Search Results (63)

Accurate source identification is essential for the prevention and control of heavy metal(loid)s (HMs) contamination in farmland soils. Conventional source-apportionment approaches often rely on limited indicators and expert judgment, which can increase uncertainty in source interpretation. In this study, 800 topsoil samples collected from farmland in Ningxia, together with 24 environmental and anthropogenic variables, were used to develop element-specific machine learning models for Cd, Cr, Hg, Pb, and As. Five algorithms, including random forest (RF), Extra Trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and least absolute shrinkage and selection operator (LASSO-stacking), were compared, and Shapley additive explanations (SHAP) were used to interpret the variables statistically associated with the spatial variability of each metal. Positive matrix factorization (PMF) was further applied to identify potential source categories. Model performance varied among metals, indicating element-specific differences in predictability and controlling factors. SHAP analysis showed that precipitation, temperature, spatial coordinates (longitude/latitude), cropping intensity, and industrial-source fine particulate matter emissions were among the most important associated factors, although their relative importance differed across elements. PMF results suggested that 73.8% of Cd was associated with agricultural inputs, 87.6% of Hg with industrial atmospheric deposition, and 68.4% of Cr and 46.7% of As with natural sources, Pb showed relatively weak predictability, suggesting that its spatial variability may be influenced by unmeasured local or legacy inputs, while model-derived associations indicated possible links with spatial gradients, terrain-hydrological conditions, wind-related variables, and soil carrier properties. Overall, this study presents an integrated framework that combines machine learning-based associated-factor analysis with receptor-model source apportionment, providing a more nuanced understanding of HMs contamination in farmland soils and supporting targeted soil pollution prevention and control. 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

Shallow aquifers in intensively managed alluvial plains worldwide are increasingly impacted by inorganic nitrogen, yet the simultaneous occurrence and interconversion of nitrate (NO₃⁻–N), nitrite (NO₂⁻–N) and ammonium (NH₄⁺–N) often confound source attribution when single indicators are used. Here, we present a transferable, process-linked framework for diagnosing “tri-nitrogen” (tri-N) pollution that integrates hydrogeochemical evolution, data-driven pattern discovery and receptor-model apportionment. We analyzed 409 shallow-groundwater samples from Shijiazhuang City (central North China Plain) for major ions and tri-N species, interpreted within Piper facies and salinization gradients, and then applied a Gaussian mixture model (GMM) to resolve multivariate hydrochemical–nitrogen end-members. Six clusters (I–VI) depict an interpretable progression from background Ca–HCO₃/Ca·Mg–HCO₃ waters to agricultural NO₃⁻–N enrichment under oxic conditions and a distinct NH₄⁺–N-rich point-source end-member under reducing conditions. An attention-based attribution model indicates that total tri-N, Na⁺, NO₃⁻–N, the NO₂⁻ fraction and SO₄²⁻ are the primary discriminators of cluster structure. Species-resolved positive matrix factorization (US EPA PMF 5.0) quantifies dominant controls, with agricultural leaching–nitrification explaining most NO₃⁻–N (Factor 6, 87.9%) and sewage/manure inputs dominating NH₄⁺–N (Factor 3, 95.3%), while NO₂⁻–N reflects mixed contributions consistent with redox-interface transitions. Beyond this case study, the combined GMM–interpretability–PMF workflow provides a general template for separating non-point versus point tri-N inputs and for prioritizing management actions in shallow aquifers where isotope or tracer data are limited. Full article

Polymetallic mining severely disrupts farmland soil ecosystems, yet the vertical migration of heavy metals, interlayer pollution disparities between topsoil and deep soil, and quantitative source apportionment of composite pollutants remain poorly understood in mining–agricultural overlapping zones. Two core hypotheses were accordingly proposed: mining-derived heavy metals can migrate downward and accumulate in deep soil layers, and the coupling of geostatistical analysis and receptor modeling enables reliable differentiation between geogenic and anthropogenic pollution sources. To test these hypotheses, 512 topsoil and 148 deep soil samples were collected from the Fenghuang Mining Area for quantification of eight metals and metalloids (including As). Geostatistical approaches, the single pollution index (Pi), and Nemerow comprehensive pollution index (P_N) were utilized to characterize spatial heterogeneity and evaluate pollution severity, while a coupled PCA–PMF receptor model was adopted for quantitative source identification; vertical comparisons of element concentrations across soil profiles further validated the robustness of source apportionment outputs. The results revealed extensive heavy metal enrichment in both soil layers, with only topsoil Cd exceeding China’s risk screening value for agricultural land. Hg exhibited pronounced spatial variability and prominent anthropogenic fingerprints, and all target metals displayed consistent spatial distribution patterns along vertical soil profiles. Four distinct pollution sources were discriminated: geogenic sources dominating Cu, Zn, Cr, and Ni accumulation, mining-industrial emissions as the major contributor to Hg pollution, mixed industrial–agricultural inputs governing As and Pb enrichment, and traffic activities serving as the primary Cd source. Cd was identified as the priority pollutant threatening local farmland security. Confirmed downward percolation of anthropogenic metals creates persistent latent ecological risks across the study area, where mining and industrial discharges represent the dominant anthropogenic pollution inputs. This work systematically elucidates the geochemical signatures, vertical migration pathways, and quantitative source contributions of heavy metals in mining-disturbed farmlands, delivering solid scientific support for targeted source control, tiered risk management, and soil ecological remediation within the Fenghuang Mining Area. Moreover, the multi-method integrated analytical framework developed herein provides transferable guidance for heavy metal pollution mitigation in global polymetallic mining–agricultural regions with analogous geological and industrial backgrounds. Full article

The Beijing–Tianjin–Hebei (BTH) region faces complex air pollution driven by alternating particulate matter (PM) and ozone (O₃) dominance, regional transport, topography, and meteorology. This study develops a hybrid framework integrating air quality index (AQI) records, pollutants, meteorological variables, and MEIC emissions from the BTH region (2018–2025) to capture spatiotemporal evolution and short-term predictability. Results show a seasonal AQI cycle (winter/spring highs, summer/autumn lows) with a summer PM–O₃ seesaw. Spatially, three zones were identified: the northern and coastal ecological barrier zone, the central compound-pollution plain zone, and the southern heavy-industrial zone. Random Forest identifies PM as the dominant AQI compositional contributor, with visibility, dew point, humidity, and MEIC emissions (particulates, NH₃, organics) as key correlates. Forecast evaluation reveals progressive improvement: ARMA captures linear baselines (R² = 0.318, MAPE = 33.26%), XGBoost improves statistical prediction by incorporating nonlinear feature interactions and lagged meteorology (R² = 0.567, MAPE = 24.81%), and LSTM shows the strongest statistical predictive performance (R² = 0.613, MAPE = 22.32%). The improvement of LSTM over XGBoost is incremental and reflects enhanced data-driven representation of short-term AQI–meteorology temporal dependence, rather than identification of physical pollution mechanisms. Regional disparities persist, with higher predictability in the southern heavy-industrial zone and lower accuracy in the northern and coastal ecological barrier zone affected by intermittent dust intrusions and frontal passages. Overall, the results suggest that LSTM may support data-driven short-term AQI warning, but source-oriented mitigation still requires process-based tools, such as chemical-transport or source-apportionment models. Full article

Source apportionment and the elucidation of driving mechanisms are essential for targeted soil pollution management. This study investigated surface soils across six towns in southern Shimen County, northwestern Hunan Province, where 662 samples were collected to determine the concentrations of As, Cd, Cr, Cu, Ni, Pb, and Zn. Multivariate statistics and the APCS-MLR receptor model were integrated to quantify pollution sources, while three machine learning models (RF, XGBoost, and LightGBM) were applied to identify key drivers of the spatial enrichment of Cd. Results showed that Cd was significantly enriched, with a mean concentration of 0.43 mg/kg (3.41 times the provincial background value). The mean concentrations of As, Cr, Cu, Ni, Pb and Zn were 11.97 mg/kg, 81.01 mg/kg, 24.15 mg/kg, 49.25 mg/kg, 29.56 mg/kg and 76.77 mg/kg, respectively, and these PTEs remained at normal background levels. Significant inter-element correlations indicated common sources. Three primary sources were quantified—natural parent material (43.83%), mining activities (30.99%), and mixed sources of coal mining and agricultural inputs (7.84%), with 17.34% attributed to unidentified mixed sources. Natural sources dominated the geogenic enrichment of Cd, Cu, Ni, Pb, and Zn; mining activities governed the accumulation of As, Cr, Cu, and Pb; a mixed source of coal mining and agricultural practices contributed substantially to Cd enrichment. Machine learning identified PM10, topography, strata, and soil type as dominant drivers, with their total feature importance reaching 70.05%. Among these factors, natural factors and anthropogenic factors accounted for 44.23% and 55.77% of the total feature importance, in turn revealing coupled natural–anthropogenic controls. This study establishes an integrated framework linking source apportionment and driver identification, providing scientific insights for potentially toxic elements (PTEs) control in analogous mining–agricultural regions. Full article

