Avoiding Pollution Hotspot in Post COVID-19 Era: An AI-based Air Pollution Exposure (APEX) Visualisation Platform for Individuals and UK Regions

避免后 COVID-19 时代的污染热点：针对个人和英国地区的基于人工智能的空气污染暴露 (APEX) 可视化平台

• 批准号: 82537
• 金额: $ 7.58万
• 依托单位: CLYTELL (UK) LIMITED
• 依托单位国家: 英国
• 项目类别: Small Business Research Initiative
• 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=82537
• 关键词: Avoiding; Pollution; Hotspot; Post; COVID

Existing navigation applications (e.g. Google Maps, Apple Maps, Waze) only provide real-time traffic information despite long-term exposure to **air pollution (i.e. inorganic and organic pollutants) being the most significant environmental threat to human health (Public Health England, 2020)**. High concentrations of inorganic air pollutants (e.g. PM2.5, PM10, CO2, NO, NO2 etc.) have been associated with devastating health diseases (e.g. stroke, lung cancer, asthma) (WHO, 2016) while organic air pollutants, particularly aerosols have recently been known to be active transporters of biological agents (e.g. Coronavirus) (Wang & Du, 2020). Hence, there is a pressing and cogent need to find innovative and sustainable ways to monitor air pollutants and curb their devastating effects on the populace (DEFRA, 2020). Despite this urgency over the years, evidence suggests that **"lack of a sustainable and generalisable air quality monitoring instruments/system"** is the main reason why there is currently no publicly available system/platform that allows the UK population to navigate pollution hotspots and its devastating effects safely. Although reference air quality monitoring instruments exist (e.g. Highways England's Air Quality Analysers), they are known to be **incredibly expensive to install and maintain**; therefore, such reference instruments cannot be relatively deployed across the entire UK to capture pollutant concentration levels. Furthermore, low-cost sensors (e.g. Zephyr, AirScan) which have recently become prevalent are known to be **plagued with accuracy, reliability and power issues**. This is coupled with the fact that it would take a significant and environmentally unfriendly investment to install low-cost sensors across every road in the UK. It was on this premise that Clytell (UK) Limited have spent the last 12 months on case-study feasibility to develop proprietary **Deep Learning Models that predict pollutant concentrations along the M1 and M56 motorway**. Using Air Quality Data from Highways England's state-of-the-art AQ Analyzers, GIS Location Data from Ordnance Survey API; Weather Data from Open Weather API and Traffic Data from MIDAS, the Deep Learning models were able to **accurately predict (@94%)** pollutant concentration of CO, CO2, NO and NO2 on these motorways. On the back of the feasibility study's significant breakthrough (i.e. that Deep Learning can indeed predict Air Quality using key historical data), Clytell (UK) Limited is proposing an **AI-based Air Pollution Exposure (APEX) Visualisation Platform for Individuals and UK Regions (APEX System)** that will extend and generalise the case-study feasibility Deep Learning Models beyond M1 and M56 motorway **to encompass the whole of the UK**.

现有的导航应用程序（例如谷歌地图，苹果地图，Waze）仅提供实时交通信息，尽管长期暴露于 ** 空气污染（即无机和有机污染物）是对人类健康最重大的环境威胁（英国公共卫生，2020年）**。高浓度的无机空气污染物（如PM2.5、PM10、CO2、NO、NO2等）与毁灭性的健康疾病（如中风，肺癌，哮喘）有关（WHO，2016），而有机空气污染物，特别是气溶胶，最近被认为是生物制剂（如冠状病毒）的主动转运体（Wang & Du，2020）。因此，迫切需要找到创新和可持续的方法来监测空气污染物并遏制其对民众的破坏性影响（DEFRA，2020）。尽管多年来一直存在这种紧迫性，但有证据表明，**“缺乏可持续和可推广的空气质量监测仪器/系统”** 是目前没有公共系统/平台允许英国人口安全地浏览污染热点及其破坏性影响的主要原因。虽然存在参考空气质量监测仪器（例如Highways England的空气质量分析仪），但众所周知，它们的安装和维护成本非常昂贵;因此，这种参考仪器不能在整个英国部署，以捕获污染物浓度水平。此外，最近流行的低成本传感器（例如Zephyr，AirScan）被称为 ** 受到精度，可靠性和功率问题的困扰。此外，在英国的每条道路上安装低成本传感器需要大量的环保投资。正是在这个前提下，Clytell（UK）Limited在过去的12个月里一直在进行案例研究可行性，以开发专有的 ** 深度学习模型，预测沿着M1和M56高速公路的污染物浓度 **。使用Highways England最先进的AQ分析仪的空气质量数据，Orlando Survey API的GIS位置数据; Open Weather API的天气数据和MIDAS的交通数据，深度学习模型能够 ** 准确地预测这些高速公路上的CO，CO2，NO和NO2的污染物浓度。在可行性研究取得重大突破的背景下，（即深度学习确实可以使用关键历史数据预测空气质量），Clytell（英国）有限公司正在为个人和英国地区提出一个基于人工智能的空气污染暴露（APEX）可视化平台（APEX系统）** 将扩展和推广案例研究可行性深度学习模型，超越M1和M56高速公路 **，涵盖整个英国 **。