Temporal trends and source apportionment of water pollution in Honghu Lake, China
Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Ho...
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| description | Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Honghu Lake, the water quality index method (CCME-WQI), multivariate statistical, and source apportionment techniques were adopted to characterize temporal trends in lake water quality (2004–2017), identify the main driving factors of water quality indicators, and quantify the contribution of various pollution sources. Besides, the water periods of the lake have been reclassified due to the seasonal variation of rainfall in the study area. The results of CCME-WQI showed that the water quality in Honghu Lake initially improved over 2004–2011, with better water quality in the wet period than in the dry periods, while the results over 2012–2017 were found to be opposite. Correlation analysis identified untreated industrial wastewater (UIW) as the main pollution source affecting COD
Mn
concentrations in Honghu Lake, while untreated domestic sewage discharge (UDS) was identified as the main pollution source affecting BOD and F. coli concentrations. The main pollution sources affecting nutrient indicators were rainfall and enclosure aquaculture (EA). Principal component analysis (PCA) combined with absolute principal component score-multiple linear regression model (APCS-MLR) further appointed the source contribution of each pollution source to water quality indicators. The results showed that EA in 2012 was reduced by 81% compared with 2004, resulting in the contribution of EA to NH
3
-N, TP, and TN decreased by 0.2 mg L
−1
, 0.039 mg L
−1
, and 0.37 mg L
−1
, respectively. Compared with 2012, UIW was reduced by 65% in 2016, resulting in the contribution of UIW to COD
Mn
decreased by 1.17 mg L
−1
. In addition, compared with 2004, UDS decreased by 85% in 2016, and the contribution of UDS to BOD and F. coli decreased by 0.7 mg L
−1
and 887 cfu L
−1
, respectively. Based on the results of APCS-MLR, it was predicted that the concentrations of COD and TP in Honghu Lake would meet the water quality requirements after 2017. However, the rainfall non-point source pollution must be further controlled to achieve the desired level of TN concentration. This study provided an accurate method for analyzing lake water pollution, and the results can provide a valuable reference for o |
| doi_str_mv | 10.1007/s11356-021-14828-z |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2544159726</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2584883098</sourcerecordid><originalsourceid>FETCH-LOGICAL-c408t-2d5baeb36c24cf80a05f504e14cbe9a77bc4d3a35228e81eff58113652f361ee3</originalsourceid><addsrcrecordid>eNqFkU1LxDAQhoMouq7-AQ8S8OLBaj7b9CiLX7AgwnoOaTvVapvUpEX015t1VwUPehqYed53mHkROqDklBKSnQVKuUwTwmhChWIqed9AE5pSkWQizzfRhORCJJQLsYN2Q3gihJGcZdtohwsqpVRygu4W0PXOmxYPHmwVsLEVDm70JWDTx8nQONuBHbCr8asZwOPete24bOPG4mtnHx5HPDfPcIJnj401e2irNm2A_XWdovvLi8XsOpnfXt3MzudJKYgaElbJwkDB05KJslbEEFlLIoCKsoDcZFlRioobLhlToCjUtVTx3lSymqcUgE_R8cq39-5lhDDorgkltK2x4MagWcpTsZTn_6NSxIfkWZRM0dEv9Ck-w8ZDIqWEUpzkKlJsRZXeheCh1r1vOuPfNCV6mY1eZaNjNvozG_0eRYdr67HooPqWfIURAb4CQhzZB_A_u_-w_QDWS5lg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2584883098</pqid></control><display><type>article</type><title>Temporal trends and source apportionment of water pollution in Honghu Lake, China</title><source>MEDLINE</source><source>Springer Nature - Complete Springer Journals</source><creator>Chen, Shuai ; Wang, Simeng ; Yu, Yanxi ; Dong, Mingjun ; Li, Yanqiang</creator><creatorcontrib>Chen, Shuai ; Wang, Simeng ; Yu, Yanxi ; Dong, Mingjun ; Li, Yanqiang</creatorcontrib><description>Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Honghu Lake, the water quality index method (CCME-WQI), multivariate statistical, and source apportionment techniques were adopted to characterize temporal trends in lake water quality (2004–2017), identify the main driving factors of water quality indicators, and quantify the contribution of various pollution sources. Besides, the water periods of the lake have been reclassified due to the seasonal variation of rainfall in the study area. The results of CCME-WQI showed that the water quality in Honghu Lake initially improved over 2004–2011, with better water quality in the wet period than in the dry periods, while the results over 2012–2017 were found to be opposite. Correlation analysis identified untreated industrial wastewater (UIW) as the main pollution source affecting COD
