The global water treatment industry urgently demands improved efficiency, energy conservation, and resource recovery. In response to these pressing challenges, artificial intelligence (AI) is rapidly emerging as a driving force for advancing water treatment technology and industry innovation, demonstrating unprecedented potential in data analysis, process prediction, strategy optimization, and resource allocation. However, the application of AI in water treatment currently lacks a systematic theoretical framework and empirical research. In particular, there is a significant gap in the implementation of AI-driven water treatment processes and the evaluation of the water industry, which urgently requires further exploration and resolution. This paper systematically sorts out the transformative logic of AI-driven water treatment technology and industry, analyzing frontier topics in the field from the perspectives of technology development paradigms, engineering application methods, and industry ecosystem models. It also proposes future research priorities and action recommendations, to provide empirical insights for the strategic deployment and execution of smart water management.

Under global warming, extreme weather events and air pollution are becoming increasingly critical challenges. Both pose serious risks to human health, economies, and societal stability, and their complex interactions can further amplify these impacts. Numerical models are essential tools for studying these phenomena; however, traditional low-resolution Earth system models often fail to accurately capture the dynamics of extreme weather and air pollution. This limitation hinders our mechanistic understanding, reduces the reliability of future projections, and constrains the development of effective adaptation strategies. Dynamical downscaling—an approach that uses high-resolution regional models nested within global models—offers a partial solution. However, this method inherits biases from the parent global models and often fails to adequately represent multi-scale and cross-sphere interactions involving the atmosphere, land, and oceans. These shortcomings underscore the growing need for developing and applying high-resolution Earth system models that can more comprehensively and accurately depict land–sea–atmosphere interactions, including heat and material exchanges and their spatial heterogeneity. This article explores the current challenges, recent advances, and future opportunities in understanding the interplay between extreme weather events and air pollution, with a focus on the critical role of high-resolution modeling.

Highly persistent per- and polyfluorinated alkyl substances (PFAS) have been extensively used worldwide for decades and are now ubiquitous in the ecosystem. To combat problems related to PFAS accumulation in the environment and their intrusion into the human body, PFAS adsorption and subsequent breakdown of carbon and fluorine chains are under intense research. Activated carbon (AC) is a widely used adsorbent for PFAS removal from water or wastewater. However, some of its shortcomings include inefficiency in short-chain PFAS removal, a lack of selectivity, overall low adsorption performance, and concerns regarding economic sustainability. Herein, we reviewed the recent innovative carbon-based technologies that aim to address these challenges. In particular, we focus on AC’s topography engineering, defunctionalization (e.g., removing surface functional groups), hydrophobicity or surface charge modification, water-confining nanopores, and AC-nanobubbles synergy. The underlying mechanisms of these novel approaches and their effectiveness in PFAS adsorption are discussed, along with their advancements and limitations. Additionally, the PFAS adsorption and regeneration ability of high-performance ACs are presented and compared. Finally, we address current challenges and offer perspectives on advancing this technology.

Insights into the microbial communities in municipal wastewater treatment plants (WWTPs) are critical for the optimization of biological nutrient removal process. However, our understanding about the spatiotemporal characteristics of the microbial communities in WWTPs remains limited. In the present study, 264 samples were collected biweekly from four spatially independent corridors in a typical municipal WWTP. The annual compositional and metagenomic characteristics were investigated based on multiple ecological indicators using statistical tests. The results revealed that the microbial community compositions from the four corridors showed significantly high similarities, as revealed by the statistical analysis at the operational taxonomic unit (OTU) level. Consistent with the OTU level results, the functionality of the microbial communities in the four independent corridors also showed significant similarity. In comparison, the dynamics of the microbial community over the year showed two successional peaks of the microbial communities with the spatial similarity, and this resulted in three alternative stable states of the microbial communities in a calendar year. The microbial communities only drifted in July and November, suggesting an uneven community succession pattern driven by seasonal variation in environmental conditions. The functional characteristics were found to be relatively conservative compared to the microbial community succession, which revealed the decoupling between the composition and functionality of the microbial community in the municipal WWTP. The present study provides an in-depth overview of the microbial communities in a municipal WWTP and will be useful for the establishment of the connection between ecological characteristics and the operational stability of WWTPs.

