Assessing the health estimation capacity of air pollution exposure prediction models.

ABSTRACT: The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations. We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM2.5), with errors a

SUBMITTER: Krall JR 

PROVIDER: S-EPMC8928613 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature