metadata: authors: 0: author: Okure,Deo
1: author: Bainomugisha,Engineer
2: author: Lozano Gracia,Nancy
3: author: Soppelsa,Maria Edisa
count: Uganda
docna: 0: docna: Characterization of Ambient Air Quality in
            Selected Urban Areas in Uganda : A Low-Cost Approach
docty: Policy Research Working Paper
subtopic: Pollution Management & Control,Air Quality & Clean Air,Brown Issues and Health,Health Care Services Industry,Intelligent Transport Systems,Social Risk Management,Disaster Management,Hazard Risk Management
teratopic: Social Development,Industry,Environment,Conflict and Development,Urban Development,Transport
abstracts: cdata!: Many cities and urban centers around the
            world experience high air pollution episodes attributable to
            increased anthropogenic alterations of natural environmental
            systems. World Health Organization estimates indicate strong
            exceedances of prescribed limits in developing countries.
            However, the evidence on local pollution measures is limited
            for such cities and Uganda is no exception. Informed by the
            practical realities of air quality monitoring, this paper
            employs a low-cost approach using passive and active
            monitors to obtain characterization of pollution levels
            based on particulate matter 2.5, nitrogen dioxide, and ozone
            over a six-month period (starting in December 2018) for
            selected urban centers in three of the four macro-regions in
            Uganda. This is the first attempt to comprehensively assess
            pollution levels at a near-national level in Uganda. A
            combination of distributed stationary monitors and mobile
            monitors installed on motorcycle taxis (boda-boda) was
            employed in selected parishes to obtain spatiotemporal
            variations in the pollutant concentrations. The results
            suggest that seasonal particulate levels heavily depend on
            precipitation patterns with a strong inverse relation, which
            further corroborates the need for longer monitoring periods
            to reflect actual seasonal variations. Informed by the
            observed level of data completeness and quality in all the
            monitoring scenarios, the paper highlights the
            practicability and potential of a low-cost approach to air
            quality monitoring and the potential to use this information
            to inform citizens.
geo_reg_key: 517191
repnme: repnme: Characterization of Ambient Air Quality in
            Selected Urban Areas in Uganda : A Low-Cost Approach
display_title: Characterization of Ambient Air
            Quality in Selected Urban Areas in Uganda : A Low-Cost Approach
listing_relative_url: /research/2021/01/32764917/characterization-ambient-air-quality-selected-urban-areas-uganda-low-cost-approach
topicv3: Disaster Risk Management m1326682 820,Air Pollution m1327122 1791,Environment, Natural Resources and Blue
            Economy m1327391 566,Health m1504661 3480,Traffic Management m1583399 4231
publishtoextweb_dt: 2021-01-20T00:00:00Z
historic_topic: Social Development,Industry,Environment,Conflict and Development,Urban Development,Transport
origu: MNA Regional Director (SMNDR)
no_of_pages: 43
author_keys: 0: author_key: 611031
1: author_key: 996678
model_extractions: Policy Research Working Paper 5912 Characterization of Ambient Air Quality in Selected Urban Areas in Uganda A Low-Cost Approach _Deo Okure Engineer Bainomugisha Nancy Lozano-Gracia Maria Edisa Soppelsa_ Urban, Disaster Risk Management, Resilience and Land Global Practice January 2021
Policy Research Working Paper 5912

# **Abstract**

Many cities and urban centers around the world experience high air pollution episodes attributable to increased anthropogenic alterations of natural environmental systems. World Health Organization estimates indicate strong exceedances of prescribed limits in developing countries. However, the evidence on local pollution measures is limited for such cities and Uganda is no exception. Informed by the practical realities of air quality monitoring, this paper employs a low-cost approach using passive and active monitors to obtain characterization of pollution levels based on particulate matter 2.5, nitrogen dioxide, and ozone over a six-month period (starting in December 2018) for selected urban centers in three of the four macro-regions in Uganda. This is the first attempt to comprehensively assess pollution levels at a near-national level in Uganda. A combination of distributed stationary monitors and mobile monitors installed on motorcycle taxis ( _boda-boda_ ) was employed in selected parishes to obtain spatiotemporal variations in the pollutant concentrations. The results suggest that seasonal particulate levels heavily depend on precipitation patterns with a strong inverse relation, which further corroborates the need for longer monitoring periods to reflect actual seasonal variations. Informed by the observed level of data completeness and quality in all the monitoring scenarios, the paper highlights the practicability and potential of a low-cost approach to air quality monitoring and the potential to use this information to inform citizens.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at nlozano@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Characterization of Ambient Air Quality in Selected Urban Areas in Uganda: A Low-Cost Approach<sup>1</sup>

Deo Okure<sup>2</sup> , Engineer Bainomugisha<sup>3</sup> , Nancy Lozano-Gracia<sup>4</sup> and Maria Edisa Soppelsa<sup>5</sup>

**_Key Words:_** Urban Air Pollution Uganda, Low-cost Air Quality Monitoring in Uganda, mobile air pollution monitoring, Spatiotemporal Variations.

