Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

The distribution and availability of phosphorus fractions in restored cut slopes soil aggregates: a case study of subalpine road,Southwest China

● There was no significant difference in soil aggregates TP along altitude gradient. ● Overall, PAC dropped steadily as aggregate size increased. ● In soil aggregate sizes, TPi > TPo > R-P at 3009,3347 and 3654 m except 3980 m. ● Active NaHCO₃-Pi was the main AP source. ● Proportion of small aggregate sizes was emphasized to increase AP storage. The distribution and availability of phosphorus (P) fractions in restored cut slope soil aggregates, along altitude gradients, were analyzed. Samples were collected at 3009, 3347, 3654 and 3980 m of altitude. We examined soil aggregates total phosphorus (TP), available phosphorus (AP) and phosphorus activation coefficient (PAC), and discovered that there was no significant difference in TP levels between all four altitudes samples (p > 0.05). However, there was a significant difference in AP at 3009, 3347 and 3980 m of altitude (p < 0.05). At the altitudes of 3009, 3347 and 3654 m, the AP accumulation in small size aggregates was more advantageous. Overall, PAC dropped steadily as soil aggregates sizes increased, as shown: PAC (3654 m) > PAC (3347 m) > PAC (3009 m) > PAC (3980 m). In all particle size soil aggregates, the distribution of the P fractions was as follows: total inorganic phosphorus (TPi) > total organic phosphorus (TPo) > residual phosphorus (R-P), at 3009, 3347 and 3654 m, but a different registry was observed at 3980 m of altitude: TPo > TPi > R-P. Through correlation and multiple stepwise regression analysis, it was concluded that active NaHCO₃-Pi was the main AP source. It was also suggested that more attention should be given to the ratio of small particle size aggregates to increase soil AP storage. In order to improve the activation capacity and supply of soil P, along with promotion of the healthy development of soil ecosystem on slope land, it was suggest that inorganic P fertilizer and P activator could be added to soil at both low (3009 m) and high altitudes (3980 m).     

State-of-the-art applications of machine learning in the life cycle of solid waste management

● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented. ● Changes of research field over time, space, and hot topics were analyzed. ● Detailed application seniors of ML on the life cycle of SW were summarized. ● Perspectives towards future development of ML in the field of SW were discussed. Due to the superiority of machine learning (ML) data processing, it is widely used in research of solid waste (SW). This study analyzed the research and developmental progress of the applications of ML in the life cycle of SW. Statistical analyses were undertaken on the literature published between 1985 and 2021 in the Science Citation Index Expanded and Social Sciences Citation Index to provide an overview of the progress. Based on the articles considered, a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have strengthened. The three topics of ML, namely, SW categories, ML algorithms, and specific applications, as applied to the life cycle of SW were discussed. ML has been applied during the entire SW process, thereby affecting its life cycle. ML was used to predict the generation and characteristics of SW, optimize its collection and transportation, and model the processing of its energy utilization. Finally, the current challenges of applying ML to SW and future perspectives were discussed. The goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of SW. ML plays an important role in the modernization and intellectualization of SW management. It is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of SW disposal.     

Development of machine learning multi-city model for municipal solid waste generation prediction

● A database of municipal solid waste (MSW) generation in China was established. ● An accurate MSW generation prediction model (WGMod) was constructed. ● Key factors affecting MSW generation were identified. ● MSW trends generation in Beijing and Shenzhen in the near future are projected. Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.     

A critical review of the emerging research on the detection and assessment of microplastics pollution in the coastal,marine, and urban Bangladesh

● Coastal and marine regions are the most studied for microplastic pollution. ● Tourism is a major cause of microplastic pollution in coastal regions. ● Sediments contain larger microplastics while fish ingest smaller microplastics. ● Inland lakes, rivers, and freshwater fish are impacted by microplastic pollution. ● Microplastics are found in edible salts, however, presence is less in refined salt. The research on the extent and effects of microplastics pollution in the Global South is only getting started. Bangladesh is a South Asian country with one of the fastest growing economies in the world, however, such exponential economic growth has also increased the pollution threats to its natural and urban environment. In this paper, we reviewed the recent primary research on the assessment of the extent of microplastics pollution in Bangladesh. From the online databases, we developed a compilation of emerging research articles that detected and quantified microplastics in different coastal, marine, and urban environments in Bangladesh. Most of the studies focused on the coastal environment (e.g., beach sediment) and marine fish, while limited data were available for the urban environment. We also discussed the relationship of the type of anthropogenic activities with the observed microplastic pollution. The Cox’s Bazar sea beach in south-east Bangladesh experienced microplastics pollution due to tourism activities, while fishing and other anthropogenic activities led to microplastics pollution in the Bay of Bengal. While microplastics larger than 1 mm were prevalent in the beach sediments, smaller microplastics with size below 0.5 mm were prevalent in marine fish samples. Moreover, the differences in microplastic abundance, size, shape, color, and polymer type found were depended on the sampling sites and relevant anthropogenic activities. It is imperative to identify major sources of microplastics pollution in both natural and urban environment, determine potential environmental and human health effects, and develop mitigating and prevention strategies for reducing microplastics pollution.     

