RESUMO
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.
RESUMO
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.