Application of Three Machine Learning Models for a Severe Ozone Episode in Mexico City

Mexico City due to its specific topography and strong ozone precursors emissions often faces high surface ozone concentrations which negatively impact the dwellers and the environment of Mexico City. This necessitates developing models with the capacity to rank meteorological and air quality variables contributing to the build-up of ozone during an ozone episode in Mexico City. Such ranking is crucial for regulatory procedures aiming at reducing ozone detrimental effects during an ozone episode. In this study, three machine learning models (Random Forest, Gradient Boosting Tree, feedforward neural network) are used to learn a prediction function that reveals the functional dependence of ozone on its predictors and can predict hourly ozone concentrations using hourly data of eight predictors (nitric oxide, nitrogen dioxide, shortwave ultraviolet-A radiation, wind direction, wind speed, relative humidity, ambient surface temperature, planetary boundary layer height). The best model, feedforward neural network with 92% accuracy, in conjunction with Shapely Additive exPlanations approach, is utilized to simulate high ozone concentrations and rank the predictors according to their importance in the build-up of ozone during a severe ozone smog episode that occurred in the period 6 - 18 March 2016. The research focuses on Mexico City, but it is equally applicable to any other city in the world.