Unmasking the sky: high-resolution PM 2.5 prediction in Texas using machine learning techniques.

Background: Although PM _2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.
Objective: This study aimed to predict PM _2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables.
Methods: We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM _2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM _2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations.
Results: Our predictive models demonstrate excellent in-sample model performance, as indicated by high R ² values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R ² values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM _2.5 concentrations over time is observed in Eastern Texas.
Impact Statement: We utilized machine learning approaches to predict PM _2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R ²  > 0.9).
(© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)