**JEL** : Q01, Q53, R00,

> 1 This work is part of the Pollution Management and Environmental Health program (PMEH) supported under TF0A3415. The PMEH program focuses on providing technical assistance to these countries, facilitating pollution management, promoting public awareness, and helping them reduce the negative effects of pollution on health. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> 2 AirQo, Makerere University - info@airqo.net

> 3 AirQo, Makerere University - info@airqo.net

> 4 World Bank - Urban, Disaster Risk Management, Resilience and Land Global Practice

> 5 World Bank - Urban, Disaster Risk Management, Resilience and Land Global Practice
# **1. Introduction**

## **1.1 Background**

Air pollution episodes are prevalent in cities and urban centers usually resulting from the cumulative anthropogenic alterations of the natural environmental systems, due to the inevitable and increasing demand for limited resources that often exceed the carrying capacity of cities (Gramsch et al., 2006; <mark>Martínez, 2015).</mark> According to WHO (2018), more than 80% of inhabitants in monitored urban centers are exposed to pollution levels that exceed WHO prescribed limits (Annex 1).

Consequently, populations in fast-growing urban centers are exposed to air pollution–linked health threats (WHO, 2018) with significant dependence on exposure periods. Broadly speaking, a 2018 WHO report indicates that 7 million people die every year from exposure to air pollution with the majority of deaths occurring in developing countries. WHO further ranks air pollution (i.e. both outdoor from industries, construction & automobiles, and indoor from solid fuel combustion) among the 24 leading risk factors for global mortality (WHO, 2009). A recent OECD (Economic Co-operation and Development Centre) study quantifying human and financial costs of Africa's outdoor air pollution found air pollution to be more deadly in Africa than malnutrition or dirty water; specifically, it estimates that air pollution in Africa kills 712,000 people a year prematurely, compared with approximately 542,000 from unsafe water, 275,000 from malnutrition and 391,000 from unsafe sanitation (OECD, 2016).

Findings from empirical epidemiological studies consistently show association of pollutant exposure and exacerbation of illnesses and increased mortality for cardiovascular and respiratory cases among vulnerable groups, e.g., older people, children, pregnant women, lower socioeconomic groupings in low-income countries, etc. (Seaton et al., 1995; <mark>Brunekreef & Holgate, 2002; B</mark> ellinger, 2005; <mark>Kampa & Castanas, 2008). Respectively, morbidity and mortality cases are largely linked to lung cancer, ischemic heart disease and stroke, Chronic Obstructive Pulmonary Disease (COPD), and respiratory infection. For example, a 9-year study (1990s) covering 38 million people in 8 European cities found a significant increase in hospital admissions for asthma and COPD, and Cardiovascular Disease (CVD) for people older than 65 years per 10μ</mark> gm<sup>-3</sup> <mark>exposure to PM</mark> 10 <mark>. More recently (2015), exposure to PM</mark> 2.5 <mark>caused 4.2 million deaths accounting for 7.6% recorded total global deaths thus making it the most important pollutant, and also ranked the 5th global risk factor in 2015. Additionally, O</mark> 3 <mark>accounted for over 0.25 million deaths (Atkinson et al., 2001; Cohen et al., 2017; WHO, 2018).</mark> A snapshot of historical pollution trends and projections in developed countries and the world’s megacities indicate higher per-capita emission levels than cities in low-income economies. However, this outlook is projected to be altered as air pollution has been increasingly severe in fast-expanding cities from non-industrialized countries often exceeding WHO limits (Mayer, 1999). Without any additional action to limit or control air pollution, challenges are only expected to increase as firms and people concentrate further in cities as economic growth rises (Bose, 2010; Kilroy et al., 2015; <mark>Martínez, 2015)</mark> .

Whereas strong regulatory regimes (e.g. restriction of lead and sulfur bearing fuels, strict emission limits, etc.) and technological advances have generally resulted in improved air quality in industrialized economies over time, e.g., China from 2013-2015 (Zheng et al., 2017), a broad plethora of literature and seminal studies still denote distributed pollution levels across a wide 2
spectrum of urban settings (i.e. from small urban centers to megacities) sufficient enough to be linked to adverse health effects.

In fact, one of the first major efforts to contextualize urban air pollution from a global perspective commissioned by the WHO and UNEP found pollution levels that could be associated with serious health effects in 20 megacities (i.e. cities with a population of more than 10 million) across the world. Generally, exceedances of WHO limits were much higher in megacities in developing countries (largely situated in Asia) with Suspended Particulate Matter (SPM) being the most severe, whereas gaseous pollution levels were somewhat spread throughout the sampled megacities. While air pollution seemed particularly more severe in megacities in developing countries, each megacity had at least one pollutant level exceeding WHO health protection guidelines with vehicle traffic being the single most important ambient pollution source accounting for CO, NOx, HC and Pb. This was largely based on data from some of the 270 established monitoring sites under the Global Environment Monitoring System/Air Programme covering 45 countries by 1992 (WHO & UNEP, 1993; Mage et al., 1996).

