Environmental Geochemistry and Health最新文献

Although microplastic pollution is a global concern, information on the distribution of microplastics in petroleum and petrochemical urban soils is limited. In this study, we investigated the occurrence, ecological risk, and human exposure risk of microplastics in different land-use types of soil in Daqing Administrative region, a prominent petroleum and petrochemical industrial base in China. Stereoscopic microscopy and Fourier transform infrared spectroscopy (μ-FTIR) were used to study the chemical composition and distribution characteristics of microplastics. We found that the abundance of microplastics in Daqing soil ranged from 714 to 11,122 items/kg, with the highest value in educational land and the lowest in parks and green land. The dominant particle size of microplastics was < 1 mm (65.7%), and the shape was mainly fiber (55.1%), with white (28.9%) and black (25.6%) as the predominant colors. The most common polymer types were rayon, polypropylene, and polyethylene. Using the potential ecological risk index (RI) and polymeric risk index (H), we found that all land-use types, except woodland (Level I), were classified into Level V of ecological risk, with the highest risk in industrial land (RI = 14,959.85, H = 588.31). The daily exposure of infants to microplastics was much higher than that of adults. These findings provide valuable data for the pollution and potential risk assessment of microplastics in urban and rural environments, suggesting the importance of taking action to minimize its harmful effects on ecological and human health. In order to control the pollution caused by microplastics, we suggest that people should reduce the unnecessary use of single-use plastic items, such as water bottles, plastic shopping bags, straws, etc. In addition, the government needs to strengthen rubbish collection to prevent plastic waste from leaking into the environment during the period from the rubbish bins to the landfills, and to build recycling systems to increase the recycling rate.

This study investigated the distributions of heavy metals (Cd, Cu, Hg, Ni, Pb, and Zn) in agricultural soils near coal-fired power plants in Taean and Seocheon, South Korea, considering wind direction and distance from the plants. Additionally, pollution assessment for these heavy metals was conducted using the geoaccumulation index (I_geo) and enrichment factor. Results showed that heavy metal concentrations in the studied soil samples were below Korean environmental criteria for agricultural soil (Cd: 4, Cu: 150, Hg: 4, Ni: 100, Pb: 200, and Zn: 300 mg/kg). However, a significant proportion of samples exceeded average levels found in uncontaminated soils. Spatial distribution analysis revealed higher concentrations of Cd and Pb southwest of the Taean plant, influenced by prevailing northeast winds. In Seocheon, soils within 4 km of the plant exhibited elevated levels of Cd and Ni, suggesting coal combustion as a potential contamination source. Pollution assessment indicated that Cd and Pb in soils near both thermal power plants were more enriched by artificial activity compared to agricultural soils in control areas. Sequential extraction results showed that heavy metals in soils within 4 km of the Seocheon plant had higher proportions of exchangeable to organic-associated forms than soils beyond 4 km, indicating a risk of high bioavailability near emission sources. This study highlights the significant impact of coal-fired power plant emissions on soil contamination, emphasizing the need for continuous monitoring and management. Environmental policies should consider wind patterns and proximity to emission sources to effectively mitigate contamination risks.

