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).     

Salinity exchange between seawater/brackish water and domestic wastewater through electrodialysis for potable water

● Present a general concept called “salinity exchange”. ● Salts transferred from seawater to treated wastewater until completely switch. ● Process demonstrated using a laboratory-scale electrodialysis system. ● High-quality desalinated water obtained at ~1 mL/min consuming < 1 kWh/m ³ energy. Two-thirds of the world’s population has limited access to potable water. As we continue to use up our freshwater resources, new and improved techniques for potable water production are warranted. Here, we present a general concept called “salinity exchange” that transfers salts from seawater or brackish water to treated wastewater until their salinity values approximately switch, thus producing wastewater with an increased salinity for discharge and desalinated seawater as the potable water source. We have demonstrated this process using electrodialysis. Salinity exchange has been successfully achieved between influents of different salinities under various operating conditions. Laboratory-scale salinity exchange electrodialysis (SEE) systems can produce high-quality desalinated water at ~1 mL/min with an energy consumption less than 1 kWh/m³. SEE has also been operated using real water, and the challenges of its implementation at a larger scale are evaluated.     

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.     

Numerical simulation of benzene transport in shoreline groundwater affected by tides under different conditions

● An approach for assessing the transport of benzene on the beach was proposed. ● The behavior of benzene in the subsurface of the beach was impacted by tide. ● Tidal amplitude influenced the travel speed and the benzene biodegradation. ● Hydraulic conductivity had the impact on plume residence time and biodegradation. ● Plume dispersed and concentration decreased due to high longitudinal dispersivity. The release and transport of benzene in coastal aquifers were investigated in the present study. Numerical simulations were implemented using the SEAM3D, coupled with GMS, to study the behavior of benzene in the subsurface of tidally influenced beaches. The transport and fate of the benzene plume were simulated, considering advection, dispersion, sorption, biodegradation, and dissolution on the beach. Different tide amplitudes, aquifer characteristics, and pollutant release locations were studied. It was found that the tide amplitude, hydraulic conductivity, and longitudinal dispersivity were the primary factors affecting the fate and transport of benzene. The tidal amplitude influenced the transport speed and percentage of biodegradation of benzene plume in the beach. A high tidal range reduced the spreading area and enhanced the rate of benzene biodegradation. Hydraulic conductivity had an impact on plume residence time and the percentage of contaminant biodegradation. Lower hydraulic conductivity induced longer residence time in each beach portion and a higher percentage of biodegradation on the beach. The plume dispersed and the concentration decreased due to high longitudinal dispersivity. The results can be used to support future risk assessment and management for the shorelines impacted by spill and leaking accidents. Modeling the heterogeneous beach aquifer subjected to tides can also be further explored in the future study.     

Surface-enhanced Raman spectroscopy for emerging contaminant analysis in drinking water

● Definition of emerging contaminants in drinking water is introduced. ● SERS and standard methods for emerging contaminant analysis are compared. ● Enhancement factor and accessibility of SERS hot spots are equally important. ● SERS sensors should be tailored according to emerging contaminant properties. ● Challenges to meet drinking water regulatory guidelines are discussed. Emerging contaminants (ECs) in drinking water pose threats to public health due to their environmental prevalence and potential toxicity. The occurrence of ECs in our drinking water supplies depends on their physicochemical properties, discharging rate, and susceptibility to removal by water treatment processes. Uncertain health effects of long-term exposure to ECs justify their regular monitoring in drinking water supplies. In this review article, we will summarize the current status and future opportunities of surface-enhanced Raman spectroscopy (SERS) for EC analysis in drinking water. Working principles of SERS are first introduced and a comparison of SERS and liquid chromatography-tandem mass spectrometry in terms of cost, time, sensitivity, and availability is made. Subsequently, we discuss the strategies for designing effective SERS sensors for EC analysis based on five categories—per- and polyfluoroalkyl substances, novel pesticides, pharmaceuticals, endocrine-disrupting chemicals, and microplastics. In addition to maximizing the intrinsic enhancement factors of SERS substrates, strategies to improve hot spot accessibilities to the targeting ECs are equally important. This is a review article focusing on SERS analysis of ECs in drinking water. The discussions are not only guided by numerous endeavors to advance SERS technology but also by the drinking water regulatory policy.     

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.     

