composting process research paper

• Search Menu
• Sign in through your institution
• Cytogenetics
• Cytotechnology
• Histotechnology
• Management/Administration
• Microbiology
• Molecular Biology
• Molecular Pathology
• Transfusion Medicine
• Advance articles
• COVID-19 articles
• Current Virtual Issue
• Cover Archive
• Author Guidelines
• Open Access
• Submission Site
• Why publish?
• Advertising and Corporate Services
• Advertising
• Reprints and ePrints
• Sponsored Supplements
• Branded Books
• About Laboratory Medicine
• About the American Society for Clinical Pathology
• Editorial Board
• Self-Archiving Policy
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

• < Previous

Composting: Art and Science of Organic Waste Conversion to a Valuable Soil Resource

This is the third article in a 3-part continuing education series on waste. On completion of this article the reader will be able to define composting; identify key parameters needed for aerobic composting; describe the various levels of composting, from home to industrial settings; and list compost quality indicators for specific end uses.

• Article contents
• Figures & tables
• Supplementary Data

Leslie R. Cooperband, Composting: Art and Science of Organic Waste Conversion to a Valuable Soil Resource, Laboratory Medicine , Volume 31, Issue 5, May 2000, Pages 283–290, https://doi.org/10.1309/W286-LQF1-R2M2-1WNT

• Permissions Icon Permissions

Composting is the manipulation of a biological process, decomposition; raw organic materials such as manure, leaves, grass clippings, food wastes, and municipal biosolids are converted to stable soil-like humic substances. Composting is an ancient technology undertaken on a variety of levels, from home to industrial. As landfills reach their capacity and ban acceptance of organic wastes, composting is an increasingly viable means of organic waste treatment. Moreover, the final product, finished compost, is a valuable soil resource with a variety of agricultural, horticultural, and silvicultural uses.

Author notes

Email alerts

More on this topic, related articles in pubmed, citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1943-7730
• Print ISSN 0007-5027
• Copyright © 2024 American Society for Clinical Pathology
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Open access
• Published: 27 January 2023

Thermophilic bacteria and their thermozymes in composting processes: a review

• Ilaria Finore 1 ,
• Antonio Feola 2 ,
• Ludovica Russo 1 ,
• Andrea Cattaneo 1 ,
• Paola Di Donato 1 , 3 ,
• Barbara Nicolaus 1 ,
• Annarita Poli 1 &
• Ida Romano 1  

Chemical and Biological Technologies in Agriculture volume  10 , Article number:  7 ( 2023 ) Cite this article

10k Accesses

32 Citations

Metrics details

In this review, the composting process of organic waste is discussed through an in-depth exploring of its thermophilic phase. It starts with the highlight on the thermodynamic evolution, which needs to be assessed when deciding to use reactors for composting, also in the context of energy generation. The composting process is mediated by different types of microorganisms, and the bacteria that play key roles are evaluated. The roles of the genera Bacillus and Thermus are considered, often described as the main components of the microbiota of compost. Due to their adaptation to the composting processes, they are candidates for technological purposes. Subsequentially, the focus is moved on the thermostable enzymes that can be isolated from them and their succession during the composting processes. Experimental examples of enzyme-related literature are reviewed, for example investigating proteases and ureases, which are found at the beginning of the process. In addition, cellulases, hemicellulases, lignin-modifying enzymes, and esterases have been described for their activities during the thermophilic phase, giving them great potential for biotechnological and industrial applications. Following, the composition of the microbial community is analyzed through the description of approaches of metagenomics. Despite it being a relatively new but fast-growing field within biology, it is intended to be a priority analysis to acquire knowledge on genomes of environmental microorganisms and communities. Finally, a space is dedicated to the description of the composting plant which treats olive oil wastes within the LIFE TIRSAV PLUS project (LIFE05 ENV/IT/00845). Through two plant solutions, being the Dynamic and the Static Composting, it provides a high-quality compost with an effective, flexible and economical process.

Graphical Abstract

Introduction

Composting represents an efficient process in which organic wastes can be recycled to obtain organic fertilizers utilized in agricultural field [ 1 ]. Starting materials, regularly utilized in compost procedure, consist of organic fractions deriving from municipal wastes, animal manures, agro-industrial waste [ 2 ], and/or their combinations. The final stable product, named compost, is a sustainable soil improver and fertilizer containing humified fraction comparable to humus which can be used to improve the physico-chemical characteristics of soil, the fertilizer efficiency, and which can promote the growth of crop [ 3 , 4 ]. Humic substances enrich the soils of nutrients such as nitrogen, potassium, calcium, and phosphorus, necessary for the plant’s growth [ 5 , 6 ]. Furthermore, composting is a smart and sustainable solution for reducing the negative environmental impacts connected to the waste management, and it is based on the circular economy model with the recycling of by-products [ 7 , 8 ].

A generic composting process is characterized by four main phases, which have been described in different works [ 8 , 9 , 10 ]. The first phase is the mesophilic or initial phase, where a rapid increase of temperature in the range 10–42 °C determines the start of the degradation of organic matter, and its duration varies between 24 and 72 h. The second one is the thermophilic phase, characterized by temperatures between 45 and 70 °C in relation to the metabolic activities of endogenous thermophilic microorganisms which degrade the organic compounds, and it can last from several days to several weeks. The temperature can remain fixed for many days according to the properties of the feedstocks, the size of the composting plant and the environment. During the following phase, indicated as the mesophilic II or maturation phase, the temperature decreases between 65 and 50 °C, and it maintains itself for 1–2 months, with the reactivation of mesophilic microorganism and the degradations of the most recalcitrant components. These first three phases can also be referred as the bio-oxidative period of composting. Finally, the last phase is the maturing or curing phase, which can last for 1–4 months with temperature comprised between 50 and 23 °C, and where the organic matter produced stabilizes.

Composting is an aerobic and exothermic process which can occur naturally in the environment, but its efficiency is deeply related to the control of specific physico-chemical parameters which can significantly affect the progress of the bio-oxidative process, such as temperature, pH, aeration rate, moisture content, carbon to nitrogen (C/N) ratio, the type of substrates and their assortment [ 9 , 11 , 12 ]. In addition, the mixing ratios chosen during the preparation phase of composting can significantly affect the progress of the bio-oxidative process. In detail, a good performance of composting, with an improved efficiency of the bio-oxidative process and a high quality of the outgoing compost, is obtainable when the compost pile reaches a temperature of 55–60 °C during the thermophilic phase, the pH is comprised between 5.0 and 8.0, the humidity in the mixture is almost 40–60%, and there is an initial C/N ratio of 20–40, but the value may vary depending on the substrate [ 13 , 14 ]. The optimal oxygen concentration is 10%, and while the oxygen content at the beginning of composting is usually adequate, during the process it may be necessary an active aeration to avoid anaerobic conditions, to remove excess moisture from the substrate, and to regulate the temperature [ 4 , 8 ]. Aeration of the compost pile can be obtained through mixing or through forced aeration, with different studies focusing on continuous and intermittent aeration during composting [ 8 , 15 ]. The C/N ratio parameter has been recognized as one the most relevant for the final quality of compost [ 16 ]. Indeed, the speed of the process results to be affected by this ratio: while low nitrogen values are a limiting factor during composting, making the degradation process slow, an excess of nitrogen is often lost by the system in the form of gases such as ammonium or other nitrogen compounds [ 17 ]. Consequently, unsuitable C/N ratios need to be adjusted by addition of further substrates to balance both elements. Other studies considered the role of turning on the process, suggesting its incumbent role in improving the biological heat efficiency and the mass transfer, also in relation to indigenous core bacteria which could be directly activated [ 18 ].

The microorganisms required for compost development are furnished by compost feedstocks, keeping an active microbial community during the process [ 19 ]. Soil microorganisms (such as bacteria, actinomycetes, fungi and protozoa) are included into the process when the waste materials are mixed with soil. Moreover, mature compost and animal manure are often used as starter in composting processes since they can be a great source of microorganism. Microbial degradation allows the conversion of organic materials into carbon dioxide (CO 2 ), which is then released into the environment. The composting process is mediated by the intervention of different types of microorganisms. The first degradation processes are carried out by mesophilic organisms that prefer temperatures between 30 and 45 °C [ 17 , 20 , 21 ]. As the temperature rises, due to the intense digestive activity of microorganisms, thermophilic populations settle and continue the conversion of organic compounds into carbon dioxide [ 22 , 23 ]. This is the “active stage” of composting because the decomposition is considerably rapid, and it persists until organic substrates are available in the piles. Then, the microbial activities decline and with them the pile temperature. During the curing phase, mesophiles populate again the compost and humic substances begin to accumulate generating the mature compost [ 17 , 20 , 24 ].

In composting, different substrates can be added to the starting materials to reduce the negative sides of this process or to improve some characteristics. These compounds can be distinguished between bulking agents, if they influence the structure of the compost in a physical way (e.g., water absorption capacity, air space, and flow between particles), or additives, if they enhance the composting process itself (e.g., reduction of leaching, greenhouse gas emissions, and odor) [ 25 , 26 ]. Additives are classified as chemical, microbial or physical, and the latter can be further sorted in organic, mineral, and reusable. In addition, the results obtained using a mixture of additives may be better than using a single compound [ 27 ].

It has been reviewed by Barthod et al. [ 25 ] how the improvement obtained through these compounds can influence different parameters of the composting procedure by way of various mechanisms. For example, the authors described how the thermophilic phase of composting can be extended by using both mineral, organic, and biological additives, thanks to their proficiency in increasing the microbial activity, while biochar, woodchips, or residual straws can increase the aeration and porosity of the composting mass. Moreover, to avoid high humidity that leads to anaerobic conditions during composting, it is required to use fibrous materials, including cornstalk, sawdust or spent mushroom substrate; on the other hand, substrates like clay can reduce large water losses. It has also been described how additives such as food waste or microbial inoculums are employed to increase the pH, which, on the contrary, can be lowered by using bamboo charcoal or zeolite. These compounds can also reduce nitrogen losses, because of their ability to adsorb ammonia. In addition, through the incorporation of rice straw, ash, or several chemical compounds, it is possible to lessen the malodorous gasses full of sulfur and nitrogen, by absorbing them or by expanding the oxygen transfer.

Abdellah et al. [ 28 ] described how the incorporation of immobilization additives leads to the diminution of heavy metals or the modification of their oxidation state, in order to decrease the bioavailability and the mobility. Biochar, lime, and graphene oxide are considered effective towards the main heavy metals (Zn, Cu, Ni, Cd, Pb, Cr, As, Hg). In particular, biochar is a carbonaceous material which can be produced by different organic substrates under high temperatures (> 400 °C) and a moderate oxygen supply [ 29 ], and its role as an organic additive is the main topic analyzed by other researchers. For example, the shell biochar used by Awasthi et al. [ 30 ] in composting improved the efficiency of the process stimulating the microbial growth and the enzymatic activities, providing the optimal physico-chemical conditions for heavy metals resistant bacteria. In another case, bamboo biochar could significantly reduce total carbon and nitrogen losses, with a positive enhancement of enzymatic activities related to their metabolism [ 29 ]. The use of wood and wheat-straw biochar done in the work by Awasthi et al. [ 31 ] resulted in an interaction with the bacterial culture, determining the adsorption of heavy metals (Cu and Zn) during the process, thus improving the quality of the compost itself.

In this review, the composting process is discussed by exploring in a more detailed way its thermophilic phase, starting with the highlight on the thermodynamic evolution, which needs to be assessed when deciding to use reactors for composting. Subsequently, the bacteria that play key roles are evaluated. The roles of the genera Bacillus and Thermus are described, being often the main components of the microbiota of compost and being candidates for technological purposes. It follows a focus on the thermozymes which can be isolated from them, and which could have potential biotechnological and industrial applications. Experimental examples of enzyme-related literature are reviewed, investigating proteases, ureases, cellulases, hemicellulases, lignin-modifying enzymes, and esterases. Following, the composition of the microbial community is analyzed through the description of metagenomics approaches, a priority analysis essential to acquire knowledge on genomes of environmental microorganisms and communities. Finally, a space is dedicated to the description of the composting plant which treats olive oil wastes within the LIFE TIRSAV PLUS project (LIFE05 ENV/IT/00845), with the analysis of two plant solutions, that is the dynamic and the static composting.

Thermodynamics of the composting process

As a result of the composting process, organic solid waste (OSW) is decomposed by microorganisms into CO 2 , H 2 O, NH 3 and a significant amount of energy is released, as made explicit below.

To sustain its metabolism, the microbial organism spends part of the produced energy, while the remainder is liberated into the surrounding system as heat [ 32 ]. When using reactors for composting, it critical to study the heat produced from organic compounds and the balance between the energy needed for the thermophile stage and the one lost through all the dissipation pathways [ 33 ].

Various approaches for quantifying the heat generation from composting have been elaborated, i.e., degradation method (DD), oxygen consumption method (OC), heat balance method (HB), CO 2 evolution method (CEM), temperature method (TEM) and heating value method (HVM), which will be discussed hereafter.

Degradation method (DD)

As previously stated, the heat generation is closely linked to the OSW degradation. Thus, a degradation model can be used to describe the composting process and its heat production. The general form can be expressed as shown in Eq.  1 :

Qbio is the heat production rate of composting (W); HVr is the heating value of the substrates (MJ kg −1 ), defined as the amount of heat released per unit of substrate degradation; r is the coefficient of degradation rate (kg s −1 ); t is the composting time (s); m is the weight of substrates (kg).

The degradation model can be considered as a function of HVr and r. For ease of calculation, HVr is regarded as a constant throughout the composting process, but in practice it gradually decreases. For r, it is difficult to accurately measure the degradation rate. Hence, many mathematical models have been formulated to describe the degradation process, such as first-order models and Monod-type models. In the first-order models, r is related to the concentration of composting substrates. These models are the most employed, because of their simplicity; however, they do not take into account the effects that other factors may have on the value of r. The Monod-type model illustrates the relationship between the microbial growth and the degradation process in composting. Due to the increased complexity and the need to obtain four or more coefficients to describe the process, this model has a more limited use.

O 2 consumption method (OC)

During the composting process, microorganisms require oxygen to start the decomposition of organic matter. The heat production rate during the metabolism is linearly correlated with the O 2 consumption rate, according to Eq.  2 :

HVo is the heating value defined as the amount of heat generated metabolically per mole of O 2 consumption (kJ mol −1 O 2 ), OCR is the O 2 consumption rate (mol kg −1 ), defined as the O 2 consumed per unit of time (mol O 2 s −1 ).

HVo is commonly seen as a constant during the composting process. Most of the time, the O 2 consumption rate is determined by measuring the O 2 concentration difference in the inlet and outlet gas. Furthermore, the O 2 consumption rate can be estimated by first-order models or Monod-type models.

Heat balance method (HB)

Throughout the composting process, the release of energy from decomposition leads to the rise in temperature of the organic matter and water, heat loss via convection and conduction, and water evaporation. The heat basically presents in two forms: sensible heat (energy associated with an increase in temperature) and latent heat (energy associated with phase transformation). The actual heat output can be calculated from the measurement of the components indicated in Eq.  3 [ 32 ]:

Qsensible is the amount of sensible heat (W), Qlatent is the amount of latent heat (W). In particular, the components of the thermal balance are convective heat loss of inlet and outlet streams (air, vapor, and water) Qgas (W), sensible heat of composting materials Qsub (W), conductive/convective losses through surface of reactor Qloss (W), latent heat loss of water evaporation Qvap (W), and radiant loss Qrad (W).

It is possible to determinate the total sensible heat, which is the temperature increase of the liquid and solid components in the reactor, by varying the temperature of the matrix per unit time. The heat balance, using accumulated heat as a variable, is calculate in Eq.  4 :

m is the mass of the matrix (kg); c is the specific heat of the matrix (kJ kg −1  °C −1 ); T is the temperature of the matrix (°C); t is the time (days); G is the dry air mass flow (kg day −1 ); Hin and Hout are the air enthalpy of the entrance and exit of dry air, respectively (kJ kg −1 ); U represents the overall heat transfer coefficient of the matrix (kW m −2  °C −1 ); A stands for the total surface area of the reactor (m 2 ); Ta is the ambient temperature (°C); BVS is the mass of degradable volatile solids (kg); and Hc is the reaction heat value of degradable organics (kJ kg −1 ). In the equation, mc is not a constant amount and it changes frequently.

Ventilation is the principal cause of heat loss. Air entering the reactor is made up of dry air and water vapor. As this goes through the reactor, these two components are heated up and release the sensible heat. Then, when the water in the treated organic solid waste leaves the reactor as water vapor, heat is carried out in the form of both sensible heat and latent heat for vaporization [ 34 , 35 ]:

Q gas is the ventilation convective heat loss (kJ); F gas is the ventilation air flow rate (kg h −1 ); Hout is the enthalpy of the air flow leaving the system (kJ kg −1 of dry air); Hin is the enthalpy of the air flow entering the system (kJ kg −1 of dry air).

Sensible heat of composting materials Q sub can be calculated as in Eq.  6 [ 19 ]:

m is the mass of composting pile (kg); MC is the moisture content (%); C s is the specific heat capacity of solid (kJ K −1  kg −1 ); C w is the specific heat capacity of water (kJ K −1  kg −1 ); T is the temperature of composting pile (K); t is the time (s).

The heat loss due to conductive transfer and radiation heat can be computed by using Eqs.  7 and 8 [ 33 ]:

Q loss is conductive heat loss from the reactor wall (kJ); U is the overall heat transfer coefficient of the matrix (kW m −2  °C −1 ); A is the reactor wall surface (m 2 ); T m is the matrix temperature (°C); T a is the ambient temperature (°C):

where Qrad is radiant heat loss from the top surface of the materials (kJ); σ is the Stefan–Boltzmann constant; A top is the surface area of the radiating body (m 2 ); T t is the temperature of the top surface (°C); F a is a configuration factor accounting for the relative position and geometry of the objects (dimensionless); F e is the emissivity factor accounting for non-black body radiation.

The latent heat loss of water evaporation from composting pile is determined from the water vapor content of exit and inlet gases as after in Eq.  9 [ 35 ]:

F gas is the flow rate of air (kg h −1 ); Q v is the enthalpy change of water vaporization (kJ kg −1 ); h out is the absolute humidity of exit air (kg kg −1 ), h in is the absolute humidity of in air (kg kg −1 ).

In large-scale reactors Qvap takes up the majority of the heat produced, while in small-scale reactors the greatest heat depletion is caused by Qloss. Qrad, instead, is so small that its contribution can be ignored [ 32 ].

CO 2 evolution method (CEM)

Variations of CO 2 concentration can manifest the degradation process since microorganisms decompose OSW and produce CO 2 . There are several ways to assess the CO 2 concentration; the main ones are the use of CO 2 meters and the estimation via respiratory quotient. The general equation of CO 2 evolution method is Eq.  10 :

HV c is the heat released per unit of CO 2 evolution (kJ mol −1 ); CER is the CO 2 evolution rate (mol s −1 ).

Temperature method (TEM)

The temperature alterations in composting can be attributed to heat production and heat loss processes. TEM is applicable only when the heat loss is a known quantity, or it is equal to zero. For rapid evaluation, the specific heat capacity of the mixed solids and liquids in the system and the pressure at which the process takes place, are assumed constants. The heat production rate can be found by monitoring the rate of temperature change during composting process, for example using a water bath to retrieve the generated heat and measure the temperature difference between the composting and water bath. Owing to these rigorous conditions, TEM is usually used in laboratory bench-scale composting units:

Δ T is the temperature change of the substrates or water (K); C mix is the specific heat capacity of substrates (kJ kg −1  K −1 ); m mix is the mass weight of OSW (kg); m water is the mass weight of water (kg); C water is the specific heat capacity of water (= 4.2 kJ kg −1  K −1 ).

Heating value method (HVM)

According to this method, it is considered that the difference between the heating value (HV) of composting substrates at the beginning and at the end of the composting is indicative of the measure of energy produced from degradation. The general equation of HVM is Eq.  12 . For these measurements, a calorimeter is usually needed [ 32 ]:

HV in is the HV of substrates at composting beginning (kJ kg −1 ); HV fin is the HV of substrate at the composting end (kJ kg −1 ).

In conclusion, different methods have been developed to evaluate the effectiveness of the composting process. Moreover, it is fundamental to investigate thoroughly all the energy generation, transfer and loss processes associated to the OSW degradation in order to optimize, control and manage this phenomenon [ 33 ]. Finally, the technological and environmental potential of composting heat reuse is a current topic of study, as it can help respond to the energy demand and reduce the pressure of climate change.

Thermophilic microorganisms isolated from compost samples

A wide variety of prokaryotes (bacteria and archaea) tolerates and usually requires high temperatures for their growth and survival; they are known as thermophilic microorganisms. A great diversity of these microorganisms has been isolated and characterized in hot environments within the past few decades. Thermophilic microbial genera have been discovered from natural (geothermal areas, terrestrial hot springs, deep-sea hydrothermal vents, etc.) and man-made environments (waste treatment plants, biological wastes, self-heated compost piles, etc.) throughout the world.

Thermophilic microorganisms are directly involved in the thermogenic phase (50–80 °C) of composting process for treating organic solid waste. The composting processes start immediately, and the temperature of compost rises with time and within a few days reaches the maximum inside the central portion of the compost pile. During the temperature change, the population of thermophiles increases and the one of mesophiles decreases. The growth of the majority of the present microorganisms is inhibited by excessively high temperatures, and the decomposition of organic matter slows down. Thermophilic bacterial strains have been isolated from almost every type and nature of compost, including autotrophs, heterotrophs and mixotrophs, and each research work has highlighted different characteristics (Table 1 ).

Bacillus , Thermus and Clostridium show metabolic activity at temperatures above 60 °C [ 36 ]. The authors have reported that during the thermogenic phase of compost, only a few species of thermophilic sporogenous bacteria (Bacillus stearothermophilus and Bacillus subtilis ) and the genus Thermus Gram-negative, aerobic, non-spore forming bacteria, showed metabolic activity above 70 °C. Bacillus spp. often occur as the major components of the microbiota (87%), including B. licheniformis, B. subtilis, B. coagulans type B , B. stearothermophilus, and B. sphaericus [ 37 ]. Bacteria belonging to the genus Thermus have been isolated mainly from hot-spring ecosystems and they have probably adapted to the conditions in the different composting processes. In contrast to the above-mentioned work, reported during the thermophilic phase of composting the presence of a low diversity of obligatory heterotrophic thermophiles related to the genus Bacillus stearothermophilus , Beffa et al. [ 38 ] have reported for the first time the isolation of the sulfur- and hydrogen-oxidizing, autotrophic thermophilic bacteria related by high DNA:DNA homology to Hydrogenobacter strains. These last strains have been isolated only from geothermal areas and their presence in the thermogenic phase (> 60 °C) of the composting process suggests that they may play a part in inorganic sulfur compound oxidation and mineralization of waste, being candidates for further studies. Organic compost samples of 2–5 weeks with temperatures ranging from 65–69 °C showed only strains related to B. stearothermophilus, whereas at temperatures above 70 °C a considerable number of Thermus strains related to Thermus thermophilus HB8 were the dominant active degraders [ 39 ].

The study conducted by Blanc et al. [ 40 ], from kitchen and garden waste compost, using a molecular approach with restriction enzyme analysis of a library of bacterial 16S rRNA gene clones, showed that among 200 clones investigated, about 70 could be identified as Thermus thermophilus and thermophilic Bacillus spp. Both taxa were found to be the dominant bacterial populations during the thermophilic phase of composting; Blanc et al. [ 41 ] reported through the Amplified Ribosomal DNA Analysis (ARDRA) method Bacillus pallidus , B. stearothermophilus and B. thermodenitrificans as predominant species in hot compost. Zhang et al. [ 42 ] from the analysis of 11 compost samples (domestic and garden waste) and using total plate counts of spore in combination with RAPD-based identification (randomly amplified polymorphic DNA), showed the thermophilic Bacillus thermodenitrificans as dominant isolate with a prominent role compared to B. stearothermophilus within the compost ecology. It was already reported [ 42 , 43 ] the abundance of B. thermodenitrificans in domestic and garden composts, indeed RAPD profiles indicated that the thermophilic strains represented ~ 76.1% of the bacterial community, whereas lower numbers (~ 10%) were represented by B. sporothermodurans , B. thermosphaericus, Aneurinibacillus sp. and Brevibacillus sp . , although the latter two strains are not typically associated with garden composts. Moreover, the study of the samples collected from different compost starting materials and managements was based on the use of different molecular phylogenetic techniques such as DGGE, T-RFLP, ARISA, 16S/23S rRNA intergenic spacer amplification, SSCP profiling and FISH hybridization, and the results showed that the thermophilic phase was characterized by the abundance of members of the genus Bacillus [ 44 , 45 , 46 , 47 ] and Thermophilus [ 48 ].

The combination of both culture-dependent and culture-independent methods (molecular techniques and isolation and identification of bacteria) is complementary, but not overlapping and a traditional microbiological approach is still effectual. New species of thermophilic bacteria have been isolated and characterized by biochemical and phylogenetic techniques, as in the case of Geobacillus toebii [ 49 ], Geobacillus toebii subsp. decanicus subsp. nov. [ 50 ], Geobacillus galactosidasius [ 51 ], Bacillus composti and Bacillus thermophilus [ 52 ], Aeribacillus composti [ 53 ].

To summarize, a considerable heterogeneity of thermophilic bacteria has been isolated from the thermogenic phase of the composting of different substrates, when they are the main responsible for the degradation of organic matter. The main genera represented are Bacillus and Thermus , often described as the main components of the microbiota of the piles of compost. These bacterial strains can be isolated from natural environments, and probably they adapted to the composting processes, becoming potential candidates for technological and industrial purposes.

Bacterial enzymatic activities in the thermophilic phase of composting

To degrade organic waste through composting, microbial communities produce and secrete a wide spectrum of thermostable enzymes, mainly hydrolytic and oxidoreductase activities [ 15 , 54 ]: enzymes such as proteases, cellulases, hemicellulases and lignin-modifying enzymes are one of the main factors driving the composting processes and could have many potential industrial applications, thus representing a field of research in industrial enzymology, a sector of biotechnology [ 23 , 54 , 55 , 56 ].

The joint activities of these extracellular enzymes are strictly dependent on the composition of the microbial communities, so that the readily degradable organic compounds and the more refractory ones are progressively decomposed and mineralized to be used as sources of carbon and nitrogen by the microorganisms, sustaining the microbial growth and ensuring the continuity of the process [ 57 , 58 , 59 ]. Additionally, the activities of the enzymes depend on different environmental factors of the composting process [ 54 ]. One of the main physico-chemical parameters associated to the efficiency of composting is temperature, and as a prolongation of the thermophilic phase can improve the quality of compost itself, extracellular enzymes secreted by microorganisms have a fundamental role, being associated to the increase of temperature [ 54 , 60 ].

During the composting process, bacteria are accounted as the leading microorganisms for quantity and diversity, which is related to their small size and their ability to grow in a wide range of pH and temperature [ 20 ]. As discussed in the previous paragraph and highlighted in literature [ 55 , 61 , 62 ], most of the extremophile bacterial species which can be found in the thermophilic phase of composting belong to the Bacillus and the Thermus genera, and with them the correlated enzymatic activities, even considering intrinsic variability depending on the compost itself. For example, Miyatake and Iwabuchi [ 62 ] studied the variation of enzymatic activity of thermophilic bacteria in high-temperature dairy cattle manure compost, finding the highest bacterial activity at 54 °C, with a decrease at 60 °C, followed by a new increase at 70 °C but associated with a reduction of the decomposition of organic matter due to the high temperature itself. Increasing attention has been given to these enzymatic activities and to their succession during the composting process since they affect the transformations that happen, thus regulating the ability to decompose organic substances. They vary based on the composition of compost, the physico-chemical parameters, and the dynamics of the microbial community at each stage of the process [ 63 , 64 , 65 ].

Proteases and ureases

During the initial phase of composting, due to the presence of oligo- and polypeptides, a high protease activity is usually found. Proteases are related to the nitrogen cycle, being responsible for the hydrolysis of proteins [ 64 , 66 ]. Urease activity is also related to the nitrogen cycle, especially in the first step of nitrogen mineralization, catalyzing the hydrolysis of urea into ammonium and carbon dioxide [ 64 , 67 ]. Due to the joint action of urease and protease, together with high pH and temperature, large amounts of ammonia are released, which are then related to the quality of the compost [ 65 ].

Table 2 reports the latest research papers with the corresponding protease and urease activities found during the composting process, with the duration of the thermophilic phase, the most prominent bacterial species during that period and the composition of the compost.

It has been described that ammonia inhibits the activity of protease and urease [ 54 ], and this is coherent with the higher activity of protease and urease, associated to Paenibacillus validus 1VC, that has been found in weeks 4–8, at the end of the thermophilic phase, of the total 24 weeks of composting in the research by Hemati et al. [ 54 ]. Ammonia is formed at the beginning of the thermophilic phase because of the degradation of amines [ 8 ], but it rapidly decreases in association with high temperature and in relation to the oxygen insufflated through the aeration system [ 54 ]. Additionally, an extraordinary high activity of urease has been recorded at the very beginning of composting (week 1), probably because of the presence of urea in the compost itself, that stimulated the expression of the enzyme.

A decrease of urease following a high activity during the first half of the thermophilic phase has been reported in H. Liu et al. [ 58 ], where the authors studied the composting of chicken manure and rice straw combined with two different C/N ratio, and in Li et al. [ 69 ], where they studied the effects of the addiction of a microbial inoculum to the treatment of pig manure and corn straw (6:1 w/w). They highlighted how there is not a significant association between the urease activity and bacteria during the thermophilic phase. Similarly, the urease activity described by J. Du et al. [ 68 ] in the composting of sewage sludge, sawdust, and biochar in different ratios generally resulted in an increase at the beginning of the process, followed by a decrease entering the thermophilic phase. In the same work, the activity of protease increased only later in the composting, at the very end of the thermophilic phase, probably because the involved microorganisms needed to adapt to be active and to start secreting these enzymes. Huang et al. [ 67 ] reported about the effects of a hyper-thermophilic pretreatment of pig manure and straw compost, that is a short-term pretreatment at high temperature, which determined a decline in the relative abundance and diversity of the microbial community, especially of the bacteria responsible for the ammonification activities during the thermophilic phase. This contributed to the decreasing activity of the enzymes expressed by these microorganisms with respect to the control pile of composting, such as protease and urease, which has also been one of the causes of a reduced ammonification rate. In turn, the lowering of nitrogen loss could have contributed to the inhibition of these enzymes.

The industrial-scale composting of food waste of 31 days researched by Zhang et al. [ 15 ] has been conducted comparing different aeration frequencies. The increase of the aeration rate determined an increasing duration of the thermophilic phase, but the authors excluded a significant correlation with the activity of urease, which gradually decreased starting from the beginning of the process, with a later reduction in the composting due to the accumulation of nitrate. Ma et al. [ 57 ] studied the composting of sludge and corn straw, finding even in this case a decrease of the urease activity during the thermophilic phase (day 5–7 over the total 11 days), while the protease activity increased and reached a peak of 3.25 mg g −1 from the 7th day, then it maintained this activity until the end of the process. A similar trend in the protease activity has been described in the already mentioned work by H. Liu et al. [ 58 ], where, during the composting of chicken manure and rice straw, it showed higher values in the thermophilic phase compared to the initial mesophilic one, from the 7 th day to the end on the process (25 days in total), being over 1012 mU g −1 .

Among different composting material tested, Q. Liu et al. [ 23 ] reported the highest protease activity during the thermophilic phase in mulberry branches and silkworm excrement (1:9 w/w) compost, reaching over 60 μg g −1  min −1 , explainable with the high crude protein content of silkworm excrements. Also, they reported a high urease activity in both mulberry branches/silkworm excrement and mulberry branches/cow dung (3:7 w/w) composts, measured over 17 μg g −1  min −1 . Similarly, Qiao et al. [ 59 ] monitored the extracellular enzymatic activities produced during the composting of three starting material, namely cow manure, mushroom residue, and sawdust, with three different C/N ratios (15, 25 and 35). They reported that the protease activity decreased during the composting process, with an activity measured at 91.36 mU g −1 during the thermophilic phase, mainly associated to Nonomuraea and Virgibacillus sp. The decreasing is associated to the initially high availability of degradable organic compounds in the starting material, which is then gradually decomposed.

Cellulases, hemicellulases and lignin-modifying enzymes

Lignocellulose is the major fraction of most organic waste; it consists mostly of cellulose, hemicellulose, and lignin, and it is the most stable and refractory fraction of organic matter in the composting process, so that its degradation is one of the major factors that could limit the composting efficiency [ 23 , 54 , 56 ]. Enzymatic activities can degrade cellulose and hemicellulose into simple sugars, and lignin into polyphenols [ 56 ]. However, the enzymatic degradation of lignin is complex due to the phenolic rings that constitute its highly branched and irregular polymer, so that the mineralization could be not completed during the composting process [ 54 , 56 ]. Fungi are known to degrade lignin in an efficient way, however, during the thermophilic phase of composting most fungi are inactivated. The composting time is mostly dependent on the activity of the enzymes secreted by lignocellulolytic thermophilic bacteria, that can speed up the process in the presence of high percentage of plant residues and green wastes [ 20 , 54 , 56 , 64 ]. Additionally, thermophilic microorganisms able to degrade lignocellulose contribute to loosen its structure, facilitating other enzymatic activities in the further phases of composting [ 70 ]. The degradation of lignocellulose happens at faster rates during the thermophilic phase due to the abundance of bacteria able to degrade it through the production and secretion of hydrolytic enzymes, whose activity increases with the rise of composting temperature [ 23 ]. This has been reported by Zhu et al. [ 56 ], who obtained an increment of simple organic matter through a thermal pretreatment of dairy manure, and even an increment of the relative abundance of thermophilic bacteria later involved in the enzymatic decomposition of lignocellulose, thus improving the quality of the compost obtained.

Cellulases and hemicellulases include numerous enzymatic activities which regulate the carbon cycle of composting, and which have an impact on the whole nutrient cycle, increasing soluble nutrients during the process [ 64 , 65 ]. In particular, cellulases and activities such as endo- and exo-glucanases, cellobiohydrolases, β-glucosidases and β-glycosidases are related to the cellulose cycle, working synergistically to degrade cellulose, while hemicellulases and enzymes such as xylanases are related to the hemicellulose cycle, for the degradation of the polymers of xylan [ 54 , 58 , 63 ]. The lignin-modifying enzymes, formerly named ligninases and lignases, have an activity related to the lignin cycle being able to catalyze the degradation of the polymer of lignin through an oxidative mechanism, with the generation of free radicals [ 54 , 71 ]. These enzymes are numerous, with some of the main groups being laccase, manganese-dependent peroxidase, and lignin peroxidase [ 71 ]. Peroxidases have a key role in the degradation of more refractory organic matter such as lignin and other similar structures [ 45 ].

