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dc.contributor.authorUddin, Md Galal
dc.contributor.authorOlbert, Agnieszka Indiana
dc.contributor.authorNash, Stephen
dc.identifier.citationUddin, 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.en_IE
dc.description.abstractWater 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.en_IE
dc.description.sponsorshipThis research was funded by the Hardiman Scholarship Programme, NUI Galway. The authors would like to thank the Environmental Protection Agency for water quality data.en_IE
dc.publisherCivil Engineering Research Association of Ireland (CERAI)en_IE
dc.relation.ispartofCivil Engineering Research Ireland (CERI) 2020en
dc.subjectModified WQI architectureen_IE
dc.subjectEmpirical Bayesian Kriging (EBK) techniqueen_IE
dc.subjectmodel prediction uncertaintyen_IE
dc.subjectCork Harbour water qualityen_IE
dc.subjectwater quality ranked mapen_IE
dc.titleAssessment of water quality using Water Quality Index (WQI) models and advanced geostatistical techniqueen_IE
dc.typeConference Paperen_IE
dc.contributor.funderHardiman Research Scholarship, National University of Ireland Galwayen_IE
dc.local.contactAgnieszka Olbert, Civil Engineering, Neb Room 2030, Nui Galway. 3208 Email:

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