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global emergency that exposed the urgent need for surveillance approaches to monitor the dynamics of viral transmission. Several epidemiological tools that may help anticipate outbreaks have been developed. Wastewater-based epidemiology is a non-invasive and population-wide methodology for tracking the epidemiological evolution of the virus. However, thorough evaluation and understanding of the limitations, robustness, and intricacies of wastewater-based epidemiology are still pending to effectively use this strategy. The aim of this study was to train highly accurate predictive models using SARS-CoV-2 virus concentrations in wastewater in a region consisting of several municipalities. The chosen region was Catalonia (Spain) given the availability of wastewater SARS-CoV-2 quantification from the Catalan surveillance network and healthcare data (clinical cases) from the regional government. By using various feature engineering and machine learning methods, we developed a model that can accurately predict and successfully generalize across the municipalities that make up Catalonia. Explainable Machine Learning frameworks were also used, which allowed us to understand the factors that influence decision-making. Our findings support wastewater-based epidemiology as a potential surveillance tool to assist public health authorities in anticipating and monitoring outbreaks.

The escalating use of plastic materials in viticulture causes release of microplastics (MPs) into vineyard soils. This study examines the impact on soil health of polypropylene (PP) raffia and polyvinyl chloride (PVC) tube strings, commonly mulched into the topsoil after use. A 120-d incubation experiment was conducted with soils exposed to high doses (10 g/kg) of microplastics (MPs) from standard, new and used strings. The study investigated alterations in the microbial community, bioavailability of macronutrients (NH4+ and NO3−, P, K, Ca, Mg), and bioavailability of micronutrients (Cu, Zn, Fe, Mg). The presence of MPs significantly stressed the soil microbial community, reducing microbial biomass by 30% after 30 d, with the exception of PVC in acid soil, which caused an unexpected increase of about 60%. The metabolic quotient (qCO2) doubled in MP-polluted soils, with PVC exerting a more pronounced effect than PP. Basal respiration increased by 25% relative to the acid control soil. PVC MPs raised soil pH from 6.2 to 7.2 and firmly reduced the bioavailability of micronutrients, particularly in acidic soils, and led to a 98% reduction in nitrate (NO3−). The availability of NH4+, P, K, Mg decreased by 10% and Cu, Fe, Mn, Zn by 30%. However, Ca availability increased by 30%, despite shifting from the acid-soluble fraction to soil organic matter and crystalline minerals. Calcareous soil was generally more resilient to changes than the acid soil. These findings underscore the urgent need to investigate the long-term effects of MPs from viticulture on soil properties and health.

Prior research has consistently demonstrated that urban economic and social systems adhere to the empirical scaling law. Furthermore, a plethora of evidence, including the scale-free networks of energy metabolism, the allometric growth patterns of species and populations, and the scaling law relationship between exergy and transformity in biosphere systems across various levels, indicates that urban ecosystems exhibit multi-level scaling law characteristics in energy metabolism under self-organization, alongside significant human activity imprints. This study synthesizes these findings to hypothesize that urban ecological components are also aligned with system-level scaling theory within the urban metabolism framework. This encompasses: 1) the existence of multistable coexistence and mutual transformation phenomena, mirroring the dynamic nature of scaling laws; and 2) a nuanced balance between the ecosystem and the socio-economic system, particularly in the realms of spatial competition and output efficiency. The ecosystem scaling theory hypotheses of urban metabolic processes offer a theoretical foundation for identifying ecological security tipping points, which are pivotal in the strategic decision-making for ecological planning and management in the future.

