# **4. Conclusion**

Air pollution concentrations in urban areas are determined by a balance of factors between pollutant generation (and subsequent accumulation) and pollution dispersion. Generally, indicative findings point towards the gradual deterioration of ambient air quality within the study areas. But more specifically, PM2.5 pollution data sets show significant spatiotemporal variations in pollution profiles across all the sampled locations. Spatiotemporal patterns generally indicate strong disproportionate correlations between pollution levels (for PM2.5, NO2 and O3) in central business districts and outside central business districts. Notably, concentrations for Kampala and Wakiso were systematically higher than upcountry locations (i.e. Eastern and Western clusters) with some PM2.5 diurnal temporal exceptions. The influences of traffic levels and urban topology (presented in seminal literature) were also confirmed in the case study areas. Despite varying pollution levels, PM2.5 and NO2 results indicate a wide measure of substantial exceedances of WHO, EU and USEPA temporal (24-hour) and annual limits.

We further conclude that inconsistent and haphazard weekly profiles and the probable absence of the ‘weekend effect’ highlights the need for multiple pollutant assessments and also reiterates the inadequacy of sparse monitoring data sets. This further corroborates the need for multiple sensor deployment with a plausible mixture of both static and complementary mobile monitors to estimate pollution levels and inform the development of high-resolution pollution maps while identifying micro-level/localized drivers. In addition, the strong influence of rainfall patterns on seasonal particulate levels justifies the need for long-term monitoring to reflect actual seasonal variations.

Informed by the observed level of completeness for static monitoring data sets and comparison of data sets for the two mobile scenarios, we further conclude that engaging volunteers for opportunistic monitoring has huge potential, not only for high-resolution air quality monitoring but also as an avenue for increasing awareness of air pollution. In other words, the practicability and the potential of a low-cost monitoring network to succeed in the setting of Uganda and perhaps Sub-Saharan African are highly demonstrated. Determining the actual costs of setting up and maintaining a large network of low-cost monitors would be an interesting line of inquiry for subsequent studies.

Although this paper is limited to providing an overview of air pollution levels within the study areas, there is a strong basis for the key findings to inform future lines of research, which could include pollution source attribution and composition, transboundary effects on local pollution, pollution trapping and street topology effects (i.e. street canyon effect), personal exposure assessment, quantifying the economic impacts of pollution, hindcasting and forecasting studies, and most importantly, exploring effective models for citizen science engagement and mitigation actions. Further still, it would also be important to continue the measurement of air pollution over a long-term period to assess annual trends as well as expanding the geographic scope of the work.

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