A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance.

ABSTRACT: Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community

SUBMITTER: Sayeed A 

PROVIDER: S-EPMC8149875 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature