RESUMO
Air pollution is a major global challenge associated with an increasing number of morbidity and mortality from lung cancer, cardiovascular and respiratory diseases, among others. However, there is scarcity of ground monitoring air quality data from Sub-Saharan Africa that can be used to quantify the level of pollution. This has resulted in limited targeted air pollution research and interventions e.g. health impacts, key drivers and sources, economic impacts, among others; ultimately hindering the establishment of effective management strategies. This paper presents a dataset of air quality observations collected from 68 spatially distributed monitoring stations across Uganda. The dataset includes hourly PM2 . 5 and PM10 data collected from low-cost air quality monitoring devices and one reference grade monitoring device over a period ranging from 2019 to 2020. This dataset contributes towards filling some of the data gaps witnessed over the years in ground level monitored ambient air quality in Sub-Saharan Africa and it can be useful to various policy makers and researchers.
RESUMO
Air pollution is prevalent in cities and urban centers in developing countries including sub-Saharan Africa, but ground monitoring data on local pollution remain inadequate, hindering effective mitigation. We employed low-cost sensing and measurement technologies to quantify pollution levels based on particulate matter (PM2.5), NO2, and O3 over a 6 month period for selected urban centers in three of the four macroregions in Uganda. PM2.5 diurnal profiles exhibited consistent patterns across all monitoring locations with higher pollution levels manifesting from 18:00 to 00:00 and from 06:00 to 09:00; while the periods from 00:00 to 05:00 and from 09:00 to 17:00 had the lowest levels. Daily PM2.5 varied widely between 34 and 107 µg/m3 over a 7 day period, well within unhealthy levels (55.5-150.4 µg/m3) for short-term exposure. The inconsistent daily trend are instructive for multiple pollutant assessment to aid specific policy initiatives. The results also show inverse relations between seasonal particulate levels and precipitation, that is, R (correlation coefficient) = -0.93 and -0.62 for Kampala and Wakiso, R = -0.49 and -0.44 for the Eastern region, and R = -0.65 and -0.96 for the Western region. NO2 monthly concentrations replicated PM2.5 spatial levels, whereas O3 exhibited inverse relations probably due to a higher retention time in less-urbanized environments. Both PM2.5 and NO2 correlated positively with the resident population. Our findings show significant spatiotemporal variations and exceedances of health guidelines by about 4-6 times across most study locations (with two exceptions) for longer-term exposure. This paper demonstrably highlights the practicability and potential of low-cost approaches for air quality monitoring, with strong prospects for citizen science. This paper also provides novel information regarding air pollution that is needed to improve control strategies for reducing exposures.