Bacteria inactivation by sulfate radical: progress and non-negligible disinfection by-products

● Status of inactivation of pathogenic microorganisms by SO₄^•− is reviewed. ● Mechanism of SO₄^•− disinfection is outlined. ● Possible generation of DBPs during disinfection using SO₄^•− is discussed. ● Possible problems and challenges of using SO₄^•− for disinfection are presented. Sulfate radicals have been increasingly used for the pathogen inactivation due to their strong redox ability and high selectivity for electron-rich species in the last decade. The application of sulfate radicals in water disinfection has become a very promising technology. However, there is currently a lack of reviews of sulfate radicals inactivated pathogenic microorganisms. At the same time, less attention has been paid to disinfection by-products produced by the use of sulfate radicals to inactivate microorganisms. This paper begins with a brief overview of sulfate radicals’ properties. Then, the progress in water disinfection by sulfate radicals is summarized. The mechanism and inactivation kinetics of inactivating microorganisms are briefly described. After that, the disinfection by-products produced by reactions of sulfate radicals with chlorine, bromine, iodide ions and organic halogens in water are also discussed. In response to these possible challenges, this article concludes with some specific solutions and future research directions.     

Property-performance relationship of core-shell structured black TiO2 photocatalyst for environmental remediation

● Properties and performance relationship of CSBT photocatalyst were investigated. ● Properties of CSBT were controlled by simply manipulating glycerol content. ● Performance was linked to semiconducting and physicochemical properties. ● CSBT (W:G ratio 9:1) had better performance with lower energy consumption. ● Phenols were reduced by 48.30% at a cost of $2.4127 per unit volume of effluent. Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure (CSBT) for environmental remediation is crucial for improving its prospects in practical applications. In this study, CSBT was synthesized using a glycerol-assisted sol-gel approach. The effect of different water-to-glycerol ratios (W:G = 1:0, 9:1, 2:1, and 1:1) on the semiconducting and physicochemical properties of CSBT was investigated. The effectiveness of CSBT in removing phenolic compounds (PHCs) from real agro-industrial wastewater was studied. The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs. It has a distinct core-shell structure and an appropriate amount of Ti³⁺ cations (11.18%), which play a crucial role in enhancing the performance of CSBT. When exposed to visible light, the CSBT performed better: 48.30% of PHCs were removed after 180 min, compared to only 21.95% for TiO₂ without core-shell structure. The CSBT consumed only 45.5235 kWh/m³ of electrical energy per order of magnitude and cost $2.4127 per unit volume of treated agro-industrial wastewater. Under the conditions tested, the CSBT demonstrated exceptional stability and reusability. The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.     

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.     

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.     

Cultivars and oil extraction techniques affect Cd/Pb contents and health risks in oil of rapeseed grown on Cd/Pb-contaminated farmland

● Organic solvent extracted fewer Cd/Pb in rapeseed oil than physical pressing. ● Brassica rapa transferred fewer Cd and Pb from seed to oil than Brassica napus . ● Carcinogenic risk mainly from Cd and worth more concern than noncarcinogenic risk. ● Organic solvent specially SLB pose less heath risk for oil than physical pressing. ● Rapeseed oil posed higher carcinogenic risk for rural residents than urban. Substitute planting with rapeseed offers promise for safely using large areas of Cd/Pb-contaminated farmland. Cd/Pb distributions during rapeseed oil production were investigated and health risks posed by the oil were assessed. Tests were performed using three cultivars (Brassica rapa SYH and ZS100 and Brassica napus QY-1) and four oil extraction techniques (mechanical and low-temperature pressing and n-hexane and subcritical low-temperature butane extraction). The amounts of Cd and Pb in oil were 0.73%–8.44% and 3.14%–11.76%, respectively, of the amounts in rapeseed and were strongly affected by the cultivar and oil extraction technique. The heavy metal (HM) concentrations were lower in solvent-extracted oil (particularly subcritical low-temperature butane extracted oil, in which HMs were not detected) than mechanically pressed oil. The Cd and Pb transfer indices were lower (meaning larger proportions of HMs were retained by the rapeseed meal) for B. rapa than B. napus. This was attributed to a high HM binding protein content of B. rapa seed. Health risks to humans were assessed using a probabilistic risk assessment model. The carcinogenic risk was mainly (97.1%–99.9%) caused by Cd and poses more concern than non-carcinogenic risk. Stronger health risks are posed by mechanically pressed than solvent-extracted oil, and higher carcinogenic risks are posed to people living in rural areas than urban areas. Substitute planting with B. rapa and extracting oil with organic solvent (preferably subcritical low-temperature butane) are optimal for safely utilizing Cd/Pb-contaminated soil. Attention should be paid to the health risks posed by Cd in oil to rural populations.     

