The Africa qualité de l'Air network (AfriqAir): A continent-spanning air quality monitoring network using lower-cost sensors
Presented by: R. Subramanian, QEERI & OSU-Efluve
Summary: Ambient air pollution is a leading cause of premature mortality with an estimated 258,000 deaths in Africa (UNICEF/GBD 2017). Such estimates have large uncertainties as many major cities in Africa do not have any ground-based air quality monitoring. Without granular data, it is impossible to quantify the magnitude of air pollution and its impacts, determine pollution sources, and develop and evaluate interventions. The lack of data is due in part to the high cost of traditional monitoring equipment and a lack of trained personnel.
We propose filling this data gap with a hybrid network of low-cost sensors combined with (and carefully calibrated to) reference monitors. About 50 real-time affordable multi-pollutant (RAMP), Clarity, and PurpleAir devices have been collaboratively deployed in Nairobi, Kigali, Accra, Abidjan, Niamey, Kinshasa, Zamdela (near Johannesburg), and other cities. All use Plantower optical nephelometers for PM2.5. RAMP and Clarity use Alphasense electrochemical sensors for NO2. Local partnerships will ensure that the sensors are well-maintained and sites determined by local experts. We will also work to develop local capacity to ensure network sustainability.
Using a calibration developed in Creteil, France, the deployments reveal morning and evening spikes in combustion-related air pollution. Median hourly NO2 in Accra and Nairobi for September-October 2019 was ~11 ppb; a similar value was observed in November-December 2019 in Zamdela. However, pilot studies in Rwanda showed that local calibration is crucial. So we acquired regulatory-grade PM2.5, NO2, and O3 monitors for Abidjan, Accra, and Nairobi. We also collocated lower-cost devices with existing reference monitors and/or integrated passive samplers in Zamdela, Kigali, Abidjan, Kampala, and Lamto. We shall present preliminary results on spatio-temporal variability of collocation-based sensor calibrations, source identification, and challenges and plans for future expansion.