Environmental Geochemistry and Health最新文献

Population density, urbanization and industrialization are the rapidly increasing and it's causes severe damages to freshwater ecosystem. The present study aims to assess the quality of groundwater using integrated methods in the industrial zone of South India. The integrated assessment of groundwater using unsupervised machine learning method associated human health risk assessment are identified as research gap in the study region. We collected 55 samples based on groundwater availability, population density, and industrial activity in summer and winter seasons. The study found that calcium-chloride and mixed calcium-magnesium-chloride types of water are the dominating category in both seasons. The groundwater pollution index (GPI) and piper trilinear and gibbs diagram indicate that evaporation, water-rock interface, and other anthropogenic activities are the dominating factors in groundwater chemistry. Analysis using the entropy water quality index (EWQI) and human health risk estimation confirmed that nitrate is the key parameter affecting groundwater sustainability. Unsupervised machine learning techniques were applied to evaluate groundwater chemistry. The principal component analysis (PCA) results revealed that seasonal variation is influenced by mineral dissolution, rainwater recharge, and anthropogenic activities. The hierarchical cluster analysis (HCA) results shows that a tight cluster forms among pH, Na, K, NO3, and F, suggesting that processes such as agricultural inputs from fertilizer use, synthetic pesticides, and natural geochemical interactions control ion exchange. The k-means clustering yielded three clusters: low-salinity fresh water, moderately mineralized water undergoing geochemical alteration, and high-hardness water indicative of specific geogenic and anthropogenic influences in both seasons. Overall, the integrated assessment of groundwater revealed that the water-rock interface, evaporation, and synthetic fertilizer use in agricultural fields are significant factors controlling groundwater quality. The key findings of this study provides clear knowledge about the nature of groundwater, influencing factors and help to improve the water resources management strategies in investigation zone.

Groundwater is a critical water resource in arid and semi-arid coal-mining regions, where mining-induced disturbances can modify aquifer conditions and degrade water quality. In this paper, hydrochemical analysis, Self-Organizing Maps (SOM), correlation analysis, and the Objective Combined Weight Water Quality Index (OCWQI) were integrated to characterize groundwater hydrochemical features, assess groundwater quality in the Dahaize coal mine area, and identify the key factors controlling water quality. Based on the Piper trilinear diagram, two dominant hydrochemical facies were identified: Ca-HCO₃ and Na-Cl. Major-ion chemistry is mainly controlled by water-rock interaction, evaporation crystallization, cation exchange, and anthropogenic inputs. Groundwater quality in the Zhiluo Group is generally poorer than that in the other aquifers. SOM and correlation analyses suggest that Na⁺, SO₄^2- and F^- are among the key parameters influencing groundwater quality and support the rationality of the OCWQI weighting. Anthropogenic activities influence groundwater hydrochemistry: elevated SO₄^2- in deeper groundwater shows a correlation with mining-affected zones, whereas NO₃^- and NH₄⁺ in shallow groundwater show patterns consistent with inputs from agricultural fertilization and domestic wastewater. This study proposes a method for evaluating the rationality of indicator weights in water quality assessment, providing a reference for groundwater quality management in coal mining areas and offering insights into water quality evolution and pollution risks in arid regions.

The long-term legacy of a decommissioned lead smelter in Santo Amaro, Bahia (Brazil), has produced one of the most metal-contaminated urban areas worldwide. This study investigates the spatial distribution, geochemical partitioning, and potential mobility of trace metals across contrasting landscape units, namely hillslope and floodplain soils. A total of 120 soil samples were analyzed using portable X-ray fluorescence (XRF), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), and the BCR sequential extraction procedure. XRF results revealed extremely high concentrations of Pb (up to 24,962 mg kg^-1), Zn (up to 8572 mg kg^-1), and Cd (up to 454 mg kg^-1), with strong spatial heterogeneity related to distance from the former smelter and landscape position. Cadmium and Pb were predominantly associated with labile and reducible fractions, indicating high chemical lability (mobility factor) and sensitivity to environmental conditions. SEM-EDS observations revealed contrasting stabilization mechanisms of Pb, associated with carbonate phases in hillslope soils and co-precipitated with Fe oxyhydroxides in floodplain soils. In contrast, Cr, Ni, and Cu were mainly associated with the residual fraction, indicating dominant lithogenic control and limited mobility. Geostatistical modeling still showed strong spatial dependence for Pb, Zn, and Cd with decreasing concentrations away from the smelter. The results demonstrate that floodplain soils do not act as permanent sinks but as transitional environments, where metals mobilized from hillslopes are temporarily retained and subsequently transferred toward downstream compartments.

This study provides a comprehensive national-scale review of river pollution in India, synthesizing evidence from 145 peer-reviewed articles, reports, and policy documents published between 2000 and 2025. Unlike earlier works limited to individual rivers or pollutants, this review integrates findings from major basins in India to capture the full complexity of contamination sources, ecological and human health risks, monitoring frameworks, and management strategies. The results establish that untreated sewage is the largest contributor, while industrial effluents enriched with heavy metals, pesticides from agricultural runoff, microplastics and pharmaceuticals from solid waste and emerging contaminants, and geogenic inputs such as arsenic and fluoride collectively degrade river quality. The synthesis reveals severe outcomes including eutrophication, biodiversity loss, microplastic bioaccumulation, antibiotic resistance, and high incidences of cancer, kidney dysfunction, fluorosis, and gastrointestinal diseases in riparian populations. Methodological advances consolidated here demonstrate how conventional physicochemical monitoring is increasingly complemented by ICP-MS, HPLC, GC-MS, statistical indices (WQI, HPI, ERI, HI, HQ), multivariate analyses (PCA, FA, CA), GIS-based hotspot mapping, and AI/ML-driven predictive models, while transport simulations using SWAT, QUAL2K, WASP, and MIKE 11 enhance source identification and risk forecasting. Mitigation outputs emphasize hybrid strategies: strict source control through CETPs, STPs, and Zero Liquid Discharge, advanced treatment using AOPs, adsorption, and membrane systems, and ecosystem-based measures such as riverbank filtration, wetlands, and phycoremediation, integrated within national programs like Namami Gange. The outputs of this review lie in providing a unified evidence base, highlighting critical research and governance gaps, and framing a technically grounded, policy-relevant roadmap for sustainable river basin management aligned with SDG 6, SDG 11, SDG 13, and SDG 15.

Chromium (Cr) contamination originating from the tanning industry presents a significant threat to soil and groundwater. Traditional static risk assessment models often lack the capacity to account for the dynamic evolution of risks due to oversights in hydrogeological processes, changes in Cr speciation, and uncertainties in parameters. In this study, an integrated analytical framework combining contaminant transport models with Bayesian networks was proposed to investigate the dynamic health risks associated with Cr exposure at 10 tannery sites in China. Key parameters were derived from laboratory experiments and field studies, enabling analysis of the dynamic changes and health risks associated with Cr pollution. By integrating solute transport with chemical equilibrium models, the dynamic changes in Cr speciation were simulated. Additionally, a Bayesian network model incorporating 43 variables was used to address multi-parameter uncertainty. Results indicated that Type III sites (Inadequate landfills) exhibited high and most persistent carcinogenic risk of Cr exposure (CR = 1.0 × 10^-5 at 6000 days, 10 times above threshold), while Type II sites (legacy tanneries) had the highest short-term carcinogenic risk (CR = 1.0 × 10^-2 at day 100). Risk levels in Type I sites (modernized tanneries) were acceptable. Sensitivity analysis revealed that groundwater Cr(VI) concentration was the dominant driver of the human health risk, followed by groundwater flow velocity and hydraulic gradient. These findings highlight the importance of considering Cr speciation dynamics and key influencing parameters in dynamic risk management, emphasizing the need for process-based assessments to effectively manage Cr pollution at tannery sites.

