| 基于原位PXRF数据的土壤锰、锌污染分布协同克里金插值与高分地图绘制 |
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| 引用本文： | 赵曼颖,赵玉鑫,沈铁志,李淑,姚光远,陈曾思澈,刘玉强,徐亚.基于原位PXRF数据的土壤锰、锌污染分布协同克里金插值与高分地图绘制[J].环境科学研究,2023,36(3):599-609. |
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| 作者姓名： | 赵曼颖 赵玉鑫 沈铁志 李淑 姚光远 陈曾思澈 刘玉强 徐亚 |
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| 作者单位： | 1.吉林建筑大学市政与环境工程学院，吉林 长春 130118 |
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| 基金项目： | 国家重点研发计划项目(No.2020YFC1806304)；江苏省产学研合作项目(No.BY2021529)；吉林省大学生创新创业训练项目(No.202110191118) |
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| 摘 要： | 高分辨率土壤重金属污染绘图(HRMMs)有助于准确识别需要进行风险管控或修复的区域.传统HRMMs基于网格模式土壤采样，开展化学分析并采用地质统计插值方法绘制污染分布地图，成本高、速度慢，且不适合高度异质性污染场地.该研究提出了一种通过多元非线性回归改善便携式X射线荧光分析(PXRF)数据，采用改进的PXRF数据进行协同克里金插值，以及HRMMs地图绘制和重金属污染分布预测的新方法.为了支持模型的建立和验证，选择我国西北某锰、锌污染场地开展研究.结果表明：(1)引入PXRF数据作为协同克里金插值的辅助变量能有效提高插值精度，而校正的PXRF数据可进一步提高空间刻画精度.重金属Mn和Zn的校正PXRF协同克里金插值较原始PXRF协同克里金插值的平均误差分别降低了4.5%和78.2%.(2)主变量点位密度的变化会改变校正后PXRF协同克里金插值的精度.以Zn为例，当主变量点位密度大于4个/(104 m2)时，校正后的PXRF协同克里金插值的精度显著降低.(3)增加辅助变量点位密度可显著提高协同克里金插值精度.当辅助变量点位密度增至7个/(104 m2
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| 关 键 词： | 协同克里金插值 主变量点位密度 辅助变量点位密度 PXRF辅助变量 |
| 收稿时间： | 2022-10-14 |
Co-Kriging Interpolation of Mn and Zn Pollution Distribution and High-Score Mapping Based on in situ PXRF Data |
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| Affiliation: | 1.School of Municipal and Environmental Engineering, Jilin Jianzhu University, Changchun 130118, China2.Chinese Research Academy of Environmental Sciences, Beijing 100012, China3.Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment, Beijing 100029, China4.China Railway Shenyang Group Co., Ltd., Shenyang 110000, China |
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| Abstract: | High-resolution heavy metal contamination mapping (HRMMs) helps to accurately identify areas requiring risk management or remediation. Traditional HRMMs based on grid model soil sampling, chemical analysis and geostatistical interpolation methods to draw pollution distribution maps were costly, slow, and not suitable for highly heterogeneous contaminated sites. This study proposes a new method to improve PXRF (Portable X-ray fluorescence analysis) data through multiple nonlinear regression, use the improved PXRF data for collaborative spatial interpolation, draw HRMMs maps and predict the distribution of heavy metal pollution. In order to support the establishment and validation of the model, an Mn and Zn contaminated site in northwest China was selected for research. The results show that: (1) The introduction of PXRF data as an auxiliary variable of Co-Kriging interpolation effectively improved the interpolation accuracy, while the corrected PXRF data further improved the spatial characterization accuracy. The average errors of the corrected PXRF Co-Kriging interpolation for Mn and Zn were 4.5% and 78.2% lower than those of the original PXRF Co-Kriging interpolation. (2) The change in the point density of the primary variable changed the accuracy of the PXRF Co-Kriging interpolation after correction. Taking Zn as an example, when the main variable point density was 4 holes/(104 m2), the corrected PXRF Co-Kriging interpolation accuracy was significantly reduced. (3) Increasing the point density of auxiliary variables significantly improved the accuracy of Co-Kriging interpolation. When the point density of the auxiliary variable was increased to 7 holes/(104 m2), the mean error and root mean square error of PXRF Co-Kriging interpolation after Zn correction were reduced by 92.4% and 34.7%, respectively. The research shows that the correction of PXRF data can effectively improve the accuracy of pollutant Co-Kriging interpolation. At the same time, Co-Kriging interpolation needs to meet certain amount of requirements for the point density of primary variables, and the higher the point density of auxiliary variables, the higher the accuracy of Co-Kriging interpolation. |
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