Ground-based monitoring of nitrogen dioxide in Kumasi, Ghana, and its comparison with satellite observations

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Abstract. Air pollution poses an increasing public health risk in many African cities, where systematic monitoring is limited by the high cost of reference-grade instrumentation. Satellite-based sensors such as TROPOMI on Sentinel-5P provide global coverage of atmospheric pollutants, including nitrogen dioxide (NO2), but their limited vertical resolution complicates the estimation of surface-level concentrations. Palmes diffusion tubes offer a low-cost, low-tech alternative for measuring NO2 at the ground level. Combining satellite observations with data from ground-based diffusion tubes presents a promising approach for generating regional air quality maps, particularly in resource-limited settings. This study reports on the implementation of a pilot network of Palmes tubes in Kumasi, Ghana. To ensure sustainability and scalability, a local air quality laboratory was established to prepare and analyse the tubes using locally sourced materials and equipment. Validation against tubes prepared and analysed by an accredited external laboratory demonstrated satisfactory agreement. Measurements from March 2025 revealed a wide range of NO2 concentrations, from an average of 7 µg m-3 in residential areas away from traffic and industry to peak values of 88 µg m-3 at heavily trafficked intersections – substantially exceeding the WHO 2021 guideline limits of 25 µg m-3 for 24-hour exposure and 10 µg m-3 for annual mean concentrations. Averaged satellite data showed the accumulation of NO₂ plumes downwind of the city, though the relationship with surface-level measurements remains complex and needs further investigation. Column-to-surface ratios in Kumasi are significantly lower than those typically observed in European cities. TROPOMI underestimates the tropospheric NO2 column densities by a factor of roughly 2.5 in March 2025, as the higher aerosol loading over Ghana reduces retrieval sensitivity in the troposphere, increasing the reliance on unrepresentative a priori profiles.

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