Estimation of PM2.5 Concentrations in China Using a Spatial Back Propagation Neural Network.

ABSTRACT: Methods for estimating the spatial distribution of PM_2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM_2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the

SUBMITTER: Wang W 

PROVIDER: S-EPMC6760143 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature