School of Land Science and Technology, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, 100083, Beijing, People's Republic of China.
Key Laboratory of Land Consolidation and Rehabilitation, The Ministry of Land and Resources, Beijing, 100035, People's Republic of China.
Environ Sci Pollut Res Int. 2019 Jun;26(16):16345-16354. doi: 10.1007/s11356-019-04973-x. Epub 2019 Apr 12.
As we all know, geochemical data usually contain outliers and they are heterogeneous, which will severely affect the use of receptor models based on classical estimates. In this paper, an advanced modified RAPCS-RGWR (robust absolute principal component scores-robust geographically weighted regression) receptor model was introduced to analyze the pollution sources of eight heavy metals (Cd, Hg, As, Pb, Ni, Cu, Zn) in a city of southern China. The results showed that source identification and source apportionment are more consistent by this advanced model even though the soil types and farming patterns are diverse. Moreover, this model decreased the occurrence of negative values of the source contribution. For these reasons, the pollution sources were classified into five types by the new model in the study area: agricultural sources, industrial sources, traffic sources, comprehensive sources, and natural sources. (1) The contributions of agricultural sources to Cr and Ni were 243.36% and 242.61%, respectively; (2) the contribution of industrial sources to Cd was 79.25%; (3) the contribution of traffic sources to Cu was 100.31%; (4) the contributions of comprehensive sources to Hg, Pb, and Zn were 253.90%, 242.31%, and 93.32%, respectively; and (5) the contribution of natural sources to As was 208.21%. Overall, the RAPCS-RGWR receptor model improved the validity of the receptor models. It is of great realistic significance to understand and popularize the advanced model in soil source apportionment in agricultural land.
众所周知，地球化学数据通常包含异常值，且具有异质性，这将严重影响基于经典估计的受体模型的使用。本文引入了一种先进的修正 RAPCS-RGWR（稳健绝对主成分得分-稳健地理加权回归）受体模型，以分析中国南方某城市八种重金属（Cd、Hg、As、Pb、Ni、Cu、Zn）的污染源。结果表明，即使土壤类型和耕作方式多样，该先进模型也能更一致地进行源识别和源分配。此外，该模型减少了源贡献出现负值的情况。由于这些原因，新模型将研究区域的污染源分为五类：农业源、工业源、交通源、综合源和自然源。（1）农业源对 Cr 和 Ni 的贡献率分别为 243.36%和 242.61%；（2）工业源对 Cd 的贡献率为 79.25%；（3）交通源对 Cu 的贡献率为 100.31%；（4）综合源对 Hg、Pb 和 Zn 的贡献率分别为 253.90%、242.31%和 93.32%；（5）自然源对 As 的贡献率为 208.21%。总体而言，RAPCS-RGWR 受体模型提高了受体模型的有效性。该模型对于理解和推广农业用地土壤源分配中的先进模型具有重要的现实意义。
