Assessment of water quality using Water Quality Index (WQI) models and advanced geostatistical technique
Uddin, Md Galal Olbert, Agnieszka Indiana Nash, Stephen
Uddin, Md Galal
Olbert, Agnieszka Indiana
Nash, Stephen
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Identifiers
http://hdl.handle.net/10379/16427
https://doi.org/10.13025/18273
https://doi.org/10.13025/18273
Repository DOI
Publication Date
2020-08-27
Type
Conference Paper
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Citation
Uddin, Md Galal, Olbert, Agnieszka Indiana, & Nash, Stephen. (2020). Assessment of water quality using Water Quality Index (WQI) models and advanced geostatistical technique. Paper presented at the CERI 2020 (virtual), Cork Institute of Technology, Cork, 27-28 August.
Abstract
Water quality index (WQI) models are popular tools to evaluate the quality of water; as such they have been developed and used by many agencies worldwide. However, the WQI model may generate excessive uncertainties in the aggregation process. This research is focused on the performance of various WQI modes. In this study, seven WQI models (Horton, CCME, NSF, West-Java, SRDD, Baccarin and Hanh) were applied in order to intercompare their performances and results generated by them. The Cork Harbour in the south of Ireland is used as a study case. Six years (2007 - 2012) of water quality monitoring data across the Harbour is used to conduct the analysis. Development of a WQI model involves four consecutive steps: (1) parameters selection, generation of (2) sub-indices, (3) weight values and (4) aggregation function; these were applied in the study. In total, nine crucial water quality parameters from 31 monitoring locations were selected in step (1) of the analysis. The EU Water Framework Directive (WFD) guidelines were applied to create the parameter sub-index rules (step 2). In step (3) the parameters weight values were generated by applying the Analytic Hierarchy Process (AHP). Finally, in step (4) the WQI model aggregation functions were applied to estimate the final WQI score for each of the seven models. Ultimately, the advanced geostatistical Empirical Bayesian Kriging (EBK) technique was used to spatially interpolate WQI calculated at the monitoring stations onto the whole domain of Cork Harbour. A comparison of the cross-validation parameters (ASE, MSE, RMSE, RMSSE and CRPS) was used to select the WQI model for the least uncertainty interpolation. The results show that the lowest uncertainty was generated by the EBK model for WQI generated by the CCME model, while the highest uncertainty obtained for the Hanh and West Java WQIs. Based on the EBK result, a ranked water quality map was proposed to be used for an assessment of surface water quality and its classification. The water quality ranked map proposed in this research can help not only to assess water quality but also to enhance understanding of water quality spatial variability in any waterbody. Based on the analysis of WQI models, it was concluded that the Cork Harbour water quality was of ‘good’ to ‘excellent’ status during the period of analysis 2007-2012.
Publisher
Civil Engineering Research Association of Ireland (CERAI)
Publisher DOI
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland