[Spatial Distribution of Soil Heavy Metals and Regional Control Strategies in China at Province Level].

It is important to understand the spatial distribution characteristics and health risks of soil heavy metals for the implementation of soil pollution control measures in different levels and regions. Based on the data of 706 core studies in the last 20 years, the spatial distribution characteristics, accumulation degree, and health risks of soil heavy metals in China were analyzed at the provincial level. The results showed that the soil heavy metals had obvious spatial differences on the provincial scale, with an overall trend of "high in the south and low in the north and high in the east and low in the west." The content of heavy metals in the soil of agricultural land and construction land was high, and the rate of exceeding the standard was higher than that of other land types. Soil heavy metal concentrations in most areas of China were higher than the regional background values and were highly cumulative. The accumulation indices were:Cd(1.80)>Pb(0.23)>Cu(0.17)>Zn(-0.05)>As(-0.56)>Cr(-0.69), with more than 85% of the provincial soils reaching moderate levels of Cd pollution. Non-ferrous metal resource-based provinces such as Yunnan, Guizhou, Guangxi, Hunan, and Jiangxi generally had higher soil heavy metal levels than those in other provinces, and local children faced higher cancer risks. Soil pollution in coastal areas such as Fujian, Zhejiang, Jiangsu, and Tianjin mainly originated from industrial production and urbanization construction. High intensity agricultural utilization was an important cause of soil heavy metal accumulation in Henan, Shandong, and Anhui.

Quantitative source apportionment and driver identification of soil heavy metals using advanced machine learning techniques.

The accurate identification of pollution sources is essential for the prevention and control of possible pollution from soil heavy metals (SHMs). However, the positive matrix factorisation (PMF) model has been widely used as a conventional method for pollution source apportionment, and the classification of source apportionment results mainly relies on existing research and expert experience, which can result in high subjectivity in the source interpretation. To address this limitation, a comprehensive source apportionment framework was developed based on advanced machine learning techniques that combine self-organizing mapping and PMF with a gradient boosting decision tree (GBDT) model. Analysis of Cd, Pb, Zn, Cu, Cr, and Ni in 272 topsoils showed that the average contents of six heavy metals were 1.72-13.79 times greater than corresponding background values, among which Cd pollution was relatively serious, with 66.91 % of the sites having higher values than the specified soil risk screening values. The PMF results revealed that 79.43 % of Pb was related to vehicle emissions and atmospheric deposition, 79.32 % of Cd and 38.84 % of Zn were related to sewage irrigation, and 85.97 % of Cr and 85.50 % of Ni were from natural sources. Moreover, the GBDT detected that industrial network density, water network density, and Fe2O3 content were the major drivers influencing each pollution source. Overall, the novelty of this study lies in the development of an improved framework based on advanced machine learning techniques that led to the accurate identification of the sources of SHM pollution, which can provide more detailed support for environmental protection departments to propose targeted control measures for soil pollution.

A comprehensive framework for identifying contributing factors of soil trace metal pollution using Geodetector and spatial bivariate analysis.

The accurate identification of pollution sources is important for controlling soil pollution. However, the widely used Positive matrix factorization (PMF) model generally relies on knowledge and experience to accurately identify pollution sources; thus, this method faces significant challenges in objectively identifying soil pollution sources. Herein, we established a comprehensive source analysis framework using factor identification and geospatial analysis, and revealed the factors contributing to trace metal(loid) (TM) pollution in soil in the Pearl River Delta (PRD), China. Using the PMF model, we initially considered that the PRD may be affected by natural, atmospheric, traffic and industrial, and agricultural sources. Moreover, Geodetector model detected the relationship between TMs and 12 environmental variables based on the strong spatial "source-sink" relationship of pollutants. The parent material and digital elevation model were the key factors predicting the accumulation of Cr, Ni, and Cu. Industries and roads were the most important determinants of Pb, Zn, and Cd, whereas atmospheric deposition was more important for Hg accumulation. The accumulation of As was found to be closely related to agricultural activities such as the application of chemical fertilizers and pesticides. The spatial autocorrelation between soil TM pollution and environmental variables further supports this hypothesis. Overall, the obtained results showed that proposed approach improved the accuracy of source apportionment and provided a basis for soil pollution control.

Relative Contribution of Metal Content and Soil Particle Mass to Health Risk of Chromium-Contaminated Soil.

Three soil samples from a chromium (Cr)-contaminated field were classified into five particle fractions (i.e., 0-50 µm, 50-100 µm, 100-250 µm, 250-500 µm, and 500-1000 µm) and were further characterized to study their physicochemical properties and Cr bioaccessibility. The results indicated that the gastrointestinal bioaccessibility estimated by the Solubility Bioaccessibility Research Consortium (SBRC) method was on average 15.9% higher than that by the physiologically based extraction test (PBET) method. The health risk of all samples was within the safe range, and the health risk based on total Cr content may be overestimated by an average of 13.2 times compared to the bioaccessibility-based health risk. The health risk investigated from metal content was mainly contributed by the 50-250 µm fraction, which was 47.5, 50.2, and 43.5% for low-, medium-, and high-level polluted soils, respectively. As for the combined effect, the fractions of 100-250 µm and 500-1000 µm contributed the highest proportion to health risk, which was 57.1, 62.1, and 64.4% for low-level, medium-level, and high-level polluted soils, respectively. These results may further deepen the understanding of health risk assessment and quantify the contribution of the soil particle mass to health risk.