Bioaccumulation of Atmospherically Deposited Cadmium in Soybean: Three-Year Field Experiment Combined with Cadmium Isotopes.

Atmospheric deposition plays a significant role in introducing cadmium (Cd) into agroecological systems; however, accurately determining its accumulation in crops through foliar and root uptake presents challenges. This study investigated the bioaccumulation of atmospherically deposited Cd in soybean using a three-year fully factorial atmospheric exposure experiment incorporating Cd isotope analysis. Results shown that atmospheric deposition accounted for 1-13% of soil Cd pools, yet contributed 11-72% of Cd to soybean tissues during the growing seasons. Over the course of soil exposure to atmospheric deposition ranging from 1 to 3 years, no notable variations were observed in Cd concentrations in soil solutions and soybean tissues, nor in isotope ratios. Newly deposited Cd was a major source in soybean plants, and the bioavailability of deposited Cd rapidly aged in soils. Atmospheric Cd enriched in lighter isotopes induced negative isotope shifts in soybean plants. By employing an optimized isotope mixing model in conjunction with a mass balance approach, foliar Cd uptake contributed 13-51%, 16-45%, and 21-56% to stem, leaf, and seed, respectively. This study highlights substantial contribution of foliar uptake of atmospheric deposition to Cd levels in soybean and controlling foliar uptake as a potential strategy in agroecological systems experiencing high atmospheric Cd deposition.

A Mine Water Source Prediction Model Based on LIF Technology and BWO-ELM.

The traditional methods for identifying water sources in coal mines lack the ability to quickly detect water sources and are prone to causing secondary pollution of samples. In contrast, laser induced fluorescence (LIF) technology has been introduced for the identification of coal mine water sources due to its high sensitivity and real-time performance. However, extreme learning machine (ELM) have shortcomings in randomly selecting weights and biases. The Beluga Whale Optimization (BWO) algorithm has efficient optimization capability, global search capability, adaptability and parallelism, and can find the optimal weights and biases in a short time. The combination of LIF technology and BWO-ELM model can be applied to quickly identify the welling water source in coal mine. Select sandstone water and old goaf water from the Huainan mining area as experimental samples, and mix them in different proportions to prepare 7 mixed water samples for testing. Utilize LIF technology to obtain spectral curve images, preprocess them with polynomial smoothing algorithm (SG) and spectral multiple scattering correction (MSC), and perform dimensionality reduction using factor analysis (FA) and linear discriminant analysis (LDA) methods. Finally, construct ELM models, Long Short Term Memory (LSTM) models, BWO-ELM models, and Particle Swarm Optimization Extreme Learning Machine(PSO-ELM) models for the dimensionality reduced data. In order to improve the reliability and accuracy of the results, the experimental results were kept to 5 decimal places. From the experimental results, it can be seen that SG-LDA-BWO-ELM has the best fitting effect, with a fitting coefficient of 0.99990, a root mean square error of 0.00041, a mean square error approaching 0, and an average absolute error of 0.00021. It has the best convergence and the smallest absolute error among all models, making it the most suitable for identifying mine water inrush. It is of great significance for preventing and controlling mine water disasters and ensuring coal mine production safety.

Contamination status, partitioning behavior, ecological risks assessment of legacy and emerging per- and polyfluoroalkyl substances in a typical heavily polluted semi-enclosed bay, China.