Mn
concentrations in Honghu Lake, while untreated domestic sewage discharge (UDS) was identified as the main pollution source affecting BOD and F. coli concentrations. The main pollution sources affecting nutrient indicators were rainfall and enclosure aquaculture (EA). Principal component analysis (PCA) combined with absolute principal component score-multiple linear regression model (APCS-MLR) further appointed the source contribution of each pollution source to water quality indicators. The results showed that EA in 2012 was reduced by 81% compared with 2004, resulting in the contribution of EA to NH
3
-N, TP, and TN decreased by 0.2 mg L
−1
, 0.039 mg L
−1
, and 0.37 mg L
−1
, respectively. Compared with 2012, UIW was reduced by 65% in 2016, resulting in the contribution of UIW to COD
Mn
decreased by 1.17 mg L
−1
. In addition, compared with 2004, UDS decreased by 85% in 2016, and the contribution of UDS to BOD and F. coli decreased by 0.7 mg L
−1
and 887 cfu L
−1
, respectively. Based on the results of APCS-MLR, it was predicted that the concentrations of COD and TP in Honghu Lake would meet the water quality requirements after 2017. However, the rainfall non-point source pollution must be further controlled to achieve the desired level of TN concentration. This study provided an accurate method for analyzing lake water pollution, and the results can provide a valuable reference for optimizing water quality management and pollution control strategies within Honghu Lake.</description><identifier>ISSN: 0944-1344</identifier><identifier>ISSN: 1614-7499</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-021-14828-z</identifier><identifier>PMID: 34155585</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Ammonia ; Aquaculture ; Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; China ; Correlation analysis ; Earth and Environmental Science ; Ecological effects ; Ecological function ; Ecosystem ; ecosystems ; Ecotoxicology ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental Monitoring ; Environmental science ; Environmental security ; Household wastes ; Indicators ; Industrial pollution ; Industrial wastes ; Industrial wastewater ; Lakes ; Nonpoint source pollution ; Nutrient concentrations ; Nutrient sources ; Point source pollution ; Pollution control ; Pollution sources ; principal component analysis ; Principal components analysis ; Quality management ; rain ; Rainfall ; regression analysis ; Regression models ; Research Article ; Rivers ; seasonal variation ; Seasonal variations ; Sewage ; Statistical analysis ; Trends ; Waste Water Technology ; Wastewater ; Water Management ; Water Pollutants, Chemical - analysis ; Water pollution ; Water Pollution - analysis ; Water Pollution Control ; Water Quality ; Water quality control ; Water quality management</subject><ispartof>Environmental science and pollution research international, 2021-11, Vol.28 (42), p.60130-60144</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-2d5baeb36c24cf80a05f504e14cbe9a77bc4d3a35228e81eff58113652f361ee3</citedby><cites>FETCH-LOGICAL-c408t-2d5baeb36c24cf80a05f504e14cbe9a77bc4d3a35228e81eff58113652f361ee3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11356-021-14828-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11356-021-14828-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34155585$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Shuai</creatorcontrib><creatorcontrib>Wang, Simeng</creatorcontrib><creatorcontrib>Yu, Yanxi</creatorcontrib><creatorcontrib>Dong, Mingjun</creatorcontrib><creatorcontrib>Li, Yanqiang</creatorcontrib><title>Temporal trends and source apportionment of water pollution in Honghu Lake, China</title><title>Environmental science and pollution research international</title><addtitle>Environ Sci Pollut Res</addtitle><addtitle>Environ Sci Pollut Res Int</addtitle><description>Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Honghu Lake, the water quality index method (CCME-WQI), multivariate statistical, and source apportionment techniques were adopted to characterize temporal trends in lake water quality (2004–2017), identify the main driving factors of water quality indicators, and quantify the contribution of various pollution sources. Besides, the water periods of the lake have been reclassified due to the seasonal variation of rainfall in the study area. The results of CCME-WQI showed that the water quality in Honghu Lake initially improved over 2004–2011, with better water quality in the wet period than in the dry periods, while the results over 2012–2017 were found to be opposite. Correlation analysis identified untreated industrial wastewater (UIW) as the main pollution source affecting COD
Mn
concentrations in Honghu Lake, while untreated domestic sewage discharge (UDS) was identified as the main pollution source affecting BOD and F. coli concentrations. The main pollution sources affecting nutrient indicators were rainfall and enclosure aquaculture (EA). Principal component analysis (PCA) combined with absolute principal component score-multiple linear regression model (APCS-MLR) further appointed the source contribution of each pollution source to water quality indicators. The results showed that EA in 2012 was reduced by 81% compared with 2004, resulting in the contribution of EA to NH
3
-N, TP, and TN decreased by 0.2 mg L
−1
, 0.039 mg L
−1
, and 0.37 mg L
−1
, respectively. Compared with 2012, UIW was reduced by 65% in 2016, resulting in the contribution of UIW to COD
Mn
decreased by 1.17 mg L
−1
. In addition, compared with 2004, UDS decreased by 85% in 2016, and the contribution of UDS to BOD and F. coli decreased by 0.7 mg L
−1
and 887 cfu L
−1
, respectively. Based on the results of APCS-MLR, it was predicted that the concentrations of COD and TP in Honghu Lake would meet the water quality requirements after 2017. However, the rainfall non-point source pollution must be further controlled to achieve the desired level of TN concentration. This study provided an accurate method for analyzing lake water pollution, and the results can provide a valuable reference for optimizing water quality management and pollution control strategies within Honghu Lake.</description><subject>Ammonia</subject><subject>Aquaculture</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>China</subject><subject>Correlation analysis</subject><subject>Earth and Environmental Science</subject><subject>Ecological effects</subject><subject>Ecological function</subject><subject>Ecosystem</subject><subject>ecosystems</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Health</subject><subject>Environmental Monitoring</subject><subject>Environmental science</subject><subject>Environmental security</subject><subject>Household wastes</subject><subject>Indicators</subject><subject>Industrial pollution</subject><subject>Industrial wastes</subject><subject>Industrial wastewater</subject><subject>Lakes</subject><subject>Nonpoint source pollution</subject><subject>Nutrient concentrations</subject><subject>Nutrient sources</subject><subject>Point source pollution</subject><subject>Pollution control</subject><subject>Pollution sources</subject><subject>principal component analysis</subject><subject>Principal components analysis</subject><subject>Quality management</subject><subject>rain</subject><subject>Rainfall</subject><subject>regression analysis</subject><subject>Regression models</subject><subject>Research Article</subject><subject>Rivers</subject><subject>seasonal variation</subject><subject>Seasonal variations</subject><subject>Sewage</subject><subject>Statistical analysis</subject><subject>Trends</subject><subject>Waste Water Technology</subject><subject>Wastewater</subject><subject>Water Management</subject><subject>Water Pollutants, Chemical - analysis</subject><subject>Water pollution</subject><subject>Water Pollution - analysis</subject><subject>Water Pollution Control</subject><subject>Water Quality</subject><subject>Water quality control</subject><subject>Water quality management</subject><issn>0944-1344</issn><issn>1614-7499</issn><issn>1614-7499</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkU1LxDAQhoMouq7-AQ8S8OLBaj7b9CiLX7AgwnoOaTvVapvUpEX015t1VwUPehqYed53mHkROqDklBKSnQVKuUwTwmhChWIqed9AE5pSkWQizzfRhORCJJQLsYN2Q3gihJGcZdtohwsqpVRygu4W0PXOmxYPHmwVsLEVDm70JWDTx8nQONuBHbCr8asZwOPete24bOPG4mtnHx5HPDfPcIJnj401e2irNm2A_XWdovvLi8XsOpnfXt3MzudJKYgaElbJwkDB05KJslbEEFlLIoCKsoDcZFlRioobLhlToCjUtVTx3lSymqcUgE_R8cq39-5lhDDorgkltK2x4MagWcpTsZTn_6NSxIfkWZRM0dEv9Ck-w8ZDIqWEUpzkKlJsRZXeheCh1r1vOuPfNCV6mY1eZaNjNvozG_0eRYdr67HooPqWfIURAb4CQhzZB_A_u_-w_QDWS5lg</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Chen, Shuai</creator><creator>Wang, Simeng</creator><creator>Yu, Yanxi</creator><creator>Dong, Mingjun</creator><creator>Li, Yanqiang</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7SN</scope><scope>7T7</scope><scope>7TV</scope><scope>7U7</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>P64</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20211101</creationdate><title>Temporal trends and source apportionment of water pollution in Honghu Lake, China</title><author>Chen, Shuai ; Wang, Simeng ; Yu, Yanxi ; Dong, Mingjun ; Li, Yanqiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-2d5baeb36c24cf80a05f504e14cbe9a77bc4d3a35228e81eff58113652f361ee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Ammonia</topic><topic>Aquaculture</topic><topic>Aquatic Pollution</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>China</topic><topic>Correlation analysis</topic><topic>Earth and Environmental Science</topic><topic>Ecological effects</topic><topic>Ecological function</topic><topic>Ecosystem</topic><topic>ecosystems</topic><topic>Ecotoxicology</topic><topic>Environment</topic><topic>Environmental Chemistry</topic><topic>Environmental Health</topic><topic>Environmental Monitoring</topic><topic>Environmental science</topic><topic>Environmental security</topic><topic>Household wastes</topic><topic>Indicators</topic><topic>Industrial pollution</topic><topic>Industrial wastes</topic><topic>Industrial wastewater</topic><topic>Lakes</topic><topic>Nonpoint source pollution</topic><topic>Nutrient concentrations</topic><topic>Nutrient sources</topic><topic>Point source pollution</topic><topic>Pollution control</topic><topic>Pollution sources</topic><topic>principal component analysis</topic><topic>Principal components analysis</topic><topic>Quality management</topic><topic>rain</topic><topic>Rainfall</topic><topic>regression analysis</topic><topic>Regression models</topic><topic>Research Article</topic><topic>Rivers</topic><topic>seasonal variation</topic><topic>Seasonal variations</topic><topic>Sewage</topic><topic>Statistical analysis</topic><topic>Trends</topic><topic>Waste Water Technology</topic><topic>Wastewater</topic><topic>Water Management</topic><topic>Water Pollutants, Chemical - analysis</topic><topic>Water pollution</topic><topic>Water Pollution - analysis</topic><topic>Water Pollution Control</topic><topic>Water Quality</topic><topic>Water quality control</topic><topic>Water quality management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Shuai</creatorcontrib><creatorcontrib>Wang, Simeng</creatorcontrib><creatorcontrib>Yu, Yanxi</creatorcontrib><creatorcontrib>Dong, Mingjun</creatorcontrib><creatorcontrib>Li, Yanqiang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ecology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Pollution Abstracts</collection><collection>Toxicology Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Environmental science and pollution research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Shuai</au><au>Wang, Simeng</au><au>Yu, Yanxi</au><au>Dong, Mingjun</au><au>Li, Yanqiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Temporal trends and source apportionment of water pollution in Honghu Lake, China</atitle><jtitle>Environmental science and pollution research international</jtitle><stitle>Environ Sci Pollut Res</stitle><addtitle>Environ Sci Pollut Res Int</addtitle><date>2021-11-01</date><risdate>2021</risdate><volume>28</volume><issue>42</issue><spage>60130</spage><epage>60144</epage><pages>60130-60144</pages><issn>0944-1344</issn><issn>1614-7499</issn><eissn>1614-7499</eissn><abstract>Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Honghu Lake, the water quality index method (CCME-WQI), multivariate statistical, and source apportionment techniques were adopted to characterize temporal trends in lake water quality (2004–2017), identify the main driving factors of water quality indicators, and quantify the contribution of various pollution sources. Besides, the water periods of the lake have been reclassified due to the seasonal variation of rainfall in the study area. The results of CCME-WQI showed that the water quality in Honghu Lake initially improved over 2004–2011, with better water quality in the wet period than in the dry periods, while the results over 2012–2017 were found to be opposite. Correlation analysis identified untreated industrial wastewater (UIW) as the main pollution source affecting COD
