Spatial characteristics of cadmium in topsoils in a typical e-waste recycling area in southeast China and its potential threat to shallow groundwater

Informal electrical and electronic waste (e-waste) recycling often creates secondary sources of cadmium (Cd) pollution. To characterize the total Cd concentration (Cd-total) in topsoil and evaluate the threat of Cd in topsoils to shallow groundwater, 187 topsoil samples and 12 shallow groundwater samples were collected in a typical e-waste recycling area in southeast China. Soil organic matter content, soil pH and Cd-total in topsoil, pH and dissolved Cd concentration in shallow groundwater were measured. Cd-total in the topsoils showed an inverse distribution trend with soil pH in that high Cd concentrations (and low pH values) were found in the surrounding area of the metal recycling industrial park where there were many family-operated e-waste recycling facilities before the industrial park was established and with low concentrations (and high pH values) in other areas, and they had similar spatial correlation structures. Cd accumulation and acidification were synchronous in topsoils, and soil pH was significantly correlated with Cd-total in topsoils with low to moderate negative correlation coefficient (r = -024), indicating that both of them maybe correlated with informal recycling. The shallow groundwater in the surrounding area of the metal recycling industrial park was seriously contaminated by Cd, and topsoil Cd accumulation and acidification in the surrounding area of e-waste recycling sites significantly increase the risk of shallow groundwater contaminated by Cd. Action is urgently required to control Cd accumulation and acidification by improving the recycling operations of e-wastes in order to reduce the risk of Cd leaching from topsoils and shallow groundwater contamination. (C) 2013 Published by Elsevier B.V.Informal electrical and electronic waste (e-waste) recycling often creates secondary sources of cadmium (Cd) pollution. To characterize the total Cd concentration (Cd-total) in topsoil and evaluate the threat of Cd in topsoils to shallow groundwater, 187 topsoil samples and 12 shallow groundwater samples were collected in a typical e-waste recycling area in southeast China. Soil organic matter content, soil pH and Cd-total in topsoil, pH and dissolved Cd concentration in shallow groundwater were measured. Cd-total in the topsoils showed an inverse distribution trend with soil pH in that high Cd concentrations (and low pH values) were found in the surrounding area of the metal recycling industrial park where there were many family-operated e-waste recycling facilities before the industrial park was established and with low concentrations (and high pH values) in other areas, and they had similar spatial correlation structures. Cd accumulation and acidification were synchronous in topsoils, and soil pH was significantly correlated with Cd-total in topsoils with low to moderate negative correlation coefficient (r = -024), indicating that both of them maybe correlated with informal recycling. The shallow groundwater in the surrounding area of the metal recycling industrial park was seriously contaminated by Cd, and topsoil Cd accumulation and acidification in the surrounding area of e-waste recycling sites significantly increase the risk of shallow groundwater contaminated by Cd. Action is urgently required to control Cd accumulation and acidification by improving the recycling operations of e-wastes in order to reduce the risk of Cd leaching from topsoils and shallow groundwater contamination. (C) 2013 Published by Elsevier B.V

A Single Soil Washing with Humic Substance Can Achieve the Risk-Based Remedial Target for Nickel Contaminated Soil

Risk-based soil remediation and management have become a global environmental issue. Here, a nickel (Ni)-contaminated site was selected to conduct the risk-based remediation strategy. The Health and Environment Risk Assessment Software was used to calculate the human health risk and the remedial target value (RTV) of Ni. Soil highly contaminated with Ni (424.30 mg kg(-1)) could cause an unacceptable carcinogenic risk (1.41 x 10(-6)), which needs further remediation. Hence, a soluble humic substance (HS) was used as the washing agent to remove Ni. After a single wash at pH 4 and 8, the Ni concentrations in soil were reduced to 278.05 and 288.27 mg kg(-1), both below the RTV (300 mg kg(-1)). Furthermore, sequential extraction analysis revealed that the residual Ni was maintained stably in the soil after HS washing. These findings suggested that HS is a promising washing agent for Ni-contaminated soil remediation under the guidance of risk control

Prediction of Heavy Metal Concentrations in Contaminated Sites from Portable X-ray Fluorescence Spectrometer Data Using Machine Learning