Airborne particulate matter enriched with heavy metals constitutes a significant health threat across the developing world. To investigate the distribution, sources, and health risks of PM_2.5-bound heavy metals, ambient samples were collected in Baoding city from 2014 to 2022 and analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Results showed that the annual mean PM_2.5 concentration in Baoding exceeded the World Health Organization (WHO) Interim Target-1. PM_2.5 levels generally declined from 2014 to 2022, with seasonal variation following the order: winter > autumn > spring > summer. Heavy metal concentrations peaked in winter, significantly exceeding those in spring, autumn, and summer, the latter exhibiting the lowest metal loading. Despite long-term air pollution controls, the annual average concentration of carcinogenic Cr(VI) may still surpass the WHO limit. Five major PM_2.5 sources were identified: coal combustion, secondary aerosols, vehicle emissions, dust, and industrial emissions. Coal combustion was the dominant source before 2017, after which secondary aerosols became predominant. Secondary aerosols also constituted the primary source in summer. Vehicle emissions and secondary aerosols contributed more to PM_2.5 in summer than in other seasons, while dust contributions were more pronounced in spring. Monte Carlo simulations indicated that PM_2.5-bound heavy metals pose non-carcinogenic risks to different populations at varying probabilities, with children exhibiting higher non-carcinogenic risks than adults. Manganese contributed most to non-carcinogenic risk. For carcinogenic risk, heavy metals showed a low probability of significant carcinogenic risk (SCR) or a high probability of acceptable risk (ACR). Carcinogenic risk probability ranked as: adult males > adult females > children, suggesting adult males may face the highest carcinogenic risk from PM_2.5-bound heavy metals. Among the five assessed metals, carcinogenic risk probability decreased in the order: As > Cr(VI) > Cd > Co > Ni, with As and Cr(VI) as dominant contributors. Both carcinogenic and non-carcinogenic risks were higher in winter than in other seasons. This study demonstrates that while coordinated air pollution controls in the Beijing-Tianjin-Hebei region have achieved some outcomes, Mn, Cr, and As still present notable potential health risks, requiring urgent attention.

This study evaluated harmful elements and associated health risks in Moatize, Mozambique's surface and groundwater, aligning with SDGs 3 (good health) and 6 (clean water). During both wet and dry seasons, 30 water samples were collected. Ions (Na⁺, K⁺, Ca²⁺, Mg²⁺, SO₄^2-, HCO₃^-, NO₃^-, F^-, and Cl^-) were analyzed using Ion Chromatography (IC), while concentrations of PTEs (Ba, Co, Cr, Cu, Mn, Mo, Pb, Sr, V, U, and Zn) were determined in water, coal, and ash samples by ICP-MS. Results indicated that PTE levels in coal and coal ash exceeded global average trace element concentrations. Moatize's water chemistry is mainly influenced by natural geological processes, especially rock weathering. Most water samples showed Pb and Se levels above the WHO (2021). Guidelines for drinking-water quality (4th ed., incorporating the first and second addenda). World Health Organization and Mozambican Water Quality Limits (MWQL). In surface water, Pb ranged from 0.006 to 0.11 mg/L during the dry season and 0.003-0.11 mg/L during the wet season; groundwater levels ranged from 0.003 to 0.016 mg/L, with two samples exceeding the WHO/MWQL limit of 0.01 mg/L. Se was elevated only in dry season groundwater (0.017-0.022 mg/L), exceeding the MWQL of 0.01 mg/L. The Pollution Index (PI) ranged from low (0) to highly polluted (392.77) due to PTEs. The most common pollutants were Pb > Se > Mn > Cu in dry season surface water and Pb > Se > Cu > Mn in wet season surface water and groundwater. Health risk assessments indicated potential non-carcinogenic issues from oral exposure (HQoral > 1), especially from NO₃^-, Cu, and Se, with children being more vulnerable. Conversely, dermal exposure (HQdermal < 1) did not pose significant health risks for any group.