Catalytic reduction of water pollutants: knowledge gaps,lessons learned,and new opportunities

● Advances, challenges, and opportunities for catalytic water pollutant reduction. ● Cases of Pd-based catalysts for nitrate, chlorate, and perchlorate reduction. ● New functionalities developed by screening and design of catalytic metal sites. ● Facile catalyst preparation approaches for convenient catalyst optimization. ● Rational design and non-decorative effort are essential for future work. In this paper, we discuss the previous advances, current challenges, and future opportunities for the research of catalytic reduction of water pollutants. We present five case studies on the development of palladium-based catalysts for nitrate, chlorate, and perchlorate reduction with hydrogen gas under ambient conditions. We emphasize the realization of new functionalities through the screening and design of catalytic metal sites, including (i) platinum group metal (PGM) nanoparticles, (ii) the secondary metals for improving the reaction rate and product selectivity of nitrate reduction, (iii) oxygen-atom-transfer metal oxides for chlorate and perchlorate reduction, and (iv) ligand-enhanced coordination complexes for substantial activity enhancement. We also highlight the facile catalyst preparation approach that brought significant convenience to catalyst optimization. Based on our own studies, we then discuss directions of the catalyst research effort that are not immediately necessary or desirable, including (1) systematic study on the downstream aspects of under-developed catalysts, (2) random integration with hot concepts without a clear rationale, and (3) excessive and decorative experiments. We further address some general concerns regarding using H₂ and PGMs in the catalytic system. Finally, we recommend future catalyst development in both “fundamental” and “applied” aspects. The purpose of this perspective is to remove major misconceptions about reductive catalysis research and bring back significant innovations for both scientific advancements and engineering applications to benefit environmental protection.     

Advancing ion-exchange membranes to ion-selective membranes: principles,status, and opportunities

● IEM ion/ion selectivities of charge, valence, & specific ion are critically assessed. ● Ion/molecule selectivities of ion/solvent and ion/uncharged solute are reviewed. ● Approaches to advance the selectivities through sorption and migration are analyzed. ● The permeability-selectivity tradeoff appears to be pervasive. ● Ion/molecule selectivities are comparatively underdeveloped and poorly understood. Ion-exchange membranes (IEMs) are utilized in numerous established, emergent, and emerging applications for water, energy, and the environment. This article reviews the five different types of IEM selectivity, namely charge, valence, specific ion, ion/solvent, and ion/uncharged solute selectivities. Technological pathways to advance the selectivities through the sorption and migration mechanisms of transport in IEM are critically analyzed. Because of the underlying principles governing transport, efforts to enhance selectivity by tuning the membrane structural and chemical properties are almost always accompanied by a concomitant decline in permeability of the desired ion. Suppressing the undesired crossover of solvent and neutral species is crucial to realize the practical implementation of several technologies, including bioelectrochemical systems, hypersaline electrodialysis desalination, fuel cells, and redox flow batteries, but the ion/solvent and ion/uncharged solute selectivities are relatively understudied, compared to the ion/ion selectivities. Deepening fundamental understanding of the transport phenomena, specifically the factors underpinning structure-property-performance relationships, will be vital to guide the informed development of more selective IEMs. Innovations in material and membrane design offer opportunities to utilize ion discrimination mechanisms that are radically different from conventional IEMs and potentially depart from the putative permeability-selectivity tradeoff. Advancements in IEM selectivity can contribute to meeting the aqueous separation needs of water, energy, and environmental challenges.     

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

● A machine learning model was used to identify lake nutrient pollution sources. ● XGBoost model showed the best performance for lake water quality prediction. ● Model feature size was reduced by screening the key features with the MIC method. ● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources. ● Next-month lake TN and TP concentrations were predicted accurately. Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R² of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.     

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.     

