Additional sampling using in-situ portable X-ray fluorescence (PXRF) for rapid and high-precision investigation of soil heavy metals at a regional scale. (1st January 2022)

• Record Type: Journal Article
• Authors: Qu, Mingkai; Guang, Xu; Liu, Hongbo; Zhao, Yongcun; Huang, Biao
• Is Part Of: Environmental pollution. Volume 292:Part A(2022)
• Publication Date: 2022-01-01
• Subjects: Soil pollution investigation -- Regional scale -- Additional sampling -- In-situ portable X-ray fluorescence -- Spatially non-stationary effects; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2021.118324
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Traditional soil heavy metal (HM) investigation usually costs a lot of human and material resources. In-situ portable X-ray fluorescence spectrometry (PXRF) is a cheap and rapid HM analysis method, but its analysis accuracy is usually affected by spatially non-stationary field environment factors. In this study, residual sequential Gaussian co-simulation (RCoSGS) was first proposed to incorporate both continuous and categorical auxiliary variables for spatial simulation of soil Cu. Next, additional in-situ PXRF sampling sites ( n = 300) were allocated in the subareas with high, medium, and low conditional variances in the proportions of 50%, 33.33%, and 16.67%, respectively. Then, robust geographically weighted regression (RGWR) was established to correct the spatially non-stationary effects of field environmental factors on in-situ PXRF and further compared with the traditionally-used multiple linear regression (MLR) and basic GWR in correction accuracy. Finally, RCoSGS with the RGWR-corrected in-situ PXRF as part of hard data (RCoSGS-PXRF) was established and further compared with the model with one or multiple auxiliary variables in the spatial simulation accuracy. Results showed that (i) RCoSGS effectively incorporated both SOM and land-use types and obtained higher spatial simulation accuracy ( RI = 37.52%) than residual sequential Gaussian simulation with land-use types ( RI = 19.44%) and sequential Gaussian co-simulation with SOM ( RI = 20.92%); (ii) RGWR significantly weakened the spatially non-stationary effects of field environmental factors on in-situ PXRF, and RGWR ( RI = 58.96%) and GWR ( RI = 39.61%) obtained higher correction accuracy than MLR; (iii) the RGWR-corrected in-situ PXRF ( RI = 66.57%) brought higher spatial simulation accuracy than both land-use types and SOM ( RI = 37.52%); (iv) RCoSGS-PXRF obtained the highest spatial simulation accuracies ( RI = 83.74%). Therefore, the proposed method is cost-effective for the rapid and high-precision investigation of soil HMs at a regional scale. Graphical abstract: Image 1 Highlights: RCoSGS was proposed to incorporate both continuous and categorical auxiliary data. Additional in-situ PXRF sampling sites were allocated based on conditional variance. RGWR weakened the spatially non-stationary effects of soil factors on in-situ PXRF. RCoSGS-PXRF was established using RGWR-corrected in-situ PXRF as part of hard data. RCoSGS-PXRF obtained the highest spatial simulation accuracies. Abstract : RCoSGS was first proposed to incorporate both continuous and categorical auxiliary variables; then, additional in-situ PXRF sampling sites were allocated based on conditional variance; finally, RGWR-corrected RXRF was incorporated into RCoSGS as hard data for spatial simulation.