Calibration of low-cost sensor data for ambient PM2.5 monitoring across urban and rural settings in South Central Uganda

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Air pollution is a leading risk factor for the global burden of disease. There are limited data from Sub-Saharan Africa (SSA). This study addresses significant data gaps in air quality monitoring in South-Central Uganda. Using a network of 27 low-cost air quality monitors in the rural Rakai region and urban cities of Masaka and Kampala, we developed a locally tailored calibration model for ambient PM 2.5 concentrations. The model incorporates ambient temperature and relative humidity, demonstrating robust performance with an R 2 of 0.93 and a root mean squared error (RMSE) of 3.97 μg/m 3 . Mean PM 2.5 concentrations during the dry and wet seasons were 20.2 μg/m 3 and 11.8 μg/m 3 in rural areas, and 29.6 μg/m 3 and 18.1 μg/m 3 in urban areas, respectively. We observed diurnal variations, with PM 2.5 levels peaking in the early morning (7 AM to 9 AM) and late evening (6 PM to 10 PM), correlating with peak traffic hours. Low-cost sensors can enhance air quality research in regions like South-Central Uganda that lack air pollution data. Calibration tailored to local conditions can improve measurement accuracy of ambient PM 2.5 concentrations.