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

TL;DR

This paper presents a machine learning framework using a novel neural network architecture to accurately predict urban air quality indices from environmental and meteorological data, addressing data sparsity issues.

Contribution

It introduces a NARX-based neural network model tailored for time series air quality prediction in urban environments, validated through a London case study.

Findings

High accuracy in AQI prediction across different urban sites

Robust performance compared to standard machine learning algorithms

Effective handling of data sparsity and missing labels

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), 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.