Although the construction of green mines has effectively reduced soil pollution issues, dust contaminants continue to pose potential risks to human health and environment. This study investigated the concentrations, sources, and human health risks of metal(oid)s present in road dust across different functional zones of the largest lead-zinc mine in Guangdong Province, China, namely the Fankou lead-zinc mine. First, a systematic collection of 45 dust samples from six functional zones was conducted, focusing on the concentrations of ten heavy metal(oid)s (HMs), including As, Cd, Pb, and Zn etc. The results indicate that mining and smelting activities at the Fankou lead-zinc mine dictate the accumulation and distribution of HMs in the dust across the various functional zones. Except for Mn, Cr, and Thallium (Tl), the concentrations of other HMs significantly exceed the soil background values. These HMs primarily originate from mixed sources, including natural, traffic, and industrial activities. In particular, the presence of As, Cd, Pb, and Zn in the dust resulted in moderate to severe pollution, posing extremely high potential ecological risks. Furthermore, the bioavailable concentrations of HMs in the dust were analyzed using two in vitro gastrointestinal simulation methods: Physiologically Based Extraction Test (PBET) and Simplified Bioaccessibility Extraction Test (SBET), allowing for a further assessment of metal bioavailability and estimation of (non)carcinogenic risk probabilities for humans. The bioaccessible heavy metal contents extracted through SBET and PBET were relatively low, with most samples exhibiting bioaccessibility below 40%. In comparison to the total concentrations of HMs in the dust, the estimated non-carcinogenic risks (HQ and HI) and carcinogenic risks (CR) associated with bioavailability (PBET and SBET) for As, Cd, Cu, Sb, Pb, and Zn were significantly reduced, falling within safe values for both adults and children. However, the carcinogenic risk posed by total As remains a concern for local adults and children, indicating that the potential carcinogenic risk of As should not be overlooked. Therefore, additional protective measures should be considered to reduce resident exposure to dust, particularly in the core production areas of the mining district.

Excessive heavy metal in drinking water are harmful to human body. Groundwater was still the drinking water resource in most of rural areas in the central of the Yangtze River Basin. Heavy metals of Fe, Mn, and As in the low plain region of the Yangtze River Basin significantly exceeded the standard, resulting in 16.67% and 5.00% of water samples in the area reaching moderate and severe heavy metal pollution states. However, the coupling effect and ecological risks of iron, manganese, and arsenic in the water environment are unknown. This paper found that the dissolution of iron-bearing and manganese-bearing minerals into groundwater affected each other, when the burial depth of groundwater was less than 20 m. Conversely, the dissolution of minerals containing iron and arsenic into the groundwater interacted with each other when the groundwater depth was greater than 20 m. The precipitation of siderite (FeCO₃) and rhodochrosite (MnCO₃) may control the dissolved Fe and Mn in groundwater. The area between Yangtze River and Han River was more affected by industrial activities, and the south area of the Yangtze River was more affected by agricultural activities. This paper not only strengthened the understanding of the risk of heavy metal pollution in local groundwater, but also provided important scientific basis for the protection of regional groundwater ecological environment.

Understanding the hydrochemical evolution of surface water and groundwater is crucial for protecting regional ecological environments. Currently, there are few quantitative studies on the relative contributions of different processes to salinity enrichment of water bodies. In this study, sixty-seven water samples were collected for chemical, and isotopic analysis, along with simulation calculations. The results reveal distinct hydrochemical types of river water, phreatic water, lake water and hot spring water in the investigated area are Ca-Mg-HCO₃, Na-Ca-HCO₃, Na-SO₄-HCO₃ and Na-HCO₃, respectively. Average temperature and depth of geothermal water storage are 196℃ and 1338 m, respectively. Average arsenic (As) content in hot spring water (298 μg/L) higher than that in lake water (39.2 μg/L), river water (9.59 μg/L) and phreatic water (4.02 μg/L). The ∑REEs content of river water in the study area is much higher than that of phreatic water and lake water. Result of δD and δ¹⁸O indicate that atmospheric precipitation is the source of recharge for all water bodies in the study area. Quantitative calculations indicate that evapo-concentration significantly enriches lake water salinity, contributing on average 90% of its salt content. In contrast, mineral dissolution contributes predominantly to the salinity of hot spring water (90.7%), phreatic water (65.8%), and river water (45.2%). Evapo-concentration emerges as the dominant mechanism for lake water salinity, while carbonate mineral dissolution primarily affects river water. Phreatic water and hot spring water are mainly controlled by the weathering and dissolution of silicate. The results can provide a theoretical basis for the study of the formation mechanism of water salinity in other regions with similar geological environment in the world.

Background: Environmental exposure to arsenic (As), lead (Pb) and cadmium (Cd) may cause chronic kidney disease (CKD), with varying independent effects and unclear combined impact. This study aimed to evaluate these effects on CKD.

