Surface water resource, such as river, is constantly contaminated by domestic and industrial pollutants. In order to properly manage the water resource, a composite index for water quality assessment, such as water quality index (WQI), has been designed to monitor and evaluate the properties of surface water. However, this index is quite subjective in terms of determination of relative weights. A principal component analysis (PCA) can be used to reduce the dimension and subjectivity of water quality variables. The purpose of this study was to implement the use of hybrid PCA and WQI methods to assess and monitor the water quality of the Bengawan Solo River, which is located in Java Island, Indonesia. The result suggested that COD, BOD, TSS, TDS, nitrate, nitrite, and ammonia were the main factors that determine water quality of the Bengawan Solo River. Furthermore, it was revealed that most samples from the river showed water quality status as slightly polluted. In addition to this, the seasonal variation of the PCWI values indicated a significant increase of water pollution in the Bengawan Solo River per year.
The quality of the river changes according to the development of the surrounding environment which is influenced by various human activities. Analysis of factors affecting Dissolved Oxygen (DO) at Bengawan Solo River is crucial for river management purpose and pollution control. Previous research suggested the use classic multiple linear regression. However, DO measurement were usually took place of sampling sites along the river channel. Therefore, there is a high chance that the measurements results may spatially correlated. As the consequence, the utilization of multiple linear regression technique for the dataset can be inappropriate. In this paper, we applied a modification of multiple linear regression model to incorporate with spatial autocorrelation that exist in the data by adding control variable such vector eigen to the model which known as Spatial Filtering with Eigenvector (SFE). The results showed that nitrate and nitrite were the predictor variables that have a negative and significant effect. However, the model contains spatial autocorrelation. The application of SFE technique by adding three eigenvectors as control variables in the model succeeded in making the residual model free from spatial autocorrelation. However, a new problem arose where there was a violation of the non-heteroscedasticity assumption.
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