TY - JOUR
T1 - A sophisticated model for rating water quality
AU - Uddin, Md Galal
AU - Nash, Stephen
AU - Rahman, Azizur
AU - Olbert, Agnieszka I.
N1 - Funding Information:
The authors gratefully acknowledge the editor's and anonymous reviewers' contributions to the improvement of this paper. This research was funded by the Hardiman Research Scholarship of the University of Galway, which funded the first author as part of his PhD program. The authors would like to acknowledge support from MaREI, the SFI Research Center for Energy, Climate, and Marine research. The authors would like to thank the Environmental Protection Agency of Ireland for providing water quality data. The authors would also like to express their gratitude to Charles Sturt University for providing all necessary assistance to this PhD project through international co-supervision. The first author would like to sincerely thank Professor Azizur Rahman for his outstanding supervision support and methodological contributions to the PhD project. Moreover, the authors also sincerely acknowledge the Eco HydroInformatics Research Group (EHIRG), School of Engineering, College of Science and Engineering, University of Galway, Ireland, for providing computational laboratory facilities to complete this research.
Funding Information:
The authors gratefully acknowledge the editor's and anonymous reviewers' contributions to the improvement of this paper. This research was funded by the Hardiman Research Scholarship of the University of Galway , which funded the first author as part of his PhD program. The authors would like to acknowledge support from MaREI , the SFI Research Center for Energy, Climate, and Marine research . The authors would like to thank the Environmental Protection Agency of Ireland for providing water quality data. The authors would also like to express their gratitude to Charles Sturt University for providing all necessary assistance to this PhD project through international co-supervision. The first author would like to sincerely thank Professor Azizur Rahman for his outstanding supervision support and methodological contributions to the PhD project. Moreover, the authors also sincerely acknowledge the Eco HydroInformatics Research Group (EHIRG), School of Engineering, College of Science and Engineering, University of Galway, Ireland, for providing computational laboratory facilities to complete this research.
Publisher Copyright:
© 2023 The Authors
PY - 2023/4/10
Y1 - 2023/4/10
N2 - Here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve the method and develop a tool that can be used by environmental regulators to abate water pollution in Ireland. The developed model has been associated with the adoption of water quality standards formulated for coastal and transitional waterbodies according to the water framework directive legislation by the environmental regulator of Irish water. The model consists of five identical components, including (i) indicator selection technique is to select the crucial water quality indicator; (ii) sub-index (SI) function for rescaling various water quality indicators' information into a uniform scale; (iii) indicators' weight method for estimating the weight values based on the relative significance of real-time information on water quality; (iii) aggregation function for computing the water quality index (WQI) score; and (v) score interpretation scheme for assessing the state of water quality. The IEWQI model was developed based on Cork Harbour, Ireland. The developed IEWQI model was applied to four coastal waterbodies in Ireland, for assessing water quality using 2021 water quality data for the summer and winter seasons in order to evaluate model sensitivity in terms of spatio-temporal resolution of various waterbodies. The model efficiency and uncertainty were also analysed in this research. In terms of different spatio-temporal magnitudes of various domains, the model shows higher sensitivity in four application domains during the summer and winter. In addition, the results of uncertainty reveal that the IEWQI model architecture may be effective for reducing model uncertainty in order to avoid model eclipsing and ambiguity problems. The findings of this study reveal that the IEWQI model could be an efficient and reliable technique for the assessment of transitional and coastal water quality more accurately in any geospatial domain.
AB - Here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve the method and develop a tool that can be used by environmental regulators to abate water pollution in Ireland. The developed model has been associated with the adoption of water quality standards formulated for coastal and transitional waterbodies according to the water framework directive legislation by the environmental regulator of Irish water. The model consists of five identical components, including (i) indicator selection technique is to select the crucial water quality indicator; (ii) sub-index (SI) function for rescaling various water quality indicators' information into a uniform scale; (iii) indicators' weight method for estimating the weight values based on the relative significance of real-time information on water quality; (iii) aggregation function for computing the water quality index (WQI) score; and (v) score interpretation scheme for assessing the state of water quality. The IEWQI model was developed based on Cork Harbour, Ireland. The developed IEWQI model was applied to four coastal waterbodies in Ireland, for assessing water quality using 2021 water quality data for the summer and winter seasons in order to evaluate model sensitivity in terms of spatio-temporal resolution of various waterbodies. The model efficiency and uncertainty were also analysed in this research. In terms of different spatio-temporal magnitudes of various domains, the model shows higher sensitivity in four application domains during the summer and winter. In addition, the results of uncertainty reveal that the IEWQI model architecture may be effective for reducing model uncertainty in order to avoid model eclipsing and ambiguity problems. The findings of this study reveal that the IEWQI model could be an efficient and reliable technique for the assessment of transitional and coastal water quality more accurately in any geospatial domain.
KW - Assessment of water quality
KW - Model efficiency
KW - Sensitivity
KW - Uncertainty
KW - Water quality model
UR - http://www.scopus.com/inward/record.url?scp=85146694550&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146694550&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2023.161614
DO - 10.1016/j.scitotenv.2023.161614
M3 - Article
C2 - 36669667
AN - SCOPUS:85146694550
SN - 0048-9697
VL - 868
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 161614
ER -