Browsing by Author "Okure, Deo"

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    Air quality management strategies in Africa: A scoping review of the content, context, co-benefits and unintended consequences
    (Environment International, 2022) Okello, Gabriel; Nantanda, Rebecca; Awokola, Babatunde; Thondoo, Meelan; Okure, Deo; Tatah, Lambed; Bainomugisha, Engineer; Oni, Tolu
    One of the major consequences of Africa’s rapid urbanisation is the worsening air pollution, especially in urban centres. However, existing societal challenges such as recovery from the COVID-19 pandemic, poverty, intensifying effects of climate change are making prioritisation of addressing air pollution harder. We undertook a scoping review of strategies developed and/or implemented in Africa to provide a repository to stakeholders as a reference that could be applied for various local contexts. The review includes strategies assessed for effectiveness in improving air quality and/or health outcomes, co-benefits of the strategies, potential collaborators, and pitfalls. An international multidisciplinary team convened to develop well-considered research themes and scope from a contextual lens relevant to the African continent. From the initial 18,684 search returns, additional 43 returns through reference chaining, contacting topic experts and policy makers, 65 studies and reports were included for final analysis. Three main strategy categories obtained from the review included technology (75%), policy (20%) and education/ behavioural change (5%). Most strategies (83%) predominantly focused on household air pollution compared to outdoor air pollution (17%) yet the latter is increasing due to urbanisation. Mobility strategies were only 6% compared to household energy strategies (88%) yet motorised mobility has rapidly increased over recent decades. A cost effective way to tackle air pollution in African cities given the competing priorities could be by leveraging and adopting implemented strategies, collaborating with actors involved whilst considering local contextual factors. Lessons and best practices from early adopters/implementers can go a long way in identifying opportunities and mitigating potential barriers related to the air quality management strategies hence saving time on trying to “reinvent the wheel” and prevent pitfalls. We suggest collaboration of various stakeholders, such as policy makers, academia, businesses and communities in order to formulate strategies that are suitable and practical to various local contexts.
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    Applying machine learning for large scale field calibration of low-cost PM2.5 and PM10 air pollution sensors
    (Applied AI Letters, 2022) Adong, Priscilla; Bainomugisha, Engineer; Okure, Deo; Sserunjogi, Richard
    Low-cost air quality monitoring networks can potentially increase the availability of high-resolution monitoring to inform analytic and evidence-informed approaches to better manage air quality. This is particularly relevant in low and middle-income settings where access to traditional reference-grade monitoring networks remains a challenge. However, low-cost air quality sensors are impacted by ambient conditions which could lead to over- or underestimation of pollution concentrations and thus require field calibration to improve their accuracy and reliability. In this paper, we demonstrate the feasibility of using machine learning methods for large-scale calibration of AirQo sensors, lowcost PM sensors custom-designed for and deployed in Sub-Saharan urban settings. The performance of various machine learning methods is assessed by comparing model corrected PM using k-nearest neighbours, support vector regression, multivariate linear regression, ridge regression, lasso regression, elastic net regression, XGBoost, multilayer perceptron, random forest and gradient boosting with collocated reference PM concentrations from a Beta Attenuation Monitor (BAM). To this end, random forest and lasso regression models were superior for PM2.5 and PM10 calibration, respectively. Employing the random forest model decreased RMSE of raw data from 18.6 μg/m3 to 7.2 μg/m3 with an average BAM PM2.5 concentration of 37.8 μg/m3 while the lasso regression model decreased RMSE from 13.4 μg/m3 to 7.9 μg/m3 with an average BAM PM10 concentration of 51.1 μg/m3. We validate our models through cross-unit and cross-site validation, allowing analysis of AirQo devices' consistency. The resulting calibration models were deployed to the entire large-scale air quality monitoring network consisting of over 120 AirQo devices, which demonstrates the use of machine learning systems to address practical challenges in a developing world setting.
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    Characterization of Ambient Air Quality in Selected Urban Areas in Uganda A Low-Cost Approach
    (Environmental Science & Technology, 2022) Okure, Deo; Bainomugisha, Engineer; Lozano-Gracia, Nancy; Soppelsa, Maria Edisa
    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.
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    Exploring PM2.5 variations from calibrated low-cost sensor network in Greater Kampala, during COVID-19 imposed lockdown restrictions: Lessons for Policy
    (Clean Air Journal, 2022) Green, Paul; Okure, Deo; Adong, Priscilla; Sserunjogi, Richard; Bainomugisha, Engineer
    Air pollution is considered a major public health risk globally, and the global South including sub-Saharan Africa face particular health risks, but there is limited data to quantify the level of pollution for different air quality contexts. The COVID-19 lockdown measures led to reduced human activities, and provided a unique opportunity to explore the impacts of reduced activities on urban air quality. This paper utilises calibrated data from a low-cost sensor network to explore insights from the diverse ambient air quality profile for four urban locations in Greater Kampala, Uganda before and during lockdown from March 31 to May 5 2020, highlighting the uniqueness of air pollution profiles in a sub-Saran Africa context. All locations saw year to year improvements in 24-hour mean PM2.5 between 9 and 25μg/m3 (i.e. 17-50% reduction from the previous year) and correlated well with reduction in traffic (up to approx. 80%) and commercial activities. The greatest improvement was observed in locations close to major transport routes in densely populated residential areas between 8 pm and 5 am. This suggests that the reduction in localised pollution sources such as nocturnal polluting activities including traffic and outdoor combustion including street cooking characteristic of fast-growing cities in developing countries, coupled with meteorological effects led to amplified reductions that continued well into the night, although meteorological effects are more generalised. Blanket policy initiatives targeting peak pollution hours could be adopted across all locations, while transport sector regulation could be very effective for pollution management. Likewise, because of the clustered and diffuse nature of pollution, community driven initiatives could be feasible for long-term mitigation.
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    Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network
    (Data in Brief, 2022) Sserunjogi, Richard; Ssematimba, Joel; Okure, Deo; Ogenrwot, Daniel; Adong, Priscilla; Muyama, Lillian; Nsimbe, Noah; Bbaale, Martin; Bainomugisha, Engineer
    Air pollution is a major global challenge associated with an increasing number of morbidity and mortality from lung can- cer, 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 manage- ment strategies. This paper presents a dataset of air quality observations collected from 68 spatially distributed monitor- ing stations across Uganda. The dataset includes hourly PM 2 . 5 and PM 10 data collected from low-cost air quality monitoring devices and one reference grade monitoring device over a pe- riod ranging from 2019 to 2020. This dataset contributes to- wards 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 re- searchers.