Likewise, subsequent studies investigating urban air quality on a global scale which also focused primarily on megacities and major urban centers found considerable levels of poor air quality. A snapshot of further insights from the global perspective studies (non-encyclopedic list) have been presented as follows: An assessment of air quality from the years 1990-2000 in the ‘principal cities’ of the world selected from Europe, Africa, North America, Latin America, Asia and Oceania by Baldasano et al (2003) which identified O3, PM10 and NO2 as pollutants of concern. Particularly, nearly half of the cities had maximum daily hourly O3 levels above 200μgm<sup>-3</sup> with North America (Mexico City), and Latin America (São Paulo, Santiago) recording the highest levels i.e. 491μgm<sup>-</sup> 3 -3 -3 , 403μgm , 351μgm r <mark>espectively, whereas most cities in Asia had high PM levels with a number of cities exceeding 300μ</mark> gm<sup>-3</sup> <mark>. These pollutant concentrations were several times higher than the WHO threshold values at the time (i.e. 120</mark> μgm<sup>-3</sup> and <mark>60-90μ</mark> gm<sup>-3</sup> for daily 8-hour O3 and suspended particulate matter respectively).<sup>6</sup> A similar study by Gurjar et al. (2008) classified air quality in 18 megacities based on a Multi-Pollutant Index (MPI) and found poor air quality in 13 of the megacities studied. Work on air pollution trends for Kolkata, Mumbai and Delhi (3 megacities in India) by Gurjar et al (2016) based on historical monitoring data (1991-2012) highlighted the severity of NOx and Particulate Matter ambient concentrations largely coming from transportation and industry. The geographic span of these sampled megacities during various timescales i.e. North America, Europe, Africa, Central and South America and Asia, highlights the historical significance of urban air pollution as a major health and environmental issue for both developed and developing countries, although there has been significant decline in SO2 and Pb levels attributable to regulatory enforcement.

Rapid population increase coupled with air pollution precursors and economic drivers e.g. motorization, combustion energy, industrialization, and other infrastructural developments may lead to inferring that air quality may be deteriorating in fast-growing urban centers in Africa. However, actual evidence is limited given the lack of adequate real-time monitoring data sets. Countries in Sub-Saharan Africa particularly have had minimal or oftentimes unsuccessful attempts to implement effective air quality control programs (Mage et al., 1996; Mayer, 1999; Baldasano et al., 2003; Petkova et al., 2013), and for a number of cases, data are nonexistent e.g. the WHO (2016) compilation of urban ambient air pollution in global cities had only 9 SubSaharan African countries with active monitoring in place, a possible reduction from what was reported in Schwela (2012).

> 6 This study referred to WHO guidelines of 1979, but the standards have since been updated and revised. the current standards are the WHO Air Quality Guidelines 2005.

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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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Low-cost monitors (both passive and active) that can cost a fraction of the reference monitors<sup>9</sup> have increasingly been adopted to supplement reference monitors. However, the monetary boundaries of ‘low-cost’ are still colloquial and very subjective e.g., US-EPA defines ‘low-cost’ as <US $2,500, and <mark>Morawska et al. (2018) review of low-cost sensing technologies preferred a US $1,000</mark> threshold (Castell et al., 2017; <mark>Morawska et al., 2018)</mark> . To date, several initiatives (see **Table 1** for a non-exhaustive list <mark>of initiatives)</mark> from academia and regulatory bodies have taken interest in low-cost active sensor platforms. The paradigm shift from traditional monitoring restricted to reference instruments towards a mixed network of sensors can be attributed to its immense and demonstrable potential for substantive reduction in monitoring costs and improvement in spatial coverage-a strong case for low-cost approaches to revolutionize urban air quality management.

This study leverages on these advances and employs low-cost monitors (primarily AirQo from Makerere University) uniquely designed to withstand the environmental and physical conditions such as dust, extreme weather conditions, unreliable power and intermittent internet connectivity. These devices are portable air quality monitors that can be deployed statically or mounted on mobile devices ( **figure 2** ) to provide pollution snapshots. AirQo monitors were supplemented by diffusive passive samplers also proven to be cost-effective for characterizing spatial variability over varying landscapes e.g. urban and rural as well as seasonal and annual trends <mark>(Van Reeuwijk et al., 1998; Chao and Law, 2000; Vardoulakis et al., 2009).</mark> _<mark>Table 1: Examples of low-cost sensor platform initiatives</mark>_

|**Initiative**|**Focus area(s)**|
|---|---|
|**EuNetAir**<br>http://www.eunetair.it/|Developing new sensing technologies for air<br>pollution control and environmental<br>sustainability|
|**CamMobSense**|Focusing on small scale sensor deployment|
|**EveryAware**<br>**http://www.everyaware.eu/**|Aiming to enhance environmental<br>awareness using portable gas sensors|
|**Citi-Sense and Citi-Sense-MOB**<br>**http://www.citi-sense.eu/**|Technological platforms for distribution<br>monitoring|
|**PiMi Airbox**|Indoor air quality monitoring|
|**Smartsantander**<br>**http://www.smartsantander.eu/**|Developed networks of internet-based<br>devices (including air quality) for smart<br>cities|
|**CAIRSENSE**|US-EPA project for testing low-cost sensors|

