Sensors as a component of urban air quality management planning: a case study with AirGradient OpenAir PM monitors from Accra, Ghana.

Millions of premature deaths across Africa every year are attributed to air pollution. Of specific mention is exposure to fine particles, PM2.5. We present in this study a novel concept using the non-parametric wind regression approach and low-capital-cost (LCC) air sensor data to identify sources of PM2.5 pollution. This study is based on PM2.5 data collected at the University of Ghana (Afri-Set), Accra using the AirGradient OpenAir PM2.5 monitor from June 01 to September 15, 2023. Using the raw, calibrated, and regulatory grade data from the Teledyne API PM Mass Monitor T640, we found a good agreement between the identified sources of PM2.5. Additionally, we observed that high PM2.5 levels (21 µgm-3) were experienced during S, W, SW and SE winds. At low wind speeds (≤ 1 ms-1), PM2.5 pollution was high suggesting a possible local source. Although there were differences in concentrations comparing the raw and the reference grade data, our results showed that PM2.5 sources were similar. A diurnal pattern of the observed PM2.5 also shows a high similarity between the 3 sets of data. Peak levels (15-20 µgm-3) were observed at 07:00 to 14:00 hrs and 18:00 to 23:00 hrs associated with SW winds. Between 00:00 and 04:00 hrs, low levels (below 15 µgm-3) were observed and associated with W and SW winds. Southerly observations were below 15 µgm-3 with high levels  (15-20 µgm-3) easterly between 04:00 and 08:00 hrs.  This indicates that the raw data from the LCC PM air sensor is suitable for developing and tracking air pollution mitigation strategies, especially in environments with similar characteristics, with some caveats. We recommend a further investigation of the site tied to prevailing background activities to provide a vivid understanding of the potential contributing factors from the observed wind directions. Keywords: Air Pollution; Source Identification; Air Sensors; Ghana; PM2.5