研究报告
刘庚,毕如田,张朝,魏文侠,李发生,郭观林.某焦化场地苯并(a)芘污染空间分布范围预测的不确定性分析[J].环境科学学报,2013,33(2):587-593
某焦化场地苯并(a)芘污染空间分布范围预测的不确定性分析
- Uncertainty analysis on spatial distribution prediction of BaP in a coking plant site
- 基金项目：环境保护公益性行业科研专项经费项目(No.201009015);国家自然科学基金青年基金项目(No.40901249)
- 作者
- 单位
- 刘庚
- 1. 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012;
2. 山西农业大学资源环境学院, 太谷 030801
- 毕如田
- 山西农业大学资源环境学院, 太谷 030801
- 张朝
- 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012
- 魏文侠
- 轻工业环境保护研究所, 北京 100012
- 李发生
- 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012
- 郭观林
- 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012
- 摘要：针对目前我国大型工业污染场地调查和污染范围确定过程中的不确定性,以我国某焦化场地为研究对象,选择场地中的特征性污染物苯并(a)芘,分别采用反距离加权模型、Johnson数据正态变换后的普通克里格模型及数据拆分后的组合预测模型,参照北京市污染场地土壤筛选值标准,将大于规定标准0.4 mg·kg-1的区域界定为污染范围,系统阐述空间插值模型对污染范围界定结果的不确定性影响.结果显示,3种插值模型界定的污染范围分别占场地总面积的70.15%、44.78%和57.06%,数据拆分后的组合预测模型插值精度最高,插值结果能较真实地反映场地实际污染情况,通过创建的预测标准误差图显示,预测标准误差大的区域主要集中在污染场地中样点较为稀疏的右上位置和有高值点的中下部区域.本研究结果对分析污染范围界定结果的不确定性和修复治理范围的确定提供了重要参考.
- Abstract：Uncertainty in site investigation and the determination of contamination boundary at large-scale contaminated sites is a critical issue in China. In order to test the influence of different prediction models on the determination of contamination boundary in a coking plant contaminated by benzo(a)pyrene (BaP), three spatial interpolation models, Inverse Distance Weighting model (IDW), Johnson's ordinary lognormal kriging model (OLKM), and Combination Prediction Model (CPM), were employed to compare their efficiencies and precisions in determining site contamination boundary. A recommended value 0.4 mg·kg-1 for BaP was used as the reference standard based on the Beijing Screening Levels for Soil Environmental Risk Assessment of Sites. The Results showed that the contamination areas calculated by IDW, OLKM, and CPM were 70.15%, 44.78% and 57.06%, respectively. The CPM was found to be more accurate than the other two models in predicting the actual pollution situations of the contaminated site. The area on the top-right corner of the site with less sampling points and the area in the mid-bottom part with high levels of contamination showed the largest standard errors based on the prediction standard errors map. This study gives useful references for dealing with uncertainty in determining remediation boundary of contaminated sites.
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