Mn
concentrations in Honghu Lake, while untreated domestic sewage discharge (UDS) was identified as the main pollution source affecting BOD and F. coli concentrations. The main pollution sources affecting nutrient indicators were rainfall and enclosure aquaculture (EA). Principal component analysis (PCA) combined with absolute principal component score-multiple linear regression model (APCS-MLR) further appointed the source contribution of each pollution source to water quality indicators. The results showed that EA in 2012 was reduced by 81% compared with 2004, resulting in the contribution of EA to NH
3
-N, TP, and TN decreased by 0.2 mg L
−1
, 0.039 mg L
−1
, and 0.37 mg L
−1
, respectively. Compared with 2012, UIW was reduced by 65% in 2016, resulting in the contribution of UIW to COD
Mn
decreased by 1.17 mg L
−1
. In addition, compared with 2004, UDS decreased by 85% in 2016, and the contribution of UDS to BOD and F. coli decreased by 0.7 mg L
−1
and 887 cfu L
−1
, respectively. Based on the results of APCS-MLR, it was predicted that the concentrations of COD and TP in Honghu Lake would meet the water quality requirements after 2017. However, the rainfall non-point source pollution must be further controlled to achieve the desired level of TN concentration. This study provided an accurate method for analyzing lake water pollution, and the results can provide a valuable reference for optimizing water quality management and pollution control strategies within Honghu Lake.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34155585</pmid><doi>10.1007/s11356-021-14828-z</doi><tpages>15</tpages></addata></record> |
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| identifier | ISSN: 0944-1344 |
| ispartof | Environmental science and pollution research international, 2021-11, Vol.28 (42), p.60130-60144 |
| issn | 0944-1344 1614-7499 1614-7499 |
| language | eng |
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| subjects | Ammonia Aquaculture Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution China Correlation analysis Earth and Environmental Science Ecological effects Ecological function Ecosystem ecosystems Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental Monitoring Environmental science Environmental security Household wastes Indicators Industrial pollution Industrial wastes Industrial wastewater Lakes Nonpoint source pollution Nutrient concentrations Nutrient sources Point source pollution Pollution control Pollution sources principal component analysis Principal components analysis Quality management rain Rainfall regression analysis Regression models Research Article Rivers seasonal variation Seasonal variations Sewage Statistical analysis Trends Waste Water Technology Wastewater Water Management Water Pollutants, Chemical - analysis Water pollution Water Pollution - analysis Water Pollution Control Water Quality Water quality control Water quality management |
| title | Temporal trends and source apportionment of water pollution in Honghu Lake, China |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T14%3A36%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Temporal%20trends%20and%20source%20apportionment%20of%20water%20pollution%20in%20Honghu%20Lake,%20China&rft.jtitle=Environmental%20science%20and%20pollution%20research%20international&rft.au=Chen,%20Shuai&rft.date=2021-11-01&rft.volume=28&rft.issue=42&rft.spage=60130&rft.epage=60144&rft.pages=60130-60144&rft.issn=0944-1344&rft.eissn=1614-7499&rft_id=info:doi/10.1007/s11356-021-14828-z&rft_dat=%3Cproquest_cross%3E2584883098%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2584883098&rft_id=info:pmid/34155585&rfr_iscdi=true |