Data-driven Air Quality Characterisation for Urban Environments: a Case Study
This addresses the need for real-time air quality awareness in cities, but it is incremental as it applies an enhanced neural network to a specific domain.
The paper tackles the problem of predicting Air Quality Index (AQI) in urban areas by proposing a machine learning method using environmental and meteorological data, achieving robust performance in a case study in London compared to standard algorithms.
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.