Ecological Risk Assessment and Source Apportionment of Heavy Metals in the Soil of an Opencast Mine in Xinjiang

Source-oriented health risk assessment and priority control factor analysis of heavy metals in urban soil of Shanghai

The characteristics and ecological risks of heavy metal pollution in urban soils were comprehensively investigated, focusing on 224 typical industries undergoing redevelopment in Shanghai. The PMF (Positive Matrix Factorization) model was used to analyze the sources of soil heavy metals, while the HRA (Health Risk Assessment) model with Monte Carlo simulation assessed health risks to humans. Health risks under different pollution sources were explored, and priority control factors were identified. Results showed that, levels of most heavy metals exceeded Shanghai soil background values. Surface soil concentrations of Cd, Hg, Pb, Cu, Zn, and Ni exceeded the background values of Shanghai's soil to varying degrees, at 5.08, 5.40, 1.81, 1.95, 1.43, and 3.53 times, respectively. Four sources were identified: natural sources (22.23 %), mixed sources from the chemical industry and traffic (26.25 %), metal product sources (36.38 %), and pollution sources from electrical manufacturing and the integrated circuit industry (15.14 %). The HRA model indicated a tolerable carcinogenic risk for adults and children, with negligible non-carcinogenic risk. Potential risk was higher for children than for adult females, and higher for adult females than for adult males, with oral ingestion as the primary exposure pathway. Metal product sources and Ni were identified as primary control factors, suggesting intensified regional control. This study provides theoretical support for urban pollution prevention and control.

Source-based health risk assessment of heavy metal contamination in soil: a case study from a polymetallic mining region in Southeastern Hubei, Central China

To conduct a precise health risk assessment of heavy metals (HMs) in soil, it is imperative to ascertain the primary sources of potential health risks. In this study, we conducted comprehensive measurements of HMs, specifically focusing on the accumulation of Cu, Cd, Sb, Zn, and Pb in local soil, which may pose threats to environmental quality. To achieve our objective, we employed a method that combines positive matrix factorization with a health risk assessment model to quantify the health risks associated with specific sources. The results obtained from the geo-accumulation index indicate that the majority of HMs found in the local soil are influenced by anthropogenic activities. Among these sources, local industrial-related activities contributed the largest proportion of HMs to the soil at 34.7%, followed by natural sources at 28.7%, mining and metallurgy-related activities at 28.2%, and traffic-related activities at 8.40%. Although the non-carcinogenic and carcinogenic risks associated with individual HMs were found to be below safety thresholds, the cumulative health risks stemming from total HMs exceeded safety limits for children. Moreover, the unacceptable health risks for children originating from industrial-related activities, natural sources, and mining and metallurgy-related activities were primarily concentrated in proximity to mining sites and industrial areas within the local region. This investigation furnishes valuable insights that can aid governmental authorities in formulating precise control policies to mitigate health threats posed by soils in polymetallic mining areas.

Assessment of Potentially Toxic Elements Pollution and Human Health Risks in Polluted Farmland Soils around Distinct Mining Areas in China-A Case Study of Chengchao and Tonglushan

This research study investigates the extent of heavy metal pollution and pollution trends in agricultural soil in mining areas during different time periods. A total of 125 soil samples were collected from two mining areas in China, the Chengchao iron mine and Tonglushan ancient copper mine. The samples were analyzed for various potentially toxic elements (PTEs). The index of geoaccumulation (Igeo), pollution index (Pi), potential ecological risk index (Eri), and hazard index (HI) were calculated to evaluate the pollution status of PTEs in the farmland around the two mining areas. The sources of PTEs were inferred by pollution distribution, and the pollution conditions of the two mining areas were compared. The results showed that the pollution of ancient copper mines was relatively severe. The main pollution elements were Cu, Cd, and As, and their average Pi values were 3.76, 4.12, and 1.84, respectively. These PTEs mainly came from mining and transportation. There are no particularly polluted elements in the Chengchao iron mine and the average Pi of all PTEs were classified as light pollution and had a wide range of sources. The findings suggest that the ancient copper mine, due to outdated mining techniques and insufficient mine restoration efforts, resulted in the spread and accumulation of PTEs in the soil over an extended period, making the farmland soil around the ancient copper mine more polluted compared to the Chengchao iron mine. In the two mining areas, there is no risk of cancer for adults and children. However, the RI values of Cr in adults and children are higher than 10^-4, which indicates that the carcinogenic risk of Cr in these soils is very high. The non-carcinogenic effects of PTEs on the human body in the soil of ancient copper mine are also higher than that of the Chengchao iron mine.

Does Environmental Regulation Promote the Infrastructure Investment Efficiency? Analysis Based on the Spatial Effects

Infrastructure investment plays a key role in steady economic growth. Investment in infrastructure is gradually increasing; however, large-scale infrastructure investment is also associated with efficiency problems and environmental problems, which need to be carefully examined. The entropy weight method and the Super-SBM model are implemented to measure environmental regulation and infrastructure investment efficiency, respectively; and the spatial Durbin model is applied to analyze the influence mechanism and spatial effects of environmental regulation on infrastructure investment efficiency. The results show that spatial agglomeration characteristics exist in both environmental regulation and infrastructure investment efficiency. Besides, environmental regulation can promote infrastructure investment efficiency in general, but it develops an inverted U-shaped relationship as the intensity increases. Lastly, the spillover effects of environmental regulation on infrastructure investment efficiency present a U-shaped relationship. The conclusion is that both environmental regulation and infrastructure investment efficiency in China increased from 2008 to 2020. Besides, moderate environmental regulation is beneficial to infrastructure investment efficiency and restrains spatial spillover, but strict environmental regulation appears to be the opposite. This research expands the literature on environmental regulation and production efficiency and provides a reference basis for formulating effective policies to improve infrastructure investment efficiency from the perspective of the ecological environment.