Geospatial Assessment of Urban Air Pollution Using Multi-Source Remote Sensing and GIS: A Case Study of Nashik City, India

Integrated weighted geospatial analysis and spatiotemporal assessment of key criteria air pollutants for hotspot identification and air quality risk assessment.

Air pollution remains a pressing concern in urban India, affecting human wellbeing and ecosystem sustainability. This investigation explores the spatial and temporal variations in 10 μm particulate matter (PM10), nitric oxides (NOX), and sulfur dioxide (SO2) air pollution in Navi Mumbai, India, from 2014-2023. Data from 23 monitoring points were analyzed using geographic information systems-based methods, including inverse distance weighting and weighted overlay analysis, to generate a comprehensive pollution index. Findings indicate high seasonal variation, with elevated PM10 and NOX levels during premonsoon and winter due to traffic, industrial activity, and unfavorable meteorological conditions. Monsoon rains significantly reduced pollutant levels. Industrial hotspots, particularly in Taloja and Kalamboli, and traffic-heavy corridors, such as Vashi and Nerul, remained persistent pollution sources. A noticeable drop in pollutant concentrations in 2020 coincided with the COVID-19 lockdown, although levels surged in subsequent years. The weighted overlay analysis proved effective in identifying pollution hotspots and offering a comprehensive understanding of air quality risks. Global comparisons highlight the specific challenges of coastal satellite cities, where industrial and harbor emissions contribute to seasonal smog. This study emphasizes the need for targeted emission controls and urban planning interventions to improve air quality and sustainability in rapidly growing regions.

Assessing the spatiotemporal characteristics of urban air pollution in Ado-Ekiti and its environs, southwest Nigeria

Air pollution poses significant environmental and public health challenges in rapidly urbanising regions of sub-Saharan Africa, where ground-based monitoring infrastructure remains limited. This study examined the spatiotemporal distribution of key air pollutants in Ado-Ekiti and its environs, Nigeria, from 2019 to 2024. Columnar concentrations of carbon monoxide (CO) and formaldehyde (HCHO) were retrieved from Sentinel-5P, while MODIS aerosol optical depth data was used to estimate particulate matter (PM2.5 and PM10). Meteorological variables (rainfall, wind speed, and wind direction components u and v) were derived from the Weather Research and Forecasting model, and one week of ground-based measurements of pollutants were collected to enable correlation analysis with satellite-derived estimates. All datasets were aggregated to monthly and annual timescales, resampled to a 1 km spatial resolution and re-projected to UTM Zone 31 N. Results indicated that particulate matter dominated the pollutant profile, with annual mean concentrations of 59.37–65.29 µg/m³ for PM2.5 and 89.35–99.51 µg/m³ for PM10, exceeding WHO guideline limits. Peak concentrations occurred during the Harmattan season, with PM₂.₅ > 100 µg/m³ and PM₁₀ > 200 µg/m³, driven primarily by Saharan dust transport and local activities. Mann-Kendall trend analysis revealed significant increasing trends in HCHO, PM2.5, and PM10, whereas CO showed no significant trend. Satellite-derived particulate estimates showed positive but weak correlations with ground-based particulate concentrations (r < 0.20), whereas rainfall significantly reduced particulate levels, and meridional winds (v) facilitated long-range PM₁₀ transport. This study provides a significant integrated satellite model for air quality assessment in data-scarce urban environments while providing evidence to support targeted emission control strategies.

Geospatial Analysis of Emissions and Air Pollution: A Case Study focusing on Dhaka University Campus, Bangladesh

Urban university campuses are becoming hotspots of environmental stress due to high energy use, dense infrastructure, and increased traffic flow. This study focuses on the Dhaka University campus to check the status of greenhouse gas emissions and air quality. These are done by integrated analyses of field data, remote sensing, and OpenStreetMap (OSM) data. In this aspect, OSM building and roads were overlaid as vector data on the raster and flow maps. Land Surface Temperature (LST) and Land Use Land cover (LULC) were mapped by Landsat 8 Operational Land Imager (OLI) using Google Earth Engine. Air quality (such as CH₄, CO₂, SO₂, NO₂, PM2.5, and PM10) using the AeroQual Series 500, vehicle counts, and electricity consumption data were all recorded during field surveys. The survey areas of the Dhaka University campus focus on the Institute of Education and Research, Mokarram Hossain Building, MBA Building, Central Library, Nilkhet Residential Quarters, Rokeya Hall, Jagannath Hall, and Residential Halls at Curzon, considered as the hotspots identified by the calculation and map representation of electricity consumption and vehicle flow data. The results indicated that high PM2.5 (AQI: 149) with hotspots are located near Raju Memorial and Doel Chattar. This concentration is well correlated with the existing infrastructure and heavy traffic. The results also significantly identified the microclimatic effect in the specific area, was demonstrated by the positive correlation between Land Surface Temperature (LST) and GHG emissions. Overall, the study shows a replicable data-driven model for climate-responsive campus planning for maintaining urban sustainability in the Global South despite some limitations. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 15(1), 2026, P 123-137

