The wastewater treatment sector contributes to greenhouse gas emissions, thereby exacerbating global warming. Achieving net-zero emissions in wastewater treatment plants (WWTPs) is pivotal to meeting carbon neutrality targets. This study evaluated the carbon footprint (CF) of six representative WWTPs in southern and northern China to elucidate the current status of carbon neutrality. The analysis covered CF composition, offsets achieved through carbon compensatory measures, and the CF seasonal and spatial variations. The average CF per population equivalent of the selected Chinese WWTPs with offsets was 58 kg CO₂-eq/PE/yr, representing a 22.8% reduction compared with the pre-offset value. Energy recovery from sludge incineration and reclaimed water reuse emerged as the vital carbon offset strategies for the studied facilities in southern and northern China, respectively. Unlike those in typical European WWTPs, indirect emissions driven by substantial electricity and chemical consumption were the primary CF sources in Chinese plants (> 60%). When pollutants removed were used as the functional unit, peak CF values were observed during summer or autumn. Energy input was determined to be the primary factor influencing the CF. Further CF reductions can be realized through process optimization, energy recovery, and chemical reduction, and other measures. This study provides a valuable reference for formulating targeted mitigation strategies in WWTPs and advancing towards carbon neutrality.

A combined system comprising a hybrid anaerobic digester (HD), a vertical subsurface flow constructed wetland (VF), and a heterogeneous photocatalysis unit was evaluated at pilot-scale for the elimination of faecal indicator microorganisms—total coliforms, Escherichia coli and Clostridium perfringens. The VF effluent was subjected to laboratory-scale experiments using different photodegradation post-treatments: UVC photolysis, heterogeneous photocatalysis with ultraviolet light (UVA/TiO₂), and sunlight-driven heterogeneous photocatalysis (Sol/TiO₂). Subsequently, the Sol/TiO₂ system was scaled up and implemented at pilot-scale (p.Sol/TiO₂). The total footprint of the combined HD+VF+p.Sol/TiO₂ system was 4.4 m². Under continuous operation, the combined HD+VF system was able to remove approximately 1.0, 1.3 and 1.1 log units for total coliforms, E. coli and C. perfringens, respectively, with the VF unit accounting for more than 80% of the overall elimination during biological treatment. Laboratory-scale experiments showed high removal efficiency, following the order UVC > UVA/TiO₂ > Sol/TiO₂. In contrast, the p.Sol/TiO₂ post-treatment (after 2 h of exposure) achieved lower removals of approximately 0.5, 1.2 and 0.1 log units for total coliforms, E. coli and C. perfringens, respectively. To our knowledge, this is the first study on the combination of VF constructed wetlands and photodegradation processes with the aim of improving the quality of reclaimed water for potential reuse. As a general conclusion, the photocatalysis pond employed in the present study improved the quality of the VF effluent, widening the possibilities for reuse of the reclaimed water.

As a national ecological civilization pilot zone with unprecedented strategic importance in China, Hainan Province requires precise identification of PM_2.5 background concentrations to achieve world-leading air quality objectives. This study utilized the three-dimensional regional air quality modeling (WRF-CMAQ) and integrated multi-source ground observation data for model validation. To quantify the PM_2.5 background values for Hainan, representing concentrations largely influenced by natural emissions and regional transport, we determined the contributions from these sources. The results identified local anthropogenic emissions (49.5%) and regional transport (29.3%) as the dominant PM_2.5 sources in Hainan, with important natural contributions (19.0%). Based on the theoretical definition of background concentrations grounded in scientific research and the objective of providing practical policy insights for Hainan, the PM_2.5 background values were defined as the PM_2.5 concentration after (1) excluding local anthropogenic emissions; (2) excluding anthropogenic emissions across Hainan and its surrounding regions; and (3) excluding local emissions combined with projected 2035 emission scenarios for surrounding areas. Based on these three definition methods, the 18 cities-averaged PM_2.5 background values for Hainan were determined as 6.9, 4.2, and 5.2 μg/m³, while the corresponding grid-averaged values calculated were 6.7, 4.6, and 4.9 μg/m³, respectively. Accounting for meteorological fluctuations could yield lower estimated PM_2.5 concentrations for Hainan. Hainan’s PM_2.5 background values showed comparability with levels documented in clean island areas globally. Our study demonstrated that Hainan maintained a relatively low PM_2.5 background level, showing substantial emission reduction potential. Through implementation of effective control measures, Hainan is projected to achieve world-leading air quality by 2035.

