Search Results (1,801)

Water pollution and its ecological consequences have become central concerns in watershed governance and aquatic ecosystem conservation. Conventional ecotoxicological research on water pollution has long relied on physicochemical monitoring, laboratory-based single-species exposure tests, and morphology-based biological surveys. Although these approaches have provided essential support for pollutant identification, toxicity characterization, and environmental standard setting, they remain insufficient for resolving community-level responses, food-web perturbations, and ecosystem degradation under multiple-stressor conditions. Environmental DNA (eDNA) has emerged as a promising molecular tool because it is non-invasive, highly sensitive, high-throughput, and capable of detecting multiple taxa simultaneously. In aquatic systems, eDNA applications have expanded from biodiversity detection to pollution diagnosis, ecological health assessment, restoration monitoring, and early warning of ecological risk, while increasingly being integrated with eRNA, multi-omics approaches, machine learning, hydrological modeling, and ecological network analysis. However, several challenges still constrain its broader application, including incomplete methodological standardization, false-positive and false-negative detections, insufficient reference databases, limited quantitative capacity, scale mismatches caused by transport and mixing, and difficulties in causal attribution. This review synthesizes recent progress in the use of eDNA for water-pollution research, with emphasis on its technical workflow, major application domains, integrative analytical frameworks, and methodological boundaries. More specifically, three main points are highlighted: (1) eDNA is shifting water-pollution research from single-species toxicity characterization toward community- and ecosystem-level ecological interpretation; (2) its greatest value lies in its integrative role at the interface of biodiversity monitoring, ecological risk assessment, and management-oriented decision support; and (3) future progress will depend on improvements in standardization, quantitative inference, regional reference databases, and multi-source data integration. Overall, this review clarifies how eDNA can contribute to more robust, ecologically meaningful, and management-relevant assessment of water pollution. Full article

Disentangling the relative roles of emissions and atmospheric processes in controlling air-pollutant concentrations remains a central challenge in air-quality management. Although machine-learning (ML) models can accurately predict pollutant concentrations, the temporal evolution of the importance of individual drivers is often difficult to interpret. Here, we introduce a framework for reconstructing time-resolved feature importance (FI) in ML air-quality models. Hourly NO₂ and PM_2.5 concentrations were simulated across clusters of observations, defined along concentration trajectories in a state–space spanned by source intensity and meteorological variables. Within each cluster, predictor importance is quantified and mapped back onto the corresponding time points, yielding continuous FI time series for all predictors. The framework is demonstrated using observations from the nationwide air-quality network in Israel, together with traffic-related source indicators and meteorological parameters. The dominant drivers differ markedly between the two pollutants: NO₂ variability is primarily associated with local emissions, mechanical transport, and turbulent mixing, whereas PM_2.5 variability reflects predictors that are related to nucleation, coagulation, hygroscopic growth, long-range transport, and chemical transformation. The feature importance exhibits pronounced seasonal, regional, and diurnal variability, including modulation around traffic rush hours. These results demonstrate the value of time-resolved interpretability for diagnosing drivers of air-pollutant variability and improving the representation of processes in statistical air-quality models. Full article

The long-term release of heavy metals from inactive tailing ponds poses a persistent threat to groundwater quality, yet the effectiveness of commonly employed risk control measures—such as anti-seepage liners and chemical stabilization—remains insufficiently evaluated under realistic field conditions. This study aims to assess the effectiveness of risk control strategies at a Pb Zn mine tailing pond in Yunnan Province, China. A dynamic 2D numerical pollutant transport model was developed, calibrated, and validated against observed hydraulic heads and metal concentrations from monitoring wells within the study aquifer. The calibrated model showed good agreement with field measurements. Simulation results indicate that anti-seepage liners alone are insufficient to ensure compliance with the Class III standards of the Chinese Groundwater Quality Standards, even under ideal conditions where all leaching from the tailing pond is prevented. In contrast, a combined strategy—chemical stabilization reducing Pb leaching from historically contaminated soils (initial Pb: 183 µg L⁻¹) by at least 70%, together with anti-seepage systems reducing infiltration flux by over 94.4%—would be sufficient to restore groundwater quality to within regulatory limits. Full article

