Huan Jing Ke Xue. 2023 Mar 8;44(3):1620-1635. doi: 10.13227/j.hjkx.202204096.
ABSTRACT
In this study, As, Cd, Cr, Hg, and Pb in the surface soil of cropland in Lanzhou were studied, and the combination of descriptive statistics, single-factor accumulation index, comprehensive accumulation index, geostatistical method, and a geographically weighted regression model (GWR) was used to investigate their accumulation status, spatial distribution, and influencing factors, in order to provide scientific basis for the precise control of heavy metal accumulation risk in the study area. The results showed that:①the single-factor accumulation index showed that the accumulation of Hg and Cd in the study area was the largest, followed by that of Pb and As, and that of Cr was the smallest; the comprehensive accumulation index showed that the accumulation rate of heavy metals was as high as 98.11%, of which 54.81% was mild accumulation and 43.30% was moderate and above accumulation. However, there was no heavy metal pollution in the surface soil of cultivated land in Lanzhou. ② Spatial autocorrelation analysis showed that there was no heavy metal pollution in the surface soil of Lanzhou, but there was accumulation in a few areas. The spatial autocorrelation analysis showed that the spatial autocorrelation of the Cr, Hg, and Pb single-factor accumulation indices was moderate, indicating that they were influenced by both random and structural factors; the spatial autocorrelation of the other two heavy metals was weak, indicating that they were mainly influenced by random factors, among which human factors were more influential. The Hg single-factor accumulation index was high in the middle and low in the fourth; the integrated accumulation index increased from northwest to southeast and decreased from the middle to both sides. ③ Geographically weighted regression (GWR) analysis showed that altitude, slope length, distance from rivers, soil organic matter (SOM) content, precipitation, air temperature, and surface temperature all showed positive driving effects, whereas the rest of the factors showed negative driving effects and significant spatial heterogeneity and instability (P<0.05). The magnitudes of the factor effects were in the order of soil characteristics>topographic characteristics>climate characteristics>location characteristics>socioeconomic characteristics>vegetation characteristics, with the largest contribution of SOM content, followed by that of altitude and air temperature, and the smallest of normalized difference vegetation index (NDVI) values. Compared with the other feature factors, the location feature factors had stronger spatial heterogeneity.
PMID:36922223 | DOI:10.13227/j.hjkx.202204096
