Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China

Abstract A suitable method and appropriate environmental variables are important for accurately predicting heavy metal distribution in soils. However, the classical methods (e.g., ordinary kriging (OK)) have a smoothing effect that results in a tendency to neglect local variability, and the commonly...

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Autores principales:	Qiquan Li, Changquan Wang, Tianfei Dai, Wenjiao Shi, Xin Zhang, Yi Xiao, Weiping Song, Bing Li, Yongdong Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f166f6465054226ad332b420d664ffc
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spelling	oai:doaj.org-article:2f166f6465054226ad332b420d664ffc2021-12-02T15:05:35ZPrediction of soil cadmium distribution across a typical area of Chengdu Plain, China10.1038/s41598-017-07690-y2045-2322https://doaj.org/article/2f166f6465054226ad332b420d664ffc2017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07690-yhttps://doaj.org/toc/2045-2322Abstract A suitable method and appropriate environmental variables are important for accurately predicting heavy metal distribution in soils. However, the classical methods (e.g., ordinary kriging (OK)) have a smoothing effect that results in a tendency to neglect local variability, and the commonly used environmental variables (e.g., terrain factors) are ineffective for improving predictions across plains. Here, variables were derived from the obvious factors affecting soil cadmium (Cd), such as road traffic, and were used as auxiliary variables for a combined method (HASM_RBFNN) that was developed using high accuracy surface modelling (HASM) and radial basis function neural network (RBFNN) model. This combined method was then used to predict soil Cd distribution in a typical area of Chengdu Plain in China, considering the spatial non-stationarity of the relationships between soil Cd and the derived variables based on 339 surface soil samples. The results showed that HASM_RBFNN had lower prediction errors than OK, regression kriging (RK) and HASM_RBFNNs, which didn’t consider the spatial non-stationarity of the soil Cd-derived variables relationships. Furthermore, HASM_RBFNN provided improved detail on local variations. The better performance suggested that the derived environmental variables were effective and HASM_RBFNN was appropriate for improving the prediction of soil Cd distribution across plains.Qiquan LiChangquan WangTianfei DaiWenjiao ShiXin ZhangYi XiaoWeiping SongBing LiYongdong WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qiquan Li Changquan Wang Tianfei Dai Wenjiao Shi Xin Zhang Yi Xiao Weiping Song Bing Li Yongdong Wang Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China
description	Abstract A suitable method and appropriate environmental variables are important for accurately predicting heavy metal distribution in soils. However, the classical methods (e.g., ordinary kriging (OK)) have a smoothing effect that results in a tendency to neglect local variability, and the commonly used environmental variables (e.g., terrain factors) are ineffective for improving predictions across plains. Here, variables were derived from the obvious factors affecting soil cadmium (Cd), such as road traffic, and were used as auxiliary variables for a combined method (HASM_RBFNN) that was developed using high accuracy surface modelling (HASM) and radial basis function neural network (RBFNN) model. This combined method was then used to predict soil Cd distribution in a typical area of Chengdu Plain in China, considering the spatial non-stationarity of the relationships between soil Cd and the derived variables based on 339 surface soil samples. The results showed that HASM_RBFNN had lower prediction errors than OK, regression kriging (RK) and HASM_RBFNNs, which didn’t consider the spatial non-stationarity of the soil Cd-derived variables relationships. Furthermore, HASM_RBFNN provided improved detail on local variations. The better performance suggested that the derived environmental variables were effective and HASM_RBFNN was appropriate for improving the prediction of soil Cd distribution across plains.
format	article
author	Qiquan Li Changquan Wang Tianfei Dai Wenjiao Shi Xin Zhang Yi Xiao Weiping Song Bing Li Yongdong Wang
author_facet	Qiquan Li Changquan Wang Tianfei Dai Wenjiao Shi Xin Zhang Yi Xiao Weiping Song Bing Li Yongdong Wang
author_sort	Qiquan Li
title	Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China
title_short	Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China
title_full	Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China
title_fullStr	Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China
title_full_unstemmed	Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China
title_sort	prediction of soil cadmium distribution across a typical area of chengdu plain, china
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/2f166f6465054226ad332b420d664ffc
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Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China

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