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.     

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.     

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.     

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.     

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 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.     

Hybrid energy harvesting systems for self-powered sustainable water purification by harnessing ambient energy

● Energy harvesters harness multiple energies for self-powered water purification. ● Hybrid energy harvesters enable continuous output under fluctuating conditions. ● Mechanical, thermal, and solar energies enable synergic harvesting. ● Perspectives of hybrid energy harvester-driven water treatment are proposed. The development of self-powered water purification technologies for decentralized applications is crucial for ensuring the provision of drinking water in resource-limited regions. The elimination of the dependence on external energy inputs and the attainment of self-powered status significantly expands the applicability of the treatment system in real-world scenarios. Hybrid energy harvesters, which convert multiple ambient energies simultaneously, show the potential to drive self-powered water purification facilities under fluctuating actual conditions. Here, we propose recent advancements in hybrid energy systems that simultaneously harvest various ambient energies (e.g., photo irradiation, flow kinetic, thermal, and vibration) to drive water purification processes. The mechanisms of various energy harvesters and point-of-use water purification treatments are first outlined. Then we summarize the hybrid energy harvesters that can drive water purification treatment. These hybrid energy harvesters are based on the mechanisms of mechanical and photovoltaic, mechanical and thermal, and thermal and photovoltaic effects. This review provides a comprehensive understanding of the potential for advancing beyond the current state-of-the-art of hybrid energy harvester-driven water treatment processes. Future endeavors should focus on improving catalyst efficiency and developing sustainable hybrid energy harvesters to drive self-powered treatments under unstable conditions (e.g., fluctuating temperatures and humidity).     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

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.     

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.     

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.     

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.     

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.     

Low-temperature caproate production,microbial diversity,and metabolic pathway in xylose anaerobic fermentation

● Converting xylose to caproate under a low temperature of 20 °C by MCF was verified. ● Final concentration of caproate from xylose in a batch reactor reached 1.6 g/L. ● Changing the substrate to ethanol did not notably increase the caproate production. ● Four genera, including Bifidobacterium , were revealed as caproate producers. ● The FAB pathway and incomplete RBO pathway were revealed via metagenomic analysis. Mixed culture fermentation (MCF) is challenged by the unqualified activity of enriched bacteria and unwanted methane dissolution under low temperatures. In this work, caproate production from xylose was investigated by MCF at a low temperature (20 °C). The results showed that a 9 d long hydraulic retention time (HRT) in a continuously stirred tank reactor was necessary for caproate production (~0.3 g/L, equal to 0.6 g COD/L) from xylose (10 g/L). The caproate concentration in the batch mode was further increased to 1.6 g/L. However, changing the substrate to ethanol did not promote caproate production, resulting in ~1.0 g/L after 45 d of operation. Four genera, Bifidobacterium, Caproiciproducens, Actinomyces, and Clostridium_sensu_stricto_12, were identified as the enriched caproate-producing bacteria. The enzymes in the fatty acid biosynthesis (FAB) pathway for caproate production were identified via metagenomic analysis. The enzymes for the conversion of (C_n+2)-2,3-Dehydroxyacyl-CoA to (C_n+2)-Acyl-CoA (i.e., EC 1.3.1.8 and EC 1.3.1.38) in the reverse β-oxidation (RBO) pathway were not identified. These results could extend the understanding of low-temperature caproate production.     

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.     

Determination of growth kinetics of microorganisms linked with 1,4-dioxane degradation in a consortium based on two improved methods

● Evaluated three methods for determining the consortia’s growth kinetics. ● Conventional method is flawed since it relies on the total biomass concentration. ● Considering only selected bacterial taxa improved the accuracy. ● Considering oligotrophs and copiotrophs further improved the accuracy. The conventional method for determining growth kinetics of microbial consortia relies on the total biomass concentration. This may be inaccurate for substrates that are uncommon in nature and can only be degraded by a small portion of the microbial community. 1,4-dioxane, an emerging contaminant, is an example of such substrates. In this work, we evaluated an improved method for determining the growth kinetics of a 1,4-dioxane-degrading microbial consortium. In the improved method, we considered only bacterial taxa whose concentration increase correlated to 1,4-dioxane concentration decrease in duplicate microcosm tests. Using PEST (Parameter Estimation), a model-independent parameter estimator, the kinetic constants were estimated by fitting the Monod kinetics-based simulation results to the experimental data that consisted of the concentrations of 1,4-dioxane and the considered bacterial taxa. The estimated kinetic constants were evaluated by comparing the simulation results with experimental results from another set of microcosm tests. The evaluation was quantified by the sum of squared relative residual, which was four orders of magnitude lower for the improved method than the conventional method. By further dividing the considered bacterial taxa into oligotrophs and copiotrophs, the sum of squared relative residual further decreased.     

