Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed the considerable impact of COVID-19 lockdowns on surface WQ. In response, this research aimed to assess the impact of COVID-19 lockdowns on surface water quality in Ireland using an advanced WQ model. To achieve this goal, six years of water quality monitoring data from 2017 to 2022 were collected for nine water quality indicators in Cork Harbour, Ireland, before, during, and after the lockdowns. These indicators include pH, water temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved oxygen (DOX), transparency (TRAN), and three nutrient enrichment indicators—dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), and total oxidized nitrogen (TON). The results showed that the lockdown had a significant impact on various WQ indicators, particularly pH, TEMP, TON, and BOD5. Over the study period, most indicators were within the permissible limit except for MRP, with the exception of during COVID-19. During the pandemic, TON and DIN decreased, while water transparency significantly improved. In contrast, after COVID-19, WQ at 7% of monitoring sites significantly deteriorated. Overall, WQ in Cork Harbour was categorized as "good," "fair," and "marginal" classes over the study period. Compared to temporal variation, WQ improved at 17% of monitoring sites during the lockdown period in Cork Harbour. However, no significant trend in WQ was observed. Furthermore, the study analyzed the advanced model's performance in assessing the impact of COVID-19 on WQ. The results indicate that the advanced WQ model could be an effective tool for monitoring and evaluating lockdowns' impact on surface water quality. The model can provide valuable information for decision-making and planning to protect aquatic ecosystems

Evaluating coastal lagoon sustainability through the driver-pressure-state-impact-response approach: a study of Khenifiss Lagoon, southern Morocco

Coastal lagoons are valuable ecosystems, providing socioeconomic benefits and supporting human populations and biodiversity. However, these systems face several challenges, making them vulnerable to both natural and human factors. In this study, we apply the Driver-Pressure-State-Impact-Response (DPSIR) Approach to conduct a comprehensive socioeconomic and environmental assessment of the Khenifiss Lagoon to promote sustainable development and support decision-makers. Located on the southern Atlantic coast of Morocco, the lagoon was designated a natural reserve in 1962, a biological reserve in 1983, and a protected wetland under the Ramsar Convention since 1980. This study represents the initial endeavor to conduct a comprehensive global and multidisciplinary environmental assessment of the lagoon by using a wide range of data sources, including relevant publications and reports, satellite images and remote sensing data, field observations, and interviews, all analyzed under the DPSIR framework. Our findings show that both natural and human factors have an impact on the ecosystem. Natural Factors associated with the geomorphological features of the region likely contribute to the silting of the lagoon, possibly intensified by a large shipwreck stuck at its inlet. Meanwhile, human factors encompass population growth (at a rate of 2% per year), tourism, shellfish farming, fishing, shellfish harvesting, and salt extraction. Our results reveal significant changes in the lagoon’s condition in recent years, including a reduction in water body extent, a probable decrease in depth, and an increase in the accumulation of solid waste, plastics, and wastewater in three sectors spanning a total surface area of 464 ha (equivalent to 7% of the lagoon), a substantial expansion of the salt mining area encompassing 368 ha, and a remarkable loss of biodiversity, manifested in declining fish stocks and seabird populations. This study showed that the lagoon is positioned as a potential site for economic growth and serves to alert stakeholders and the local population to the ecosystem’s environmental issues. Based on the findings of this study, we highly recommend regulating human activities within the lagoon, the removal of the wreck at the entrance, proper waste management, community awareness programs, and strict monitoring and enforcement of regulations to protect the environment

Assessment of water quality using Water Quality Index (WQI) models and advanced geostatistical technique

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.This research was funded by the Hardiman Scholarship Programme, NUI Galway. The authors would like to thank the Environmental Protection Agency for water quality data.non-peer-reviewe

Application of water quality index models to an Irish estuary

The paper investigates the application of different Water Quality Index (WQI) models for to estuarine waters. WQI models are aggregation based mathematical models that convert extensive water quality data into a single value. They typically contain four crucial components with the functions of (1) selecting parameters, (2) developing sub-index rules, (3) generating weighting values, and (4) aggregating the sub-indices. They are attractive because of their relative simplicity and ease of application. However, there is a level of uncertainty in the final aggregated indices due to the potentially large spatial and temporal variations in the input water parameter values. Here we apply seven different WQI models to Cork Harbour, an estuary on the southwest coast of Ireland. The water quality data input data included measurements of nine water quality monitoring parameters from 31 monitoring sites in Cork Harbour. The spatial uncertainty of the WQI models was estimated based on the standard deviation of the computed indices. The spatial uncertainty of the input water quality data was also determined and compared with that of the WQIs for any correlationThis research was funded by the Department of Civil Engineering, NUI Galway. The authors would like to thank the Environmental Protection Agency for water quality data

A review study of water quality index models and their use for assessing surface water quality

The water quality index (WQI) model is a popular tool for evaluating surface water quality. It uses aggregation techniques that allow conversion of extensive water quality data into a single value or index. Globally, the WQI model has been applied to evaluate water quality (surface water and groundwater) based on local water quality criteria. Since its development in the 1960s, it has become a popular tool due to its generalised structure and ease-of-use. Commonly, WQI models involve four consecutive stages; these are (1) selection of the water quality parameters, (2) generation of sub-indices for each parameter (3) calculation of the parameter weighting values, and (4) aggregation of sub-indices to compute the overall water quality index. Several researchers have utilized a range of applications of WQI models to evaluate the water quality of rivers, lakes, reservoirs, and estuaries. Some problems of the WQI model are that they are usually developed based on site-specific guidelines for a particular region, and are therefore not generic. Moreover, they produce uncertainty in the conversion of large amounts of water quality data into a single index. This paper presents a comparative discussion of the most commonly used WQI models, including the different model structures, components, and applications. Particular focus is placed on parameterization of the models, the techniques used to determine the sub-indices, parameter weighting values, index aggregation functions and the sources of uncertainty. Issues affecting model accuracy are also discussed.The authors would like to acknowledge the Hardiman Research Scholarship of the National University of Ireland Galway, which funded the first author as part of his PhD program. The reserach was also supported by MaREI, the SFI Research Centre for Energy, Climate, and Marine [Grant No: 12/RC/2302_P2]

Assessment of water quality using Water Quality Index (WQI) models and advanced geostatistical technique

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.This research was funded by the Hardiman Scholarship Programme, NUI Galway. The authors would like to thank the Environmental Protection Agency for water quality data