Development of a Geospatial Air-Quality Intervention Priority Index Based on Satellite Atmospheric, Urban Exposure, Environmental, and Vulnerability Indicators: Application to Kinshasa, 2023–2025

Introduction: Rapid urban growth in African cities increases population exposure to air pollution, while ground-based monitoring remains limited. This study developed a geospatial air-quality intervention priority index for Kinshasa, Democratic Republic of the Congo, by integrating satellite-derived atmospheric pollution, urban exposure, environmental attenuation, topographic context, and human vulnerability indicators.

Methods: The study covered 23 urban communes of Kinshasa, excluding Maluku, from 2023 to 2025. Satellite products representing absorbing aerosols, carbon monoxide, nitrogen dioxide, and ozone were processed into monthly, seasonal, and annual composites. After robust normalization, these variables were combined into a satellite-derived atmospheric pollution component using the Analytic Hierarchy Process. This component was then integrated with local 30-metre indicators derived from roads, markets, transport stops, fuel stations, schools, health facilities, land use, vegetation, water bodies, and topography. The weighting structure was checked through the consistency ratio of the Analytic Hierarchy Process and interpreted with caution because weights influence the final priority ranking. Spatial-resolution mismatch was addressed by treating the satellite component as contextual information, not as a 30-metre estimate of pollutant concentrations.

Results: The index identified higher intervention priorities in 2024 and during the main dry season. The highest commune-level priorities were found in Matete, Ndjili, Kisenso, Kalamu, Bandalungwa, Kasa-Vubu, Ngiri-Ngiri, Kinshasa, Lingwala, and Ngaba. Validation based on administrative aggregation, spatial plausibility checks, OpenStreetMap infrastructure, and field knowledge showed that priority zones corresponded to highly exposed and vulnerable urban environments.

Conclusions: The proposed index provides a reproducible decision-support framework for air-quality management in data-scarce cities. It does not estimate ground-level concentrations, but translates satellite-derived pollution information and local vulnerability into spatially explicit intervention priorities.