# Air Quality Forecasting

Regional air-quality forecasting has relied on online-coupled numerical chemistry-weather models whose skill is sensitive to meteorological initial conditions, emission inventories and lateral boundary conditions. CWAI treats meteorology, particles and reactive gases as one joint atmospheric state, driven by two consecutive states, emissions and static surface descriptors through a hierarchical Swin Transformer, and rolls out 40 six-hour steps to a 10-day forecast. Against WRF-Chem over 62 January and July 2020 starts, CWAI had lower initialization-weighted RMSE for all six surface pollutants, from 15.8% for PM2.5 to 25.8% for NO2.

Regional air-quality forecasting has relied on online-coupled numerical chemistry-weather models whose skill is sensitive to meteorological initial conditions, emission inventories and lateral boundary conditions. CWAI treats meteorology, particles and reactive gases as one joint atmospheric state, driven by two consecutive states, emissions and static surface descriptors through a hierarchical Swin Transformer, and rolls out 40 six-hour steps to a 10-day forecast. Against WRF-Chem over 62 January and July 2020 starts, CWAI had lower initialization-weighted RMSE for all six surface pollutants, from 15.8% for PM2.5 to 25.8% for NO2.

Ammonia emissions inversions rely on ensembles to carry meteorology-dependent error covariances, but each ensemble means repeatedly running the GEM-MACH chemistry model. EnsAI replaces that with a U-Net that maps ammonia emissions perturbations plus temperature and wind fields directly to surface concentration perturbations, producing a week of simulated data per member in 7 seconds against roughly 6.5 hours for GEM-MACH on 720 nodes. Inversions using the EnsAI ensemble gave annual total emissions 2.6% lower than the GEM-MACH ensemble inversions, with monthly totals within 10% for every month.

Ammonia emissions inversions rely on ensembles to carry meteorology-dependent error covariances, but each ensemble means repeatedly running the GEM-MACH chemistry model. EnsAI replaces that with a U-Net that maps ammonia emissions perturbations plus temperature and wind fields directly to surface concentration perturbations, producing a week of simulated data per member in 7 seconds against roughly 6.5 hours for GEM-MACH on 720 nodes. Inversions using the EnsAI ensemble gave annual total emissions 2.6% lower than the GEM-MACH ensemble inversions, with monthly totals within 10% for every month.

Ozone concentration forecasting now runs on two-stage decomposition and two-scale feature selection. The raw series is first decomposed by ICEEMDAN, the most complex component IMF1 is decomposed again by VMD, and the remaining subsequences are reconstructed by fuzzy entropy; partial autocorrelation and random forest then select input dimensions before a temporal convolutional network predicts. On daily ozone data from Beijing and Shanghai, the combined model reaches MAE, RMSE and SMAPE of 0.53509, 0.73346 and 0.8433%, the best among four comparison experiments.

Ozone concentration forecasting now runs on two-stage decomposition and two-scale feature selection. The raw series is first decomposed by ICEEMDAN, the most complex component IMF1 is decomposed again by VMD, and the remaining subsequences are reconstructed by fuzzy entropy; partial autocorrelation and random forest then select input dimensions before a temporal convolutional network predicts. On daily ozone data from Beijing and Shanghai, the combined model reaches MAE, RMSE and SMAPE of 0.53509, 0.73346 and 0.8433%, the best among four comparison experiments.

On hourly PM2.5 data from two roadside stations in Guildford, UK, a quantum neural network clearly beat classical MLP and LSTM baselines on raw, undecomposed series, with the depth-4 model lifting Pearson correlation from roughly 0.29–0.31 to 0.599. Adding five-level discrete wavelet decomposition, a 172-qubit depth-4 model reached the study's highest average correlation of 0.729 and lowest NMSE of 0.488 over the full six-hour horizon, though the classical MLP came close at that point. The results come from the Dynex neuromorphic quantum annealing emulation platform and have not been validated on physical quantum hardware.

On hourly PM2.5 data from two roadside stations in Guildford, UK, a quantum neural network clearly beat classical MLP and LSTM baselines on raw, undecomposed series, with the depth-4 model lifting Pearson correlation from roughly 0.29–0.31 to 0.599. Adding five-level discrete wavelet decomposition, a 172-qubit depth-4 model reached the study's highest average correlation of 0.729 and lowest NMSE of 0.488 over the full six-hour horizon, though the classical MLP came close at that point. The results come from the Dynex neuromorphic quantum annealing emulation platform and have not been validated on physical quantum hardware.