Responses of microbial interactions to elevated salinity in activated sludge microbial community

● Salinity led to the elevation of NAR over 99.72%. ● Elevated salinity resulted in a small, complex, and more competitive network. ● Various AOB or denitrifiers responded differently to elevated salinity. ● Putative keystone taxa were dynamic and less abundant among various networks. Biological treatment processes are critical for sewage purification, wherein microbial interactions are tightly associated with treatment performance. Previous studies have focused on assessing how environmental factors (such as salinity) affect the diversity and composition of the microbial community but ignore the connections among microorganisms. Here, we described the microbial interactions in response to elevated salinity in an activated sludge system by performing an association network analysis. It was found that higher salinity resulted in low microbial diversity, and small, complex, more competitive overall networks, leading to poor performance of the treatment process. Subnetworks of major phyla (Proteobacteria, Bacteroidetes, and Chloroflexi) and functional bacteria (such as AOB, NOB and denitrifiers) differed substantially under elevated salinity process. Compared with subnetworks of Nitrosomonadaceae, Nitrosomonas (AOB) made a greater contribution to nitrification under higher salinity (especially 3%) in the activated sludge system. Denitrifiers established more proportion of cooperative relationships with other bacteria to resist 3% salinity stress. Furthermore, identified keystone species playing crucial roles in maintaining process stability were dynamics and less abundant under salinity disturbance. Knowledge gleaned from this study deepened our understanding of microbial interaction in response to elevated salinity in activated sludge systems.     

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.     

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.     

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.     

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.     

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.     

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.     

Microbial biodegradation of plastics: Challenges,opportunities, and a critical perspective

● Health hazards of plastic waste on environment are discussed. ● Microbial species involved in biodegradation of plastics are being reviewed. ● Enzymatic biodegradation mechanism of plastics is outlined. ● Analytical techniques to evaluate the plastic biodegradation are presented. The abundance of synthetic polymers has increased due to their uncontrolled utilization and disposal in the environment. The recalcitrant nature of plastics leads to accumulation and saturation in the environment, which is a matter of great concern. An exponential rise has been reported in plastic pollution during the corona pandemic because of PPE kits, gloves, and face masks made up of single-use plastics. The physicochemical methods have been employed to degrade synthetic polymers, but these methods have limited efficiency and cause the release of hazardous metabolites or by-products in the environment. Microbial species, isolated from landfills and dumpsites, have utilized plastics as the sole source of carbon, energy, and biomass production. The involvement of microbial strains in plastic degradation is evident as a substantial amount of mineralization has been observed. However, the complete removal of plastic could not be achieved, but it is still effective compared to the pre-existing traditional methods. Therefore, microbial species and the enzymes involved in plastic waste degradation could be utilized as eco-friendly alternatives. Thus, microbial biodegradation approaches have a profound scope to cope with the plastic waste problem in a cost-effective and environmental-friendly manner. Further, microbial degradation can be optimized and combined with physicochemical methods to achieve substantial results. This review summarizes the different microbial species, their genes, biochemical pathways, and enzymes involved in plastic biodegradation.     

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.     

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.     