> 9 Measurement instruments certified by regulatory bodies, e.g., US-EPA operating under a rigorous set of regulatory protocols, are usually referred to as the ‘gold standard’ of air quality monitoring.

5
|**Village Green**<br>**https://www.epa.gov/air-research/village-**<br>**green-project**|US-EPA community-based project aimed at<br>raising awareness on local air quality|
|---|---|
|**AirVisual**<br>**https://www.airvisual.com/**|Aimed at creating a crowd sourced global<br>network for static air quality monitors|
|**PurpleAir**|a US-based low-cost air quality monitoring<br>network|
|**https://www2.purpleair.com/**||

Data collection focused on anticipated emissions from anthropogenic processes within the predefined study areas with a primary focus on Particulate Matter (PM2.5 and PM10), supplemented by NO2 and O3 passive data sets. These pollutants are not only indicative of the ambient air pollution profile in Uganda but are also within the context of WHO-classical and US-EPA-criteria pollutants i.e. pollutants considered to have adverse effects on human health upon inhalation and ingestion (through contaminated food & water), and to a small extent, dermal contact (cited in Kampa & Castanas, 2008) as informed by empirical clinical and epidemiological studies (see table 2).

_Table 2: Classical and Criteria Pollutants (WHO, 2005; EPA, 2018)_

|**Classical**<br>**Pollutants**|**Criteria**<br>**Pollutants**|**Major Sources**|
|---|---|---|
|PM|PM|Automobile-generated particulates, combustion<br>emissions, industrial sources, dispersion and ambient<br>suspension of loose surface particulates by wind, etc.|
|NO2|NO2|Direct combustion of fossil fuel from thermal fixation of<br>atmospheric nitrogen in the combustion environment|
|O3|O3|Series of reactions between NO2and VOCs, CO, CH4,<br>etc. initiated by sunlight|
|SO2|SO2|largely combustion of Sulphur bearing fuels|
||CO<sup>10</sup>|Incomplete combustion of fuels and inadequate<br>ventilation due to limited oxygen supply e.g. tobacco<br>smoke, engine exhaust, etc.|

# **2.2 Geographic Scope (Study Area)**

This study was restricted to predefined geographical project boundaries in selected sub-counties<sup>11</sup> within three of the four macro-regions of Uganda (i.e. Central, Southern and Eastern) defined to

> 10 Only available from US-EPA criteria pollutants.

> 11 Sub-counties are the official administrative units which collectively make up a district, which then are aggregated to form macro-regions (presently four in total).

6
be representative of the spatial distribution of manufacturing firms in Uganda, and consistent with the official administrative boundaries of Uganda. Because the air pollution assessment was designed to accompany a firm survey designed to be representative of the firm distribution across the country, exclusion of the Northern region was informed by low population density and low firm density of the targeted firm categories. The study set out to collect 6-month pollution data with primary focus around the major emission sources in urban centers<sup>12</sup> and targeted firms within the sampled locations.

As mentioned above, the decisions for deployment of monitors started from the pre-selected subcounties and was supplemented by collation of grey literature to help establish parish level boundaries for the monitoring exercise (i.e. re-aligning with the pre-selected boundaries from the firm-level survey) and map out tentative locations for stationary monitors including boundaries for mobile monitoring-partly informed by the authors’ knowledge of the local environment.

The field validation exercise to all the selected sub-counties followed, with the objective to confirm static deployment locations and identify boundaries/routes for supplementary mobile monitoring for areas where static would not have adequate spatial coverage. Spatial boundaries and buffer zones of key pollution generating activities with the potential to affect local air quality were also identified during the field validation exercise. As anticipated, major pollution generating activities including target firm categories were largely situated in urban areas. For stationary deployment, a maximum of 1-2km impact zones (region within the physical environment and activities that can affect local air quality) were considered as most major emission sources within the study areas are closer to the ground (<3m above the ground) having largely localized effects. This was partly informed by study objectives and the fact that there is no official air quality monitoring in Uganda (i.e. there are no Air Quality Management Areas).<sup>13</sup> Lists of all the sampled sub-counties with respective parishes were uploaded onto a portable mapping tool (ArcGIS collector) for precision during the field visits and corresponding GPS coordinates for all potential locations in visited subcounties were obtained ( **figure 1** ).

