Abstract
A large amount of heavy metal (HM) inputs exists in the farming areas of the Hebei plain of northern China. However, the potential ecological risk, source, and input flux of HMs in these areas have not been well-investigated. In this study, atmospheric deposition, fertilizer, irrigation water, and agricultural soil samples were collected from farming areas (~74,111 km2) in Hebei Province, China. The HM index of geoaccumulation (Igeo) and potential ecological risk index (RI) of soil was calculated for eight HMs. The source and input flux of each element were predicted using the input flux and principal component score–multiple linear regression (PCS–MLR) methods. The results showed that Cd and Hg increased Igeo values, and the maximum levels of As (29.5 mg/kg), Cu (228.9 mg/kg), Cd (4.52 mg/kg), and Zn (879.0 mg/kg) were greater than the health risk screening values in the soil quality standard of China. The potential ecological risk factor (Er) of Cd demonstrated a moderately potential ecological risk, accounting for 67.72%. The distribution map showed that Cd was mainly concentrated in eastern area of Baoding (BD) in the study area. The result of the atmospheric dry and wet deposition contributed more to soil pollution than the usage of fertilizer or irrigation water by calculating the input flux. The order was Zn (94%) > Cu (92%) > Pb (89%) > Cr (86%) > Cd (72%) > Hg = Ni (71%) > As (59%). Principal component analysis (PCA) results showed that there were four sources of HMs in soil. Geological sources contribute to the accumulation of As, Cr, and Ni in soil. Cu and Pb in the soil were attributable to the input from vehicular emissions and irrigation water. Cd and Zn in the soil were attributable to the farming activity, whereas Hg originates from the combustion of coal. The results of PCS–MLR demonstrated that the contribution rate of As, Ni, and Cr in the study area was 30.06%, 71.86%, 57.71% for the first group (natural source); Cu, Pb and Zn were 71.78%, 63.59%, and 30.72% for the second group (vehicle emissions); Zn was 60.93% for the third group (fertilizer application and irrigation water); and Hg was 85.16%, for the fourth group (coal combustion). These factors provide a valuable reference for remediating HM pollution.
Highlights
In agroecological environments, heavy metal pollution (HMP) is attributed to improper application of chemical fertilizers and pesticides, as well as irrigation water and industrial factors [1–3]
Where Qa,i is the amount of HMs (i = As, Cd, Cr, Cu, Ni, Pb, Zn, and Hg) present in the study area because of atmospheric deposition, Ci is the content of HM i because of atmospheric deposition, W is the annual amount of deposition obtained based on the sampling bottle, K is the conversion coefficient (10,000 m2 ), and S is the area in which the individual soil sample can be observed (706.5 cm2 )
The distribution map demonstrated that the concentration of HMs in the JZN plain is greater than that in the JD plain
Summary
Heavy metal pollution (HMP) is attributed to improper application of chemical fertilizers and pesticides, as well as irrigation water and industrial factors [1–3]. Based on a multiobjective geochemical survey conducted to identify pollutants from industrial, mining, and agricultural activities, the fertilizer usage rate, livestock, poultry manure, pesticides, irrigation water, atmospheric deposition of industrial waste and automobile exhaust, and other human activities were the primary causes of soil pollution [11,12]. The source apportionment (PCS–MLR) was used to analyze the contribution level of each pollution source with respect to the HMs. the aim of this study is to (1) analyze HMs concentration, the HMP level, and potential ecological risks of Hebei Plain, China, caused by industry and agriculture, (2) evaluate. HMs spatial distribution characteristics, (3) calculate the HMs input fluxes of fertilizers, irrigation water, and atmospheric depositions to agricultural soils, (4) identify the pollution source and contribution rate of HMs observed in the study area via principal component score–multiple linear regression (PCS–MLR), and (5) provide valuable management to control the pollution source. We will provide valuable data for controlling and remediation HMP to Hebei agricultural soils
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