Gaussian Processes for Monitoring Air-Quality in Kampala

Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in low and middle-income countries have to rely on a sparse allocation of them. In this paper, we investigate the use of Gaussian Processes for both nowcasting the current air-pollution in places where there are no sensors and forecasting the air-pollution in the future at the sensor locations. In particular, we focus on the city of Kampala in Uganda, using data from AirQo's network of sensors. We demonstrate the advantage of removing outliers, compare different kernel functions and additional inputs. We also compare two sparse approximations to allow for the large amounts of temporal data in the dataset

Exploring PM2.5 variations from calibrated low-cost sensor network in Greater Kampala, during COVID-19 imposed lockdown restrictions: Lessons for Policy

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 μg/m3 and 25 μg/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

Air quality management strategies in Africa: A scoping review of the content, context, co-benefits and unintended consequences.

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

The impact of urban mobility on air pollution in Kampala, an exemplar sub-Saharan African city

This paper analyses the impact of urban mobility (UM) on air pollution by studying the effects of an intervention on local air quality. The study focuses on the PM2.5 level in Kampala, the capital of Uganda, and considers COVID-19 as an unintentional intervention. The PM2.5 level of the city was obtained from a network of low-cost calibrated sensors, while UM is characterized by open-access Google reports. The period under consideration excludes the weeks immediately before and after the first lockdown. PM2.5 data were deweathered using the machine learning technique of random forest (RF) to exclude the variation of meteorological factors, seasonality, and weekday-weekend effect, and then the impact of the pandemic was parametrised. The traffic pattern is discussed, and air mass clustering and pollution polar plots are used to analyse the distribution of long- and short-range sources, respectively. The percentage change from the baseline (PCfB) of the average of UM dimensions is then assessed against that of deweathered PM2.5 level to investigate the impact of UM on the PM2.5 level. Our analysis shows a strong correlation between urban mobility and roadside PM2.5 levels and a weaker relationship with urban PM2.5 levels. The profile of long-range emission sources was consistent over the study period, with more than 61% of the modelled air masses that arrived in Kampala first passing over Kenya and Tanzania. Overall, the COVID-19 pandemic reduced PM2.5 levels in Kampala by about 10%, which is a relatively small compared to other cities that have been studied around the world