The contaminant status, spatial distribution, partitioning behavior, and ecological risks of 26 legacy and emerging perfluoroalkyl and polyfluoroalkyl substances (PFASs) in Laizhou Bay, China were investigated. The concentrations of ∑PFASs in surface and bottom seawater ranged from 37.2 to 222 ng/L and from 34.2 to 305 ng/L with an average of 116 ± 62.7 and 138 ± 93.8 ng/L, respectively. There were no significant differences in the average concentrations between the surface and bottom seawater (P > 0.05). Perfluorooctanoic acid (PFOA) and short-chain PFASs dominated the composition of PFASs in seawater. The concentrations of ∑PFASs in sediments ranged from 0.997 to 7.21 ng/g dry weight (dw), dominated by perfluorobutane sulfonate (PFBS), perfluorobutanoic acid (PFBA), and long-chain PFASs. The emerging alternatives of perfluoro-1-butane-sulfonamide (FBSA) and 6:2 fluorotelomer sulfonic acid (6:2 FTSA) were detected for the first time in Laizhou Bay. The ∑PFASs in seawater in the southwest of the bay were higher than those in the northeast of the bay. The ∑PFASs in sediments in the northeast sea area were higher than those in the inner area of the bay. Log Kd and log Koc values increased with increasing carbon chain length for PFASs compounds. Ecological risk assessments indicated a low ecological risk associated with HFPO-DA but a moderate risk associated with PFOA contamination in Laizhou Bay. Positive matrix factorization (PMF) analysis revealed that fluoropolymer manufacturing, metal plating plants, and textile treatments were identified as major sources contributing to PFASs contamination.

Metals and phosphorus in the sediments of streams emptying into the Çanakkale strait (Dardanelles): Spatial distribution, pollution status, risk assessment and source identification.

The Çanakkale Strait is exposed to various pollutants due to its strategic location. It is thought that stream inputs may contribute significantly to metal and phosphorus (P) accumulation in the strait. In this study, the spatial distribution, pollution status, ecological risks and possible sources of twelve metals and P in the sediments of seven important streams emptying into the strait were analyzed. The results showed that Zn (226 mg/kg), Ba (67.2 mg/kg) and Pb (10.4 mg/kg) concentrations were higher in the Umurbey Stream due to mining activities, while P concentration (295 mg/kg) was higher in the Çanakkale Stream due to both agricultural activities and domestic wastewater discharges. Modified hazard quotient (mHQ), enrichment factor (EF) and contamination factor (Cf) values revealed that Zn and Pb showed high and moderate contamination in the US3 and US4 sampling sites of the Umurbey Stream, respectively. Similarly, P showed moderate contamination in the ÇS3 site of the Çanakkale Stream. Nemerow pollution index (NPI) showed that the US3 (2.41) and US4 (4.28) sites of the Umurbey Stream were slightly and moderately polluted, respectively. Toxic risk index (TRI) values demonstrated that the sediments in only the US4 site (5.17) of the Umurbey Stream may pose a low toxic risk due to high Zn content. Similarly, based on comparison results with sediment quality guidelines (SQGs), it was found that high Zn content may lead to adverse effects on sediment-dwelling organisms in the US4 site. In addition, the PEC-quotient value in the US4 site exceeded 0.5, confirming the finding that the sediments in this site could be toxic to benthic organisms. Finally, correlation, cluster and factor analyzes were used to determine possible sources of elements. Mining activities, natural sources and mixed sources (agricultural activities and natural sources) were identified as the main sources of elements in the sediments of the streams. This study can provide an important reference for evaluating stream sediment pollution and managing marine pollution.

Seasonal variations and sources of atmospheric EPFRs in a megacity in severe cold region: Implications for the influence of strong coal and biomass combustion.

Environmentally persistent free radicals (EPFRs) can pose exposure risks by inducing the generation of reactive oxygen species. As a new class of pollutants, EPFRs have been frequently detected in atmospheric particulate matters. In this study, the seasonal variations and sources of EPFRs in a severe cold region in Northeastern China were comprehensively investigated, especially for the high pollution events. The geomean concentration of EPFRs in the total suspended particle was 6.58 × 1013 spins/m3 and the mean level in winter was one order of magnitude higher than summer and autumn. The correlation network analysis showed that EPFRs had significantly positive correlation with carbon component, K+ and PAHs, indicating that EPFRs were primarily emitted from combustion and pyrolysis process. The source appointment by the Positive Matrix Factorization (PMF) model indicated that the dominant sources in the heating season were coal combustion (48.4%), vehicle emission (23.1%) and biomass burning (19.4%), while the top three sources in the non-heating season were others (41.4%), coal combustion (23.7%) and vehicle emissions (21.2%). It was found that the high EPFRs in cold season can be ascribed to the extensive use of fossil fuel for heating demand; while the high EPFRs occurred in early spring were caused by the large-scale opening combustion of biomass. In summary, this study provided important basic information for better understanding the pollution characteristics of EPFRs, which suggested that the implementation of energy transformation and straw utilization was benefit for the control of EPFRs in severe cold region.