Methods: 1,398 individuals were included. Urine arsenic (UAs) was determined by atomic fluorescence method. Urinary cadmium (UCd) and blood lead (BPb) levels were determined by graphite-furnace atomic absorption spectrometry. CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m² or proteinuria. Generalized linear models (GLM), restricted cubic spline (RCS) models, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models were employed to study the independent and combined effects of exposure to As, Pb and Cd on CKD risk.

Results: Compared with non-CKD subjects, UAs, UCd, BPb, and creatinine adjusted urinary cadmium (UCdCr) were all significantly higher in CKD subjects. Compared with the lowest quartiles, the ORs (95%CIs) of CKD risk in the highest quartiles were 2.09 (1.16-3.74) for UAs, 2.84(1.56-5.18) for UCd, and 1.79 (1.05-3.06) for UCdCr, respectively. UAs, UCd, and UCdCr were all significantly positively associated with CKD risk in p-trend tests. RCS models revealed non-linear links between UAs, UCd, UCdCr and CKD risk, while a linear dose-response existed for BPb and CKD risk. The OR (95%CI) in WQS models were 1.72 (1.25-2.36) with UAs being the highest weighing metal(loid). BKMR models showed co-exposure mixture linked to higher CKD risk when the ln-transformed metal(loid)s above their 55th percentile. The ln-transformed UAs and UCdCr was significantly positively associated with CKD risk when the other two ln-transformed metals levels were all fixed at their different percentile levels. Synergism between Cd and Pb was also apparent.

Conclusions: Single As, and Cd exposure were positively associated with an increased CKD risk. Co-exposure to As, Pb and Cd was positively associated with CKD risk, with As playing a dominant role.

Analyzing the formation mechanisms of industrial parks and quantitatively evaluating the related hazard levels are important for understanding the development and planning of industrial parks, but there is currently a lack of relevant research. In this study, Beijing was taken as a case study. The analysis results showed that (1) the overall spatial distribution of industrial parks in Beijing followed a clustering pattern, with nested spatial distribution pattern, where larger structures contributed 53.96% of the variance; (2) for the overall spatial distribution of industrial parks, kernel density of enterprises was the main influencing factor, which there were synergistic enhancement effects with almost all other influencing factors, especially urban construction, the number of financial institutions, the population density, this can share transportation and other resources, achieving coordinated development. According to these main factors, the prediction model of the future spatial distribution pattern of industrial parks in Beijing was established; (3) for site selection of each industrial park, twenty-two industrial parks near industrial enterprises in Beijing were more affected by industrial enterprise clustering, and the remaining 65 industrial parks were strongly affected by terrain, (4) The industrial parks in the central and southern parts of Beijing presented a relatively high hazard level to the surrounding sensitive receptors. These results provide theoretical support for the development and layout of industrial parks.

The outbreak of the coronavirus disease 2019 (Covid-19) led to the high consumption of antibiotics such as azithromycin as well as corticosteroids such as prednisone, prednisolone, and dexamethasone used to treat the disease. Seemingly, the concentrations of these four Covid-19 drugs increased in wastewater effluents and surface water resources. This is due to the failure of traditional wastewater treatment facilities (WWTFs) to eliminate pharmaceuticals from wastewater. Therefore, the objective of the current research was to review the present state of literature on the occurrence of four Covid-19 drugs in water resources, the associated risks and toxicity, their fate, as well as the emergence of combined pollutants of Covid-19 drugs and heavy metals. From late 2019 to date, azithromycin was observed at concentrations of 935 ng/L, prednisone at 433 ng/L, prednisolone at 0.66 ng/L, and dexamethasone at 360 ng/L, respectively, in surface water resources. These concentrations had increased substantially in water resources and were all attributed to pollution by wastewater effluents and the rise in Covid-?19 infections. This phenomenon was also exacerbated by the observation of the pseudo-persistence of Covid-19 drugs, long half-life periods, as well as the excretion of Covid-19 drugs from the human body with about 30?90% of the parent drug. Nonetheless, the aquatic and human health toxicity and risks of Covid-19 drugs in water resources are unknown as the concentrations are deemed too low; thus, neglecting the possible long-term effects. Also, the accumulation of Covid-19 drugs in water resources presents the possible development of combined pollutants of Covid-19 drugs and heavy metals that are yet to be investigated. The risks and toxicity of the combined pollutants, including the fate of the Covid-19 drugs in water resources remains a research gap that undoubtably needs to be investigated.

