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

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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**.