Deep learning methods are increasingly employed to forecast air quality from an ever-increasing stream of data and algorithms. However, the efficacy of current approaches may be questionable when evaluated not solely in terms of greater forecasting fidelity, but also concerning the decision-making process in pollution early warning. Here, rather than amending classical machine learning algorithms, we argue that now is the time to push the frontiers of air pollutant forecasting beyond state-of-the-art approaches. This can be achieved through near real-time assimilation of multi-scale observations for laying the foundation of training data, enhanced attribution methods for impending heavy pollution, diagnostics for forecasting uncertainty, and advanced climate-chemistry emulators for improving seasonal forecasting. To harness this potential, it is essential to address several key challenges in deep learning methods, particularly generalization ability in extreme events, physics-informed interpretable approaches, and the mitigation technology of cumulative errors in multi-process coupled systems. This interdisciplinary endeavor will remain a central pursuit in the quest to anticipate and manage environmental change.

Severe ozone (O₃) pollution has always been a serious problem faced by areas with rapid economic development, and the regional O₃ transport between cities is a major cause of this problem. Therefore, we used a bidirectional long short-term memory (Bi-LSTM) model to quantitatively identify the regional O₃ transport in Hangzhou Bay, China. Combined with the meteorological removal method, we were able to model O₃ concentrations that were not affected by transport. The contribution of regional transport to Shanghai’s O₃ was quantified and validated using two different simulation schemes, which yielded highly consistent results of 18.41 μg/m³ (24% contribution) and 20.52 μg/m³ (27% contribution). According to the model simulation results, we found that approximately 24% of the O₃ pollution in Shanghai originates from other cities in the summer when the O₃ pollution is high. In addition, the regional O₃ transport was mainly concentrated during the high-value weather of O₃ pollution in Shanghai, and transport on non-pollution days was not apparent. Therefore, the regional O₃ transport from other cities is an important source of O₃ pollution in Shanghai. Overall, our study demonstrates the potential of machine-learning models coupled with meteorological removal for quantifying the inter-city influence of atmospheric pollutants.

Climate change presents a critical global challenge, threatening human well-being, ecosystems, economies, and societies. While mitigation efforts remain essential and critically important, the growing urgency of climate impacts necessitates immediate and effective adaptation measures. Effective adaptation strategies require advanced modeling tools with higher resolution, integration of ecosystem and social dynamics, and the ability to assess diverse adaptation scenarios. Local-scale models, which are performed at the scale of an administrative region, a country, or a specified region, are particularly valuable as they can incorporate specific adaptation measures and generate precise, context-specific insights. These models play a key role in formulating tailored climate adaptation strategies and action plans. This paper explores the significance and challenges in developing such models, emphasizing the pressing need to accelerate their advancement. We call on the scientific community and policymakers to prioritize the development of tailored local-scale modeling tools and services to enhance resilience and better support adaptive responses to the complex and evolving challenges posed by climate change and rapid urbanization at the local level.

Electroactive microorganisms are integral to biogeochemical cycles through extracellular electron transfer and have potential applications in environmental remediation. However, their long-term competitive interactions and evolutionary dynamics with non-electroactive microorganisms remain poorly understood. In this study, we conducted a 320-day cultivation experiment in which monocultures of the electroactive Shewanella oneidensis MR-1, the non-electroactive Citrobacter freundii An1, and their cocultures were compared under three single electron acceptor conditions: anaerobic (no exogenous electron acceptor), ferrihydrite, or oxygen. After 320 d, S. oneidensis MR-1 presented the highest relative abundance of 30.94% ± 0.74% in the ferrihydrite cocultures. S. oneidensis MR-1 maintained ferrihydrite reduction capacity after cultivation under all three conditions, indicating the long-term stability of its extracellular electron transfer. Moreover, no other phenotypic evolution was observed in S. oneidensis MR-1 after ferrihydrite or anaerobic cultivation. In contrast, both monocultured and cocultured S. oneidensis MR-1 exhibited enhanced adaptation to oxygen, characterized by increased growth rates, metabolic activity, and reduced cell aggregation. Notably, substrate consumption increased in monocultures but decreased in cocultures, suggesting an optimization of metabolic efficiency in the latter. Genome sequencing revealed mutations in genes associated with cell division, adenosine triphosphate synthesis, lactate metabolism, and flagellar/pilus expression in S. oneidensis MR-1. Interestingly, the ferrihydrite-adapted groups also exhibited enhanced adaptation to oxygen. 83.96% of mutations were shared across all culture systems and enriched in environmental signal-sensing pathways, indicating that parallel genomic evolution facilitated cross-environmental adaptation. Our findings reveal the ecological evolution of electroactive microorganisms in diverse redox environments and establish a foundation for engineering electroactive communities.