Pollution of organophosphorus pesticides in the Dongting Lake, China and its relationship with dissolved organic matter: Occurrence, source identification and risk assessment.

The escalating global demand for food and industrialization has placed significant pressure on the integrity and management of inland lake ecosystems. Herein, the organophosphorus pesticides (OPPs) pollution status and their relationship with dissolved organic matter (DOM) in Dongting Lake were investigated to identify the ecological risks and potential sources of OPPs. The total concentrations of 18 detected OPPs were in the range of 13.49-375.24 ng/L, with higher concentration observed in east and west lake regions. Among these, fenthion was the dominant contributor, accounting for 64% of total OPPs, posing significant ecological risk to aquatic organisms. Nearly all of sites showed high combined risk of total OPPs. Parallel factor analysis (PARAFAC) and fluorescence regional integration (FRI) technique showed that DOM was mainly composed of terrestrial humic-like and tryptophan-like substances. Moreover, correlation analysis revealed a close association between DOM optical parameters and OPP concentrations. Specifically, OPPs exhibited a significantly positive correlation with tyrosine-like substances, while displaying a negative correlation with fulvic acid-like substances. These results indicated that OPP concentrations may decrease with increasing humification levels and declining tyrosine-like substance contents. This study underscores the critical role of DOM in assessing the occurrence and sources of OPPs in aquatic environments, providing valuable insights for effective environmental management strategies.

Health risk assessment of heavy metals based on source analysis and Monte Carlo in the downstream basin of the Zishui.

In this study, stone coal mines in the lower reaches of the Zijiang River were adopted as the research object. To analyze the spatial distribution, sources, and health risks of heavy metals in the surrounding soil of stone coal mines, 82 topsoil samples were collected in the study area, and the contents of 8 heavy metals including Cd, Hg, As, Cr, Pb, Cu, Ni, and Zn were determined. The spatial distribution of heavy metals was analyzed using ArcGIS, and the pollution sources of heavy metals were identified using Positive matrix factorization (PMF). Then, Monte Carlo and health risk assessment models were used to evaluate the health risks of different populations. The results showed that the average content of heavy metals followed the order of Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, and the contents of all heavy metals were higher than the soil background values of Hunan Province. The high-value areas of heavy metals content were mostly concentrated in the central region close to areas with a notable concentration of stone coal mines. PMF identified four pollution sources, namely, mining activities (26.9%), atmospheric deposition (18.8%), natural sources (32.8%) and agricultural sources (21.5%). The carcinogenic and non-carcinogenic risks for children were higher than those for adults, with As and Cd posing higher carcinogenic risks to children. Based on the source of health risks, it was determined that the health risks could be primarily attributed to agricultural sources, and As was the main heavy metal causing health risks. This study provides theoretical support for treating heavy metal pollution in mining basins.

Occurrence of halogenated organic contaminants in surface sediments of the Yangtze River estuary and its adjacent marine area.

