Search Results (1,137)

In this study, global spatial autocorrelation, local spatial autocorrelation, Spearman correlation analysis, gray correlation analysis, entropy weight method, and the gravity model were used to analyze the spatiotemporal variation and environment-urban-economy-associated factors of air quality of 31 provinces in China during 2015~2022. From 2015 to 2022, the Air Quality Index (AQI) exhibited a downward trend in 30 out of 31 Chinese provinces, with the exception of Shaanxi Province. Concurrently, the annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO declined across the study period. High-high clusters and low-high outliers were observed in northern China, whereas low-low clusters and high-low outliers were distributed in southern China. Twelve provinces (38.7%) showed positive correlation (0.095~0.95), 18 provinces (58.1%) showed negative correlation (−0.76~0.095), and only Anhui showed no correlation between AQI and O₃. The comprehensive AQI quality presented a dual-core model in Sichuan (in the southwest) and Henan (in the central part) of China, while the comprehensive AQI improvement rate presented a single-core model in Jiangsu in the east of China. The gravity models incorporating AQI and GDP revealed that both air quality and economic performance improved over the study period. The spatial pattern of pollution evolved from a multi-core structure to a non-core structure, whereas the pattern of economic growth transitioned from a non-core structure to a dual-core structure, with the Beijing-Tianjin-Hebei region and the Yangtze River Delta emerging as the primary urban agglomerations. Full article

Groundwater resources are critical in sustaining rapidly growing coastal urban regions like Dili City, Timor-Leste, where aquifers are prone to contamination. To inform groundwater pollution prevention and control in the Quaternary intergranular aquifer, a GIS-based groundwater vulnerability assessment was carried out using DRASTIC, modified DRASTIC, and modified DRASTIC–AHP methodologies. It confirmed that the central to northern urban area was the most vulnerable, while the southern part was the least vulnerable to contamination. Model performance was validated by correlating vulnerability indices with measured groundwater quality parameters, showing that the modified DRASTIC–AHP was the most accurate. The areas classified as having very low, low, moderate, high and very high vulnerability were 23.1%, 23.1%, 20.6%, 12.8%, and 19.2%, respectively, with high vulnerability along the northern coastline and Comoro River alluvial channel. Sensitivity analysis supports model robustness and identifies recharge, aquifer media, and hydraulic conductivity as the dominant controlling factors. The integrated modeling and sensitivity framework provides an efficient basis for prioritizing protection measures and infrastructure upgrades (e.g., sewerage) to reduce contamination risks. A key management implication is that centralized wastewater management is preferable to current practices for mitigating ongoing groundwater degradation. Full article

Research on the urban circular economy (CE) in developing regions often overlooks cross-sectoral interactions, social dimensions, data uncertainty, circularity metrics, and nonlinear trends, underscoring the need for integrated adaptive assessment. To address these gaps, we propose an integrated framework combining a nonlinear autoregressive with exogenous inputs (NARX) neural network and a fuzzy dynamic network slack-based measure (DNSBM) model to evaluate and improve urban CE performance across economic, environmental, and social dimensions in 107 cities of the Yangtze River Economic Belt (YREB) from 2011 to 2023. The results show a steady increase in aggregate efficiency and robustness across α-cut levels, alongside marked regional and stage heterogeneity. Downstream cities perform better because of more effective resource coordination, whereas upstream cities show greater potential for improvement. The main constraint is the social health dimension, reflecting persistent underinvestment in public health. ANN-based slack adjustment enhances efficiency estimation accuracy. Most cities need to reduce redundant inputs, curb pollution emissions, and increase health investment. This study contributes a closed-loop, multidimensional framework that captures temporal dynamics, data uncertainty, and cross-sectoral feedback and supports performance optimization and region-specific sustainability pathways. Full article

