Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources

Abstract Fast economic development, burgeoning population growth, and rapid urbanization have led to complex pollution sources contributing to water quality deterioration simultaneously in many developing countries including China. This paper explored the use of spatial regression to evaluate the im...

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Autores principales:	Xiaoying Yang, Qun Liu, Xingzhang Luo, Zheng Zheng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/88e28a3197804bf084c58f227d73ce84
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spelling	oai:doaj.org-article:88e28a3197804bf084c58f227d73ce842021-12-02T15:06:23ZSpatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources10.1038/s41598-017-08254-w2045-2322https://doaj.org/article/88e28a3197804bf084c58f227d73ce842017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08254-whttps://doaj.org/toc/2045-2322Abstract Fast economic development, burgeoning population growth, and rapid urbanization have led to complex pollution sources contributing to water quality deterioration simultaneously in many developing countries including China. This paper explored the use of spatial regression to evaluate the impacts of watershed characteristics on ambient total nitrogen (TN) concentration in a heavily polluted watershed and make predictions across the region. Regression results have confirmed the substantial impact on TN concentration by a variety of point and non-point pollution sources. In addition, spatial regression has yielded better performance than ordinary regression in predicting TN concentrations. Due to its best performance in cross-validation, the river distance based spatial regression model was used to predict TN concentrations across the watershed. The prediction results have revealed a distinct pattern in the spatial distribution of TN concentrations and identified three critical sub-regions in priority for reducing TN loads. Our study results have indicated that spatial regression could potentially serve as an effective tool to facilitate water pollution control in watersheds under diverse physical and socio-economical conditions.Xiaoying YangQun LiuXingzhang LuoZheng ZhengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaoying Yang Qun Liu Xingzhang Luo Zheng Zheng Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources
description	Abstract Fast economic development, burgeoning population growth, and rapid urbanization have led to complex pollution sources contributing to water quality deterioration simultaneously in many developing countries including China. This paper explored the use of spatial regression to evaluate the impacts of watershed characteristics on ambient total nitrogen (TN) concentration in a heavily polluted watershed and make predictions across the region. Regression results have confirmed the substantial impact on TN concentration by a variety of point and non-point pollution sources. In addition, spatial regression has yielded better performance than ordinary regression in predicting TN concentrations. Due to its best performance in cross-validation, the river distance based spatial regression model was used to predict TN concentrations across the watershed. The prediction results have revealed a distinct pattern in the spatial distribution of TN concentrations and identified three critical sub-regions in priority for reducing TN loads. Our study results have indicated that spatial regression could potentially serve as an effective tool to facilitate water pollution control in watersheds under diverse physical and socio-economical conditions.
format	article
author	Xiaoying Yang Qun Liu Xingzhang Luo Zheng Zheng
author_facet	Xiaoying Yang Qun Liu Xingzhang Luo Zheng Zheng
author_sort	Xiaoying Yang
title	Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources
title_short	Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources
title_full	Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources
title_fullStr	Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources
title_full_unstemmed	Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources
title_sort	spatial regression and prediction of water quality in a watershed with complex pollution sources
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/88e28a3197804bf084c58f227d73ce84
work_keys_str_mv	AT xiaoyingyang spatialregressionandpredictionofwaterqualityinawatershedwithcomplexpollutionsources AT qunliu spatialregressionandpredictionofwaterqualityinawatershedwithcomplexpollutionsources AT xingzhangluo spatialregressionandpredictionofwaterqualityinawatershedwithcomplexpollutionsources AT zhengzheng spatialregressionandpredictionofwaterqualityinawatershedwithcomplexpollutionsources
_version_	1718388461074907136

Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources

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