[Distribution Prediction of Soil Heavy Metals Based on Remote Sensing Temporal-Spatial-Spectral Features and Random Forest Model].

Obtaining soil heavy metal content characteristics and spatial distribution is crucial for preventing soil pollution and formulating environmental protection policies. We collected 304 surface soil samples (0-20 cm) in the Changqing district. At the same time, the spectral, temporal, and spatial features of soil heavy metals were derived from multi-remote sensing data; the temporal-spatial-spectral features closely related to soil heavy metals were selected via correlation analysis and used as input independent variables. The measured soil arsenic (As) content was used as the dependent variable to establish a spatial prediction model based on the random forest (RF) algorithm. The results showed the following:the As content in the soils exceeded the background value by 43.17% but did not exceed the risk screening values and intervention values, indicating slight heavy metal pollution in the soil. The accuracy ranking of the spatial prediction models with one feature type from high to low was spatial features (ratio of performance to inter-quartile range (RPIQ)=3.87)>temporal features (RPIQ=2.57)>spectral features (RPIQ=2.50). The spatial features were the most informative for predicting soil heavy metals. The models using temporal-spatial, temporal-spectral, and spatial-spectral features were superior to those using only one feature type, and the RPIQ values were 4.81, 4.21, and 4.70, respectively. The RF model with temporal-spatial-spectral features achieved the highest spatial prediction accuracy (R2=0.90; root mean square error (RMSE)=0.77; RPIQ=5.68). The As content decreased from the northwest to the southeast due to Yellow River erosion and industrial activities. The spatial prediction of soil heavy metals incorporating remote sensing temporal-spatial-spectral features and the random forest model provides effective support for soil pollution prevention and environmental risk control.

A joint method to assess pollution status and source-specific human health risks of potential toxic elements in soils.

Potential toxic elements (PTEs) in soils follow various exposure pathways (e.g., ingestion, dermal contact, and inhalation) when migrating to the human body, and can threaten human health. The objective of this study was to quantitatively evaluate the pollution status and human health risk for different age groups (i.e., children and adults) caused by source-specific PTEs in the soil. A total of 425 topsoil samples (0-20 cm) were collected in the Zhangqiu District of China, and contents of eight PTEs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were analyzed simultaneously. The PTE sources were identified using a geostatistical method, and positive matrix factorization was used to apportion the contribution rate of each PTE source for human health risk of different age groups based on a health risk assessment model. Results showed that: (1) As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn contents all exceeded the background values in soil, indicating that there had been a slight enrichment of soil PTEs over time; (2) Cr and Ni were derived from natural sources, while Cd, Cu, and Zn were derived from transportation sources; industrial emissions and coal burning intensified pollution from Hg, Pb, and As in soils, as shown in the average contribution percentages of natural sources (24.36%), transportation sources (33.79%), and industrial sources (41.85%); (3) the non-carcinogenic risk caused by soil PTEs did not endanger human health, but the carcinogenic risk was close to the risk threshold, and should be given attention; the largest carcinogenic risk (36.53%) and non-carcinogenic risk (36.01%) for children were attributed to industrial sources, while the largest carcinogenic risk (34.98%) and non-carcinogenic risk (37.06%) for adults were attributed to transportation sources. The results presented in this study provide support for effectively preventing PTE health risks in different age groups from source-specific PTE pollution of the soil.

[Quantitative Evaluation of Human Health Risk of Heavy Metals in Soils Based on Positive Matrix Factorization Model and Geo-statistics].

The aim of this study was to quantitatively assess the human health risks derived from different exposure paths of heavy metals in the soil. Zhangqiu county was selected as the study area, and 425 soil samples were collected to measure the As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn contents. A descriptive statistical method was used to assess the heavy metal pollution status of the soils, and the quantitative sources for human health were then determined based on positive matrix factorization (PMF) and geo-statistical techniques. The results show that the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in the soils exceed background values, but do not exceed the risk screening values, indicating that there is slight heavy metals pollution in the soil. The sources of heavy metals in the soil can be divided into three categories. The spatial distribution of Cr and Ni is approximately the same, is similar to the spatial distribution trend of parent materials, and belongs to natural sources. Cd, Cu, and Zn are controlled by transportation. The spatial distribution is significantly affected by the location of road lines. The hot spot areas of Hg, Pb, and As correspond to the locations of the industrial park and the urban area. Industrial emissions and coal combustion increase the accumulation of Hg in the soil, and As, Pb, and Hg are classified as industrial sources. The contribution rate of industrial source is 41.85%, with transportation sources and natural sources being 33.79% and 24.36%, respectively. The non-carcinogenic and carcinogenic risks under the exposure paths of hand, breathing, and skin are within the acceptable level. For children, the sources of heavy metals with the largest carcinogenic (36.53%) and non-carcinogenic (36.01%) risks are industrial sources. However, transportation is the largest source of carcinogenic (34.98%) and non-carcinogenic (37.06%) risk for adults. Differential avoidance of heavy metal sources and exposure pathways is vital to reducing human health risks.