  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.
