Improving assessment quality of soil natural attenuation capacity at the point and regional scales.

Abstract

Soil natural attenuation capacity (NAC) is an important ecosystem service that maintains a clean environment for organisms in the soil, which in turn supports other services. However, spatially varying indicator weights were rarely considered in the traditionally-used soil NAC assessment model (e.g., ecosystem-service performance model) at the point scale. Moreover, in the spatial simulation of soil NAC, the traditionally-used geostatistical models were usually susceptible to spatial outliers and ignored valuable auxiliary information (e.g., land-use types). This study first proposed a novel soil NAC assessment method based on the ecosystem-service performance model and moving window-entropy weight method (MW-EW) (NACMW-EW). Next, NACMW-EW was used to assess soil NAC in a typical area in Guixi City, China, and further compared with the traditionally-used NACtra and NACEW. Then, robust sequential Gaussian simulation with land-use types (RSGS-LU) was established for the spatial simulation of NACMW-EW and compared with the traditionally-used SGS, SGS-LU, and RSGS. Last, soil NAC's spatial uncertainty was evaluated based on the 1000 realizations generated by RSGS-LU. The results showed that: (i) MW-EW effectively revealed the spatially varying indicator weights but EW couldn't; (ii) NACMW-EW obtained more reasonable results than NACtra and NACEW; (iii) RSGS-LU (RMSE = 0.118) generated higher spatial simulation accuracy than SGS-LU (RMSE = 0.123), RSGS (RMSE = 0.132), and SGS (RMSE = 0.135); and (iv) the relatively high (P[NACMW-EW(u) > 0.57] ≥ 0.95) and low (P[NACMW-EW(u) > 0.57] ≤ 0.05) threshold-exceeding probability areas were mainly located in the south and east of the study area, respectively. It is concluded that the proposed methods were effective tools for soil NAC assessment at the point and regional scales, and the results provided accurate spatial decision support for soil ecosystem service management.