Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices

Naz, Iram; Ahmad, Ijaz; Aslam, Rana Waqar; Quddoos, Abdul; Yaseen, Andaleeb

doi:10.3390/w16010063

Cited by 11 publications

(4 citation statements)

References 84 publications

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“…An approach in which the ratios of the concentrations of the water quality measures and their suggested standard values are weighted and combined to provide a weighted WQI score is often employed. The weighted WQI technique has recently been used in investigations of groundwater quality [45][46][47][48][49]. The number of parameters (observations) utilized and their related weights vary for each technique used to compute WQI scores, but all approaches are identical in principle [50,51].…”

Section: R E T R a C T E Dmentioning

confidence: 99%

RETRACTED: Exploring Groundwater Quality Assessment: A Geostatistical and Integrated Water Quality Indices Perspective

Masood,

Rashid,

Haider

et al. 2023

Water

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Groundwater is an important source of freshwater. At the same time, anthropogenic activities, in particular, industrialization, urbanization, population growth, and excessive application of fertilizers, are some of the major reasons for groundwater quality deterioration. Therefore, the present study is conducted to evaluate groundwater quality by using integrated water quality indices and a geospatial approach to identify the different water quality zones and propose management strategies for the improvement of groundwater quality. Groundwater quality was evaluated through the physicochemical parameters (pH, chloride (Cl−), fluoride(F−), iron (Fe−2), nitrate (NO3−1), nitrite (NO2), arsenic (As), total hardness, bicarbonate (HCO3−), calcium (Ca+2), magnesium (Mg+2), color, taste, turbidity, total dissolved solids (TDS)) and microbiological parameters including total coliforms, fecal coliforms, and Escherichia coli of samples collected from the water and sanitation agency (WASA) and urban units. Irrigation parameters crucial to the assessment, including (electrical conductivity (EC), residual sodium carbonates (RSC), and sodium adsorption ratio (SAR)), were also collected at more than 1100 sites within the study area of upper and central Punjab. After collecting the data of physicochemical parameters, the analysis of data was initiated to compute the water quality index for groundwater quality, a four-step protocol in which the Analytical Hierarchy Process (AHP) was used to determine the weights of selected parameters by generating a pairwise matrix, on the relative importance of parameters using the Satty scale. The index was then classified into five classes for quality assessment of drinking water (excellent, good, medium, bad, and very bad) and four classes for irrigation water quality assessment (excellent, good, permissible, and unsuitable). After computing the index values for drinking as well as irrigation purposes, the values were interpolated, and various maps were developed to identify the status of groundwater quality in different zones of the study area. Mitigation strategies for water pollution involve source control, such as monitoring industrial discharge points and managing waste properly. Additionally, treating wastewater through primary, secondary, or tertiary stages significantly improves water quality, reducing contaminants like heavy metals, microbiological agents, and chemical ions, safeguarding water resources. The findings highlight significant regional variations in water quality issues, with heavy metal concerns concentrated notably in Lahore and widespread emerging microbiological contamination across all studied divisions. This suggests a systemic problem linked to untreated industrial effluents and poorly managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions indicate water quality ranging from marginal to unfit, underscoring the urgency for remediation. Conversely, other divisions fall within a medium class, potentially suitable for drinking purposes. Notably, microbiological contamination at 27% poses a major challenge for water supply agencies, emphasizing the critical need for pre-disposal primary, secondary, and tertiary treatments. These treatments could potentially rehabilitate 9%, 35%, and 41% of the study area, respectively, pointing toward tangible, scalable solutions critical for safeguarding broader water resources and public health. With the current pace of water quality deterioration, access to drinking water is a major problem for the public. The government should prioritize implementing strict monitoring mechanisms for industrial effluent discharge, emphasizing proper waste management to curb groundwater contamination. Establishing comprehensive pre-disposal treatments, especially primary, secondary, and tertiary stages, is imperative to address the prevalent heavy metal and microbiological issues, potentially rehabilitating up to 41% of affected areas. Additionally, creating proactive policies and allocating resources for sustainable groundwater management are crucial steps for ensuring broader water resource security and public health in the face of deteriorating water quality. Therefore, urgent regional action is needed to address escalating anthropogenic threats to groundwater, emphasizing the crucial need for proactive measures to safeguard public health and ensure sustainable water resources.

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Section: R E T R a C T E Dmentioning

confidence: 99%

RETRACTED: Exploring Groundwater Quality Assessment: A Geostatistical and Integrated Water Quality Indices Perspective

Masood,

Rashid,

Haider

et al. 2023

Water

Self Cite

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“…This signals escalating urbanization pressures around Khinjhir Lake, with a greater encroachment of human settlements. The RF model, which produced higher accuracy metrics, estimated a 1.1% and 0.9% rise in the built-up area from 2000 to 2010 and 2010 to 2020, respectively [68]. The CART model calculated a 1.1% and 0.8% increase in built-up proportions over the corresponding periods.…”

Section: Accuracy Assessmentmentioning

confidence: 94%

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Aslam,

Shu,

Naz

et al. 2024

Remote Sensing

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Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.

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“…However, it did not attempt to improve the implementation of MCDA in supporting soil remediation or suggest the optimal criteria. Finally, relevant papers (e.g., [93,94]) published after the completion of the database coding were not included.…”

Section: Limitationsmentioning

confidence: 99%

Multi-Criteria Decision Analysis to Support the Remediation of Polluted Soils: A Review of Case Studies

Abrams,

Hendrickx,

Turcanu

et al. 2024

Land

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For the successful remediation of polluted sites, priority setting among the possible remediation technologies is of major importance. The related decisions are typically conditioned by a limited set of alternative remediation techniques and multiple, often contradicting criteria. These characteristics make the decision problem suitable for applying a formalised discrete multi-criteria decision analysis (MCDA). This paper reports on the outcome of a systematic review of articles published between 1995 and 2020 encompassing 43 MCDA applications to support the selection of the remediation technique for polluted soils. It focuses on the comparison between implementations of the MCDA methodology. The review identifies four gaps where progress can be made to mobilise the full strength of the MCDA methodology to support the remediation of polluted soils: (i) early stakeholder engagement, (ii) inclusion of social criteria, (iii) an informed choice of the weighting and aggregation method, (iv) and sensitivity analysis.

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Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices

Cited by 11 publications

References 84 publications

RETRACTED: Exploring Groundwater Quality Assessment: A Geostatistical and Integrated Water Quality Indices Perspective

RETRACTED: Exploring Groundwater Quality Assessment: A Geostatistical and Integrated Water Quality Indices Perspective

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Multi-Criteria Decision Analysis to Support the Remediation of Polluted Soils: A Review of Case Studies

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