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

Abstract

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.