Multivariate water quality analysis of Lake Cajititlán, Mexico
Lake Cajititlán is a shallow body of water located in an endorheic basin in western Mexico. This lake receives excess fertilizer runoff from agriculture and approximately 2.3 Hm 3 per year of poorly treated wastewater from three municipal treatment plants. Thirteen water quality parameters were moni...
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Veröffentlicht in: | Environmental monitoring and assessment 2020-01, Vol.192 (1), p.5-5, Article 5 |
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Sprache: | eng |
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Zusammenfassung: | Lake Cajititlán is a shallow body of water located in an endorheic basin in western Mexico. This lake receives excess fertilizer runoff from agriculture and approximately 2.3 Hm
3
per year of poorly treated wastewater from three municipal treatment plants. Thirteen water quality parameters were monitored at five sampling points within the lake over 9 years. The objective of this work was to characterize the spatial and temporal variations of the water quality and to identify the sources of data variability in order to assess the influence and the impact of different natural and anthropogenic processes. One-way ANOVA tests, principal component analysis (PCA), cluster analysis (CA), and discriminant analysis (DA) were implemented. The one-way ANOVA showed that biochemical oxygen demand and pH present statistically significant spatial variations and that alkalinity, total chloride, conductivity, chemical oxygen demand, total hardness, ammonia, pH, total dissolved solids, and temperature present statistically significant temporal variations. PCA results explained both natural and anthropogenic processes and their relationship with water quality data. The CA results suggested there is no significant spatial variation in the water quality of the lake because of lake mixing caused by wind. The most significant parameters for spatial variations were pH, NO
3
−
, and NO
2
−
, consistent with the configuration of point and nonpoint sources that affect the lake’s water quality. The temporal DA results suggested that conductivity, hardness, NO
2
−
, pH, and temperature were the most significant parameters to discriminate between seasons. The temporal behavior of these parameters was associated with the transport pathways of seasonal contaminants. |
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ISSN: | 0167-6369 1573-2959 |
DOI: | 10.1007/s10661-019-7972-4 |