Technologies for pollutant removal and resource recovery from blackwater: a review

● Blackwater is the main source of organics and nutrients in domestic wastewater. ● Various treatment methods can be applied for resource recovery from blackwater. ● Blackwater treatment systems of high integration and efficiency are the future trend. ● More research is needed for the practical use of blackwater treatment systems. Blackwater (BW), consisting of feces, urine, flushing water and toilet paper, makes up an important portion of domestic wastewater. The improper disposal of BW may lead to environmental pollution and disease transmission, threatening the sustainable development of the world. Rich in nutrients and organic matter, BW could be treated for resource recovery and reuse through various approaches. Aimed at providing guidance for the future development of BW treatment and resource recovery, this paper presented a literature review of BWs produced in different countries and types of toilets, including their physiochemical characteristics, and current treatment and resource recovery strategies. The degradation and utilization of carbon (C), nitrogen (N) and phosphorus (P) within BW are underlined. The performance of different systems was classified and summarized. Among all the treating systems, biological and ecological systems have been long and widely applied for BW treatment, showing their universality and operability in nutrients and energy recovery, but they are either slow or ineffective in removal of some refractory pollutants. Novel processes, especially advanced oxidation processes (AOPs), are becoming increasingly extensively studied in BW treatment because of their high efficiency, especially for the removal of micropollutants and pathogens. This review could serve as an instructive guidance for the design and optimization of BW treatment technologies, aiming to help in the fulfilment of sustainable human excreta management.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

China’s carbon neutrality: an extensive and profound systemic reform

● China has pledged ambitious carbon peak and neutrality goals for mitigating global climate change. ● Major challenges to achieve carbon neutrality in China are summarized. ● The new opportunities along the pathway of China’s carbon neutrality are discussed from four aspects. ● Five policy suggestions for China are provided. China is the largest developing economy and carbon dioxide emitter in the world, the carbon neutrality goal of which will have a profound influence on the mitigation pathway of global climate change. The transition towards a carbon-neutral society is integrated into the construction of ecological civilization in China, and brings profound implications for China’s socioeconomic development. Here, we not only summarize the major challenges in achieving carbon neutrality in China, but also identify the four potential new opportunities: namely, the acceleration of technology innovations, narrowing regional disparity by reshaping the value of resources, transforming the industrial structure, and co-benefits of pollution and carbon mitigation. Finally, we provide five policy suggestions and highlight the importance of balancing economic growth and carbon mitigation, and the joint efforts among the government, the enterprises, and the residents.     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.     

Factors affecting the distribution of microplastics in soils of China

● Microplastic (MP) abundance in soil of China was highly heterogeneous. ● MP abundance was higher near large rivers and central land affected by monsoons. ● MP abundance was correlated with longitude, mulching film, and average temperature. ● Factors suitable for predicting MP pollution using models were discussed. Microplastics (MPs) are found worldwide in high abundance, posing a potential threat to ecosystems. Despite the ubiquity of MPs in the environment, very little is known about the regional distribution of MPs and underlying factors affecting this distribution in the field, which likely include human activity, but also features of the environment itself. Here, out of a total of 1157 datapoints investigated in 53 Chinese studies, 9.68% datapoints were removed as outliers in the heterogeneity analysis. This review revealed that the abundance of MPs was highly heterogeneous. In addition, microplastic (MP) distribution maps based on China demonstrated that the highest abundance of MPs tended to occur near large rivers and central land affected by the intersection of two monsoons. The model-fitting and previous studies showed that MP abundance in China was correlated with longitude, agricultural mulching film usage per capita, temperature, and precipitation. However, due to the heterogeneity of MPs and the low matching degree between the current environmental data and the sampling points, this pattern was not as evident as reported in any single study. Factors affecting the distribution of MPs can not be captured by linear relationships alone, and systematic selection of suitable environmental factors and further model optimization are needed to explore the cause of MP pollution in soil. Overall, this review revealed an uneven distribution of MPs and serves as a reference for model prediction to assess and control plastic pollution in natural soil environments.     