> 12 Since there is no universal threshold/definition for urban centers (Chomitz et al., 2005; UN, 2017), we adopted the official classification of urban areas in Uganda, i.e., gazetted administrative centers including District Headquarters, Cities, Municipalities, Towns, Town Boards and all Trading Centers with a population of more than 1,000 persons (UBOS, 2001) and all sampled locations met this criterion. This threshold is also comparable to other jurisdictions, e.g., Chomitz et al. (2005) and UN (2017), and adopting a different threshold would not have significantly altered the sampling, in fact even the US census threshold of 288 persons per square kilometer adopted for the parish level firm survey was way below the average parish level population density.

> 13 It is typical for regulatory practice, e.g., UK-EA, to define impact zones for air quality risk assessments prior to establishing monitoring sites.

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using AirQo monitors (https://www.airqo.net/) for real-time particulate matter sampling and 2- month passive sampling using Palmes-type diffusion tubes supplied by Gradko International for supplementary gaseous monitoring (NO2 and O3). Monitoring devices were installed between 2.5 to 4m high (i.e. within breathing zone) to ensure pollution levels captured is reflective of population exposure. For O3 and NO2, monitoring tubes were removed at the end of every 4 weeks and shipped to a UK based ISO 17025 accredited laboratory for pollutant analyses and results relayed over emails. Volunteers were opportunistically identified and recruited to host the monitoring devices.

Mobile sampling was conducted along predefined routes converging at a stationary monitoring location. These were largely ‘popular’ _boda-boda_ routes within the respective geographic boundaries. It is standard practice for _boda-boda_ riders to operate from within specific geographic boundaries, and rider recruitment (from the predefined areas of interest) was largely informed by this fact. All devices deployed on _boda-boda_ had inbuilt GPS modules to track and attribute monitoring data to specific locations ( **see figures 2a and 12** ). Data was then streamed in near-realtime to an online platform over a local cell-phone network.

Under mobile monitoring, riders were presented with 2 scenarios:

**Scenario 1 (Opportunistic Monitoring):** Mobile data routes were identified and highlighted to the riders but _boda_ movements were not restricted i.e. riders were free to collect data alongside their regular schedules.

**Scenario 2 (Restrictive Monitoring):** Riders were asked to spend about 30 minutes in selected spots during specific times of the day i.e. morning before 08:30, afternoon between 14:00 and 16:30, and evening between 17:00 to 21:00 (riders usually stop working after 21:00). In addition, they were asked to specifically ride through the defined routes on their way to/from the selected spots. In both cases, monitoring was done during active hours of the day to correspond with riders’ typical working hours i.e. (from around 06:00 to 21:00) and the stoppage times were chosen to correspond to the highs and lows of expected typical diurnal profile ( **figure 4** ).

Real-time PM data from active monitors (AirQo and PurpleAir tools) were automatically uploaded to the cloud platform at a frequency ranging 30-80 seconds, with variations depending on mode i.e. whether static or mobile and available power options. The resulting data sets informed the average pollutant concentrations within specific static deployment neighborhood along with spatiotemporal variations from mobile sampling.<sup>14</sup>

# **2.3.1 Meteorological Data**

Supplementary meteorological data (humidity, temperature and precipitation) were obtained from weather stations situated within the national climatic zones in line with sampled locations. These compilations included both historical data sets and measurements obtained during the pollution assessment period. Sampled precipitation compilations were superimposed on pollution plots to indicatively assess the meteorological influence on seasonal variations.

> 14 All active monitors were collocated (in a controlled environment) with a high-end factory calibrated - instrument Turnkey Osiris (https://turnkey <u>instruments.com/product/osiris/) prior to deployment to establish any</u> correction factors, whereas analysis for the passive monitors was handled by an ISO 17025 accredited laboratory.

