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

据估计，环境空气污染每年造成300多万人过早死亡。颗粒物（PM）污染尤其可能是造成死亡人数增加的原因之一。不幸的是，撒哈拉以南非洲地区对空气污染的监测有限，部分原因是精确的监测设备过于昂贵，难以在国家和地方一级制定或评估政策。低成本的颗粒传感器是可用的，但其有限的精度意味着数据不能可靠地使用而不进行校正。该项目将测试这样一个假设，即当与参考仪器结合使用并与物理洞察力结合使用时，低成本的传感器网络可以用来产生模型，以准确地预测PM，获得洞察力，并计划政策。我们的重点是坎帕拉，在过去的四年里，项目团队在那里建立了一个低成本的传感器网络。坎帕拉是一个快速发展的城市，颗粒物污染持续处于危险水平，经常超过世卫组织指导方针年平均限值的十倍。造成这种情况的因素有很多，包括坎帕拉的地理位置、部分未使用金属的道路网络，以及家庭焚烧垃圾和使用固体燃料炉灶做饭等活动。目的和目标：项目团队之前安装了一个低成本的传感器网络，并使用称为高斯过程的数学模型提供整个城市的污染预测。这种类型的模型只使用测量之间的相关性，这意味着外部输入，如风向，没有得到适当的处理。此外，这种类型的模型不能用于预测干预措施的效果（例如对道路封闭的影响进行建模），因为这涉及到训练数据之外的外推。我们之前曾与坎帕拉首都管理局（KCCA）合作，在整个城市安装了50个传感器，在这个项目中，我们将与他们合作，制定可能的干预措施，以改善空气质量，模拟其潜在影响，然后衡量其有效性。该项目的数学目标是围绕空间和时间模型的新建模范式的发展，以及这些模型在观测数据上训练模型所面临的挑战。目的是三重的。首先，它们将允许我们包含物理过程的真实近似值，例如城市周围污染的移动。其次，他们会让我们弄清楚是什么在何时何地产生污染。第三，他们将帮助KCCA回答“如果”的问题，例如：“如果我们关闭Luwum街，禁止机动车通行呢？”模型的预测也必须报告其可信度，以便KCCA和其他机构知道结果是否可信。应用和效益：坎帕拉的空气质量即使有微小的改善，也会改善其人口的健康。通过向政策制定者和民间社会提供预测工具，我们将使他们能够规划和评估改善空气质量的政策干预措施。我们预计将产生相当大的国际影响，首先是通过邻国城市当局的实施。第二，支持该领域的学术研究。第三，支持开发更清洁的燃料等实际干预措施，支持积极出行和其他有关“双重负担”的问题。总而言之，该项目将带来与空气质量改善有关的生活质量的重大改善。坎帕拉首都管理局（KCCA）是坎帕拉的地方政府和民政当局，有可能采取行动改善空气质量。但他们缺乏在这一领域制定或激励政策决策的信息和证据。该项目将以对决策者最有用的格式和背景，以明确和可操作的方式提供打包和呈现的数据。

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.