Current scenario and challenges of plastic pollution in Bangladesh: a focus on farmlands and terrestrial ecosystems

● A global snapshot of plastic waste generation and disposal is analysed. ● Effect of plastic pollution on environment and terrestrial ecosystem is reviewed. ● Ecotoxicity and food security from plastic pollution is discussed. Plastic is considered one of the most indispensable commodities in our daily life. At the end of life, the huge ever-growing pile of plastic waste (PW) causes serious concerns for our environment, including agricultural farmlands, groundwater quality, marine and land ecosystems, food toxicity and human health hazards. Lack of proper infrastructure, financial backup, and technological advancement turn this hazardous waste plastic management into a serious threat to developing countries, especially for Bangladesh. A comprehensive review of PW generation and its consequences on environment in both global and Bangladesh contexts is presented. The dispersion routes of PW from different sources in different forms (microplastic, macroplastic, nanoplastic) and its adverse effect on agriculture, marine life and terrestrial ecosystems are illustrated in this work. The key challenges to mitigate PW pollution and tackle down the climate change issue is discussed in this work. Moreover, way forward toward the design and implementation of proper PW management strategies are highlighted in this study.     

Discovery of emerging organic pollutants in the atmosphere through an omics approach

● We review the framework of discovering emerging pollutants through an omics approach. ● High-resolution MS can digitalize atmospheric samples to full-component data. ● Chemical features and databases can help to translate untargeted data to compounds. ● Biological effect-directed untargeted analyses consider both existence and toxicity. Ambient air pollution, containing numerous known and hitherto unknown compounds, is a major risk factor for public health. The discovery of harmful components is the prerequisite for pollution control; however, this raises a great challenge on recognizing previously unknown species. Here we systematically review the analytical techniques on air pollution in the framework of an omics approach, with a brief introduction on sample preparation and analysis, and in more detail, compounds prioritization and identification. Through high-resolution mass spectrometry (HRMS, typically coupled with chromatography), the complicated environmental matrix can be digitalized into “full-component” data. A key step to discover emerging compounds is the prioritization of compounds from massive data. Chemical fingerprints, suspect lists and biological effects are the most vital untargeted strategies for comprehensively screening critical and hazardous substances. Afterward, compressed data of compounds can be identified at various confidence levels according to exact mass and the derived molecular formula, MS libraries, and authentic standards. Such an omics approach on full-component data provides a paradigm for discovering emerging air pollutants; nonetheless, new technological advancements of instruments and databases are warranted for further tracking the environmental behaviors, hence to evaluate the health risk of key pollutants.     

A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

● Established a quantification method of pollutant emission standard. ● Predicted the SO₂ emission intensity of single coking enterprises in China. ● Evaluated the influence of pollutant discharge standard on prediction accuracy. ● Analyzed the SO₂ emissions of Chinese provincial and municipal coking enterprises. Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO₂ emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and theR² increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO₂ emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO₂ emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants.     

Differences in distributions,assembly mechanisms,and putative interactions of AOB and NOB at a large spatial scale

● Nitrifiers in WWTP were investigated at large spatial scale. ● AOB populations varied greatly but NOB populations were similar among cities. ● Drift dominated both AOB and NOB assembling processes. ● DO did not show a significant effect on NOB. ● NOB tended to cooperate with AOB and non-nitrifying microorganisms. Ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) play crucial roles in removing nitrogen from sewage in wastewater treatment plants (WWTPs) to protect water resources. However, the differences in ecological properties and putative interactions of AOB and NOB in WWTPs at a large spatial scale remain unclear. Hence, 132 activated sludge (AS) samples collected from 11 cities across China were studied by utilizing 16S rRNA gene sequencing technology. Results indicated that Nitrosomonas and Nitrosospira accounted for similar ratios of the AOB community and might play nearly equal roles in ammonia oxidation in AS. However, Nitrospira greatly outnumbered other NOB genera, with proportions varying from 94.7% to 99.9% of the NOB community in all WWTPs. Similar compositions and, hence, a low distance–decay turnover rate of NOB (0.035) across China were observed. This scenario might have partly resulted from the high proportions of homogenizing dispersal (~13%). Additionally, drift presented dominant roles in AOB and NOB assembling mechanisms (85.2% and 81.6% for AOB and NOB, respectively). The partial Mantel test illustrated that sludge retention time and temperature were the primary environmental factors affecting AOB and NOB communities. Network results showed that NOB played a leading role in maintaining module structures and node connections in AS. Moreover, most links between NOB and other microorganisms were positive, indicating that NOB were involved in complex symbioses with bacteria in AS.     

Plastic materials and water sources actively select and shape wastewater plastispheres over time

● Wastewater MPs exhibited resistomes and therefore health threats. ● High density of alkB gene indicates both HDPE and PET can be utilized by microbes. ● Plastics and waters actively selected and shaped the plastispheres over time. ● A broader phylogenetic spectrum of MHET-degrading microorganisms was annotated. The daily use of plastics presents a serious pollution issue due to their extremely slow degradation. Microplastics and the biofilm that grows on plastics (i.e., the plastisphere) are important subsets of plastic wastes. Many studies have been conducted to reveal the structures of the plastispheres, the driving factors for the formation of the plastisphere, and the ability of the plastispheres to degrade plastics in a variety of water bodies. However, the plastispheres related to wastewater are understudied. In this study, we used a microcosmic strategy to study the evolution of the plastispheres associated with microplastics (MPs) over time in wastewater. We found that plastic materials and water sources did not actively select and shape the plastispheres at an early stage, but the active selection for a unique niche of the plastisphere occurred after 14 d of growth. In addition, we confirmed that the alkB gene was densely present, and metagenomics showed some additional chemical reactions, which suggests that MPs are consumed by the microbes in the plastispheres. Additionally, metagenomics identified some metagenome-assembled genomes (MAGs) associated with high-density polyethylene (HDPE) and polyethylene terephthalate (PET). The identification of HDPE-associated MAGs and PET-associated MAGs further supports the notion that the selection for a unique niche of the plastisphere is driven by plastic materials and water sources (in this study, after 14 d of growth). Our discoveries bring new views on the behavior of the wastewater-associated plastisphere, especially how long it takes a wastewater plastisphere to form.     

Characterization and variation of dissolved organic matter in composting: a critical review

● Effect of composting approaches on dissolved organic matter (DOM). ● Effect of composting conditions on the properties of DOM. ● Character indexes of DOM varied in composting. ● The size, hydrophobicity, humification, and electron transfer capacity increased. ● The hydrophilicity, protein-like materials, and aliphatic components reduced. As the most motive organic fraction in composting, dissolved organic matter (DOM) can contribute to the transfer and dispersal of pollutants and facilitate the global carbon cycle in aquatic ecosystems. However, it is still unclear how composting approaches and conditions influence the properties of compost-derived DOM. Further details on the shift of DOM character indexes are required. In this study, the change in properties of compost-derived DOM at different composting approaches and the effect of composting conditions on the DOM characteristics are summarized. Thereafter, the change in DOM character indexes’ in composting was comprehensively reviewed. Along with composting, the elements and spectral properties (chromophoric DOM (CDOM) and fluorescent DOM (FDOM)) were altered, size and hydrophobicity increased, and aromatic-C and electron transfer capacity were promoted. Finally, some prospects to improve this study were put forward. This paper should facilitate the people who have an interest in tracing the fate of DOM in composting.     

Methanation and chemolitrophic nitrogen removal by an anaerobic membrane bioreactor coupled partial nitrification and Anammox

● Efficient carbon methanation and nitrogen removal was achieved in AnMBR-PN/A system. ● AOB outcompeted NOB in PN section by limiting aeration and shortening SRT. ● The moderate residual organic matter of PN section triggered PD in anammox unit. ● AnAOB located at the bottom of UASB played an important role in nitrogen removal. An AnMBR-PN/A system was developed for mainstream sewage treatment. To verify the efficient methanation and subsequent chemolitrophic nitrogen removal, a long-term experiment and analysis of microbial activity were carried out. AnMBR performance was less affected by the change of hydraulic retention time (HRT), which could provide a stable influent for subsequent PN/A units. The COD removal efficiency of AnMBR was > 93% during the experiment, 85.5% of COD could be recovered in form of CH₄. With the HRT of PN/A being shortened from 10 to 6 h, nitrogen removal efficiency (NRE) of PN/A increased from 60.5% to 80.4%, but decreased to 68.8% when the HRT_PN/A further decreased to 4 h. Microbial analysis revealed that the highest specific ammonia oxidation activity (SAOA) and the ratio of SAOA to specific nitrate oxidation activity (SNOA) provide stable NO₂⁻-N/NH₄⁺-N for anammox, and anammox bacteria (mainly identified as Candidatus Brocadia) enriched at the bottom of Anammox-UASB might play an important role in nitrogen removal. In addition, the decrease of COD in Anammox-UASB indicated partial denitrification occurred, which jointly promoted nitrogen removal with anammox.