Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Beijing Municipal Key Laboratory of Agriculture Environment Monitoring, Beijing 100097, China.
Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Beijing Municipal Key Laboratory of Agriculture Environment Monitoring, Beijing 100097, China; Collaborative Innovation Center for Key Technology of Smart Irrigation District in Hubei, Yichang 443002, China.
Sci Total Environ. 2017 Feb 15;580:430-439. doi: 10.1016/j.scitotenv.2016.10.088. Epub 2016 Dec 28.
The aim of this study was to measure the improvement in mapping accuracy of spatial distribution of Cd in soils by using geostatistical methods combined with auxiliary factors, especially qualitative variables. Significant correlations between Cd content and correlation environment variables that are easy to obtain (such as topographic factors, distance to residential area, land use types and soil types) were analyzed systematically and quantitatively. Based on 398 samples collected from a Cd contaminated area (Hunan Province, China), we estimated the spatial distribution of Cd in soils by using spatial interpolation models, including ordinary kriging (OK), and regression kriging (RK) with each auxiliary variable, all quantitative variables (RKWQ) and all auxiliary variables (RKWA). Results showed that mapping with RK was more consistent with the sampling data of the spatial distribution of Cd in the study area than mapping with OK. The performance indicators (smaller mean error, mean absolute error, root mean squared error values and higher relative improvement of RK than OK) indicated that the introduction of auxiliary variables can improve the prediction accuracy of Cd in soils for which the spatial structure could not be well captured by point-based observation (nugget to sill ratio=0.76) and strong relationships existed between variables to be predicted and auxiliary variables. The comparison of RKWA with RKWQ further indicated that the introduction of qualitative variables improved the prediction accuracy, and even weakened the effects of quantitative factors. Furthermore, the significantly different relative improvement with similar R and varying spatial dependence showed that a reasonable choice of auxiliary variables and analysis of spatial structure of regression residuals are equally important to ensure accurate predictions.
本研究旨在通过使用地统计学方法结合辅助因子（尤其是定性变量）来衡量土壤中 Cd 空间分布的制图精度的提高。系统地、定量地分析了 Cd 含量与易获得的相关环境变量（如地形因子、与居民区的距离、土地利用类型和土壤类型）之间的显著相关性。基于从中国湖南省 Cd 污染区采集的 398 个样本，我们使用空间插值模型（包括普通克里金（OK）和带有每个辅助变量的回归克里金（RK）、所有定量变量（RKWQ）和所有辅助变量（RKWA））来估计土壤中 Cd 的空间分布。结果表明，与 OK 相比，RK 制图与研究区域 Cd 空间分布的采样数据更一致。性能指标（较小的平均误差、平均绝对误差、均方根误差值以及 RK 相对于 OK 的相对改进较高）表明，引入辅助变量可以提高那些空间结构无法被基于点的观测（块金效应值到基台值比=0.76）很好地捕捉的土壤中 Cd 的预测精度，并且待预测变量与辅助变量之间存在很强的关系。RKWA 与 RKWQ 的比较进一步表明，定性变量的引入提高了预测精度，甚至削弱了定量因素的影响。此外，具有相似 R 值但空间依赖性不同的相对改进差异显著，表明合理选择辅助变量和分析回归残差的空间结构对于确保准确预测同样重要。