Heavy metals are ubiquitous environmental pollutants, contaminating air, soil, and water via the erosion of natural deposits, as well as originating from anthropogenic sources, such as agriculture, industries, transportation, and landfills. The increasing utilization of heavy metals over the years, combined with the persistent nature of metals in the environment poses a direct threat to human and environment health. Although regulatory limits have been established for toxic metals, assessing the associated health risks using real-life exposure scenarios remains challenging. In this review, we summarize the development and use of in vitro models based two- and three-dimensional cell culture systems, focusing on exposure to heavy metals via the dermal, inhalation, and ingestion routes using environmental samples. We also highlight recent developments in three-dimensional cell culture techniques and their potential for implementation in evaluating environmental samples for heavy metal toxicity. In addition, we assess the comparative strengths and specific applications of different modeling approaches, emphasizing the value of integrating advanced in vitro systems in environmental toxicology.

Pesticides and DBPs coexist in tap waters at trace levels, demanding attention for long term health protection. To allocate resource for water contaminant control, regulated chemicals need to be prioritized. The current prioritization is primarily based on the toxicity additivity assumption that ranks toxicity-weighted concentration of chemicals. However, recent findings revealed that the non-additive synergistic and antagonistic toxicological interactions are also prevalent in waters, potentially biasing previous prioritization rankings. To demonstrate a possible framework for improved prioritization, we identified the cytotoxic interactions, component contributions, and forcing compounds among six common toxic pesticides and DBPs based on the Chou-Talalay approach. In the “Malathion + DBPs” combination, the interaction type shifted from additivity to antagonism as the concentration increased, indicating concentration dependency. In the “Chlorothalonil + DBPs” combination, the strong antagonism led to a convergence of cytotoxicity value among the three mixtures. A comparison of cytotoxicity of “Deltamethrin + IAN/BAN” revealed that the interaction type affected the mixture-induced cytotoxicity. To unravel the cytotoxic interactions and forcing chemicals at both environmentally-relevant and bioaccumulation-attainable concentrations, we analyzed the componential contributions among “pesticides + DBPs” mixtures at LC_0.1 and LC₅₀ levels and identified the forcing cytotoxic compounds in each. At LC_0.1, pesticides need to be prioritized in only two combinations out of nine; at LC₅₀, DBPs should be prioritized only in “Chlorothalonil + DBPs” combinations. These results provide a framework for the prioritization among “pesticides + DBPs” in water and possibly other classes of contaminants.

Conductive additive such as biochar have been extensively employed to enhance anaerobic digestion (AD) performance for over a decade. Among the proposed mechanisms, conductive additive-facilitated direct interspecies electron transfer (DIET) is frequently cited as a key contributor to these performance improvements. Because this process is believed to bypass traditional diffusible intermediates (e.g., H₂ or formate), it can enable more efficient energy transfer between syntrophic partners and accelerate substrate degradation, potentially leading to higher methane yields and improved overall stability of the anaerobic digestion process. However, benefits regarding conductive additive-facilitated DIET often rely on indirect indicators rather than direct experimental evidence. Here, we advocate for a critical reassessment on the benefits of conductive additive for DIET in AD. Specifically, we emphasize the importance of establishing standardized experimental protocols and obtaining direct evidence to confirm the occurrence and significance of DIET in conductive additive-amended AD system. Furthermore, it is essential to distinguish DIET from other enhancement mechanisms such as pH buffering and toxin adsorption that may independently contribute to improved AD performance, with the goal of advancing its practical implementation.

Rapid urbanization reshapes landscape patterns and intensifies stormwater runoff pressure, yet the shifting cost-effectiveness of green infrastructure across different urban development phases remains poorly quantified. Focusing on Beijing’s 150 km² urban subcenter, this study quantified 21 block-level landscape metrics, which were distilled via principal component analysis into five landform indicators: dominance, fragmentation, edge, aggregation, and shape. K-means clustering classified each block into constructed, constructing, or unconstructed phases. A life-cycle cost analysis then estimated the bioretention investment required to meet an 80%–85% annual runoff volume control target. The constructing phase, characterized by contiguous impervious surfaces at the urban edge, demands 45% more bioretention investment per unit area than the unconstructed phase and 4% more than the constructed phase. As land transitions from unconstructed to constructed, bioretention costs increase by approximately 109% for agricultural land and 86% for green space, whereas changes for residential and commercial areas remain minimal. These results indicate that uniform runoff control investment policies risk underfunding rapidly developing fringes and overfunding consolidated urban centers. A phase-specific and land use–sensitive investment strategy is therefore necessary to avoid capital inefficiency while achieving runoff control goals. By linking dynamic landscape evolution with infrastructure economics, this study provides a forward-looking tool to guide runoff control investment during urban expansion.