Anoplophora glabripennis (ALB) is an invasive wood-boring cerambycid that causes extensive damage to hardwood host trees through sequential tissue penetration from the bark to the sapwood. Developmental biology of ALB is well established. However, interactions among its life cycle, environmental contaminants, and fungal associates remain poorly understood. In particular, the ecological relationships among microplastics, entomopathogenic fungi, and the fungal symbiont Fusarium solani (FSSC) within ALB-associated woody tissues remain largely uncharacterized. This review develops a conceptual anatomical framework integrating ALB developmental biology, fungal associations, frass deposition pathways, and potential microplastic interactions within woody host tissues. The framework was constructed through ecological literature synthesis and anatomical reconstruction. To our knowledge, this represents the first conceptual framework integrating ALB developmental anatomy, fungal symbiosis, and potential microplastic interactions within host tree gallery systems. A longitudinal cross-sectional model was developed to illustrate oviposition, larval gallery formation, pupation, and adult emergence in relation to the outer bark, cambium/phloem, sapwood, and heartwood. FSSC isolates previously documented on ALB egg surfaces following oviposition and within ALB frass were examined, thereby positioning the fungal symbiont both within and outside galleries produced by ALB throughout its life cycle. Previous studies have demonstrated that microplastics can be taken up by plant stem tissues and accumulate on the forest floor through atmospheric deposition. This widespread presence suggests that micro- and nanoplastics may penetrate sapwood and heartwood galleries through xylem and phloem flow. These transport pathways may overlap with regions where late-instar larvae actively forage. The integrative framework presented here highlights potential ecological interactions within the gallery microhabitat and provides a foundation for future experimental investigations into contaminant–pathogen–host dynamics in xylophagous insects. While we refrain from proposing specific management strategies, we present a conceptual framework to elucidate how microplastics may serve as incidental contact points for cerambycid anatomy and fungal propagules. We hypothesize that these interactions link microplastic pollution to invertebrate ecology. Microplastics may function as substrates for fungal spores within forest canopies and gallery systems, potentially influencing fungal persistence, contaminant transport, and ecological dynamics within infested forest habitats. Full article

The transport sector contributes significantly to greenhouse gases and airborne pollutants. This study focuses on the co-benefits related to decreases in air pollutants and CO₂ emissions under various mitigation scenarios. The associated mitigations in PM_2.5 concentrations are predicted by establishing a random forest (RF) model and the health benefits are evaluated with the global exposure mortality model (GEMM). Environmental tax values and carbon trading prices are integrated alongside traditional elasticity coefficients and coordinate-based approaches to transform reductions into economic advantages. The results indicate that in the most favorable scenario (ELC), CO₂ emissions are expected to peak in 2032 with a reduction of 51.71%; this is supported by the marginal CO₂ emission curve, which intersects the zero axis around that same year, while the air pollution equivalents (APeq) are projected to decline by 25.65% in 2050. The efficiency of synergistic reductions between air pollutants and CO₂ ranks as SO₂ > NO_X > CO > HC > PM_2.5 > PM₁₀, and the elasticity coefficients for all pollutants are gradually aligning toward 1, suggesting that stricter mitigation efforts will enhance co-benefits. Furthermore, the economic benefits attributable to CO₂ reduction are anticipated to be 10.81 billion CNY by 2050, and 17,297 premature deaths associated with PM_2.5 exposure could be prevented. The findings in this study could provide essential insights for the co-management of CO₂ and air pollutants from road mobile sources. Full article

