Environmental Geochemistry and Health最新文献

The assessment of soil and nutrient erosion along the scattered Brahmaputra River in the Jorhat district of Assam has been attempted utilizing field and laboratory assessments, satellite remote sensing techniques, and the Revised Morgan-Morgan-Finney (RMMF) soil erosion model. River portions were mapped based on visually interpreted and geo-coded false colour composites (FCC) of Landsat-9 OLI data and Survey of India toposheets (1:50,000). Soil samples were collected from 62 different sites within a region of 86.78 km² on the banks of the Jorhat River. The main findings revealed that approximately 55.24% of the study region was inundated at least once during the flood period from May to July 2023. The study area showed total sand content between 26% and 84%, silt 5%-48%, clay 6%-36% and organic matter content 0.20%-2.08%. Erodibility indices like Clay Ratio (mean 4.91), Modified Clay Ratio (mean 4.67), Dispersion ratio (mean 0.19), Erosion Ratio (mean 0.11) and Erosion Index (mean 0.20) indicated that most soils in the study area were susceptible to erosion. Using the Revised Morgan-Morgan-Finney model, annual soil loss was estimated, with runoff transport capacity ranging from 5.16 to 164.68 t ha⁻¹ (mean: 37.27 t ha⁻¹), and total annual detachment ranging from 11.56 to 129.14 t ha⁻¹ (mean: 77.59 t ha⁻¹) and transport capacity was considered as estimated soil loss as it was lower than total detachment. Pearson's correlation matrix shows that aggregate stability had negative association with sand fraction and positive associations (p <  = 0.05) with clay and organic matter. Also, hydraulic conductivity (HC), water holding capacity (WHC) and available water (AW) demonstrated negative (p <  = 0.05) associations with soil loss. The total fertile soil loss was estimated as 322,668 tons annually, with annual losses of organic carbon, total nitrogen, total phosphorus, and total potassium estimated at 1,444.32 tons, 23.52 tons, 227.15 tons, and 4,710.64 tons, respectively. Change detection (from 1986 to 2022) was estimated based on various land use patterns, where riverine sand, built-up land, cropland showed percent increase as 61.16%, 52.75% and 14.38% respectively. But, negative percent change of 73.37%, 12.72% and 32.65% were showed against Land use/land cover (LULC) classes of barren land, river/waterbodies and grassland respectively. Hence, expanding cropland and bare sand on coarse-textured, low organic matter soil drove the extreme soil and nutrient erosion in the study area. This study underscores the potentiality of combining the Revised MMF model with geospatial analysis to effectively identify and quantify soil erosion in the study area.

Heavy metals (HMs) are mostly toxic to all forms of life and are tenacious environmental pollutants. Rapid industrialization, urban development, and unsustainable agricultural implications lead to their accumulation in soil and water ecosystems, promoting serious ecological and health risks. In course of time, the growing global population, demand for food, water, energy, and technology have increased heavy metal-contaminated wastewater discharges. Conventional physicochemical treatment approaches, although widely applied, often suffer from limitations such as incomplete metal removal, significant energy requirement and cost effectiveness. Microorganisms such as bacteria, algae, and fungi demonstrate great efficiency in HM detoxification and degradation by virtue of their natural biological properties. These microorganisms have the capability to reduce toxic metal ions in their surroundings to non-toxic or fixed forms. The current review aims to critically assess the efficiency of individual bioremediation processes in microorganisms like bacteria, fungi, algae, and their combinatorial states like bacteria-algae, algae-fungi, and algae-algae consortia. A great deal of emphasis has been given to elucidate the mechanistic insights specific to consortia particularly mutualistic carbon/nutrient exchange, bioprecipitation, EPS-mediated capture, enzymatic transformation, adsorption/chelation and synergistic indirect effects. Moreover, the importance of interspecies interactions through metabolite transfer and signaling has been underlined in terms of system stability and remediation ability. Future research direction includes reactor-scale integration studies, modeling efforts, and the use of artificial intelligence/machine learning tools. Thus, engineered bacterial communities based on omics analysis can provide a basis to improve bioremediation strategies in terms of efficiency, stability, and sustainability for the remediation of HMs in wastewaters.

