Pattern recognition of water quality variance in Yamuna River (India) using hierarchical agglomerative cluster and principal component analyses
The monitoring and assessment of a river system is a complex process and not restricted to urban areas only. The discharge of wastewater drains in the river increases the river system complexity further. The abstraction of freshwater at regular intervals and the discharge of the wastewater from vari...
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| Veröffentlicht in: | Environmental monitoring and assessment 2021-08, Vol.193 (8), p.494-494, Article 494 |
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| description | The monitoring and assessment of a river system is a complex process and not restricted to urban areas only. The discharge of wastewater drains in the river increases the river system complexity further. The abstraction of freshwater at regular intervals and the discharge of the wastewater from various sources cause significant spatial and temporal variation in water quality. The multivariate statistical analysis is performed to identify water quality parameters’ variability on the 5-year dataset from four monitoring sites. Hierarchical agglomerative cluster analysis (HACA) and principal component analysis (PCA) are applied to characterize the water quality parameters and identify the significant pollution sources. The clusters are formed considering the similarities between parameters, and eigenvalues are determined from the covariance of parameters. The box plots are designed to identify the spatial and temporal variations. The highest variability of the first principal component is 60.78% of the total variance at the second sampling location, the ITO bridge. The significant varifactors obtained from the PCA indicate the parameters responsible for the maximum variation in water quality. The study reveals the importance of multivariate statistical techniques in identifying a pattern of variability of parameters and developing management strategies to improve river water quality by identifying dominant parameters causing the maximum degradation in water quality. |
| doi_str_mv | 10.1007/s10661-021-09318-1 |
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The discharge of wastewater drains in the river increases the river system complexity further. The abstraction of freshwater at regular intervals and the discharge of the wastewater from various sources cause significant spatial and temporal variation in water quality. The multivariate statistical analysis is performed to identify water quality parameters’ variability on the 5-year dataset from four monitoring sites. Hierarchical agglomerative cluster analysis (HACA) and principal component analysis (PCA) are applied to characterize the water quality parameters and identify the significant pollution sources. The clusters are formed considering the similarities between parameters, and eigenvalues are determined from the covariance of parameters. The box plots are designed to identify the spatial and temporal variations. The highest variability of the first principal component is 60.78% of the total variance at the second sampling location, the ITO bridge. The significant varifactors obtained from the PCA indicate the parameters responsible for the maximum variation in water quality. The study reveals the importance of multivariate statistical techniques in identifying a pattern of variability of parameters and developing management strategies to improve river water quality by identifying dominant parameters causing the maximum degradation in water quality.</description><identifier>ISSN: 0167-6369</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-021-09318-1</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Atmospheric Protection/Air Quality Control/Air Pollution ; Bridges ; Cluster analysis ; Complexity ; Discharge ; Earth and Environmental Science ; Ecology ; Ecotoxicology ; Eigenvalues ; Environment ; Environmental Management ; Environmental monitoring ; Environmental science ; Freshwater ; Inland water environment ; Monitoring/Environmental Analysis ; Multivariate analysis ; Multivariate statistical analysis ; Parameter identification ; Parameters ; Pattern recognition ; Pollution sources ; Principal components analysis ; River water ; River water quality ; Rivers ; Statistical analysis ; Statistical methods ; Temporal variations ; Urban areas ; Variability ; Variance ; Wastewater ; Wastewater discharges ; Water quality</subject><ispartof>Environmental monitoring and assessment, 2021-08, Vol.193 (8), p.494-494, Article 494</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021</rights><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c352t-2b49dd0f88fb5e7feb460e2f68bdcefd922e246a697bb4cd7c1ff4b524d1373b3</citedby><cites>FETCH-LOGICAL-c352t-2b49dd0f88fb5e7feb460e2f68bdcefd922e246a697bb4cd7c1ff4b524d1373b3</cites><orcidid>0000-0003-4942-8510</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10661-021-09318-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-021-09318-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27911,27912,41475,42544,51306</link.rule.ids></links><search><creatorcontrib>Arora, Sameer</creatorcontrib><creatorcontrib>Keshari, Ashok K.</creatorcontrib><title>Pattern recognition of water quality variance in Yamuna River (India) using hierarchical agglomerative cluster and principal component analyses</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><description>The monitoring and assessment of a river system is a complex process and not restricted to urban areas only. 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The discharge of wastewater drains in the river increases the river system complexity further. The abstraction of freshwater at regular intervals and the discharge of the wastewater from various sources cause significant spatial and temporal variation in water quality. The multivariate statistical analysis is performed to identify water quality parameters’ variability on the 5-year dataset from four monitoring sites. Hierarchical agglomerative cluster analysis (HACA) and principal component analysis (PCA) are applied to characterize the water quality parameters and identify the significant pollution sources. The clusters are formed considering the similarities between parameters, and eigenvalues are determined from the covariance of parameters. The box plots are designed to identify the spatial and temporal variations. The highest variability of the first principal component is 60.78% of the total variance at the second sampling location, the ITO bridge. 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| subjects | Atmospheric Protection/Air Quality Control/Air Pollution Bridges Cluster analysis Complexity Discharge Earth and Environmental Science Ecology Ecotoxicology Eigenvalues Environment Environmental Management Environmental monitoring Environmental science Freshwater Inland water environment Monitoring/Environmental Analysis Multivariate analysis Multivariate statistical analysis Parameter identification Parameters Pattern recognition Pollution sources Principal components analysis River water River water quality Rivers Statistical analysis Statistical methods Temporal variations Urban areas Variability Variance Wastewater Wastewater discharges Water quality |
| title | Pattern recognition of water quality variance in Yamuna River (India) using hierarchical agglomerative cluster and principal component analyses |
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