Data-driven Air Quality Characterisation for Urban Environments: a Case Study

Zhou, Yuchao, De, Suparna, Ewa, Gideon, Perera, Charith and Moessner, Klaus (2018) Data-driven Air Quality Characterisation for Urban Environments: a Case Study IEEE Access, 6 (1). pp. 77996-78006.

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Abstract

The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX) model, especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Zhou, Yuchao yz0003@surrey.ac.uk De, Suparna S.De@surrey.ac.uk Ewa, Gideon Perera, Charith Moessner, Klaus K.Moessner@surrey.ac.uk
Date :	3 December 2018
Funders :	Horizon 2020
DOI :	10.1109/ACCESS.2018.2884647
Copyright Disclaimer :	© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Uncontrolled Keywords :	Air Quality Estimation; Air Pollution; Machine Learning Prediction; Neural Network
Depositing User :	Clive Harris
Date Deposited :	05 Dec 2018 11:42
Last Modified :	15 Jan 2019 16:09
URI:	http://epubs.surrey.ac.uk/id/eprint/849982

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