Table 3 reports the latest research papers with the corresponding cellulases, hemicellulases and lignin-modifying enzymes found during the composting process, with the duration of the thermophilic phase, the most prominent bacterial species during that period and the composition of the compost.

Hemati et al. [ 54 ] measured the highest cellulase activity within the 4th and the 12th week of the total 24 weeks of composting, between the end of thermophilic phase and the starting of the cooling phase and associated it to Bacillus nealsonii 104C. In the high lignin compost (agricultural residue/sawdust 15:85), the increase in temperature during the thermophilic phase promoted the denaturation of polymers, making more substrates available, increasing the microbial community and the enzymatic activities, and lowering the decrease of the cellulase activity with respect to the low lignin composting process (agricultural residue/sawdust 97:3). Additionally, in the same work the authors distinguished the activity of generic cellulases from the one of β-glucosidase, that was higher at the beginning of the composting process, in weeks 2–4 (thermophilic phase), and it has been associated to Bacillus nealsonii 104C. β-glucosidases are active on cellulose and other disaccharides, they hydrolyze β-D-glucose chains releasing β-glucose, and their activity is higher in presence of easily metabolizable substrates available to microorganisms, and in presence of high concentration of water-soluble carbon, such as at the beginning of composting [ 54 , 66 , 68 ].

A similar trend in the cellulase activity but on a shorter composting period has been reported by Jiang et al. [ 65 ], with a peak on the 4 th day on the total 24 days of composting of sewage sludge and saw dust at a ratio of 4:1 (w/w fresh weight). Moreover, their addiction of “garbage enzymes” obtained through the composting of kitchen wastes increased the relative abundance of bacteria of the phylum Firmicutes during the thermophilic phase, with a significant enhancement of cellulase activity. Instead, during the thermophilic phase of the composting of sludge and corn straw done by Ma et al. [ 57 ], between the 5th and the 7th day of the total 11 days of the process, the cellulase activity mostly associated to Bacillus sp. gradually increased at 250 µg g −1  min −1 . It did not reach a peak in this phase probably because of the high cellulose content of the substrate, allowing further cellulase production in the following phase: the authors highlighted that since cellulose is a moderately refractory organic matter, its degradation mainly happens in the middle and late phases of composting. A comparable increase in the cellulase activity, even in this case associated to Bacillus sp., has been reported by Du et al. [ 68 ] in the middle of the thermophilic phase, nevertheless with a peak at the end of the 11 days process, probably because cellulase is a complex polysaccharide, and microorganisms need to gather enough nutrients to decompose it, with a subsequent production of cellulases later in the process. Additionally, the authors differentiated the activity of β-glucosidase, again associated mainly to Bacillus sp. which was higher at the beginning of composting until the start of the thermophilic phase.

Siu-Rodas et al. [ 61 ] isolated three strains of Bacillus subtilis from the thermophilic phase of composting, namely A-, M- and N-SRETCR, that showed endo- and exo-cellulase activity at acid pH (4.8 and 5.8, respectively), with potential applications in animal foods, fruit juice extraction and clarification and paper bleaching industries, and basic pH (9.3), suitable for applications in paper and detergent industries. Additionally, the cellulase from the strain A-SRETCR showed a higher activity at 60 °C. During the 44 weeks composting of textile waste, Biyada et al. [ 49 ] detected different bacterial genera, such as Devosia , Flavobacterium , Pseudoxanthomonas , Pseudomonas , and Achromobacter , with cellulase and xylanase activities associated to the thermophilic phase. The authors related the high concentration of cellulolytic bacteria during this phase to the high temperature itself that could have assisted the degradation of cellulose.

The composting process of 25 days of chicken manure and rice husk in two different C/N ratios, namely 9.61 and 17.3, has been researched in H. Liu et al. [ 58 ], and the latter was found more suitable, with an increase in the relative percentage of Bacillus sp. also during the thermophilic phase. The authors related this abundance to cellulase and β-glycosidase activities, leading to a prolonged thermophilic phase (from the 3 rd to the 16 th day) and a fast and broad degradation of cellulose and hemicellulose with respect to the compost with 9.61 C/N ratio, highlighting a critical role in the progressing of the composting itself. The cellulase activity peaked on the 3 rd day, measuring 11 U g −1 , while the β-glycosidase reached a high value later in the composting process, being measured 3.80 U g −1 during the same day. A similar trend has been highlighted in the already cited study of industrial-scale composting of food waste by Zhang et al. [ 15 ]. The authors reported a high cellulase activity at the beginning of the process and then entering the thermophilic phase, followed by a decrease of its activity with the increase of the temperature in the second half of the thermophilic phase. Even in the already mentioned composting process studied by Qiao et al. [ 59 ], the authors reported an increase of the cellulase activity during the thermophilic phase, measured at 14.03 U g −1 . This resulted in an efficient decrease of cellulose of 78.9%, 64.6% and 61.6% at the end of the overall 39–days process for the three piles of compost considered (cow manure, mushroom residue, and sawdust, respectively). Additionally, they reported that the β-glycosidase activity decreased through the composting, measured at 16.39 U g −1 during the thermophilic phase, being related to the presence of substrates that could be readily metabolized.

Depending on the composting materials used, Liu et al. [ 23 ] reported different predominant enzymatic activities during the thermophilic phase (from day 4 to day 16 of the total 25 days), when the degradation of organic matter mainly happens. Specifically, the highest β-glucosidase and endo-glucanase activities have been measured for the composting of mulberry branches and cow dung (3:7 w/w), 1.31 and 17.15 μg g −1  min −1 , respectively, while the activity of exo-glucanase and xylanase were higher in composting of mulberry branches and silkworm excrement (1:9 w/w) and again in the mulberry branches and cow dung compost.

The xylanase activity in the above-mentioned work by Hemati et al. [ 54 ] had a similar trend to the one of cellulase in the same work, with the highest activity at the end of the thermophilic phase, when the high temperature determines changes in the polymers, and it has been associated to the Paenibacillus validus 1VC isolate. On the contrary, the xylanase activity described in the composting of chicken manure and rice husk researched by H. Liu et al. [ 48 ] had a peak at the beginning of the process and another peak in the middle of the thermophilic phase, being 25.4∙10 3 U g −1 on the 9th day over the total 25 days. The composting of tomato plant waste and pine chips over the course of 6 months in the research done by López et al. [ 70 ] resulted in a thermophilic phase that lasted for 11 days, and the authors isolated bacteria with xylanase activity, mostly belonging to the phylum Firmicutes. Given their wide thermostability, the authors suggested as they could have a key role in the degradation of cellulose and hemicellulose during the composting process.

Hemati et al. [ 54 ] detected an enzymatic activity generally called ligninase, with the highest activity in the thermophilic phase and they associated it with Paenibacillus koreensis 12C and Paenibacillus validus 1VC from the 2nd to the 4th week of the total 24 weeks of composting. In the cited work by Ma et al. [ 57 ], the authors described a peroxidase related to lignin and to other refractory organic matter degradation, with a high activity during the thermophilic phase and a peak of 192.50 μmol h −1  g −1 on the 7th day of the total 11 days of composting. A peroxidase activity strongly associated to Bacillus sp. has been described also in J. Du et al., [ 68 ], with an increase through the composting process, and pointing at a high lignin degradation only at the end of it.

Du et al. [ 60 ] studied the addition of exogenous enzymes on the thermophilic composting of wildlife manure. The treatment with 0.1% of the enzymatic complex composed of cellulase 10,000 U g −1 , xylanase 20,000 U g −1 , β-glucanase 5000 U g −1 and pectinase 1000 U g −1 resulted in an improvement of the compost maturity, with an increase of duration of temperatures over 60 °C (33 over 38 days of total composting) during the thermophilic phase. The authors suggested that the ligninolytic enzymes improved microbial activity, thus promoting the elimination of organic micro-pollutants and pathogens through a favorable thermophilic phase. Moreover, they reported predominant relative abundance of Firmicutes and of Actinobacteria during the first and the latter half of the thermophilic phase, respectively. A conceptually opposite experiment has been performed in the work by Li et al. [ 69 ], where the authors studied the impacts of microbial inoculation screened from a high-temperature compost on the activity of the enzymes during the composting itself. The addiction of 1.0% of liquid inoculum of Acinetobacter pittii , Bacillus subtilis subsp. Stercoris , and Bacillus altitudinis in the compost of pig manure and corn straw (6:1 w/w) determined an increase of cellulase activity with respect to the untreated composting process, being 2.40 against 2.02 mg glucose g −1 24 h −1 , respectively, on the 24th day of composting of the total 32 days, during the thermophilic phase.

Aside from the activities usually considered by most researchers, there are also papers which focus on other enzymes, such as esterases, giving insight on their activity through the composting process and during the thermophilic phase.

Esterases catalyze the hydrolysis of an ester group, with the release of the esterified acid [ 72 ]. Phosphatases are related to the phosphorus cycle, being able to catalyze the hydrolysis of phospholipids, with the release of free phosphorus, which is assimilated by microorganisms [ 15 , 54 ].

Table 4 reports works from the literature with esterase activities found during the composting process, with the duration of the thermophilic phase, the most prominent bacterial species during that period and the composition of the compost.

Zhang et al. [ 15 ] selected an alkaline phosphatase, compatible with the pH of the industrial food-waste compost considered, ranging from 7.5 to 8.5. In the composting processes, the enzyme showed an increasing activity in the first half of the thermophilic phase, with a decrease in the latter one; however, in the process with the higher aeration frequency, the phosphatase activity was not maintained during the thermophilic phase. Hemati et al. [ 54 ] reported about an alkaline phosphomonoesterase, whose activity slightly increased during the first two weeks of the thermophilic phase, and then decreased until the end of this phase. In the high lignin composting, the highest activity has been associated to Bacillus nealsonii 104C in the 4th week. The composting experiment performed by Li et al. [ 69 ] with pig manure and corn straw showed a continuous decrease of an alkaline phosphatase during the process, however with a positive correlation with Rummeliibacillus pycnus , of the phylum Firmicutes. An alkaline and an acid phosphatase have been distinguished in H. Liu et al. [ 58 ], both showing a decreasing trend in the activity through the composting process, with an activity in the pile of chicken manure/rice husk (17.3 C/N) measured at 46.0 and 0.97 U g −1 , respectively, on the 9 th day of composting, in the middle of the thermophilic phase. On a more generic level, bacterial isolates from manure compost in Charbonneau et al. [ 55 ] related to Geobacillus thermodenitrificans , strains CMB-A1, A2, A3, A7 and A10, showed a thermotolerant esterase activity at 60–65 °C on tributyrin and olive oil. Additionally, in the same work the authors noted a high lipolytic activity for Aneurinibacillus thermoaerophilus strain CMB-C1 and for Bacillus smithii , from pH 5–9 and pH 5–7, respectively.

In conclusion, different thermostable enzymes can be obtained from thermophilic bacteria isolated from composting processes, mainly hydrolytic and oxidoreductase activities, and they could have biotechnological and industrial applications. Their succession during the composting process regulates the decomposition of organic substances. For example, protease and urease activities are found at the beginning of the process, with a general decrease during the thermophilic phase with the accumulation of ammonia. The progressing of composting mostly related to lignocellulolytic thermophilic bacteria, whose cellulase, hemicellulases and lignin-modifying enzymes work synergistically to degrade lignocellulose, a major fraction in most organic waste. The degradation of cellulose has been reported in different periods of the thermophilic phase. Even lignin-related enzymes and esterases discussed in research have a prominent activity during this phase. The characteristics of resistance to high temperature and high or low pH sometimes reported for these enzymes are of great interest for their technological potential, being suitable for industrial processes where mesophilic enzymes could fail.

Composition and diversity of the microbial community during composting by metagenomics approaches

Metagenomics is the study of genetic material extracted directly from environmental samples, which produces a profile of the microbial communities, opening an ideal window on the microbes present in a habitat and overcoming the “pure-culture” paradigm [ 73 ]. This implies that the metagenomics studies focus on a whole microbiota, with reference to all microbial species ranging from the ecological community of commensal to symbiotic and pathogenic strains of a peculiar niche. The term metagenomics, coined in 1998 by Handelsman et al. [ 74 ], it has been recently redefined by Chen and Pachter as “the application of modern genomics technique without the need for isolation and laboratory cultivation of individual species” [ 75 ]. In fact, these studies revealed that usually less than 1% of microorganisms from natural sources could be cultivated under laboratory conditions [ 76 ], and that the uncultured species not only constitute a major part of the microbial communities, but they could also perform key functions in ecological processes. Therefore, despite that metagenomics is a relatively new but fast-growing field within biology, it is intended to be a priority analysis for the purpose of acquiring knowledge on genomes of environmental microbes, as well as of entire microbial communities. Metagenomics analysis targeting total DNA isolated from the environment or from animal sources could be performed using two types of approaches, being “Sequencing metagenomics or sequencing-based metagenomics” and “Functional metagenomics or function-based metagenomics”. The first, through (1) Target oriented method or DNA metabarcoding and (2) Total metagenome method or shotgun metagenomics, allows the characterization of a microbial community in terms of taxonomic composition and the prediction of functional diversity. The second is aimed at the discovery of new genes encoding enzymatic activities of biotechnological interest, and to assess the functional activities and ecological roles of particular microorganisms.

The biological decomposition of organic matter is especially due to the presence of thermophilic and mesophilic microorganisms, and bacterial phyla including Bacteroidetes, Actinobacteria Proteobacteria and Firmicutes are usually found in the composting process [ 77 ]. The succession of microbial communities is the key for an efficient and a profitable process, influencing the quality and the maturity of compost and the rate of biodegradation [ 78 ]. In a standard composting process, the aerobic microbial metabolism leads to temperature increases above 50 °C, followed by high temperatures that are maintained until most biodegradable materials are completely digested. Then, the material slowly cools down as well as the microbial activity, and the organic matter stabilizes [ 79 ]. The resident microbial community includes mesophilic and thermophilic bacteria and fungi which continuously fit to the changing nutrient supply and which alter the environmental conditions. Moreover, since fungi were not detected in a composting sample with temperature above 65 °C, it was suggested that bacteria are the dominant degraders and the main responsible of the recycling of the organic wastes in thermophilic phase of composting processes, while fungi play a functional role during the cooling and curing phases.

The types of raw materials and the operations of the composting process would give different microbial communities. Overall, the genera Pseudomonas, Acinetobacter, Steroidobacter, Bacilli and Sphingobacterium were the most abundant genera in rice straw, sugar cane bagasse, and coffee hulls composting processes with addition of cow manure [ 22 ]. In sludge and cattle dung composting, Ureibacillus, Tepidimicrobium, Kribbella and Bordetella were reported to be the dominant ones [ 80 ], while in maize straw composting processes, Sporosarcina, Bacillus, Cellvibrio, Devosia and Cellulomonas were the most abundant [ 81 ].

Therefore, to improve the quality of compost and to shorten the duration of the process modulating the functional microbes within the procedure, it is required to investigate the microbial communities, their interaction, and their response to physico-chemical properties of compost, such as temperature, moisture content, C/N ratio, water content, aeration, and other factors. Moreover, because composting is considered an interesting and potential source of novel thermophilic microorganisms and a biotechnologically source of new thermozymes useful for industrial applications, the metagenomics and metatranscriptomics tools are valuable approaches to study these microorganisms and their metabolism. Therefore, these microorganisms and enzymes could be employed in biomass valorization, green processes, and sustainable transformation [ 78 ], as well as being helpful in the characterizations of the bioprocess itself [ 24 ]. These microorganisms have a crucial role in the global carbon cycle and act as sources of biochemical catalysts for advanced biofuels production [ 82 ].

Wang et al. [ 82 ] used a metagenomic analysis of the rice straw-adapted (RSA) microbial consortia enriched from compost ecosystems to elucidate the systematic and functional properties of this microbiome. The authors employed high‑throughput 16S rRNA gene pyrosequencing and phylogenetic classification, the analysis of the 16S pyrotag library, and 5 Gbp of metagenomic sequence beside the DNA library construction and sequencing. They showed that the phylum Actinobacteria was the predominant group among the Bacteria in the RSA consortia, followed by Proteobacteria, Firmicutes, Chloroflexi, and Bacteroidetes. The Carbohydrate Active EnZyme (CAZyme) profile revealed that CAZyme genes in the RSA consortia were also widely distributed within these bacterial phyla. About 46.1% of CAZyme genes were from actinomycetal communities, which included cellobiohydrolase, β-glucosidase, acetylxylan esterase, arabinofuranosidase, pectinlyase, and ligninase genes. The presence of this distinct repertoire of CAZyme genes in the RSA consortia suggests a synergistic system efficient in the processing and metabolizing of carbohydrates in the compost habitats based on lignocellulosic biomass.

In another study, Tian et al. [ 21 ] described the composting experiments conducted using dairy manure and rice chaff as starting materials of the process, in the Lian Ye composting plant (Jiang Yin, China). This study provided an improved understanding of the bacterial composition and dynamics during the composting process since a large number of clones were analyzed and a high coverage value of each clone library was obtained. The experimental procedure implied the construction of four 16S rRNA gene clone libraries from compost samples taken at fixed days as 0, 12, 42 and 112, and about eight hundred randomly selected clones were sequenced. The results showed that the microbial communities varied significantly during the process. Firmicutes and Proteobacteria were the two most abundant phyla at all stages of sampling. Bacteroidetes and Chloroflexi resulted as ubiquitous, while the phylum Actinobacteria was prevalent only at the thermophilic stage (day 42) in which most of sequences fell into the genus Arthrobacter . The authors registered that a short variation in bacterial diversity at the phylum and genus levels was checked as the composting process went on, contrary to what happened at the level of the species that instead, varied greatly. In addition, the candidate phylum BCR1 (Bacterial rice cluster 1) was originally revealed by phylogenetic analysis of 16S rRNA genes amplified from anoxic bulk soil of flooded rice microcosms and found by molecular methods in diverse environments (soils, activated sludge, anaerobic digesters and wastewater treatment reactors, marine and freshwater sediments, geothermal springs), and sequences belonging to it were recorded also in the Lian Ye composting plant. In conclusion, the lower bacterial diversity at the species level was found at the thermophilic stage of process. In contrast, a high level of mesophilic bacterial diversity was observed in the cured compost.

In a recent paper, Zhong et al. [ 22 ] studied the dynamic changes in structure and potential function of microbial population during dairy manure composting process. The bacterial community dynamics and diversity were monitored throughout the composting processes by the Illumina MiSeq platform for high-throughput sequencing. Potential function of bacterial community was predicted by advanced bioinformatics tools (e.g., co-occurrence network analysis and PICRUSt). Although the highest alpha diversity was observed in the initial samples, most of the species were not functional microbes for the composting reaction because a self-purification mechanism appears in order to eliminate the undesired microbes and develops the microbial community able to degrade the raw material. The evolution of bacterial community during composting was observed, and a total of 21 phyla were detected throughout the dairy manure composting process, with Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Chloroflexi, and Planctomycetes as the dominant. In the initial phases of composting, genera from Firmicutes and Actinobacteria such as Corynebacterium, Clostridium sensu stricto, Clostridium XI, Romboutsia and Turicibacter , were significantly higher. Corynebacterium, Clostridium, Romboutsia and Turicibacter have been reported to be the dominant species in raw dairy manure. In addition, other genera such as Bacillus and Geobacillus from the phylum Firmicutes, were found in the thermophilic phase of composting and they are generally considered relevant thermophilic decomposers. In the cooling phase, the relative abundance of the phylum Proteobacteria was significantly predominant and at the genus level , Chelatococcus, Filomicrobium, Chelativorans, Kofleria, Azomonas, Povalibacter and Luteimonas were identified as indicators for this phase. Moreover, the genera Thermobifida and Thermomonospora affiliating to Actinobacteria were also found in the cooling phase. Other dominant Actinobacteria, such as Mycobacterium , Nonomuraea and Actinomadura had significantly higher relatively abundance in maturation phase. The phylum Bacteroidetes was dominant in the cooling phase while the phylum Planctomycetes was dominant in the maturation step. Regarding the function profile, PICRUSt indicated that the bacterial metabolism changed as the composting progressed, suggesting that a specific metabolic function was required in different composting steps. These findings will provide significant information for a better understanding of the function and the structure of microbial community during the composting ecosystem.

Even in the case of food processes that have suffered an error during the line of production, the resulted waste can be used in the composting processes for the recovery of organic material. This is the case described by Papale et al. [ 83 ], in which waste biomass coming from a local coffee company, which supplied burnt ground coffee after an incorrect roasting process, was employed as a biomass to be used in the composting plan. Genomic and predictive metabolic analysis of the 16S rRNA V3–V4 amplicon and culture-dependent analysis were both used to identify the main microbial factors that characterized the composting process. The abundance of the Bacillales order (phylum Firmicutes) indicated a shift from the mesophilic to the thermophilic phase of the composting processes, confirming the ability to degrade coffee constituents, such as cellulose and hemicellulose, by members of Bacillales. The finding of archaeal component dominated by ammonia-oxidizing Archaea (AOA) within Thaumarchaeota, led to suppose that ammonification (a process in which ammonium can be released from organic nitrogen when present in the fresh substrates) could be a prevalent process in the early thermophilic phase of the ground coffee compost.

Next‑generation sequencing and amplification of 16S rRNA and Internal Transcribed Spacer (ITS) gene amplicons genomic regions of bacteria and fungi, respectively, were employed to analyse the DNA extracted from textile waste compost samples taken both at mesophilic (week 9) and thermophilic phases (week 28) of the process [ 20 ]. It was observed that Proteobacteria, Bacteroidetes, and Actinobacteria were the dominant bacterial phyla present in the mesophilic phase, but they did not find them in the thermophilic phase. Composting of textile waste exhibited a sustained thermophilic profile (above 55 °C) that usually precludes fungal activity. Nonetheless, the presence of fungi at the thermophilic phase was observed. In particular, the phyla Rozellomycota, Basidiomycota, and Ascomycota were recorded both in mesophilic and in thermophilic steps.

To summarize, depending on the raw materials and the operations of the composting process, different microbial communities can arise, each characterized by different internal interaction and response to physico-chemical properties. As a direct consequence, the application of metagenomics to study these microorganisms. The functional metagenomics essentially applies two different methodological approaches, being the Sequencing-based approach and the Functional-based screening, and both constitute the most important way to discover novel extremozymes useful for industrial purposes.

Although molecular techniques are of increasing usage in the elucidation of microbial community, the isolation of microorganisms remains an essential task both to relate taxonomic and metabolic diversity of organisms and to recover relevant species as purified microorganism for further use. In fact, composting ecosystem represents a rich source for the isolation of microorganisms that are useful, e.g., inoculants for composting and producers of enzymes that hydrolyze polymers or degrade recalcitrant compounds. Despite the broad research in this field, the potential of this ecosystem for the discovery of novel microorganisms and secondary metabolites is far from being fully exploited, especially considering that each process and raw materials may provide different strains.

Composting example: recovery of olive oil waste in the LIFE TIRSAV PLUS project

In the EU producing countries, it takes place 70–75% of the world’s olive oil production, with 1,920,000 tn produced in the 2019/2020 olive-growing season (COI 2020 data). Olive groves are located in nine EU Member States in the Mediterranean region and cover an area slightly lower than 5 million hectares. The environmental impact of the olive sector is partly linked to the production of oil mill waste (OMW): olive mill wastewater (OMWW) and olive pomace waste (OPW). OMW is also a valuable resource of compounds useful through their recovery and enhancement.

As part of the LIFE TIRSAV PLUS project (LIFE05 ENV/IT/00845), it has been developed an industrial composting process able to recover all the waste from the olive sector to produce quality compost. The composting process developed is independent from the extraction systems in use at the mills, such as: the two-phase continuous cycle processes, whose products are olive oil and wet pomace, and the three-phase continuous cycle systems which produce olive oil, wastewater, and olive pomace. In addition, the flexibility of the TIRSAV PLUS composting system allows the recovery of different organic waste from other production chains within the same process. The physico-chemical characteristics of the oil mill waste need the definition of appropriate mixing ratios between the various matrices used, due to their low total nitrogen content (0.96% on average) which is unbalanced compared to the total organic carbon values (60.45%) for the purposes of the controlled bio-oxidation process.

OMW is a relevant organic resource to be returned to agricultural soils, however composting technologies aimed at the recovery of these organic matrices have never become established in the olive oil industry due to the initial investment, the necessary management costs, and the expertise required. The composting process of TIRSAV PLUS is based on the combination of management and process choices able to adapt to the territorial and productive context in which the plant operates. The management solutions adopted in response to the problems highlighted are based on the centralization of composting activities within a single production area, meaning one plant for several mills. This choice allows to reduce management costs and ensures effective process control for a specialized operator.

The composting method developed by LIFE TIRSAV PLUS employs two plant solutions. The first is the Static Composting (SC), a static system which consists of a pre-treatment phase, a slow ripening phase and eventually a refining phase. The second is the dynamic composting (DC), a dynamic system divided into a pre-treatment phase, a comparatively rapid ripening phase, and in the end a refining phase (Fig.  1 ).

Scheme of the composting process of the oil waste composting plant realized by the LIFE TIRSAV PLUS project. DC dynamic composting, OMPW olive mill pomace waste, OMWW olive mill wastewater, PR pruning residues, SBS Sawdust, bark, and shavings, SC static composting, WW wool waste

The first phase of the process, common to both SC and DC systems, concerns the preparation of the mixtures to be started at composting. The choice of the composition of the mixtures depends on the composting system to be activated and on the substrate to be produced, i.e., mixed compost soil improver or green compost soil improver. The starting matrices were vegetation waters, virgin olive pomace, wet pomace, Pruning Residues (PR), lignocellulosic materials such as Sawdust, Bark and Shavings (SBS), and raw Wool Waste (WW). Table 5 shows some of the experimental mixtures developed during the first phase of the TIRSAV PLUS process, indicated as A, B and C.

The mixing ratios aimed to obtain mixtures with physico-chemical characteristics as close as possible to the standard parameters required by an efficient composting process. The analytical values of the experimented mixtures A, B and C are reported in Table 6 .

The pre-treatment phase, from a plant engineering point of view, consists of three stages: (1) the pruning residues shredding; (2) the mixing of olive pomace and vegetation water from three-stage continuous cycle mills (in the case of the two-stage extraction systems this step is not activated); and (3) the second-level mixing and homogenization of all matrices provided in the composition of the recipes (Fig.  2 ).

Functional diagram of the pre-treatment phase. OMPW olive mill pomace waste, OMWW olive mill wastewater, PR pruning residues, SBS sawdust, bark, and shavings, WW wool waste

The mixtures produced in the pre-treatment phase are sent to the composting lines. The composting process consists of 4 phases, meaning mesophilic I, thermophilic, mesophilic II and maturation. As part of the LIFE TIRSAV PLUS project, two process lines have been created that refer to the DC and SC systems.

In the case of the DC System, it is structured in three combined phases (Fig.  3 ):

A single system solution in biocontainer for the mesophilic I and thermophilic phases (accelerated bio-oxidation). The duration of this phase varies from 14 to 21 days and the process is entirely controlled by a Programmable Logic Controller (PLC) system that monitors temperature and humidity.

A second phase where the material at the exit of the biocontainer is placed in heaps on panels with forced ventilation (mesophilic phase II) and covered with waterproof and breathable sheets. This phase lasts about 30 days, and the temperature is the controlled process parameter.

The third stage is the final ripening. It is managed in static and not ventilated heaps under roofing, and it does not require specific equipment except for a front loader for the periodic turning of the heaps. The duration of the final maturation period is variable, and it depends on the type of mixtures, ranging from 50 to 60 days.

Functional scheme of the phases of maturation DC and SC. SC static composting, DC dynamic composting

Instead, in the slow ripening line SC (Fig.  3 ), the process can follow two alternative process lines. In the first, the mixture is loaded into aerated plastic containers (bins or big bags) and stored in a dry place. In the second line, the mixtures are placed in static heaps indoors, about 2.5 m high, which are turned over periodically until complete ripening. For both SC lines there is no automatic process control system but a manual and periodic temperature measurement. The ripening time can be very long, ranging from 3 to 6 months, and the quality of the compost is not always optimal for any market due to the coarse size and the excessive dehydration. The SC process has been designed to significantly reduce the plant and the management costs, and to meet the needs of companies interested in returning a mature compost in their own fields.

At the end of the maturation process, the compost is processed with a dimensional separation to eliminate the coarse elements, such as lignocellulosic residues not completely degraded, lumps of compact compost and impurities. The compost is stored in big bags, or it is given directly to companies for use in the field. The fine fraction (residues of lignocellulosic and lumps of compost) is reused for the preparation of the mixtures to be started at composting. The compost produced by the plant meets high-quality criteria, making it certifiable and usable in organic farming. The main analytical values of some of the soil improvers produced by the LIFE TIRSAV PLUS plants are shown in Table 7 . The two composts are classified according to the Italian legislation on fertilizers as mixed compost and green compost.

To summarize, the composting method of oil residues developed by the LIFE TISAV PLUS project includes the preparation and the pretreatment of the starting mixtures, followed by the employment of two plant solutions, being the dynamic composting and the static composting. This results in an effective, flexible, and economical process which significantly reduces the environmental impact of the olive sector and contributes to the maintenance of soil fertility, with the production of a high-quality compost.

Availability of data and materials

The data presented in this study are available in the article.

Abbreviations

Ammonia-oxidizing archaea

Amplified ribosomal DNA analysis

Bacterial rice cluster 1

Carbohydrate active enzyme

CO 2 evolution method

Dynamic composting

Degradation

Heat balance

Heating value

Heating value method

Internal transcribed spacer

Oxygen consumption

Olive mill pomace waste

Oil mill waste

Olive mill wastewater

Olive pomace waste

Olive pomace wastewater

Organic solid waste

Programmable logic controller.PR: Pruning residues

Randomly amplified polymorphic DNA

Rice straw-adapted

Sawdust, bark and shavings

Static composting

Temperature method

Raj D, Antil RS. Evaluation of maturity and stability parameters of composts prepared from agro-industrial wastes. Bioresour Technol. 2011;102:2868–73. https://doi.org/10.1016/j.biortech.2010.10.077 .

Article   CAS   Google Scholar  

Shepherd MW, Liang P, Jiang X, Doyle MP, Erickson MC. Microbiological analysis of composts produced on South Carolina poultry farms. J Appl Microbiol. 2010;108:2067–76. https://doi.org/10.1111/j.1365-2672.2009.04610.x .

Article   Google Scholar  

Gajalakshmi S. High-rate composting–vermicomposting of water hyacinth ( Eichhornia crassipes , Mart. Solms). Bioresour Technol. 2002;83:235–9. https://doi.org/10.1016/s0960-8524(01)00216-4 .

Li D, Yuan J, Ding J, Wang H, Shen Y, Li G. Effects of carbon/nitrogen ratio and aeration rate on the sheep manure composting process and associated gaseous emissions. J Environ Manage. 2022;323:1–11. https://doi.org/10.1016/j.jenvman.2022.116093 .

Singh J, Kalamdhad AS. Assessment of compost quality in agitated pile composting of water hyacinth collected from different sources. Int J Recycl Org Waste Agricult. 2015;4:175–83. https://doi.org/10.1007/s40093-015-0097-z .

Prasad R, Singh J, Kalamdhad A. Assessment of nutrients and stability parameters during composting of water hyacinth mixed with cattle manure and sawdust. Res J Chem Sci. 2013;3:70–7.

Google Scholar  

Awasthi SK, Kumar M, Sarsaiya S, Ahluwalia V, Chen H, Kaur G, Sirohi R, Sindhu R, Binod P, Pandey A, Rathour R, Kumar S, Singh L, Zhang Z, Taherzadeh MJ, Awasthi MK. Multi-criteria research lines on livestock manure biorefinery development towards a circular economy: from the perspective of a life cycle assessment and business models strategies. J Clean Prod. 2022;341:1–20. https://doi.org/10.1016/j.jclepro.2022.130862 .

Nemet F, Perić K, Lončarić Z. Microbiological activities in the composting process: a review. J Agric Environ Sci. 2021;8:41–53. https://doi.org/10.1016/j.biortech.2018.10.020 .

Wainaina S, Awasthi MK, Sarsaiya S, Chen H, Singh E, Kumar A, Ravindran B, Awasthi SK, Liu T, Duan Y, Kumar S, Zhang Z, Taherzadeh MJ. Resource recovery and circular economy from organic solid waste using aerobic and anaerobic digestion technologies. Bioresour Technol. 2020;301:1–14. https://doi.org/10.1016/j.biortech.2020.122778 .

Bhattacharya A, Pletschke BI. Thermophilic Bacilli and their enzymes in composting. In: Maheshwari D, editor. Composting for sustainable agriculture. Cham: Springer; 2014. p. 103–24.

Gao M, Liang F, Yu A, Li B, Yang L. Evaluation of stability and maturity during forced-aeration composting of chicken manure and sawdust at different C/N ratios. Chemosphere. 2010;78:614–9. https://doi.org/10.1016/j.chemosphere.2009.10.056 .

Awasthi MK, Sarsaiya S, Wainaina S, Rajendran K, Kumar S, Quan W, Duan Y, Awasthi SK, Chen H, Pandey A, Zhang Z, Jain A, Taherzadeh MJ. A critical review of organic manure biorefinery models toward sustainable circular bioeconomy: technological challenges, advancements, innovations, and future perspectives. Renew Sustain Energ Rev. 2019;111:115–31. https://doi.org/10.1016/j.rser.2019.05.017 .

Neklyudov AD, Fedotov GN, Ivankin AN. Intensification of composting processes by aerobic microorganisms: a review. Appl Biochem Microbiol. 2008;44:6–18. https://doi.org/10.1007/s10438-008-1002-6 .

Golueke CG, Diaz LF. Potential useful products from solid wastes. Waste Manag Res. 1991;9:415–23. https://doi.org/10.1177/0734242X9100900159 .

Zhang S, Wang J, Chen X, Gui J, Sun Y, Wu D. Industrial-scale food waste composting: effects of aeration frequencies on oxygen consumption, enzymatic activities, and bacterial community succession. Bioresour Technol. 2021;320:1–10. https://doi.org/10.1016/j.biortech.2020.124357 .

Dadi D, Daba G, Beyene A, Luis P, Van der Bruggen B. Composting and co-composting of coffee husk and pulp with source-separated municipal solid waste: a breakthrough in valorization of coffee waste. Int J Recycl Org Waste Agric. 2019;8:263–77. https://doi.org/10.1007/s40093-019-0256-8 .