Over the past two decades, air-quality prediction has moved from a mainly single-model paradigm toward ensemble systems that make explicit use of diversity across models, observations, and data streams. This review connects developments that are often treated separately: chemical transport model (CTM) ensembles, tree-based and hybrid machine learning ensembles, deep learning architectures, physics-informed neural networks, and distributed approaches such as federated learning. Evidence summarized from recent systematic reviews and coordinated modeling initiatives indicates that, within comparable validation settings, ensembles often outperform individual models for PM_2.5, PM₁₀, O₃, NO₂, CO, and SO₂ across a broad range of spatial scales and standard error metrics, including RMSE, MAE, and correlation. Operational CTM ensembles, such as the Copernicus Atmosphere Monitoring Service (CAMS) European system with eleven regional models, improve both forecast skill and uncertainty characterization for ozone and particulate matter. In data-driven applications, tree-based ensembles (Random Forest, gradient boosting, XGBoost, LightGBM) and hybrid deep architectures (CNN–LSTM models, attention-based multi-branch networks, graph neural networks) now form a core part of the state of the art for AQI (Air Quality Index) and particulate-matter estimation from structured and multi-source data. Reported performance can be very high on well-structured tabular datasets, with $R^2$ values above 0.99 in selected benchmarks and RMSE reductions of 23–45% relative to classical statistical baselines in multi-modal studies; however, these values are not directly interchangeable because pollutant type, prediction horizon, monitoring density, and validation design differ among studies. This review proposes a practical taxonomy of ensemble strategies and uses it to explain why diversity, rather than model count alone, is central to reliable air-quality prediction. Drawing on coordinated European and North American model-evaluation initiatives (AQMEII, HTAP) and on case studies in topographically and meteorologically complex Italian regions (the Po Valley, the Naples metropolitan area, and Campania), we show that effective ensemble design requires a balance among diversity, redundancy, computational feasibility, and interpretability. On the basis of a structured narrative synthesis, the main research gaps concern physics-informed and explainable ensemble frameworks, transferable and adaptive models, standardized benchmarks, severe-pollution-episode forecasting, and scalable distributed architectures. Open questions include how to design compact non-redundant CTM sub-ensembles and how to couple deep learning with chemical-transport physics in next-generation operational systems. Full article

Road transport is a leading source of urban nitrogen oxides (NO_x) and fine particulate matter (PM_2.5)—a public-health and urban-sustainability challenge—and Low-Emission Zones (LEZs) are Europe’s principal response. Yet most are governed statically, unable to track conditions changing by the hour and the street. A digital twin, treated as decision infrastructure rather than a 3D model, recasts the LEZ as an adaptive decision infrastructure: a closed loop of sensing, modelling and rule-based adjustment. We develop a scalable, five-phase lifecycle methodology with auditability and GDPR-by-design built in, and derive three falsifiable hypotheses—efficiency, data integration, responsiveness—defining a research agenda. We test only the first. A diagnostic reading of London’s ULEZ shows its unimplemented phases are precisely those that close the loop. A proof-of-concept on real hourly NO₂ from five London sites (2023–2024) tests efficiency: at equal abatement effort, adaptive targeting avoids significantly more elevated-pollution hours than a uniformly stricter policy (about 37% versus 27%; 95% CI excludes parity), the advantage rising with forecast quality. This demonstrates the mechanism in reduced form, not a generalizable figure for a deployed system. By making regulation more responsive and accountable, it advances the Sustainable Development Goals on health, sustainable cities and climate (SDGs 3, 11, 13). Full article

