Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data.

ABSTRACT: Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM_2.5 concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM_2.5

SUBMITTER: Ge Y 

PROVIDER: S-EPMC10540507 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature