摘要
为获得吉林省某日遗化武污染场地土壤中砷含量的空间分布规律,在面积26.19 hm2场地内以网格法采集了2668个表层(-0.5~0 m)土壤样品和637个亚层(-1.0^-0.5 m)土壤样品并进行砷含量的测定。利用地统计学理论探讨了研究区域土壤砷含量的半变异函数及最佳拟合模型,在地理信息系统(GIS)平台上插值分析,并结合土壤垂直剖面(-1.0~0 m)砷含量的分析,得到研究区土壤空间分布规律。结果表明,研究区域表层砷含量为中等程度的空间相关,其最佳半变异函数模型为指数模型,最佳插值模型为简单克里格插值;亚层砷含量与表层砷含量空间结构相似,但表、亚层砷含量呈弱相关;土壤剖面分析表明砷污染主要集中在表面土层,并且随着土壤层深度的增加,大多数样点砷含量降低;砷含量的空间变异受销毁含砷日遗化武、挖掘回收日遗化武、农耕等人为因素影响显著。
A total of 2668 surface soil samples(-0.5 ~ 0 m) and 637 sub-layer soil samples(-1.0 ^-0.5 m) were collected from a contaminated site with an area of 26.19 hectares and analyzed for concentration of arsenic, so as to get the spatial distribution of arsenic concentrations in the soil contaminated by the chemical weapons abandoned by Japan invaders in Jilin Province. The semivariogram of arsenic content in the soil in survey region was discussed with a geostatistics method, and the best fitted model was explored. The geographic information system(GIS) was used for interpolation analysis. Combined with the concentration of arsenic in vertical sections(-1.0 ~ 0 m), the spatial distribution of arsenic in research region was accessed. The results indicated that spatial correlation of arsenic content in surface soil was moderate. The optimal semivariogram model was the exponential model, and the best interpolation model was simple Kriging interpolation. The arsenic content of sub-layer was similar to that of surface soil in space structure, with weak correlations with each other. Analysis on the arsenic content in vertical sections showed that contamination was mainly concentrated in surface soil. The arsenic content in most of the collected samples reduced as the soil layer depth increased. Spatial variability of arsenic could be significantly affected by anthropogenic factors such as excavation and destruction of chemical weapons with arsenic and farming.
出处
《土壤通报》
CAS
CSCD
北大核心
2014年第2期486-492,共7页
Chinese Journal of Soil Science
关键词
日本遗弃在华化学武器
砷污染土壤
地统计学
半变异函数
空间变异
Chemical weapons abandoned by Japan
As-contaminated Soil
Geostatistics
Semivariogram
Spatial variability
