Spatio-Temporal Trends and Identification of Correlated Variables with Water Quality for Drinking-Water Reservoirs.
Identifieur interne : 000060 ( PubMed/Checkpoint ); précédent : 000059; suivant : 000061Spatio-Temporal Trends and Identification of Correlated Variables with Water Quality for Drinking-Water Reservoirs.
Auteurs : Qing Gu [République populaire de Chine] ; Ke Wang [République populaire de Chine] ; Jiadan Li [République populaire de Chine] ; Ligang Ma [République populaire de Chine] ; Jinsong Deng [République populaire de Chine] ; Kefeng Zheng [République populaire de Chine] ; Xiaobin Zhang [République populaire de Chine] ; Li Sheng [République populaire de Chine]Source :
- International journal of environmental research and public health [ 1660-4601 ] ; 2015.
Descripteurs français
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- MESH :
- chemical : Drinking Water.
- geographic : China.
- Cluster Analysis, Geographic Information Systems, Water Quality, Water Resources, Water Supply.
Abstract
It is widely accepted that characterizing the spatio-temporal trends of water quality parameters and identifying correlated variables with water quality are indispensable for the management and protection of water resources. In this study, cluster analysis was used to classify 56 typical drinking water reservoirs in Zhejiang Province into three groups representing different water quality levels, using data of four water quality parameters for the period 2006-2010. Then, the spatio-temporal trends in water quality were analyzed, assisted by geographic information systems (GIS) technology and statistical analysis. The results indicated that the water quality showed a trend of degradation from southwest to northeast, and the overall water quality level was exacerbated during the study period. Correlation analysis was used to evaluate the relationships between water quality parameters and ten independent variables grouped into four categories (land use, socio-economic factors, geographical features, and reservoir attributes). According to the correlation coefficients, land use and socio-economic indicators were identified as the most significant factors related to reservoir water quality. The results offer insights into the spatio-temporal variations of water quality parameters and factors impacting the water quality of drinking water reservoirs in Zhejiang Province, and they could assist managers in making effective strategies to better protect water resources.
DOI: 10.3390/ijerph121013179
PubMed: 26492263
Affiliations:
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pubmed:26492263Le document en format XML
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<front><div type="abstract" xml:lang="en">It is widely accepted that characterizing the spatio-temporal trends of water quality parameters and identifying correlated variables with water quality are indispensable for the management and protection of water resources. In this study, cluster analysis was used to classify 56 typical drinking water reservoirs in Zhejiang Province into three groups representing different water quality levels, using data of four water quality parameters for the period 2006-2010. Then, the spatio-temporal trends in water quality were analyzed, assisted by geographic information systems (GIS) technology and statistical analysis. The results indicated that the water quality showed a trend of degradation from southwest to northeast, and the overall water quality level was exacerbated during the study period. Correlation analysis was used to evaluate the relationships between water quality parameters and ten independent variables grouped into four categories (land use, socio-economic factors, geographical features, and reservoir attributes). According to the correlation coefficients, land use and socio-economic indicators were identified as the most significant factors related to reservoir water quality. The results offer insights into the spatio-temporal variations of water quality parameters and factors impacting the water quality of drinking water reservoirs in Zhejiang Province, and they could assist managers in making effective strategies to better protect water resources.</div>
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<Abstract><AbstractText>It is widely accepted that characterizing the spatio-temporal trends of water quality parameters and identifying correlated variables with water quality are indispensable for the management and protection of water resources. In this study, cluster analysis was used to classify 56 typical drinking water reservoirs in Zhejiang Province into three groups representing different water quality levels, using data of four water quality parameters for the period 2006-2010. Then, the spatio-temporal trends in water quality were analyzed, assisted by geographic information systems (GIS) technology and statistical analysis. The results indicated that the water quality showed a trend of degradation from southwest to northeast, and the overall water quality level was exacerbated during the study period. Correlation analysis was used to evaluate the relationships between water quality parameters and ten independent variables grouped into four categories (land use, socio-economic factors, geographical features, and reservoir attributes). According to the correlation coefficients, land use and socio-economic indicators were identified as the most significant factors related to reservoir water quality. The results offer insights into the spatio-temporal variations of water quality parameters and factors impacting the water quality of drinking water reservoirs in Zhejiang Province, and they could assist managers in making effective strategies to better protect water resources.</AbstractText>
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<MeshHeading><DescriptorName UI="D040362" MajorTopicYN="N">Geographic Information Systems</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D060753" MajorTopicYN="Y">Water Quality</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D062066" MajorTopicYN="N">Water Resources</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014881" MajorTopicYN="Y">Water Supply</DescriptorName>
</MeshHeading>
</MeshHeadingList>
<OtherID Source="NLM">PMC4627024</OtherID>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">cluster analysis</Keyword>
<Keyword MajorTopicYN="N">reservoir</Keyword>
<Keyword MajorTopicYN="N">spatial distribution</Keyword>
<Keyword MajorTopicYN="N">temporal variation</Keyword>
<Keyword MajorTopicYN="N">water quality</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year>
<Month>07</Month>
<Day>30</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2015</Year>
<Month>10</Month>
<Day>16</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2015</Year>
<Month>10</Month>
<Day>23</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2015</Year>
<Month>10</Month>
<Day>23</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2016</Year>
<Month>5</Month>
<Day>24</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">26492263</ArticleId>
<ArticleId IdType="pii">ijerph121013179</ArticleId>
<ArticleId IdType="doi">10.3390/ijerph121013179</ArticleId>
<ArticleId IdType="pmc">PMC4627024</ArticleId>
</ArticleIdList>
</PubmedData>
</pubmed>
<affiliations><list><country><li>République populaire de Chine</li>
</country>
<region><li>Zhejiang</li>
</region>
<settlement><li>Hangzhou</li>
</settlement>
<orgName><li>Université de Zhejiang</li>
</orgName>
</list>
<tree><country name="République populaire de Chine"><region name="Zhejiang"><name sortKey="Gu, Qing" sort="Gu, Qing" uniqKey="Gu Q" first="Qing" last="Gu">Qing Gu</name>
</region>
<name sortKey="Deng, Jinsong" sort="Deng, Jinsong" uniqKey="Deng J" first="Jinsong" last="Deng">Jinsong Deng</name>
<name sortKey="Li, Jiadan" sort="Li, Jiadan" uniqKey="Li J" first="Jiadan" last="Li">Jiadan Li</name>
<name sortKey="Ma, Ligang" sort="Ma, Ligang" uniqKey="Ma L" first="Ligang" last="Ma">Ligang Ma</name>
<name sortKey="Sheng, Li" sort="Sheng, Li" uniqKey="Sheng L" first="Li" last="Sheng">Li Sheng</name>
<name sortKey="Wang, Ke" sort="Wang, Ke" uniqKey="Wang K" first="Ke" last="Wang">Ke Wang</name>
<name sortKey="Zhang, Xiaobin" sort="Zhang, Xiaobin" uniqKey="Zhang X" first="Xiaobin" last="Zhang">Xiaobin Zhang</name>
<name sortKey="Zheng, Kefeng" sort="Zheng, Kefeng" uniqKey="Zheng K" first="Kefeng" last="Zheng">Kefeng Zheng</name>
</country>
</tree>
</affiliations>
</record>
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