Spatial prediction and standard-exceeding risk assessment of soil available phosphorus based on geographically weighted regression kriging
WANG Yan1,2, QU Ming-kai2, CHEN Jian2, YANG Lan-fang1, HUANG Biao2, ZHAO Yong-cun2
1. Faculty of Resource and Environmental Science, Hubei University, Wuhan 430062, China;
2. Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Science, Nanjing 210008, China
In this study, geographically weighted regression kriging (GWRK) model was established to predict the spatial distribution pattern of soil available phosphorus in Jintan County, Jiangsu Province. Geographically weighted regression (GWR) was first used to quantify the local spatial regression relationships between soil available phosphorus and its three main influencing factors (i.e., soil total phosphorus, soil pH, and soil organic matter). And then the final prediction value of GWRK is the sum of the GWR prediction value and the regression residuals value interpolated by ordinary kriging (OK). In this study 52 independent verification samples were used to compare the prediction accuracy of the GWRK model and the traditional OK model. Finally, the standard-exceeding risk of the soil available phosphorus was assessed based on the results generated by the GWRK model. The GWR analysis showed that the relationships between soil available phosphorus and its three main influencing factors (i.e., soil total phosphorus, soil pH, and soil organic matter) were spatial non-stationary, with local regression coefficient changing with spatial position. Model comparison showed that the GWRK prediction result had lower mean absolute error (MAE), root mean square error (RMSE) and higher Pearson correlation coefficient (r). In addition, the relative improvement index (RI) of GWRK over OK was 19.61%. The risk assessment results showed that the 175.58km2 areas was divided into the risk area of the soil available phosphorus content exceeded the Environmental safety threshold (40mg/kg), which accounted for about 18% of the whole area. Therefore, the GWRK model could effectively assess the spatial distribution pattern of available content of the soil elements. And the local regression coefficient of GWR could provide more accurate spatial decision support for the regulation of soil elements available content at a regional scale.
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