Transparent exopolymer particles (TEP) are abundant gel-like colloids pivotal in marine carbon cycling and water treatment processes. Their environmental roles are governed by hierarchical architectures, yet in-situ structural characterization remains challenging due to transparency, fragility, and polymorphism. To address this, we developed an integrated image analysis suite combining advanced processing with statistical modeling, enabling simultaneous 2D/3D quantification of TEP morphology and intra-particle heterogeneity. This framework generates multidimensional descriptors (e.g., fractal dimensions, density gradients) for individual aggregates and assemblies. Applied to cation-mediated aggregation, it revealed divergent bridging behaviors. Mg²⁺ induced moderate size changes (2.99–4.08 μm), while Ca²⁺ drove exponential growth (1.81–187.76 μm) when ionic strength increasing from 1 to 5 mmol/L. Concurrent form factor reductions (Mg: 0.31 to 0.16; Ca: 0.44 to 0.19) quanti-tatively distinguish aggregation pathways. The method deciphers ion-specific assembly mechanisms and resolves subtle colloidal interactions, establishing a paradigm for colloidal system analysis with possible applications extending beyond TEP research to other subjects such as microplastic aggregation.

Antibiotic residues in wastewater promote the emergence of resistant bacteria, posing a serious potential threat to human health and ecosystems. Effective degradation strategies are crucial for removing antibiotics from wastewater. In this study, a photocatalytic polymer membrane was used to treat three antibiotics, i.e., sulfamethoxazole, chloramphenicol, and ofloxacin. In parallel with chemical analysis, the acute and chronic toxicity of the antibiotics and their degradation mixtures to the freshwater green alga Scenedesmus vacuolatus was assessed. Photocatalytic membrane treatment of 10 mg/L aqueous solutions (and 1100 mg/L for ofloxacin) achieved complete parent-compound removal, with half-lives ranging from 6.2–102.3 min. Toxicity measured at successive irradiation times revealed initial detoxification followed by increased toxicity due to transformation products and by-products caused by membrane photoaging, limiting the total detoxification effectiveness. The results underscore the promise of photocatalytic membranes for antibiotic removal while highlighting the critical importance of photostable polymer–photocatalyst materials to prevent secondary ecotoxicological effects in water treatment applications. These results further demonstrate the need to combine chemical and toxicological methods to validate new technologies for wastewater treatment.

Ammonia fuel, as a promising zero-carbon energy carrier, faces environmental challenges due to high-concentration NH₃ slip in exhaust emissions. Here we develop an efficient Ag-Mn/Al₂O₃ bimetallic catalyst for NH₃ selective catalytic oxidation (NH₃-SCO). Mechanistic investigations reveal the dual role of Mn in this catalytic system: On one hand, Mn preferentially occupies the hydroxyl anchoring sites on Al₂O₃ surface, inducing Ag species aggregation to form nanoparticles with superior O₂ activation capability; on the other hand, highly dispersed Mn species provide abundant Lewis acid sites, significantly enhancing NH₃ adsorption and activation. Through this synergistic effect, the 2Ag3Mn/Al₂O₃ catalyst achieves complete conversion of 7.5 mg/L NH₃ at 300 °C. Further studies demonstrate a distinct temperature-dependent effect on the catalyst’s reaction mechanism, transitioning from –NH-dominated pathway at low temperatures to i-SCR mechanism at high temperatures. This work not only elucidates the mechanism of Mn’s dual role in structural regulation and functional sites but also provides new insights into the rational design of efficient NH₃-SCO catalysts.

