High-accuracy spatial prediction of soil pollutants and their speciation in strong human-affected areas

Strong human activities greatly challenge the high-accuracy spatial prediction of soil pollutants and their speciation. This study first determined three auxiliary variables of soil total arsenic (TA) in a typical strong human-affected area, namely in-situ portable X-ray fluorescence (PXRF) TA calibrated by robust geographically weighted regression (RGWR), atmospheric deposition information simulated by atmospheric diffusion model (AERMOD), and land-use types. Then, robust residual cokriging with the above three auxiliary variables (RRCoK-RCPXRF/AD/LUT) was proposed to spatially predict soil TA. Finally, RGWR-robust ordinary kriging (RGWR-ROK) with the RRCoK-predicted soil TA was proposed to spatially predict soil As(III). The results show that: (i) RGWR obtained a higher spatial calibration accuracy (RI = 64.78%) for in-situ PXRF TA than the basic geographically weighted regression and traditionally-used ordinary least squares; (ii) The effect of auxiliary variables and model robustness on the prediction accuracy of soil TA is significant (RI > 14.33%); (iii) RRCoK-RCPXRF/AD/LUT achieved a higher prediction accuracy (RI = 58.87%) for soil TA than the other six traditional models; and (iv) RGWR-ROK achieved a higher prediction accuracy (RI = 55.26%) for soil As(III) than the other three traditional models. Therefore, this study provided a cost-effective solution for high-accuracy spatial prediction of soil pollutants and their speciation in strong human-affected areas.