Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest

Abstract

Fifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQINSF) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

Application of Multivariate Statistical Techniques and Water Quality Index for the Assessment of Water Quality and Apportionment of Pollution Sources in the Yeongsan River, South Korea

“…The combination of a WQI and MSTs may be useful for determining the water quality of rivers. MSTs such as cluster analysis (CA), stepwise discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA), co-occurrence network analysis, and Mann-Kendall trend analysis are widely used for evaluating and interpreting both temporal and spatial variations in large and complex water quality datasets, as well as for source apportionment of pollution [1,3,6,8,9,[24][25][26][27][28]. Mann-Kendall trend analysis has been applied to observe longterm patterns of water quality parameters [29].…”

Assessment of the Seasonal Water Quality Changes in Semi-Urban Surface Tanks of Noyyal River Basin

“…The three samples were collected for all the 5 tanks during summer, winter and monsoon seasons in the year 2020 -2021 and to observe the change in the quality of water at different timings based on the wastewater generation and wastewater flow into the tanks in the study area as well as the time of high concentration from the remote point to the sampling point. The seasons are well defined through basin and the rainfall management has balance in its distribution [7]. The samples were sealed and transferred to the laboratory and the analysis were carried out by using standard APHA procedures [8].…”

“…Twoway ANOVA test results showed that interaction effects of any variables (Akbulut et al2010). 4.PCA showed the parameters contributing for the water quality from the [7].…”

Spatiotemporal variation evaluation of water quality in middle and lower Han River, China

“…Taking China for illustration, there were 874 records of local water contamination during 2006 ~ 2016, resulting in great troubles for domestic life and making millions of economic loss 5 . Reasonable evaluation of water quality variation has been demonstrated as a practical tool for water quality warning and protection 6 – 8 .…”