Generative adversarial networks to model air pollution under uncertainty

Abstract

Urbanization trends worldwide show a clear preference for motorized road mobility, which has led to a degradation of air quality in recent years. Modelling and forecasting ambient air pollution is a relevant problem because it helps decision-makers and urban city planners understand this phenomenon, which is a significant threat to citizens’ health. Generally, datadriven models suffer from a lack of data. This article addresses the issue of having limited access to road traffic density and pollution concentration data by applying deep generative models, specifically, Conditional Generative Adversarial Networks (CGAN). The main idea is to train CGANs to generate synthetic nitrogen dioxide concentration values given the road traffic density. The experimental data analysis from Montevideo (Uruguay) shows that the proposed method generates realistic (accurate and diverse) pollution data while using reduced computational resources.