Exploring spatial distributions of increments in soil heavy metals and their relationships with environmental factors using GWR-Reference-Cited by-同舟云学术

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Exploring spatial distributions of increments in soil heavy metals and their relationships with environmental factors using GWR

Published:2021-02-10 Issue:10 Volume:35 Page:2173-2186
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Li Hao,Fu Peihong,Yang Yong,Yang Xue,Gao Hongjie,Li Ke

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-021-01986-2.pdf

Reference46 articles.

1. Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28(4):281–298

2. Brunsdon C, Fotheringham AS, Charlton M (1998) Geographicallyweighted regression - modelling spatial non-stationarity. Society 47(3):431–443

3. Carlon C, Dalla Valle M, Maromini A (2004) Regression models to predict water-soil heavy metals partition coefficients in risk assessment studies. Environ Pollut 127(1):109–115

4. Chen R, Chen H, Song L, Yao Z, Meng F, Teng Y (2019) Characterization and source apportionment of heavy metals in the sediments of Lake Tai (China) and its surrounding soils. Sci Total Environ 694:133819

5. Chen W, Peng L, Hu K, Zhang Z, Peng C, Teng C, Zhou K (2020) Spectroscopic response of soil organic matter in mining area to Pb/Cd heavy metal interaction: a mirror of coherent structural variation. J Hazard Mater 393:122425

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