Quantifying the environmental impacts of urban expansion in Srinagar City, Jammu & Kashmir, India through remote sensing and geostatistical analysis

Application of GIS and Remote Sensing in Ambient Air Quality Assessment in Developing Countries: Methods, Challenges and Future Directions

Ambient air pollution remains one of the major environmental challenges affecting human health and ecosystem sustainability, particularly in developing countries where rapid urbanization, industrial activities, transportation growth, and limited air quality monitoring infrastructure contribute to increasing pollution levels. Conventional ground-based monitoring networks provide valuable air quality information but are often constrained by high operational costs, limited spatial coverage, and inadequate monitoring stations in many developing regions. This review examines the application of Geographic Information Systems (GIS) and remote sensing technologies in ambient air quality assessment in developing countries, with emphasis on major data sources and methodologies, pollutant monitoring and spatial analysis, existing challenges, and emerging trends and future directions. A structured literature review was conducted using Scopus, Web of Science, ScienceDirect, Google Scholar, and PubMed, focusing on peer-reviewed journal articles, review papers, and relevant scientific reports published in English between 2006 and 2026. Studies addressing the application of GIS, remote sensing, satellite observations, and geospatial modelling to ambient air quality assessment in developing countries or regions with limited monitoring infrastructure were prioritized, while selected global studies with significant methodological relevance were also considered. The review synthesizes applications involving satellite observations, Aerosol Optical Depth (AOD), spatial interpolation, geostatistical techniques, ground-based monitoring, meteorological data, and integrated statistical and machine-learning approaches for assessing particulate matter (PM₂.₅ and PM₁₀) and gaseous pollutants, including nitrogen dioxide (NO₂), sulphur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃). The findings demonstrate that integrating GIS and remote sensing can improve pollution mapping, hotspot identification, spatial and temporal assessment, exposure evaluation, and evidence-based environmental management, particularly where conventional monitoring networks are inadequate. However, major challenges include limited ground-validation data, spatial and temporal resolution constraints, atmospheric and environmental uncertainties, technical capacity limitations, data accessibility, financial constraints, and uncertainty associated with modelling and data integration. Emerging directions include artificial intelligence and machine learning, cloud-based geospatial platforms, low-cost air-quality sensors, unmanned aerial vehicles, integration of environmental, climate and health data, and development of localized monitoring frameworks. The novelty of this review lies in its focused synthesis of how GIS and remote sensing can be integrated with complementary monitoring and modelling approaches to address air-quality information gaps in developing countries. Such integration has practical implications for improving environmental monitoring, exposure assessment, pollution-control planning, public-health protection, and sustainable environmental management. Overall, GIS and remote sensing should complement rather than replace conventional monitoring systems, with future progress depending on improved ground validation, local capacity, data integration, and institutional collaboration.

Spatiotemporal assessment of urban PM₁₀ and population exposure using integrated satellite, meteorological, and socio-spatial data

The pollution with particulate matter is a major risk factor for population health, especially in urban environments, where the interaction between anthropogenic sources, meteorological conditions and built environment characteristics generates complex spatial patterns of exposure. In this context, the present study aims to estimate the spatial distribution of PM₁₀ concentrations and assess population exposure in a medium-sized city in Romania by integrating satellite data, in situ measurements and socio-spatial indicators. The methodology is based on MODIS-MAIAC satellite products to retrieve Aerosol Optical Depth (AOD) values, which are correlated with PM₁₀ data and meteorological variables from four ground-based air quality monitoring stations. Based on these data, a multiple regression model was developed to estimate PM₁₀ concentrations, and subsequently, a composite exposure index was constructed by integrating relevant socio-spatial indicators, such as population density, built environment characteristics, the Normalized Difference Vegetation Index (NDVI), and terrain elevation. The results highlight pronounced seasonal variability in PM₁₀ concentrations, with maximum values in the cold season (up to 41.8 μg/m 3 ) and an annual average of approximately 20.5 μg/m 3 , exceeding the thresholds recommended by the World Health Organisation but falling within the limits set by the European Union. The direct relationship between AOD and PM₁₀ is relatively weak ( R  = 0.28), but integrating meteorological variables significantly improves the model’s performance. The analysis of the composite index indicates that approximately 20.1% of the population of the Oradea Metropolitan Area lives in areas with a high predisposition to exposure, particularly near industrial areas and in densely built urban environments. The results confirm that population exposure to PM₁₀ is determined not exclusively by concentration levels but by the complex interactions among pollution, population distribution, and the characteristics of the built environment. The proposed integrated approach provides a robust tool for identifying vulnerable areas and supporting urban planning and air quality management, contributing to the development of strategies to reduce risks to human health.