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<!-- Start of picture text --> 350 —--—)— MityanaMukono(Central)<br>—@- Masaka<br>Entebbe<br>——2- Mubende<br>300 as Luwera<br>—mr 250<br>=<br>ch<br>—<br>5 200<br>s<br>ra<br>aw<br>=5 iso<br>oO<br>w<br>mTM<br>= Loo<br>50<br><!-- End of picture text --> <!-- Start of picture text --> _ ke<br><!-- End of picture text --> a ssegseseeseseesessse2ss33ee83383828 ASSS8SSS8SBSESBSRSARRASHARSHSRSRARAS Time(Hours) <!-- Start of picture text --> 350 —-—-—~— KapchorwaNamayings<br>—8— Bugiri<br>Tororo<br>-*- Mbale(Industrial Division)<br>Soo<br>25.0<br>=<br>=<br>—<br>2 200<br>SSoS<br>+<br>=<br>aD<br>a<br>5 150<br>a)<br>ra<br>=<br>x<br>Loo<br>so<br>Ssasanse==-----a : , ee a ————<br>>= ae<br>a<br>SSSSS8S2S8SBBSBSBES8 SB SSB SBSBESBRSBRARARRARAARARSARAR8282828838Tirme(Hours)5832383 88338385888AS<br><!-- End of picture text -->
<!-- Start of picture text --> 350 the Kasese<br>oo Fortportal<br>=o0<br>25.0<br>ge<br>—<br>S 200<br>e<br>a<br>5 10<br>a<br>=<br>ao<br>Loo<br>so<br>a<br>ssesseseseseessee8828238388828382828<br>ASSSBSSSBSBESSERSBRRARARSHRARSRBRATime(Hours) RAS<br><!-- End of picture text --> <!-- Start of picture text --> AOI Category Corresponding<br>PM2.5 Conc./ugmTM<br>Unhealthy 55_5-150.4<br>Very unhealthy 150.5-250.4<br>Hazardous Above 250.5<br><!-- End of picture text -->
<!-- Start of picture text -->
-§r- Rubaga<br>-—@ NamugongolKireka)<br>100 —@ Bweyogerere<br>-@ Ndejje(Seguku)<br>a” ov Bunamwaya<br>‘Ss @ Kawempe<br>= 90<br>Cc<br>2<br>oa) - --@<br>© 80 °<br>~ .<br>Ccg@ “ ..- e<br>c<br>Oo 70<br>O<br>wo<br>Ps<br>a 60<br>@-----------@------——aa ee9" * a tae ae<br>50 * —_ _—~ ~ = — ~—. —eird<br>Monday Tuesday Wednesday Thursday Friday Saturday Sunday<br>Days<br><!-- End of picture text -->
<!-- Start of picture text --> 70 _ He —@ Mukono(Central)<br>te — so —- Mityana<br>_<br>65 ao a . ~@we MasakaEntebbe<br>a” L -@ Mubende<br>= 60 weg luwero<br>=<br>:<br>ro) oS<br>i<br>o 50 ue ee<br>8 ae Wasp -------- 8.<br>O 45 o o Ween<br>Nqi ~e<br>=<br>Q. ao<br>35 —_ >— Oo ee _<br>x Tose<br>Monday Tuesday Wednesday Thursday Friday Saturday Sunday<br>Days<br><!-- End of picture text --> <!-- Start of picture text --> —@ Iganga(Busiembatya)<br>60 -§- Iganga(Central)<br>__ __-@----* Busia<br>eee<br>—_ ee Oem -nnn gene -@%e- J injajinja(Buwenge) (Main street)<br>><br>=<br>Cc<br>2<br>>_4 50 ae<br>~<br>c<br>ov<br>e 45<br>ia —<<br>= >: a *<br>a”) x<br>: o<br>35<br>Monday Tuesday Wednesday Thursday Friday Saturday Sunday<br>Days<br><!-- End of picture text --> <!-- Start of picture text --> 60.0<br>—— we Kasese<br>— NN aN “@ Bundibugyo<br>TM 57.5] ~ NN<br>E _ ~ . oe Fortportal<br>> ~ ~, ~<br>= 55.0 ~we - * oeNON<br>G ~ —A—— ‘\<br>58525 ~~~Aa \,\<br>i<br>50.0 ‘<br>= SO \<br>é\<br>c475 *<br>U 45.0 ee«<br>= 42.5 ae en ue<br>ogy” s<br>40.0 ‘t<br>Monday Tuesday Wednesday Thursday Friday Saturday Sunday<br>Days<br><!-- End of picture text -->
<!-- Start of picture text --> 120 -§- Rubaga<br>-@ Namugongo(Kireka)<br>110 —@ Bweyogerere<br>“@ Ndejje(Seguku)<br>a” ve Bunamwaya<br>& 100 ___.- ~( Kawempe<br>>= 2<br>© 90<br>° Ak<br>b -<br>c<br>ov<br>2 70 .<br>a 50 — / “<br>40 -o--—— oe<br>March April May june july August<br>Months<br><!-- End of picture text -->
<!-- Start of picture text --> —@ Mukono(Central) x<br>80 | -@ Masaka 4<br>% Entebbe SY —<br>-@ Mubende / ox<br>-~TM_ 70 we Luwero y, fo<br>€ y<br>ax y<br>oO 60 \ y<br>c s \ I ueeooe ---.<br>@ 50 Ss =—= 2<br>U SS — J / ae<br>Cc YS ra _— — — —*<br>Ss “e % Looe<br>in 40 ae “ /<br>a ss v /<br>oO *. Sy / J :<br>30 — ay SY<br>_—*<br>20<br>March April May june july August<br>Months<br><!-- End of picture text -->

