Pollution characteristics and ecological risk assessment of heavy metals in paddy fields of Fujian province, China.

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ID: 109142
2020
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Abstract
To analyze the concentration, spatial distribution patterns, and ecological risks of heavy metals (Cd, Cr, Pb, As, Cu, Ni and Co), 272 topsoil samples (0-20 cm) were collected from paddy fields in Fujian province in July 2017. The results revealed that the mean concentration of all heavy metals exceeded the background values in Fujian province, with the mean concentration of Cd being 5.20 times higher than its background. However, these concentrations of heavy metals were lower than their corresponding national standards (GB 15618-1995). Spatially, for Cd, the high concentration areas were located mainly in southeast of Sanming city and northeast of Quanzhou city. For Pb and As, the places of highest concentration were mainly in southeast of Quanzhou city and Zhangzhou city, and the main areas of high Ni concentration were distributed southeast of Nanping city. The geo-accumulation index ([Formula: see text]) of Cd and As were indicative of moderate contaminations, and the index of Co, Cu and Cr suggested that these were practically uncontaminated. The nemerow integrated pollution index ([Formula: see text]) showed that the entire study area was prone to a low level of pollution, but at the county level, Yongcun county and Zhaoan county are in an warning line area of pollution. According to the potential ecological risk ([Formula: see text]), the ecological risk belongs to the low risk of paddy fields in Fujian province. However, Cd should be given attention ([Formula: see text] = 25.09), as it contributed to the majority of potential ecological risks in Fujian province.
Reference Key
kang2020pollutionscientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kang, Zhiming;Wang, Songliang;Qin, Junhao;Wu, Renyue;Li, Huashou;
Journal Scientific reports
Year 2020
DOI
10.1038/s41598-020-69165-x
URL
Keywords
Deep learning machine vision paddy field detecting and tracking moving obstacles

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