The Cau river basin in northern Vietnam is experiencing increasing pressures on water resources due to rapid urbanization, industrial development, agricultural expansion, and inadequate wastewater management. Understanding the interactions between surface water, groundwater, and water quality is essential for developing effective and sustainable water management strategies. This study developed and applied a coupled MIKE SHE–MIKE 11 framework to simulate surface–groundwater connectivity and its influence on water quality dynamics in the Cau river basin. Hydrometeorological and water quality datasets collected during 2023–2024 were used to calibrate and test the integrated model at key monitoring locations, including Cha, Phuc Loc Phuong, and Dap Cau stations. The hydrological component demonstrated satisfactory performance, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.55 to 0.79 for water level simulations, indicating a reliable representation of surface and subsurface flow processes. Simulated river–aquifer exchange fluxes revealed pronounced spatial variability across the basin. Upstream reaches predominantly functioned as groundwater recharge zones, whereas the middle and downstream sections exhibited dynamic bidirectional exchanges governed by river stage fluctuations, hydraulic gradients, and local hydrogeological conditions. Water quality simulations for BOD₅, COD, NH₄⁺, total nitrogen (TN), and total phosphorus (TP) showed good agreement with observations, with calibration and testing errors generally remaining below 25%. Incorporating surface–groundwater interactions improved the representation of pollutant transport, residence time, and nutrient accumulation processes compared with conventional river-only simulations. The results demonstrate that river–aquifer connectivity plays a critical role in regulating both hydrological processes and water quality conditions in the basin. The coupled modeling framework provides a robust scientific basis for identifying critical interaction zones, assessing pollution risks, optimizing monitoring programs, and supporting integrated water resource planning. By explicitly linking hydrological connectivity with water quality dynamics, the proposed framework serves as a practical decision-support tool for sustainable water resource management in the Cau river basin and other river–aquifer systems facing increasing environmental pressures and progressive water quality degradation. Full article

Private vehicle overreliance in Klang Valley, Malaysia contributes to severe traffic congestion, air pollution, and carbon emissions, yet public transport modal shift remains far below national policy targets. This study extends the Theory of Planned Behaviour (TPB) by incorporating two external constructs, environmental concern (EC) and technology adoption (TA), to investigate the behavioural intention of 483 private vehicle users to switch to rail transit. Hypotheses were tested using partial least squares structural equation modelling (PLS-SEM). The results show that, with the exception of subjective norm ($\beta =0.064$, $p=0.180$), all constructs exert significant positive effects on behavioural intention. Technology adoption emerged as the strongest direct predictor ($\beta =0.417$), followed by perceived behaviour control ($\beta =0.340$). Environmental concern operated exclusively through indirect pathways, mediated by attitude and perceived behaviour control, and exhibited the largest effect size on the TPB components ($f^2$ = 0.380–0.449, large effect). Full article

To address the difficulty in predicting the migration trajectories of microplastics in aquatic environments, this study develops a hydrodynamically driven migration model applicable to multiple types of microplastics. Based on hydraulic experiments, hydrodynamic thresholds are established to characterize transitions among drifting, suspension, and sedimentation. The model integrates hydrodynamic forces, gravity, buoyancy, and interparticle interactions, enabling accurate simulation of migration pathways and ultimate destinations. Compared with conventional models, the key innovation lies in incorporating differences in size, shape, and material, allowing differentiated representation and prediction of diverse microplastics. The pollutant accumulation patterns obtained by simulating microplastic migration in the Mulanxi River basin using this model are consistent with actual observational results, further demonstrating the model’s reliability and applicability. Results from the Xianyou Section show that microplastics smaller than 0.5 mm account for 71.62%, dominated by fragmentary and fibrous types. There are significant differences in the migration behaviour of microplastics made from different materials; these differences are primarily attributable to variations in their density and physicochemical properties. Furthermore, transport rates at the downstream end are positively correlated with proximity to pollution sources and the abundance of lightweight microplastics. The total flux reaches 9.37 × 10¹¹ particles, with an overall transport rate of 68.34%. This study enhances the mechanistic understanding and predictive capability of microplastic transport in freshwater systems, providing new theoretical and methodological support for pollution control. Full article