Mineral scaling represents a major constraint that limits the efficiency of membrane desalination, which is becoming increasingly important for achieving sustainable water supplies in the context of a changing climate. Different mineral scales can be formed via distinct mechanisms that lead to a significant variation of scaling behaviors and mitigation strategies. In this article, we present a comprehensive review that thoroughly compares gypsum scaling and silica scaling, which are two common scaling types formed via crystallization and polymerization respectively, in membrane desalination. We show that the differences between scale formation mechanisms greatly affect the thermodynamics, kinetics, and mineral morphology of gypsum scaling and silica scaling. Then we review the literatures on the distinct behaviors of gypsum scaling and silica scaling during various membrane desalination processes, examining their varied damaging effects on desalination efficiency. We further scrutinize the different interactions of gypsum and silica with organic foulants, which result in contrasting consequences of combined scaling and fouling. In addition, the distinctive mitigation strategies tailored to controlling gypsum scaling and silica scaling, including scaling-resistant membrane materials, antiscalants, and pretreatment, are discussed. We conclude this article with the research needs of attaining a better understanding of different mineral scaling types, aiming to inspire researchers to take scale formation mechanism into consideration when developing more effective approaches of scaling control in membrane desalination.

Developing an anthropogenic carbon dioxides (CO2) emissions monitoring and verification support (MVS) capacity is essential to support the Global Stocktake (GST) and ratchet up Nationally Determined Contributions (NDCs). The 2019 IPCC refinement proposes top-down inversed CO2 emissions, primarily from fossil fuel (FFCO2), as a viable emission dataset. Despite substantial progress in directly inferring FFCO2 emissions from CO2 observations, substantial challenges remain, particularly in distinguishing local CO2 enhancements from the high background due to the long atmospheric lifetime. Alternatively, using short-lived and co-emitted nitrogen dioxide (NO2) as a proxy in FFCO2 emission inversion has gained prominence. This methodology is broadly categorized into plume-based and emission ratios (ERs)-based inversion methods. In the plume-based methods, NO2 observations act as locators, constraints, and validators for deciphering CO2 plumes downwind of sources, typically at point source and city scales. The ERs-based inversion approach typically consists of two steps: inferring NO2-based nitrogen oxides (NOx) emissions and converting NOx to CO2 emissions using CO2-to-NOx ERs. While integrating NO2 observations into FFCO2 emission inversion offers advantages over the direct CO2-based methods, uncertainties persist, including both structural and data-related uncertainties. Addressing these uncertainties is a primary focus for future research, which includes deploying next-generation satellites and developing advanced inversion systems. Besides, data caveats are necessary when releasing data to users to prevent potential misuse. Advancing NO2-based CO2 emission inversion requires interdisciplinary collaboration across multiple communities of remote sensing, emission inventory, transport model improvement, and atmospheric inversion algorithm development.

Microbial electrochemical system (MES) offers sustainable solutions for environmental applications such as wastewater treatment, energy generation, and chemical synthesis by leveraging microbial metabolism and electrochemical processes. This review explores the transformative role of 3D printing in MES research, focusing on reactor body design, electrode fabrication, and bioprinting applications. Rapid prototyping facilitated by 3D printing expedites MES development while unlocking design flexibility, which enhances performance in optimising fluid dynamics and mass transfer efficiency. Tailored ink materials further improve the conductivity and biocompatibility of electrodes, paving the way for environmental applications. 3D-printed bio-anodes and bio-cathodes offer enhanced electrogenesis and boosted electron acceptance processes, respectively, by fine-tuning electrode architectures. Additionally, 3D bioprinting presents opportunities for scaffold fabrication and bioink formulation, enhancing biofilm stability and electron transfer efficiency. Despite current challenges, including material selection and cost, the integration of 3D printing in MES holds immense promise for advancing energy generation, wastewater treatment, resource recovery, carbon utilisation, and biosensing technologies.