<!-- Start of picture text --> —@ Iganga(Busiembatya) .<br>-#- Iganga(Central) ae ye<br>eo -@ve JinjaBusia(Main street) we tse<br>= 60 eS -@ Jjinja(Buwenge)<br>a a “<br>= SS / (x<br>3 és, “ ]<br>50 sy / / ;<br>iy SY /<br>& Ne } .<br>VvU / / ~<br>in ~\ j<br>March April May june july August<br>Months<br><!-- End of picture text --> <!-- Start of picture text -->
- \ -@oe BundibugyoKasese<br>E 70 \ ve Fortportal<br>=<br>a<br>6 —__—_*——"*<br>B60 ——k}—_————*<br>o<br>rmc \<br>vt)<br>U 50 a<br>c eer<br>- ae “a<br>nj 40 A ee ao<br>s es<br>30<br>March April May June July August<br>Months<br><!-- End of picture text -->
<!-- Start of picture text --> 250 250<br>—— Average PM 2.5(Kireka) —@ Total Rainfall(Kampala)<br>—% Average PM 2.5(Kawempe)<br>200 200<br>_ 150 150 E<br>==<br>a£<br>~ £<br>=bt| foraco<br>100 1004s2<br>50 50<br>0 March April= May June July (e)<br>Months<br><!-- End of picture text -->

<!-- Start of picture text --> 250 250<br>—+— Average PM 2.5(Soroti) —@®- Total Rainfall(Soroti)<br>—*% Average PM 2.5(Tororo) —@ Total Rainfall(Tororo)<br>200 200<br>_ 150 150 E<br>E=<br>a<br>—> £c<br>“ ‘so<br>= =<br>100 100 ©<br>—_<br>50 50<br>0 -<br>January February March April May June July fe)<br>Months<br><!-- End of picture text -->
<!-- Start of picture text --> 250 250<br>—— Average PM 2.5(Kabarole) —@ Total Rainfall(Kabarole)<br>—* Average PM 2.5(Kasese) —@ Total Rainfall(Kasese)<br>200 200<br>_ 150 150 e<br>5—=c<br>s é<br>z 5<br>100 1006<br>e<br>50 50<br>0 -<br>January February March April May fe)<br>Months<br><!-- End of picture text -->
<!-- Start of picture text --> 80.00<br>70.00<br>60.00<br>ful<br>&— 50.00<br>Ej 40.00<br>2 30.00<br>§<br>20.00<br>10.00<br>0.00<br>Entebbe Ndejje(Seguku) Bunanwaya Rubaga Namugongo Bweyogerere<br>(Kireka}<br>Sampled Locations<br>e NO2 Concentration (Dec/Jan) g NO? Concentration (Jan/Feb)<br>8 03 Concentration (Dec/Jan) O03 Concentration (Jan/Feb)<br>60.00<br>50.00<br>= 40.00<br>Fi<br>jy 20.00<br>e<br>g<br>20.00<br>10.00<br>0.00<br>Mityana-Central Mubende MasakaNyenda Mukono Central Wobulenzi<br>Division (Kisekende}<br><!-- End of picture text -->