Antil RS, Raj D, Abdalla N, Inubushi K. Physical, chemical and biological parameters for compost maturity assessment: a review. In: Maheshwari DK, editor. Composting for sustainable agriculture. Cham: Springer; 2014. p. 83–101.

Wang B, Wang Y, Wei Y, Chen W, Ding G, Zhan Y, Liu Y, Xu T, Xiao J, Li J. Impact of inoculation and turning for full-scale composting on core bacterial community and their co-occurrence compared by network analysis. Bioresour Technol. 2022;345:1–7. https://doi.org/10.1016/j.biortech.2021.126417 .

Karnchanawong S, Nissaikla S. Effects of microbial inoculation on composting of household organic waste using passive aeration bin. Int J Recycl Org Waste Agric. 2014;3:113–9. https://doi.org/10.1007/s40093-014-0072-0 .

Biyada S, Merzouki M, Dėmčėnko T, Vasiliauskienė D, Ivanec-Goranina R, Urbonavičius J, Marčiulaitienė E, Vasarevičius S, Benlemlih M. Microbial community dynamics in the mesophilic and thermophilic phases of textile waste composting identified through next-generation sequencing. Sci Rep. 2021;11:1–11. https://doi.org/10.1038/s41598-021-03191-1 .

Tian W, Sun Q, Xu D, Zhang Z, Chen D, Li C, Shen Q, Shen B. Succession of bacterial communities during composting process as detected by 16S rRNA clone libraries analysis. Int Biodeterior Biodegradation. 2013;78:58–66. https://doi.org/10.1016/j.ibiod.2012.12.008 .

Zhong X-Z, Li X-X, Zeng Y, Wang S-P, Sun Z-Y, Tang Y-Q. Dynamic change of bacterial community during dairy manure composting process revealed by high-throughput sequencing and advanced bioinformatics tools. Bioresour Technol. 2020;306:1–10. https://doi.org/10.1016/j.biortech.2020.123091 .

Liu Q, He X, Luo G, Wang K, Li D. Deciphering the dominant components and functions of bacterial communities for lignocellulose degradation at the composting thermophilic phase. Bioresour Technol. 2022;348:1–10. https://doi.org/10.1016/j.biortech.2022.126808 .

He Y, Xie K, Xu P, Huang X, Gu W, Zhang F, Tang S. Evolution of microbial community diversity and enzymatic activity during composting. Res Microbiol. 2013;164:189–98. https://doi.org/10.1016/j.resmic.2012.11.001 .

Barthod J, Rumpel C, Dignac M-F. Composting with additives to improve organic amendments: a review. Agron Sustain Dev. 2018;38:1–23. https://doi.org/10.1007/s13593-018-0491-9 .

Zhou Y, Xiao R, Klammsteiner T, Kong X, Yan B, Mihai FC, Liu T, Zhang Z, Awasthi MK. Recent trends and advances in composting and vermicomposting technologies: a review. Bioresour Technol. 2022;360:1–13. https://doi.org/10.1016/j.biortech.2022.127591 .

Shan G, Li W, Gao Y, Tan W, Xi B. Additives for reducing nitrogen loss during composting: a review. J Clean Prod. 2021;307:1–15. https://doi.org/10.1016/j.jclepro.2021.127308 .

Abdellah YAY, Shi Z-J, Luo Y-S, Hou W-T, Yang X, Wang R-L. Effects of different additives and aerobic composting factors on heavy metal bioavailability reduction and compost parameters: a meta-analysis. Environ Pollut. 2022;307:1–11. https://doi.org/10.1016/j.envpol.2022.119549 .

Awasthi MK, Duan Y, Awasthi SK, Liu T, Zhang Z. Influence of bamboo biochar on mitigating greenhouse gas emissions and nitrogen loss during poultry manure composting. Bioresour technol. 2020;303:1–10. https://doi.org/10.1016/j.biortech.2020.122952 .

Awasthi SK, Duan Y, Liu T, Zhang Z, Pandey A, Varjani S, Awasthi MK, Taherzadeh MJ. Can biochar regulate the fate of heavy metals (Cu and Zn) resistant bacteria community during the poultry manure composting? J Hazard Mater. 2021;406:1–9. https://doi.org/10.1016/j.jhazmat.2020.124593 .

Awasthi MK, Duan Y, Awasthi SK, Liu T, Chen H, Pandey A, Zhang Z, Taherzadeh MJ. Emerging applications of biochar: Improving pig manure composting and attenuation of heavy metal mobility in mature compost. J Hazard Mater. 2020;389:1–9. https://doi.org/10.1016/j.jhazmat.2020.122116 .

Fan S, Li A, ter Heijne A, Buisman CJN, Chen WS. Heat potential, generation, recovery and utilization from composting: a review. Resour Conserv Recycl. 2021;175:1–15. https://doi.org/10.1016/j.resconrec.2021.105850 .

Ajmal M, Aiping S, Uddin S, Awais M, Faheem M, Ye L, Rehman K, Ullah MS, Shi Y. A review on mathematical modeling of in-vessel composting process and energy balance. Biomass Convers Biorefin. 2022;12:4201–13. https://doi.org/10.1007/s13399-020-00883-y .

He P, Zhao L, Zheng W, Wu D, Shao L. Energy balance of a biodrying process for organic wastes of high moisture content: a review. Dry Technol. 2013;31:132–45. https://doi.org/10.1080/07373937.2012.693143 .

Wang Y, Huang G, Zhang A, Han L, Ge J. Estimating thermal balance during composting of swine manure and wheat straw: a simulation method. Int J Heat Mass Transf. 2014;75:362–7. https://doi.org/10.1016/j.ijheatmasstransfer.2014.03.083 .

de Bertoldi M, Vallini G, Pera A. The biology of composting: a review. Waste Manag Res. 1983;1:157–76. https://doi.org/10.1016/0734-242X(83)90055-1 .

Strom PF. Identification of thermophilic bacteria in solid-waste composting. Appl Environ Microbiol. 1985;50:906–13. https://doi.org/10.1128/aem.50.4.906-913.1985 .

Beffa T, Blanc M, Aragno M. Obligately and facultatively autotrophic, sulfur- and hydrogen-oxidizing thermophilic bacteria isolated from hot composts. Arch Microbiol. 1996;165:34–40. https://doi.org/10.1007/s002030050293 .

Beffa T, Blanc M, Lyon PF, Vogt G, Marchiani M, Fischer JL, Aragno M. Isolation of Thermus strains from hot composts (60 to 80 °C). Appl Environ Microbiol. 1996;62:1723–7. https://doi.org/10.1128/aem.62.5.1723-1727.1996 .

Blanc M, Marilley L, Beffa T, Aragno M. Thermophilic bacterial communities in hot composts as revealed by most probable number counts and molecular (16S rDNA) methods. FEMS Microbiol Ecol. 1999;28:141–9. https://doi.org/10.1111/j.1574-6941.1999.tb00569.x .

Blanc M, Marilley L, Beffa T, Aragno M. Rapid identification of heterotrophic, thermophilic, spore-forming bacteria isolated from hot composts. Int J Syst Bacteriol. 1997;47:1246–8. https://doi.org/10.1099/00207713-47-4-1246 .

Zhang YC, Ronimus RS, Turner N, Zhang YI, Morgan HW. Enumeration of thermophilic Bacillus species in composts and identification with a random amplification polymorphic DNA (RAPD) protocol. Syst Appl Microbiol. 2002;25:618–26. https://doi.org/10.1078/07232020260517760 .

Dees PM, Ghiorse WC. Microbial diversity in hot synthetic compost as revealed by PCR-amplified rRNA sequences from cultivated isolates and extracted DNA. FEMS Microbiol Ecol. 2001;35:207–16. https://doi.org/10.1111/j.1574-6941.2001.tb00805.x .

Ishii K, Fukui M, Takii S. Microbial succession during a composting process as evaluated by denaturing gradient gel electrophoresis analysis. J Appl Microbiol. 2000;89:768–77. https://doi.org/10.1046/j.1365-2672.2000.01177.x .

Schloss PD, Hay AG, Wilson DB, Walker LP. Tracking temporal changes of bacterial community fingerprints during the initial stages of composting. FEMS Microbiol Ecol. 2003;46:1–9. https://doi.org/10.1016/S0168-6496(03)00153-3 .

Peters S, Koschinsky S, Schwieger F, Tebbe CC. Succession of microbial communities during hot composting as detected by PCR–single-strand-conformation polymorphism-based genetic profiles of small-subunit rRNA genes. Appl Environ Microbiol. 2000;66:930–6. https://doi.org/10.1128/AEM.66.3.930-936.2000 .

Rawat S, Johri BN. Role of thermophilic microflora in composting. In: Satyanarayana T, Littlechild J, Kawarabayasi Y, editors. Thermophilic microbes in environmental and industrial biotechnology. Dordrecht: Springer; 2013. p. 137–69.

Chapter   Google Scholar  

Székely AJ, Sipos R, Berta B, Vajna B, Hajdú C, Márialigeti K. DGGE and T-RFLP analysis of bacterial succession during mushroom compost production and sequence-aided T-RFLP profile of mature compost. Microb Ecol. 2009;57:522–33. https://doi.org/10.1007/s00248-008-9424-5 .

Sung MH. Geobacillus toebii sp. nov., a novel thermophilic bacterium isolated from hay compost. Int J Syst Evol Microbiol. 2002;52:2251–5. https://doi.org/10.1099/ijs.0.02181-0 .

Poli A, Romano I, Caliendo G, Nicolaus G, Orlando P, de Falco A, Lama L, Gambacorta A, Nicolaus B. Geobacillus toebii subsp. decanicus subsp. Nov., a hydrocarbon-degrading, heavy metal resistant bacterium from hot compost. J Gen Appl Microbiol. 2006;52:223–34. https://doi.org/10.2323/jgam.52.223 .

Poli A, Laezza G, Gul-Guven R, Orlando P, Nicolaus B. Geobacillus galactosidasius sp. nov., a new thermophilic galactosidase-producing bacterium isolated from compost. Syst Appl Microbiol. 2011;34:419–23. https://doi.org/10.1016/j.syapm.2011.03.009 .

Yang G, Chen M, Yu Z, Lu Q, Zhou S. Bacillus composti sp. nov. and Bacillus thermophilus sp. nov., two thermophilic, Fe(III)-reducing bacteria isolated from compost. Int J Syst Evol Microbiol. 2013;63:3030–6. https://doi.org/10.1099/ijs.0.049106-0 .

Finore I, Gioiello A, Leone L, Orlando P, Romano I, Nicolaus B, Poli A. Aeribacillus composti sp. nov., a thermophilic bacillus isolated from olive mill pomace compost. Int J Syst Evol Microbiol. 2017;67:4830–5. https://doi.org/10.1099/ijsem.0.002391 .

Hemati A, Aliasgharzad N, Khakvar R, Khoshmanzar E, Asgari Lajayer B, van Hullebusch ED. Role of lignin and thermophilic lignocellulolytic bacteria in the evolution of humification indices and enzymatic activities during compost production. J Waste Manag. 2021;119:122–34. https://doi.org/10.1016/j.wasman.2020.09.042 .

Charbonneau DM, Meddeb-Mouelhi F, Boissinot M, Sirois M, Beauregard M. Identification of thermophilic bacterial strains producing thermotolerant hydrolytic enzymes from manure compost. Indian J Microbiol. 2012;52:41–7. https://doi.org/10.1007/s12088-011-0156-8 .

Zhu N, Gao J, Liang D, Zhu Y, Li B, Jin H. Thermal pretreatment enhances the degradation and humification of lignocellulose by stimulating thermophilic bacteria during dairy manure composting. Bioresour Technol. 2021;319:1–9. https://doi.org/10.1016/j.biortech.2020.124149 .

Ma C, Jin H, Hu B, Liu N, Zhang K, Zhao J-H, Zhang H-Z. Changes in enzyme activity and bacterial succession during sewage sludge composting. Nat Environ Pollut Technol. 2020;19:695–701. https://doi.org/10.46488/NEPT.2020.v19i02.024 .

Liu H, Huang Y, Wang H, Shen Z, Qiao C, Li R, Shen Q. Enzymatic activities triggered by the succession of microbiota steered fiber degradation and humification during co-composting of chicken manure and rice husk. J Environ Manage. 2020;258:1–9. https://doi.org/10.1016/j.jenvman.2019.110014 .

Qiao C, Ryan Penton C, Liu C, Shen Z, Ou Y, Liu Z, Xu X, Li R, Shen Q. Key extracellular enzymes triggered high-efficiency composting associated with bacterial community succession. Bioresour Technol. 2019;288:1–8. https://doi.org/10.1016/j.biortech.2019.121576 .

Du G, Feng W, Cai H, Ma Z, Liu X, Yuan C, Shi J, Zhang B. Exogenous enzyme amendment accelerates maturity and changes microflora succession in horse and wildlife animal manure co-composting. Environ Sci Pollut Res. 2021;28:21610–20. https://doi.org/10.1007/s11356-020-11568-4 .

Siu-Rodas Y, Calixto-Romo M de los A, Guillén-Navarro K, Sánchez JE, Zamora-Briseño JA, Amaya-Delgado L. Bacillus subtilis with endocellulase and exocellulase activities isolated in the thermophilic phase from composting with coffee residues. Rev Argent Microbiol. 2018;50:234–43; doi: https://doi.org/10.1016/j.ram.2017.08.005

Miyatake F, Iwabuchi K. Effect of high compost temperature on enzymatic activity and species diversity of culturable bacteria in cattle manure compost. Bioresour Technol. 2005;96:1821–5. https://doi.org/10.1016/j.biortech.2005.01.005 .

Kong Z, Wang X, Wang M, Chai L, Wang X, Liu D, Shen Q. Bacterial ecosystem functioning in organic matter biodegradation of different composting at the thermophilic phase. Bioresour Technol. 2020;317:1–12. https://doi.org/10.1016/j.biortech.2020.123990 .

Bougnom BP, Boyomo O, Nwaga D, Essia Ngang JJ, Etoa FX. Compost: a tool to sustainable urban and peri-urban agriculture in Sub-Saharan Africa? In: Maheshwari D, editor. Composting for sustainable agriculture. Cham: Springer; 2014. p. 269–83.

Jiang J, Wang Y, Yu D, Yao X, Han J, Cheng R, Cui H, Yan G, Zhang X, Zhu G. Garbage enzymes effectively regulated the succession of enzymatic activities and the bacterial community during sewage sludge composting. Bioresour Technol. 2021;327:1–10. https://doi.org/10.1016/j.biortech.2021.124792 .

Castaldi P, Garau G, Melis P. Maturity assessment of compost from municipal solid waste through the study of enzyme activities and water-soluble fractions. Waste Manag. 2008;28:534–40. https://doi.org/10.1016/j.wasman.2007.02.002 .

Huang Y, Li D, Wang L, Yong C, Sun E, Jin H, Huang H. Decreased enzyme activities, ammonification rate and ammonifiers contribute to higher nitrogen retention in hyperthermophilic pretreatment composting. Bioresour Technol. 2019;272:521–8. https://doi.org/10.1016/j.biortech.2018.10.070 .

Du J, Zhang Y, Qu M, Yin Y, Fan K, Hu B, Zhang H, Wei M, Ma C. Effects of biochar on the microbial activity and community structure during sewage sludge composting. Bioresour Technol. 2019;272:171–9. https://doi.org/10.1016/j.biortech.2018.10.020 .

Li C, Li H, Yao T, Su M, Li J, Liu Z, Xin Y, Wang L, Chen J, Gun S. Effects of microbial inoculation on enzyme activity, available nitrogen content, and bacterial succession during pig manure composting. Bioresour Technol. 2020;306:1–12. https://doi.org/10.1016/j.biortech.2020.123167 .

López MJ, Jurado MM, López-González JA, Estrella-González MJ, Martínez-Gallardo MR, Toribio A, Suárez-Estrella F. Characterization of thermophilic lignocellulolytic microorganisms in composting. Front Microbiol. 2021;12:1–13. https://doi.org/10.3389/fmicb.2021.697480 .

Dhagat S, Jujjavarapu SE. Utility of lignin-modifying enzymes: a green technology for organic compound mycodegradation. J Chem Technol Biotechnol. 2022;97:343–58. https://doi.org/10.1002/jctb.6807 .

Gopalan N, Nampoothiri KM. Biotechnological production of enzymes using agro-industrial wastes. In: Dhillon GS, Kaur S, editors. Agro-industrial wastes as feedstock for enzyme production. San Diego: Elsevier; 2016. p. 313–30.

Handelsman J. Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev. 2004;68:669–85. https://doi.org/10.1128/MMBR.68.4.669-685.2004 .

Handelsman J, Rondon MR, Brady SF, Clardy J, Goodman RM. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chem Biol. 1998;5:R245–9. https://doi.org/10.1016/S1074-5521(98)90108-9 .

Chen K, Pachter L. Bioinformatics for whole-genome shotgun sequencing of microbial communities. PLoS Comput Biol. 2005;1:1–7. https://doi.org/10.1371/journal.pcbi.0010024 .

Amann RI, Ludwig W, Schleifer KH. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol Rev. 1995;59:143–69. https://doi.org/10.1128/mr.59.1.143-169.1995 .

Antunes LP, Martins LF, Pereira RV, Thomas AM, Barbosa D, Lemos LN, Silva GMM, Moura LMS, Epamino GWC, Digiampietri LA, Lombardi KC, Ramos PL, Quaggio RB, de Oliveira JCF, Pascon RC, Cruz JB, Silva AM, Setubal JC. Microbial community structure and dynamics in thermophilic composting viewed through metagenomics and metatranscriptomics. Sci Rep. 2016;6:1–13. https://doi.org/10.1038/srep38915 .

Jurado M, López MJ, Suárez-Estrella F, Vargas-García MC, López-González JA, Moreno J. Exploiting composting biodiversity: study of the persistent and biotechnologically relevant microorganisms from lignocellulose-based composting. Bioresour Technol. 2014;162:283–93. https://doi.org/10.1016/j.biortech.2014.03.145 .

López-González JA, López MJ, Vargas-García MC, Suárez-Estrella F, Jurado M, Moreno J. Tracking organic matter and microbiota dynamics during the stages of lignocellulosic waste composting. Bioresour Technol. 2013;146:574–84. https://doi.org/10.1016/j.biortech.2013.07.122 .

Lv B, Xing M, Yang J, Zhang L. Pyrosequencing reveals bacterial community differences in composting and vermicomposting on the stabilization of mixed sewage sludge and cattle dung. Appl Microbiol Biotechnol. 2015;99:10703–12. https://doi.org/10.1007/s00253-015-6884-7 .

Wei H, Wang L, Hassan M, Xie B. Succession of the functional microbial communities and the metabolic functions in maize straw composting process. Bioresour Technol. 2018;256:333–41. https://doi.org/10.1016/j.biortech.2018.02.050 .

Wang C, Dong D, Wang H, Müller K, Qin Y, Wang H, Wu W. Metagenomic analysis of microbial consortia enriched from compost: new insights into the role of Actinobacteria in lignocellulose decomposition. Biotechnol Biofuels. 2016;9:1–17. https://doi.org/10.1186/s13068-016-0440-2 .

Papale M, Romano I, Finore I, Lo Giudice A, Piccolo A, Cangemi S, Di Meo V, Nicolaus B, Poli A. Prokaryotic diversity of the composting thermophilic phase: the case of ground coffee compost. Microorganisms. 2021;9:1–19. https://doi.org/10.3390/microorganisms9020218 .

Download references

Acknowledgements

Not applicable.

The present work was partially supported by Fondazione Giorgio Armani and LIFE TIRSAV PLUS project (LIFE05 ENV/IT/00845).

Author information

Authors and affiliations.

Institute of Biomolecular Chemistry (ICB), Consiglio Nazionale Delle Ricerche (CNR), Via Campi Flegrei 34, Pozzuoli, 80078, Naples, Italy

Ilaria Finore, Ludovica Russo, Andrea Cattaneo, Paola Di Donato, Barbara Nicolaus, Annarita Poli & Ida Romano

Studio BAT-Biologia, Ambiente E Territorio, Via Civitella 25, 84060 Moio Della Civitella, Salerno, Italy

Antonio Feola

Department of Science and Technology, University “Parthenope”, Centro Direzionale–Isola C4, 80143, Naples, Italy

Paola Di Donato

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, AP, IR, IF and BN; literature search, AP, AC, LR, IR, IF and PDD; data curation, AC, LR, AF and PDD; writing—original draft preparation, AP, AF, IF and AC; writing—review and editing, AP, IR, IF, AC and LR: supervision, AP, IF and BN; funding acquisition, AP, BN and AF. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Annarita Poli .

Ethics declarations

Ethics approval and consent to participate, consent for publication.

All the authors gave their consent for publication of this article.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Finore, I., Feola, A., Russo, L. et al. Thermophilic bacteria and their thermozymes in composting processes: a review. Chem. Biol. Technol. Agric. 10 , 7 (2023). https://doi.org/10.1186/s40538-023-00381-z

Download citation

Received : 29 October 2022

Accepted : 11 January 2023

Published : 27 January 2023

DOI : https://doi.org/10.1186/s40538-023-00381-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Thermophilic phase
• Thermodynamics
• Metagenomics
• Thermophilic bacteria

• Open access
• Published: 14 January 2017

Comparative effectiveness of different composting methods on the stabilization, maturation and sanitization of municipal organic solid wastes and dried faecal sludge mixtures

• Tesfu Mengistu 1 ,
• Heluf Gebrekidan 1 ,
• Kibebew Kibret 1 ,
• Kebede Woldetsadik 2 ,
• Beneberu Shimelis 1 &
• Hiranmai Yadav 1  

Environmental Systems Research volume  6 , Article number:  5 ( 2018 ) Cite this article

21k Accesses

39 Citations

1 Altmetric

Metrics details

Composting is one of the integrated waste management strategies used for the recycling of organic wastes into a useful product. Composting methods vary in duration of decomposition and potency of stability, maturity and sanitation. This study was aimed to investigate the comparative effectiveness of four different methods of composting viz. windrow composting (WC), Vermicomposting (VC), pit composting (PC) and combined windrow and vermicomposting (WVC) on the stabilization, maturation and sanitization of mixtures of municipal solid organic waste and dried faecal sludge.

The composting treatments were arranged in a completely randomized block design with three replications. The changes in physico-chemical and biological characteristics of the compost were examined at 20 days interval for 100 days using standard laboratory procedures. The analysis of variance was performed using SAS software and the significant differences were determined using Fisher’s LSD test at P ≤ 0.05 level.

The evolution of composting temperature, pH, EC, \({\text{NH}}_{ 4}^{ + }\) , \({\text{NO}}_{ 3}^{ - }\) , \({\text{NH}}_{ 4}^{ + }\) : \({\text{NO}}_{ 3}^{ - }\) ratio, OC, C:N ratio and total volatile solids varied significantly among the composting methods and with composting time. The evolution of total nitrogen and germination index also varied significantly (P ≤ 0.001) with time, but their variation among the composting methods was not significant (P > 0.05). Except for PC, all other methods of composting satisfied all the indices for stability/maturity of compost at the 60th day of sampling; whereas PC achieved the critical limit values for most of the indices at the 80th day. A highly significant differences (P ≤ 0.001) were noted among the composting methods with regard to their effectiveness in eliminating pathogens (faecal coliforms and helminth eggs). The WVC method was most efficient in eliminating the pathogens complying with WHO’s standard.

Turned windrow composting and composting involving earthworms hastened the biodegradation process of organic wastes and result in the production of stable compost earlier than the traditional pit method of composting. The WVC method is most efficient in keeping the pathogens below the threshold level. Thus, elimination of pathogens from composts being a critical consideration, this study would recommend this method for composting organic wastes involving human excreta.

As in many other cities of the developing countries, the rapid urbanization and high population growth of Dire Dawa (Ethiopia’s 2nd largest city) have resulted into a significant increase in generation of wastes from domestic and commercial activities, posing numerous questions concerning the adequacy of the current waste management systems, and their associated environmental, economical and social implications. A report by Beneberu et al. ( 2012 ) depicted that, despite the great efforts made by the Dire Dawa city municipality, it has been hardly possible to meet the ever-increasing waste management service demand of the city adequately and effectively. The per capita waste generation rate of the city is reported to be 0.3 kg day −1 and the city generates an estimated quantity of 77 tonnes of solid wastes per day (Community Development Research 2011 ). The same report indicated that, as there is very limited or no effort to recycle, reuse or recover the waste that is being generated; waste disposal has been the major mode of waste management practice. It has been observed that the indiscriminate dumping of wastes into the landfill is resulting in unexpectedly faster filling up of the city’s sanitary landfill which would, thus, likely be abandoned in the near future than anticipated 30 years (Beneberu et al. 2012 ).

In addition to the municipal solid wastes (MSW), the human excreta also constitute a significant component of wastes generated from Dire Dawa city. Faecal sludge (FS) accumulating in the commonly used on-site sanitation systems are periodically collected and dumped indiscriminately into its well-engineered sludge dewatering and drying bed. The faecal sludge, after being dried in the beds, since it has no purpose in Dire Dawa, was observed to be excavated from the drying beds and disposed in the landfill site. It is, therefore, of paramount importance to establish economically viable, environmentally sustainable and socially acceptable method of waste management for the sustainable development of the city.

Bundela et al. ( 2010 ) suggested that agricultural application of organic solid wastes, as nutrient source for plants and as soil conditioner, is the most cost effective municipal solid waste (MSW) disposal option because of its advantages over traditional means, such as land filling or incineration. Though, human wastes are a rich source of organic matter and inorganic plant nutrients and therefore used to support food production, their use without prior stabilization represents a high risk because of the potentially negative effects of any phytotoxic substances or pathogens they may contain (Garcia et al. 1993 ). Application of raw wastes may inhibit seed germination, reduce plant growth and damage crops by competing for oxygen or causing phytotoxicity to plants due to insufficient biodegradation of organic matter (Brewer and Sullivan 2003 ; Cooperband et al. 2003 ). Moreover, the reuse of untreated faeces for agricultural purposes can cause a great health risk, because a great number of pathogens such as bacteria, viruses and helminthes can be found in human excreta (Gallizzi 2003 ). Therefore, the management of urban solid wastes involving human excreta for recycling in agriculture should necessarily incorporate sanitization, stabilization and maturation aspects to minimize potential disease transmission and to obtain a more stabilized and matured product for application to soil (Carr et al. 1995 ).

Composting and vermicomposting are two of the best-known processes for biological stabilization of solid organic wastes by transforming them into a safer and more stabilized material that can be used as a source of nutrients and soil conditioner in agricultural applications (Lazcano et al. 2008 ; Bernal et al. 2009 ; Domínguez and Edwards 2010 ). Composting involves the accelerated degradation of organic matter by microorganisms under controlled conditions, in which the organic material undergoes a characteristic thermophilic stage that allows sanitization of the waste by elimination of pathogenic microorganisms (Lung et al. 2001 ). Vermicomposting, on the other hand, is emerging as the most appropriate alternative to conventional aerobic composting (Yadav et al. 2010 ) and it involves the bio-oxidation and stabilization of organic material by the joint action of earthworms and microorganisms (Lazcano et al. 2008 ). More recently, combining thermophilic composting and vermicomposting has been considered as a way of achieving stabilized substrates (Tognetti et al. 2007 ). Thermophilic composting results in sanitization of wastes and elimination of toxic compounds while the subsequent vermicomposting reduces particle size and increases nutrient availability (Mupondi et al. 2010 ).

Composting methods differ in duration of decomposition and potency of stability and maturity (Iqbal et al. 2012 ). Due to the ecological and health concerns of human wastes, extensive research has been conducted to study the composting process and to evaluate methods to describe the stability, maturity and sanitation of compost prior to its agricultural use (Brewer and Sullivan 2003 ; Zmora-Nahum et al. 2005 ). Although several studies have addressed the optimization of composting, vermicomposting or composting with subsequent vermicomposting of various organic wastes (Dominguez et al. 1997 ; Frederickson et al. 1997 ; Ndegwa and Thompson 2001 ; Tognetti et al. 2005 , 2007 ; Lazcano et al. 2008 ; Mupondi et al. 2010 ), information on the effectiveness of the different composting methods on biodegradation and sanitization of mixtures of MSW and dried faecal sludge (DFS) is scant. Moreover, regarding the sanitization efficiency of the different composting techniques, controversial reports have been presented in different literatures. Several researchers reported the effectiveness of thermophilic composting in eliminating pathogenic organisms (Koné et al. 2007 ; Vinnerås 2007 ; Mupondi et al. 2010 ). However, a few studies on composting of source-separated faeces claimed that a sufficiently high temperature for pathogen destruction is difficult to achieve (Bjorklund 2002 ; Niwagaba et al. 2009 ). Similarly, in vermicomposting, some studies have provided evidence of suppression of pathogens (Monroy et al. 2008 ; Rodriguez-Canche et al. 2010 ; Eastman et al. 2001 ), while others (Bowman et al. 2006 ; Hill et al. 2013 ) demonstrated the insignificant effect of vermicomposting in reducing Ascaris summ ova as compared to composting without worms. The effectiveness of vermicomposting for pathogen destruction was still remaining unclear due to conflicting information in the literature (Hill et al. 2013 ); the present scenario thus, calls for further exploration. Accordingly, the present study attempted to investigate the comparative effectiveness of four different methods of composting viz. windrow composting (WC), Vermicomposting (VC), pit composting (PC), and combined windrow and vermicomposting (WVC) on the stabilization, maturation and sanitization of mixtures of MSW and dried faecal sludge.

Experimental site, wastes and earthworms utilized

The study was carried out at Dire Dawa, a city in Eastern Ethiopia located at 9° 6′ N, 41° 8′ E and at an altitude of 1197 m above sea level. The Municipal solid organic waste used in this study was obtained from a door-to-door waste collection service provided by the Sanitation and Beautification Agency (SBA) of Dire Dawa city, in which the wastes were collected from various locations in the city. The dried faecal cake which was about to be excavated from the drying bed and dumped to the landfill site was collected from the dumping site. The garbage receives mixed organic and inorganic domestic wastes, upon arrival to the composting site; the wastes were spread flat on the ground and sorted manually into organic and non-organic fractions. All the compostable components were shredded manually into small pieces of particle sizes ranging from 3 to 5 cm as described by Pisa and Wuta ( 2013 ). The shredded MSW and dried faecal sludge were then mixed manually in a 2:1 mix ratio. The earthworm species ( Eisenia foetida ) were obtained from Haramaya University. Matured earthworms and their cocoons were brought to Dire Dawa, where they were made to be multiplied (reared) for about 4 months using cow dung as medium.

Composting treatments

The methods of composting tested were: turned windrow composting (WC), pit composting (PC) (a composting method commonly practiced by farmers of the study area), vermicomposting (VC) and combined windrow and vermicomposting (WVC). The composting was done in outdoor but under shade condition. Three replicates of each of the four composting methods were made being arranged in a completely randomized block design. Each composting pile was covered with a layer of dry grass (5 cm) to prevent excessive loss of moisture.

Windrow composting : In the thermophilic composting, the homogenized feedstock of 1 m 3 volume (~275 kg dry weight) was heaped into conical piles in about 1 m 2 area after being wetted with water to 50–60% (Maso and Blasi 2008 ).

Pit composting a homogenized feedstock with the same moisture level as in ‘a’ was filled in a pit with dimension of 1 × 1 × 1 m (length width and depth).

Vermicomposting : Vermicomposting was performed in vermicompost bed measuring 1 × 1 × 0.3 m (length, width and height respectively) framed with bricks where the walls and bottom of the structure was lined with polyethylene sheet. In order to drain the excess water, the bottom of the polyethylene sheet was made to have tiny holes. Mature earthworms ( E. foetida ) were introduced at the recommended stocking rate of 250 adult worms per 20 kg of bio-waste (Padmavathiamma et al. 2008 ). The moisture content of the material was maintained between 70 and 80% (Maso and Blasi 2008 ).

Combined windrow composting and vermicomposting : Thermophilic composting of the wastes was done in same manner as in windrow composting and the piled substrate was allowed to be composted until the temperature was dropped to mesophilic phase. After the completion of the thermophilic phase (15 days after the initiation of the process), the subsequent vermicomposting continued using earthworms ( E. foetida ) as described under vermicomposting (Mupondi et al. 2010 ) .

The pilled heaps in WC were turned and mixed every week while the substrates in other methods of composting were left intact. The moisture content of each pile was checked every week and adjusted accordingly. The compost mass in WVC received the same treatment as WC and VC during the thermophilic and mesophilic phases of composting respectively. The temperatures in each heap was measured daily with a temperature probe from randomly selected places (centre, bottom and top) throughout the process.

Compost sampling and analysis

Sampling procedure.

To evaluate the various physical, chemical and biological transformations of the compost, representative samples were collected from four different points of the compost pile (bottom, surface, side and centre) of each pile at every 20 days (20, 40, 60, 80 and 100 days). All the samples were sealed in plastic containers and transported immediately to the laboratory using an ice box. Up on their arrival to the laboratory, the samples were stored in a refrigerator at 4 °C until they were analysed. Physico-chemical and microbial analyses were carried out at Haramaya University following standard procedures.

Physico-chemical analysis of compost

Moisture content was determined as weight loss upon drying in an oven at 105 °C to a constant weight (Lazcano et al. 2008 ). Total nitrogen (TN) and organic carbon (OC) were determined using dried compost samples which were ground to pass through a 2-mm sieve as described by Pisa and Wuta ( 2013 ). For the determination of total N, samples were decomposed using concentrated H 2 SO 4 and catalyst mixture in Kjeldahl flask and subsequently, N content in the digest was determined following steam distillation and titration method (Bremner and Mulvaney 1982 ).Organic carbon was estimated by dichromate wet digestion and rapid titration methods as described by Walkley and Black ( 1934 ). Total volatile solids was determined as weight loss on ignition at 550 °C for 4 h in a muffle furnace as described by Lazcano et al. ( 2008 ). Ammonium N ( \({\text{NH}}_{ 4}^{ + }\) –N) was determined from 0.2 ml aliquot of 0.5 M K 2 SO 4 extract of the filtrate after colour development with sodium nitroprusside, whereas, Nitrate N ( \({\text{NO}}_{ 3}^{ - }\) –N) was determined in a separate aliquot (0.5 ml) after colour development with 5% salicylic acid using a spectrophotometer (Okalebo et al. 2002 ). Analysis for pH and electrical conductivity (EC) were performed in extracts of 1:10 (w/v) compost: distilled water ratio as described by Ndegwa and Thompson ( 2001 ). The C:N ratio was calculated using the individual values of OC and TN.

