Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
Environ Pollut. 2021 Sep 15;285:117261. doi: 10.1016/j.envpol.2021.117261. Epub 2021 Apr 28.
Previous ecological risk assessments were mainly concentration-oriented rather than source-oriented. Moreover, land use is usually related to source emissions but was rarely used to improve the source apportionment accuracy. In this study, the land-use effects of heavy metals (HMs) in surface (0-20 cm) and subsurface (20-40 cm) soils were first explored using ANOVA in a suburb of Changzhou City, China; next, based on robust absolute principal component scores-robust geographically weighted regression (RAPCS/RGWR), this study proposed RAPCS/RGWR with land-use type (RAPCS/RGWR-LUT) and compared its source apportionment accuracy with those of basic RAPCS/RGWR and commonly-used absolute principal component scores/multiple linear regression (APCS/MLR); then, the source-oriented ecological risks were apportioned based on RAPCS/RGWR-LUT and Hakanson potential ecological risk index method; finally, this study proposed robust residual kriging with land-use type (RRK) for spatially predicting the source-oriented ecological risks, and compared its spatial prediction accuracy with those of robust ordinary kriging (ROK) and traditionally-used ordinary kriging (OK). Results showed that: (i) by incorporating land-use effects, RAPCS/RGWR-LUT obtained higher source apportionment accuracy than RAPCS/RGWR and APCS/MLR; (ii) the two most important external input sources of the ecological risks were 'atmospheric deposition' (PERI = 47.11 and PERI = 35.27) and 'agronomic measure' (PERI = 28.93 and PERI = 20.37); (iii) the biggest ecological risk factor was soil Cd (ER = 57.14 and ER = 47.62), which was mainly contributed by 'atmospheric deposition' (ER=33.14 and ER=25.71); (iv) RRK obtained higher spatial prediction accuracy than ROK and OK; (v) the high-risk areas derived from 'atmospheric deposition' were mainly located in the southwest of the study area, and the high-risk areas derived from 'agronomic measure' were scattered in the agricultural land in the north and south of the study area. The above information provided effective spatial decision support for reducing the source-oriented input of the ecological risks of soil HMs in a large-scale area.
先前的生态风险评估主要以浓度为导向，而不是以源为导向。此外，土地利用通常与源排放有关，但很少用于提高源解析的准确性。本研究首先在中国常州市郊区利用方差分析探讨了重金属（HMs）在表层（0-20cm）和次表层（20-40cm）土壤中的土地利用效应；接下来，基于稳健绝对主成分得分-稳健地理加权回归（RAPCS/RGWR），本研究提出了基于土地利用类型的 RAPCS/RGWR（RAPCS/RGWR-LUT），并比较了其与基本 RAPCS/RGWR 和常用的绝对主成分得分/多元线性回归（APCS/MLR）的源解析准确性；然后，基于 RAPCS/RGWR-LUT 和 Hakanson 潜在生态风险指数方法对源导向生态风险进行了分配；最后，本研究提出了基于土地利用类型的稳健剩余克立格（RRK），用于空间预测源导向生态风险，并比较了其与稳健普通克立格（ROK）和传统普通克立格（OK）的空间预测准确性。结果表明：（i）通过纳入土地利用效应，RAPCS/RGWR-LUT 获得了比 RAPCS/RGWR 和 APCS/MLR 更高的源解析准确性；（ii）生态风险的两个最重要的外部输入源是“大气沉降”（PERI=47.11 和 PERI=35.27）和“农业措施”（PERI=28.93 和 PERI=20.37）；（iii）最大的生态风险因素是土壤 Cd（ER=57.14 和 ER=47.62），主要由“大气沉降”（ER=33.14 和 ER=25.71）贡献；（iv）RRK 获得了比 ROK 和 OK 更高的空间预测准确性；（v）源自“大气沉降”的高风险区域主要位于研究区西南部，源自“农业措施”的高风险区域则散布在研究区北部和南部的农业用地中。以上信息为在大面积区域减少土壤 HMs 源导向输入提供了有效的空间决策支持。
