Pollutant-Specific Deep Learning Architectures for Multi-Species Air Quality Bias Correction: Application to NO2 and PM10 in California

Accurate air quality forecasting remains challenging due to persistent biases in chemical transport models. Addressing this challenge, the current study develops pollutant-specific deep learning frameworks that correct systematic errors in the Community Multiscale Air Quality (CMAQ) simulations of nitrogen dioxide (NO2) and coarse particulate matter (PM10) over California. Building upon a previous study on ozone bias correction, a hybrid CNN–Attention–LSTM architecture is adapted, and a weighted Huber loss function is introduced for PM10 to enhance the detection of extreme pollution events through a gated tail-weighting mechanism. Using data from twenty EPA monitoring stations (ten per pollutant) for 2010–2014, the proposed approach achieves substantial performance gains over the CMAQ baseline. For NO2, RMSE decreases by ~51% with an average systematic bias reduction of ~80% and a random error reduction of ~42%. For PM10, RMSE improves by ~49% while the systematic and random errors decrease by ~94% and ~33%, respectively. The PM10 model also shows high consistency with observations (Index of Agreement improvement of ~105%) and a strong ability to capture peak events (F1 score improvement of ~270%), while the NO2 model achieves large gains in explanatory power (R2 improvement averaging ~816%). Both pollutants also demonstrate enhanced temporal agreement between predictions and observations, as confirmed by the Dynamic Time Warping analysis (NO2: ~55%, PM10: ~58%). These results indicate that pollutant-specific loss functions and architectural tuning can significantly improve both accuracy and event sensitivity, offering a transferable framework for bias correction across multiple pollutants and regions.