Nonetheless, a consolidated review of urban air quality based on Particulate Matter by Petkova et al. (2013) for cities in Africa found exceedances of WHO annual and 24-hour limits with levels higher than what is ordinarily observed in already industrialized countries in Europe and America. This is typical in Uganda where available and collective empirical evidence from published and grey literature dating back to the year 1987<sup>7</sup> points towards a gradual deterioration of air quality in Kampala (Uganda’s largest city).

This paper summarizes the key findings and insights from an empirical air quality data collection exercise using low-cost tools. The study advances previous efforts using a low-cost approach and attempts to comprehensively characterize ambient air pollution for selected urban areas in 25 districts in Uganda. This is the first attempt to comprehensively characterize air pollution on a near-national scale, as all previous initiatives focused on Kampala with a single exception i.e. Kirenga et al. (2015) that evaluated Kampala and Jinja. A review of individual published studies indicates a broad measure of limitations and shortfalls that could have rendered air quality characterization (based on specific study findings) inconclusive e.g. limited averaging periodsimpeding assessment of temporal variations, pollutants profile, and spatial coverage.<sup>8</sup> This work provides an important step toward filling this data and knowledge gap.

In what follows, this paper starts by providing a description of the materials and methods used for the low cost and innovative data collection effort used in this study. Section 3 presents the main characterization of pollution measures throughout sampled parishes in Uganda, a first effort of this kind. We conclude with some insights for policy making and suggestions for future research contributing to improving the understanding of air pollution patterns in the developing world.

# **2. Materials and Methods**

## **2.1 The Case for Low-Cost Air Quality Monitoring**

The first step towards managing and reducing air pollution is monitoring. One needs to quantify its scale, magnitude and spatial distribution to really understand where the challenges lie. However, air quality data inadequacy is often linked to the lack of institutional capacity mainly attributed to the associated resource burdens of air quality monitoring e.g. distinctive traditional certified reference instruments are expensive to set up and maintain (Castell et al., 2017 and Hugh et. al 2019). This often leads to sparse distribution of monitoring networks in developing countries; moreover, the unique practical realities of urban centers in resource-strained countries make traditional reference monitoring unaffordable. Consequently, various international environmental agencies and regulatory bodies including US-EPA, European Environment Agency and the United Nations Environment Programme (UNEP) recognize the dire need for supplementary monitoring techniques to provide indicative air quality data.

> 7 These studies include Nyangababo (1987), Nyangababo & Salmeen (1987), Matagi (2002), ICF International (2009), Schwander et al (2014), Kirenga et al (2015), KCCA (unpublished) (2019).

> 8 For example, Nyangababo (1987), and Nyangababo and Salmeen (1987) use lichens and moss for assessment of heavy metal traces in ambient air from industries and traffic; whereas the study is a good reference baseline for ambient heavy metal concentrations in Kampala, it cannot be a representative indication of the current air quality situation given the drastic changes in the physical environment within the last 30 years. Matagi (2002) provides a qualitative profile of emission sources but lacked quantitative measurement data. Schwander et al. (2014) whose findings were based on two 24-hour measurements in a single location can only be used to characterise levels within the monitoring location and restricted to 24-hour averaging periods. Kirenga et al., (2015) was the first study to focus on 2 major urban areas in Uganda (Kampala and Jinja), however, this was restricted to one-month monitoring and therefore findings are restricted to concentrations experienced within one meteorological month, disregarding the pronounced seasonal fluctuations in pollution levels.

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