Iram Naz

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

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Rana Waqar Aslam

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Abdul Quddoos

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Aqil Tariq

Mississippi State University

Hong Shu

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Asif Sajjad

Quaid-i-Azam University

Walid Soufan

King Saud University

Salim Heddam

University of Skikda

Muhammad Rashid

University of Engineering and Technology Lahore

Andaleeb Yaseen

Ca' Foscari University of Venice

Saif Haider

University of Engineering and Technology Lahore

Cooperative Institutions

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Wuhan University

King Saud University

University of Engineering and Technology Lahore

Qassim University

Al-Azhar University

Peking University

Mississippi State University

Center for Cultural Heritage Technology

Italian Institute of Technology

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SAR image integration for multi-temporal analysis of Lake Manchar Wetland dynamics using machine learning

Scientific Reports (2024)

Chaoyong Wang Rana Waqar Aslam Abdul Quddoos Iram Naz Aqil Tariq

The Manchar Lake wetland complex, Pakistan's largest freshwater-lake, faces unprecedented ecological challenges amidst climate change and human pressures, necessitating urgent, data-driven conservation strategies. This study employs cutting-edge multi-sensor remote sensing techniques to quantify and analyze the dynamic changes in this critical ecosystem from 2015 to 2023, aiming to provide a comprehensive understanding of wetland dynamics for informed management decisions. Integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 multispectral imagery, we assessed changes in wetland extent, vegetation health, and land-use patterns using spectral indices and topographic data. Our methodology achieved classification accuracies exceeding 92% across all study years, revealing significant ecosystem fluctuations. Water body extent exhibited a non-linear trend, expanding from 318.5 km² (5%) in 2015 to 397.0 km² (7%) in 2019, before contracting to 369.9 km² (6%) in 2023. This pattern was corroborated by MNDWI values. Concurrently, vegetation covers dramatically increased from 405.5 km² (7%) in 2019 to 1081.6 km² (18%) in 2023. The Enhanced Vegetation Index (EVI) reflected this trend, decreasing from 0.61 in 2015 to 0.41 in 2019, before recovering to 0.53 in 2023. Land use changes were substantial, with agricultural areas increasing from 118.4 km² (2%) in 2015 to 498.0 km² (8%) in 2023. SAR data consistently supported these observations. Topographic analysis, including the Topographic Wetness Index (TWI), provided crucial insights into wetland distribution and resilience. This comprehensive analysis highlights the complex interplay between natural processes and human influences shaping the Manchar-Lake ecosystem, underscoring the urgent need for adaptive management strategies in the face of rapid environmental change.

Dynamics

10.1038/s41598-024-76730-1

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Citations (2)

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

Water (2023)

Iram Naz Ijaz Ahmad Rana Waqar Aslam Abdul Quddoos Andaleeb Yaseen

This study undertook an assessment of 24 physiochemical parameters at over 1094 sites to compute the water quality index (WQI) across the upper and central Punjab regions of Pakistan. Prior to the WQI calculation, an analytical hierarchy process (AHP) was employed to assign specific weights to each water quality parameter. The categorization of WQI into distinct classes was achieved by constructing a pairwise matrix based on their relative importance utilizing Saaty’s scale. Additionally, the groundwater quality status for irrigation and drinking purposes across various zones in the study area was delineated through the integration of WQI and geostatistical methodologies. The findings revealed discernible heavy metal issues in the Lahore division, with emerging microbiological contamination across the entire study region, potentially attributed to untreated industrial effluent discharge and inadequately managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions fell within the marginal to unfit categories, indicating water quality concerns. In contrast, the indices for other divisions were in the medium class, suggesting suitability for drinking purposes. Scenario analysis for developing mitigation strategies indicated that primary treatment before wastewater disposal could rehabilitate 9% of the study area, followed by secondary (35%) and tertiary (41%) treatments. Microbiological contamination (27%) emerged as the predominant challenge for water supply agencies. Given the current trajectory of water quality deterioration, access to potable water is poised to become a significant public concern. Consequently, government agencies are urged to implement appropriate measures to enhance overall groundwater quality for sustainable development.

Sewerage

10.3390/w16010063

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Citations (25)

Assessment of Urban Environmental Quality by Socioeconomic and Environmental Variables Using Open‐Source Datasets

Transactions in GIS (2024)

