Unmasking the sky: high-resolution PM_2.5 prediction in Texas using machine learning techniques

Abstract

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 R² values generated from the gradient boosting models (0.94–0.97) and random forest models (0.81–0.90). However, the out-of-sample R² 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 (R² > 0.9).