广东省东南部菜地水田砷含量空间分布

针对稀疏采样条件下农用地土壤砷含量空间分布难以高效准确识别的问题,以广东省东南部农用地为例,采集表层土壤(0~20 cm)样本104个,借助空间分析与多元回归建模手段,分菜地、水田和果园3类农用地分析土壤砷含量统计特征、模拟揭示土壤砷含量空间分异的成因与空间分布格局。结果表明:1)研究区农用地土壤砷含量总体上达标,但个别样点土壤砷含量值高达137.80 mg/kg,显著高于国家土壤环境质量二级标准(GB156182-1995);2)3类农用地中菜地土壤砷含量最高、水田次之、果园相对较低,均值分别为11.04、9.89和2.54 mg/kg;3)各类农用地土壤砷污染来源不同,菜地砷污染主要在不同地形条件下,受河流污水灌溉和烟囱排放的废气沉降随地表径流累积影响,在空间上呈现北部偏高、其他地区偏低的分布格局;4)水田砷污染受河流污水灌溉影响显著,空间上呈现南北部偏高、中部地区偏低的污染格局。研究结果对在稀疏采样监测地区开展农用地土壤重金属污染制图与精细防控策略的制定提供了一种行之有效的方法。

Spatial distribution of As in vegetable field and paddy in southeast of Guangdong province

Abstract: The soil As pollution in agricultural land has received increasing attention globally due to its significant potential harm to the environment and human health. Knowing the source as well as the characteristics of pollutants is the premise for their control and treatment. But now, sampling for understanding pollutants in soil is difficult and costly resulting in limited number of soil samples from the As contaminated land. For the problem that it is hard to accurately and efficiently map the spatial distribution of soil As concentration based on limited soil samples. In this study, we collected a total of 104 top soil samples (0~20cm) from agricultural land, in a southeast of Guangdong province. A combined approach of spatial analysis and multiple regression modeling was developed to recognize the statistical characteristic of As concentration in sampling soil, and to reveal their formation causes of spatial variation and associated spatial patterns in vegetable field, paddy and orchard. Results showed that in general, As concentration of soil samples was largely not exceeding the national standard in the agricultural land of the studied area. However, As concentration of few soil samples was still exceeding the National Environment Quality Standard for soil (GB15618-1995), with the maximum value was 137.80 mg/kg. Among three different types of agricultural lands, the soil As concentration in vegetable field was the highest, followed by the paddy and orchard, with mean values of 11.04, 9.89 and 2.54 mg/kg, respectively. The sources of soil As contamination were variable in different agricultural land. In vegetable field, soil As concentration was closely interrelated with shortest distances between samples and chimneys and rivers, and the slope of sample sites. Significance value of the soil As concentration simulation model in vegetable field was 0.010 (P<0.05). In paddy, the dominant factor of soil As concentration was the shortest distances between samples and rivers. Significance value of the soil As concentration simulation model in paddy was 0.044 (P<0.05). However, the multiple linear regression models of soil As concentration simulation in orchard were not significant (P>0.05) in this study. The results suggested that the soil As contamination in vegetable field mainly was affected by the river sewage irrigation and surface runoff of polluted gas from chimneys. The area with high soil As concentration was distributed in the northern part, and lower in other regions. In paddy, the soil As contamination was significantly only affected by the sewage irrigation, with higher As contamination in the north and south, and lower in other regions. But no obvious source of soil As pollution was found in orchard soil. This study would provide a scientific method for accurately mapping the spatial distribution of heavy metal pollution in agricultural land and consequently guide the policy making of precise prevention and control based on sparse sampling data.