Compost phytotoxicity test

For determining compost phytotoxicity, a modified phytotoxicity test employing seed germination was used (Zucconi et al. 1981 ). A 10 g of screened compost sample was shaken with 100 ml of distilled water for an hour, then the suspension was centrifuged at 3000 rpm for 15 min and the supernatant was filtered through a Whatman No 42 filter paper. Number 2 Whatman filter paper was placed inside a sterilized petri dish and wetted with 9 ml of the extract, 30 tomato seeds ( Solanum esculentum L.) were placed on the paper. Nine ml of distilled water was used as a control and all experiments were run in triplicate (Wu et al. 2000 ). The petri dishes were kept in the dark for 4 days at room temperature. At the end of the 4th day, the germination index (GI) was calculated using the following formula (Selim et al. 2012 ).

Faecal coliform analysis

For the determination of faecal coliforms in the initial raw materials and in the composts the procedures described by Mupondi et al. ( 2010 ) were employed. Aseptically weighed 10 g samples of either waste mixture or fresh compost were added to 90 ml of distilled water previously autoclaved at 121 °C for 15 min and the suspensions were then mixed using a blender to ensure thorough mixing. Additional serial dilutions were made up to 10 −6 . A 0.1 ml aliquot of each dilution was plated, in triplicate, in appropriate media-Violet Red Bile Agar (VBA) (Vuorinen and Saharinen 1997 ). The plates were then maintained in an incubator at a constant temperature of 44 °C for 24 h. For each of the treatment samples the numbers of faecal coliforms were expressed as log 10 CFU (colony forming unit) per gram of fresh sample and average values were calculated.

Helminth eggs recovery

The determination of helminth egg in this study was done based on the US EPA protocol ( 1999 ) modified by Schwartzbrod ( 2003 ). The analysis was carried out in triplicate for the initial raw waste and compost samples. The concentration of number of eggs per gram of dry weight of sample was computed according to the following formula (Ayres and Mara 1996 ):

where N = number of eggs per gram of dry weight of sample, Y = number of eggs in the McMaster slide (mean of counts from three slides), M = estimated volume of product at final centrifugation, C = volume of the McMaster slide, S = dry weight of the original sample.

Data analysis

The data obtained from this study were subjected to statistical analysis of variance (ANOVA) procedures using SAS software and the significant differences were determined using Fisher’s LSD test at P  ≤ 0.05 level.

Results and discussion

Characteristics of the raw waste materials.

The results of the analysis for the raw wastes are presented in Table  1 . The pH of the municipal solid waste (MSW) was alkaline and that of dried faecal sludge (DFS) was acidic in reaction. EC of MSW was much greater than that of DFS. The alkaline pH and high EC value in MSW could be attributed to the presence of wood ash which was observed to occur in considerable amount during the screening of the waste. The total N content of DFS was more double than that of MSW, indicating that it could be used to reduce the C:N ratio of the MSW.

The total helminth egg count for the dried faecal sludge and mixture of faecal sludge and MSW was 80.56 g −1 TS and 38.89 g −1 TS respectively, which is far greater than the recommended value for materials used in agriculture as per WHO’s guidelines (≤3–8 eggs g −1 TS) (Xanthoulis and Strauss 1991 ). Similarly, the total faecal coliform count of all the raw materials was found to exceed the standard threshold limit of <1000 cfu g −1 (WHO 2006 ). Therefore, it suggests that the raw wastes cannot be used directly for agriculture without being treated as it may result in soil contamination. The germination index values of the wastes was also far below the standard limit (>80%) substantiating the presence of phytotoxic substances which would make the raw wastes unfit for application in agricultural soils (Additional file 1 : Table S1).

Evolution of composting temperature

Considerable variations in temperature conditions were observed among the different composting methods on course of the composting period (Fig.  1 ). Though there were series of rise and fall in temperature, the general pattern of temperature for treatments (particularly for WC and PC) was similar. There was a rapid rise in temperature during the first few days of the composting process followed by a fall with time and finally it began to gradually reach to the ambient temperature. These temperature patterns denoted the thermophilic, mesophilic and maturation phases of a composting process, respectively. The rapid progress from initial mesophilic phase to thermophilic phase in WC and PC indicates a high proportion of readily degradable substances and self-insulating capacity of the waste (Sundberg et al. 2004 ). The change in temperature pattern observed in this study is in accord with other composting study (Tognetti et al. 2007 ).

Changes in ambient air temperature and temperature in the experimental piles during the composting process ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting)

Temperatures reached the thermophilic range (>45 °C) on the second and third day for the WC and PC which lasted for 15 and 19 days, respectively after initiation of the process. During these days of the process, a higher temperature was recorded for the WC than the PC. A peak average temperature ranging between 60.7 and 62.67 °C was recorded during the 3rd to 6th days for WC. Correspondingly for PC, the highest average temperature of 50.2–52.4 °C was registered during the 3rd to 9th day (Additional file 1 ). The increase in temperature within the composting mass was caused when the heat generated from the respiration and decomposition of sugar, starch and protein by the population of microorganisms accumulates faster than it is dissipated to the surrounding environment (Jusoh et al. 2013 ).

During the subsequent mesophilic phase (45–35 °C), however, PC registered a relatively higher temperature than WC. This phase was lasted for 13 days, from 16th to 28th day for WC and from 20th to 32nd day for PC and from the respective days on temperature values <35 °C and very close to the ambient temperature was recorded for both composting methods. The ambient temperature during the experimental period ranged from 23.7 to 33.7 °C (Fig.  1 ).

The vermicomposting unit (VC), where low temperature was induced intentionally by spreading the material in ground beds, tended to show the lowest temperature all through the process. The temperature profile for the WVC during the thermophilic phase showed similar pattern as that of the WC and has taken a different track during the subsequent vermicomposting process resembling the sole vermicomposting unit.

The size, initial moisture content and aeration of the piled substrate might have attributed for the variation in temperature of the different composting methods. Initially, to protect the earthworms from extreme thermophilic temperature and to keep an optimum condition for their performance, the height and moisture content of the pile in the vermicomposting unit were maintained to 30 cm and 80% compared to 1 m height/depth and 60%, respectively, in the WC and PC piles. As a result, the vermicompost with small volume of organic pile and relatively high moisture content does not heat up as such because the heat generated by the microbial population is lost quickly to the atmosphere, whereas in the WC and PC heat build-up particularly in the centre of the pile might have been insulated by the outer layer letting the temperature inside the pile to be raised. It is a well-established fact that, the smaller the bioreactor or compost pile, the greater the surface area-to-volume ratio, and therefore the larger the degree of heat loss to conduction and radiation ( http://www.cfe.cornell.edu/compost/invertebrates.html ).

The possible explanation for the variation in temperature profile of the WC and PC, given the same volume and moisture content of the pile, may be the differences in aeration (air circulation) in the piled substrates. The weekly turning of the compost mass in WC might have promoted the free circulation of air to enhance the microbial activity in the oxidation process and thereby raise the temperature; whereas in PC, the substrates being stacked in the pit without being turned the circulation of air in the pile might have been relatively restricted to impair the microbial activity and thereby the heat generated during the process. Finstein et al. ( 1986 ) who demonstrated the linear relationship between the oxygen consumed and heat produced during aerobic metabolism, support the finding of this study.

Evolution of pH

The first pH reading being taken at the 20th day after the initiation of the process, a sharp and significant (P ≤ 0.001) rise in pH than the initial state was observed in all the treatments. The rise in pH during these days is considered to be the result of the metabolic degradation of organic matter containing nitrogen (proteins, amino acids etc.) leading to formation of amines and ammonia salts through mineralization of organic nitrogen (Dumitrescu et al. 2009 ). As Smith and Hughes ( 2002 ) and Mupondi et al. ( 2006 ) suggested, it might also be attributed to the decomposition of organic acids to release alkali and alkali earth cations previously bound by organic matter. An increase in pH during composting of different substrates was also reported in many other studies (Sundberg et al. 2004 ; Tognetti et al. 2007 ; Gao et al. 2010 ).

The analysis of variance (ANOVA) showed a non-significant variation (P > 0.05) of pH values among the different methods of composting at the 20th day of sampling. Nevertheless, as composting progressed, significant variation (P ≤ 0.01) in pH was noted among the different composting methods (Fig.  2 ). Except for PC, which exhibited a further rise in pH, all other methods of composting showed a fairly stable pH during the 20th to 60th day of the process. This was followed by a slight fall to nearly neutral pH value during 80th to 100th day. In PC, a rise in pH value was observed to extend to the 60th day (8.03), after which it declined slightly at the 80th day and finally dropped to 7.83 at the 100th day.

Changes in pH in different composting methods with time. ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting, LSD least significant difference). Different letters indicate significant differences at P ≤ 0.05

Generally, from the 20th day till the end of the process (100th day), PC registered the highest pH value than the rest of the composting methods which were noted for their statistical parity (P > 0.05) (Fig.  2 ). This may possibly be caused due to the relatively higher concentration of ammonium ion maintained in PC. The relative decline in pH during the latter stage of the composting process might be caused due to the nitrification process which is responsible for the release of H + ion (Huang et al. 2001 ). This is also evident from \({\text{NO}}_{ 3}^{ - }\) data which was observed to increase remarkably during later stages of the process. Overall, the pH values achieved in all treatments at the end of the experiment were within the range acceptable for plant growth as recommended by Tognetti et al. ( 2005 ).

Evolution of electrical conductivity (EC)

The electrical conductivity values varied significantly (P ≤ 0.01) among the composting methods and over the different composting period. Generally, as indicated in Fig.  3 , all the treatments showed similar pattern of change in EC where the value decreased steadily with the progress in the composting process. It was found to be reduced by about 55.53, 54.66, 47.97, and 37.40% respectively for VC, WVC, PC, and WC at the 100th day as compared to the initial value of the raw material at day 0. The obtained results are in agreement with Yadav et al. ( 2012 ) and Gao et al. ( 2010 ) who reported an eventual decrease in EC value with progress in composting and vermicomposting. However this is in contrast with other studies (Gómez-Brandón et al. 2008 ) which reported increased EC values with composting time.

Changes in EC in composting mixtures of different composting methods with time. ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

The progressive decline of EC value with time would justify that, firstly; there might be leaching of mineralized ions during periodic showering of water on the composting mass, secondly; as composting process progressed, humification would inevitably proceed and the resulting humic fractions might have complexed the soluble salts which in turn tend to decrease the amount of mobile free ions and thereby the EC (Rao 2007 ).

The ANOVA results revealed that the EC value during the entire composting period was significantly higher (P ≤ 0.001) for WC followed by PC, whereas VC which was in statistical parity with WVC recorded the lowest value (Fig.  3 ). This would justify that the piled substrates in PC, VC and WVC which were not turned, but rather watered periodically on top to maintain the moisture at optimum; the soluble ions might have gradually been leached down. Moreover in VC and WVC, owing to the smaller size of the pile and a relatively large quantity of water added, the leaching of those ions might have been even more pronounced than the PC. In WC on the other hand, the weekly turning and mixing up of the substrate might have helped the redistribution of the mineralized ions in the compost mass and hence the loss of those ions from the system through leaching might have relatively been reduced. This finding is in line with Lazcano et al. ( 2008 ) and Frederickson et al. ( 2007 ) who reported a significantly lower EC value for VC and WVC than WC. The EC value in the final product of all treatments was far below the threshold value of 3000 µS cm −1 indicating a material which can be safely applied to soil (Soumaré et al. 2002 ).

Evolution of total organic carbon

With advancement of the composting process, the total organic carbon content of the compost decreased consistently and significantly (P ≤ 0.01) for all the treatments (Fig.  4 ). The decrease in organic carbon content at the end of the composting process with respect to WVC, VC, WC and PC was 54.74, 54.52, 52.00, and 48.80%, respectively of their initial carbon content. The present finding is also in consent with the findings of Tiquia et al. ( 2002 ), who reported a total carbon loss that ranged from 50 to 63% in turned windrows and 30–54% in unturned windrows. Similarly, reviewing the works of other authors, Yadav et al. ( 2010 ) reported total organic carbon reduction values ranging between 26 and 66% during vermicomposting of wastes of various sources. The variation in the amount of OC lost from the different composting method may possibly be caused by differences in the aeration of the piled substrate. Turning the compost pile (in WC) and continuous borrowing and fragmenting of the material by earthworms (in VC and WVC) might have altered the aeration of the compost mass and accelerated the degradation process to enhance the loss of carbon as carbon dioxide. The results are in agreement with the findings of Guo et al. ( 2012 ) who demonstrated higher losses of carbon in treatments receiving higher rates of aeration.

Changes in total organic carbon in composting mixture of different composting methods with time. ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

Evolution of total nitrogen

Changes in the total nitrogen of the different composting methods varied significantly (P ≤ 0.01) with the different sampling period, while the variation among the composting methods was found to be statistically insignificant (P > 0.05) (Fig.  5 ). The total nitrogen content of the initial raw material of all treatments was reduced significantly (P ≤ 0.01) during the first 20 days of composting. However, during the subsequent sampling, there was a gradual increment of total nitrogen, the maximum value being recorded at the 100th day. The decline in the total nitrogen during the first 20 days might be attributed to the loss of nitrogen in the form of ammonia which is apparent during the active phase of composting. Witter and Lopez-Real ( 1988 ) reported nitrogen losses that could amount to 50% and considered that nearly all nitrogen lost is due to ammonia volatilization.

Changes in total nitrogen in composting mixture of different composting methods with time ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

The rise in total nitrogen after the 20th day may be caused due to a concentration effect that resulted from degradation of organic C compounds which in turn leads to weight loss and therefore, a relative increase of N concentration (Dias et al. 2010 ). As Bernal et al. ( 1998 ) explained the concentration of N usually increases during composting when the loss of volatile solid (organic matter) is greater than the loss of NH 3 . This would generally indicate that there was a relatively greater increase in total N compared with the decrease in the organic carbon content. The results of the present study would, therefore, justify that during the first 20 days of composting, losses of N through NH 3 volatilization occurred at a greater rate than organic matter degradation, while during the subsequent periods, the rate of N loss as NH 3 might be slower than the rate of dry matter loss as CO 2 . In addition, the N level might have also been increased due to the fixation of atmospheric N within the compost heap by the free living N fixing microorganisms’ activity that commonly occurs during the later stage of the composting process (Seal et al. 2012 ). In their co-composting study of pig manure and corn stalks, Guo et al. ( 2012 ) reported results that were in agreement with the trends of the present study—a general decrease of total nitrogen during the thermophilic phase followed by an increase then after.

Evolution of C:N Ratio

The C:N ratio of the composting material of all the treatments narrowed consistently and significantly (P ≤ 0.01) with the advancement of the composting time (Fig.  6 ). The initial C:N ratio of the raw material at day 0 was 19:1 which was within the recommended range suitable for composting (35–12) (Epstein 1997 ). This was found to decrease to nearly 11:1, 9:1, 10:1 and 9:1 at the 100th day of sampling for PC, VC, WC and WVC, respectively. Obviously, throughout the composting process the organic matter is decomposed by microorganisms through which the organic carbon was oxidized to CO 2 gas to the atmosphere and thus lowers the C:N ratio (Jusoh et al. 2013 ). This is in conformity with the findings of other studies (Kumar et al. 2009 ; Khwairakpam and Kalamdhad 2011 ).

Changes in C:N ratio of composting mixture in different composting methods with time ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

C:N ratio value for PC was significantly (P ≤ 0.01) higher than the other methods of composting which were statistically at par (P > 0.05) with each other (Fig.  6 ). The variation seemed to arise mainly due to the differences in the amount of total organic carbon as could be witnessed from previous discussion and the same justification given above can also be claimed for the variation in C:N ratio among the different composting methods. Generally, the C:N ratios in the final product of all the treatments were found to be satisfactory because matured compost material usually has a C:N ratio of 15 or less (Hock et al. 2009 ).

As Gómez-Brandón et al. ( 2008 ) pointed out C:N ratio may not be a good indicator of compost stability because it can level off before the compost stabilizes. When wastes rich in nitrogen are used as source material for composting, the C:N ratio can be within the values of stable compost even though it may still be unstable. By the same token, Zmora-Nahum et al. ( 2005 ) reported a C:N ratio lower than the cut-off value of 15 very early during the composting of cattle manure, while important stabilization processes were still taking place. Correspondingly, in the present study, three of the four treatments (VC, WVC and WC) and PC achieved a C:N ratio of <15 at the 40th and 60th day of sampling, respectively, while the degradation of the organic material was still significant till the 60th and 80th days for the respective treatments. As evidenced earlier a statistically stable values for total organic carbon was observed during the 60th to 100th and 80th to 100th day of sampling for the respective treatments.

Evolution of \({\text{NH}}_{ 4}^{ + }\) , \({\text{NO}}_{ 3}^{ - }\) and \({\text{NH}}_{ 4}^{ + }\) :NO 3 ratio

The concentration of \({\text{NO}}_{ 3}^{ - }\) –N and \({\text{NH}}_{ 4}^{ + }\) –N varied significantly (P ≤ 0.001) for the different composting methods and over the different composting period, notwithstanding that all the treatments have generally shown similar pattern of changes in both ammonium and nitrate concentrations (Figs.  7 , 8 ). As can be seen from the graph (Fig.  7 ), all the composting methods showed a rise in \({\text{NH}}_{ 4}^{ + }\) –N concentration during the 20th day of sampling which was then declined sharply as evidenced at the 40th day and coming to decrease slightly from the 40th day until the end of the experiment (100th day).

Changes in \({\text{NH}}_{ 4}^{ + }\) concentration of composting mixture in different composting methods with time ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

Changes in \({\text{NO}}_{ 3}^{ - }\) concentration of composting mixture in different composting methods with time ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

The rise in \({\text{NH}}_{ 4}^{ + }\) –N concentration during the first 20 days was likely to be caused as a result of the mineralization of organic matter (the conversion of organic N to \({\text{NH}}_{ 4}^{ + }\) via the ammonification process), thus reflecting active transformation of organic matter and unstable substrate (Tognetti et al. 2005 ; Guo et al. 2012 ). Whereas the decrease in \({\text{NH}}_{ 4}^{ + }\) –N during the subsequent sampling periods was probably due to NH 3 volatilization (Gao et al. 2010 ), the microbial immobilization as nitrogenous compounds such as amino acids, nucleic acids and proteins and/or its oxidation to \({\text{NO}}_{ 3}^{ - }\) through nitrification process (Guo et al. 2012 ). An increase in \({\text{NH}}_{ 4}^{ + }\) –N concentration during the initial stage of composting and its reduction afterwards was reported by Gao et al. ( 2010 ).

The analysis of variance indicated that PC registered the highest concentration of \({\text{NH}}_{ 4}^{ + }\) –N during all the sampling period. However, a statistically significant (P ≤ 0.01) variation of \({\text{NH}}_{ 4}^{ + }\) –N among the treatments was recorded only at the 20th and 40th day of sampling (Fig.  7 ). Turning the piled substrate in WC and the smaller size and increased surface area of the vermibed in VC and WVC might have resulted in increased loss of ammonia leading to a relatively low level of ammonium at this day of sampling (20th day). The compost pile in PC, on the other hand, being not turned and mixed, the loss of N in the form of ammonia might have relatively been reduced and this might have contributed for the increased level of ammonium nitrogen in PC than the other methods of composting. Similar results were reported by Guo et al. ( 2012 ) who noted highest level of ammonium nitrogen in treatments with low than high aeration rate.

Regarding the \({\text{NO}}_{ 3}^{ - }\) –N, for all the treatments its level was sharply and significantly (P ≤ 0.01) decreased at the 20th day sampling than the initial. This might be caused due to either the leaching of nitrate by water during periodic watering of the composting mass or its immobilization by the decomposing microorganisms. During the subsequent composting period (20th to 60th days), however, the \({\text{NO}}_{ 3}^{ - }\) –N level came to be relatively stable and during these days the variation in \({\text{NO}}_{ 3}^{ - }\) –N level among all the treatments was insignificant (P > 0.05) (Fig.  8 ). This was followed by a sharp rise of \({\text{NO}}_{ 3}^{ - }\) –N after the 60th day (for WC, VC and WVC) and 80th day (for PC) as evidenced on the 80th and 100th day of sampling, respectively. At the end of the process (100th day), PC exhibited a significantly lower value of \({\text{NO}}_{ 3}^{ - }\) –N than the other methods of composting. It seems that due to the better aeration by earthworms (in VC and WVC) and turning of the piles (in WC), the oxidation of \({\text{NH}}_{ 4}^{ + }\) to \({\text{NO}}_{ 3}^{ - }\) might have been enhanced in the respective methods of composting than in PC.

The \({\text{NH}}_{ 4}^{ + }\) –N content of the starting material was clearly higher (1014.28 mg kg −1 ) than the \({\text{NO}}_{ 3}^{ - }\) –N content (684.5 mg kg −1 ), giving the \({\text{NH}}_{ 4}^{ + }\) : \({\text{NO}}_{ 3}^{ - }\) ratio to be 1.48. On course of the composting process the ratio was found to be raised sharply at the 20th day of sampling for all the treatments. This is followed by a drastic decline during the 40th day and coming to be declining gradually during the subsequent periods of composting (60–100 days) (Fig.  9 ). PC registered the highest ratio during all the sampling periods; however, a statistically significant variation among the composting treatments was noted only at the 20th and 40th day of sampling (Fig.  9 ). At the 20th day, the highest (13.57) and lowest (9.42) ratio was recorded for PC and WVC, respectively. At the 100th day of sampling the value was found to drop to 0.06, 0.026, 0.016 and 0.02, respectively for PC, VC, WC and WVC.

Changes in \({\text{NH}}_{ 4}^{ + }\) : \({\text{NO}}_{ 3}^{ - }\) ratio of composting mixture in different composting methods with time ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

Critical limit values of <400 mg kg −1 for \({\text{NH}}_{ 4}^{ + }\) –N (Zucconi and de Bertoldi 1987 ), >300 mg kg −1 for \({\text{NO}}_{ 3}^{ - }\) –N (Forster et al. 1993 ) and <1 for \({\text{NH}}_{ 4}^{ + }\) -: \({\text{NO}}_{ 3}^{ - }\) ratio (Brewer and Sullivan 2003 ) has been established as a stability/maturity indices for composts of various origins. Concomitantly, except for PC all the other composting treatments satisfied the critical limits for stability/maturity at the 60th day of sampling. Whereas, PC achieved these values( \({\text{NO}}_{ 3}^{ - }\) –N and \({\text{NH}}_{ 4}^{ + }\) : \({\text{NO}}_{ 3}^{ - }\) ratio) at the 80th day, implying that PC was late to achieve the index value for maturity than the other three methods of composting and the same explanation given above pertaining to differences in aeration would also be suggested for the variation in these values among the treatments.

Evolution of total volatile solids (TVS)

The average total volatile solid (TVS) content of the raw waste was 523.4 mg kg −1 which steadily decomposed throughout the experimental period. The change in TVS with composting time showed the same pattern as the change in total organic carbon in that it decreases significantly (P ≤ 0.01) with the advancement of composting time. The greatest reduction in TVS was noted during the first 20 days of composting signifying the fast degradation of the substrate during this active phase of composting (Fig.  10 ). The decrease in TVS content of the sample indicates the degradation of organic matter of the waste during the composting process (Levanon and Pluda 2002 ). Values of TVS varied significantly (P ≤ 0.01) among the different methods of composting (Fig.  10 ). On the course of composting, the highest and lowest values of TVS were recorded for PC and WVC, respectively.

Changes in total volatile solids of composting mixture in different composting methods with time ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

The analysis of variance revealed that the values of TVS for the three methods of composting (WC, VC and WVC) after the 60th day was insignificant (P > 0.05) indicating the stability of the product at the 60th day. Whereas, for PC a statistically stable value was achieved at the 80th day of composting, implying the relatively longer period of time the latter has taken for the product to be stable. This is due to the relatively slow rate of degradation of the organic matter in PC. The important role played by the earthworms in reducing the TVS through degrading wastes was reported by Yadav et al. ( 2012 ).

Phytotoxicity assessment

All the composting treatments followed the same general pattern of changes in germination index (GI) over the different sampling period and the variation in GI values among the treatments was insignificant (P > 0.05; Fig.  11 ). However, the values varied significantly (P ≤ 0.01) with the composting time. The lowest value of this variable was recorded at the 20th day of sampling which was of course statistically not different from the starting material (day 0). This was observed to increase with the advancement of composting period up to the 60th day and from the 60th day on it came to a more or less stable value with insignificant variation (Fig.  11 ). Tiquia and Tam ( 1998 ) also reported findings that are similar to the results of this study.

Changes in germination index (GI) of composting mixture in different composting methods with time ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

The reason for the low germination index value of in the initial sample and the sample taken at the 20th day of the composting process could be attributed to the presence of phytotoxic compounds in the raw wastes and their production in the substrate during the active phase of composting. Phytotoxic compounds, such as; ammonium ions, fatty acids, and low molecular weight phenolic acids are reported to impair seed germination and root elongation (Delgado 2010 ; Gómez-Brandón et al. 2008 ). It was also evident from the chemical analysis of the raw material and compost samples of this study that the highest level of ammonium was recorded at the 20th day of sampling followed by the initial substrate at day 0. The detrimental effect of high levels of ammonium to seed germination and root elongation was reported in many other studies (Tiquia and Tam 1998 ; Selim et al. 2012 and Guo et al. 2012 ).

The rise in GI late at the 60th day might be due to the degradation of the phytotoxic compounds which were present in the initial raw wastes or produced during the active phase of composting as intermediate products of microbial metabolism (Bernal et al. 1998 ). According to Haq et al. ( 2014 ) compost with GI of more than 80% is considered to be matured and practically free of phytotoxic substances. In this study as indicated in the graph (Fig.  11 ), all the treatments were found to have a GI value of >80% at the 60th day of sampling, implying that, about 60 days were needed to overcome the threshold limit of 80% by reducing the phytotoxicity of the compost to levels consistent for a safe soil application (Soares et al. 2013 ).

Pathogen inactivation

Total faecal coliforms.

Except for VC all other methods of composting showed a substantial reduction in population of faecal coliforms at the 20th day of sampling. These treatments were effective in keeping the population of the faecal coliforms in the compost below the minimum allowable limit (<1000 cfu g −1 ) right at the 20th day. The reduction in the population of faecal coliforms in these methods of composting might be related to the high temperature generated in the compost pile during the thermophilic phase. Perhaps in this study the first sampling was taken at the 20th day, but it is likely that these methods could have attained such low population even much earlier than the 20th day. As per the reports of WHO ( 2006 ) and Schönning and Stenström ( 2004 ), pathogen inactivation in composting is achieved when temperatures above 50 °C are maintained for at least 1 week. Temperatures exceeding 50 °C were also recorded in those methods (WC, PC and WVC) involving thermophilic phase of the current study.

Some inconsistencies in reduction pattern of the faecal coliforms were detected in WC during the mesophilic and curing phase, where the population of these pathogens came to rise and fall at different sampling periods (Fig.  12 ). This may be due to the contamination of the compost mass from the external source during the periodic and manual turning of the compost pile.

Elimination of faecal coliform during co-composting of dried faecal sludge and municipal solid organic wastes with time. ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting). Different letters indicate significant differences at P ≤ 0.05

Regarding VC, contrary to the former methods, the number of the faecal coliforms was found to increase remarkably at the 20th day of sampling, this was then declined steadily during the subsequent sampling periods (Fig.  12 ). The increasing of faecal coliforms in VC during the 20th day of sampling could be attributed to creation of a good environment for multiplication of this pathogen through rehydration and subsequent availability of easily degradable substrates by dissolution following rehydration (Mupondi et al. 2010 ). The reports by Schönning and Stenström ( 2004 ) and WHO ( 2006 ) also indicated that certain types of pathogenic bacteria can increase in numbers when conditions favouring their growth are established in their storage medium/environment.

The reduction of the faecal coliforms population during the subsequent period of vermicomposting may be attributed to some activities of earthworms which possibly include: selective predation/consumption (Edward and Bohlen 1996 ; Kumar and Shweta 2011 ); mechanical destruction through action of gizzard (Edwards and Subler 2011 ); microbial inhibition through humic and coelomic acids or other enzymes secreted within the digestive tract (Edwards and Subler 2011 ); stimulation of microbial antagonists (Kumar and Shweta 2011 ); and indirectly through stimulation of endemic or other microbial species which outcompete, antagonize, or otherwise destroy pathogens (Edwards and Subler 2011 ).

Helminth egg count

During the composting process, there was a general reduction in the number of helminth eggs for all the treatments (Fig.  13 ). The total helminth egg count was found to decrease from 38.89 g −1 TS of the starting material to 8.33 (WC), 19.44(VC), 14.81 (PC) and 2.78 (WVC) in the final product as evidenced at the 100th day. These values correspond to a 78.57, 50, 61.9 and 92.86% total reduction of eggs for the respective treatments. It has been observed that the extent to which the helminth eggs were eliminated varied significantly with time and among the treatments (P ≤ 0.01). Those treatments involving thermophilic composting (WC, PC and WVC) demonstrated a drastic reduction of eggs during the first 20 days of the process when the active thermophilic phase was prevailing. This amounts to 84.85% (WC), 73.08% (PC) and 74.36% (WVC) of the total reductions recorded in the respective treatments. Whereas the treatment without a thermophilic phases (VC), the greatest reduction of helminth eggs was observed during the latter stages of the composting process. More than 75% of the total reduction was recorded after the 60th day of the process while only 23.81% of it was recorded during the first 40 days of the composting process.

Helminth eggs removal dynamics during co-composting of faecal sludge and municipal organic solid waste. ( WC windrow composting, VC vermicomposting, PC pit composting, WVC combined windrow and vermicomposting LSD Least significant difference). Different letters indicate significant differences at P ≤ 0.05

The highest reduction of eggs was achieved in WVC method followed by windrow method of composting (WC), while the sole vermicomposting method (VC) registered the lowest value (Fig.  13 ). However, only the former treatment (WVC) is complying with the WHO guidelines of <3–8 Ascaris egg g −1 TS while all the rest treatments were found to have egg counts more than the threshold limit. The result of this study clearly demonstrated that the high temperature produced in the thermophilic phase of the composting process is much more effective in sanitizing pathogenic parasites of faecal sludge than the earthworms did. It has been suggested that high temperature may increase the permeability of the Ascaris eggs’ shell, allowing transport of harmful compounds, as well as increasing the desiccation rate of the eggs (Koné et al. 2010 ).

Even though numerous authors reported the full elimination of parasitic eggs under thermophilic condition (Plym-Forshell 1995 ; Gantzer et al. 2001 ), this had not come about in the present study where helminth eggs were still detected despite the fact that the thermophilic condition (≥45 °C) was maintained for about 15–19 days. It is likely that the lethal temperature, being not evenly distributed throughout the piled biomass, the complete destruction of the eggs may not be ensured. The substrates that lay on the top of the pile, being exposed to the open atmosphere, might have experienced a relatively cooler temperature than the inner laid ones. Strauch ( 1991 ) suggested that composting ensures hygienization of the material on condition that all biomass is exposed to a sufficiently high temperature (55 °C for 14 days).

The temperature reading of the present study indicates that, on average, a high temperature of (>55 °C) was recorded only for 8 days in windrows and during which the pile was turned only once letting it to experience the high temperature of >55 °C for only a day after this first turning. This would therefore suggest that, had the piled feedstock been turned more frequently such that every 2 or 3 days, the biomass would have enjoyed the lethal high temperature uniformly and for relatively longer period of time and thus would have resulted in increased efficiency of helminth egg elimination. This justification is of course in argument with the reports of Koné et al. ( 2007 ) who demonstrated the non-significant effect of turning frequency on the inactivation efficiency of helminths egg. However, it has been explained that the size of the piled feedstock determines the magnitude of heat generated and the time duration in which the thermophilic phase would be maintained during the composting process. The larger the size of the pile the higher the magnitude of heat generated and the longer the thermophilic phase would be maintained within the pile, and thus the less frequently it can be turned. In cases where the pile size is smaller, the thermophilic phase would last for a short period of time; therefore, unless turned frequently there would be no chance for the out laid biomass to enjoy the lethal high temperature which is usually formed inside the pile. In the United States of America, the compost is regarded as hygienically safe if a temperature >55 °C is maintained in windrows for at least 15 days with a minimum of 5 turnings during the high temperature period (USEPA 1999 ).

Conclusions

The biodegradation process of organic wastes is markedly influenced by the methods of composting employed. Turned windrows (WC) and composting involving earthworms (VC and WVC) hasten the biodegradation process of organic wastes and result in the production of stable compost earlier than the traditional pit method of composting (PC). Even though all the tested methods of composting remarkably reduced the pathogenic organisms (faecal coliforms and helminth eggs), it was only the WVC method that qualify the standard set by WHO, keeping the concentration of helminth egg below the threshold level. Thus, elimination of pathogens from composts being a critical consideration, this study would recommend the WVC method for composting organic wastes involving human excreta.

Abbreviations

analysis of variance

colony forming unit

dried faecal sludge

electrical conductivity

germination index

faecal sludge

municipal solid waste

organic carbon

pit composting

total:nitrogen

total volatile solids

United States Environmental Protection Agency

vermicomposting

windrow composting

windrow plus vermicomposting

world health organization

Ayres RM, Mara DD (1996) Analysis of wastewater for use in agriculture—a laboratory manual of parasitological and bacteriological techniques. World Health Organization (WHO), Geneva

Beneberu S, Eline B, Harole Y, Zelalem L (2012) Current solid waste management practices for productive reuse in Dire Dawa City, Ethiopia (Draft project Report)

Bernal MP, Paredes C, Sanchez-Monedero MA, Cegarra J (1998) Maturity and stability parameters of composts prepared with a wide range of organic wastes. Bioresour Technol 63:91–99

Article   CAS   Google Scholar  

Bernal MP, Alburquerque JA, Moral R (2009) Composting of animal manures and chemical criteria for compost maturity assessment: a review. Bioresour Technol 100:5444–5453

Bjorklund A (2002) The potential of using thermal composting for disinfection of separately collected faeces in Cuernacava, Mexico. Minor Field Studies No. 200. Swedish University of Agricultural Sciences, International Office. ISSN 1402-3237

Bowman DD, Liotta JL, McIntosh M, Lucio-Forster A (2006) Ascaris suum egg inactivation and destruction by the vermicomposting worm, Eisenia foetida . Residuals Biosolids Manag 2:11–18

Google Scholar  

Bremner JM, Mulvaney CS (1982) Nitrogen—total. In: Page AL, Miller RH, Keeney DR (eds) Methods of soil analysis, Part 2. Chemical and Microbiological Properties. Agronomy Monograph No. 9. ASA-SSSA, Madison, Wisconsin, USA, pp 595–624

Brewer LJ, Sullivan DM (2003) Maturity and stability evaluation of composted yard trimmings. Compost Sci Util 11(2):96–112

Article   Google Scholar  

Bundela PS, Gautam SP, Pandey AK, Awasthi MG, Sarsaiya S (2010) Municipal solid waste management in Indian cities—a review. Int J Environ Sci 1(4):591–606

Carr L, Grover R, Smith B, Richard T, Halbach T (1995) Commercial and on-farm production and marketing of animal waste compost products. In: Steele K (ed) Animal waste and the land–water interface. Lewis Publishers, Boca Raton, pp 485–492

Community Development Research (2011) Ethiopia solid waste and landfill (country profile and action plan). Global Methane Initiative http://www.globalmethane.org/ . Accessed on August 2012

Cooperband LR, Stone AG, Fryda MR, Ravet JL (2003) Relating compost measures of stability and maturity to plant growth. Compost Sci Util 11(2):113–124

Delgado M (2010) Phytotoxicity of uncomposted and composted poultry manure, African. J Plant Sci 4:154–162

Dias BO, Silva CA, Higashikawa FS, Roig A, Sánchez-Monedero MA (2010) Use of biochar as bulking agent for the composting of poultry manure: effect on organic matter degradation and humification. Bioresour Technol 101:1239–1246

Domínguez J, Edwards CA (2010) Relationships between composting and vermicomposting: relative values of the products. In: Edwards CA, Arancon NQ, Sherman RL (eds) Vermiculture technology: earthworms, organic waste and environmental management. CRC Press, Boca Raton, pp 1–14

Dominguez J, Edwards CA, Subler S (1997) Comparison of vermicomposting and composting. Bio-Cycle 38(4):57–59

CAS   Google Scholar  

Dumitrescu L, Manciulea I, Sauciuc A, Zaha C (2009) Obtaining fertilizer compost by composting vegetable waste, sewage sludge and sawdust. In: Bulletin of the Transilvania, vol 2, no 51. University of Braşov, pp 117–122

Eastman BR, Kane PN, Edwards CA, Trytek L, Gunadi B, Stermer AL, Mobley JR (2001) The effectiveness of vermiculture in human pathogen reduction for USEPA biosolids stabilization. Compost Sci Util 9(1):38–49

Edward CA, Bohlen PJ (1996) Biology and ecology of earthworms. Chapman and Hall, London

Edwards CA, Subler S (2011) Human pathogen reduction during vermicomposting. In: Edwards CA, Arancon NQ, Sherman R (eds) Vermiculture technology Florida. CRC Press Taylor and Francis Group, Florida, pp 249–261

Epstein E (1997) The science of composting. Technomic Publishing Company Inc, Lancaster

Finstein MS, Miller FC, Strom PF (1986) Monitoring and evaluating composting process performance. J Water Pollut Control Fed 58(4):272–278

Forster JC, Zech W, Wiirdinger E (1993) Comparison of chemical and microbiological methods for the characterization of the maturity of composts from contrasting sources. Biol Fertil Soils 16:93–99

Frederickson J, Butt KR, Morris RM, Daniels C (1997) Combining vermiculture with traditional green waste composting systems. Soil Biol Biochem 29(3/4):725–730

Frederickson J, Howell G, Hobson AM (2007) Effect of pre-composting and vermicomposting on compost characteristics. Eur J Soil Biol 43:S320–S326

Gallizzi K (2003) Co-composting reduces helminth eggs in faecal sludge: a field study in Kumasi, Ghana. SANDEC, Dübendorf, p 45

Gantzer C, Gaspard P, Galvez L, Huyard A, Dumouthier N, Schwartzbrod J (2001) Monitoring of bacteria and parasitological contamination during various treatment of sludge. Water Resour 35(16):3763–3770

Gao M, Liang F, Yub A, Li B, Yang L (2010) Evaluation of stability and maturity during forced-aeration composting of chicken manure and sawdust at different C/N ratios. Chemosphere 78:614–619

Garcia C, Hernandez T, Costa F (1993) Evaluation of the organic matter composition of raw and composted municipal wastes. Soil Sci Plant Nutr 39:99–108

Gómez-Brandón M, Lazcano C, Domínguez J (2008) The evaluation of stability and maturity during the composting of cattle manure. Chemosphere 70:436–444

Guo R, Li G, Jiang T, Schuchardt F, Chen T, Zhao Y, Shen Y (2012) Effect of aeration rate, C/N ratio and moisture content on the stability and maturity of compost. Bioresour Technol 112:171–178

Haq T, Ali TA, Begum R (2014) Seed germination bioassay using maize seeds for phytoxicity evaluation of different composted materials. Pak J Bot 46(2):539–542

Hill GB, Lalander C, Baldwin SA (2013) The effectiveness and safety of vermi-versus conventional composting of human feces with Ascaris suum ova as model helminthic parasites. J Sustain Dev 6(4):1–10

Hock LS, Baharuddin AS, Ahmed MN, Md. Shah UK, Abdul Rahaman NA, Abd-Aziz S, Hassan MA, Shirai Y (2009) Physicochemical changes in windrow co-composting process oil palm mesocarpfiber and palm oil effluent anaerobic sludge. Aust J Basic Appl Sci 3(3):2809–2819

http://www.cfe.cornell.edu/compost/invertebrates.html (1 of 4) [1/16/2001 8:49:10 AM]. Cornell composting Science and Engineering

Huang GF, Fang M, Wu QT, Zhou LX, Liao XD, Wong JWC (2001) Co-composting of pig manure with leaves. Environ Technol 22:1203–1212

Iqbal MK, Khan RA, Nadeem A, Hussnain A (2012) Comparative study of different techniques of composting and their stability evaluation in municipal solid waste. J Chem Soc Pak 34(2):273–282

Jusoh ML, Manaf LA, Abdul Latif P (2013) Composting of rice straw with effective microorganisms (EM) and its influence on compost quality. Iran J Environ Health Sci Eng 10:17

Khwairakpam M, Kalamdhad AS (2011) Vermicomposting of vegetable wastes amended with cattle manure. Res J Chem Sci 1(8):49–56

Koné D, Cofie O, Zurbru C, Gallizzi K, Moser D, Drescher S, Strauss M (2007) Helminth eggs inactivation efficiency by faecal sludge dewatering and co-composting in tropical climates. Water Res 14(9):4397–4402

Koné D, Cofie O, Nelson K (2010) Low-cost options for pathogen reduction and nutrient recovery from faecal sludge. In: Drechsel P, Scott CA, Raschid-Sally L, Redwood M, Bahri A (eds) Wastewater irrigation and health: assessing and mitigating risk in low-income countries. International Water Management Institute (IWMI), Earthscan, International Development Research Centre (IDRC), Colombo, pp 171-188. 

Kumar R, Shweta (2011) Removal of pathogens during vermi-stabilization. J Environ Sci Technol 4(6):621–629

Kumar PR, Jayaram A, Somashekar RK (2009) Assessment of the performance of different compost models to manage urban household organic solid wastes. Clean Technol Environ Policy 11:473–484

Lazcano C, Gómez-Brandón M, Domínguez J (2008) Comparison of the effectiveness of composting and vermicomposting for the biological stabilization of cattle manure. Chemosphere 72:1013–1019

Levanon D, Pluda D (2002) Chemical, physical and biological criteria for maturity in composts for organic farming. Compost Sci Util 10(4):339–346

Lung AJ, Lin CM, Kim JM, Marshall MR, Nordstedt R, Thompson NP, Wei CI (2001) Destruction of Escherichia coli O157:H7 and Salmonella enteritidis in cow manure composting. J Food Prot 64:1309–1314

Maso MA, Blasi AB (2008) Evaluation of composting as a strategy for managing organic wastes from a municipal market in Nicaragua. Bioresou Technol 99:5120–5124

Monroy F, Aira M, Domínguez J (2008) Changes in density of nematodes, protozoa and total coliforms after transit through the gut of four epigeic earthworms (Oligochaeta). Appl Soil Ecol 39:127–132

Mupondi LT, Mnkeni PNS, Brutsch MO (2006) The effects of goat manure, sewage sludge and effective microorganisms on the composting of pine bark. Compost Sci Util 14:201–210

Mupondi LT, Mnkeni PN, Muchaonyerwa P (2010) Effectiveness of combined thermophilic composting and vermicomposting on biodegradation and sanitization of mixtures of dairy manure and waste paper. Afr J Biotechnol 9(30):4754–4763

Ndegwa PM, Thompson SA (2001) Integrating composting and vermicomposting in the treatment and bioconversion of solids. Bioresour Technol 76:107–112

Niwagaba C, Nalubega M, Vinnerås B, Sundberg C, Jonsson H (2009) Benchscale composting of source-separated human faeces for sanitation. Waste Manag 29:585–589

Okalebo JR, Guthua KW, Woomer PJ (2002) Laboratory methods of soil and plant analysis—a working manual. TSBF-CIAT and SACRED Africa, Nairobi

Padmavathiamma PK, Li LY, Kumari UR (2008) An experimental study of vermin biowaste composting for agricultural soil improvement. Bioresour Technol 99:1672–1681

Pisa C, Wuta M (2013) Evaluation of composting performance of mixtures of chicken blood and maize stover in Harare, Zimbabwe. Int J Recycl Org Waste Agric 2(5):1–11

Plym-Forshell L (1995) Survival of Salmonellas and Ascarissuum eggs in a thermophilic biogas plant. Acta Vet Scandinavica 36:79–85

Rao KJ (2007) Composting of municipal and agricultural wastes. In: Proceedings of the international conference on sustainable solid waste management, Chennai, India, 5–7 September 2007

Rodriguez-Canche LG, Cardoso-Vigueros L, Maldonado-Montiel T, Martinez Sanmiguel M (2010) Pathogen reduction in septic tank sludge through vemicomposting using Eisenia fetida . Bioresour Technol 101:3548–3553

Schönning C, Stenström TA (2004) Guidelines for the safe use of urine and faeces in ecological sanitation. Report 2004-1. Ecosanres, SEI. Sweden. www.ecosanres.org

Schwartzbrod J (2003) Quantification and viability determination for helminth eggs in sludge (modified EPA method 1999). University of Nancy, Nancy

Seal A, Bera R, Chatterjee AK, Dolui AK (2012) Evaluation of a new composting method in terms of its biodegradation pathway and assessment of compost quality, maturity and stability. Arch Agron Soil Sci 58(9):995–1012

Selim SM, Zayed MS, Atta MH (2012) Evaluation of phytotoxicity of compost during composting process. Nat Sci 10(2):69–77

Smith DC, Hughes JC (2002) Changes in chemical properties and temperature during the degradation of organic wastes subjected to simple composting protocols suitable for small-scale farming, and quality of the mature compost. S Afr J Plant Soil 19:53–60

Soares MR, Matsinhe C, Belo S, Quina MJ, Quinta-Ferreira R (2013) Phytotoxicity evolution of biowastes undergoing aerobic decomposition. J Waste Manag. doi: 10.1155/2013/479126

Soumaré M, Demeyer A, Tack FMG, Verloo MG (2002) Chemical characteristics of Malian and Belgian solid waste composts. Bioresour Technol 81:97–101

Strauch D (1991) Survival of pathogenic micro-organisms and parasites in excreta, manure and sewage sludge. Revue Sci Tech (Int Off Epizoot) 10:813–846

Sundberg C, Smars S, Jonsson H (2004) Low pH as an inhibiting factor in the transition of mesophilic to thermophilic phase in composting. Bioresour Technol 95:145–150

Tiquia SM, Tam NFY (1998) Elimination of phytotoxicity during co-composting of spent pig-manure sawdust litter and pig sludge. Bioresour Technol 65:43–49

Tiquia SM, Richard TL, Honeyman MS (2002) Carbon, nutrient, and mass loss during composting. Nutr Cycl Agroecosyst 62:15–24

Tognetti C, Loas F, Mazzarino MJ, Hernandez MT (2005) Composting vs. vermicomposting: a comparison of end product quality. Compost Sci Util 13(1):6–13

Tognetti C, Mazzarino MJ, Laos F (2007) Improving the quality of municipal organic waste compost. Bioresour Technol 98:1067–1076

USEPA (United States Environmental Protection Authority) Pathogen Equivalency Committee (PEC) (1999) Control of pathogens and vector attraction in sewage sludge. In: USEPA Environmental Regulations and Technology, Office of Research and Development EPA/625/R-92/013, Washington, DC, p 177

Vinnerås B (2007) Comparison of composting, storage and urea treatment for sanitising of faecal matter and manure. Bioresour Technol 98:3317–3321

Vuorinen AH, Saharinen MH (1997) Evolution of microbiological and chemical parameters during manure and straw co-composting in a drum composting system. Agric Ecosyst and Environ 66:19–29

Walkley A, Black IA (1934) An examination of the Degtjareff method for determining soil organic matter and proposed modification of the titration method. Soil Sci Soc Am J 37:29–34

WHO (2006) Guidelines for the safe use of wastewater, excreta and greywater, vol 4. Excreta and grey water use in agriculture. ISBN: 92 4 154685 9

Witter E, Lopez-Real J (1988) Nitrogen losses during the composting of sewage sludge, and the effectiveness of clay soil, Zeolite and compost in adsorbing volatilised ammonia. Biol Wastes 23:279–294

Wu L, Ma LQ, Martinez GA (2000) Comparison of methods for evaluating stability and maturity of bio-solids compost. J Environ Qual 29(2):424–429

Xanthoulis D, Strauss M (1991) Reuse of wastewater in agriculture at Ouarzazate, Morocco (Project UNDP/FAO/WHO MOR 86/018). Unpublished mission reports

Yadav KD, Tare V, Ahammed MM (2010) Vermicomposting of source separated human faeces for nutrient recycling. Waste Manag 30:50–56

Yadav KD, Tare V, Ahammed MM (2012) Integrated composting–vermicomposting process for stabilization of human faecal slurry. Ecol Eng 47:24–29

Zmora-Nahum S, Markovitch O, Tarchitzky J, Chen Y (2005) Dissolved organic carbon (DOC) as a parameter of compost maturity. Soil Biol Biochem 37:2109–2116

Zucconi F, de Bertoldi M (1987) Compost specification for the production and characterization of compost from municipal solid waste. In: de Bertoldi M, Ferranti MP, Hermite PL, Zucconi F (eds) Compost: production, quality and use. Elsevier Applied Science Publishers, Barking, pp 30–50

Zucconi F, Forte M, Monaco A, De Bertoldi M (1981) Biological evaluation of compost maturity. Biocycle 22(4):27–29

Download references

Authors’ contributions

TM conceived and carried out the study; performed the analyses and drafted the manuscript. HG participated in the design of the study; KK, KW, BS and HY participated in the design of the study supervised the analysis process and helped draft the manuscript. All authors read and approved the final manuscript.

Acknowledgements

This study was financially supported by the Ministry of Education of the Federal Democratic Republic of Ethiopia. Authors wish to thank the Sanitation and Beautification Agency (SBA) and Water Supply and Sewerage Authority (WSSA) of Dire Dawa City Administration for their cooperation in collecting and providing the compostable material.

Competing interests

The authors declare that they have no competing interests

Author information

Authors and affiliations.

School of Natural Resources Management and Environmental Sciences, Haramaya University, P.O.Box 138, Dire Dawa, Ethiopia

Tesfu Mengistu, Heluf Gebrekidan, Kibebew Kibret, Beneberu Shimelis & Hiranmai Yadav

School of Plant Sciences, Haramaya University, P.O.Box 138, Dire Dawa, Ethiopia

Kebede Woldetsadik

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Tesfu Mengistu .

Additional file

Additional file 1. table s1 mean daily temperature values of different composting methods, rights and permissions.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Mengistu, T., Gebrekidan, H., Kibret, K. et al. Comparative effectiveness of different composting methods on the stabilization, maturation and sanitization of municipal organic solid wastes and dried faecal sludge mixtures. Environ Syst Res 6 , 5 (2018). https://doi.org/10.1186/s40068-017-0079-4

Download citation

Received : 13 May 2016

Accepted : 01 January 2017

Published : 14 January 2017

DOI : https://doi.org/10.1186/s40068-017-0079-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Faecal coliform
• Faecal sludge
• Helminth egg
• Municipal solid waste
• Sanitization
• Stabilization
• Vermicomposting

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A review of composting process models of organic solid waste with a focus on the fates of c, n, p, and k.

1. Introduction

• What are the key features of existing composting models that involve the fates of C, N, P, and K? (RQ1);
• How could the gaps between the existing model and the target model be well defined and presented? (RQ2).

2.1. Literature Screening

2.2. data extraction, 2.3. checklist for model assessment, 3.1. overview of reviewed models, 3.2. composting substrates and target variables, 3.3. modeling approaches, 3.3.1. mechanism-derived models, 3.3.2. data-driven models, 3.4. application scales, 3.5. sensitivity analysis and validation, 3.6. gaps with the target models reflected by the checklist, 4. discussion, 5. conclusions, supplementary materials, author contributions, acknowledgments, conflicts of interest, abbreviations.

• Yalcinkaya, S. A spatial modeling approach for siting, sizing and economic assessment of centralized biogas plants in organic waste management. J. Clean. Prod. 2020 , 255 , 120040. [ Google Scholar ] [ CrossRef ]
• Dhanya, B.S.; Mishra, A.; Chandel, A.K.; Verma, M.L. Development of sustainable approaches for converting the organic waste to bioenergy. Sci. Total Environ. 2020 , 723 , 138109. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Guo, H.-N.; Wu, S.-B.; Tian, Y.-J.; Zhang, J.; Liu, H.-T. Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review. Bioresour. Technol. 2021 , 319 , 124114. [ Google Scholar ] [ CrossRef ]
• Chen, T.; Zhang, S.; Yuan, Z. Adoption of solid organic waste composting products: A critical review. J. Clean. Prod. 2020 , 272 , 122712. [ Google Scholar ] [ CrossRef ]
• Mor, S.; Ravindra, K.; Dahiya, R.P.; Chandra, A. Leachate characterization and assessment of groundwater pollution near municipal solid waste landfill site. Environ. Monit. Assess. 2006 , 118 , 435–456. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Onwosi, C.O.; Igbokwe, V.C.; Odimba, J.N.; Eke, I.E.; Nwankwoala, M.O.; Iroh, I.N.; Ezeogu, L.I. Composting technology in waste stabilization: On the methods, challenges and future prospects. J. Environ. Manag. 2017 , 190 , 140–157. [ Google Scholar ] [ CrossRef ]
• Mayer, F.; Bhandari, R.; Gäth, S. Critical review on life cycle assessment of conventional and innovative waste-to-energy technologies. Sci. Total Environ. 2019 , 672 , 708–721. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wainaina, S.; Awasthi, M.K.; Sarsaiya, S.; Chen, H.; Singh, E.; Kumar, A.; Ravindran, B.; Awasthi, S.K.; Liu, T.; Duan, Y.; et al. Resource recovery and circular economy from organic solid waste using aerobic and anaerobic digestion technologies. Bioresour. Technol. 2020 , 301 , 122778. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Aguelmous, A.; El Fels, L.; Souabi, S.; Zamama, M.; Hafidi, M. The fate of total petroleum hydrocarbons during oily sludge composting: A critical review. Rev. Environ. Sci. Biotechnol. 2019 , 18 , 473–493. [ Google Scholar ] [ CrossRef ]
• Kulkarni, M.B.; Ghanegaonkar, P.M. Biogas generation from floral waste using different techniques. Glob. J. Environ. Sci. Manag. 2019 , 5 , 17–30. [ Google Scholar ] [ CrossRef ]
• Fernandez-Bayo, J.D.; Yazdani, R.; Simmons, C.W.; VanderGheynst, J.S. Comparison of thermophilic anaerobic and aerobic treatment processes for stabilization of green and food wastes and production of soil amendments. Waste Manag. 2018 , 77 , 555–564. [ Google Scholar ] [ CrossRef ]
• Liu, Z.; Wang, X.; Wang, F.; Bai, Z.; Chadwick, D.; Misselbrook, T.; Ma, L. The progress of composting technologies from static heap to intelligent reactor: Benefits and limitations. J. Clean. Prod. 2020 , 122328. [ Google Scholar ] [ CrossRef ]
• Cerda, A.; Artola, A.; Font, X.; Barrena, R.; Gea, T.; Sánchez, A. Composting of food wastes: Status and challenges. Bioresour. Technol. 2018 , 248 , 57–67. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Li, H.; Chen, Z.; Fu, D.; Wang, Y.; Zheng, Y.; Li, Q. Improved ADM1 for modelling C, N, P fates in anaerobic digestion process of pig manure and optimization approaches to biogas production. Renew. Energy 2020 , 146 , 2330–2336. [ Google Scholar ] [ CrossRef ]
• Faverial, J.; Cornet, D.; Paul, J.; Sierra, J. Multivariate Analysis of the Determinants of the End-Product Quality of Manure-Based Composts and Vermicomposts Using Bayesian Network Modelling. PLoS ONE 2016 , 11 , e0157884. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Galvez-Sola, L.; Morales, J.; Mayoral, A.M.; Marhuenda-Egea, F.C.; Martinez-Sabater, E.; Perez-Murcia, M.D.; Bustamante, M.A.; Paredes, C.; Moral, R. Estimation of phosphorus content and dynamics during composting: Use of near infrared spectroscopy. Chemosphere 2010 , 78 , 13–21. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tiquia, S.M.; Richard, T.L.; Honeyman, M.S. Carbon, nutrient, and mass loss during composting. Nutr. Cycl. Agroecosyst. 2002 , 62 , 15–24. [ Google Scholar ] [ CrossRef ]
• Eghball, B.; Power, J.F.; Gilley, J.E.; Doran, J.W. Nutrient, Carbon, and Mass Loss during Composting of Beef Cattle Feedlot Manure. J. Environ. Qual. 1997 , 26 , 189–193. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhao, S.; Schmidt, S.; Qin, W.; Li, J.; Li, G.; Zhang, W. Towards the circular nitrogen economy—A global meta-analysis of composting technologies reveals much potential for mitigating nitrogen losses. Sci. Total Environ. 2020 , 704 , 135401. [ Google Scholar ] [ CrossRef ]
• Qu, J.; Zhang, L.; Zhang, X.; Gao, L.; Tian, Y. Biochar combined with gypsum reduces both nitrogen and carbon losses during agricultural waste composting and enhances overall compost quality by regulating microbial activities and functions. Bioresour. Technol. 2020 , 314 , 123781. [ Google Scholar ] [ CrossRef ]
• Abdel-Shafy, H.I.; Mansour, M.S.M. Solid waste issue: Sources, composition, disposal, recycling, and valorization. Egypt. J. Pet. 2018 , 27 , 1275–1290. [ Google Scholar ] [ CrossRef ]
• Oladele, S.O.; Adetunji, A.T. Agro-residue biochar and N fertilizer addition mitigates CO2-C emission and stabilized soil organic carbon pools in a rain-fed agricultural cropland. Int. Soil Water Conserv. Res. 2020 . [ Google Scholar ] [ CrossRef ]
• Petric, I.; Selimbašić, V. Development and validation of mathematical model for aerobic composting process. Chem. Eng. J. 2008 , 139 , 304–317. [ Google Scholar ] [ CrossRef ]
• Mason, I.G. Mathematical modelling of the composting process: A review. Waste Manag. 2006 , 26 , 3–21. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, Y.; Niu, W.; Ai, P. Assessing thermal conductivity of composting reactor with attention on varying thermal resistance between compost and the inner surface. Waste Manag. 2016 , 58 , 144–151. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, Y.; Ai, P.; Cao, H.; Liu, Z. Prediction of moisture variation during composting process: A comparison of mathematical models. Bioresour. Technol. 2015 , 193 , 200–205. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ma, S.; Sun, X.; Fang, C.; He, X.; Han, L.; Huang, G. Exploring the mechanisms of decreased methane during pig manure and wheat straw aerobic composting covered with a semi-permeable membrane. Waste Manag. 2018 , 78 , 393–400. [ Google Scholar ] [ CrossRef ]
• He, X.; Han, L.; Ge, J.; Huang, G. Modelling for reactor-style aerobic composting based on coupling theory of mass-heat-momentum transport and Contois equation. Bioresour. Technol. 2018 , 253 , 165–174. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zambra, C.E.; Moraga, N.O.; Escudey, M. Heat and mass transfer in unsaturated porous media: Moisture effects in compost piles self-heating. Int. J. Heat Mass Transf. 2011 , 54 , 2801–2810. [ Google Scholar ] [ CrossRef ]
• Zambra, C.E.; Rosales, C.; Moraga, N.O.; Ragazzi, M. Self-heating in a bioreactor: Coupling of heat and mass transfer with turbulent convection. Int. J. Heat Mass Transf. 2011 , 54 , 5077–5086. [ Google Scholar ] [ CrossRef ]
• Alavi, N.; Sarmadi, K.; Goudarzi, G.; Babaei, A.A.; Bakhshoodeh, R.; Paydary, P. Attenuation of tetracyclines during chicken manure and bagasse co-composting: Degradation, kinetics, and artificial neural network modeling. J. Environ. Manag. 2019 , 231 , 1203–1210. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fernandez-Mena, H.; MacDonald, G.K.; Pellerin, S.; Nesme, T. Co-benefits and Trade-Offs from Agro-Food System Redesign for Circularity: A Case Study With the FAN Agent-Based Model. Front. Sustain. Food Syst. 2020 , 4 , 41. [ Google Scholar ] [ CrossRef ]
• Bonifacio, H.F.; Rotz, C.A.; Richard, T.L. A Process-Based Model for Cattle Manure Compost Windrows: Part 1. Model Description. Trans. ASABE 2017 , 60 , 877–892. [ Google Scholar ] [ CrossRef ]
• Lauwers, J.; Appels, L.; Thompson, I.P.; Degrève, J.; van Impe, J.F.; Dewil, R. Mathematical modelling of anaerobic digestion of biomass and waste: Power and limitations. Prog. Energy Combust. Sci. 2013 , 39 , 383–402. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Walling, E.; Trémier, A.; Vaneeckhaute, C. A review of mathematical models for composting. Waste Manag. 2020 , 113 , 379–394. [ Google Scholar ] [ CrossRef ]
• Li, S.; Fang, Y.; Wu, X. A systematic review of lean construction in Mainland China. J. Clean. Prod. 2020 , 257 , 120581. [ Google Scholar ] [ CrossRef ]
• Heyman, A.; Law, S.; Berghauser Pont, M. How is Location Measured in Housing Valuation? A Systematic Review of Accessibility Specifications in Hedonic Price Models. Urban Sci. 2019 , 3 , 3. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Jia, F.; Peng, S.; Green, J.; Koh, L.; Chen, X. Soybean supply chain management and sustainability: A systematic literature review. J. Clean. Prod. 2020 , 255 , 120254. [ Google Scholar ] [ CrossRef ]
• Wijewardhana, U.A.; Meyer, D.; Jayawardana, M. Statistical models for the persistence of threatened birds using citizen science data: A systematic review. Glob. Ecol. Conserv. 2020 , 21 , e00821. [ Google Scholar ] [ CrossRef ]
• Harris, R.C.; Sumner, T.; Knight, G.M.; White, R.G. Systematic review of mathematical models exploring the epidemiological impact of future TB vaccines. Hum. Vaccin. Immunother. 2016 , 12 , 2813–2832. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Downs, S.H.; Black, N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J. Epidemiol. Community Health 1998 , 52 , 377. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Shiflet, A.B.; Shiflet, G.W. Introduction to Computational Science: Modeling and Simulation for the Sciences , 2nd ed.; Princeton University Press: Princeton, NJ, USA, 2014; ISBN 9781400850556. [ Google Scholar ]
• Lin, Y.P.; Huang, G.H.; Lu, H.W.; He, L. Modeling of substrate degradation and oxygen consumption in waste composting processes. Waste Manag. 2008 , 28 , 1375–1385. [ Google Scholar ] [ CrossRef ]
• Oudart, D.; Robin, P.; Paillat, J.M.; Paul, E. Modelling nitrogen and carbon interactions in composting of animal manure in naturally aerated piles. Waste Manag. 2015 , 46 , 588–598. [ Google Scholar ] [ CrossRef ]
• Calisti, R.; Regni, L.; Proietti, P. Compost-recipe: A new calculation model and a novel software tool to make the composting mixture. J. Clean. Prod. 2020 , 122427. [ Google Scholar ] [ CrossRef ]
• Douglas, P.; Tyrrel, S.F.; Kinnersley, R.P.; Whelan, M.; Longhurst, P.J.; Walsh, K.; Pollard, S.J.T.; Drew, G.H. Sensitivity of predicted bioaerosol exposure from open windrow composting facilities to ADMS dispersion model parameters. J. Environ. Manag. 2016 , 184 , 448–455. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Oudart, D.; Paul, E.; Robin, P.; Paillat, J.M. Modeling organic matter stabilization during windrow composting of livestock effluents. Environ. Technol. 2012 , 33 , 2235–2243. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Ge, J.; Huang, G.; Huang, J.; Zeng, J.; Han, L. Particle-Scale Modeling of Methane Emission during Pig Manure/Wheat Straw Aerobic Composting. Environ. Sci. Technol. 2016 , 50 , 4374–4383. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vasiliadou, I.A.; Muktadirul Bari Chowdhury, A.K.M.; Akratos, C.S.; Tekerlekopoulou, A.G.; Pavlou, S.; Vayenas, D.V. Mathematical modeling of olive mill waste composting process. Waste Manag. 2015 , 43 , 61–71. [ Google Scholar ] [ CrossRef ]
• Villaseñor, J.; Rodríguez Mayor, L.; Rodríguez Romero, L.; Fernández, F.J. Simulation of carbon degradation in a rotary drum pilot scale composting process. J. Environ. Manag. 2012 , 108 , 1–7. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, Y.; Lashermes, G.; Houot, S.; Doublet, J.; Steyer, J.P.; Zhu, Y.G.; Barriuso, E.; Garnier, P. Modelling of organic matter dynamics during the composting process. Waste Manag. 2012 , 32 , 19–30. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lashermes, G.; Zhang, Y.; Houot, S.; Steyer, J.P.; Patureau, D.; Barriuso, E.; Garnier, P. Simulation of Organic Matter and Pollutant Evolution during Composting: The COP-Compost Model. J. Environ. Qual. 2013 , 42 , 361–372. [ Google Scholar ] [ CrossRef ]
• St Martin, C.C.G.; Bekele, I.; Eudoxie, G.D.; Bristol, D.; Brathwaite, R.A.I.; Campo, K.-R. Modelling response patterns of physico-chemical indicators during high-rate composting of green waste for suppression of Pythium ultimum. Environ. Technol. 2014 , 35 , 590–601. [ Google Scholar ] [ CrossRef ]
• Li, W.; Wu, C.; Wang, K.; Meng, L.; Lv, L. Nitrogen loss reduction by adding sucrose and beet pulp in sewage sludge composting. Int. Biodeterior. Biodegrad. 2017 , 124 , 297–303. [ Google Scholar ] [ CrossRef ]
• Huang, G.; Wang, X.; Han, L. Rapid estimation of nutrients in chicken manure during plant-field composting using physicochemical properties. Bioresour. Technol. 2011 , 102 , 1455–1461. [ Google Scholar ] [ CrossRef ]
• Petric, I.; Mustafić, N. Dynamic modeling the composting process of the mixture of poultry manure and wheat straw. J. Environ. Manag. 2015 , 161 , 392–401. [ Google Scholar ] [ CrossRef ]
• Li, Z.; Lu, H.; Ren, L.; He, L. Experimental and modeling approaches for food waste composting: A review. Chemosphere 2013 , 93 , 1247–1257. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kabbashi, N. Sewage sludge composting simulation as carbon/nitrogen concentration change. J. Environ. Sci. 2011 , 23 , 1925–1928. [ Google Scholar ] [ CrossRef ]
• Boniecki, P.; Dach, J.; Pilarski, K.; Piekarska-Boniecka, H. Artificial neural networks for modeling ammonia emissions released from sewage sludge composting. Atmos. Environ. 2012 , 57 , 49–54. [ Google Scholar ] [ CrossRef ]
• Haug, R.T. The Practical Handbook of Compost Engineering ; Lewis Publishers: Boca Raton, FL, USA, 1993; ISBN 0873713737. [ Google Scholar ]
• Wang, Y.; Huang, G.; Zhang, A.; Han, L.; Ge, J. Estimating thermal balance during composting of swine manure and wheat straw: A simulation method. Int. J. Heat Mass Transf. 2014 , 75 , 362–367. [ Google Scholar ] [ CrossRef ]
• Hamelers, H.V.M. A Mathematical Model for Composting Kinetics ; Rulkens, W.H., van Straten, G., Eds.; Wageningen University: Wageningen, The Netherlands, 2001; ISBN 90-5808-445-0. [ Google Scholar ]
• Hamelers, H.V.M. Modeling composting kinetics: A review of approaches. Rev. Environ. Sci. Biotechnol. 2004 , 3 , 331–342. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bonifacio, H.F.; Rotz, C.A.; Richard, T.L. A Process-Based Model for Cattle Manure Compost Windrows: Part 2. Model Performance and Application. Trans. ASABE 2017 , 60 , 893–913. [ Google Scholar ] [ CrossRef ]
• Sun, W.; Huang, G.H.; Zeng, G.; Qin, X.; Yu, H. Quantitative effects of composting state variables on C/N ratio through GA-aided multivariate analysis. Sci. Total Environ. 2011 , 409 , 1243–1254. [ Google Scholar ] [ CrossRef ]
• Bayram, A.; Kankal, M.; Ozsahin, T.; Saka, F. Estimation of the carbon to nitrogen (C:N) ratio in compostable solid waste using artificial neural networks. Fresenius Environ. Bull. 2011 , 20 , 3250–3257. [ Google Scholar ]
• Hosseinzadeh, A.; Baziar, M.; Alidadi, H.; Zhou, J.L.; Altaee, A.; Najafpoor, A.A.; Jafarpour, S. Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions. Bioresour. Technol. 2020 , 303 , 122926. [ Google Scholar ] [ CrossRef ]
• Díaz, M.J.; Eugenio, M.E.; López, F.; García, J.C.; Yañez, R. Neural Models for Optimizing Lignocellulosic Residues Composting Process. Waste Biomass Valor 2012 , 3 , 319–331. [ Google Scholar ] [ CrossRef ]
• Mancebo, U.; Hettiaratchi, J.P.A. Rapid assessment of methanotrophic capacity of compost-based materials considering the effects of air-filled porosity, water content and dissolved organic carbon. Bioresour. Technol. 2015 , 177 , 125–133. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Varma, V.S.; Kalamdhad, A.S.; Kumar, B. Optimization of waste combinations during in-vessel composting of agricultural waste. Waste Manag. Res. 2017 , 35 , 101–109. [ Google Scholar ] [ CrossRef ]
• Chen, H.; Sun, S.; Zhang, B. Forecasting N 2 O emission and nitrogen loss from swine manure composting based on BP neural network. MATEC Web Conf. 2019 , 277 , 1010. [ Google Scholar ] [ CrossRef ]
• Abdallah, M.; Abu Talib, M.; Feroz, S.; Nasir, Q.; Abdalla, H.; Mahfood, B. Artificial intelligence applications in solid waste management: A systematic research review. Waste Manag. 2020 , 109 , 231–246. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Solle, D.; Hitzmann, B.; Herwig, C.; Pereira Remelhe, M.; Ulonska, S.; Wuerth, L.; Prata, A.; Steckenreiter, T. Between the Poles of Data-Driven and Mechanistic Modeling for Process Operation. Chem. Ing. Tech. 2017 , 89 , 542–561. [ Google Scholar ] [ CrossRef ]
• Roy, D.; Azaïs, A.; Benkaraache, S.; Drogui, P.; Tyagi, R.D. Composting leachate: Characterization, treatment, and future perspectives. Rev. Environ. Sci. Biotechnol. 2018 , 17 , 323–349. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Fernandez-Mena, H.; Gaudou, B.; Pellerin, S.; MacDonald, G.K.; Nesme, T. Flows in Agro-food Networks (FAN): An agent-based model to simulate local agricultural material flows. Agric. Syst. 2020 , 180 , 102718. [ Google Scholar ] [ CrossRef ]
• Rotz, C.A.; Corson, M.; Chianese, D.; Coiner, C. The Integrated Farm System Model ; Pasture Systems and Watershed Management Research Unit Agricultural Research Service United States Department of Agriculture: Washington, DC, USA, 2012.

