Intra-urban air pollution exposure prediction: a smart platform and applications using a land-use regression model

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Air pollution is a global threat with an estimated 7 million people dying worldwide from exposure to both indoor and outdoor air pollution every year, making it the world’s largest environmental health risk. Improving poor air quality is internationally recognised and enshrined in the targets of global sustainable development goals This problem is particularly evident in China, as its burgeoning urban environments face substantial pressures to both grow economically as well as to simultaneously improve resident’s health, quality of life, and productivity.This project seeks to develop a publicly accessible smart platform to forecast air pollution exposure at an intraurban scale. This project is designed to provide state-of-the-art tools to yield air quality data and build up local expertise and monitoring capabilities. We will be identifying sources of pollution, assessing their significance, informing efforts at their reduction, and building expertise and capacity in country.This will provide several environmental, societal, and economic benefits. Easy to access, reliable data, will empower citizens to reduce their exposure levels to air pollution through planning of alternative routes, moreover, it will also lead to behavioural change as citizens will become more aware of the results of their choices on the local environment. This project will also enable legislators to develop innovative solutions to solve air quality policy issues. The development of a smart platform including cost-effective tools to monitor air quality and identify sources for adaptive management responses will make a major contribution to people's health and wellbeing, as well as to ecosystems health.