Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States.

ABSTRACT: A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and tempora

SUBMITTER: Di Q 

PROVIDER: S-EPMC5761665 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature