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Recent advances in low-cost (LC) sensor technology fostered their deployment in low-income and undersampled countries such as Sub-Saharan Africa (SSA) regions, affected by the highest particulate matter (PM) concentrations and population exposure. The present study is the first addressed in Niamey, Niger, and focuses on assessing LC sensor data and global reanalysis products. Three LC PM2.5 and PM10 monitoring stations were deployed and successfully operated across ∼8 months at different (urban, suburban and rural) locations. Observed PM2.5 and PM10 concentrations revealed consistent patterns, higher during the dry Harmattan season, while appreciably lower during the humid monsoon season. In Niamey, PM2.5 mean concentrations (6.1–20.1 μg/m3) were similar to those observed over higher-income countries, confirming the hypothesis of strictly depending on urbanisation, and thus on anthropogenic activities. Conversely, PM10 concentrations (55.3–142.8 μg/m3) were remarkably higher than most of those measured elsewhere worldwide, and predominantly constituted (81–89%) by coarse fraction. PM10 origin, inferred by backtrajectory analysis, was mainly natural (Saharan dust) during the Harmattan season, and both natural and anthropogenic during the monsoon season. Low-resolution gridded estimations by the Copernicus Atmosphere Monitoring Service (CAMS) were not capable of adequately resolving the spatial variability of PM2.5 and PM10 observations in Niamey, further highlighting the importance of sensor network data to improve air quality knowledge. To tackle the harmful effects of Saharan dust on population, and create robust datasets integrated with gridded products in this challenging region, effort should be put toward creation of trans-national integrated monitoring networks based on LC sensors across SSA.
Gualtieri et al. (Sat,) studied this question.