This study investigated the pollution level and assessed potential human health risk of trace elements in soil from Apo Mechanic Village, Gudu Market, and Goza Municipal dumpsite in Abuja, Nigeria where primitive recycling and recovery of valuable materials, open burning, dismantling, and dumping of wastes are being carried out. A total of 56 soil samples were collected from the three study sites and samples from their corresponding control sites, and analyzed for Zn, Cd, Cu, Ni, Pb, and Cr. The result showed that the element concentrations at the three study sites were significantly higher (P < 0.05) than their respective control sites, indicating the influence of waste recycling, dismantling, burning, and dumping activities at the sites. The average pollution load index (PLI) at the sites ranged from 5.80 to 37.09 indicating that the sites are highly polluted, with Cd and Pb being the highest contributors. Human health risk assessment revealed that there is a potential non-carcinogenic risk of Pb in adults and children through ingestion across the three study sites, and Pb in adults via dermal contact across the sites. There is also a non-carcinogenic risk of Cd and Ni through ingestion in children at the Apo site. Extremely high carcinogenic risk of Cr was found for both adults and children at all the three study sites, and carcinogenic risk of Cd in children at Apo and Goza sites and Pb in children at Goza site. This calls for an urgent need to enforce environmental regulations and prevention and monitoring of the crude and primitive waste dumping, dismantling, and burning activities at these sites as the investigated elements, particularly Pb, Cd, and Cr posed non-cancer and cancer health risks to workers and nearby residents.

Accurate source apportionment is essential for identifying major contributors to heavy-metal pollution and guiding effective water management. However, most receptor-model studies overlook the spatial dimension of pollution sources. To address this gap, this study integrates a GIS-based Urban-Rural Gradient Index (URGI) with Positive Matrix Factorization (PMF) to provide spatially explicit source interpretation. The URGI, derived from land-use and population rasters, quantified the degree of urbanization and human activity intensity and was coupled with the PMF contribution matrix to distinguish urban- and rural-related sources.The framework was applied to the Nanchang section of the Fu River during both wet and dry periods, analyzing nine heavy metals (As, V, Mn, Fe, Zn, Sr, Ba, Cd, and Cr). Hotspots of V, Mn, Sr, and Ba were concentrated in highly urbanized areas, may not directly due to local emissions but because these elements share common occurrence patterns and broad migration pathways that spatially intersect with urban regions. In contrast, Fe, Zn, and As showed weak or negative responses to the URGI, with rural hotspots mainly driven by natural background inputs and hydrological mobilization rather than anthropogenic activities. PMF resolved five major sources-natural sources, urban-industrial sources, rural-composite sources, atmospheric deposition sources, and traffic sources-with contributions of 64.02%, 16.82%, 0.18%, 15.63%, and 3.35% in the dry period, and 42.97%, 27.34%, 3.76%, 14.41%, and 11.51% in the wet period. Natural sources dominated in both seasons, while urban-industrial and traffic sources increased during the wet period.Coupling URGI with PMF strengthened the spatial interpretability of source factors, reduced subjectivity in factor identification, and revealed clear urban-rural contrasts in source contributions, supporting targeted prevention and precision management of heavy-metal pollution in complex watershed environments.

Air pollution poses a significant public health risk, as pollutants, emitted from both natural and anthropogenic sources, can penetrate deep into the respiratory system, leading to a wide range of respiratory diseases. While numerous studies have examined the role of meteorological factors in modulating air quality, limited research has focussed specifically on their effectiveness in regions characterized by intensive mineral extraction activities, particularly coal mining zones where emission loads remain persistently high. In this context, the concentration levels of PM₁₀, PM_2.5, SO₂, and NO₂ were continuously monitored for one year using an automated ambient air quality monitoring system to investigate their seasonal behaviour and meteorological interactions. The recorded concentration ranged from 17.49 to 393.40 µg/m³ for PM₁₀, 5.45 to 231.53 µg/m³ for PM_2.5, 12.3 to 62.05 µg/m³ for NO₂, and 11.59 to 182 µg/m³ for SO₂. The pollutant concentrations peaked during winter and declined during summer and monsoon seasons. The trend analysis using Theil-Sen estimator revealed significant negative trends for all four pollutants PM₁₀ (- 181.79 units), PM_2.5 (- 106.11 units), SO₂ (- 22.76 units), and NO₂ (- 29.89 units). The linear regression analysis demonstrated a strong correlation between PM₁₀ and PM_2.5 (R² = 0.89), whereas a weak association (R² = 0.01) was observed between NO₂ and SO₂. Nonlinear regression further indicated temperature as a key influencing factor, showing a strong inverse relationship with NO₂ (- 13.218) and a moderate negative impact on PM_2.5 (- 1.517). Overall, the findings of this study underscore highlight the seasonal vulnerability of coal mining regions to pollutant accumulation and highlights the limitations of mechanisms under elevated emission scenarios. Furthermore, this study establishes temperature as a boundary-layer control variable and emphasizes that effective air-quality management in coal-mining regions must integrate real-time meteorological forecasting with emission scheduling for sustainable air-quality compliance.

Iron and steel industries are among the largest contributors to heavy metal contamination in aquatic environments. In India, nearly 270 million tonnes of steel are produced annually. Still, only 30-35% is utilised, with large quantities of steel slag generated as a by-product, which is often dumped or used in landfills. Steel slag, a by-product of this industry, is widely employed in construction activities such as marine installations, artificial reef construction, construction fills, soil drains, and wastewater treatment. The present study investigated the stress response of the green mussel Perna viridis to steel slag exposure under controlled conditions by assessing stress markers and characterising the slag before and after seawater exposure. Following 14 and 28 days of exposure to 10% steel slag in seawater, adult mussels exhibited only ~ 10% mortality. Water quality parameters remained stable throughout the experiment. Antioxidant enzyme activities in mussel tissues were unaffected at both time points. Elemental analysis of steel slag after seawater exposure revealed the presence of ferrous and magnesium oxides, which are not toxic to P. viridis. Magnesium, iron, and phosphorus were the dominant elements in the tested slag fractions. Overall, the findings suggest that steel slag has minimal adverse effects on the growth and survival of P. viridis. These findings provide preliminary evidence that, under controlled conditions, steel slag may be considered a relatively safe material for marine and coastal applications. However, long-term ecological assessments are warranted before large-scale deployment in seabed restoration, artificial reef construction, or other reclamation activities.

Composts of edible fungal substrates (CEFS) and Chinese herbal residues (CCHR) are potential ecological organic fertilizers, which are sustainable for blueberry cultivation. But how the two composts affect the fertility and the migration or transformation of heavy metals (HMs) in blueberry soil is unknown. In this study, we conducted a field trial by comparing CEFS and CCHR to the special fruit organic fertilizer in the market (SFOF) and potassium sulfate compound fertilizers (PSCF) according to a three-year continuous fertilization experiment in blueberry. We aimed to reveal the effect of different fertilizers on the integrated soil fertility (ISF), nutrients availability, and the heavy metals (HMs) risks in blueberry soil and fruit; simultaneously to clarify the relationships between nutrients input and HMs risks for the blueberry according to RDA analysis. Results showed that the highest integrated soil fertility (ISF) was obtained in the CEFS + PSCF and CCHR, and blueberry was mostly affected by the input of nitrogenous and organic matters, and restricted by heavy metal Cd. CEFS and CCHR demonstrated a better fertility to enhance the ISF by 139.71%-149.31%, improve the effectiveness of soil Nitrogen, Phosphorus. In addition, application of CEFS and CCHR was conductive to reduce the bioavailability of heavy metals as decreasing the HOAc-extractable Cd by 23.94%-39.37%. Correlation analysis revealed that the input of organic matters was positive to the improvement of ISF and the reducing of Cd bioavailability, but excess organic matters would affect blueberry's absorption of Potassium, thus it is necessary to replenish potassium fertilizer in time for blueberry. Our results would provide a theoretical basis for application of CEFS and CCHR in the safety production of blueberry.

This study provides a detailed size-resolved analysis of atmospheric particulate matter (PM) collected in two contrasting European capitals, Oslo and Budapest, during summer and winter campaigns. Using 13-stage cascade impactors, we assessed the mass size distributions of over 30 elements and analyzed arsenic (As) compounds to identify their sources and potential health risks. Advanced data analyses, including Kaplan-Meier estimation for censored data, Atmospheric Particle Size Distribution (APSD) analysis, and Enrichment Factor (EF) calculations, revealed distinct behaviors among the elements. We observed a clear separation of sources based on particle size. Crustal elements such as aluminum (Al), iron (Fe), and calcium (Ca) were primarily found in the coarse mode (greater than 2.5 µm), originating from natural soil and resuspended road dust. In contrast, anthropogenic tracers like sulfur (S), As, cadmium (Cd), and lead (Pb) were concentrated in the accumulation mode (approximately 0.1-1.0 µm), which is characteristic of high-temperature combustion and secondary aerosol formation. A significant finding of our study was the predominance of inorganic arsenic (As_inorg) over organic species [dimethylarsinic acid (DMA) and trimethylarsine oxide (TMAO)] across all campaigns. As_inorg consistently peaked in the fine fraction and closely tracked the distribution of total As, indicating a substantial potential for deep respiratory deposition. Source apportionment analysis revealed notable seasonal and geographical differences. In Oslo, there was an accumulation-mode enrichment of vanadium (V) and nickel (Ni), indicating that shipping emissions were a major source of pollution. In Budapest, winter pollution was influenced by distinct local factors: potassium (K) shifted to the accumulation mode, pointing to biomass burning, while Pb exhibited a significant increase in the coarse mode, suggesting the resuspension of legacy soil contamination. These findings highlight the importance of size-resolved speciation for accurate source identification and health risk assessment in urban environments.

Reliable geochemical baselines are largely absent for northern Ghana, limiting efforts to distinguish natural element variability from human-induced contamination. This study addresses that gap by evaluating soil geochemical compositions in the Bongo and Talensi districts, where limited prior characterization has hindered accurate environmental assessment. Using an integrated geostatistical and machine-learning framework, regional background and baseline values were established to support environmental monitoring, land-use planning, and resource management. Three complementary geostatistical approaches; Iterative (3σ), Frequency Distribution, and Concentration-Area (C-A), were combined with clustering and regression-based learning to delineate geochemical zones and identify key elemental drivers. Machine-learning analysis identified Cd, Sb, Ge, and Ag as the main predictors distinguishing the Bongo and Talensi geochemical provinces. The Bongo province, underlain by felsic granitoids, is dominated by silicate weathering and low trace-metal variability, whereas Talensi reflects metavolcanic and hydrothermally influenced soils with localized metal enrichment. Chromium (Cr) exhibited the highest mean concentration (276.6 mg/kg), exceeding international soil-quality limits, while Cu (7.9 mg/kg), Pb (4.8 mg/kg), and Zn (26.2 mg/kg) remained within safe thresholds. The enrichment of Cr and related baseline ratios indicate that trace-metal variations arise chiefly from natural bedrock composition. The results provide reliable geochemical baselines essential for contamination assessment, environmental regulation, and mineral exploration. This study delivers the first integrated geostatistical-machine-learning framework for northern Ghana, offering a practical tool for sustainable land-use and resource-management decision-making.

Mining activities are not only sources of potentially toxic element (PTE) pollution, but are also closely associated with natural radioisotopes. This study combined uranium radioisotopes to better understand the behavior of mine-derived PTEs in lake sediments. We collected surface sediments near an abandoned mine in Lake Daecheong, South Korea, and determined the concentration distribution of PTEs (Pb, Zn, Cu, Cr, Ni, As, Cd, and Hg) and uranium radioisotopes (²³⁵U and ²³⁸U) using an inductively coupled plasma mass spectrometer and a gamma spectrometer, respectively. The mean Zn, Cu, Ni, and Cd concentrations in the tributary near the mine were significantly higher than those of other PTEs, and their distributions tended to decrease downstream. The mean concentrations and distributions of ²³⁵U and ²³⁸U showed a consistent trend similar to that of PTEs. PTE pollution was extremely high only in sites downstream of the tributary directly affected by the mine. Zn, Cu, Ni, Cd, ²³⁵U, and ²³⁸U were closely related and were the most important factors controlling PTE origin. Consequently, the surface sediments were dominated by mine-derived PTEs (Zn, Cu, Ni, and Cd), suggesting a close relationship between the locations and PTE concentrations, highlighting mines as sources. Moreover, uranium radioisotopes were highly correlated with mine-derived PTEs, which will help improve our understanding of PTE behavior. Therefore, uranium radioisotopes can be used as tracers to assess the origin of PTEs from mining activities.