Halogenated organic contaminants, such as chlorinated and brominated polycyclic aromatic hydrocarbons (Cl/Br-PAHs), are some of the most important emerging environmental pollutants. However, empirical data on Cl/Br-PAHs in estuarine and marine ecosystems are limited, rendering assessments of Cl/Br-PAH contamination in estuarine and offshore environments uncertain. Here the occurrence, sources, and ecological risks of 7 Cl-PAHs and 18 Br-PAHs were determined in surface sediments of the Yangtze River Estuary (YRE), a highly urbanized and industrialized area, and its adjacent marine area. The concentrations of Cl-PAHs ranged from 4.50 to 18.38 ng g-1 (average 7.19 ng g-1), while those of Br-PAHs ranged from 4.80 to 61.18 ng g-1 (average 14.11 ng g-1). The dominant Cl-PAH and Br-PAH in surface sediment were 9-chlorofluorene (17.79%) and 9-bromofluorene (58.49%), respectively. The distributions and compositions of Cl/Br-PAHs in the surface sediments varied considerably due to complex hydrodynamic and depositional conditions in the YRE and its adjacent marine area, as well as differences in physicochemical properties of different Cl/Br-PAHs. Positive matrix factorization revealed that the primary sources of Cl/Br-PAHs in the study area were e-waste dismantling (33.6%), waste incineration (23.2%), and metal smelting (11.0%). According to the risk quotient, the Cl/Br-PAHs in sediments posed no toxic risk to aquatic organisms.

Groundwater hydrochemistry, source identification and health assessment based on self-organizing map in an intensive mining area in Shanxi, China.

The Changzhi Basin in Shanxi is renowned for its extensive mining activities. It's crucial to comprehend the spatial distribution and geochemical factors influencing its water quality to uphold water security and safeguard the ecosystem. However, the complexity inherent in hydrogeochemical data presents challenges for linear data analysis methods. This study utilizes a combined approach of self-organizing maps (SOM) and K-means clustering to investigate the hydrogeochemical sources of shallow groundwater in the Changzhi Basin and the associated human health risks. The results showed that the groundwater chemical characteristics were categorized into 48 neurons grouped into six clusters (C1-C6) representing different groundwater types with different contamination characteristics. C1, C3, and C5 represent uncontaminated or minimally contaminated groundwater (Ca-HCO3 type), while C2 signifies mixed-contaminated groundwater (HCO3-Ca type, Mixed Cl-Mg-Ca type, and CaSO4 type). C4 samples exhibit impacts from agricultural activities (Mixed Cl-Mg-Ca), and C6 reflects high Ca and NO3- groundwater. Anthropogenic activities, especially agriculture, have resulted in elevated NO3- levels in shallow groundwater. Notably, heightened non-carcinogenic risks linked to NO3-, Pb, F-, and Mn exposure through drinking water, particularly impacting children, warrant significant attention. This research contributes valuable insights into sustainable groundwater resource development, pollution mitigation strategies, and effective ecosystem protection within intensive mining regions like the Changzhi Basin. It serves as a vital reference for similar areas worldwide, offering guidance for groundwater management, pollution prevention, and control.

Tracing sulfate sources and transformations of surface water using multiple isotopes in a mining-rural-urban agglomeration area.

Rapid urbanization and mining activities are exacerbating sulfate (SO42-) pollution in surface water, and the information on its sources and transformations is crucial for understanding the sulphur cycle in mining areas. In this study, the SO42- in the surface water of Huaibei mining area were monitored and the main sources of pollution and biogeochemical processes were identified using stable isotopes (δD, δ18O-H2O, δ34S-SO42- and δ18O-SO42-) and water chemistry. The results demonstrated the SO42- content in the Huihe River and Linhuan subsidence water area (SWA) is higher than that in other rivers and SWAs, which exceeded the environmental quality standard of surface water. The SO42- content of different rivers and SWAs showed seasonal differences, and the dry season was higher than the wet season. In addition, the SO42- in Tuohe River and Suihe River is primarily caused by urban sewage and agriculture activities, while in Zhonghu and Shuoxihu SWA is mainly contributed by natural evaporate dissolution. Notably, the input of SO42- in the Huihe River and Linhuan SWA caused by mining activities cannot be disregarded. The aerobic environment and isotopic fractionation of surface water indicate that sulfide oxidation is not the major cause of SO42- formation. This work has revealed the multiple sources and transformation mechanisms of SO42-, and provided a reference for the development of comprehensive management and effective remediation strategies of SO42- contamination in surface water around mining areas.

Comprehensive source identification of heavy metals in atmospheric particulate matter in a megacity: A case study of Hangzhou.

Megacities face significant pollution challenges, particularly the elevated levels of heavy metals (HMs) in particulate matter (PM). Despite the advent of interdisciplinary and advanced methods for HM source analysis, integrating and applying these approaches to identify HM sources in PM remains a hurdle. This study employs a year-long daily sampling dataset for PM1 and PM1-10 to examine the patterns of HM concentrations under hazy, clean, and rainy conditions in Hangzhou City, aiming to pinpoint the primary sources of HMs in PM. Contrary to other HMs that remained within acceptable limits, the annual average concentrations of Cd and Ni were found to be 20.6 ± 13.6 and 46.9 ± 34.8 ng/m³, respectively, surpassing the World Health Organization's limits by 4.1 and 1.9 times. Remarkably, Cd levels decreased on hazy days, whereas Ni levels were observed to rise on rainy days. Using principal component analysis (PCA), enrichment factor (EF), and backward trajectory analysis, Fe, Mn, Cu, and Zn were determined to be primarily derived from traffic emissions, and there was an interaction between remote migration and local emissions in haze weather. Isotope analysis reveals that Pb concentrations in the Hangzhou region were primarily influenced by emissions from unleaded gasoline, coal combustion, and municipal solid waste incineration, with additional impact from long-range transport; it also highlights nuanced differences between PM1 and PM1-10. Pb isotope and PCA analyses indicate that Ni primarily stemmed from waste incineration emissions. This explanation accounts for the observed higher Ni concentrations on rainy days. Backward trajectory cluster analysis revealed that southern airflows were the primary source of high Cd concentrations on clean days in Hangzhou City. This study employs a multifaceted approach and cross-validation to successfully delineate the sources of HMs in Hangzhou's PM. It offers a methodology for the precise and reliable analysis of complex HM sources in megacity PM.

Gated graph neural networks for identifying contamination sources in water distribution systems.

Contamination events in water distribution networks (WDN) pose significant threats to water supply and public health. Rapid and accurate contamination source identification (CSI) can facilitate the development of remedial measures to reduce impacts. Though many machine learning (ML) methods have been proposed for fast detection, there is a critical need for approaches capturing complex spatial dynamics in WDNs to enhance prediction accuracy. This study proposes a gated graph neural network (GGNN) for CSI in the WDN, incorporating both spatiotemporal water quality data and flow directionality between network nodes. Evaluated across various contamination scenarios, the GGNN demonstrates high prediction accuracy even with limited sensor coverage. Notably, directional connections significantly enhance the GGNN CSI accuracy, underscoring the importance of network topology and flow dynamics in ML-based WDN CSI approaches. Specifically, the method achieves a 92.27% accuracy in narrowing the contamination source to 5 points using just 2 h of sensor data. The GGNN showcases resilience under model and measurement uncertainties, reaffirming its potential for real-time implementation in practice. Moreover, our findings highlight the impact of sensor sampling frequency and measurement accuracy on CSI accuracy, offering practical insights for ML methods in water network applications.

Morphological Enrichment and Environmental Factors Correlation of Heavy Metals in Dominant Plants in Typical Manganese Ore Areas in Guizhou, China.

Bioavailable heavy metal and their efficient phytoremediation in mining areas have major implications for environmental and human health. In this study, we investigated 12 dominant plants in a typical Mn ore area of Zunyi, Guizhou Province, China, to determine the heavy metal contents, morphologies, and environmental factors affecting Mn, Cd, Pb, Cu, Zn, and Cr in the plant parts and rhizosphere soil. The bioavailabilities and degrees of metals were evaluated using the ratios of the secondary to primary phase distributions and potential ecological risk indices. Principal component analysis, cluster analysis, positive matrix factorisation modelling, and redundancy analysis were used to trace the origins and correlations among the metals. The results indicate that the bioavailabilities were the highest for Mn and Cd in the study area, and all of the target heavy metals had bioavailabilities above the moderate ecological harm level. Statistical modelling indicates that there are four main pollution sources: mining, smelting, processing operations, and atmospheric deposition. The dominant plants had high heavy metal enrichments, bioconcentration factors, and translocation factors for Mn, Cu, Cr, Cd, and Zn. The redundancy analysis indicates that soil total N, total P, and pH affect metal absorption and distributions in Compositae and non-Compositae plants in low-N, low-P, and slightly alkaline mining environments. This study provides a feasible basis for the screening of heavy metal enrichment plants and the improvement of remediation technology in manganese ore area under the extreme environment of poor nutrition.

Identification of illicit discharges in sewer networks by an SWMM-Bayesian coupled approach.

Illicit discharges into sewer systems are a widespread concern within China's urban drainage management. They can result in unforeseen environmental contamination and deterioration in the performance of wastewater treatment plants. Consequently, pinpointing the origin of unauthorized discharges in the sewer network is crucial. This study aims to evaluate an integrative method that employs numerical modeling and statistical analysis to determine the locations and characteristics of illicit discharges. The Storm Water Management Model (SWMM) was employed to track water quality variations within the sewer network and examine the concentration profiles of exogenous pollutants under a range of scenarios. The identification technique employed Bayesian inference fused with the Markov chain Monte Carlo sampling method, enabling the estimation of probability distributions for the position of the suspected source, the discharge magnitude, and the commencement of the event. Specifically, the cases involving continuous release and multiple sources were examined. For single-point source identification, where all three parameters are unknown, concentration profiles from two monitoring sites in the path of pollutant transport and dispersion are necessary and sufficient to characterize the pollution source. For the identification of multiple sources, the proposed SWMM-Bayesian strategy with improved sampling is applied, which significantly improves the accuracy.

Characterization of the inhalable fraction (< 10 µm) of soil from highly urbanized and industrial environments: magnetic measurements, bioaccessibility, Pb isotopes and health risk assessment.

Soil in urban and industrial areas is one of the main sinks of pollutants. It is well known that there is a strong link between metal(loid)s bioaccessibility by inhalation pathway and human health. The critical size fraction is < 10 µm (inhalable fraction) since these particles can approach to the tracheobronchial region. Here, soil samples (< 10 µm) from a highly urbanized area and an industrialized city were characterized by combining magnetic measurements, bioaccessibility of metal(loids) and Pb isotope analyses. Thermomagnetic analysis indicated that the main magnetic mineral is impure magnetite. In vitro inhalation analysis showed that Cd, Mn, Pb and Zn were the elements with the highest bioaccessibility rates (%) for both settings. Anthropogenic sources that are responsible for Pb accumulation in < 10 µm fraction are traffic emissions for the highly urbanized environment, and Pb related to steel emissions and coal combustion in cement plant for the industrial setting. We did not establish differences in the Pb isotope composition between pseudo-total and bioaccessible Pb. The health risk assessment via the inhalation pathway showed limited non-carcinogenic risks for adults and children. The calculated risks based on pseudo-total and lung bioaccessible concentrations were identical for the two areas of contrasting anthropogenic pressures. Carcinogenic risks were under the threshold levels (CR < 10-4), with Ni being the dominant contributor to risk. This research contributes valuable insights into the lung bioaccessibility of metal(loids) in urban and industrial soils, incorporating advanced analytical techniques and health risk assessments for a comprehensive understanding.

Evaluation of ecological and health risks of trace elements in soils of olive orchards and apportionment of their sources using the APCS-MLR receptor model.

Izmir, Turkey's third most populous city, is in an important position in terms of both agriculture and industry. The province, which contributes 9.3% to the country's industrial production, also has an important potential in terms of olive cultivation. However, until now, no research has been undertaken to analyze the content of trace elements (TEs) in the soil of olive orchards in Izmir. This study was carried out to determine the pollution level and ecological risks of TEs in the olive orchards soils of Izmir province, to reveal their potential sources and to evaluate their health risks. Among the TEs, the average content of only Ni (37.9 mg/kg) exceeded the world soil average content (29 mg/kg), while the average content of only Cd (0.176 mg/kg) exceeded the upper continental crust content (0.09 mg/kg). Enrichment factor revealed that there was significant enrichment for Cd in 73.6%, Ni in 11.6% and Cr in 5.4% of olive orchards, respectively, due to polluted irrigation water and agrochemicals. Similarly, ecological risk factor indicated that there were moderate and considerable ecological risks for Cd in 48.8% and 23.3% of olive orchards, respectively. Absolute principal component scores-multiple linear regression (APCS-MLR) model showed that Ni and Cr in the study area are affected by agricultural sources, Al, Co, Cu, Fe, Mn, Pb and Zn originate from lithogenic sources, and Cd originates from mixed sources. Based on health risk evaluation methods, non-carcinogenic and carcinogenic effects would not be expected for residents. This study provides significant knowledge for evaluating soil TE pollution in olive orchards and serves a model for source apportionment and human health risk evaluation of TEs in other agricultural regions.

Health risk assessment and potential sources of metals in riparian soils of the Wujiang River, China.

In order to understand the pollution status of metals in the riparian soils along the Wujiang River, 26 sampling sites in the mainstream and tributary streams were selected for investigation. The geo-accumulation index (Igeo), Nemerow integrated pollution index, and potential ecological risk index were applied to evaluate the contamination status and ecological risks of metals. Results revealed that the average concentrations of As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn were 12.20, 0.51, 84.01, 57.42, 922.57, 38.37, 38.06, and 127.82 mg/kg, respectively. The metal contamination degree and ecological risks in the upper reaches were significantly higher than those in the middle and lower reaches of the Wujiang River. Cd was the dominant contamination metal. Significant non-carcinogenic and carcinogenic risks of metals were found in children based on the hazard index and carcinogenic risk. As was the main non-carcinogenic and carcinogenic pollutant metal in both adults and children. According to principal component analysis, hierarchical clustering analysis, and absolute principal component scores-multiple linear regression, anthropogenic sources (mining and agricultural activities) contributed most to Zn, Pb, Cr, Cd, Cu, and Ni, with contribution rates of 89.14, 82.32, 74.46, 72.12, 68.52, and 61.02%, respectively. Natural sources contributed most to Mn, with a contribution rate of 83.07%. Unidentified sources contributed most to As, with a contribution rate of 47.27%.

Analysing N-nitrosamine occurrence and sources in karst reservoirs, Southwest China.

N-nitrosamines in reservoir water have drawn significant attention because of their carcinogenic properties. Karst reservoirs containing dissolved organic matter (DOM) are important drinking water sources and are susceptible to contamination because of the fast flow of various contaminants. However, it remains unclear whether N-nitrosamines and their precursor, DOM, spread in karst reservoirs. Therefore, this study quantitatively investigated the occurrence and sources of N-nitrosamines based on DOM properties in three typical karst reservoirs and their corresponding tap water. The results showed that N-nitrosamines were widely spread, with detection frequencies > 85%. Similar dominant compounds, including N-nitrosodimethylamine, N-nitrosomethylethylamine, N-nitrosopyrrolidine, and N-nitrosodibutylamine, were observed in reservoirs and tap water, with average concentrations of 4.7-8.9 and 2.8-6.7 ng/L, respectively. The average carcinogenic risks caused by these N-nitrosamines were higher than the risk level of 10-6. Three-dimensional fluorescence excitation-emission matrix modeling revealed that DOM was composed of humus-like component 1 (C1) and protein-like component 2 (C2). Fluorescence indicators showed that DOM in reservoir water was mainly affected by exogenous pollution and algal growth, whereas in tap water, DOM was mainly affected by microbial growth with strong autopoietic properties. In the reservoir water, N-nitrosodiethylamine and N-nitrosopiperidine were significantly correlated with C2 and biological indicators, indicating their endogenously generated sources. Based on the principal component analysis and multiple linear regression methods, five sources of N-nitrosamines were identified: agricultural pollution, microbial sources, humus sources, degradation processes, and other factors, accounting for 46.8%, 36.1%, 7.82%, 8.26%, and 0.96%, respectively. For tap water, two sources, biological reaction processes, and water distribution systems, were identified, accounting for 75.7% and 24.3%, respectively. Overall, this study presents quantitative information on N-nitrosamines' sources based on DOM properties in typical karst reservoirs and tap water, providing a basis for the safety of drinking water for consumers.

Risk assessment and source identification of soil heavy metals: a case study of farmland soil along a river in the southeast of a mining area in Southwest China.

To investigate the heavy metals (HMs) contamination of surface farmland soil along the river in the southeast of a mining area in southwest China and identify the contamination sources, 54 topsoil samples were collected and the concentrations of seven elements (Zn, Ni, Pb, Cu, Hg, Cr, and Co) were determined by inductively coupled plasma optical emission spectrometry (ICP-OES) and atomic fluorescence spectrometry (AFS). The geo-accumulation index ([Formula: see text]) and comprehensive potential ecological risk index ([Formula: see text]) were used for analysis to determine the pollution degree of HMs and the risk level of the study area. Meanwhile, the Positive Matrix Factorization (PMF) model was combined with a variety of statistical methods to determine the sources of HMs. To explore the influence of the river flowing through the mining area on the concentrations of HMs in the farmland soil, 15 water samples were collected and the concentrations of the above seven elements were determined. The results showed that the concentrations of Pb, Cu, and Zn in soil all exceeded the risk screening value, and Pb in soil of some sampling sites exceeded control value of "Agricultural Land Soil Pollution Risk Control Standard".[Formula: see text] showed that Pb was heavily contaminated, while Cu and Zn were moderately contaminated. RI showed that the study area was at moderate risk. PMF and various statistical methods showed that the main source of HMs was the industrial source. In the short term, the river flowing through the mine has no significant influence on the concentration of HMs in the soil. The results provide a reference for the local government to control contamination and identify the sources of HMs.

Pollution and mobility of heavy metals in the soils of a typical agricultural zone in eastern China.

The pollution of heavy metals (HMs) in agricultural soils profoundly threatens national food safety, and the mobility and environmental behaviors of HMs are closely implicated in crop safety. Here, we assessed the pollution level and mobility of ten HMs and explored their environmental behaviors in the soils of three different land uses from a main crop production zone in eastern China. The concentrations of HMs in the soils were higher in the farmland than the woodland and wasteland, and Cd showed a relatively higher pollution and ecological risk levels compared to other metals. Cadmium was dominated by the reducible (41%) and exchangeable (23%) fractions, and the rest of HMs were mainly in the residual fraction (> 60%). The significant correlation between the exchangeable and DGT-labile Cd indicates relatively higher mobility of Cd in the soils. Soil pH, organic matters and mineral elements had significant correlation with the exchangeable and reducible fractions of most of the HMs (e.g., Cd, Co, Mn, Ni, Pb and V; p < 0.05), indicating their good predictors of the HMs mobility. However, this was not the case for the DGT-labile fraction, which suggests a marked difference in the controlling mechanisms of the mobility versus potential bioavailability of HMs in the soils. The results of this study indicate that both the chemically extracted fractions and the bioavailable fractions of HMs need be considered when effectively assessing the safety of agricultural soils.