The Huixian Wetland faces severe contamination from heavy metals (HMs) (particularly Hg and Cd) from industrial, agricultural, and aquaculture activity; however, comprehensive source-oriented health-risk assessment studies integrating multiple analytical methods for this karst wetland ecosystem remain limited. This study employed a multi-model approach to investigate the sources of heavy-metal contamination and the associated health risks in sediments from the Guilin-Huixian Karst Wetland. The absolute principal component score-multiple linear regression (APCS-MLR) and predictor model factorization (PMF) models, were used for source apportionment whereas the health-risk assessment combined the United States Environmental Protection Agency (USEPA) framework with a Monte Carlo model to quantify parameter uncertainties and identify high-risk populations. We collected sediment samples from 18 sampling points (58 sediment samples) and the contents were analyzed for seven HMs (As, Hg, Cu, Cr, Ni, Pb, and Zn). Results revealed that the average As, Hg, Cu, Cr, Ni, Pb, and Zn concentrations were 2.10-, 1.54-, 1.47-, 1.92-, 1.94-, 2.22-, and 3.15-times higher, respectively, than the background values of the Guangxi soils, with pollution severity ranked as Zn > Pb > Cr > As > Ni > Hg > Cu. Source apportionment identified three primary pollution sources: (1) traffic-related emissions, contributing significantly to Ni (81.08%), Pb (63.07%), As (48.32%), and Cu (28.87%); (2) industrial activity, contributing to Hg (36.81%) and Cr (25.79%); and (3) Notably, Zn exhibited the highest enrichment level (3.15 times the background value), but source attribution differed markedly between models. APCS-MLR attributed 80.77% to unidentified mixed sources due to weak inter-element correlations (KMO = 0.514), whereas PMF identified traffic emissions as the primary source (Factor 2: 47.23%), better resolving the contribution from tire wear and brake pad abrasion. Health-risk assessment indicated non-carcinogenic risk (CR) probabilities of 2.8% for adults and 43.1% for children, whereas unacceptable CR probabilities were 0.03% for adults and 18.5% for children, with As identified as the primary carcinogenic substance. These findings highlight the importance of traffic emissions, agricultural non-point source pollution, enhanced industrial waste management, and regular monitoring with source-specific remediation of key pollutants, particularly As, to ensure regional ecological security and protect the health of residents.

Carbofuran (CBF) is a highly toxic carbamate pesticide that poses significant risks to environmental and human health, necessitating sensitive and reliable monitoring in environmental matrices. In this study, a N and S-doped nonacobalt octasulfide carbon (Co₉S₈@NSC) nanocomposite was synthesized via a one-pot high-temperature pyrolysis method and employed as a modifier for a glassy carbon electrode (GCE) to develop an efficient voltammetric sensor for CBF detection. The structural and morphological characteristics of the synthesized nanocomposite were systematically characterized using X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. The electrochemical properties were evaluated by cyclic voltammetry and linear sweep voltammetry. The Co₉S₈@NSC-modified GCE exhibited superior electrocatalytic activity toward CBF oxidation, as evidenced by an enhanced oxidation peak current and a reduced oxidation potential compared with bare and other modified electrodes. The sensor demonstrated a wide linear response range from 10 to 260 µM with an ultralow detection limit of 0.003 µM. Furthermore, the fabricated sensor showed excellent selectivity in the presence of inorganic ions, biological molecules, and other pesticide-related compounds as potential interferents, while maintaining a good reproducibility and storage stability. The practical applicability of the proposed sensor was validated through the successful determination of CBF in wastewater, soil, and orange peel extract samples, yielding satisfactory recoveries in the range of 97.4-99.9%. These findings demonstrate that the Co₉S₈@NSC-based electrochemical sensor is a promising platform for environmental monitoring of CBF contamination in complex real matrices.