Tan Lingye Nayyer Saleem Rana Waqar Aslam Asif Sajjad Iram Naz

ABSTRACT In this era of rapid development, environmental quality is an essential aspect of sustainable development. A healthy urban environment supports, regulates, and provides livable conditions. In urban areas, environmental quality is considerably affected by socioeconomic factors such as population expansion and economic development. For decision‐making, it is also significant for stakeholders and policymakers to understand the impact of urban expansion on environmental quality. While previous studies have examined urban environmental quality, they often focused on single cities or limited environmental parameters. This research addresses these limitations by conducting a comparative analysis of two major Asian cities with similar demographic features, utilizing a comprehensive set of socioeconomic and environmental variables. Our innovative approach combines open‐source datasets with advanced remote sensing techniques to provide a more holistic assessment of urban environmental quality over two decades. We analyzed urban environmental quality for last two decades with the selected urban environmental quality parameters: surface greenness, surface moisture, and land surface temperature. Lahore (Pakistan) and Wuhan (China) cities were selected for having approximately same demographic features. Correlation matrix has been used to assess the relationship between these environmental variables and social‐economic variables: urban expansion and carbon emission. Correlation coefficient indicated that urban expansion correlates negatively with surface greenness and surface moisture for both Lahore (−0.67 and −0.71) and Wuhan District (−0.5 and −0.75), respectively, while it had a positive relation with land surface temperature: 0.65 and 0.57 for Lahore and Wuhan District, respectively. These effects are more prominent within 10 km distance from the city center, where a substantial urban expansion is observed during the time window.

Environmental Quality

10.1111/tgis.13250

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Citations (2)

Multi-temporal image analysis of wetland dynamics using machine learning algorithms

Journal of Environmental Management (2024)

Rana Waqar Aslam Iram Naz Hong Shu Jianguo Yan Abdul Quddoos

Dynamics

10.1016/j.jenvman.2024.123123

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Integrated Geospatial and Geostatistical Multi-Criteria Evaluation of Urban Groundwater Quality Using Water Quality Indices

Water (2024)

Iram Naz Hong Fan Rana Waqar Aslam Aqil Tariq Abdul Quddoos

Groundwater contamination poses a severe public health risk in Lahore, Pakistan’s second-largest city, where over-exploited aquifers are the primary municipal and domestic water supply source. This study presents the first comprehensive district-wide assessment of groundwater quality across Lahore using an innovative integrated approach combining geographic information systems (GIS), multi-criteria decision analysis (MCDA), and water quality indexing techniques. The core objectives were to map the spatial distributions of critical pollutants like arsenic, model their impacts on overall potability, and evaluate targeted remediation scenarios. The analytic hierarchy process (AHP) methodology was applied to derive weights for the relative importance of diverse water quality parameters based on expert judgments. Arsenic received the highest priority weight (0.28), followed by total dissolved solids (0.22) and hardness (0.15), reflecting their significance as health hazards. Weighted overlay analysis in GIS delineated localized quality hotspots, unveiling severely degraded areas with very poor index values (>150) in urban industrial zones like Lahore Cantt, Model Town, and parts of Lahore City. This corroborates reports of unregulated industrial effluent discharges contributing to aquifer pollution. Prospective improvement scenarios projected that reducing heavy metals like arsenic by 30% could enhance quality indices by up to 20.71% in critically degraded localities like Shalimar. Simulating advanced multi-barrier water treatment processes showcased an over 95% potential reduction in arsenic levels, indicating the requirement for deploying advanced oxidation and filtration infrastructure aligned with local contaminant profiles. The integrated decision support tool enables the visualization of complex contamination patterns, evaluation of remediation options, and prioritizing risk-mitigation investments based on the spatial distribution of hazard exposures. This framework equips urban planners and utilities with critical insights for developing targeted groundwater quality restoration policies through strategic interventions encompassing treatment facilities, drainage infrastructure improvements, and pollutant discharge regulations. Its replicability across other regions allows for tackling widespread groundwater contamination challenges through robust data synthesis and quantitative scenario modeling capabilities.

Geostatistics

10.3390/w16172549

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Citations (5)

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

Water (2023)

Muhammad Umer Masood Muhammad Rashid Saif Haider Iram Naz Chaitanya B. Pande

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.

Sodium adsorption ratio

Total dissolved solids

Turbidity

Total suspended solids

10.3390/w16010138

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Citations (6)

Assessing climatic impacts on land use and land cover dynamics in Peshawar, Khyber Pakhtunkhwa, Pakistan: a remote sensing and GIS approach

GeoJournal (2024)

Rana Waqar Aslam Iram Naz Abdul Quddoos Muhammad Rizwan Quddusi

Khyber pakhtunkhwa

Land Cover

Geomatics

10.1007/s10708-024-11203-6

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Citations (9)

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

Remote Sensing (2024)

Rana Waqar Aslam Hong Shu Iram Naz Abdul Quddoos Andaleeb Yaseen

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.

Ramsar site

Vulnerability

10.3390/rs16050928

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Citations (31)

Monitoring landuse change in Uchhali and Khabeki wetland lakes, Pakistan using remote sensing data

Gondwana Research (2024)

Rana Waqar Aslam Hong Shu Aqil Tariq Iram Naz Muhammad Nasar Ahmad

Paddy field

10.1016/j.gr.2023.12.015

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Citations (47)

RETRACTED: Masood et al. Exploring Groundwater Quality Assessment: A Geostatistical and Integrated Water Quality Indices Perspective. Water 2024, 16, 138

Water (2024)

Muhammad Umer Masood Muhammad Rashid Saif Haider Iram Naz Chaitanya B. Pande

The Water Editorial Office retracts the article “Exploring Groundwater Quality Assessment: A Geostatistical and Integrated Water Quality Indices Perspective” [...]

10.3390/w16091195

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Citations (1)