This study aims to explore the effects of micro-polluted river water on nitrogen and microbial communities of paddy field soil under different irrigation modes. The experiment was conducted in a water-saving park in Nanjing. By establishing three water quality conditions—clean water, micro-polluted river water, and alternating irrigation—and two moisture conditions—flood irrigation and controlled irrigation—this study investigates the effects of different irrigation patterns on soil nitrogen and microbial communities. The results indicate that, under flood irrigation, the input of micro-polluted river water can effectively alleviate NH₄⁺-N loss during the heading stages of rice growth by 49.3%. Moisture conditions are the primary factor influencing microbial community structure. Although the input of micro-polluted river water reduces community stability, rotation irrigation can increase microbial abundance and enhance network complexity, thereby enhancing the system’s resilience. Redundancy analysis shows that soil moisture, pH, and ion content are the key environmental factors driving microbial distribution. The clean and polluted water rotation irrigation model performs best in maintaining soil nitrogen and microbial health. Rotation irrigation promotes the enrichment of key functional groups, such as Actinobacteria, effectively increasing rice yield. This study provides a theoretical basis for promoting sustainable agricultural production through water resource management. Full article

As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for the operation and management of such systems. In this study, field experiments were carried out in the Qianhai–Nanshan Deep Tunnel to investigate complex water quality behavior, leading to the development of chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N) models that incorporate temporal variation, temperature, and burial depth. Results indicate that temperature is the dominant factor influencing water quality in deep tunnel storage. Increased ground temperature promotes the degradation and mass transport of pollutants within the tunnel system. Owing to temperature–depth effects, the deeply buried Qianhai tunnel significantly reduces river discharge pollution after water storage, with COD and NH₃–N removal rates reaching 74.9% and 26.8%, respectively. Temperature-controlled experiments showed that COD and NH₃–N reduction rates varied between 60–94% and 10–30% across a temperature range of 20–34 °C. The proposed model was validated against experimental data, achieving Nash–Sutcliffe efficiency coefficients of 0.7–0.8. This study provides a methodological foundation for simulating complex aquatic environments and offers a decision-support tool for optimizing the operational strategies of deep tunnel systems. However, the model’s current generalization capability is constrained by the limited experimental conditions (20–34 °C, 12 days) and the lack of experimental replicates, which should be systematically addressed in future studies. Full article

Watershed Ecological Compensation (WEC) is a vital tool for water environmental governance, yet existing research often focuses on either upstream or downstream regions in isolation, lacking a systematic assessment of basin-wide aggregate effects. Taking China’s Xin’an River Basin as a case study, this paper investigates the impacts of cross-provincial WEC on pollutant emissions, economic performance, and green economic efficiency. Theoretical analysis based on a social welfare maximization framework indicates that WEC helps reduce emissions and enhance green economic efficiency, though its impact on economic output is conditional. Using the Synthetic Control Method (SCM) for empirical analysis, the results show that the policy significantly reduced industrial COD emissions by an average of 111 t/10⁸ m³ per year and notably improved green economic efficiency, with industrial COD emissions per unit of GDP decreasing by 3.5 t per 100 million RMB annually. However, no significant impact on overall basin-wide economic development was observed. Robustness tests using Synthetic Difference-in-Differences (SDID) and staggered DID models further confirm the reliability of these findings. This study provides theoretical and empirical support for the policy effectiveness of WEC in pollution control and green development. Full article

Characterizing surface runoff and the transport process of non-point source pollutants (NSPs) carried by this runoff is crucial for identifying key source areas, estimating pollution loads entering water bodies, and implementing pollution control, which is particularly important in regions dominated by smallholder farming in China. Currently, most of the commonly used NSP models originated from international countries and have shortcomings such as high data requirements, high generalization degrees, and requiring the calibration of numerous parameters in the application process. Therefore, a distributed non-point source pollution model based on the time-varying gain and stormwater runoff response was constructed, designed for application at the watershed scale. This study describes the construction of the model, introducing its principles and structure through three key modules: a rainfall–runoff module, a soil erosion module, and a pollutant migration and transformation module. The proposed model was used to simulate the rainfall–runoff, soil erosion, and nutrient migration and transformation processes at different spatiotemporal scales. Although it achieved the best performance at the monthly and annual scales, its simulation results at the daily and hourly scales still met the relevant requirements, with relative errors within 20% and Nash–Sutcliffe Efficiency (NSE) coefficients of approximately 0.7. The annual sediment delivery ratios for the Yangliu Small Watershed and the basin above the Ankang section in 2022 were determined to be 0.445 and 0.36, respectively. The pollutant processes corresponding to different runoff events in the Yangliu Small Watershed were simulated, and the average NSE for total nitrogen (TN), ammonia nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), total phosphorus (TP), and soluble reactive phosphorus (SRP) were determined to be 0.69, 0.74, 0.79, 0.71, and 0.71, respectively. For the basin above the Ankang section, the NSE coefficients for the simulation of NH₃-N and TP pollutant processes were 0.78 and 0.83, respectively. The model demonstrated robust applicability across various spatial (ranging from small to large watersheds) and temporal (hourly−daily−monthly−annual) scales, and exhibited stability across different basins in a semi-humid region of China. The model is characterized by a parsimonious parameter set, ease of calibration, and strong spatiotemporal versatility, thus providing an efficient and reliable tool for non-point source pollution simulation. Full article

