Air pollution in Kigali, Rwanda: spatial and temporal variability, source contributions, and the impact of car-free Sundays

Abstract

Ambient air pollution, particularly fine particulate mass (PM2.5) and ozone (O3), is associated with premature human mortality and other health effects, but monitoring is scarce to non-existent in large parts of Africa. Lower-cost real-time affordable multi-pollutant (RAMP) monitors and a black carbon monitor were deployed in Kigali, Rwanda to fill the air quality data gap here. PM2.5 data were corrected using data from a coincident, short-term campaign that used standard filter-based gravimetry, while gas data were verified by collocation with reference carbon monoxide (CO) and O3 monitors at the Rwanda Climate Observatory at Mt Mugogo, Rwanda. Over March 2017-July 2018, the ambient average PM2.5 in Kigali was 52 µg/m3, significantly higher than World Health Organization (WHO) Interim Target 1. Study average BC was 4 µg/m3, comparable to mid-sized urban areas in India and China and significantly higher than BC in cities in developed countries. Spatial variability across Kigali is limited, while PM2.5 at Mt Mugogo is moderately correlated with PM2.5 in Kigali. A sharp diurnal profile is observed in both PM2.5 and BC, with the Absorption Angstrom Exponent (AAE) indicating that the morning peak is associated with rush-hour traffic-related air pollution (TRAP) while the late evening peak can be attributed to both traffic and domestic biofuel use. PM2.5 in the dry seasons is almost two times that during the following wet seasons while BC is 40-60% higher. Local sources contribute at least half the ambient PM2.5 during wet seasons and one-fourth during dry seasons. Vehicular restrictions on some Sundays appear to reduce PM2.5 and BC by 10-12 µg/m3 and 1 µg/m3 respectively, but this needs further investigation. Dry season ozone in Kigali can exceed WHO guidelines. These lower-cost monitors can play an important role in the continued monitoring essential to track the effectiveness of pollution-control policies recently implemented in Rwanda.

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

“…Surface concentrations and diurnal trends of ARISense CO and PM in Malawi were comparable to studies in Kenya, Rwanda, Ethiopia, Uganda and South Africa (Scheel et al, 1998;Toihir et al, 2015;Laakso et al, 2008;DeWitt et al, 2019;Subramanian et al, 2020;Nthusi, 2017;McFarlane et al, 2021) 500 ppb, at their rural and urban sites (Subramanian et al, 2020). Both studies of Rwanda found mean ambient O3 concentrations of 30 to 40 ppb (DeWitt et al, 2019;Subramanian et al, 2020). The annual mean ARISense O3 values were up to a factor of 10 lower, however, we identified quality assurance issues in the calibrated O3 values, particularly for the second half of the deployment data.…”

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources

“…In contrast, the only other source of AQ data in the city at that time, an integrated filter sampler, did not detect these benefits. Integrated filter samplers collect the cumulative mass of pollutants over extended time intervals (in this case, a day), and so could not identify such time‐of‐day‐specific changes in AQ (Subramanian et al., 2020 ). Low‐cost sensors can also be used for temporary deployments after natural disasters.…”

“…The forecasts also provide information when clouds or gaps in coverage limit the detection of pollutants by satellites (Figure 7 ), as well as between overpass times. Conversely, the lack of satellite data does not allow one to verify the forecasted values under cloudy conditions unless surface data, such as from low‐cost sensors, are available (e.g., Subramanian et al., 2020 ).…”

“…Smoke from both agricultural burning and wildfires can lead to acute exposures of large populations to very unhealthy levels of pollution. Here, we show an example of how regional pollution generated from seasonal agricultural burning degraded AQ in Kigali, Rwanda, beyond typical air pollution levels associated with traffic and domestic biofuels (Subramanian et al, 2020), and how NASA resources could be used to monitor the regional build-up of pollution as well as to forecast the long-range transport of the smoke and potential episodes of unhealthy air in Kigali.…”

Design of Smart IoT Device for Monitoring Short-term Exposure to Air Pollution Peaks

“…Air pollution spikes have a short lifetime, requiring monitoring in a smaller time resolution [19]. These spikes need to be monitored at each appearance not to affect the living.…”