Correlational analysis of water quality in Bago City coastal rivers using visual variables and analytical parameters

https://doi.org/10.70228/CBJ2024053

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ABSTRACT

An extensive understanding of the patterns of water quality is vital in maintaining the ecological condition of rivers. Relationships between water quality data and different assessment methods could generate models to be utilized as an assessment tool. This study aims to determine the correlation between visual variables and water quality parameters. Sarno River Visual Assessment Protocol (SRVAP), a revised visual assessment protocol, was used to determine the river’s ecological condition. Based on the field evaluation, the SRVAP scores were obtained by assessing eleven visual elements. At the same time, the water quality data was analyzed using standard methods in terms of ten (10) analytical parameters. Based on the results, hydrologic alteration had the highest contribution to the score since there was no perceptible water withdrawal among the three rivers. Correlation between analytical parameters and visual variables (SRVAP) was obtained through multiple linear regression (MLR) analysis to develop a water quality model for the coastal rivers of Bago City. Multiple runs of the fit model using backward selection resulted in chemical oxygen demand (p = 0.0389) and phosphates (p =0.0389) having significant p values, thus obtaining the model equation as:
SRVAP = 4.18734694 + 0.0022789368*COD + 4.2465179384*Phosphates
The study’s results show that chemical oxygen demand and phosphates are considered predictors of the visual assessment protocol. The application of the model equation to other coastal rivers may be further investigated.

Keywords: Sensorial Methods, Multiple Linear Regression, coastal rivers, water quality