Abstract

Air quality forecasting is crucial for public health, especially in rural, suburban, and developing areas lacking reliable monitoring data. Hybrid monitoring (surface, satellite, and models) offers a scalable, cost‐ effective solution for tracking pollution and trends. This work presents a machine learning model that integrates ground measurements with global model outputs assimilating satellite observations to forecast air quality. Ground measurements of fine particulate matter (PM2.5) from over 60 U.S. embassies and consulates were used to calibrate global model outputs for local air quality forecasting. Multi‐channel input data was prepared using the Goddard Earth Observing System forward processing for meteorology and aerosol forecasts over 72 hr. An advanced convolutional neural network addressed high‐dimensional data and nonlinearities between inputs and outputs. A global model was developed and fine‐tuned with continent‐specific local models. The global model achieved Root Mean Squared Error (RMSE) and slope of 5.64 μg/m3 and 0.96, respectively. Local models showed improved performance with RMSE of 3.21 μg/m3 and slope of 0.98, outperforming the global model in Air Quality Index predictions by 6.57% in accuracy and greater stability during variability. The forecasts are publicly accessible via an application programming interface, providing global air quality predictions for 269 U. S. embassy and consulate sites to support public health and operational planning.

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Publication Date

Summer 6-2025

Document Type

Article

Department, Program, or Center

Chester F. Carlson Center for Imaging Science

College

College of Science

Campus

RIT – Main Campus

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