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, microplastics, and wet-weather mixed-source processes when characteristics similar to event-driven transport, threshold exceedance, and adaptive control are identified. Drawing on a structured literature search of studies published from 2000 to December 2025, this narrative review synthesizes evidence from 138 selected references on how extreme rainfall, drought–rewetting, warming, and freeze–thaw processes alter source activation, hydrological connectivity, biogeochemical processing, and receiving-water hazards. Our resilience assessment is based on resistance, recovery, robustness, and persistence, which we interpret using exposure, sensitivity, and adaptive capacity. It is shown that standard average-load and fixed-baseline measurements may not detect short pollution pulses, cross-scenario failure, and long-term drift; operational measurement must thus involve event thresholds, recovery trajectories, tail-risk measures, and propagation of uncertainty. Extrapolation, interpretability, data demand, and applicability for data-sparse basins are used to compare process-based, data-driven, and hybrid models. Adaptation options are associated with measurable triggers as part of a monitoring–trigger–action cycle with location-specific instructions for monsoon-agricultural, cold-region, semi-arid and urban systems. The novel aspect of this framework is the integration of mechanism-based evidence, quantitative resilience indicators, model uncertainty, and adaptive governance into one decision-focused workflow. This sustainability-oriented framework advances long-term watershed management by linking water-quality protection and resilient development. Full article

This study numerically investigates how a finite, circular patch of emergent vegetation alters microplastic (MP) transport, concentration, and retention in open-channel flow. A validated numerical model was developed to represent the vegetation patch as a porous zone and simulate MP transport. The framework was validated against laboratory data for two configurations: a low-blockage case and a high-blockage case. After validation, 36 MP cases, comprising four polymer densities, three particle diameters ranging from 0.1 to 0.5 mm, and two categories of shape factors (elongated and spherical), were released upstream and tracked over 180–420 s. Results show that vegetation density, represented by the blockage parameter and solid volume fraction, primarily controls the interception of microplastics. Dense patches create persistent recirculation and low-velocity zones that increase residence time and trapping, whereas sparse patches induce only transient disturbances, allowing rapid downstream advection. Quantitatively, retention in the dense configuration was ≈62% for the smaller MP sizes (0.1–0.2 mm) versus ≈35% in the sparse configuration at 300 s. Polymer density, particle shape, and particle size had only secondary effects under the tested moderate flow conditions. Smaller microplastics and elongated particles showed slightly higher retention. The findings identify dense vegetation as a selective hydrodynamic filter, demonstrating that vegetation-induced flow restructuring is the dominant control on MP fate. These effects should be considered in river restoration and pollution mitigation strategies. Full article

Ozone (O₃), as a key secondary pollutant, exhibits pronounced spatiotemporal heterogeneity, posing significant challenges to coordinated regional air pollution control. However, systematic understanding of high-resolution O₃ spatial inversion and its driving mechanisms in arid urban agglomerations remains limited. In this study, the Tianshan North Slope Urban Agglomeration (TNSUA) was selected as the study area, and a multi-model comparative framework was established to comprehensively evaluate the O₃ inversion performance of 16 machine learning and deep learning models, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extremely Randomized Trees (ET), and Gradient Boosting Decision Tree (GBDT). Based on the optimal model performance, high-precision daily O₃ spatial reconstruction for the year 2023 was achieved across the study region. The contributions of individual driving factors and their nonlinear response relationships were quantitatively interpreted using Shapley Additive Explanations (SHAP). Furthermore, a backward trajectory model combined with the Weighted Potential Source Contribution Function (WPSCF) and Weighted Concentration Weighted Trajectory (WCWT) methods was employed to identify potential source regions and transport pathways of O₃. The results indicate that: (1) The XGBoost model exhibited the best performance (R² = 0.93, RPD > 3). The reconstructed results reveal that high O₃ concentrations in 2023 were primarily distributed in southern Urumqi, southern Changji, and southern Tacheng, with southern Urumqi identified as the most prominent hotspot. (2) The spatial variability of O₃ was predominantly driven by downward shortwave radiation (DSR) and air temperature (TEM), both of which showed significant nonlinear responses and threshold effects on O₃ formation. (3) Source apportionment analysis indicates that westerly transport serves as a major exogenous contribution pathway, with potential source regions mainly located in the surrounding areas of the northern Tianshan slope as well as Central Asia, particularly eastern Kazakhstan and northern Kyrgyzstan. This study systematically elucidates the formation mechanisms of O₃ pollution in arid urban agglomerations from three aspects—high-precision inversion, driving mechanism analysis, and cross-regional transport identification—thereby providing a scientific basis for precise air pollution control strategies. Full article

