Abstract

There must be some uncertainty in the remediation areas delineated based on limited sample points, and resampling in the high-uncertainty areas is particularly necessary. In situ field portable X-ray fluorescence spectrometry (FPXRF), a rapid and cheap analysis method for soil heavy metals, is strongly affected by many spatially non-stationary soil factors. This study first delineated the high-uncertainty area (threshold-exceeding probabilities (PTE) between 30% and 70%) of soil Pb based on the 1000 realizations produced by sequential Gaussian simulation (SGS) with 93 ICP-MS Pb concentrations measured in a peri-urban agriculture area, China. Next, in situ FPXRF was used to increase sample density in this high-uncertainty area. Then, robust geographically weighted regression (RGWR) was used to correct the in situ FPXRF Pb, and the correction accuracies of RGWR, basic GWR, and traditionally-used ordinary least squares regression (OLSR) were compared. Finally, to explore the best way to combine these corrected in situ FPXRF concentrations in delineating the remediation area, we compared the following spatial simulation methods: basic SGS, sequential Gaussian co-simulation (CoSGS) with the RGWR-corrected in situ FPXRF Pb as auxiliary soft data (CoSGS-CorFPXRF), and SGS with the RGWR-corrected in situ FPXRF Pb as part of hard data (SGS-CorFPXRF). Results showed that (i) RGWR produced higher correction accuracy (RI=71.5%) than GWR (RI=59.68%) and OLSR (RI=25.58%) for the in situ FPXRF Pb; (ii) SGS-CorFPXRF produced less uncertainty (G=0.97) than CoSGS-CorFPXRF (G=0.95) and SGS (G=0.91) in the spatial simulation; (iii) High-uncertainty area (30%<PTE<70%) was reduced from 36.55% to 8.7% of the whole study area. It is concluded that the recommended methods are cost-effective to reduce the uncertainty in delineating the remediation areas of soil heavy metals.

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