Ship exhaust emissions have become an increasingly prominent global atmospheric environmental issue, triggering a series of ecological disturbances and adverse public health consequences. However, comprehensive analyses of the research progress and evolution trends in this field remain scarce. This study systematically retrieved 1346 scholarly publications in the ship exhaust emissions field for the period 2011–2025 from the Web of Science Core Collection and carried out a bibliometric analysis encompassing publication outputs, contributing countries/regions, and keyword characteristics. The findings reveal a sustained and robust growth trajectory in global research output, with annual publications increasing nearly fivefold over the 15-year study period. Notably, academic interest in this field has increased significantly since 2020 due to the implementation of the global sulfur cap regulation. Core thematic clusters (mean silhouette S = 0.7205) in this field include source apportionment, numerical modeling analysis, atmospheric criteria pollutants, and technological emission reduction strategies. The geographical distribution of research output shows a significant positive correlation with the importance of regional maritime economies. China, the United States, and Germany are the leading contributors in terms of publication outputs, while frequent research collaborations have been observed among European countries. Since 2021, the emergence of Automatic Identification System data as a keyword with high burst strength (intensity = 3.60) marks a paradigm shift toward a “big data-enabled refined management” framework. Concurrently, the sustained burst activity of keywords including nitrogen oxides, volatile organic compounds, and traffic-related emissions from 2023 to 2025 indicates rapidly growing scholarly attention to secondary aerosol precursors from shipping, and the critical need for coordinated multi-pollutant control strategies. Future research directions for ship exhaust emissions are expected to transition from fundamental characterization research to big data-driven monitoring and estimation methods, as well as advanced emission reduction technologies. The bibliometric insights derived from this study provide a valuable reference framework for subsequent in-depth studies on ship exhaust emissions. Full article

High-fluoride (F⁻) groundwater is a widespread environmental problem that poses significant risks to human health in many regions worldwide. Understanding the origin, circulation, and evolution of fluoride-rich groundwater is therefore essential for effective groundwater management and mitigation strategies. In recent years, stable isotope techniques have helped to address key gaps in understanding the hydrogeochemical processes governing F⁻ enrichment, particularly regarding the source identification and water-rock interaction mechanisms that remain poorly constrained. This study reviews the applications of hydrogen–oxygen, strontium–calcium, and lithium–boron isotopes in research on high-F⁻ groundwater systems. Hydrogen and oxygen isotopes (δ²H and δ¹⁸O) are widely used to identify groundwater recharge sources, mixing processes, and evaporative effects, thereby providing key constraints on the origin of fluoride-rich groundwater. Strontium and calcium isotopes (⁸⁷Sr/⁸⁶Sr and δ^44/40Ca) serve as effective tracers of water-rock interactions and associated hydrogeochemical processes, including mineral weathering and dissolution, cation exchange, and secondary mineral precipitation, which play critical roles in fluoride mobilization and enrichment. In addition, lithium, and boron isotopes (δ⁷Li and δ¹¹B) provide valuable insights into the influence of geothermal fluids and deep hydrothermal processes on fluoride accumulation in groundwater systems. Overall, the integrated application of these stable isotope systems offers a robust framework for elucidating the formation mechanisms and evolutionary pathways of high-F⁻ groundwater. Moving beyond qualitative source identification, future research should prioritize the development of Bayesian isotope mixing models that explicitly quantify uncertainty in fluoride source apportionment and utilize sensitivity analysis to test competing hydrogeochemical mechanisms. Full article

Water pollution control for wetland lakes has undergone a fluctuating development process. Effective pollution management requires not only scientific water quality monitoring data but also clear identification of pollution sources within the study area. Accordingly, this study investigated Mudong Lake, the core area of the Huixian Wetland, and conducted water quality monitoring in January 2023 (dry season) and June 2023 (wet season). Based on the Water Quality Index (WQI) assessment results, water quality was better in the wet season than in the dry season. To identify pollution sources, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was applied. The results showed that pollution in the dry season was mainly derived from aquaculture and agricultural non-point source pollution, anthropogenic point source pollution, and internal release from sediments, while pollution in the wet season exhibited mixed characteristics, driven by agricultural non-point sources, domestic sewage discharge, and natural factors. Source apportionment analysis indicated that composite pollution sources (domestic sewage and aquaculture wastewater), agricultural non-point source pollution, and other unidentified sources contributed 43.71%, 34.11%, and 22.18% of the total pollution load, respectively. The findings of this study can provide a scientific basis for pollution control, emission reduction, and the targeted management of Mudong Lake. Full article