Click here to enlarge figure

Share and Cite

Yang, Z.; Muhayodin, F.; Larsen, O.C.; Miao, H.; Xue, B.; Rotter, V.S. A Review of Composting Process Models of Organic Solid Waste with a Focus on the Fates of C, N, P, and K. Processes 2021 , 9 , 473. https://doi.org/10.3390/pr9030473

Yang Z, Muhayodin F, Larsen OC, Miao H, Xue B, Rotter VS. A Review of Composting Process Models of Organic Solid Waste with a Focus on the Fates of C, N, P, and K. Processes . 2021; 9(3):473. https://doi.org/10.3390/pr9030473

Yang, Zheng, Furqan Muhayodin, Oliver Christopher Larsen, Hong Miao, Bing Xue, and Vera Susanne Rotter. 2021. "A Review of Composting Process Models of Organic Solid Waste with a Focus on the Fates of C, N, P, and K" Processes 9, no. 3: 473. https://doi.org/10.3390/pr9030473

Article Metrics

Article access statistics, supplementary material.

PDF-Document (PDF, 260 KiB)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Environ Res Public Health

Composting of Organic Solid Waste of Municipal Origin: The Role of Research in Enhancing Its Sustainability

Grazia policastro.

1 Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy

2 Telematic University Pegaso, 80132 Naples, Italy

Alessandra Cesaro

Associated data.

The data analyzed during this work are available in the cited literature.

The organic solid waste of municipal origin stands as one of the residual streams of greatest concern: the great amounts continuously produced over time as well as its biochemical and physical characteristics require its proper handling via biological processes, pursuing the recovery of material and/or the generation of energy. At the European level, most of the industrial plants treating the organic fraction of municipal solid waste (OFMSW) rely on composting, which is a well-established and reliable process that is easy to operate in different socio-economic contexts. Nevertheless, when regarded in a life cycle perspective as well as in the view of the principles of circular economy underlying waste management, several issues (e.g., the presence of toxic substances in compost) can be recognized as technical challenges, requiring further studies to identify possible sustainable solutions. This work aims at discussing these challenges and figuring out the state of the art of composting in a circular perspective. Firstly, the main mentioned issues affecting compost quality and process sustainability are briefly reviewed. Next, to promote the effective use of composting in light of the circular economy principles, research experiences are critically presented to highlight the current technical challenges concerning the environmental and health impact reduction and possible scientific perspectives to overcome issues affecting the compost quality. Based on the critical analysis of reviewed studies, it emerged that further research should be aimed at unveiling the hazard potential of emerging contaminants as well as to address the understanding of the mechanisms underlying their potential removal during composting. Moreover, the adoption of a multidisciplinary perspective in the design of research studies may play a key role towards the definition of cost-effective and environmentally friendly strategies to overcome the technical issues affecting the process.

1. Introduction

The organic fraction of municipal solid waste (OFMSW) is an important share of European household waste [ 1 ]. The most recent report of the European Environmental Agency highlights that approximately 85 million tons of biodegradable waste were generated in European Union member states; the greatest portion (around 60%) was composed by food scraps and leftovers, while green waste accounted for approximately 34% [ 1 ]. The high amounts as well as the biochemical characteristics of OFMSW make its proper management an issue of great relevance at the European and international levels. The OFMSW is a carbon-rich source that, when improperly managed, may produce severe environmental burdens [ 2 ], whose reduction has been pursued by promoting the recovery of resources and energy. In this regard, the comparison of the production of compost from source selected OFMSW and the landfilling of biologically stabilized mechanically sorted OFMSW through a Life Cycle Analysis (LCA) demonstrated that the latter was responsible for the greater impact in terms of global warming [ 3 ].

Currently, the principles of the circular economy are underpinning waste management, and an approach directed towards the recovery of materials and/or energy from OFMSW is even more important [ 1 ]. To this end, the biological processing of OFMSW appears as the most suitable solution. Recent research advances are addressing the generation of value added biochemicals [ 4 ] and energy carriers such as hydrogen [ 5 ] and hythane [ 6 ]. However, most of them are still in their infancy in terms of being proposed at industrial scale, where composting and anaerobic digestion remain the prevailing options [ 7 ]. A comparative review of these processes pointed out that anaerobic digestion may end up being more profitable than composting depending on the process scale, but the former process is definitely more environmentally friendly in terms of greenhouse gas (GHG) emissions, as the biogas may be addressed to energy recovery [ 7 ]. Nonetheless, the European Environment Agency recently highlighted that additional environmental benefits can be gained by treating OFMSW with a sequential anaerobic digestion and composting process. Although anaerobic digestion is thus expected to increase significantly, in Europe most of the OFMSW treatment capacity is still provided by composting [ 1 ].

Composting is a well-known biological process, carried out under aerobic conditions, to stabilize organic substrate into a product with fertilizer properties, namely the compost [ 8 ]. However, compost is not always a harmless product. Indeed, it may contain various chemical and biological contaminants, and exerting health and/or environmental risks. Such contaminants may expose different population groups to health hazards, including composting plant workers, consumers of compost-treated agricultural products, and children playing on compost-treated parks [ 8 ]. The main categories of contaminants which can be contained in the compost are physical pollutants including micro plastics (MPs) and chemical compounds, including persistent organic contaminants such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins and -furans (PCDD/Fs), pesticides, and phthalates [ 9 ]. Moreover, the possible presence of pathogens leading to microbiological risk has to be considered and properly handled. Furthermore, the composting process can release gaseous compounds of environmental concern such as NH 3 and GHG (e.g. CO 2 ) [ 9 ]. Process hazards and environmental impacts depend on composting methods and can be mitigated by the proper operation of the process. The generation of high-quality compost is pivotal to ensuring the effective and safe use of this product on soil due to its agronomic properties. In this regard, research plays a key role in the individuation of challenges and strategies to enhance the process sustainability and perspectives. Indeed, as reported in Figure 1 , the number of studies on composting has dramatically increased in recent decades.

Trend of indexed papers containing the word “composting” from 1995 to 2020 (source of data: Scopus database, accessed in December 2022).

The operation of composting in accordance with both the needs of environmental impact reduction and the principle of the circular economy requires a careful analysis, aiming at figuring out the state of the art and identifying the role of research in enhancing its performance. Nonetheless, an updated study meeting these specific needs is absent from the current literature. Indeed, previous works mainly focused on the generic overview of composting challenges and potentials [ 10 , 11 ], as well as on single specific aspects, such as the reduction of nitrogen loss [ 12 ], the use of composting products [ 13 ], the role of biochar in mitigating GHG emissions in composting [ 14 ], composting mathematical models [ 15 ], and organic pollutants [ 16 ]. In the following paragraphs, industrial composting is briefly outlined, and the more recent research experiences are discussed to individuate main challenges and potential risks as well as to highlight possible strategies and scientific perspectives to promote the sustainability of composting in light of the circular economy principles. After the literature review, an updated and critical analysis of the mentioned challenges, the existing potential strategies as well as possible future research perspectives in this field is provided, in order to deliver a tool for researchers interested in developing new methods and/or improve those currently used, and is aimed at promoting the sustainability of composting.

2. Industrial Composting: Main Features and Technical Challenges

2.1. main features of the industrial composting process.

As shown in Figure 2 , the composting process begins as soon as the raw organic materials are mixed together: During the initial stage (organic matter degradation), oxygen and the easily available compounds are consumed by the microorganisms. The temperature of the composting materials then increases rapidly (stabilization phase). As active composting slows, temperature gradually drops (cooling phase) until the compost reaches the environmental temperature. A final curing period usually follows the active composting (mineralization/partial humification) [ 11 ].

Composting phases.

As reported in Table 1 , composting facilities can consist of both closed and open systems.

Composting systems: main advantages and drawbacks.

The former, including biocells, bioreactors and in-vessel composting, require higher capital and operating costs, but allow a better process monitoring. This, in turn, results in lower environmental nuisance as well as in more favorable process conditions, which usually accelerate the biodegradation, shortening the composting period [ 17 , 18 , 19 ]. Conversely, open systems, such as open windrow and aerated piles, are much less costly: they do not rely on reactors and often air supply is provided by periodically turning the material under processing. In this way, temperature and moisture are also roughly regulated. The lower monitoring extent, in this case, results in longer composting times [ 20 , 21 ] and higher land occupation is also required. In order to overcome the limits of the single composting methods, two-stage processes have also been implemented: in this case, closed systems are used to manage the first biodegradation stages that are more energy intensive and characterized by greater emissions, whereas the final maturation stage is left to open systems [ 22 , 23 ]. Among others, the choice of the composting process depends on the environmental and economic conditions, but it is clear that diverse configurations can be applied in a wide variety of contexts to pursue organic waste recovery [ 24 , 25 ]. Compost generation accounts, indeed, for the interest in composting as a material recovery process pursuing circular economy principles. Moreover, it is recognized as a key process in the food-energy-water nexus, since compost can be used as soil amendment for food production [ 7 ].

2.2. Technical Challenges

The application of compost on soil brings several benefits, enhancing its main physic-chemical properties [ 26 ]. It increases the soil essential levels of both organic matter and nutrients and enhances its bulk density, porosity, water holding capacity, and cation-exchange capacity. Moreover, as a substitute for chemical fertilizer, the use of compost contributes to the reduction of the environmental impacts associated with the production and utilization of chemical fertilizers [ 27 ]. However, undesired substances and materials can be found in compost, hindering its safe agronomic use. Compost quality and the emissions of CO 2 and other GHG are recognized as the main challenges for the sustainability of the process [ 9 ]. Both require the identification of proper solutions, and to this end the analysis of the influencing factors is fundamental.

2.2.1. Organic Waste as a Source of Contaminants

The composition and characteristics of the organic substrate destined to composting influences the presence of undesired substances and components in compost [ 28 , 29 ]. The release of CO 2 is also influenced by the chemical composition of the substrate that drives the biochemical reaction, but little has been reported up to now. Conversely, the issue of organic waste contamination has been significantly debated [ 28 , 29 ].

Non-degradable materials, which may enter composting together with the organic waste, end up in the compost if not removed via mechanical pretreatments or during compost refining stages, with adverse effects on soil [ 30 ]. Compost physical contaminants, such as glass, plastics, and synthetic fibers, tend to be incorporated at the soil depth of cultivation, where they may either envelop or act as nucleating agents for mineral grains and organic matter, blocking some pores and potentially reducing water percolation and gas exchange [ 30 ].

The presence of physical contaminants can be properly controlled by promoting the source segregation of the organic waste destined for composting. Alvarez et al. [ 31 ] studied the correlations between the socio-economic/demographic factors and the percentage of undesirable materials present in biowaste samples to find that in separately collected streams, it ranged between 10 and 20%; conversely, in cities with poor participation in the separate collection schemes, unwanted materials may account up to the 50% of the biowaste. More recently, Echavarri-Bravo et al. [ 32 ], through an inter-laboratory trial to evaluate the presence of physical contaminants in compost, posed the issue of their proper detection. The outcomes of their work showed that physical contaminants are heterogeneously present in the source sorted organic waste of municipal origin, and they may require replicate analysis to provide a fair assessment of product quality [ 32 ].

Although the improvement of separate collection can enhance the quality of the organic waste, potential miss-sorting and the collection system itself [ 33 ] require the mechanical pretreatment of the waste destined to composting [ 34 , 35 ]. On the other hand, despite the beneficial effects of the pretreatment stage on physical contaminants reduction, such pretreatment determines losses of biodegradable materials, mainly through sieving [ 23 ], and promotes the reduction of plastic items to micro-plastics (MPs) via shredding and crushing processes. Compost is considered as one of the main sources of MPs in agricultural environments [ 36 ]; they may adversely affect the carbon cycle in soil and bring toxic elements (i.e. heavy metals) that have been reported to be associated to MPs during composting [ 37 ]. Gui et al. [ 38 ] showed the gradual increase of MPs abundance during composting, as well as the change in their shape and size distribution. Therefore, the long-term application of compost onto the soil could result in the accumulation of MPs and consequent impacts on the soil ecosystem.

Although MPs represent a pressing issue, there remains a scarcity of data about their presence, especially for the smaller fractions, due to the difficulties associated with their separation and analysis [ 39 ]. However, some attempts have already been made, and a recent study showed that the abundance of MPs tend to increase during the pretreatment of the organic waste destined to composting, whereas manual sorting had no effect on micro-plastics as it aimed at the removal of the larger plastic items, the mechanical shearing and tearing forces exerted by the crushing and pressing steps was the cause of the increase of micro-plastic abundance in the samples entering the composting process [ 38 ]. This outcome confirms the key role of source selection in preventing the generation of MPs that can end up in compost. In this regard, studying the refined compost produced from five OFMSW facilities differing for the collection systems and treatment technologies, Edo et al. [ 40 ] found that smaller plants with OFMSW door-to-door collection systems produced compost with less plastic of all sizes, whereas compost from big facilities fed by OFMSW from street bin collection displayed the highest contents of plastics. Additionally, the authors reported that no compostable plastic debris was found in the analyzed samples, suggesting that biodegradable polymers that may be present in the incoming waste do not contribute to the spreading of anthropogenic pollution [ 40 ]. Biodegradable polymers have been introduced in recent decades to overcome the issue of traditional plastic pollution. The increasing use of such new materials determines their increasing frequent presence in the waste destined to composting. Although, as mentioned, most biodegradable polymers have been found to be suitable for composting, few of them, such as polylactic acid (PLA), may negatively affect the process. More specifically, it has been found that PLA degradation generates lactic acid, which significantly reduces the pH of compost, affecting seed germination [ 41 ].

In contrast with non-degradable, physical pollutants, the pre-processing of the organic waste cannot act on the presence of persistent organic contaminants. Polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins and -furans (PCDD/Fs), pesticides, and phthalates have reportedly been found in compost [ 42 , 43 , 44 , 45 ], and their fate during the composting process varies depending on the molecular weight, as discussed later in this work.

2.2.2. The Influence of Process Conditions in Driving Composting Sustainability

The composting process aims at organic matter biostabilization, which is mainly affected by the supply of oxygen, the availability of nutrients, the temperature, and the time [ 46 ]; the optimization of these operating parameters and conditions are crucial to ensure the biodegradation of organic matter to an adequate extent, determining compost biological stability and maturity.

Compost stability refers to the degree of decomposition of the organic matter, whereas the maturity describes the suitability as soil amendment, indicating the degree of humification [ 47 ]. Relevant amounts of organic acids, free ammonia-nitrogen (NH 3 ) or other water-soluble compounds that can restrict root development and limit seed germination may be found in unstable composts, and thus the maturity further implies the absence of both phytotoxic compounds in addition to pathogens [ 47 , 48 , 49 ].

Stability and maturity are a consequence of the proper biodegradation of organic compounds in the presence of nutrients such as nitrogen (N), phosphorous (P), and potassium (K). Carbon and nitrogen are fundamental for microorganisms to gain energy and build new cells, and thus the C/N ratio is used as a process control parameter [ 50 ]. During composting, the C/N ratio decreases as a consequence of the decrease of both elements, which occurs at a rate that is higher for C than for N [ 50 ]. Several studies reported that a C/N ratio between 25–30 is optimal for proper composting, but values as high as 40 or 50 have been recommended as well [ 51 , 52 , 53 ]. The C/N ratio may be adjusted by selecting suitable bulking agents, which also play a role in affecting aeration by influencing the porosity of the substrate under composting.

Oxygen supply is the most important parameter in ensuring the proper process development, affecting the microbial activity during the process [ 54 ]. Aeration frequency was found to influence the succession of the bacterial community during the industrial food waste composting by affecting both oxygen concentration and the release of various enzymes by these bacteria [ 54 ]. Similar outcomes were obtained by Wang et al. [ 55 ], who further observed how the aeration rate influenced the leachate production and contributed to the decomposition of toxic substances in the leachate itself.

Cerda et al. [ 48 ] reported proper composting development for aeration ranging between 0.2 and 0.6 L/kg OM min, whereas Xu et al. [ 56 ], studying bacterial dynamics together with gaseous emissions and humification during the composting of food waste, found that aeration intensities higher than 0.36 L/kg OM min reduced the emissions of GHG and hydrogen sulphide and promoted the production of the humus precursor. The same authors recommended reducing the aeration intensity in the final stages of composting in order to avoid the bacterial consumption of the humus precursors.

Aeration is not only crucial to providing oxygen supply, as it also helps with regulating the temperature and the moisture in the mass under composting as well as in removing CO 2 [ 57 ]. The moisture, in turn, has been recently pointed out to affect GHG emissions during food and garden waste composting; increasing the moisture content of the waste under composting resulted in more pores filled with water, which determined, in turn, the creation of anaerobic zones where methane (CH 4 ) was produced; nevertheless, total nitrous oxide (N 2 O) was found to increase for decreasing moisture content [ 58 ]. This condition stands as a technical challenge to be addressed to ensure the sustainability of the process while providing proper aeration conditions. Several studies indeed report that aeration demand for temperature and moisture regulation is much higher than that of biochemical reactions [ 59 , 60 ], and thus excess oxygen is usually supplied in industrial scale plants.

Most industrialized countries have regulated composting as an OFMSW recovery process, defining specific guidelines. Beyond setting threshold limit values for some target compost parameters, these provide indications about the process operating conditions, including the definition of the waste substrates to be excluded as well as the minimum temperature to be reached to ensure the proper sanitation of the final product [ 61 ]. Composting is indeed a self-heating process and temperatures tend to increase in the initial stages as a result of the higher biochemical reaction intensity, and to decline in the final maturation steps, reaching values comparable to the environmental ones. Due to the importance of temperature during composting, this process is divided into the so-called mesophilic, thermophilic and maturation stages, where the former two (i.e. mesophilic and thermophilic) basically refer to the accelerated bio-oxidation phase. In order to produce a hygienic compost, the thermophilic stage should last one week and reach temperatures as high as 55 °C to ensure pathogen destruction [ 61 ]. The temperature of the waste under composting is influenced by the external one, so that heating methods have been developed to promote the microbial activity and increase the temperature in cold climate regions [ 62 ]. The role of high temperature is indeed fundamental not only for hygiene reasons; it promotes organic matter degradation, shortening the maturity period [ 63 ]. Additionally, it may act in controlling the presence of some contaminants. In this regard, Chen et al. [ 64 ] reported an almost 43% removal of polystyrene-MPs from sewage sludge after 45 days of hyperthermophilic composting. They concluded that this outcome was due to the excellent bio-oxidation performance exhibited by hyperthermophilic bacteria [ 64 ]. However, under thermophilic conditions, the high temperature, humidity and oxygen content could improve the degradation of MPs and increase the release of toxic elements (plasticizer, chlorine and heavy metals). In addition, such conditions could produce the reactive oxygen species that reduces the richness and biodiversity of microbial communities during the conventional composting of cow manure and sawdust [ 65 ].

The operating conditions may thus play a role in promoting the contaminant removal; besides MPs, the concentration of selected organic contaminants can be also reduced. This is the case for low molecular weight PAHs, which were observed to decrease up to 90% during composting [ 66 ]. Conversely, the concentration of high molecular weight PAHs, PCBs and pesticides was found to remain stable or to increase, likely due to the moisture content reduction during the final steps of composting [ 66 , 67 , 68 , 69 ]. Similarly, Graça et al. [ 69 ] showed that the use of wood shavings as a bulking agent promoted the proper succession of bacteria during composting, leading to the degradation of low molecular weight PAHs and phthalates, whereas Lin et al. [ 70 ] demonstrated the possibility to treat the high concentration of benzophenone during the co-composting of food waste, sawdust and mature compost, reaching a 97% removal efficiency after 35 days of incubation.

It is worth highlighting that composting has been proposed as a bioremediation practice for sites contaminated with organic pollutants, including polycyclic aromatic hydrocarbons, pesticides, and petroleum products [ 71 ], as well as polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated di-benzofurans (PCDFs) [ 72 ]. This indicates a potential for the process to reduce the presence of some toxic compounds (which may be contained in the incoming waste) in the final compost.

3. Enhancing Compost Quality and Reducing Gaseous Emissions: The Role of Research

In most industrialized countries where OFMSW recycling practices are well established, the characteristics of compost for its use on soil are clearly identified. Although a lack of uniformity can be observed, the agronomic value (C/N ratio, minimum carbon content) and the presence of heavy metals, inerts and pathogens are usually well established [ 73 ]. Similarly, the stability and maturity are already monitored in waste-based composts. The assessment of both biological stability and maturity during the industrial scale composting of the organic fraction of municipal solid waste showed the key role of these parameters in the process monitoring [ 74 ]. The analysis of the Dynamic Respiration Index (DRI) over time was found to provide useful indications about the development of the biological stabilization process, although it may not address the correct identification of the possible causes for unstable composts. Similarly, the sole result of phytotoxicity tests cannot provide comprehensive information given the tight link between stability and maturity [ 74 ], especially for municipal waste based-compost [ 75 ]. The proper monitoring of the selected conventional parameters of stability and maturity may provide further indication about the biodiversity in composting processes [ 76 ].

3.1. Persistent and/or Emerging Organic Contaminants

The greatest concern about the safe use of compost on soil must be ascribed to those contaminants not yet regulated, and this issue has been largely debated in the scientific literature. The presence of persistent organic contaminants has been investigated to provide a wider perspective, and it was found that they do not usually pose a severe risk. The content of compounds causing dioxin-like effects such as PAHs, PCBs and other chlorinated compounds, analyzed in compost samples collected in 16 European countries, was found to be mostly below the most restrictive limit values [ 77 ]. Similarly, Langdon et al. [ 78 ] found that many contaminants in composted municipal organic waste samples produced in New South West Australia can be considered as not posing a risk. Nevertheless, for some others, no criteria were available to assess their hazard potential. To this end, a priority ranking was proposed based on the assessment of a risk quotient (RQ) for either ecological or human receptors. This was calculated considering the maximum concentration in soil resulting in two different scenarios of compost application on soil ( Table 2 ).

Prioritization of selected contaminants based on the risk assessment conducted by Langdon et al. [ 78 ]).

The need to further explore the adverse effects of most emerging compounds comes along with the need to verify whether the proper adjustment of the composting operating conditions may contribute to their degradation. In this regard, further research should focus on understanding the mechanisms underlying the biologically-mediated oxidation of these organic pollutants during waste composting and, based on scientific literature, similar considerations are raised for microplastics. As mentioned, the establishment of specific temperature profiles may be beneficial for the process [ 64 ]. However, further research efforts should be directed towards the understanding of its role on different plastic polymers and size range as well as toward the definition of strategies to reach high temperatures in cold climate regions. In this regard, a suitable solution would be the use of new microbial strains working at psychrophilic conditions, as suggested by Jiang et al. [ 79 ]. Generally speaking, the research on microbial communities has indicated that many compounds used for microbial inoculation help to improve the temperature, to extend the high temperature periods, and to enhance kinase activity, chemical composition and enzymes so that more studies are required in this field [ 11 ].

Depending on the chemical characteristics and concentration of contaminants, the microbial pools as well as the environmental composting conditions that they contribute to create may differently affect the fate of the contaminant itself [ 11 ]. The identification of both optimization strategies and effective monitoring represents other areas of key research to address the technical challenges related to composting.

Process optimization entails the iterative adjustment of the operating conditions to identify those ensuring the sustainable production of a high-quality compost. In this view, an approach based exclusively on an experimental campaign, especially if carried out at industrial scale, may require significant time and effort. Modelling can represent a suitable tool to better understand the composting process [ 80 ], identifying the causes of possible failures so as to take prompt action. In this regard, Onwosi et al. [ 81 ] briefly reviewed possible statistical and kinetic approaches. The former relies on the use of techniques, including the one-at-a-time approach, factorial design and the fuzzy logic model, which allow the comprehension of the effects of some variables of the process under investigation. On the other hand, the kinetic approach uses mathematical models. The development of effective models is worth studying, with the aim being to address new solutions to be adopted at larger scales.

3.2. Gaseous Emissions

The optimization of the composting process is also fundamental to reducing GHG emissions. Recent research advances have focused on the use of semi-permeable membranes coupled with intermittent aeration: under the optimum conditions investigated at industrial scale, a global warming potential (GWP) reduction up to 10% was observed [ 82 ] and the carbon dioxide, methane, nitrous oxide, and ammonia emissions outside the membrane during the aeration interval were decreased by 64%, 70%, 55%, and 11%, respectively, compared with that inside the membrane [ 83 ].

The optimization of the composting process for GHG may also entail the use of additives [ 84 , 85 ]. Yang et al. [ 50 ] proposed the addition of two mineral additives, namely phosphor-gypsum and superphosphate, to reduce gaseous emissions during kitchen waste composting. They found that additives reduced CH 4 emissions by 80.5–85.8% and decreased NH 3 emissions by 18.9–23.5%. A decrease in GHG emissions by 7.3–17.4% was also observed. The extensive data analysis carried out by Cao et al. [ 84 ] to quantify the impact of different additives on NH 3 and GHG emissions showed that it was possible to gain greater yields, reducing the loss of total nitrogen as well as the emissions of NH 3 , N 2 O and CH 4 by 46.4%, 44.5%, 44.6% and 68.5%. The corresponding reduction in the global warming potential was 54.2%. The same authors pointed out that all the additive categories (namely physical chemical and biological) significantly reduced TN loss and NH 3 emission, although, under optimal conditions, the chemical additives resulted in higher effectiveness [ 84 ].

Novel additives may thus be identified, or proper activation procedures may be developed to enhance the efficiency of traditional additives with regard to odour and gaseous emissions. It is worth highlighting that, in a circular perspective, carbon dioxide, although contributing to GHG emissions, may be more interestingly captured and recycled. Thomson et al. [ 86 ] proposed this option by approaching the composting process optimization in the view of resource recapture with the aim of using CO 2 and other composting outputs (like heat and the compost itself) within controlled environmental agriculture practices.

In view of enhancing composting sustainability in a circular perspective, its integration within anaerobic digestion facilities has been proved to be a solution. For instance, Di Maria et al. [ 87 ] compared composting with integrated anaerobic/aerobic treatments aiming at different energetic use of the biogas produced during the anaerobic stage. Their findings demonstrated that the latter was the preferred option in terms of avoided impacts, especially when considering the upgrading of biogas into biomethane instead of conventional exploitation in co-generators [ 87 ]. These results were more recently confirmed by Le Pera et al., [ 88 ] highlighting another area of further research based on LCA studies to identify the potential impact of composting and optimize the process by addressing the reduction of emissions.

4. Critical Analysis of Reviewed Studies and Concluding Remarks

The reliability of composting and its easy implementation are the main drivers for this process to be the preferred technical solution to manage the organic fraction of municipal solid waste at the European level. Nevertheless, with the shift of the paradigm towards the circular economy, some aspects have emerged as technical constraints limiting the sustainability of this process. The need to ensure the effective and safe use of compost on soil has addressed the discussion on compost quality and the presence of contaminants. Similarly, odour, ammonia and GHG emissions require proper handling to reduce the environmental burdens of the process.

The literature review highlights the central position of these aspects in the scientific debate. Different kinds of persistent organic contaminants have been detected and regarded in the view of the potential risk posed by their release into the environment; similarly, the fate of microplastics during composting has been investigated to verify which process stages contribute the most to their accumulation into the process product. Further studies are needed to unveil the hazard potential of emerging contaminants as well as to address the understanding of the mechanisms underlying their potential removal during composting and to propose novel solutions to be applied at larger scale.

Another issue of concern was found to be related to gaseous emissions: beyond odour control solutions, it is fundamental to reduce the environmental burdens associated with GHG emissions. Novel approaches rely on intermittent aeration and the use of semi-permeable membranes or that of additives, but additional efforts should be devoted to the identification of both the optimal operating conditions and the operating costs to implement these solutions within industrial plants. Moreover, in the view of a circular perspective, any solution addressing the capture and recycling of gaseous compounds may play a pivotal role in the near future. This is particularly true for GHGs such as CO 2 .

It is worth highlighting that research exclusively based on experimental campaigns may turn out to be expensive and time-consuming, so that additional approaches based on either mathematical modelling or life cycle assessment studies may be used to support the identification of the most suitable solutions. However, even though different approaches may serve diverse purposes, adopting a holistic and multidisciplinary perspective in the design of research studies dealing with the composting process may play a key role towards the definition of reliable, cost-effective and environmentally friendly strategies to enhance composting performances. Conversely, despite the essential role of research in this field, the limitation of lab-scale experimental tests has to be overcome via the validation of the most promising findings at real scale.

Acknowledgments

Alessandra Cesaro would like to thank the former Italian Ministry of Education, University and Research (MIUR) who provided financial support for her position in the frame of the research project entitled “Dipartimenti di Eccellenza” per Ingegneria Civile Edile e Ambientale -CUPE65D18000820006.

Funding Statement

This research received no external funding.

Author Contributions

G.P. and A.C. contributed to the study conception and design, material preparation, data collection and analysis and wrote the first draft of the manuscript. A.C. performed review and editing. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

share this!

August 14, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

Could manure and compost act like probiotics, reducing antibiotic resistance in urban soils?

by Kimbra Cutlip, University of Maryland

Urban soils often contain chemical contaminants, such as heavy metals or trace amounts of antibiotics, along with higher levels of antibiotic-resistant bacteria. New research from the University of Maryland suggests that, in some cases, boosting urban soil health with compost and treated manure may reduce the amount of "bad" bacteria. Understanding these dynamics has important implications for improving the quality and safety of fresh produce in urban agriculture.

The study was published the Journal of Food Protection.

"Urban farming brings people together and now we see that it can help clean up the environment, at least from certain antibiotic-resistant bacteria," said Ryan Blaustein, an assistant professor in the Department of Nutrition and Food Science at UMD and an author of the study. "Growing organically may promote healthier vegetable 'microbiomes' that we are exposed to as consumers."

Urban farmers and community gardeners often amend their soil with biological additives, like animal manure, or composts made from mixtures of plant material and food scraps that may include fruits and vegetables, eggs, milk, meat, or shellfish waste.

These types of soil amendments are regulated, and must be properly composted or pasteurized before application, because they carry a risk of introducing microbes like salmonella and E. coli, which cause food-borne illness. But little is known about the potential effects of using organic soil amendments on antibiotic resistance in bacteria in urban food systems.

To help fill this gap, Blaustein and his colleagues analyzed soils and leafy green vegetables like kale and lettuce from seven urban farms and community gardens around Washington, D.C. They tested for levels of total bacteria and bacteria resistant to antibiotics like ampicillin and tetracycline. At each location, they tested leafy greens as well as soil that had been treated with manure or compost and soil that had not been treated.

Their results showed that amended soils treated with manure or compost had much more total bacteria than untreated soils, but not necessarily more harmful bacteria or antibiotic-resistant strains. Meaning, the proportion of resistant bacteria and food safety indicators were actually lower in the amended soil. Further studies need to be done to determine the long-term impacts, but their results suggest that manure and compost could act like probiotics for the soil, perhaps introducing or stimulating beneficial bacteria that outcompetes and suppresses the antibiotic-resistant bacteria .

The researchers also found that the pH in soil was strongly associated with concentrations of tetracycline-resistant bacteria, suggesting that managing pH has applications for controlling associated risks. In addition, they saw large differences in bacteria levels between sites, sometimes within the same farm, depending on what amendments were used and what greens were grown. Blaustein said these results highlight the need to build a systems-level understanding of soils in urban farming environments.

This information has important implications for understanding the role of compost and manure for improving soil health and managing harmful bacteria and ensuring a healthy food supply from urban agricultural settings.

Journal information: Journal of Food Protection

Provided by University of Maryland

Explore further

Feedback to editors

Researchers discover smarter way to recycle polyurethane

15 hours ago

DNA study challenges thinking on ancestry of people in Japan

A visionary approach: How a team developed accessible maps for colorblind scientists

New tool simplifies cell tracking data analysis

16 hours ago

How some states help residents avoid costly debt during hard times

Review of 400 years of scientific literature corrects the Dodo extinction record

Study confirms likely identity of the remains of Bishop Teodomiro

Computer simulations suggest more than half of people on Earth have limited access to safe drinking water

Hailstone library to improve extreme weather forecasting

Exploring Huntington's disease: Researchers discover that protein aggregates poke holes in the nuclear membrane

Relevant physicsforums posts, hiking illness danger -- rhabdomyolysis.

13 hours ago

Strategies and Tips for First Responders Interacting with Autism Spectrum Disorder Patients

14 hours ago

Cannot find a comfortable side-sleeping position

Using capsaicin to get really high, therapeutic interfering particle.

