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Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10–13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP–LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality. [ABSTRACT FROM AUTHOR]

Long-term coal mining activities have led to severe heavy metal pollution in the soil environment of coal mining areas, posing significant threats to both ecological environments and human health. In this study, surface soil samples were collected from the overlying soil of coal gangue and the surrounding areas of the Panyi coal mine in Huainan. The concentrations of Cd, Zn, Cu, Ni, and Pb elements were determined. A self-organizing map (SOM) and an absolute principal component score multiple linear regression (APCS-MLR) receptor model were employed for the quantitative analysis of the soil's heavy metal pollution sources and contributions. Additionally, this study focused on the analysis of the pathways of the relatively serious pollution of Cd. The results revealed that the average concentrations of heavy metals (Cd, Pb, Zn, Cu, Cr, and Ni) in the study area were 4.55, 0.59, 1.54, 0.69, 0.59, and 0.71 times the local soil background values, respectively. The concentrations of Cd and Zn exceeded the risk screening values at some sampling points, with exceedance rates of 44% and 8%, respectively, indicating a relatively serious Cd contamination. The sources of heavy metals in the soil in the study area were classified into four categories: mining activities, agricultural activities, weathering of natural matrices, and other unknown sources, with average contributions of 55.48 percent, 24.44 percent, 8.91 percent and 11.86 percent, respectively. Based on the spatial distribution of Cd, it was inferred that atmospheric deposition is one of the important pollution pathways of Cd in the study area. Cd profile distribution patterns and a surface water pollution survey showed that the farmland areas were affected by the irrigation water pathway to some extent. The vertical distribution of heavy metal content in the forest area showed a strong disorder, which was related to the absorption function of plant roots. The results of this study can help to improve the environmental management of heavy metal pollution so as to protect the ecological environment and human health. [ABSTRACT FROM AUTHOR]

Population expansion within agricultural lands applies pressure on natural resources, particularly water resources, and leads to contamination through different types of pollutants, such as heavy metals, that consequently alter the ecosystem and impact human health. In the present work, several heavy metals in sediment along the Kitchener drain were assessed using different soil quality and health indices; the Kitchener drain is one of the major drains in the Nile Delta. Sediments were collected from six stations along the drain from upstream to downstream. Soil physical and chemical properties were analyzed as well as four metal pollution indices and five ecological risk indices. Additionally, carcinogenic and noncarcinogenic risks for adults and children were evaluated. The data showed that the Kitchener drain is mainly contaminated with Cd, Pb, and Zn, where the concentrations decreased from upstream to downstream. The eco-toxicological indexes showed that Pb, Zn, and Cr were the most hazardous metals along the drain, mainly at upstream stations. The human health risk indices data revealed that the noncarcinogenic risk of the studied metals can be ordered as follows: Co > Cr > Pb > Mn > Ni > Cd > Cu > Zn for adults, while for children it was Cr > Mn > Co > Pb > Ni > Cd > Cu > Zn. The carcinogenic risk data showed that heavy metals ranged from low to medium in all sites, except for Pb and Zn, which have high carcinogenic risks. The present study showed more contamination upstream compared to downstream which can be attributed to urbanization and human activity, as shown from the land use/landcover map. This highlighted that the major drains inside the Nile Delta suffer from different anthropogenic activities that should be taken into consideration by researchers, scientists, and policymakers. Also, the source of heavy metal pollution, particularly upstream, should be controlled or treated before discharge into the drain. On the other side, downstream (toward the Mediterranean Sea), the heavy metals could affect the trophic levels of the marine ecosystem on the Mediterranean Sea which should be taken into consideration. [ABSTRACT FROM AUTHOR]

In order to study the characteristics and sources of heavy metal pollution within different grassland soil categories in Altay, 147 soil samples were collected in Fuyun County according to grassland type, and the contents of seven heavy metals (Cu, Pb, Cr, Zn, As, Ni, and Cd) were determined. The heavy metal pollution in the soil was evaluated using the Nemerow pollution and geological accumulation index methods. The absolute principal component score–multiple linear regression model (APCS-MLR) model was used to analyze the sources of heavy metals and the contribution rate of the pollution sources. The results showed that (1) the heavy metal content of 0~10 cm soil in the mining area was generally higher than the remaining two layers. Natural grassland was the most seriously contaminated, and the order of its contamination was Zn > Pb > Ni > Cd > Cu > Cr > As. (2) The evaluation results of the comprehensive pollution index showed that Ni and Cd were the main pollutants in five grassland types, and the evaluation results of the geological accumulation index method showed that natural grasslands were more polluted. (3) Three pollution sources were obtained according to the analysis using the APCS-MLR model, including natural and mining activity sources (32.6%), industrial coal combustion and sewage sources (24.7%), and an unknown source (42.7%). The results of the study can provide a basis for pollution control and ecological protection in the grasslands of Altay. [ABSTRACT FROM AUTHOR]