Deep Learning for Prediction of the Air Quality Response to Emission Changes.

ABSTRACT: Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM). By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods

SUBMITTER: Xing J 

PROVIDER: S-EPMC7375937 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature