实验室新闻

    科研进展

    实验室公告

    学术活动公告

 
Source apportionment of soil heavy metals using robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR) receptor model
 点击数:136次 添加时间:  [关闭] [收藏]

作者:Qu, MK (Qu, Mingkai); Wang, Y (Wang, Yan); Huang, B (Huang, Biao) ; Zhao, YC (Zhao, Yongcun) 

 

题目:Source apportionment of soil heavy metals using robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR) receptor model

 

刊物:SCIENCE OF THE TOTAL ENVIRONMENT,卷: 626  页: 203-210

DOI: 10.1016/j.scitotenv.2018.01.070

出版年: JUN 1 2018

 

文章下载:https://ac.els-cdn.com/S0048969718300913/1-s2.0-S0048969718300913-main.pdf?_tid=895b5789-2be1-437b-b1c7-fec998e2504a&acdnat=1523928035_f1b7a4b9d500bdb706402908b4004b51

 

摘要: 

The traditional source apportionment models, such as absolute principal component scores-multiple linear regression (APCS-MLR), are usually susceptible to outliers, which may bewidely present in the regional geochemical dataset. Furthermore, the models are merely built on variable space instead of geographical space and thus cannot effectively capture the local spatial characteristics of each source contributions. To overcome the limitations, a new receptor model, robust absolute principal component scores-robust geographicallyweighted regression (RAPCS-RGWR), was proposed based on the traditional APCS-MLR model. Then, the new method was applied to the source apportionment of soilmetal elements in a region ofWuhan City, China as a case study. Evaluations revealed that: (i) RAPCS-RGWR model had better performance than APCS-MLR model in the identification of the major sources of soilmetal elements, and (ii) source contributions estimated by RAPCS-RGWR model were more close to the true soil metal concentrations than that estimated by APCS-MLR model. It is shown that the proposed RAPCS-RGWR model is a more effective source apportionment method than APCS-MLR (i.e., nonrobust and global model) in dealing with the regional geochemical dataset. (c) 2018 Elsevier B.V. All rights reserved.

 

 

 

 

 

 

 


 

版权所有:中国科学院南京土壤研究所 苏ICP备05004320号 地 址:江苏省南京市北京东路71号 邮编:210008 网站管理