China’s stringent clean air policies have substantially reduced nitrogen oxides (NO_x) emissions, leading to a general decline in nitrogen dioxide (NO₂). However, surface ozone (O₃) pollution remains severe, creating a complex challenge due to the non-linear relationship between O₃ and its precursors. To disentangle the drivers behind these trends, this study quantifies the impacts of interannual variations in top-down constrained NO_x emissions on surface NO₂ and O₃ concentrations from 2014 to 2021 across mainland China and five national urban agglomerations. We employed the WRF-CMAQ model with a fixed-emission simulation approach, using an observationally optimized NO_x emission inventory derived from the assimilation of surface NO₂ measurements. Results reveal that NO₂ reductions were predominantly emission-driven (>80% post-2017), with declines most pronounced in winter. A strong linear consistency was found between interannual changes in top-down NO_x emissions and attributed NO₂ concentration variations, validating the methodology. In contrast, O₃ responses to NO_x reductions were spatially and seasonally heterogeneous, reflecting a non-linear photochemical regime. In major urban agglomerations (e.g., Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD)), NO_x reductions post-2018 showed limited effectiveness in mitigating summertime O₃ and even increased O₃ in spring and autumn, indicating a prevalent VOC-sensitive regime where NO_x reduction can disinhibit O₃ formation. Conversely, certain provinces (e.g., Anhui, Shanxi, Jilin) exhibited O₃ decreases, suggesting a NO_x-sensitive regime. The area benefiting from NO_x reductions expanded steadily in summer after 2017 but not in other seasons. This study confirms the efficacy of NO_x-focused policies for reducing primary NO₂ pollution but highlights that mitigating persistent O₃ requires a strategic shift to synergistic, region-specific control of volatile organic compounds alongside NO_x, informed by local chemical sensitivity. Full article

Large-scale hydropower development provides substantial socio-economic and energy benefits but simultaneously introduces complex ecological and environmental challenges that require comprehensive scientific assessment. This study systematically evaluates the effects of the leading reservoir (Longpan hydropower station, referring to the uppermost and principal flow-regulating dam in the cascade) in the middle reaches of the Jinsha River’s operation on the water environment of the mainstream Yangtze River, China, with the aim of clarifying its water quality responses and supporting evidence-based basin management. Based on an analysis of the current water quality conditions of the Yangtze River and a comparative review of the operational experience of the Three Gorges Reservoir, this research explores the mechanisms through which large reservoirs alter hydrological and ecological processes. These mechanisms include reduced flow velocity, prolonged water residence time, weakened pollutant dispersion, and increased risk of algal blooms in tributaries. To quantitatively assess these impacts, an improved river dilution–mixing model was developed and applied to simulate the water quality response during the dry season (February–April) under different discharge scenarios. Key downstream monitoring sections were examined. The modeling results indicate that the operation of the Leading reservoir can moderately reduce dry-season concentrations of key pollutants (e.g., total phosphorus, permanganate index) at downstream sections by approximately 2–5% on average, with spatially heterogeneous effects. Although the overall improvement magnitude remains limited, the combined effects of sediment deposition and in situ degradation may yield more pronounced real-world benefits. The findings underscore the importance of optimizing the regulatory function of the Longpan Reservoir through coordinated operation within the cascade reservoir system. It is recommended to integrate water resource allocation, water quality management, and aquatic ecosystem protection, alongside enhanced pollution control and ecological restoration in key zones. The methodology and findings provide a referenced framework for assessing the water-environmental implications of large-scale reservoir regulation in other major river systems. Full article

Microplastics (MP) are transported through rivers, acting as major conduits to oceans, yet standard transport models often fail to capture polymer-specific dynamics like settling and removal. This study proposes two novel analytical frameworks to address this: a modified Advection–Dispersion Equation (ADE) incorporating first-order sinking and removal, and a multi-phase model accounting for hydrodynamic–particle coupling. We derived exact closed-form solutions for a finite pulse input and validated the baseline model against established results. Our results demonstrate that the conventional ADE significantly overestimates peak MP concentrations, while the modified ADE reveals a “stretching” effect that extends the duration of ecosystem exposure. Our analysis indicates that sinking is the primary driver of mass loss to sediments, with higher sinking rates reducing aqueous concentrations by approximately 50% compared to non-settling scenarios. However, removal employs negligible influence during the initial pulse phase but shows cumulative impact over long transport distances. The study highlights the critical need to incorporate sediment accumulation terms into risk assessments, as ignoring sinking leads to underestimating benthic pollution and overestimating marine flux. Additionally, the multi-phase formulation provides a theoretical basis for modeling dense plastic spills where particles alter flow momentum. Full article

Spatial mapping of PM_2.5 in complex urban and suburban terrains remains challenging for classical geostatistical interpolation. This study evaluates a Random Forest (RF) framework for high-resolution air pollution mapping and compares its performance with ordinary kriging in the Kraków region. The analysis integrates measurements from 51 low-cost air quality sensors with topographic and meteorological predictors, including elevation, temperature, relative humidity, and wind speed. Five representative hours during a relatively windless, inversion dominated day were selected to examine hourly variability in pollution patterns. Model robustness was assessed using leave-one-out (LOO) cross-validation, while interpretability was addressed through permutation-based predictor importance analysis. The RF model achieved high predictive accuracy (R² = 0.85 to 0.95) and good spatial stability with an LOO standard error below 5%. Elevation consistently emerged as the dominant predictor, confirming the key role of terrain-controlled accumulation, while temperature and humidity gained importance during evening and nighttime hours. The RF approach captured fine-scale transport features along river valleys that were not resolved by ordinary kriging, which produced smoother but less interpretable surfaces. The results demonstrate that RF mapping provides an accurate and explainable support to traditional geostatistical methods for analyzing urban air pollution dynamics in complex terrain. Full article

Agricultural rivers are often silent receivers of microplastics (MPs) from diffuse, non-point sources; however, their pollution dynamics during rainfall events remain poorly understood. In this study, MP transport was investigated at three sampling points in an agricultural river catchment, where mulching films are used, and sewage sludge is not applied. Sampling was conducted in the Umeda River and its tributaries during six sampling events. MP flux exhibited a strong positive correlation with river discharge (L–Q relationship; n = 1.49–1.61, R² = 0.67–0.87). The L–Q model indicates that a tenfold increase in discharge results in approximately a 600-fold increase in MP flux and a 1000-fold increase in total suspended solid flux. MP abundance during rainfall was up to four times higher than that during baseflow, ranging from 73 ± 64 to 200 ± 111 particles/m³, while peak flux reached 6736 particles/s, with an MP mass of 811 mg/s. Regarding particle characteristics, rainfall enhanced the heterogeneity of MPs, although fragments and polyethylene/polypropylene polymers remained consistently dominant across all hydrological stages. First-flush behavior was observed at HU, with more than half of the total MP mass exported within the initial 50% of the event flow volume. These findings help to inform mitigation strategies that should prioritize a reduction in upstream plastic inputs in order to effectively manage MP transport in agricultural rivers. Full article

With rapid industrialization and urbanization, water pollution in urban rivers has become increasingly severe, posing significant threats to regional ecological environments and water security. The Puning section of the Lianjiang River in Guangdong Province, China, suffers from complex pollution originating from multiple sources, including domestic sewage, industrial wastewater, and agricultural non-point source pollution. This underscores an urgent need for integrated river pollution control in the region. In this study, a coupled hydrodynamic-water quality model was established to systematically analyze and simulate water quality conditions in the Puning section. A total of 22 pollution control scenarios were proposed and evaluated. The results indicated that most monitoring sections in the Puning segment failed to meet the Class V surface water quality standards, with notably high concentrations of chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), and total phosphorus (TP). Numerical simulations revealed that the multi-source interception scenario—simultaneously intercepting inflows at Tiande Bridge (Baikeng Lake), Dayangmei (Liusha Zhonghe), and Liudoupu (Liusha Zhonghe)—performed best in reducing pollutant concentrations. Specifically, this scenario achieved reductions exceeding 90% in NH₃-N and TP concentrations. Furthermore, the study demonstrates that for basins with complex pollution sources, integrated management strategies—including the construction of wastewater treatment facilities, control of point and non-point sources, and ecological restoration measures—can effectively improve water quality and optimize the water environmental capacity. These findings provide theoretical and technical support for water environment management in the Lianjiang River Basin and offer valuable insights for water quality management in similar regions. Full article