Research on soil organic carbon (SOC) is crucial for improving soil carbon sinks and achieving the "double-carbon" goal. This study introduces ten auxiliary variables based on the data from a 2021 land quality survey in Zhengzhou and a multi-objective regional geochemical survey. It uses geostatistical ordinary kriging (OK) interpolation, as well as classical machine learning (ML) models, including random forest (RF) and support vector machine (SVM), to map soil organic carbon density (SOCD) in the topsoil layer (0 - 20 cm) of cultivated land. It partitions the sampling data to assess the generalization capability of the machine learning models, with Zhongmu County designated as an independent test set (dataset2) and the remaining data as the training set (dataset1). The three models are trained using dataset1, and the trained machine learning models are directly applied to dataset2 to evaluate and compare their generalization performance. The distribution of SOCD and SOCS in soils of various types and textures is analyzed using the optimal interpolation method. The results indicated that: (1) The average SOC densities predicted by OK interpolation, RF, and SVM are 3.70, 3.74, and 3.63 kg/m², with test set precisions (R²) of 0.34, 0.60, and 0.81, respectively. (2) ML achieves a significantly higher predictive precision than traditional OK interpolation. The RF model's precision is 0.21 higher than the SVM model and more precise in estimating carbon stock. (3) When applied to the dataset2, the RF model exhibited superior generalization capabilities (R² = 0.52, MSE = 0.32) over the SVM model (R² = 0.32, MSE = 0.45). (4) The spatial distribution of surface SOCD in the study area exhibits a decreasing gradient from west to east and from south to north. The total carbon stock in the study area is estimated at approximately 10.76 × 10⁶t. (5) The integration of soil attribute variables, climatic variables, remote sensing data, and machine learning techniques holds significant promise for the high-precision and high-quality mapping of soil organic carbon density (SOCD) in agricultural soils.

The high levels of nitrate (NO₃^-) in the surface water have contributed to eutrophication and other eco-environmental damages worldwide. Although the excessive NO₃^- concentrations in rivers were often attributed to anthropogenic activities, some undisturbed or slightly disturbed rivers also had high NO₃^- levels. This study utilized multi-pronged approaches (i.e., river natural abundance isotopes, ¹⁵N-labeling techniques, and qPCR) to provide a comprehensive explanation of the reason for the high NO₃^- levels in a river draining forest-dominated terrene. The river natural abundance isotopes (δ¹⁵N/δ¹⁸O-NO₃^-) indicated that the soil source (i.e., soil organic nitrogen-SON and chemical fertilizer-CF) were the primary contributors to the NO₃^-, and the NO₃^- removal was probably prevalent in the basin scale. The ¹⁵N-labeling techniques quantitatively showed that denitrification and anammox were stronger than nitrification in the soils and sediments. Structural equation models suggested that nitrification in the soils was regulated by NH₄⁺-N contents, which, in turn, were closely related to fertilization in spring. Denitrification and anammox were largely controlled by elevation and functional gene abundances (i.e., nirK and hzsB, respectively). The hydrological isotopes (i.e., δD/δ¹⁸O-H₂O) indicated that the transport of NO₃^- from soil to the river was related to the intensity of runoff leaching to the soil, In contrast, the riverine NH₄⁺ was largely from point sources; thus, increasing runoff led to a dilution effect. This study clearly showed that soil biogeochemistry and hydrological condition of a river basin jointly shaped the high NO₃^- levels in the almost undisturbed river.