Although prior research focused on Baghdad has identified variability in fine particulate matter concentrations (PM_2.5) and their origins, there remains uncertainty in the identification of the relative importance of local and long-range PM_2.5 sources. This study analysed hourly air pollutant concentrations and meteorological data from three monitoring sites over the year 2019 in Baghdad, namely Al-Wazeriya (WZ), Al-Andalus Square (AS), and Al-Saiydiya (SA) sites, to determine the nature of PM_2.5 sources. Multi-stage statistical models were utilised to address inherent data limitations and varying sampling dates caused by limitations on power supplies to monitoring equipment, thus improving the identification of urban particulate sources. Bivariate polar plots, concentration ratios, and conditional bivariate probability function (CBPF) plots were used to identify local sources of PM_2.5. Potential Source Contribution Function (PSCF) and concentration weighted trajectory (CWT) methods were employed for distant and regional source apportionment. Domestic diesel generators are suggested to be the primary local source of PM_2.5 pollutants in Baghdad’s WZ area (categorised as residential with significant traffic volumes). Gasoline- and diesel-fueled motor vehicles significantly contribute to PM_2.5 concentrations in the AS and SA areas, which are commercial areas with the latter having close proximity to motorway sources. Additional impacts result from gas flaring and thermal power plants in these regions. Long-range PM_2.5 transport may be attributed to the combustion of low-quality heavy fuel oils from several potential sources, including Nahrawan brick factories, oil fields, and Al-Musayyab thermal power plants, primarily towards the northeast, east, and southeast of Baghdad. Transboundary contributions to PM_2.5 concentrations in Baghdad were also identified, from industrial sources in western Iran and eastern Syria, as well as dust particulates, and oil and gas production from southwestern Iran’s Khuzestan Province, Kuwait, and the Arabian Gulf. Low to medium wind speeds (1–4 ms⁻¹) were linked with the highest source contributions, suggesting local emission sources to be the most significant contributors to high PM_2.5 at the studied sample locations. Full article

Heavy metal contamination in agricultural soils threatens ecosystem safety and sustainable land use, particularly in geologically sensitive areas. This study aimed to assess the pollution status, ecological risks and source contributions of eight heavy metals (Hg, Cd, Pb, As, Cr, Cu, Ni and Zn) in soils from a dry-hot agricultural region of central Yunnan, China. To improve source apportionment, this study applied and compared three models: APCS-MLR, PMF, and Random Forest. Analysis of 1790 soil samples showed mean concentrations (mg/kg) of 0.03 for Hg, 0.17 for Cd, 25.01 for Pb, 7.46 for As, 85.91 for Cr, 36.20 for Cu, 31.75 for Ni, and 69.24 for Zn. Pollution assessment indicated that Cu and Cd were the main pollutants, while ecological risk assessment identified Cd and Hg as the dominant ecological risk factors. Four major sources were identified: industrial hybrid sources, natural background, atmospheric deposition and agricultural activities, with industrial hybrid sources contributing the largest share. These results indicate that integrating APCS-MLR, PMF, and Random Forest provides a more reliable framework for source identification and supports targeted soil pollution control in regions affected by both natural and anthropogenic inputs. Full article

Identifying the specific sources of heavy metal accumulation in intensive agricultural landscapes is essential for ensuring soil sustainability and food security. In this study, we independently carried out a high-density regional geochemical survey and high-resolution field sampling in the Yangtze–Huaihe Watershed, Eastern China, and used the original sample dataset to distinguish between geogenic backgrounds and anthropogenic enrichments. By employing the APCS-MLR model, four distinct pollution sources were quantitatively identified: natural pedogenesis, agricultural activities, traffic emissions, and industrial inputs. Results demonstrated that while most heavy metal concentrations remained below national safety thresholds, Cd and Hg exhibited significant topsoil enrichment, signaling potential ecological risks. Source apportionment revealed that natural sources primarily controlled As, Cr, Ni, and Pb, with the contribution ranging from 41% to 70%. In contrast, traffic emissions (e.g., tire wear and fuel combustion) emerged as the dominant source for Cd (68%), Zn (55%), and Cu (34%), while industrial activities accounted for a substantial 89% of Hg accumulation via atmospheric deposition. Notably, despite the region’s intensive cultivation, agricultural practices played a surprisingly minor role in heavy metal accumulation. These findings highlight that the accumulations from traffic and industry now account for approximately 50% of the total heavy metal load in the region. Our results underscore the critical importance of high-resolution spatial data for precise source identification and suggest that implementing vegetative buffer zones and stricter industrial emission controls are imperative to mitigate further soil degradation in similar agricultural watersheds. Full article