Aug 14, 2024

Neutron contamination threshold in tissue using LINAC

Aug 8, 2024

More from Biology and Medical

Related Stories

Study finds manure control effective in reducing soil antibiotic-resistant bacteria

Jan 31, 2024

Bacteria can spread antibiotic resistance through soil

Oct 6, 2017

New technology could reduce spread of antibiotic resistance genes through compost

Feb 7, 2018

How antibiotic use in animals is contributing to antibiotic resistance

Nov 20, 2017

Comparing the pathogen numbers in backyard and commercial composts

Aug 26, 2021

Compost improves apple orchard sustainability

Jun 21, 2021

Recommended for you

The banana apocalypse is near, but biologists might have found a key to their survival

21 hours ago

X-ray irradiation technique helps to control cancer-causing poison in corn

Aug 15, 2024

Beer in space: Researchers study microgravity's effect on fermentation

Skin cell discovery could help Atlantic salmon fend off sea lice

Newly discovered ability of comammox bacteria could help reduce nitrous oxide emissions in agriculture

Unraveling modern bread wheat from the genes up

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Phys.org in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

• See us on facebook
• See us on twitter
• See us on youtube
• See us on linkedin
• See us on instagram

Massive biomolecular shifts occur in our 40s and 60s, Stanford Medicine researchers find

Time marches on predictably, but biological aging is anything but constant, according to a new Stanford Medicine study.

August 14, 2024 - By Rachel Tompa

We undergo two periods of rapid change, averaging around age 44 and age 60, according to a Stanford Medicine study. Ratana21 /Shutterstock.com

If it’s ever felt like everything in your body is breaking down at once, that might not be your imagination. A new Stanford Medicine study shows that many of our molecules and microorganisms dramatically rise or fall in number during our 40s and 60s.

Researchers assessed many thousands of different molecules in people from age 25 to 75, as well as their microbiomes — the bacteria, viruses and fungi that live inside us and on our skin — and found that the abundance of most molecules and microbes do not shift in a gradual, chronological fashion. Rather, we undergo two periods of rapid change during our life span, averaging around age 44 and age 60. A paper describing these findings was published in the journal Nature Aging Aug. 14.

“We’re not just changing gradually over time; there are some really dramatic changes,” said Michael Snyder , PhD, professor of genetics and the study’s senior author. “It turns out the mid-40s is a time of dramatic change, as is the early 60s. And that’s true no matter what class of molecules you look at.”

Xiaotao Shen, PhD, a former Stanford Medicine postdoctoral scholar, was the first author of the study. Shen is now an assistant professor at Nanyang Technological University Singapore.

These big changes likely impact our health — the number of molecules related to cardiovascular disease showed significant changes at both time points, and those related to immune function changed in people in their early 60s.

Abrupt changes in number

Snyder, the Stanford W. Ascherman, MD, FACS Professor in Genetics, and his colleagues were inspired to look at the rate of molecular and microbial shifts by the observation that the risk of developing many age-linked diseases does not rise incrementally along with years. For example, risks for Alzheimer’s disease and cardiovascular disease rise sharply in older age, compared with a gradual increase in risk for those under 60.

The researchers used data from 108 people they’ve been following to better understand the biology of aging. Past insights from this same group of study volunteers include the discovery of four distinct “ ageotypes ,” showing that people’s kidneys, livers, metabolism and immune system age at different rates in different people.

Michael Snyder

The new study analyzed participants who donated blood and other biological samples every few months over the span of several years; the scientists tracked many different kinds of molecules in these samples, including RNA, proteins and metabolites, as well as shifts in the participants’ microbiomes. The researchers tracked age-related changes in more than 135,000 different molecules and microbes, for a total of nearly 250 billion distinct data points.

They found that thousands of molecules and microbes undergo shifts in their abundance, either increasing or decreasing — around 81% of all the molecules they studied showed non-linear fluctuations in number, meaning that they changed more at certain ages than other times. When they looked for clusters of molecules with the largest changes in amount, they found these transformations occurred the most in two time periods: when people were in their mid-40s, and when they were in their early 60s.

Although much research has focused on how different molecules increase or decrease as we age and how biological age may differ from chronological age, very few have looked at the rate of biological aging. That so many dramatic changes happen in the early 60s is perhaps not surprising, Snyder said, as many age-related disease risks and other age-related phenomena are known to increase at that point in life.

The large cluster of changes in the mid-40s was somewhat surprising to the scientists. At first, they assumed that menopause or perimenopause was driving large changes in the women in their study, skewing the whole group. But when they broke out the study group by sex, they found the shift was happening in men in their mid-40s, too.

“This suggests that while menopause or perimenopause may contribute to the changes observed in women in their mid-40s, there are likely other, more significant factors influencing these changes in both men and women. Identifying and studying these factors should be a priority for future research,” Shen said.

Changes may influence health and disease risk

In people in their 40s, significant changes were seen in the number of molecules related to alcohol, caffeine and lipid metabolism; cardiovascular disease; and skin and muscle. In those in their 60s, changes were related to carbohydrate and caffeine metabolism, immune regulation, kidney function, cardiovascular disease, and skin and muscle.

It’s possible some of these changes could be tied to lifestyle or behavioral factors that cluster at these age groups, rather than being driven by biological factors, Snyder said. For example, dysfunction in alcohol metabolism could result from an uptick in alcohol consumption in people’s mid-40s, often a stressful period of life.

The team plans to explore the drivers of these clusters of change. But whatever their causes, the existence of these clusters points to the need for people to pay attention to their health, especially in their 40s and 60s, the researchers said. That could look like increasing exercise to protect your heart and maintain muscle mass at both ages or decreasing alcohol consumption in your 40s as your ability to metabolize alcohol slows.

“I’m a big believer that we should try to adjust our lifestyles while we’re still healthy,” Snyder said.

The study was funded by the National Institutes of Health (grants U54DK102556, R01 DK110186-03, R01HG008164, NIH S10OD020141, UL1 TR001085 and P30DK116074) and the Stanford Data Science Initiative.

• Rachel Tompa Rachel Tompa is a freelance science writer.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Hope amid crisis

Psychiatry’s new frontiers

Combined Treatment for Chicken Manure and Kitchen Waste Enhances Composting Effect by Improving pH and C/N

• Original Paper
• Published: 16 August 2024

Cite this article

• Feng Ma 1 ,
• Qinghui Chen 1 ,
• Haoyu Quan 1 ,
• Chaoyue Zhao 1 ,
• Youzhao Wang 1 &
• Tong Zhu   ORCID: orcid.org/0000-0002-3460-7316 1  

Due to its environmental friendliness, composting is widely used for the treatment of chicken manure and kitchen waste. However, the composting effect still needs to be improved due to the low pH of kitchen waste and low C/N of chicken manure. This paper analyzed the composting effects of chicken manure, kitchen waste, and combined chicken manure and kitchen waste. The maximum composting temperature, relative abundance of thermophilic bacteria, organic matter degradation, and water content decrease for the combined treatment were 77.00 °C, 67.04%, 15.00%, and 28.74%, respectively, which were significantly greater than those for the separate treatment of chicken manure and kitchen waste. Moreover, the results revealed that the composting products of the combined treatment had a higher degree of harmlessness and humification based on seed germination, Escherichia coli , and humification indicator, which is advantageous for the resource application of the products. In conclusion, combined treatment can enhance the composting effect due to the pH and C/N of the material being improved by mixing chicken manure and kitchen waste. This study provides a powerful assistance for understanding the composting mechanism of combined treatment of chicken manure and kitchen waste, laying the theoretical foundation for engineering application.

Graphical Abstract

Combined treatment enhances composting effect by improving pH and C/N of material.

Combined treatment increases temperature and thermophilic bacterial reproduction.

Combined treatment promotes organic matter degradation and water evaporation.

Combined treatment raises harmlessness and humification of composting products.

Statement of Novelty

Chicken manure is characterized by low C/N and high pH, while kitchen waste is characterized by high C/N and low pH. They are poorly composted separately, and need to add conditioners in the process. Therefore, combined treatment of chicken manure and kitchen waste was considered to achieve suitable C/N and pH, which enhances the composting effect and also reduces the use of conditioners. This study provides a novel approach to the composting of chicken manure and kitchen waste.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Environmental Chemistry

Data Availability

Data are available on request to the authors.

Yu, Z., Liu, X., Zhao, M., Zhao, W., Liu, J., Tang, J., Liao, H., Chen, Z., Zhou, S.: Hyperthermophilic composting accelerates the Humification process of Sewage Sludge: Molecular characterization of dissolved Organic Matter using eem-parafac and two-dimensional correlation spectroscopy. Bioresour Technol. 274 , 198–206 (2019). https://doi.org/10.1016/j.biortech.2018.11.084

Article   Google Scholar  

Shen, C., Shangguan, H., Fu, T., Mi, H., Lin, H., Huang, L., Tang, J.: Electric Field-Assisted Aerobic Co-composting of Chicken Manure and Kitchen Waste: Ammonia Mitigation and Maturation Enhancement. Bioresour Technol. 391 , 129931 (2024). https://doi.org/10.1016/j.biortech.2023.129931

Zhao, J., Wang, Z., Li, J., Yan, B., Chen, G.: Pyrolysis of Food Waste and Food Waste Solid Digestate: A comparative investigation. Bioresour Technol. 354 , 127191 (2022). https://doi.org/10.1016/j.biortech.2022.127191

Chen, Y., Qin, Z., Zhang, P., Li, X., Feng, L.: Polycyclic aromatic hydrocarbons stimulate Acidogenesis, Acetogenesis and Methanogenesis during anaerobic co-digestion of Waste activated Sludge and Food Waste. Bioresour Technol. 360 , 127567 (2022). https://doi.org/10.1016/j.biortech.2022.127567

Li, M., Li, S., Meng, Q., Chen, S., Wang, J., Guo, X., Ding, F., Shi, L.: Feedstock optimization with Rice Husk Chicken Manure and mature Compost during Chicken Manure Composting: Quality and Gaseous emissions. Bioresour Technol. 387 , 129694 (2023). https://doi.org/10.1016/j.biortech.2023.129694

Zhang, W., Yu, C., Wang, X., Hai, L.: Increased Abundance of Nitrogen Transforming Bacteria by higher C/N ratio reduces the total losses of N and C in Chicken Manure and Corn Stover Mix Composting. Bioresour Technol. 297 , 122410 (2020). https://doi.org/10.1016/j.biortech.2019.122410

Badgett, A., Milbrandt, A.: Food Waste Disposal and Utilization in the United States: A spatial cost benefit analysis. J. Clean. Prod. 314 , 128057 (2021). https://doi.org/10.1016/j.jclepro.2021.128057

Tawfik, A., Eraky, M., Osman, A.I., Ai, P., Zhou, Z., Meng, F., Rooney, D.W.: Bioenergy Production from Chicken manure: A review. Environ. Chem. Lett. 21 (5), 2707–2727 (2023). https://doi.org/10.1007/s10311-023-01618-x

Li, L., Guo, X., Zhao, T., Li, T.: Green Waste Composting with Bean dregs, Tea Residue, and Biochar: Effects on Organic Matter Degradation, Humification and Compost Maturity. Environ. Technol. Innov. 24 , 101887 (2021). https://doi.org/10.1016/j.eti.2021.101887

Wang, Y., Ma, F., Zhu, T., Liu, Z., Ma, Y., Li, T., Hao, L.: Electric Heating promotes Sludge composting process: Optimization of Heating Method through Machine Learning algorithms. Bioresour Technol. 382 , 129177 (2023). https://doi.org/10.1016/j.biortech.2023.129177

Cui, P., Liao, H., Bai, Y., Li, X., Zhao, Q., Chen, Z., Yu, Z., Yi, Z., Zhou, S.: Hyperthermophilic Composting reduces Nitrogen loss Via Inhibiting ammonifiers and enhancing nitrogenous humic substance formation. Sci. Total Environ. 692 , 98–106 (2019). https://doi.org/10.1016/j.scitotenv.2019.07.239

Huang, Y., Danyang, L., Shah, G.M., Chen, W., Wang, W., Xu, Y., Huang, H.: Hyperthermophilic pretreatment composting significantly accelerates humic substances formation by regulating precursors Production and Microbial communities. Waste Manag. 92 , 89–96 (2019). https://doi.org/10.1016/j.wasman.2019.05.021

Liao, H., Lu, X., Rensing, C., Friman, V.P., Geisen, S., Chen, Z., Yu, Z., Wei, Z., Zhou, S., Zhu, Y.: Hyperthermophilic composting accelerates the removal of Antibiotic Resistance genes and Mobile Genetic Elements in Sewage Sludge. Environ. Sci. Technol. 52 (1), 266–276 (2018). https://doi.org/10.1021/acs.est.7b04483

Ma, F., Zhu, T., Yao, S., Quan, H., Zhang, K., Liang, B., Wang, Y., Zhu, Y., Zhao, C., Lyu, Z.: Coupling effect of high temperature and thermophilic Bacteria indirectly accelerates the Humification process of municipal Sludge in Hyperthermophilic Composting. Process. Saf. Environ. Prot. 166 , 469–477 (2022). https://doi.org/10.1016/j.psep.2022.08.052

Thomas, C., Idler, C., Ammon, C., Amon, T.: Effects of the C/N ratio and moisture content on the survival of Esbl-Producing Escherichia Coli during Chicken Manure Composting. Waste Manag. 105 , 110–118 (2020). https://doi.org/10.1016/j.wasman.2020.01.031

Wan, L., Wang, X., Cong, C., Li, J., Xu, Y., Li, X., Hou, F., Wu, Y., Wang, L.: Effect of inoculating microorganisms in Chicken Manure composting with Maize Straw. Bioresour Technol. 301 , 122730 (2020). https://doi.org/10.1016/j.biortech.2019.122730

Ren, S., Guo, X., Lu, A., Guo, X., Wang, Y., Sun, G., Guo, W., Ren, C., Wang, L.: Effects of Co-composting of Lincomycin Mycelia Dregs with Furfural Slag on Lincomycin Degradation, Maturity and Microbial communities. Bioresour Technol. 265 , 155–162 (2018). https://doi.org/10.1016/j.biortech.2018.05.087

Qiao, C., Penton, C.R., Liu, C., Tao, C., Deng, X., Ou, Y., Liu, H., Li, R.: Patterns of Fungal Community Succession triggered by C/N ratios during composting. J. Hazard. Mater. 401 , 123344 (2021). https://doi.org/10.1016/j.jhazmat.2020.123344

Xiong, J., Zhuo, Q., Su, Y., Qu, H., He, X., Han, L., Huang, G.: Nitrogen Evolution during membrane-covered aerobic composting: Interconversion between Nitrogen forms and Migration pathways. J. Environ. Manag. 345 , 118727 (2023). https://doi.org/10.1016/j.jenvman.2023.118727

Zhu, P., Qin, H., Zhang, H., Luo, Y., Ru, Y., Li, J., San, K.W., Wang, L., Yu, X., Guo, W.: Variations in Antibiotic Resistance genes and removal mechanisms Induced by C/N ratio of substrate during composting. Sci. Total Environ. 798 , 149288 (2021). https://doi.org/10.1016/j.scitotenv.2021.149288

Ruiz-Barrera, O., Ontiveros-Magadan, M., Anderson, R.C., Byrd, J.A., Hume, M.E., Latham, E.A., Nisbet, D.J., Arzola-Alvarez, C., Salinas-Chavira, J., Castillo-Castillo, Y.: Nitro-Treatment of Composted Poultry Litter; effects on Salmonella, E. Coli and Nitrogen Metabolism. Bioresour Technol. 310 , 123459 (2020). https://doi.org/10.1016/j.biortech.2020.123459

Zhong, H., Liu, X., Zhu, L., Yang, Y., Yan, S., Zhang, X.: Bioelectrochemically-assisted vermibiofilter process enhancing stabilization of Sewage Sludge with Synchronous Electricity Generation. Bioresour Technol. 289 , 121740 (2019). https://doi.org/10.1016/j.biortech.2019.121740

Villafuerte, A.B., Soria, R., Rodríguez-Berbel, N., Zema, D.A., Lucas-Borja, M.E., Ortega, R., Miralles, I.: Short-term evaluation of Soil Physical, Chemical and biochemical properties in an abandoned cropland treated with different Soil Organic amendments under Semiarid conditions. J. Environ. Manag. 349 , 119372 (2024). https://doi.org/10.1016/j.jenvman.2023.119372

Liu, L., Liu, S., Zhu, S., Zhou, X., Ma, Y., Pan, N., Li, D., Li, Y., Li, C.: Effects of different concentrations of Biological Maturity agents on Nitrogen and Microbial Diversity of Auricularia Heimuer Residue Compost. Bioresour Technol. 388 , 129641 (2023). https://doi.org/10.1016/j.biortech.2023.129641

Ji, Z., Zhang, L., Liu, Y., Li, X., Li, Z.: Evaluation of composting parameters, technologies and Maturity indexes for aerobic manure composting: A Meta-analysis. Sci. Total Environ. 886 , 163929 (2023). https://doi.org/10.1016/j.scitotenv.2023.163929

Chang, M., Zhu, T., Xiao, T., Wang, J., Wang, N., Song, Y., Wang, Y.: Novel process for Organic Wastewater Treatment using Aerobic Composting Technology: Shifting from Pollutant removal towards Resource Recovery. Sci. Total Environ. 913 , 169522 (2024). https://doi.org/10.1016/j.scitotenv.2023.169522

Quan, H., Zhu, T., Ma, F., Zhang, K., Zhu, Y., Wang, Y., Lyu, Z.: Enhanced Bio-drying Effect in Low-Temperature: Characteristics of Sludge Hyperthermophilic Aerobic Bio-drying by inoculating with thermophilic Bacteria and full-scale operation. Dry. Technol. 1–14 (2023). https://doi.org/10.1080/07373937.2023.2210213

Ma, F., Wang, Y., Quan, H., Wang, Z., Zhao, C., Li, X., Liang, B., Zhang, H., Hao, L., Zhu, T.: Exploring the Humification process of municipal Sludge in Hyperthermophilic composting through Metagenomic and Untargeted Metabolomic. Bioresour Technol. 387 , 129575 (2023). https://doi.org/10.1016/j.biortech.2023.129575

Hénault-Ethier, L., Martin, V.J.J., Gélinas, Y.: Persistence of Escherichia Coli in batch and continuous Vermicomposting systems. Waste Manag. 56 , 88–99 (2016). https://doi.org/10.1016/j.wasman.2016.07.033

Ge, M., Shen, Y., Ding, J., Meng, H., Zhou, H., Zhou, J., Cheng, H., Zhang, X., Wang, J., Wang, H.: New Insight into the impact of moisture content and Ph on dissolved Organic Matter and Microbial dynamics during cattle Manure Composting. Bioresour Technol. 344 , 126236 (2022). https://doi.org/10.1016/j.biortech.2021.126236

Haynes, R.J., Zhou, Y.F.: Comparison of the Chemical, Physical and Microbial properties of composts produced by Conventional Composting or Vermicomposting using the same feedstocks. Environ. Sci. Pollut Res. 23 (11), 10763–10772 (2016). https://doi.org/10.1007/s11356-016-6197-0

Tan, H., Yu, Y., Zhu, Y., Liu, T., Miao, R., Hu, R., Peng, W., Chen, J.: Impacts of size reduction and alkaline-soaking pretreatments on Microbial Community and Organic Matter Decomposition during Wheat Straw Composting. Bioresour Technol. 360 , 127549 (2022). https://doi.org/10.1016/j.biortech.2022.127549

Hashemi, H., Ebrahimi, A., Khodabakhshi, A.: Investigation of anaerobic biodegradability of real compost leachate emphasis on Biogas Harvesting. Int. J. Environ. Sci. Technol. 12 (9), 2841–2846 (2015). https://doi.org/10.1007/s13762-015-0820-3

Xu, Z., Li, R., Zhang, X., Wang, S., Xu, X., Daniel Tang, H., Emmanuel Scriber, K., Zhang, K., Quan, Z.: Molecular mechanisms of Humus formation mediated by New Ammonifying microorganisms in Compost. Chem. Eng. J. 483 , 149341 (2024). https://doi.org/10.1016/j.cej.2024.149341

Zhang, J., Wu, Z., Huang, Y., Zhan, X., Zhang, Y., Cai, C.: Industrial-Scale composting of Swine Manure with a Novel Additive-Yellow Phosphorus Slag: Variation in Maturity indicators, Compost Quality and Phosphorus Speciation. Bioresour Technol. 384 , 129356 (2023). https://doi.org/10.1016/j.biortech.2023.129356

Chen, P., Zhang, L., Li, Y., Liang, J.: Insight to Maturity during Biogas Residue from Food Waste Composting in terms of Multivariable Interaction. Environ. Sci. Pollut Res. 29 (47), 71785–71795 (2022). https://doi.org/10.1007/s11356-022-20616-0

Yang, Y., Wang, G., Li, G., Ma, R., Kong, Y., Yuan, J.: Selection of sensitive seeds for Evaluation of Compost Maturity with the seed Germination Index. Waste Manag. 136 , 238–243 (2021). https://doi.org/10.1016/j.wasman.2021.09.037

Ye, Z., Ding, H., Yin, Z., Ping, W., Ge, J.: Evaluation of Humic Acid Conversion during composting under Amoxicillin stress: Emphasizes the driving role of Core Microbial communities. Bioresour Technol. 337 , 125483 (2021). https://doi.org/10.1016/j.biortech.2021.125483

Wang, L., Wang, T., Xing, Z., Zhang, Q., Niu, X., Yu, Y., Teng, Z., Chen, J.: Enhanced lignocellulose degradation and composts fertility of cattle manure and wheat straw composting by Bacillus inoculation. J. Environ. Chem. Eng. 11 (3), 109940 (2023). https://doi.org/10.1016/j.jece.2023.109940

Du, J., Li, Y., Xu, J., Huang, M., Wang, J., Chao, J., Wu, J., Sun, H., Ding, H., Ye, H.: Characterization of Key odorants in Langyatai Baijiu with Jian Flavour by Sensory-Directed Analysis. Food Chem. 352 , 129363 (2021). https://doi.org/10.1016/j.foodchem.2021.129363

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2020YFC1806402), the Shenyang Science and Technology Plan Project (No. 20–202–4–37). Feng Ma was financially supported by the scholarship (No. 202306080070) from China Scholarship Council (CSC). The authors would like to thank Dr. Shuichi TORII (Kumamoto University) for his helpful discussion.

This work was supported by the National Key Research and Development Program of China (No. 2020YFC1806402), the Shenyang Science and Technology Plan Project (No. 20–202–4–37).

Author information

Authors and affiliations.

Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, China

Feng Ma, Qinghui Chen, Haoyu Quan, Chaoyue Zhao, Youzhao Wang & Tong Zhu

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation was performed by Feng Ma, Qinghui Chen, and Haoyu Quana. Data collection and analysis were performed by Feng Ma, Chaoyue Zhao, and Youzhao Wang. Funding Acquisition was performed by Tong Zhu. The first draft of the manuscript was written by Feng Ma and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Tong Zhu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Ma, F., Chen, Q., Quan, H. et al. Combined Treatment for Chicken Manure and Kitchen Waste Enhances Composting Effect by Improving pH and C/N. Waste Biomass Valor (2024). https://doi.org/10.1007/s12649-024-02676-0

Download citation

Received : 09 April 2024

Accepted : 03 August 2024

Published : 16 August 2024

DOI : https://doi.org/10.1007/s12649-024-02676-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Temperature
• Combined treatment
• Kitchen waste
• Chicken manure
• Find a journal
• Publish with us
• Track your research

Starting this year, all licensed sage grouse hunters will be required to carry a free, annual sage grouse hunting permit. This new regulation aims to enhance the Wyoming Game and Fish Department’s efforts to survey sage grouse hunters and gather valuable data on their hunting experiences. 

Who needs the free sage grouse permit?

• All licensed sage grouse hunters must carry the permit alongside their game bird license. This requirement applies to daily, annual, lifetime and pioneer license holders.
• Wyoming hunters under the age of 14 are exempt from needing the permit as they do not require a license to hunt game birds. However, nonresident hunters under the age of 14 who purchase a game bird license and take their own limit will need a sage grouse permit. 
• Falconers hunting sage grouse must possess the sage grouse permit and their falconry and game bird licenses. 

“Wyoming has about 60,000 game bird license holders each year, with only a few thousand estimated to hunt sage grouse,” said Nyssa Whitford, Game and Fish sage-grouse/sagebrush biologist. “The new annual permit system will allow Game and Fish to send targeted surveys to sage grouse hunters, ensuring more accurate and timely data to aid in the management of this iconic species.” 

The sage grouse permit can be acquired at  Game and Fish offices or  online through the department’s website . It can be obtained as a paper copy or electronically via a mobile device. A screenshot or downloaded PDF of the permit on a mobile device is acceptable in the field. The permit will  not be available at license selling agents. 

Planning on hunting sage grouse this year? Make sure you have the following items with you while in the field: 

• Annual sage grouse permit
• Game bird license
• Hunter education certification. (Hunter education is required in Wyoming for all hunters born after Jan. 1, 1966, with exemptions for military and law enforcement.)
• Conservation stamp (Does not apply to daily and pioneer license holders.)

Learn more about the sage grouse hunting seasons and requirements in the  2024 Game Bird and Small Game Regulations .

Breanna Ball

Related news.

Casper Game and Fish offers training on lymph node removal for chronic wasting disease testing

Sybille Wildlife Research Center working on CWD composting study

Apply now for Wyoming preference points

Want the latest updates.

Sign up to get the latest news and events sent directly to your inbox.

This paper is in the following e-collection/theme issue:

Published on 16.8.2024 in Vol 26 (2024)

Human-Comparable Sensitivity of Large Language Models in Identifying Eligible Studies Through Title and Abstract Screening: 3-Layer Strategy Using GPT-3.5 and GPT-4 for Systematic Reviews

Authors of this article:

Original Paper

• Kentaro Matsui 1, 2 , MD, PhD   ; 
• Tomohiro Utsumi 2, 3 , MD   ; 
• Yumi Aoki 4 , PhD   ; 
• Taku Maruki 5 , MD   ; 
• Masahiro Takeshima 6 , MD, PhD   ; 
• Yoshikazu Takaesu 7 , MD, PhD  

1 Department of Clinical Laboratory, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan

2 Department of Sleep-Wake Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan

3 Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan

4 Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan

5 Department of Neuropsychiatry, Kyorin University School of Medicine, Tokyo, Japan

6 Department of Neuropsychiatry, Akita University Graduate School of Medicine, Akita, Japan

7 Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan

Corresponding Author:

Yoshikazu Takaesu, MD, PhD

Department of Neuropsychiatry

Graduate School of Medicine

University of the Ryukyus

Okinawa, 903-0215

Phone: 81 98 895 3331

Email: [email protected]

Background: The screening process for systematic reviews is resource-intensive. Although previous machine learning solutions have reported reductions in workload, they risked excluding relevant papers.

Objective: We evaluated the performance of a 3-layer screening method using GPT-3.5 and GPT-4 to streamline the title and abstract-screening process for systematic reviews. Our goal is to develop a screening method that maximizes sensitivity for identifying relevant records.

Methods: We conducted screenings on 2 of our previous systematic reviews related to the treatment of bipolar disorder, with 1381 records from the first review and 3146 from the second. Screenings were conducted using GPT-3.5 (gpt-3.5-turbo-0125) and GPT-4 (gpt-4-0125-preview) across three layers: (1) research design, (2) target patients, and (3) interventions and controls. The 3-layer screening was conducted using prompts tailored to each study. During this process, information extraction according to each study’s inclusion criteria and optimization for screening were carried out using a GPT-4–based flow without manual adjustments. Records were evaluated at each layer, and those meeting the inclusion criteria at all layers were subsequently judged as included.

Results: On each layer, both GPT-3.5 and GPT-4 were able to process about 110 records per minute, and the total time required for screening the first and second studies was approximately 1 hour and 2 hours, respectively. In the first study, the sensitivities/specificities of the GPT-3.5 and GPT-4 were 0.900/0.709 and 0.806/0.996, respectively. Both screenings by GPT-3.5 and GPT-4 judged all 6 records used for the meta-analysis as included. In the second study, the sensitivities/specificities of the GPT-3.5 and GPT-4 were 0.958/0.116 and 0.875/0.855, respectively. The sensitivities for the relevant records align with those of human evaluators: 0.867-1.000 for the first study and 0.776-0.979 for the second study. Both screenings by GPT-3.5 and GPT-4 judged all 9 records used for the meta-analysis as included. After accounting for justifiably excluded records by GPT-4, the sensitivities/specificities of the GPT-4 screening were 0.962/0.996 in the first study and 0.943/0.855 in the second study. Further investigation indicated that the cases incorrectly excluded by GPT-3.5 were due to a lack of domain knowledge, while the cases incorrectly excluded by GPT-4 were due to misinterpretations of the inclusion criteria.

Conclusions: Our 3-layer screening method with GPT-4 demonstrated acceptable level of sensitivity and specificity that supports its practical application in systematic review screenings. Future research should aim to generalize this approach and explore its effectiveness in diverse settings, both medical and nonmedical, to fully establish its use and operational feasibility.

Introduction

Large language models (LLMs) with extensive parameters, honed on substantial textual data, have seen striking advancements recently. Following OpenAI’s third-generation Generative Pre-trained Transformer (GPT-3), LLMs now possess advanced competencies in various natural language processing tasks [ 1 ]. Among these, ChatGPT, which is built on GPT-3.5—an iteration that improves upon GPT-3 by integrating both supervised and reinforcement learning techniques—has received particular attention [ 2 , 3 ]. GPT-3.5 has shown exceptional performance in the medical domain, achieving remarkable results on medical licensing examinations across different regions [ 4 ]. Furthermore, GPT-4, the successor to GPT-3.5, has exhibited superior performance [ 5 ], with its contextual understanding abilities potentially exceeding those of humans [ 6 , 7 ]. Beyond its use for language editing [ 8 , 9 ], both GPT-3.5 and GPT-4 have proven to be effective tools for analyzing and comprehending the abstracts of research papers, offering potential benefits in the screening process for systematic reviews.

Systematic reviews and subsequent meta-analyses bear crucial clinical significance. The screening of titles and abstracts is a crucial step in this process [ 10 - 13 ], often involving more than 1000 papers identified via targeted keyword searches [ 14 ]. This screening process can take approximately 1 hour for every 60-120 papers [ 10 ], which is a substantial drain on human and time resources. In addition, human error is inevitable in the screening process [ 15 - 17 ], and the number of such errors can increase as the amount of paper to be screened increases possibly due to fatigue and cognitive overload [ 18 , 19 ]. To mitigate this labor-intensive task, attempts have been made to use text mining and machine learning technologies [ 17 , 20 - 29 ]. Although these methods have successfully reduced the workload, they risk omitting relevant papers, which could result in a high false-negative rate. Specifically, several studies reported the exclusion of records that should have been included in the meta-analysis [ 20 , 21 , 23 , 29 ]. Consequently, using machine learning techniques, such as natural language processing, to assist with abstract screening has not yet become widely adopted [ 14 , 30 ]. For systematic reviews, maintaining high sensitivity for studies eligible for full-text assessment, ideally at 100% [ 10 ], is crucial if they are to be fully supplanted by an automated process.

With the advanced language-processing capabilities of GPT-3.5 and GPT-4 [ 2 , 5 ], there has been an expectation of achieving higher accuracy in screening processes. Kohandel Gargari et al [ 31 ] conducted title and abstract screening using GPT-3.5, but the sensitivity for identifying relevant papers remained at a maximum of 69%, even after attempting various prompt modifications. Khraisha et al [ 32 ] explored the use of GPT-4 across different systematic review processes and found that the sensitivity for title and abstract screening ranged between 42% and 50%. Guo et al [ 33 ] have also demonstrated the use of GPT-4 in title and abstract screenings; however, the sensitivity for relevant papers was limited to 76%, highlighting the challenge of unintentionally excluding necessary records. Notably, Tran et al [ 34 ] used GPT-3.5 for title and abstract screening with rigorous prompt adjustments, achieving a high sensitivity of 97.1% for relevant papers. While this high-sensitivity level might already be suitable for practical use in the systematic review process, its specificity was limited to 37.7% [ 34 ].

The aim of this study is to develop a title- and abstract-screening method using GPT-3.5 and GPT-4 that achieves as high a sensitivity as possible. Although the method of using GPT-3.5 by Tran et al [ 34 ] achieved high sensitivity for identifying relevant papers, we aim to maintain high sensitivity while also improving specificity through a unique approach that incorporates GPT-4. To achieve this, we subdivided the process of determining inclusion for systematic reviews [ 11 ] involving 3 layers of screening. By breaking down the screening process into multiple steps, each addressing a specific aspect, we aimed to optimize the performance of the language models. In this study, we regarded the results of human screening as the gold standard and calculated the sensitivity and specificity of the GPT-3.5 and GPT-4 screening results in comparison with them. Furthermore, we carefully examined the records that were erroneously excluded by GPT-3.5/GPT-4. This examination was conducted to assess the appropriateness of their exclusion.

Language Model Details

GPT-3.5 and GPT-4, LLMs used in this study, are accessible through ChatGPT. However, ChatGPT does not support processing multiple queries against the titles and abstracts of scholarly papers simultaneously. To address this limitation, we leveraged the application programming interfaces (APIs) of GPT-3.5 and GPT-4, known as gpt-3.5-turbo and gpt-4-turbo-preview, respectively [ 35 ].

For gpt-3.5-turbo, we used the most current model available, gpt-3.5-turbo-0125. This model could be used at a low cost of US $0.50 per 1M tokens for input and US $1.50 per 1M tokens for output, with approximately 750 tokens corresponding to 1000 words [ 36 ]. Similarly, for GPT-4, we used the latest model available, gpt-4-0125-preview, which was available at a cost of US $10.00 per 1M tokens for input and US $30.00 per 1M tokens for output [ 36 ].

Calling the GPT-3.5 and GPT-4 API

In this study, we used Google Spreadsheet and Google Apps Script to interface with the GPT-3.5 and GPT-4 APIs for batch processing. Specifically, we created the “GPT35” function to call the gpt-3.5-turbo-0125 API within Google Spreadsheet. Users can invoke this function by entering “=GPT35([prompt])” into a cell, enabling the intuitive batch processing of multiple titles and abstracts. Similarly, we established the “GPT4” function to access the gpt-4-0125-preview API.

Both the gpt-3.5-turbo-0125 and gpt-4-0125-preview have a parameter called “temperature,” which introduces “variability” in the responses—the higher the temperature, the greater the randomness, with a range between 0 and 2 [ 37 ]. As described later in this study, the decision to include or exclude records was delegated to GPT-3.5 and GPT-4. At the preliminary trials, it was observed that setting the temperature above 0 resulted in varying responses from one trial to another. In addition, setting the temperature above 0 can lead to unexpected responses. When instructed to respond with either “E” (for the exclusion) or “I” (for the inclusion), if the temperature is 0, the output will be strictly “E” or “I.” However, if the temperature is above 0, even if it is only 0.1, the response might be, for example, “The answer is ‘E’.” In light of these observations, and primarily to ensure reproducibility, this study fixed the temperature at 0 for all screenings. The Apps Script used in this study is shown in Multimedia Appendix 1 .

