Exposure estimates of PM2.5 using the land-use regression with machine learning and microenvironmental exposure models for elders: Validation and comparison

Abstract

Estimating short-term exposure to PM2.5 has been achieved for population health studies using the land use regression with machine learning (LUR_ML) and microenvironmental exposure (ME) models. However, there is a lack of clarity regarding the performance of these models in predicting PM2.5 exposure for individuals residing in diverse environments, and the factors influencing the variations in accuracy between these models. This study performed the LUR_ML and ME models to estimate daily exposure concentrations of PM2.5 for elders residing in urban, suburban, rural, and industrial regions in Taiwan. The accuracy of the model predictions was assessed by comparing them with personal PM2.5 monitoring for both overall and regional assessments. The LUR_ML model demonstrated reasonably moderate agreement (R2 = 0.516) overall with personal exposure to PM2.5, while the ME models exhibited relatively higher predictions (R2 = 0.535–0.575) and lower biases. The agreement of PM2.5 predictions varies across regions, particularly in areas with higher exposure contrast. The ME model 1, utilizing region-specific microenvironmental measurements rather than generic data, highlights the potential for accurate prediction of personal PM2.5 exposure. This study contributed to the understanding of variations in prediction accuracy across different regions and support the need for improved exposure models of air pollution.