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.     

Evaluating the impact of sulfamethoxazole on hydrogen production during dark anaerobic sludge fermentation

● SMX promotes hydrogen production from dark anaerobic sludge fermentation. ● SMX significantly enhances the hydrolysis and acidification processes. ● SMX suppresses the methanogenesis process in order to reduce hydrogen consumption. ● SMX enhances the relative abundance of hydrogen-VFAs producers. ● SMX brings possible environmental risks due to the enrichment of ARGs. The impact of antibiotics on the environmental protection and sludge treatment fields has been widely studied. The recovery of hydrogen from waste activated sludge (WAS) has become an issue of great interest. Nevertheless, few studies have focused on the impact of antibiotics present in WAS on hydrogen production during dark anaerobic fermentation. To explore the mechanisms, sulfamethoxazole (SMX) was chosen as a representative antibiotic to evaluate how SMX influenced hydrogen production during dark anaerobic fermentation of WAS. The results demonstrated SMX promoted hydrogen production. With increasing additions of SMX from 0 to 500 mg/kg TSS, the cumulative hydrogen production elevated from 8.07 ± 0.37 to 11.89 ± 0.19 mL/g VSS. A modified Gompertz model further verified that both the maximum potential of hydrogen production (P_m) and the maximum rate of hydrogen production (R_m) were promoted. SMX did not affected sludge solubilization, but promoted hydrolysis and acidification processes to produce more hydrogen. Moreover, the methanogenesis process was inhibited so that hydrogen consumption was reduced. Microbial community analysis further demonstrated that the introduction of SMX improved the abundance of hydrolysis bacteria and hydrogen-volatile fatty acids (VFAs) producers. SMX synergistically influenced hydrolysis, acidification and acetogenesis to facilitate the hydrogen production.     

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.     

Facile fabrication of dolomite-doped biochar/bentonite for effective removal of phosphate from complex wastewaters

● Dolomite-doped biochar/bentonite was synthesized for phosphate removal. ● DO/BB exhibited a high phosphate adsorption capacity in complex water environments. ● PVC membrane incorporated with DO/BB can capture low concentration phosphate. ● Electrostatic interaction, complexation and precipitation are main mechanisms. The removal of phosphate from wastewater using traditional biological or precipitation methods is a huge challenge. The use of high-performance adsorbents has been shown to address this problem. In this study, a novel composite adsorbent, composed of dolomite-doped biochar and bentonite (DO/BB), was first synthesized via co-pyrolysis. The combination of initial phosphate concentration of 100 mg/L and 1.6 g/L of DO/BB exhibited a high phosphate-adsorption capacity of 62 mg/g with a removal efficiency of 99.8%. It was also stable in complex water environments with various levels of solution pH, coexisting anions, high salinity, and humic acid. With this new composite, the phosphate concentration of the actual domestic sewage decreased from 9 mg/L to less than 1 mg/L, and the total nitrogen and chemical oxygen demand also decreased effectively. Further, the cross-flow treatment using a PVC membrane loaded with DO/BB (PVC-DO/BB), decreased the phosphate concentration from 1 to 0.08 mg/L, suggesting outstanding separation of phosphate pollutants via a combination of adsorption and separation. In addition, the removal of phosphate by the PVC-DO/BB membrane using NaOH solution as an eluent was almost 90% after 5 cycles. The kinetic, isotherm and XPS analysis before and after adsorption suggested that adsorption via a combination of electrostatic interaction, complexation and precipitation contributed to the excellent separation by the as-obtained membranes.