Process of Screening and Prompt Engineering

Generally, in a systematic review, a comprehensive examination is conducted on studies that address a relevant clinical question. After a comprehensive literature search is performed to identify all potential studies for review, each record is assessed to determine whether it addresses the clinical question [ 11 ]. In this study, we used either GPT-3.5 or GPT-4 to assess the inclusion or exclusion of relevant papers at each of the following three layers: (1) research design, (2) target population, and (3) intervention and control [ 11 ]. Records not deemed for exclusion at any of these layers were classified as “included.” We present the workflow of the process we conducted in Figure 1 .

The characteristics of the 2 systematic review papers [ 38 , 39 ] used in this study are summarized in Table 1 . The first paper by Takeshima et al [ 38 ] investigated the efficacy of bright light therapy in patients with bipolar disorder. In this study, the titles and abstracts of a total of 1381 records were initially screened in duplicate, with the task being divided between 2 pairs of independent evaluators. The first pair reviewed the initial 753 records, while the second pair assessed the remaining 628 records. Of these, 30 records were targeted for a full-text assessment, and eventually 6 records (encompassing 6 studies) were selected for meta-analysis. The second paper by Maruki et al [ 39 ] verified the difference in therapeutic effects between the usage of 2 types: second-generation antipsychotics (SGAs) and mood stabilizers (MSs), versus the usage of either type alone, targeting patients with bipolar disorder. In this study, the titles and abstracts of a total of 3146 records were initially screened in duplicate, with the screening divided between 2 pairs of evaluators. The first pair reviewed the initial 1694 records, while the second pair evaluated the remaining 1452 records. Of these, 96 records were targeted for a full-text assessment, and eventually 9 records (encompassing 5 studies) were selected for meta-analysis. We used the data on the inclusion or exclusion decisions of each human evaluator made prior to reaching a consensus among evaluators.

The screening process was divided into three layers: (1) research design, (2) target population, and (3) intervention and control. The prompts for each layer must be specifically tailored to each systematic review. At this point, manual prompt adjustments could lead to issues with reproducibility in future research. Therefore, in this study, we used GPT-4 (gpt-4-0125-preview, temperature=0) to automatically extract the information and generate the content for the prompts related to “research design,” “target population,” “intervention,” and “control.” The prompts used for extraction, along with the content defined for “research design,” “target population,” “intervention,” and “control,” are detailed in Textbox 1 . In this study, we extracted information by inserting the text from the “inclusion criteria” paragraph of the Methods section of each paper into the specified location in the prompt ( Textbox 1 ).

The structure of the prompts for each of the 3 layers is shown in Textbox 2 . Within these prompts, we specified that if a decision cannot be made, records should be considered potentially eligible for full-text assessment and not excluded. In this study, the information supplied to GPT-3.5 and GPT-4 was limited to the titles and abstracts of the records; details such as authors, their affiliations, or journal names were not included in the prompts.

In the screening process using GPT-3.5 or GPT-4, we initially verified whether the research design of all records satisfied the inclusion criteria. For records not excluded in the first layer, we subsequently confirmed whether the target population aligned with the inclusion criteria. Moreover, for records that were not excluded in the first and second layers, we assessed whether both the intervention and control groups met the inclusion criteria ( Figure 1 ).

• Research design: [insert your answer here]
• Target population: [insert your answer here]
• Intervention: [insert your answer here]
• Control: [insert your answer here]
• Research design: Randomized controlled trials (RCTs) at the individual or cluster level, including crossover studies reporting results from the first period.
• Target population: Patients with a clinical diagnosis of bipolar disorder (BD), type I or type II.
• Intervention: Any kind of light therapy, including 'light therapy,' 'bright light therapy,' 'phototherapy,' or chronotherapy in any intensity and color.
• Control: Sham treatment (e.g., low-intensity light, dim red light, or negative ion) or treatment as usual (no light treatment).
• Research design: Randomized controlled trials (RCTs) at the individual or cluster level, including crossover studies before crossover
• Target population: Participants diagnosed with bipolar I or II depression, including mixed features and/or rapid cycling.
• Intervention: Adjunctive therapy with second-generation antipsychotics (SGA) or mood stabilizers (MS) during baseline treatment with SGA or MS.
• Control: Adjunctive therapy with a placebo during baseline treatment with second-generation antipsychotics (SGA) or mood stabilizers (MS).
• Prompt for research design#Title and abstractTitle: [ Title of the record was inserted here ]Abstract: [ Abstract of the record was inserted here ]#Research design[ The ‘research design’ specified in Textbox 1 was inserted here ]#QueryYou are a researcher rigorously screening titles and abstracts of scientific papers for inclusion or exclusion in a review paper.Does the paper with the above title and abstract meet the specified research design? If yes, highly suspected, or difficult to determine, answer 'I'. If not, answer 'E'.#RulesYou can reply using only 'E' or 'I'.#Your answer:
• Prompt for target population#Title and AbstractTitle: [ Title of the record was inserted here ]Abstract: [ Abstract of the record was inserted here ]#Target population[ The ‘target population’ specified in Textbox 1 was inserted here ]#QueryYou are a researcher rigorously screening titles and abstracts of scientific papers for inclusion or exclusion in a review paper.Does the paper with the above title and abstract meet the specified target population? If yes, highly suspected, or difficult to determine, answer ‘I’. If not, answer ‘E’.#RulesYou can reply using only ‘E’ or ‘I’.#Your answer:
• Prompt for intervention and control#Title and abstractTitle: [ Title of the record was inserted here ]Abstract: [ Abstract of the record was inserted here ]#Intervention[ The ‘intervention’ specified in Textbox 1 was inserted here ]#Control[ The ‘control’ specified in Textbox 1 was inserted here ]#QueryYou are a researcher rigorously screening titles and abstracts of scientific papers for inclusion or exclusion in a review paper.Does the paper with the above title and abstract meet the specified intervention and control criteria? If yes, highly suspected, or difficult to determine, answer 'I'. If not, answer 'E'.#RulesYou can reply using only 'E' or 'I'.#Your answer:

Data Analysis

In this study, we analyzed the results from human evaluators of systematic review papers, comparing these with the records identified by GPT-3.5 or GPT-4. We considered the records included in the full-text assessment to be correct. We assessed the inclusion or exclusion decisions made by each human evaluator (before consensus was reached) against those determined by GPT-3.5 or GPT-4, focusing on sensitivity and specificity. Sensitivity was defined as the proportion of correctly identified eligible records for full-text assessment by human evaluators, GPT-3.5, or GPT-4. Formally, sensitivity is calculated as follows:

Sensitivity = True positives / (True positives + False negatives)

True positives = Number of records correctly identified as eligible

False negatives = Number of records incorrectly identified as ineligible.

Similarly, specificity was defined as the proportion of correctly identified ineligible records (for full-text assessment) by human evaluators, GPT-3.5, or GPT-4. Formally, specificity is calculated as follows:

Specificity = True negatives / (True negatives + False positives)

True negatives = Number of records correctly identified as ineligible

False Positives = Number of records incorrectly identified as eligible.

For records eligible for full-text assessment but excluded by either GPT-3.5 or GPT-4, we reviewed the title and the abstract to assess whether the exclusion decision was justified. Following this review, we recalculated sensitivity and specificity after adjusting for these justified exclusions. Furthermore, for records that were incorrectly excluded by GPT-3.5 or GPT-4, we conducted a narrative verification of the erroneous judgments by asking each LLM to explain the reasons behind their decisions. We modified the prompt used for screening ( Textbox 2 ) by replacing the “#Rules” statement with “Specify the reason for your answer.” This modification allowed GPT-3.5 or GPT-4 to provide their judgment results along with the underlying reasons.

Ethical Considerations

This study used only publicly available data from research papers and does not involve human subjects or personal data. Therefore, it does not require a human subject ethics review or exemption.

Results on Takeshima et al Paper

Figure 2 [ 38 ] shows the number of records excluded by GPT-3.5 and GPT-4 at each layer of research design, target population, and intervention and control, applied to records in the paper by Takeshima et al [ 38 ].

GPT-3.5 excluded 84 records at the research design layer, 877 records at the target population layer, and 0 record at the intervention and control layer, ultimately determining 420 out of 1382 records for inclusion. None of the 6 records (including 6 papers) that were included in the meta-analysis were excluded by GPT-3.5. The sensitivity for included records was 0.900 and the specificity was 0.709. Among the eligible records for full-text assessment, GPT-3.5 classified 3 (10.0%) records as excluded. Of these, the exclusion of 2 records by GPT-3.5 was justified, while the remaining 1 (3.3%) record was deemed to require full-text assessment ( Table 2 ). After adjustments for these justified judgments ( Multimedia Appendix 2 ), the sensitivity improved to 0.966 and the specificity remained at 0.710. For the one record that GPT-3.5 determined to be excluded at the target population layer, it was suggested that GPT-3.5 concluded that the record “included both bipolar disorder and unipolar mood disorder, which did not match the selection criteria.”

a Number of records for which exclusion was not justified.

GPT-4 excluded 589 records at the research design layer, 760 records at the target population layer, and 1 record at the intervention and control layer, ultimately determining 31 out of 1381 records for inclusion. None of the 6 records (including 6 papers) that were included in the meta-analysis were excluded by GPT-4. The sensitivity for included records was 0.806 and the specificity was 0.996. Among the eligible records for full-text assessment, GPT-4 classified 6 (20.0%) records as excluded. Of these, the exclusion of 5 records by GPT-4 was justified, while the remaining 1 (3.3%) record was considered to require full-text assessment ( Table 2 ). After adjustments for these justified judgments ( Multimedia Appendix 2 ), the sensitivity improved to 0.962 and the specificity remained at 0.996. GPT-4 included all 6 records (including 6 papers) that were included in the meta-analysis. For the one record that GPT-4 judged to be excluded at the research design layer, it was revealed that GPT-4 deduced that “although this study mentioned registration in an RCT, it investigated the associations between sleep, physical activity, and circadian rhythm indicators” (from the perspective of whether to include the study in the meta-analysis, GPT-4’s judgment is likely to be correct; however, considering the purpose of the initial screening, we determined that it would be appropriate to include the study).

Results of the Paper by Maruki et al

Figure 3 [ 39 ] shows the number of records excluded by GPT-3.5 and GPT-4 at each layer of research design, target population, and intervention and control, applied to records in the Maruki et al [ 39 ] paper.

GPT-3.5 excluded 220 records at the research design layer, 126 records at the target population layer, and 10 records at the intervention and control layer, ultimately determining 2790 out of 3146 records for inclusion. None of the 9 records (including 9 papers) that were included in the meta-analysis were excluded by GPT-3.5. The sensitivity for included records was 0.958 and the specificity was 0.116. Among the eligible records for full-text assessment, GPT-3.5 classified 4 (4.2%) records as excluded. None of these records’ exclusion by GPT-3.5 was justified, and all were considered to require full-text assessment ( Table 3 and Multimedia Appendix 2 ). For the 2 records that GPT-3.5 inferred to be excluded at the research design layer, it was revealed that GPT-3.5 determined that “although they were RCTs, either the individual or cluster level was not specified” for both records. For the 2 records that GPT-3.5 deemed to be excluded at the target population layer, it was suggested that GPT-3.5 surmised that “although the records involved bipolar disorder, they did not match the selection criteria due to the presence of comorbidities (one record had generalized anxiety disorder, and the other had alcohol dependence).”

GPT-4 excluded 1287 records at the research design layer, 503 records at the target population layer, and 830 records at the intervention and control layer, ultimately determining 526 out of 3146 records for inclusion. None of the 9 records (including 9 papers) that were included in the meta-analysis were excluded by GPT-4. The sensitivity for included records was 0.875 and the specificity was 0.855. Among the eligible records for full-text assessment, GPT-4 classified 12 (12.5%) records as excluded. Of these, the exclusion of 8 records by GPT-4 was justified, while the remaining 4 (4.2%) records were considered to require full-text assessment ( Table 3 ). After adjustments for these justified judgments ( Multimedia Appendix 2 ), the sensitivity improved to 0.943 and the specificity remained at 0.855. “For the one record that GPT-4 determined to be excluded at the target population layer, it was suggested that GPT-4 inferred that ‘although the record involved bipolar disorder, it did not match the selection criteria due to the presence of a comorbidity (alcohol dependence).’ For the three records that GPT-4 judged to be excluded at the Intervention and control layer, in each case, GPT-4 cited the reason for exclusion as ‘the intervention criteria are the addition of either SGA or MS to SGA or MS, but this study does not mention the use of SGA.’”

In the list used in the paper by Maruki et al [ 39 ], there were a total of 355 records where part of the title and abstract were corrupted into irrelevant Chinese characters (eg, “This was an eight窶陣eek, open窶人abel, prospective study”). Despite these errors, all cases could be appropriately discerned, likely due to the context-sensitive judgment capability of GPT-3.5 and GPT-4.

Comparison of GPT-3.5, GPT-4, and Human Evaluators

Both the study by Takeshima et al [ 38 ] and the study by Maruki et al [ 39 ] involved 2 individuals conducting screening for the initial segment, while a different set of 2 individuals was responsible for the screening of the latter segment. The sensitivity and specificity of human evaluators and GPT-3.5 and GPT-4 for each segment are shown in Table 4 . The adjusted results, in cases where the exclusion of GPT-3.5 or GPT-4 was justified, are shown in the numbers within parentheses ( Table 4 ).

a LLMs: large language models.

b Not applicable.

c Values after adjusting for cases where exclusion was justified.

Time and Cost Required for Screenings

In our Google Spreadsheet setup, both GPT-3.5 and GPT-4 managed to process approximately 110 records per minute across each of the 3 layers. Consequently, the estimated ideal completion time was between 20 and 30 minutes for the study by Takeshima et al [ 38 ], and between 60 and 80 minutes for the study by Maruki et al [ 39 ]. However, in practice, due to errors with the Google Spreadsheet and API, the screening process took about 1 hour for the study by Takeshima et al [ 38 ] and about 2 hours in total for the study by Maruki et al [ 39 ]. Furthermore, due to daily API call limits, the work had to be spread out over 3 days. The screening for these 2 studies incurred a total cost of US $59, with US $4 for calls to GPT-3.5 and US $55 for calls to GPT-4.

Principal Findings

This study demonstrates the use of a 3-layer screening method using GPT-3.5 and GPT-4 for title and abstract screenings in systematic reviews, highlighting its remarkable speed and sensitivity comparable with that of human evaluators. However, GPT-3.5 demonstrated low specificity for relevant records, rendering it less practical. In contrast, the use of GPT-4 showed both high sensitivity and specificity, particularly where adjustments for justified exclusions led to an improvement in sensitivity. Although achieving 100% sensitivity remained unattainable, a 3-layer screening method with GPT-4 may potentially be practical for use in the systematic review process and can reduce human labor.

Previous research demonstrating the effectiveness of automated screening using text mining has encountered sensitivity issues [ 20 - 29 ]. Specifically, the exclusion of important studies that should have been included in their meta-analysis [ 20 , 21 , 23 , 29 ], a limitation not observed in our approach, hampered their application to clinical practice. False negatives in machine learning–based screening can arise from several factors: complexity in research design, characteristics of the target demographic, types of interventions, complexity in selection criteria, a significant scarcity of relevant records within the data set (leading to data imbalance), and inconsistency in the terminology used for judgment [ 21 , 23 , 29 ]. Our method using GPT-3.5 or GPT-4 was able to address issues related to data set imbalance and terminology inconsistency, as we used the same prompt across records, and assess the inclusion or exclusion one by one. In addition, previous text mining screenings may not have effectively addressed garbled text, such as “open-label” mistakenly appearing as “open窶人abel” [ 40 ], an issue that LLMs can potentially mitigate through their attention mechanisms [ 41 ]. Moreover, the outstanding knowledge base of GPT-4 [ 6 , 7 ] likely helped address the complexity in research design, target demographics, and intervention, as well as selection criteria—areas where GPT-3.5 might have fallen short. These distinctions possibly account for the notable differences in specificity observed between GPT-3.5 and GPT-4. Recently, Guo et al [ 33 ] conducted title and abstract screening using GPT-4. Their approach diverges from our 3-layer method; it integrated inclusion and exclusion criteria within the context, generating decisions and reasoning through a single prompt. While we believe that our 3-layer method could potentially offer greater sensitivity than theirs, it remains difficult to definitively assert a significant improvement in sensitivity over the method by Guo et al [ 33 ], given the limited sample size and the differences in data sets. Tran and colleagues’ approach [ 34 ], despite using GPT-3.5, demonstrated remarkable sensitivity. It is important to note, however, that the manual creation of their highly effective prompt raises questions regarding its replicability and broader applicability.

Both human-conducted and LLM-conducted systematic reviews have their inherent pitfalls. Errors made by humans are inevitable, with their accuracy estimated to be around 10% [ 15 ], and slightly higher for false exclusions, at approximately 13%-14% [ 16 , 17 ]. These values represent the performance of experts in the relevant field, and the accuracy may be lower for individuals with less expertise or shallow screening experience; therefore, guidelines have recommended piloting and training the abstract screening team [ 12 ]. In this study, we observed that human evaluation in the paper by Takeshima et al [ 38 ] exhibited slightly more false negatives than that in the paper by Maruki et al [ 39 ]. Although the reasons for the judgment discrepancies were not investigated in this study’s data set, they may be attributed to the larger volume of records screened [ 14 ] and the potentially more complex and challenging research question in the paper by Maruki et al [ 39 ]. Using 2 reviewers to screen records can significantly lower the likelihood of false negatives [ 16 ] and has been recommended [ 11 , 13 ]. Yet, simultaneously, there has been a case that the systematic review screenings, albeit rare, are conducted by a single reviewer, because of time constraints [ 13 , 42 ]. Hence, the unavoidable errors and substantial time and effort required for screening represent significant drawbacks of human screening in systematic reviews [ 10 , 13 ].

Conversely, methods using LLMs also present several drawbacks. One primary concern is their susceptibility to misinformation and quality issues inherent in their training data [ 43 ]. Notably, in this study, the specificity of the GPT-3.5 screenings in Maruki et al [ 39 ] paper was markedly low. While the causes are not definitive, this may be attributed to an insufficient understanding of bipolar disorder, MSs, and second-generation antipsychotics. Tran and colleagues [ 34 ] incorporated relevant knowledge into their manually created prompts; it might have enhanced sensitivity but not specificity; and this could also be due to GPT-3.5’s knowledge limitations. Furthermore, the decision-making processes of LLMs lack transparency, making them difficult to interpret [ 43 ]. This lack of interpretability is compounded by the “grounding problem,” where LLMs struggle to grasp concrete facts and real-world scenarios due to their lack of real-world experiences and sensory input [ 1 , 44 ]. We attempted to verify incorrectly excluded records by querying GPT-3.5 and GPT-4 with the original screening prompts, their responses, and justifications. Our findings revealed that GPT-3.5’s lower accuracy was primarily due to a lack of knowledge about the target domain, while GPT-4’s incorrect exclusions were mainly due to misinterpretations of the inclusion criteria. These findings highlight the ongoing challenges in understanding and interpreting the decision-making processes of LLMs. Although GPT-4 demonstrates advancements in comprehension, factuality, specificity, and inference, it is still more susceptible to factual errors [ 45 ]. In addition, it has been suggested that LLMs’ accuracy diminishes with longer prompts [ 46 ]; lengthy abstracts might have contributed to decreased accuracy in decision-making. A potential future risk is that the normalization of AI-based judgments could result in the oversight of human expert verification, potentially diminishing the quality of systematic reviews.

On the positive side, compared with the human screening time reported in previous studies [ 10 ], our method enabled remarkably faster screening. Although our approach uses a 3-layer structure, which might seem time-consuming at first glance, by limiting GPT-3.5/GPT-4 responses to “E” (Exclude) or “I” (Include), we efficiently screened a large volume of records in batch. Unlike humans, LLMs do not experience fatigue and subsequent decline in performance; moreover, they are presumed to have better reproducibility in their judgments. While using GPT-4’s API comes with associated costs [ 36 ], the increased efficiency compared with human effort more than compensates for these expenses. Using LLMs for title and abstract screening could also enable screening a much larger number of records, previously deemed impractical due to time limitations. Our 3-layer method using GPT-4 exhibits high sensitivity and a useful level of specificity and yet opportunities for further refinement exist. Future studies could enhance accuracy through methods such as optimizing prompts [ 47 ] and integrating multiple LLMs for decision assessment [ 48 ], which may contribute to higher precision. In the meantime, swift advancements in LLM technology are set to continuously evolve; future breakthroughs in LLMs may readily overcome our current challenges—possibly, only by a simple prompt.

Limitations

This study has some limitations. First, the 2 systematic reviews used in this investigation [ 38 , 39 ] were confined to clinical studies within psychiatry, limiting the generalizability of our findings. In addition, the sample size was small, and the investigation remained exploratory, with the results lacking statistical substantiation. Future studies should aim to replicate these findings across a broader range of medical fields and specialized domains to enhance their applicability and reliability. Second, the artificial intelligence industry is progressing rapidly, with information becoming obsolete within a matter of months or even weeks. The models we used in this study, gpt-3.5-turbo-0125 and gpt-4-0125-preview, are currently the most up-to-date. However, updates to these models might alter screening outcomes. Third, to ensure consistency in our findings, we set the temperature parameter to 0. However, a temperature of 0 does not always guarantee absolute uniformity in output sentences [ 35 ]. However, our observations indicate no variation in results across multiple tests with the same model in this study. Fourth, this study did not investigate the discrepancies in screening results between GPT-3.5 and GPT-4, nor did it examine the impact of prompt variations on performance. In addition, this research did not directly compare the performance of the proposed approach with existing systematic literature review strategies. Furthermore, this study was not designed to explore the risks associated with using LLMs for screening purposes. Finally, gpt-3.5-turbo-0125’s training data include information up to September 2021, whereas gpt-4-0125-preview’s training data extend to December 2023 [ 35 ]. Consequently, the systematic review paper by Takeshima et al [ 38 ] might have been incorporated into GPT-3.5’s training data set, with both systematic review papers possibly included in GPT-4’s data set. Nevertheless, as the study’s prompts did not explicitly reference these reviews, we consider that their impact is minimal.

Conclusions

We developed a practical screening method using GPT-3.5 and GPT-4 in the title- and abstract-screening process of systematic reviews. Our 3-layer method not only achieved better sensitivity for relevant records than previous machine learning–based screening methods [ 20 , 21 , 23 , 29 ] but also demonstrated a remarkable potential to reduce human reviewers’ workload significantly. Although GPT-3.5 showed lower specificity, which may limit its applicability, the use of GPT-4 within our method yielded sensitivity comparable with human evaluators, making it suitable for use in systematic review screenings. Despite the focus on psychiatric fields and the small sample size of our study, our findings highlight the potential for broader application. We emphasize the importance of further validation across multiple domains to establish a universal screening methodology. Concurrently, developing more effective approaches in response to the advancing capabilities of LLMs is warranted in future research.

Acknowledgments

This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant 22K15778). During the preparation of this work, the authors used ChatGPT (GPT-4 and GPT-4o, by OpenAI), Claude (Claude 3 Opus, by Anthropic), and Gemini (Gemini 1.5 Pro, by Google) to enhance the readability and proofread the English text. After using these services, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.

Conflicts of Interest

None declared.

Script for the Google Spreadsheet.

Records eligible for full paper screening but excluded by GPT-3.5 or GPT-4.

• Brown TB, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877-1901.
• Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, et al. Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst. 2022;35:27730-27744. [ CrossRef ]
• Introducing ChatGPT. URL: https://openai.com/blog/chatgpt [accessed 2023-07-01]
• Levin G, Horesh N, Brezinov Y, Meyer R. Performance of ChatGPT in medical examinations: a systematic review and a meta-analysis. BJOG. 2024;131(3):378-380. [ CrossRef ] [ Medline ]
• GPT-4. URL: https://openai.com/research/gpt-4 [accessed 2024-02-29]
• Bojic L, Kovacevic P, Cabarkapa M. GPT-4 surpassing human performance in linguistic pragmatics. arXiv. Preprint posted online. Dec 15, 2023.
• Eriksen AV, Möller S, Ryg J. Use of GPT-4 to diagnose complex clinical cases. NEJM AI. 2023;1(1):AIp2300031. [ CrossRef ]
• Kim SG. Using ChatGPT for language editing in scientific articles. Maxillofac Plast Reconstr Surg. 2023;45(1):13. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Matsui K, Koda M, Yoshida K. Implications of nonhuman "Authors". JAMA. 2023;330(6):566. [ CrossRef ] [ Medline ]
• Lefebvre C, Glanville J, Briscoe S, Littlewood A, Marshall C, Metzendorf MI, et al. Searching for and selecting studies. In: Cochrane Handbook for Systematic Reviews of Interventions. New York, NY. John Wiley & Sons; 2019:67-107.
• Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ. Cochrane Handbook for Systematic Reviews of Interventions. New York, NY. John Wiley & Sons; 2019.
• Polanin JR, Pigott TD, Espelage DL, Grotpeter JK. Best practice guidelines for abstract screening large‐evidence systematic reviews and meta‐analyses. Res Synth Methods. 2019;10(3):330-342. [ CrossRef ]
• Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160. [ FREE Full text ] [ CrossRef ] [ Medline ]
• O'Hearn K, MacDonald C, Tsampalieros A, Kadota L, Sandarage R, Jayawarden SK, et al. Evaluating the relationship between citation set size, team size and screening methods used in systematic reviews: a cross-sectional study. BMC Med Res Methodol. 2021;21(1):142. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wang Z, Nayfeh T, Tetzlaff J, O'Blenis P, Murad MH. Error rates of human reviewers during abstract screening in systematic reviews. PLoS One. 2020;15(1):e0227742. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gartlehner G, Affengruber L, Titscher V, Noel-Storr A, Dooley G, Ballarini N, et al. Single-reviewer abstract screening missed 13 percent of relevant studies: a crowd-based, randomized controlled trial. J Clin Epidemiol. 2020;121:20-28. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wilson E, Cruz F, Maclean D, Ghanawi J, McCann SK, Brennan PM, et al. Screening for in vitro systematic reviews: a comparison of screening methods and training of a machine learning classifier. Clin Sci (Lond). 2023;137(2):181-193. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bannach-Brown A, Przybyła P, Thomas J, Rice ASC, Ananiadou S, Liao J, et al. Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error. Syst Rev. 2019;8(1):23. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cierco Jimenez R, Lee T, Rosillo N, Cordova R, Cree IA, Gonzalez A, et al. Machine learning computational tools to assist the performance of systematic reviews: a mapping review. BMC Med Res Methodol. 2022;22(1):322. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shemilt I, Simon A, Hollands GJ, Marteau TM, Ogilvie D, O'Mara-Eves A, et al. Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews. Res Synth Methods. 2014;5(1):31-49. [ CrossRef ] [ Medline ]
• Rathbone J, Hoffmann T, Glasziou P. Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers. Syst Rev. 2015;4:80. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Olofsson H, Brolund A, Hellberg C, Silverstein R, Stenström K, Österberg M, et al. Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan. Res Synth Methods. 2017;8(3):275-280. [ CrossRef ] [ Medline ]
• Gates A, Johnson C, Hartling L. Technology-assisted title and abstract screening for systematic reviews: a retrospective evaluation of the Abstrackr machine learning tool. Syst Rev. 2018;7(1):45. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gartlehner G, Wagner G, Lux L, Affengruber L, Dobrescu A, Kaminski-Hartenthaler A, et al. Assessing the accuracy of machine-assisted abstract screening with DistillerAI: a user study. Syst Rev. 2019;8(1):277. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gates A, Gates M, Sebastianski M, Guitard S, Elliott SA, Hartling L. The semi-automation of title and abstract screening: a retrospective exploration of ways to leverage abstrackr's relevance predictions in systematic and rapid reviews. BMC Med Res Methodol. 2020;20(1):139. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hamel C, Kelly SE, Thavorn K, Rice DB, Wells GA, Hutton B. An evaluation of DistillerSR's machine learning-based prioritization tool for title/abstract screening—impact on reviewer-relevant outcomes. BMC Med Res Methodol. 2020;20(1):256. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Reddy SM, Patel S, Weyrich M, Fenton J, Viswanathan M. Comparison of a traditional systematic review approach with review-of-reviews and semi-automation as strategies to update the evidence. Syst Rev. 2020;9(1):243. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pham B, Jovanovic J, Bagheri E, Antony J, Ashoor H, Nguyen TT, et al. Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow. Syst Rev. 2021;10(1):156. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Valizadeh A, Moassefi M, Nakhostin-Ansari A, Hosseini Asl SH, Saghab Torbati M, Aghajani R, et al. Abstract screening using the automated tool rayyan: results of effectiveness in three diagnostic test accuracy systematic reviews. BMC Med Res Methodol. 2022;22(1):160. [ FREE Full text ] [ CrossRef ] [ Medline ]
• O'Connor AM, Tsafnat G, Thomas J, Glasziou P, Gilbert SB, Hutton B. A question of trust: can we build an evidence base to gain trust in systematic review automation technologies? Syst Rev. 2019;8(1):143. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kohandel Gargari O, Mahmoudi MH, Hajisafarali M, Samiee R. Enhancing title and abstract screening for systematic reviews with GPT-3.5 turbo. BMJ Evid Based Med. 2024;29(1):69-70. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Khraisha Q, Put S, Kappenberg J, Warraitch A, Hadfield K. Can large language models replace humans in systematic reviews? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages. Res Synth Methods. 2024;15(4):616-626. [ CrossRef ] [ Medline ]
• Guo E, Gupta M, Deng J, Park YJ, Paget M, Naugler C. Automated paper screening for clinical reviews using large language models: data analysis study. J Med Internet Res. 2024;26:e48996. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tran VT, Gartlehner G, Yaacoub S, Boutron I, Schwingshackl L, Stadelmaier J, et al. Sensitivity and specificity of using GPT-3.5 turbo models for title and abstract screening in systematic reviews and meta-analyses. Ann Intern Med. 2024;177(6):791-799. [ CrossRef ] [ Medline ]
• Models. URL: https://platform.openai.com/docs/models [accessed 2024-02-29]
• Pricing. URL: https://openai.com/pricing [accessed 2024-03-06]
• API Reference. URL: https://platform.openai.com/docs/api-reference/ [accessed 2024-02-29]
• Takeshima M, Utsumi T, Aoki Y, Wang Z, Suzuki M, Okajima I, et al. Efficacy and safety of bright light therapy for manic and depressive symptoms in patients with bipolar disorder: a systematic review and meta-analysis. Psychiatry Clin Neurosci. 2020;74(4):247-256. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Maruki T, Utsumi T, Takeshima M, Fujiwara Y, Matsui M, Aoki Y, et al. Efficacy and safety of adjunctive therapy to lamotrigine, lithium, or valproate monotherapy in bipolar depression: a systematic review and meta-analysis of randomized controlled trials. Int J Bipolar Disord. 2022;10(1):24. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Benchimol J, Kazinnik S, Saadon Y. Text mining methodologies with R: An application to central bank texts. Machine Learn with Appl. 2022;8:100286. [ CrossRef ]
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Inf Process syst. 2017;30:5998-6008.
• Nussbaumer-Streit B, Mayr V, Dobrescu AI, Chapman A, Persad E, Klerings I, et al. Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review. Cochrane Database Syst Rev. 2020;4(4):CD013574. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wang B, Xie Q, Pei J, Chen Z, Tiwari P, Li Z, et al. Pre-trained language models in biomedical domain: a systematic survey. ACM Comput Surv. 2023;56(3):1-52. [ CrossRef ]
• Mollo DC, Millière R. The vector grounding problem. arXiv. Preprint posted online. Apr 04, 2023.
• Zheng S, Huang J. Chang KC-C. why does chatgpt fall short in providing truthful answers. arXiv. Preprint posted online. Dec 03, 2023.
• Levy M, Jacoby A, Goldberg Y. Same task, more tokens: the impact of input length on the reasoning performance of large language models. arXiv. Preprint posted online. Jul 10, 2024.
• Giray L. Prompt engineering with ChatGPT: a guide for academic writers. Ann Biomed Eng. 2023;51(12):2629-2633. [ CrossRef ] [ Medline ]
• Li J, Zhang Q, Yu Y, Fu Q, Ye D. More agents is all you need. arXiv. Preprint posted online. Feb 03, 2024.

Abbreviations

Edited by S Ma; submitted 14.09.23; peer-reviewed by D Fraile Navarro, T Nguyen, A Nakhostin-Ansari; comments to author 23.01.24; revised version received 10.03.24; accepted 25.06.24; published 16.08.24.

©Kentaro Matsui, Tomohiro Utsumi, Yumi Aoki, Taku Maruki, Masahiro Takeshima, Yoshikazu Takaesu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Advertisement

Supported by

The Australian Professor Who Turned Breaking on Its Head

Rachael Gunn, known as B-girl Raygun, displayed some … unique moves as she competed in a field with breakers half her age. The judges and the internet were underwhelmed.

• Share full article

By Dodai Stewart and Talya Minsberg

Reporting from Paris

Breaking made its debut as an Olympic sport Friday, and among the competitors was Dr. Rachael Gunn, also known as B-girl Raygun, a 36-year-old professor from Sydney, Australia, who stood out in just about every way.

By day, her research interests include “dance, gender politics, and the dynamics between theoretical and practical methodologies.” But on the world’s stage in Paris, wearing green track pants and a green polo shirt instead of the street-style outfits of her much younger fellow breakers, she competed against the 21-year-old Logan Edra of the United States, known as Logistx.

During the round robin, as Raygun and Logistx faced off, Raygun laid on her side, reached for her toes, spun around, and threw in a kangaroo hop — a nod to her homeland. She performed a move that looked something like swimming and another that could best be described as duckwalking. The high-speed back and head spins that other breakers would demonstrate were mostly absent.

The crowd cheered Raygun politely. The judges weren’t as kind. All nine voted for Logistx in both rounds of the competition; Logistx won, 18-0.

Online, Raygun’s performance quickly became a sensation, not necessarily in a flattering way.

“The more I watch the videos of Raygun, the Aussie breaker, the more I get annoyed,” one viewer posted on X, formerly known as Twitter. “There’s 27.7 million Australians in the world and that’s who they send to the Olympics for this inaugural event??? C’mon now!”

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .