Forecasting ground-level ozone concentration levels using machine learning

Abstract

Ground-level ozone (GLO) has been widely recognized as a critical air pollutant that has the potential to induce various adverse environmental and health effects. To eliminate its hazardous impacts, the development of an accurate and effective approach to forecast the upcoming pollution concentration levels is in urgent need. Recent studies show that machine learning algorithms have excellent abilities in ground-level ozone concentration forecasting, however many of their forecasting models do not consider the contributing effects on meteorological measurements and traffic situations which were also identified as potential influencing factors to ground-level ozone concentration by some previous research. In the meantime, most of the existing models target short-term forecasting with rough temporal resolutions, such as daily and weekly scales. This paper aims to propose a methodology that can provide long-term GLO concentration forecasting with a high temporal resolution. To achieve this, a frequent pattern mining approach is utilized to analyze the local intercorrelation between GLO concentration and contributing factors such as meteorological parameters and transportation situations. Then, a series of machine learning algorithms were identified to forecast the ground-level ozone concentration levels using traffic and meteorological measurements data. A case study was conducted in the Houston region with 10 years of historical measurements, each of the historical ground-level ozone concentration records is associated with a series of meteorology and traffic situation parameters. Data from 2010 to 2019 was used to select and train the machine learning models, and data from 2020 was used to perform the final validation and evaluation. Results show that the extreme gradient boost (XGBoost) machine learning algorithm provides the most accurate prediction of the hourly ground-level ozone concentration on a yearly scale, which shows the annually forecasting ability and the robustness of the model built. The proposed approach could be applied to other similar regions and other critical air pollutants that are also influenced by transportation and meteorological factors.