# Sampled Locations

ge NO2 Concentration (Dec) g NO2 Concentration (Jan) 803 Concentration (Dec) = 03 Concentration (Jan)
<!-- Start of picture text --> 100.00<br>90.00<br>80.00<br>«70.00<br>§ 60.00<br>= 50.00<br>= 40.00<br>) 30.00<br>20.00<br>10.00<br>0.00<br>os=mh _Or ~ aah ra &é= om= <of <e&<br>x & < AS x 2 = g #<br>& & 8 s = o o e<br>eoco sea g g £ ad afPd<br>s e a<br>a? Sampled Locations<br>e NO2 Concentration (Dec/Jan) @ NO? Concentration (Jan/Feb)<br>@ 03 Concentration (Dec/Jan) = O3 Concentration (Jan/Feb)<br><!-- End of picture text --> <!-- Start of picture text --> 70.00<br>60.00<br>50.00<br>turd<br>3 40.00<br>“30.00<br>S<br>20.00<br>10.00<br>0.00<br>Bundibugyo Kabarole (Western Kasese Rubirizi Kabale (Nothern<br>(Nyahuka TC) Diw.) (Nyamwamba} (Nyakesh anu) Div.)<br>Sampled Locations<br>g NO2 Concentration (Dec) mg NO2 Concentration {Jan}<br>@ 03 Concentration (Dec) O03 Concentration (Jan)<br><!-- End of picture text -->
<!-- Start of picture text --> 04 PHZ.5 Concentrationon 08th Apr, 2019<br>Falt:0i<br>. |<br>af eydt<br>m0 moo moo 1100 ao nine Moo 1000 1600 Lo 00<br>|oo<br>tr. PHS Concentrationon 00th Apr, 2019<br>§esaswt|<br>bo<br>md<br>5 By<br>& |<br>|<br>oF<br>100 tho 130 ite it 100 io 1700 into 100<br>Teverecantal<br><!-- End of picture text -->
<!-- Start of picture text --> = ¢<br>Boy| PM2.5 Concentrationom 18th Apni, 201<br>Oo4<br>5<br># ROY<br>o“iyE*|<br>z 10 |<br>ay<br>Oy<br>Poke one co) m0 bog oo Pty) wo 00 “Oo ee) mos 100 700 71:00<br>wo4<br>PH2.5 Concentrationon 19th April, 201<br>my<br>§ |<br>Eno!<br>TM<br>-J |<br>Em}<br>0<br>a<br>00 te) ooo 0-00 1to io Leto 400 1d re) inte 00 eto 200 to<br>TeveciHowesl<br><!-- End of picture text -->
<!-- Start of picture text --> 8 a0 PM2.5 Concentration on 27th April, 2019<br>oO<br>5 150<br>t<br>& 100<br>a<br>ww 50)<br>is)<br>z 0<br>06:00 07-00 06:00 08:00 1:00 1 10 3:00 0 B.0 16.00 Tr 16:00 B00<br>5 PM2.5 Concentration on 28th April, 2019<br>2 400<br>b<br>&<br>u<br>E<br>5 20<br>il<br>is}<br>z<br>06:00 07-00 06:00 08:00 10:00 11:00 ara 3:00 1400 5:00 16:00 Te 16:00 19:00 20-00<br>e 400 :<br>8 PM2.5 foncentration on O8th May, 2019<br>& 300<br>&<br>u<br>2 00<br>a<br>a<br>in 100<br>M<br>z 0<br>m0 60 i OOO TOC (GS a BOD<br>8 300 PM2.5 Concentration on 09th May, 2019<br>5<br>5 200<br>u<br>i=<br>a<br>© 100<br>il<br>is)<br>z 0<br>23:00 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 06:00 09:00 10:00 11:00 12:00 15:00 14:00 15:00 16:00 17:00 16:00 19:00 20:00 21:00 22:00 23.00 00:00 01:00<br>Time(Hours)<br><!-- End of picture text -->
<!-- Start of picture text --> my<br>Bo}| PM2.5 Concentrationom 18th Apni, 201<br>Eas<br>5=*ao!<br>z wo |<br>w|<br>|<br>of<br>ar ne Ped moo woe Le rie 100 wo i060 M00 ioe moe reo 700 71.00<br>wi |<br>} PH2.5 Concentrationon 19th April, 201<br>my<br>: |<br>emo<br>Eno!<br>2<br>oE woo}<br>‘<br>L}9:00 om:00 oho boo ibn too Leto M00 ito oo iho 100 eto aot ite<br>TeveciHowesl<br>_ Ey efcenirateneen Lath Agen, 204<br>E | |<br>= m0} | !<br>E |<br>of<br>00 OO Sto OF OO OO OO Oe Oe 1 1 MO OO oo OO LO OO FOO Zoo meO OO<br>ney | Pep Concentrationon lath Apeil, 201<br>a} }<br>ey<br>i | |<br>wmoMy }<br>4<br>ce ee i ee ee ee ee<br>TeretHioeta<br><!-- End of picture text -->
<!-- Start of picture text --> (Kireka_Namugongo) ri \e * a<br>° _—page poiont ii ; }I||, Nasa :, Natonko WePF a<br>y Static Location as ans Buwate if at a<br>Fri_Apr_19_2019_all_records ; Kira! s, a © pamoyt<br>|, - Elawate a Jianda ‘. Cave eo<br>ieee {| ts ae e | : ie<br>B Komamboga . | “ i sin pas<br>= 150 -— 250 ‘ 5 * ‘ il : ig! hyirize a Bajo<br>@ >55- 150 "4 Yeze vefre st? iF Kyallsyemtata | I WL vy<br>a > 35-55 \ Kulambire i nee Ne a ss<br>aes ye i iy ~~ oe, ah, ; a _g@iSeeta<br>B o-12 ‘& ne “ , | a yt em a.<br>Bukoto be ‘s kyamecod q - a eo =< mm ( y 4<br>_ gore, Co ae ee fa<br>i Se ae \ op ee ere ag ase<br>Peo Naguru oF i my 5 600) ell.<br>ia. | 5 - Yaa i oh Ee , Ben<br>ee ; ie a Pu te Z cued Eo “S. Namilyang<br>aay © al ae So ar ie ee . |] P\<br>iS, iy, PS a ot<br>2km<br>a<br><!-- End of picture text -->

<!-- Start of picture text --> .<br>: ¢<br>:<br>wu me =<br>s * “ ;<br>Je Vee A<br>: et 5 oe Py<br>halTo. me ao ie<br>| E aS \<br>2"ea<br><!-- End of picture text -->

<!-- Start of picture text --> Kireka Kampala Latest<br>oe<br>Very unhealthy<br>Last updated on 7-Jul-2019 at 12:23 hrs EAT<br>Past 24 hrs Past1 hr Latest<br>| roll ad<br>400<br>= 300<br>a<br>2 200<br>nt"PISOQMMDADLAONG PPAAo bE PHnit a LEib kis OMDN D r<br><!-- End of picture text -->
# **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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