Deep Learning Model to Estimate Air Pollution Using M-BP to Fill in Missing Proxy Urban Data

Abstract

Air quality has deteriorated rapidly in Hong Kong and China in the past two decades, with NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> levels frequently exceeding WHO safety guidelines. While poor air quality has clear public health impacts, there are very limited air quality monitoring (AQM) stations, severely constraining evidence-based air quality decision-making, leading to severe criticisms about the utility of the current official Air Quality Health Index to the public. Since air pollution is highly location-dependent, a city-wide deployment of traditional, highly sophisticated air quality monitors would be prohibitively expensive. In this paper, we propose a deep learning model to estimate air pollution throughout the city, utilizing the readily available urban data as proxy data. As with many big data driven approaches, the proxy data may be sparse/missing. We propose the M-BP algorithm to recover/fill in such missing data. Our results show that the proposed model gives better estimates compared with existing big data approaches.