Future air quality predictions using a machine learning-based model over the contiguous United States

Air quality regulations have reduced emissions of pollutants in the U.S., but many prognostic studies suggest that future air quality might be degraded by global climate change. The simulated climate by various climate models shows a large variation in the future decades, and it is important to account for such variations to study future air quality. We have developed a machine learning (ML) based air quality model to study, in an efficient way, how future air quality might be influenced by climate change. Our ML model uses two-phase random forest to predict the O3 and PM2.5 concentrations with training datasets of key meteorological information and air quality pollutant emissions. To evaluate the model performance, we used the input datasets for the U.S. Environmental Protection Agent (EPA) the Community Multiscale Air Quality Modeling System (CMAQ) simulations and compared our model predictions against the CMAQ output as a benchmark. The ML model is well performed for hourly O3 predictions over the whole domain in four selected months (January, February, July, and August), and the R2 values are in 0.5 – 0.7, the normalized mean bias (NMB) values are within ±3%, the overall normalized mean error (NME) values are below 20%. Predicting PM2.5 is more challenging than predicting O3, but our ML model performance is still acceptable. The overall R2 values of PM2.5 predictions are in 0.4 – 0.6, and the NMB values are within ±6%, but the NME can be up to 60%. Our ML model with GPU acceleration runs less than one hour using a single GPU processor to predict 11-year one-month (total 11 months) simulations. It uses significantly less computing resources compared to the 3D models, like CMAQ, while it results in comparable predictability to CMAQ. It shows that our ML model a reliable and efficient tool to assess the air quality under various climate change scenarios.