Portable X-ray fluorescence (pXRF) spectrometers provide simple, rapid, nondestructive, and cost-effective analysis of the metal contents in soils. The current method for improving pXRF measurement accuracy is soil sample preparation, which inevitably consumes significant amounts of time. To eliminate the influence of sample preparation on PXRF measurements, this study evaluates the performance of pXRF measurements in the prediction of eight heavy metals’ contents through machine learning algorithm linear regression (LR) and multivariate adaptive regression spline (MARS) models. Soil samples were collected from five industrial sites and separated into high-value and low-value datasets with pXRF measurements above or below the background values. The results showed that for Cu and Cr, the MARS models were better than the LR models at prediction (the MARS-R2 values were 0.88 and 0.78; the MARS-RPD values were 2.89 and 2.11). For the pXRF low-value dataset, the multivariate MARS models improved the pXRF measurement accuracy, with the R2 values improved from 0.032 to 0.39 and the RPD values increased by 0.02 to 0.37. For the pXRF high-value dataset, the univariate MARS models predicted the content of Cu and Cr with less calculation. Our study reveals that machine learning methods can better predict the Cu and Cr of large samples from multiple contaminated sites

Prediction of Dichloroethene Concentration in the Groundwater of a Contaminated Site Using XGBoost and LSTM

Chlorinated aliphatic hydrocarbons (CAHs) are widely used in agriculture and industries and have become one of the most common groundwater contaminations. With the excellent performance of the deep learning method in predicting, LSTM and XGBoost were used to forecast dichloroethene (DCE) concentrations in a pesticide-contaminated site undergoing natural attenuation. The input variables included BTEX, vinyl chloride (VC), and five water quality indicators. In this study, the predictive performances of long short-term memory (LSTM) and extreme gradient boosting (XGBoost) were compared, and the influences of variables on models’ performances were evaluated. The results indicated XGBoost was more likely to capture DCE variation and was robust in high values, while the LSTM model presented better accuracy for all wells. The well with higher DCE concentrations would lower the model’s accuracy, and its influence was more evident in XGBoost than LSTM. The explanation of the SHapley Additive exPlanations (SHAP) value of each variable indicated high consistency with the rules of biodegradation in the real environment. LSTM and XGBoost could predict DCE concentrations through only using water quality variables, and LSTM performed better than XGBoost

Remediation technologies for neonicotinoids in contaminated environments: Current state and future prospects

Neonicotinoids (NEOs) are synthetic insecticides with broad-spectrum insecticidal activity and outstanding efficacy. However, their extensive use and persistence in the environment have resulted in the accumulation and biomagnification of NEOs, posing significant risks to non-target organisms and humans. This review provides a summary of research history, advancements, and highlighted topics in NEOs remediation technologies and mechanisms. Various remediation approaches have been developed, including physiochemical, microbial, and phytoremediation, with microbial and physicochemical remediation being the most extensively studied. Recent advances in physiochemical remediation have led to the development of innovative adsorbents, photocatalysts, and optimized treatment processes. High-efficiency degrading strains with well-characterized metabolic pathways have been successfully isolated and cultured for microbial remediation, while many plant species have shown great potential for phytoremediation. However, significant challenges and gaps remain in this field. Future research should prioritize isolating, domesticating or engineering high efficiency, broad-spectrum microbial strains for NEO degradation, as well as developing synergistic remediation techniques to enhance removal efficiency on multiple NEOs with varying concentrations in different environmental media. Furthermore, a shift from pipe-end treatment to pollution prevention strategies is needed, including the development of green and economically efficient alternatives such as biological insecticides. Integrated remediation technologies and case-specific strategies that can be applied to practical remediation projects need to be developed, along with clarifying NEO degradation mechanisms to improve remediation efficiency. The successful implementation of these strategies will help reduce the negative impact of NEOs on the environment and human health

Numerical Research on Migration Law of Typical Chlorinated Organic Matter in Shallow Groundwater of Yangtze Delta Region