Investigating multiple source apportionment methods and quantitatively characterizing heavy metal contamination in soils are of critical importance for effective pollution control and prevention. This study systematically investigates multiple source apportionment methods for soil heavy metals, with quantitative characterization of contamination features crucial for effective pollution control. Taking Jingxi City in Guangxi, China, as a case study, we conducted a comprehensive analysis of 8816 soil samples using multi-source big data integration. By synergistically applying machine learning algorithms, the potential ecological risk index, and bivariate local Moran’s index, we achieved dual objectives: quantitative inversion of eight heavy metal concentrations and simultaneous ecological risk assessment with pollution source identification. Through comparative model evaluation, the XGBoost algorithm demonstrated optimal predictive performance. Contribution analyses revealed that soil properties (Fe₂O₃, Al₂O₃, and phosphorus content), road distribution, and elevation significantly regulate heavy metal accumulation. Spatial risk mapping identified cadmium, mercury, and arsenic contamination hotspots as critical environmental threat zones. The bivariate local Moran’s index model elucidated spatial coupling characteristics between ecological risks and environmental drivers, providing spatially explicit decision-making support for precision environmental management. Our multidimensional analytical framework incorporates spatial visualization of heavy metal distribution, hierarchical ecological risk assessment, and pollution source contribution analysis, ultimately establishing a scientific decision-making system for land safety utilization and pollution risk management. This integrated approach offers methodological references for regional heavy metal pollution control in karst environments. Full article

Phosphorus enrichment remains a major driver of eutrophication in lake-feeding rivers, yet effective regulation is hindered by insufficient understanding of the spatiotemporal variability and dominant sources of total phosphorus (TP) at the basin scale. The Xiangjiang River, a major inflow to Dongting Lake, provides a representative system for examining TP dynamics in a human-impacted watershed. An interpretable association rule mining framework was applied to multi-source water quality, hydrological, agricultural, and socio-economic data (2020–2024) to characterize TP variation and quantify source contributions. TP concentrations exhibit pronounced seasonal and hydrological variability, with higher levels during spring and the flood season and lower levels during autumn and low-flow periods, together with a longitudinal increasing pattern from upstream to downstream. Quantitative source apportionment indicates that agricultural non-point sources dominate TP contributions at the basin scale, domestic sources provide a stable secondary contribution, and industrial sources exert localized influences. The spatial organization of source contributions closely corresponds to land-use patterns, with relatively consistent source structures among sites despite local heterogeneity. These results demonstrate the utility of an interpretable association rule mining framework for resolving TP source structures in heterogeneous river basins. The proposed framework offers a transferable approach for phosphorus source identification and supports basin-scale nutrient management and targeted control of agricultural non-point source pollution. Full article

Nitrate in Baiyangdian Lake is directly linked to the sustainability of watershed ecological functions, acting as a key priority for regional ecological protection. Subsequent to the completion of a series of ecological restoration projects, its sources have undergone inevitable shifts, rendering the original pollution control framework incompatible with the new context. Thus, accurate identification of nitrate sources and their seasonal variation characteristics constitutes a core prerequisite for enhancing the targeting of pollution management. This study integrated dual stable isotopes (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻) in water and potential source samples, along with hydrochemical data, and applied the Bayesian stable isotope mixing model (MixSIAR) to elucidate the sources of NO₃⁻ in Baiyangdian Lake. The results indicated that denitrification exerted a weak influence on the isotopic composition of NO₃⁻ in Baiyangdian Lake. Plots of the NO₃⁻/Cl⁻ versus Cl⁻ ratios for water samples and δ¹⁵N-NO₃⁻ versus δ¹⁸O-NO₃⁻ ratios for both water samples and potential sources confirmed anthropogenic sources as the primary nitrate contributors. The δ¹⁵N-NO₃⁻ vs. 1/[NO₃⁻] plot revealed that the number of NO₃⁻ sources exceeded two. The MixSIAR model demonstrated that wastewater treatment plant (WWTP) discharge was the dominant source throughout the four seasons, accounting for 49–62% with the highest contribution in winter and the lowest in summer. Soil nitrogen release contributed 19–32%, reaching its annual peak in summer. Sediment release accounted for 11–13%, maintaining a relatively low contribution across all seasons. Chemical fertilizer, manure, and sewage (M&S), and atmospheric deposition each contributed less than 6.5%, with negligible contributions. A significant reduction in the contributions of sediment release and M&S reflected the optimization effect of long-term regional ecological restoration efforts. WWTPs point source discharge and seasonal non-point source input from soil nitrogen collectively constituted the core sources of nitrate in Baiyangdian Lake. These findings provide crucial scientific support for the precise source apportionment and differentiated management of nitrate pollution in the basin. Full article