Against the backdrop of the ongoing advancement of China’s dual-carbon goals and the coordinated strategy for ecological protection and high-quality development in the Yellow River Basin (YRB), it is important to clarify the spatiotemporal dynamics of air pollution in the densely populated urban agglomerations of the mid–lower YRB. Using station-based daily observations from 2015 to 2024, this study examines six major air pollutants (PM_2.5, PM₁₀, CO, NO₂, O₃ and SO₂) across the Shandong Peninsula, Central Plains, and Guanzhong Plain urban agglomerations. Sen’s slope estimator and the Mann–Kendall test are applied to quantify long-term trends, while partial correlation analysis and the GeoDetector model are used to diagnose pollutant co-variations and the drivers of spatial heterogeneity. Results indicate that while PM_2.5, PM₁₀, NO₂, SO₂, and CO concentrations significantly decreased, O₃ exhibited a statistically significant upward trend (Z = 2.32, p = 0.02), particularly with pronounced summer maxima. PM_2.5 shows clear seasonal variation, with elevated levels during winter and reduced levels during summer. Marked spatial contrasts are also observed: elevated particulate matter and CO are concentrated in the northern part of the Central Plains, while higher O₃ levels are more evident in coastal areas, particularly within the Shandong Peninsula urban agglomeration. In terms of inter-pollutant relationships, particulate matter and CO are positively associated with SO₂, whereas O₃ is negatively correlated with NO₂. GeoDetector results further suggest that air temperature, wind speed, and topography are the key factors associated with the spatial differentiation of pollutant levels; notably, the interaction between wind speed and temperature provides the greatest explanatory power, with effects that vary seasonally. These findings provide a scientific basis for region-specific air-pollution control and for advancing the co-benefits of carbon reduction and pollution mitigation in the YRB. Full article

The coupling of physical transport and phase-transfer processes represents a fundamental mechanism governing metal cycling in estuarine systems under tidal oscillations. Taking Quanzhou Bay as a model system, we conducted continuous observations and sample collection at the river channel (Q1), the turbidity maximum zone (Q2), and the outer bay channel (Q3). The metals (Al, Ti, Ba, Cu, Mn, and Zn) were measured by ICP-MS to systematically investigate the distribution, transport, and inter-media transfer across multiple water layers under varying estuarine processes. Our findings demonstrate that particulate metal concentrations in Quanzhou Bay exhibit strong synchrony with suspended sediment concentrations (SSC) over tidal cycles, displaying a distinct sediment-following pattern controlled by alternating end members. Particulate metal fluxes during flood and ebb-tides generally followed the hierarchy Q1 > Q2 >> Q3. Notably, stations Q1 and Q2 were dominated by flood-tide fluxes with net transport directed landward, whereas Q3 was characterized by ebb tide dominance with net flux directed seaward—revealing a spatial division of labor between “inner bay retention/reallocation” and “outer bay channel export”. In contrast, dissolved metals exhibited marked element-specific responses to tidal forcing: Al and Ti increased during flood tides at stations Q1 and Q2, while Ba and Cu showed opposite trends, and Mn and Zn displayed more conservative behavior. Concurrently, solid/liquid partition coefficient (logKd) values for Al, Ti and Ba, Cu exhibited inverse patterns over tidal cycles, suggesting divergent adsorption–desorption regulation under identical hydrodynamic conditions that drives differential phase-transfer dynamics. These disparities likely reflect intrinsic chemical properties and source variations among the elements. This study elucidates, at the tidal timescale, the coupled processes of “alternating end-member control—estuarine filter modulation—concurrent channelized export and inner bay retention” in Quanzhou Bay, providing critical process-level insights for metal flux quantification and bay pollution remediation initiatives in an ecological restoration project. Full article