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

使用低成本传感器网络对坎帕拉空气污染进行基于物理的概率建模

基本信息

批准号：
EP/T00343X/2
负责人：
Richard Wilkinson
金额：
$ 40.48万
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FT00343X%2F2
关键词：
Physically informed probabilistic modelling air

项目摘要

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.

环境空气污染估计每年会导致超过300万人过早死亡。颗粒物（PM）污染尤其可能是导致此损失的原因。不幸的是，撒哈拉以南非洲的空气污染的监测有限，部分原因是准确的监控设备太昂贵，因此很难在国家和地方一级制定或评估政策。可以使用低成本颗粒传感器，但是它们的精度有限意味着没有校正就无法可靠地使用数据。该项目将检验以下假设：与参考仪器结合使用并与物理见解相结合时，低成本传感器网络可用于生产模型，以准确预测PM，获得洞察力和计划策略。我们专注于坎帕拉（Kampala），项目团队在过去的四年中建立了低成本的传感器网络。坎帕拉（Kampala）是一个迅速发展的城市，具有持续的危险颗粒污染水平，经常超过WHO指南的年平均限制的十倍。许多因素为此做出了贡献，包括坎帕拉的地理位置，其部分未指定的道路网络，以及在固体燃料炉子上进行家庭燃烧和烹饪等活动：AIMS和目标：该项目团队以前已经安装了低成本的传感器网络，并在整个城市中使用了一种数学模型来预测诸如高斯流程的城市中的预测。这种类型的模型仅使用测量之间的相关性，这意味着无法正确处理外部输入（例如风向）。此外，这种类型的模型不能用于预测干预措施的效果（例如，建模道路封闭的影响），因为这涉及在培训数据之外推断。我们以前曾与坎帕拉首都市管理局（KCCA）合作，在整个城市安装了五十个传感器，在这个项目中，我们将与他们一起制定可能的干预措施，以提高空气质量，对其潜在的影响进行建模，然后衡量其有效性。项目的数学目标专门围绕着对这些模型和挑战的模型进行新的模型，并围绕这些模型进行训练，并围绕这些模型来训练这些模型。目的是三倍。首先，它们将允许我们包括实际过程的现实近似，例如城市周围的污染运动。其次，他们将让我们确定在何时何地产生污染的原因。第三，他们将帮助KCCA回答“如果呢？”问题，例如“如果我们关闭卢沃姆街到达汽车交通怎么办？”这些模型的预测还必须报告他们的信心，以便KCCA和其他人知道结果是否可以信任。应用程序和好处：即使是坎帕拉空气质量的较小改善也会改善其人口的健康状况。通过为决策者和民间社会提供预测的工具，我们将使他们能够计划和评估政策干预措施以提高空气质量。我们预计，首先是通过城市当局在邻国实施的国际影响力。第二，通过支持该领域的学术研究。第三，通过支持开发实际干预措施，例如清洁燃料，支持“双重负担”围绕“双重负担”的活动旅行以及其他问题，该项目将导致与改善空气质量相关的生活质量的高度改善。坎帕拉首都管理局（KCCA）是坎帕拉地方政府和民事当局，有可能采取行动来改善空气质量。但是他们缺乏在该领域做出或激发政策决策的信息和证据。该项目将以最有用的格式和上下文提供清晰可行的方式包装和呈现的数据。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Shallow and Deep Nonparametric Convolutions for Gaussian Processes

高斯过程的浅层和深层非参数卷积

DOI：
10.48550/arxiv.2206.08972
发表时间：
2022
期刊：
影响因子：
0
作者：
McDonald T
通讯作者：
McDonald T

Adjoint-aided inference of Gaussian process driven differential equations

高斯过程驱动微分方程的伴随辅助推理

DOI：
10.48550/arxiv.2202.04589
发表时间：
2022
期刊：
影响因子：
0
作者：
Gahungu P
通讯作者：
Gahungu P

Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility.

DOI：
10.1007/s11356-022-24605-1
发表时间：
2023-03
期刊：
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
影响因子：
5.8
作者：
Galiwango, Ronald;Bainomugisha, Engineer;Kivunike, Florence;Kateete, David Patrick;Jjingo, Daudi
通讯作者：
Jjingo, Daudi

AI-driven environmental sensor networks and digital platforms for urban air pollution monitoring and modelling

人工智能驱动的环境传感器网络和数字平台，用于城市空气污染监测和建模