广东省东南部菜地水田砷含量空间分布
[Abstract]:In order to solve the problem that the spatial distribution of arsenic content in agricultural land is difficult to identify efficiently and accurately under sparse sampling conditions, 104 samples of surface soil (0 ~ 20 cm) were collected by spatial analysis and multivariate regression modeling, taking agricultural land in the southeast of Guangdong Province as an example. The statistical characteristics of arsenic content in soil were analyzed by three types of agricultural land, namely vegetable field, paddy field and orchard, and the spatial distribution pattern and cause of spatial differentiation of arsenic content in soil were revealed by simulation. The results showed that: 1) the arsenic content of agricultural land in the study area was up to the standard on the whole, but the value of arsenic content in the soil reached 137.80 mg/kg, significantly higher than that of the second grade national standard for soil environmental quality (GB156182-1995); 2) the content of arsenic in vegetable soil was the highest in the three types of agricultural land, followed by paddy field, and the orchard was relatively low, with the mean values of 11.04 mg/kg; and 2.54 mg/kg;, respectively. 3) the sources of arsenic pollution in different agricultural land are different. The arsenic pollution of vegetable land is mainly affected by the accumulation of surface runoff by river sewage irrigation and exhaust gas deposition of chimney discharge under different topographic conditions, and the north side of the soil is relatively high in space. Low distribution pattern in other areas; 4) arsenic pollution in paddy fields was significantly affected by river sewage irrigation, and the spatial distribution of arsenic pollution was higher in the north and south and lower in the central region. The results provide an effective method for mapping and fine control strategies of heavy metal pollution in agricultural soil in sparse sampling monitoring area.
【作者单位】: 中南大学有色金属成矿预测与地质环境监测教育部重点实验室;中南大学冶金与环境学院国家重金属污染防治工程技术研究中心;
【基金】:中南大学‘创新驱动计划’项目(2015CXS005) 中国博士后科学基金特别资助项目(2013T60780)
【分类号】:X53
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