Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam

Air pollution concentrations in Ho Chi Minh City (HCMC) have been found to surpass the WHO standard, which has become a very serious problem affecting human health and the ecosystem. Various machine learning algorithms have recently been widely used in air quality forecasting studies to predict possible impacts. Training and constructing several machine learning models for different air pollutants, such as NO₂, SO₂, O₃, and CO forecasts, is a time-consuming process that necessitates additional effort for deployment, maintenance, and monitoring. In this paper, an effort has been made to develop a multi-step multi-output multivariate model (a global model) for air quality forecasting, taking into account various parameters such as meteorological conditions, air quality data from urban traffic, residential, and industrial areas, urban space information, and time component for the prediction of NO₂, SO₂, O₃, CO hourly (1 h to 24 h) concentrations. The global forecasting model can anticipate multiple air pollutant concentrations concurrently, based on past concentrations of covariate characteristics. The datasets on air pollution time series were gathered from six HealthyAir air quality monitoring sites in HCMC between February 2021 and August 2022. Darksky weather provided the hourly concentrations of meteorological conditions for the same period. This is the first model built using real-time air quality data for NO₂, SO₂, CO, and O₃ forecasting in HCM city. To assess the effectiveness of the proposed model, it was evaluated using real data from HealthyAir stations and quantified using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation indices. The results show that the global air quality forecasting model beats earlier models built for air quality forecasting of each specific pollutant in HCMC.