Application of multimetric model involving macrobenthos bioindicators in assessing organic contamination in Rawang Sub-Basin, Selangor River, Malaysia

• Main Author: Hettige, Nadeesha Dilani
• Format: Thesis
• Language: English
• Published: 2021
• Subjects: Water quality biological assessment; Stream ecology; Environmental monitoring
• Online Access: http://psasir.upm.edu.my/id/eprint/104748/1/FPAS%202022%2010%20IR.pdf; http://psasir.upm.edu.my/id/eprint/104748/
• Institution: Universiti Putra Malaysia

Organic pollution due to unsustainable fish farming activities is a common problem in the Rawang sub-basin of the Selangor River. However, no comprehensive study has evaluated the organic contamination potential caused by fish farm effluents using local macrobenthos communities. The objectives of this study were: i) to assess macrobenthos assemblages, habitat quality, and water quality within the fish farming areas, ii) to establish potential macrobenthos as bioindicators for organic pollution assessment, and iii) to improvise a multimetric model using macrobenthos for organic pollution assessment. This study chose six sampling sites based on accessibility and proximity to fish farms, with one sampling site as a reference site. Macrobenthos and water sampling were completed from April 2019 to March 2020, and physical habitats were assessed during rainy and dry seasons. The macrobenthos assemblages, habitat quality, and water quality were evaluated using different indices. Principal Components Analysis (PCA) and Canonical Correspondence Analysis (CCA) helped determine the potential of organic pollution indicators. The reference site was excluded when verifying the bioindicators and the model improvised due to the significant differences in water quality parameters and the macrobenthos composition. Suitable bioindicators for applying a model for organic contamination assessment were determined using PCA. The backward multiple linear regression (MLR) was employed to determine the significant difference of identified bioindicators with water quality parameters. Based on the score value (PCA variance coefficient) of each macrobenthos family, the cumulative score value of each sampling site was calculated by considering each replicate as a sampling site to increase the number of samples (i.e., 18 = sampling sites 6 x 3 replicates). The cumulative score values of the sampling sites were classified using cluster analysis. The resultant dendrogram produced three clusters. The cluster range value and mean confidence intervals were used to obtain a distinct classification of water quality classes. The improvised model of water quality standards was validated internally and externally. Results revealed that organic effluent originating from fish farming practices affected river health. The unsustainable fish farming activities mainly influenced the organic contamination water quality parameters (EC, DO, BOD, COD, pH, and ammoniacal-nitrogen) and macrobenthos bioindicators. The CCA showed many pollution-tolerant and moderately pollution-tolerant taxa (Aeolosomatidae, Chironomidae, Lumbriculidae, Naididae, Planorbidae, and Tubificidae) were affected by the high BOD, COD, turbidity, TSS, EC, and ammoniacal-nitrogen. The families Gomphidae, Aytidae, Leptophlebiidae, Thiaridae, and Viviparidae were sensitive to pollution and affected by DO concentration. Based on the multivariate statistical analysis, nine macrobenthos families (Baetidae, Libellulidae, Protoneuridae, Chironomidae, Corbiculidae Hydropchysidae, Tubificidae, Lumbriculiade, and Naididae) were identified as bioindicators to improvise the model. Based on the mean confidence intervals for each cluster range, three different value scales were developed to represent the contamination level (i.e., <0.69 as organically polluted, 0.69 - 0.87 as slightly organic polluted, and >0.87 as clean status). The results produced after validation were better than the water quality status from other studies based on the BMWP/BMWPThai score. This study concludes that an improvised multimetric model can evaluate river organic contamination successfully.