Ozonation of aromatic monomer compounds in water: factors determining reaction outcomes

● p- CNB and IBP were selected, to explore factors determining ozonation outcomes. ● •OH contributed only 50 % to IBP removal, compared to the 90 % for p -CNB removal. ● IBP achieved fewer TOC removal and more by-product types and quantities. ● A longer ring-opening distance existed during the degradation of IBP. ● Multiple positions on both branches of IBP were attacked, consuming more oxidants. For aromatic monomer compounds (AMCs), ozonation outcomes were usually predicted by the substituents of the benzene ring based on the electron inductive effect. However, the predicted results were occasionally unreliable for complex substituents, and other factors caused concern. In this study, p-chloronitrobenzene (p-CNB) and ibuprofen (IBP) were selected for ozonation. According to the electron inductive theory, p-CNB should be less oxidizable, but the opposite was true. The higher rates of p-CNB were due to various sources of assistance. First, the hydroxyl radical (•OH) contributed 90 % to p-CNB removal at pH 7.0, while its contribution to IBP removal was 50 %. Other contributions came from molecular O₃ oxidation. Second, p-CNB achieved 40 % of the total organic carbon (TOC) removal and fewer by-product types and quantities, when compared to the results for IBP. Third, the oxidation of p-CNB started with hydroxyl substitution reactions on the benzene ring; then, the ring opened. However, IBP was initially oxidized mainly on the butane branched chain, with a chain-shortening process occurring before the ring opened. Finally, the degradation pathway of p-CNB was single and consumed fewer oxidants. However, both branches of IBP were attacked simultaneously, and three degradation pathways that relied on more oxidants were proposed. All of these factors were determinants of the rapid removal of p-CNB.     

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.     

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.     

The impact of different voltage application modes on biodegradation of chloramphenicol and shift of microbial community structure

● Presented coupled system enhanced biodegradation of antibiotic chloramphenicol. ● HRT and electrical stimulation modes were key influencing factors. ● Electrical stimulation had little effect on the chloramphenicol metabolic pathway. ● Microbial community structure varied with the voltage application mode. Exoelectrogenic biofilms have received considerable attention for their ability to enhance electron transfer between contaminants and electrodes in bioelectrochemical systems. In this study, we constructed anaerobic-aerobic-coupled upflow bioelectrochemical reactors (AO-UBERs) with different voltage application modes, voltages and hydraulic retention times (HRTs). In addition, we evaluated their capacity to remove chloramphenicol (CAP). AO-UBER can effectively mineralize CAP and its metabolites through electrical stimulation when an appropriate voltage is applied. The CAP removal efficiencies were ~81.1%±6.1% (intermittent voltage application mode) and 75.2%±4.6% (continuous voltage application mode) under 0.5 V supply voltage, which were ~21.5% and 15.6% greater than those in the control system without voltage applied, respectively. The removal efficiency is mainly attributed to the anaerobic chamber. High-throughput sequencing combined with catabolic pathway analysis indicated that electrical stimulation selectively enriched Megasphaera, Janthinobacterium, Pseudomonas, Emticicia, Zoogloea, Cloacibacterium and Cetobacterium, which are capable of denitrification, dechlorination and benzene ring cleavage, respectively. This study shows that under the intermittent voltage application mode, AO-UBERs are highly promising for treating antibiotic-contaminated wastewater.     

The role of lipids in fermentative propionate production from the co-fermentation of lipid and food waste

● Lipid can promote PA production on a target from food waste. ● PA productivity reached 6.23 g/(L∙d) from co-fermentation of lipid and food waste. ● Lipid promoted the hydrolysis and utilization of protein in food waste. ● Prevotella , Veillonella and norank _f _Propioni bacteriaceae were enriched. ● Main pathway of PA production was the succinate pathway. Food waste (FW) is a promising renewable low-cost biomass substrate for enhancing the economic feasibility of fermentative propionate production. Although lipids, a common component of food waste, can be used as a carbon source to enhance the production of volatile fatty acids (VFAs) during co-fermentation, few studies have evaluated the potential for directional propionate production from the co-fermentation of lipids and FW. In this study, co-fermentation experiments were conducted using different combinations of lipids and FW for VFA production. The contributions of lipids and FW to propionate production, hydrolysis of substrates, and microbial composition during co-fermentation were evaluated. The results revealed that lipids shifted the fermentation type of FW from butyric to propionic acid fermentation. Based on the estimated propionate production kinetic parameters, the maximum propionate productivity increased significantly with an increase in lipid content, reaching 6.23 g propionate/(L∙d) at a lipid content of 50%. Propionate-producing bacteria Prevotella, Veillonella, and norank_f_Propionibacteriaceae were enriched in the presence of lipids, and the succinate pathway was identified as a prominent fermentation route for propionate production. Moreover, the Kyoto Encyclopedia of Genes and Genomes functional annotation revealed that the expression of functional genes associated with amino acid metabolism was enhanced by the presence of lipids. Collectively, these findings will contribute to gaining a better understanding of targeted propionate production from FW.