Physically-informed probabilistic modelling of air pollution in Kampala using a low cost sensor network

Ambient air pollution is estimated to contribute to over three million premature deaths each year. Particulate matter (PM) pollution in particular is a likely contributor to this toll. Unfortunately there is only limited monitoring of air pollution in Sub-saharan Africa, in part because accurate monitoring equipment is too expensive, making it hard to develop or assess policy at national and local level. Low-cost particulate sensors are available, but their limited accuracy means that the data cannot be used reliably without correction. This project will test the hypothesis that when used in combination with a reference instrument and combined with physical insight, low-costs sensor networks can be used to produce models to accurately predict PM, gain insight, and plan policy. We focus on Kampala, where the project team have built a low-cost sensor network over the previous four years. Kampala is a rapidly growing city with persistent dangerous levels of particulate pollution, which regularly exceeds ten-times the WHO's guideline annual mean limit. Many factors contribute to this, including Kampala's geography, its partly unmetalled road network, and activities such as domestic burning of garbage and cooking on solid fuel stoves.

Aims and Objectives: The project team have previously installed a low-cost sensor network, and provide predictions of pollution across the city using a mathematical model known as a Gaussian process. This type of model only uses correlations between measurements, which means that external inputs, such as wind-direction, are not properly handled. Moreover, this type of model can't be used to anticipate the effect of an intervention (for example modelling the impact of a road closure), as this involves extrapolating outside of the training data. We have previously worked with the Kampala Capital City Authority (KCCA) to install fifty sensors across the city, and in this project, we will work with them to develop possible interventions to improve air quality, model their potential impact, and then measure their effectiveness.

The project's mathematical aims are specifically around the development of a new modelling paradigm for models of space and time, and the challenges these pose for training the models on observational data. The purpose is threefold. Firstly, they will allow us to include realistic approximations of physical processes, such as the movement of pollution around a city. Secondly, they will let us work out what is producing the pollution, where and when. Thirdly, they will help the KCCA answer "what if?" questions, e.g. "What if we close Luwum Street to motor traffic?" The models predictions must also report their confidence, so that the KCCA and others know if the results can be trusted.

Applications and benefits: Even small improvements in air quality in Kampala would improve the health of its population. By providing policy makers and civil society with the tools for making predictions, we will enable them to plan and assess policy interventions to improve air quality. We anticipate considerable international impact, first through implementation by city authorities in neighbouring countries. Second, by supporting academic research in the field. And third, by supporting the development of practical interventions such as cleaner fuels and support active travel and other issues around 'double burden'.

In summary, the project will lead to considerable high-impact improvements in quality-of-life associated with improved air quality. The Kampala Capital City Authority (KCCA), the local government and civil authority for Kampala, have the potential take action to achieve improvements in air quality. But they lack the information and evidence to make or motivate policy decisions in this domain. This project will provide the data, packaged and presented in a clear and actionable manner, in a format and context most useful to policy makers.

Our physically-informed probabilistic model of the air pollution in Kampala will impact the following areas:

Public policy: The local government in Kampala, the KCCA, are our partners for this project and have previously helped us to build the low cost sensor network. Improving air quality is one of their strategic priorities. They plan to take, and have previously taken, action to reduce air pollution in the city, for example, by the use of temporary road closures, or restricting the domestic burning of waste. The model we will build in this project will allow the KCCA to better understand the air pollution challenge, and to understand the sources of pollution. Our model will allow them to assess the likely effect of interventions enabling them to maximize their effectiveness.

Society: Air pollution is a problem across much of the Global South, particularly in rapidly urbanising cities with marginalized and low income communities, such as Kampala, but its management is severely restricted by the lack of affordable systems for systematic monitoring. Historical and ongoing measurements of air quality in Kampala far exceed recommendations by the WHO. Kampala's population of 1.5M people would have substantially improved life expectancies for even a small improvements in air quality. This project will provide policy makers and civil society the tools they need to develop and assess possible policy interventions to improve air quality. Beyond the local improvements to health, we anticipate considerable international impact, first through the implementation of similar low cost networks by city authorities in neighbouring countries. Second, by supporting academic research in air pollution. And third, by supporting the development of practical interventions such as cleaner fuels and active travel (thus targeting the growing issue of 'double burden' in the Global South).

Economy: The OECD estimates the annual welfare costs associated with outdoor air pollution in Sub-Saharan Africa are $40B. Any improvement in air quality in Kampala will positively benefit civil society by reducing the costs of poor health, and improving the fitness and health of the population

Knowledge: One of the key outputs will be an open, robust and simple to use system for monitoring air pollution using a mixture of sensor types that will allow policymakers to use a physics-informed model to answer 'what if?' questions. This will have clear scientific utility, both in the domain of air pollution modelling but also in the wider machine learning community, which is increasingly focusing on the development of methods that move beyond purely data-driven algorithms.

People: This project will build research and technical capacity in data and computer science at Makerere University, strengthening the foundations for improved graduate education. The additional research capacity is synergistic with the support the project will provide for networking, via the Data Science Africa network (a cooperative scientific programme across African countries).