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焦化场地内外土壤重金属空间分布及驱动因子差异分析
引用本文:顾高铨,万小铭,曾伟斌,雷梅.焦化场地内外土壤重金属空间分布及驱动因子差异分析[J].环境科学,2021,42(3):1081-1092.
作者姓名:顾高铨  万小铭  曾伟斌  雷梅
作者单位:中国科学院地理科学与资源研究所,北京 100101;中国科学院大学,北京100049
基金项目:国家重点研发计划项目(2018YFC1800302)
摘    要:焦化场地作为典型的工业污染场地,其特征污染物重金属严重危害人体健康,研究其场地内外污染物的空间分布及驱动因子,对于后续的采样设计、风险评估、污染防控等工作具有重要指导意义.本研究基于反距离加权法分析某在产焦化厂内部及外部的重金属As、Cd、Cr、Cu、Hg、Ni、Pb和Zn的空间分布,并利用地理探测器分析焦化厂内部及外部的重金属空间分布驱动因子差异.结果表明,该焦化厂内部及周边除As、Ni和Zn外,其余重金属的超背景值率均在50%以上,且内外部重金属变异系数超过30%,空间分布连续性较差.其中内部平均变异程度为:Hg > Cd > As > Cu > Zn > Cr > Pb > Ni,外部平均变异程度为:Hg > Cu > Cd > As > Zn > Pb > Cr > Ni.根据分异及因子探测结果,理化性质因子中对焦化厂内部及外部重金属空间分布贡献最大的均为土壤全氮、有机质和有效中微量元素含量;距离污染源因子中对内部重金属空间分布贡献最大的为粗苯、冷鼓工段,对外部重金属空间分布贡献最大的为焦炉熄焦工段,并且污染源及土壤理化性质的交互因子对内部重金属空间分布的贡献度略高于外部.根据结果可知,决定焦化厂内部及外部的重金属空间分布的理化性质驱动因子较为一致,其主要基于土壤养分元素对重金属有效性的影响.而决定焦化厂内外部重金属分布的污染源存在差异,内部重金属分布主要受到焦化产品精制工艺中排放含重金属废气及废水等的驱动,外部重金属分布主要受到炼焦制气工艺中排放废气沉降的驱动.

关 键 词:重金属  变异系数  反距离加权法  地理探测器  驱动因子
收稿时间:2020/8/21 0:00:00
修稿时间:2020/10/21 0:00:00

Analysis of the Spatial Distribution of Heavy Metals in Soil from a Coking Plant and Its Driving Factors
GU Gao-quan,WAN Xiao-ming,ZENG Wei-bin,LEI Mei.Analysis of the Spatial Distribution of Heavy Metals in Soil from a Coking Plant and Its Driving Factors[J].Chinese Journal of Environmental Science,2021,42(3):1081-1092.
Authors:GU Gao-quan  WAN Xiao-ming  ZENG Wei-bin  LEI Mei
Institution:Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:Coking plants are typical industrial pollution sites and may release heavy metals into the environment, posing a threat to human health. Scholars have discovered that different types of heavy metals are released during different coking production processes and lead to spatial differences in heavy metals. Research on the spatial distribution and driving factors of pollutants in the soil inside and outside coking plants is important for sampling design, risk assessment, pollution prevention and control, etc.. Inverse distance weight was used to analyze the spatial distribution of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn inside and outside of the coking plant. A geo-detector was used to find out the difference in the driving factors for the spatial distribution of heavy metals between soil from inside and outside the coking plant. The results showed that except As, Ni, and Zn, the overall background value rate of other heavy metals was above 50%, and the continuity of the spatial distribution of heavy metals in the soil was poor. The coefficient of variation (CV) exceeded 30%, representing a moderate variation. The average degree of CV inside the coking plant was Hg > Cd > As > Cu > Zn > Cr > Pb > Ni, and the external average degree of CV was Hg > Cu > Cd > As > Zn > Pb > Cr > Ni. An analysis of heavy metal content showed that the content of As, Cd, Cr, Pb, and Zn outside the coking plant was bigger than inside. According to geo-detector results, the physicochemical properties factors with a large contribution rate to the spatial distribution of heavy metals inside and outside the coking plant was the soil''s total nitrogen, organic matter, and available medium-micro element content. Pollution source factors that contributed the most to the spatial distribution of heavy metals inside were the crude benzol and cold drum section, while the coke oven and quench section determined the outside spatial distribution of heavy metals. The q value of the strongest factor inside the coking plant was more than 0.5 while outside the coking plant it was less than 0.5. According to the interaction detector result, the interaction factors values of pollution sources and soil physicochemical properties to the inside spatial distribution of heavy metals was higher than outside. According to the distribution and geo-detector results, the strongest physicochemical properties driving factors that determined the inside and outside spatial distribution of heavy metals were relatively consistent. These factors were soil nutrient factors, which mainly influenced the availability of heavy metals. The differences in the production processes led to the difference between the inside and outside spatial distribution of heavy metals. The content of heavy metals outside the coking plant was higher than inside because the heavy metals came from various pollution sources. The driving forces for the distribution of heavy metals inside the plant were higher than outside and showed that the heavy metals inside of the plant were mainly from the coking plant. Heavy metal distribution inside the coking plant was mainly driven by the pollution source factor of the coking refining process and coking water, while heavy metal distribution outside the coking plant was mainly driven by the coking gas production process and other emission pollution source factors.
Keywords:heavy metals  coefficient of variation  inverse distance weighting  geo-detector  driving factor
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