With the reform of China’s urbanization increasing in popularity, the security issues posed by urban groundwater, especially groundwater in industrial areas, have attracted scholars’ attention. This research aimed to predict and quantify the migration process of contaminants in a microconfined aquifer by conducting a groundwater contamination investigation in an abandoned chemical plant in the Jiangsu Province of China. First, data such as regional hydrogeological parameters and types of contaminants were obtained via hydrogeological drilling, groundwater well monitoring, pumping tests, and laboratory permeability tests, which helped identify the most serious pollution factor: chloroform. Then, a groundwater flow model was built using the Groundwater Modeling System (GMS) and verified using the general-purpose parameter estimation (PEST) package. In addition, based on the three-dimensional multi-species model for transport (MT3DMS) in GMS, a transport model was established. The results illustrate that the plume range of chloroform diffuses with water flow, but, because of its slow diffusion rate and inability to degrade naturally, the concentration of the contaminant has remained several times higher than the safety standard for a long time. The contaminant spread vertically to the soil layer above the microconfined aquifer under pressure, resulting in direct pollution. In addition, the contaminant in the microconfined aquifer is anticipated to migrate down to the clay layer and become enriched. However, the first confined aquifer has not been seriously polluted in the past 20 years. Finally, a sensitivity analysis of the parameters shows that groundwater contamination in the Yangtze delta region is greatly affected by precipitation recharge and hydraulic conductivity

Derivation of human health and odor risk control values for soil ammonia nitrogen by incorporating solid-liquid partitioning, ammonium/ammonia equilibrium: A case study of a retired nitrogen fertilizer site in China

Nitrogen fertilizer supports agricultural intensification, but its manufacturing results in substantial contaminated sites. Ammonia nitrogen is the main specific pollutant in retired nitrogen fertilizer sites with potential human health and odor risks. However, few studies focus on ammonia nitrogen risk assessment at contaminated sites, particularly considering its solid-liquid partitioning process (Kd) and ammonium/ammonia equilibrium process (R) in the soil. This study took a closed nitrogen fertilizer factory site as an example and innovatively introduced Kd and R to scientifically assess the human health and odor risk of ammonia nitrogen. The risk control values (RCVs) of ammonia nitrogen based on human health and odor risk were also derived. The maximum concentration of ammonia nitrogen was 3380 mg/kg in the unsaturated soil, which was acceptable for human health because the health RCVs were 5589 ∼ 137,471 mg/kg in various scenarios. However, odor risk was unacceptable for RCVs were 296 ∼ 1111 mg/kg under excavation scenarios and 1118 ∼ 35,979 mg/kg under non-excavation scenarios. Of particular concern, introducing Kd and R in calculation increased the human health and odor RCVs by up to 27.92 times. Despite the advancements in ammonia risk assessment due to the introduction of Kd and R, odor risk during excavation remains a vital issue. These findings inform a more scientific assessment of soil ammonia risk at contaminated sites and provide valuable insights for the management and redevelopment of abandoned nitrogen fertilizer plant sites

Asbestos-Environment Pollution Characteristics and Health-Risk Assessment in Typical Asbestos-Mining Area

Asbestos has been confirmed as a major pollutant in asbestos-mining areas that are located in western China. In general, asbestos-fibre dust will is released into the environment due to the effect of intensive industrial activities and improper environmental management, such that the health of residents in and around mining areas is jeopardised. A typical asbestos mining area served as an example in this study to analyse the content and fibre morphology of asbestos in soil and air samples in the mining area. The effects of asbestos pollution in and around the mining areas on human health were also assessed based on the U.S. Superfund Risk Assessment Framework in this study. As indicated by the results, different degrees of asbestos pollutions were present in the soil and air, and they were mainly concentrated in the mining area, the ore-dressing area, and the waste pile. The concentration of asbestos in the soil ranged from 0.3% to 91.92%, and the concentration of asbestos fibres in the air reached 0.008–0.145 f·cc−1. The results of the scanning-electron microscope (SEM) energy suggested that the asbestos was primarily strip-shaped, short columnar, and granular, and the asbestos morphology of the soils with higher degrees of pollution exhibited irregular strip-shaped fibre agglomeration. The excess lifetime cancer risk (ELCR) associated with the asbestos fibres in the air of the mining area was at an acceptable level (10−4–10−6), and 40.6% of the monitoring sites were subjected to unacceptable non-carcinogenic risks (HQ > 1). Moreover, the waste pile was the area with the highest non-carcinogenic risk, followed by the ore dressing area, a residential area, and a bare-land area in descending order. In the three scenarios of adult offices or residences in the mining area, adults’ outdoor activities in the peripheral residence areas, and children’s outdoor activities, the carcinogenic-and non-carcinogenic-risk-control values in the air reached 0.1438, 0.2225 and 0.1540 f·cc−1, and 0.0084, 0.0090 and 0.0090 f·cc−1, respectively. The results of this study will lay a scientific basis for the environmental management and governance of asbestos polluted sites in China