Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
Environ Pollut. 2021 Feb 1;270:116220. doi: 10.1016/j.envpol.2020.116220. Epub 2020 Dec 6.
High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.
高密度样本通常是在大面积范围内获得高精度源解析结果的前提条件。原位现场便携式 X 射线荧光光谱法（FPXRF）是一种快速廉价的增加土壤重金属（HM）样本量的方法。此外，分类土地利用类型可能与源贡献密切相关。然而，上述信息很少被纳入源解析中。在本研究中，首先使用稳健的地理加权回归（RGWR）来校正与土壤相关的因子（例如土壤水分和土壤有机质）对中国武汉市城乡边缘地区原位 FPXRF 的空间变异性影响，并比较了 RGWR 的校正精度与传统非空间多元线性回归（MLR）和基本 GWR 的校正精度。然后，使用方差分析（ANOVA）来划分土地利用类型对 HM 浓度的影响。最后，基于稳健的空间受体模型（即稳健绝对主成分得分/RGWR [RAPCS/RGWR]），本研究提出了带有分类土地利用类型和 RGWR 校正原位 FPXRF 数据的 RAPCS/RGWR（RAPCS/RGWR_LU&FPXRF），并比较了其与不带或带一种辅助数据的 RAPCS/RGWR 的性能。结果表明：（i）原位 FPXRF 数据的校正模型的性能顺序为 RGWR＞GWR＞MLR，对于每种 HM，RGWR 相对于 MLR 的相对改进范围为 52.6%至 70.71%；（ii）分类土地利用类型显著影响土壤 Zn、Cu、As 和 Pb 的浓度；（iii）RAPCS/RGWR_LU&FPXRF 获得了最高的源贡献估计精度，而基本 RAPCS/RGWR 获得了最低的源贡献估计精度。综上所述，土地利用类型和 RGWR 校正的原位 FPXRF 数据与源贡献密切相关，RAPCS/RGWR_LU&FPXRF 是一种经济有效的大面积土壤重金属源解析方法。
