Machine Learning Methods for Statistical Prediction of PM2.5 in Urban Agglomerations with Complex Terrain, Using Grenoble As an Example

Abstract

In this study, we propose several methods based on machine learning approaches for predicting air pollution levels in cities located in mountain valleys, with Grenoble (France) as a case study. Pollution forecasting is performed using both regression and classification of exceeding threshold levels. We employ a data-driven approach, utilizing various machine-learning models. Based on historical data from 2012 to 2018, collected at several meteorological stations in the Grenoble Valley, multiple machine learning models were trained to predict the daily average concentrations of fine particulate matter PM10 and PM2.5 three days ahead. Days with high PM concentrations exceeding the threshold values set by the World Health Organization (WHO) are of particular interest in our study. It was found that the presence of local meteorological conditions leads to the formation of temperature inversions, which are statistically associated with air pollution levels in this region. Although local meteorological conditions primarily determine the pollution level, the machine learning models considered in our study can be adapted for other cities in valleys by training them on relevant data.