Surface water quality data by principal component analysis
Keywords:
multivariate statistical techniques, water resources monitoring, water resources management.
Abstract
This study used multivariate techniques for data analysis in order to determine the natural and anthropogenic factors that contribute to the spatial and temporal variations of water quality in urban watersheds of Caxias do Sul, Brazil. Principal Component Analysis (PCA) was used to analyze data collected at 30 points between September 2012 and January 2014. Monitoring was conducted bimonthly in six urban basins, where a total of 21 physical, chemical and biological parameters were analyzed. We found that PCA can explain 71.3% of the total variance in water quality, and that domestic and industrial pollution are the main contributors to the water quality variation in the region, especially from the galvanic manufacturing sector. Furthermore, we observed a trend of self-attenuation of pollutants in water downstream from urban areas and great anthropogenic influence as the pressure from urbanized areas decreases.
Published
20/10/2015
Issue
Section
Papers
Authors maintain the copyrights for their work. However, they grant rights of first publication to Ambiente e Agua - An Interdisciplinary Journal of Applied Science. In compensation, the journal can transfer the copyrights, allowing non-commercial use of the article including the right of sending the article to other data bases or publication media. The journal uses the CC BY 4.0 license"