The remediation of nitroaromatic-contaminated water systems remains a critical environmental challenge, creating the urgent need for cost-effective, non-precious metal catalysts to increase wastewater biodegradability. In this study, we present an Fe/N-functionalized three-dimensional porous carbon (Fe–N–C) catalyst synthesized via a straightforward pyrolysis approach that enables efficient nitrobenzene (NB) degradation via electrochemical and chemical reduction pathways. The high-temperature pyrolysis process facilitates the iron-catalyzed reconstruction of the carbon matrix, resulting in a hierarchically porous structure with increased graphitization and uniformly distributed macrocyclic Fe–N₄ coordination sites. These structural features give the Fe–N–C catalyst exceptional electron transfer kinetics, catalytic activity, and pH adaptability, surpassing conventional graphite (GR) and nitrogen-doped carbons (NPCs) in NB reduction. Systematic evaluation of the electrochemical reduction performance revealed that the Fe–N–C electrode achieved the highest NB removal efficiency. To further assess the versatility of the catalyst, a functionalized Fe–N–C/zero-valent iron (ZVI) composite was engineered by integrating Fe–N–C as a catalytic layer onto the reductant ZVI. Compared with ZVI alone, this composite markedly increased the NB reduction efficiency. These findings provide valuable insights into the electrochemical reduction process of Fe–N–C and new directions for the rational design of efficient nitrobenzene reduction systems.

The emissions of volatile sulfur compounds (VSCs) from wastewater treatment plants (WWTPs) pose odor nuisances and health risks to workers and surrounding residents, thus becoming a major environmental concern for these facilities. This study investigated the long-term monitoring of emissions of VSCs from an anaerobic/oxic (A/O) WWTP and employed the AERMOD model to simulate the dispersion of VSCs within an area of 5 × 5 km² for assessing the impact of these emissions on odor and health risks. The obtained results indicated that the emissions of VSCs from the WWTP decreased in the order of summer > autumn > spring > winter. The pretreatment unit accounted for 90.72% of the total VSCs emissions. The dispersion of VSCs was significantly influenced by the direction and speed of wind, with the largest dispersion range observed in autumn and the smallest one in spring. Dimethyl disulfide (DMDS), dimethyl sulfide (DMS), and hydrogen sulfide (H₂S) were identified as the primary VSCs contributing to odor impacts. The source tracing analysis revealed that the fugitive emissions of VSCs from the grill and sand-water separation unit (SWSU) contributed most significantly to their dispersion, odor pollution, and health risks, accounting for 74.87% and 11.33%, respectively. Enclosure of the grill and SWSU with covers, adjustment of the position of the exhaust pipe of the deodorizing facility, and increment in the height of the exhaust pipe are expected to be effective measures in mitigating the negative impacts of VSCs. These measures would provide new insights into dispersion modelling, risk prediction, and emission control of gaseous pollutant emitted from other factories.

Sewer corrosion is a critical issue that significantly threatens sewer systems, contributing to approximately 40% of sewer infrastructure deterioration. Although numerous review studies have been conducted in this field, gaps persist in identifying the complex factors driving corrosion and understanding their interrelationships. These deficiencies impede the development of accurate corrosion prediction models and the identification of more effective mitigation strategies. This research aims to deepen the understanding of the underlying causes of sewer corrosion, evaluate the latest advancements in prediction models, and explore current mitigation techniques. A novel hybrid approach is employed, combining bibliometric, scientometric, and systematic analysis. While widely used in other fields, this methodology is new in sewer corrosion. The key findings of this study include a comprehensive identification of the various factors influencing corrosion, an overview of existing corrosion prediction models, and an evaluation of currently employed mitigation strategies. Additionally, this research highlights critical research gaps and suggests future avenues for investigation, with the potential to support municipalities in more efficient and flexible management of sewer infrastructure.

Floating photovoltaic (FPV) systems provide dual benefits in renewable energy generation and water resource utilization, supporting global decarbonization efforts. This study conducts a full life cycle assessment (LCA) of FPV systems, covering material production, construction, operation, and decommissioning stages. Particular attention was given to ecological risks, an often overlooked aspect in previous environmental evaluations. A structured ecological risk matrix is developed integrating physical, chemical, and biological indicators to evaluate environmental disturbances caused by FPV systems. The framework was applied to eight representative water bodies in China, including Taihu Lake (characterized by high nutrient loading), Danjiangkou Reservoir (a major drinking water source), and Poyang Lake (an ecologically sensitive wetland). Each site is evaluted using a semi-quantitative scoring system based on ecological sensitivity and FPV disturbance potential. Risk levels were classified to guide deployment suitability (0–6), identify high-risk areas, and support mitigation strategies. The findings revealed significant spatial heterogeneity in ecosystem vulnerability and highlighted the lack of standardized protocols for ecological risk assessment in FPV projects. In response, this study proposed context-specific design recommendations, including adjustments to module density, transparency, and anchoring methods, to minimize ecological impacts. This research provides a transferable tool for incorporating ecological metrics into FPV planning and regulatory review, particularly in freshwater ecosystems. It contributes to the development of risk-informed deployment guidelines and emphasizes the need for long-term ecological monitoring in FPV expansion.