Visible light induces bacteria to produce superoxide for manganese oxidation

● Term of manganese-oxidizing microorganisms should be reconsidered. ● Visible light induces heterotrophic bacteria to produce superoxide. ● Heterotrophic bacteria oxidize Mn(II) ions with a fast oxidation rate. ● Superoxide oxidizing Mn(II) ions is an unintended side reaction of bacteria. ● Superoxide is an important oxidation force of Mn(II) in the environment. Manganese oxides are widely distributed in soils and sediments, affecting the migration and transformation of heavy metals and organic pollutants. The microbial conversion of soluble Mn(II) into insoluble Mn(III/IV) oxides is considered to be the initial source of manganese oxides in the environment; however, whether this process is related to a physiological role remains unclear. Here, we explored the microbial manganese oxidation process under visible light by using coastal surface seawater microorganisms. Visible light greatly promotes the oxidation rate of Mn(II), and the average rate reaches 64 μmol/(L·d). The generated manganese oxides were then conducive to Mn(II) oxidation, thus the rapid manganese oxidation was the result of the combined action of biotic and abiotic, and biological function accounts for 88 % ± 4 %. Extracellular superoxide produced by microorganisms induced by visible light is the decisive factor for the rapid manganese oxidation in our study. But the production of these superoxides does not require the presence of Mn(II) ions, the Mn(II) oxidation process was more like an unintentional side reaction, which did not affect the growth of microorganisms. More than 70 % of heterotrophic microorganisms in nature are capable of producing superoxide, based on the oxidizing properties of free radicals, all these bacteria can participate in the geochemical cycle of manganese. What’s more, the superoxide oxidation pathway might be a significant natural source of manganese oxide.     

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.     

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.     

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.     

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.     

A critical review on thermodynamic mechanisms of membrane fouling in membrane-based water treatment process

● Fundamentals of membrane fouling are comprehensively reviewed. ● Contribution of thermodynamics on revealing membrane fouling mechanism is summarized. ● Quantitative approaches toward thermodynamic fouling mechanisms are deeply analyzed. ● Inspirations of thermodynamics for membrane fouling mitigation are briefly discussed. ● Research prospects on thermodynamics and membrane fouling are forecasted. Membrane technology is widely regarded as one of the most promising technologies for wastewater treatment and reclamation in the 21st century. However, membrane fouling significantly limits its applicability and productivity. In recent decades, research on the membrane fouling has been one of the hottest spots in the field of membrane technology. In particular, recent advances in thermodynamics have substantially widened people’s perspectives on the intrinsic mechanisms of membrane fouling. Formulation of fouling mitigation strategies and fabrication of anti-fouling membranes have both benefited substantially from those studies. In the present review, a summary of the recent results on the thermodynamic mechanisms associated with the critical adhesion and filtration processes during membrane fouling is provided. Firstly, the importance of thermodynamics in membrane fouling is comprehensively assessed. Secondly, the quantitative methods and general factors involved in thermodynamic fouling mechanisms are critically reviewed. Based on the aforementioned information, a brief discussion is presented on the potential applications of thermodynamic fouling mechanisms for membrane fouling control. Finally, prospects for further research on thermodynamic mechanisms underlying membrane fouling are presented. Overall, the present review offers comprehensive and in-depth information on the thermodynamic mechanisms associated with complex fouling behaviors, which will further facilitate research and development in membrane technology.     

Insights into the changes of amino acids,microbial community,and enzymatic activities related with the nutrient quality of product during the composting of food waste

● The highest seed germination index was achieved at 0.3 g/g total solids of food waste. ● Proline was identified as the key amino acid related with the composting process. ● Amino acid metabolism sequences predominated during the whole composting process. This study systematically investigated the changes of amino acids as the composting process of food waste proceeded. It is found that the addition of 0.3 g/g total solids of food waste achieved the highest seed germination index of the product (268 %). The microbial community results indicated that the abundance of amino acid metabolism sequences remained at high levels during the whole composting process. Proline was identified as the key amino acid related with the nutrient quality of product during the composting of food waste. Further plant germination and hydroponic experiments found, that compared with those without the addition of proline, the addition of 50 mg/L proline increased seed germination rate by 20 %, increased shoot length by 3 %, increased root biomass of seedlings by 82 %, and increased leaf biomass of seedlings by 76 %, respectively. Firmicutes, γ-Pseudomonadota, Chloroflexi and Planctomycetes were the key identified bacteria related with the increase of proline during the composting of food waste. Meanwhile, the enzymatic tests of the activities of superoxide dismutase, peroxidase and malondialdehyde indicated that proline did not cause oxidative damage on the growth of plants. This study provided novel insights into the changes of amino acids, microbial community, and enzymatic activities related with the nutrient quality of product during the composting of food waste.     

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).     

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.     

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.