Climate change may intensify the deterioration of river water quality by altering streamflow regimes, precipitation patterns, and organic matter transport pathways. In this study, a Hydrological Simulation Program-FORTRAN (HSPF)-based streamflow and total organic carbon (TOC) water quality model for the Tamjin River Basin, Korea, was developed, and future TOC pollution was evaluated under quantile delta mapping (QDM) bias-corrected Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5) climate scenarios. Unlike previous studies that generally applied climate bias correction, watershed modeling, or pollutant-load assessment as separate procedures, this study links QDM-preserved climate change signals, process-based HSPF simulations, and TOC-specific discharge-load, delivered-load, exceedance-frequency, and load-reduction indicators within a single management framework. The model showed acceptable performance, with Nash–Sutcliffe efficiency (NSE) values of 0.67 and 0.68 for streamflow at Jangheung Dam and Gamcheon Bridge, respectively, and a TOC deviation of volume (DV) of 0.6% at Tamjin5. Under the SSP5-8.5 no-action scenario for the 2040s, the mean streamflow decreased by 33.1%, whereas the mean TOC concentration increased by 76.8% relative to the baseline. The number of days exceeding 4 mg/L TOC increased from 41 to 216 days yr⁻¹, and the Korean TOC-based water quality class deteriorated from Ib to III. In contrast, the 20% and 30% load reduction scenarios offset approximately 33.8% and 67.9% of the climate-driven increase in TOC, respectively, with the 30% reduction scenario showing greater effectiveness during low-flow seasons. Elevated TOC levels may have implications for downstream water treatment because organic matter can increase chemical demand and disinfection-byproduct formation potential. However, these treatment-related effects were not directly evaluated in this study. These results suggest that TOC should be considered as a complementary indicator to conventional biochemical oxygen demand (BOD)-based management when developing climate-resilient water-quality strategies for the Tamjin River Basin. Full article

Landfill leachate represents a long-term source of contamination that may significantly affect groundwater and receiving water bodies through the migration of organic, inorganic, and toxic pollutants. This study evaluated the long-term migration of landfill leachate and its potential environmental impacts using the LandSim Release 2 probabilistic software model applied to two municipal waste landfills in the Republic of Serbia: the regional sanitary landfill “Gigoš” in Jagodina and the sanitary landfill “Meteris” in Vranje. The modelling framework integrated laboratory leachate analyses, hydrogeological conditions, engineered barrier system characteristics, and receptor-oriented contaminant transport assessment. Model validation was performed through comparison of simulated and laboratory-measured concentrations. Two scenarios were analyzed for each site: an engineered sanitary landfill scenario with a functional containment system and a conservative barrier-failure scenario representing complete loss of engineered barrier functionality. Ten representative leachate parameters were included, covering nitrogen compounds, inorganic ions, toxic substances, and heavy metals/metalloids. The results showed that engineered protection systems significantly delay contaminant migration and reduce receptor concentrations, while barrier-failure conditions lead to earlier pollutant breakthrough and higher environmental risk. The simulations demonstrated that under the engineered sanitary landfill scenario, receptor concentrations of all analyzed contaminants remained below the corresponding maximum allowable concentrations, with contaminant migration occurring only after several centuries. In contrast, the conservative barrier-failure scenario resulted in substantially earlier contaminant breakthrough, with nitrogen compounds and phenols representing the greatest environmental concern due to their rapid migration and exceedance of regulatory thresholds, while the “Meteris” landfill generally exhibited higher receptor concentrations than the “Gigoš” landfill. These findings highlight the importance of predictive modelling and long-term monitoring for sustainable landfill management and groundwater protection. Full article