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Water quality assessment of east Tiaoxi River, China, based on a comprehensive water quality index model and Monte-Carlo simulation

The comprehensive water quality index (CWQI) reflects the comprehensive pollution status of rivers through mathematical statistics of several water quality indicators. Using computational mathematical simulations, high-confidence CWQI predictions can be obtained based on limited water quality monito...

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Autores principales: Jin, Wei, Li, Yuan, Lu, Li, Zhang, Dong, He, Shanying, Shentu, Jiali, Chai, Qiwei, Huang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203578/
https://www.ncbi.nlm.nih.gov/pubmed/35710812
http://dx.doi.org/10.1038/s41598-022-14293-9
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author Jin, Wei
Li, Yuan
Lu, Li
Zhang, Dong
He, Shanying
Shentu, Jiali
Chai, Qiwei
Huang, Lei
author_facet Jin, Wei
Li, Yuan
Lu, Li
Zhang, Dong
He, Shanying
Shentu, Jiali
Chai, Qiwei
Huang, Lei
author_sort Jin, Wei
collection PubMed
description The comprehensive water quality index (CWQI) reflects the comprehensive pollution status of rivers through mathematical statistics of several water quality indicators. Using computational mathematical simulations, high-confidence CWQI predictions can be obtained based on limited water quality monitoring samples. At present, most of the CWQI reported in the literature are based on conventional indicators such as nitrogen and phosphorus levels, and do not include the petroleum hydrocarbons levels. This article takes a typical river in eastern China as an example, based on the 1-year monitoring at 20 sampling sets, a CWQI containing five factors, TN, NH(4)(+)-N, TP, ∑n-Alks, and ∑PAHs was established, and further predicted by a Monte-Carlo model. The predicted CWQI for each monitoring section is above 0.7, indicating that most of the monitoring sections are moderately polluted, and some sections are seriously polluted. The Spearman rank correlation coefficient analysis results show that TN, ∑PAHs, and ∑n-Alks are the main factors influencing the water quality, especially the petroleum hydrocarbons have a significant impact on the middle and lower reaches due to shipping. In the future, more attention should be paid to petroleum hydrocarbon organic pollutants in the water quality evaluation of similar rivers.
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spelling pubmed-92035782022-06-18 Water quality assessment of east Tiaoxi River, China, based on a comprehensive water quality index model and Monte-Carlo simulation Jin, Wei Li, Yuan Lu, Li Zhang, Dong He, Shanying Shentu, Jiali Chai, Qiwei Huang, Lei Sci Rep Article The comprehensive water quality index (CWQI) reflects the comprehensive pollution status of rivers through mathematical statistics of several water quality indicators. Using computational mathematical simulations, high-confidence CWQI predictions can be obtained based on limited water quality monitoring samples. At present, most of the CWQI reported in the literature are based on conventional indicators such as nitrogen and phosphorus levels, and do not include the petroleum hydrocarbons levels. This article takes a typical river in eastern China as an example, based on the 1-year monitoring at 20 sampling sets, a CWQI containing five factors, TN, NH(4)(+)-N, TP, ∑n-Alks, and ∑PAHs was established, and further predicted by a Monte-Carlo model. The predicted CWQI for each monitoring section is above 0.7, indicating that most of the monitoring sections are moderately polluted, and some sections are seriously polluted. The Spearman rank correlation coefficient analysis results show that TN, ∑PAHs, and ∑n-Alks are the main factors influencing the water quality, especially the petroleum hydrocarbons have a significant impact on the middle and lower reaches due to shipping. In the future, more attention should be paid to petroleum hydrocarbon organic pollutants in the water quality evaluation of similar rivers. Nature Publishing Group UK 2022-06-16 /pmc/articles/PMC9203578/ /pubmed/35710812 http://dx.doi.org/10.1038/s41598-022-14293-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Jin, Wei
Li, Yuan
Lu, Li
Zhang, Dong
He, Shanying
Shentu, Jiali
Chai, Qiwei
Huang, Lei
Water quality assessment of east Tiaoxi River, China, based on a comprehensive water quality index model and Monte-Carlo simulation
title Water quality assessment of east Tiaoxi River, China, based on a comprehensive water quality index model and Monte-Carlo simulation
title_full Water quality assessment of east Tiaoxi River, China, based on a comprehensive water quality index model and Monte-Carlo simulation
title_fullStr Water quality assessment of east Tiaoxi River, China, based on a comprehensive water quality index model and Monte-Carlo simulation
title_full_unstemmed Water quality assessment of east Tiaoxi River, China, based on a comprehensive water quality index model and Monte-Carlo simulation
title_short Water quality assessment of east Tiaoxi River, China, based on a comprehensive water quality index model and Monte-Carlo simulation
title_sort water quality assessment of east tiaoxi river, china, based on a comprehensive water quality index model and monte-carlo simulation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203578/
https://www.ncbi.nlm.nih.gov/pubmed/35710812
http://dx.doi.org/10.1038/s41598-022-14293-9
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