AirMPA: A Meteorology-to-Pollution Adapter for Global Air Quality Forecasting

Yiheng Wang; Kai Zheng; Yuetan Lin; Fanglu Fan; Chenliang Tao; Guochao Chen; Hongliang Zhang; Hao Li

physics.ao-phOct 19, 2026

AirMPA: A Meteorology-to-Pollution Adapter for Global Air Quality Forecasting

Authors: Yiheng Wang, Kai Zheng, Yuetan Lin, Fanglu Fan, Chenliang Tao, Guochao Chen, Hongliang Zhang, Hao Li

Organizations: Fudan University · Shanghai Academy of AI for Science · University of Shanghai for Science and Technology

Abstract

Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health. Traditional numerical forecasting systems suffer from incomplete parameterizations, uncertain inputs, and high computational cost. Existing deep learning approaches improve efficiency, but often entangle meteorological and pollutant variables within unified forecasting frameworks, limiting flexibility and pollutant-specific representation learning. To address these challenges, we propose AirMPA, a decoupled autoregressive framework for meteorology-to-pollution forecasting. AirMPA predicts 13 atmospheric pollutants at 0.4° spatial resolution and 12-h temporal resolution using meteorological fields, emission priors, and historical pollutant concentrations. The model is built on an improved Swin-Transformer architecture with zonal release attention and dual-branch fusion, enabling more effective modeling of global transport continuity and heterogeneous pollutant dynamics. During training, AirMPA uses the fifth-generation atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) as meteorological forcing, while during inference it directly ingests forecast fields from external meteorological models, enabling stable autoregressive prediction up to 5 days ahead. Experiments show that AirMPA consistently outperforms Aurora and surpasses the Copernicus Atmosphere Monitoring Service (CAMS) in approximately 90% of pollutant-lead-time evaluation cases over the 5-day forecast horizon, while outperforming CAMS on all variables beyond 48 h. These results demonstrate the potential of decoupled meteorology-to-pollution forecasting for global air quality prediction.

Figures & tables

Figure 1: AirMPA architecture. (a) Overview of the AirMPA architecture and data flow. (b) Illustration of the zonal release attention mechanism, which mitigates the artificial discontinuity at the global longitude boundary. (c) Autoregressive inference pipeline for multi-step pollutant forecasting.

Figure 2: Comparison of forecasting performance across AirMPA (with FuXi), AirMPA (with ERA5), CAMS, and Aurora, evaluated by latitude-weighted RMSE (lower is better).

Figure 3: Spatial distribution of RMSE and RMSE differences for PM 2.5 , total column O 3 , surface NO 2 , and surface O 3 over the evaluation period. In each panel, rows from top to bottom show AirMPA RMSE, RMSE difference ( $\mathrm{CAMS}-\mathrm{AirMPA}$ ), and RMSE difference ( $\mathrm{Aurora}-\mathrm{AirMPA}$ ). Red regions indicate better performance of AirMPA.

Figure 4: Comparison of surface O 3 forecasts at 12:00 on August 14, 2022. From top to bottom, the panels show results from CAMS, Aurora, AirMPA, and CAMS FC initial field at the valid forecast time.

Metric	1d	3d	5d	Overall
Better cases	11/13	11/13	10/13	32/39
Mean $\Delta$ RMSE	$-5.99\%$	$-8.17\%$	$-7.43\%$	$-7.20\%$

Table 1: Decoupled Swin versus Aurora.

\Delta

RMSE denotes the mean relative RMSE change against Aurora; negative values indicate improvement. Better cases count lower-RMSE results across 13 pollutants at each lead time.

Input	1d	3d	5d	Overall
ERA5	$-10.72\%$	$-24.89\%$	$-31.21\%$	$-22.27\%$
FuXi	$-8.63\%$	$-17.65\%$	$-17.80\%$	$-14.69\%$
HRES	$-8.81\%$	$-18.15\%$	$-18.80\%$	$-15.26\%$

Table 2: AirMPA performance under different meteorological inputs. Values are mean relative RMSE changes against CAMS across 13 pollutants; negative values indicate improvement. Overall averages the 39 pollutant–lead-time cases. ERA5 provides an offline reanalysis reference.

Model	PM 2.5 $\downarrow$		O 3 $\downarrow$		TC NO 2 $\downarrow$		Average RMSE $\downarrow$
	1d	5d	1d	5d	1d	5d	1d	5d
Swin Transformer (Baseline)	4.31	6.08	2.595	4.435	0.651	0.870	17.68	32.20
w/ Emission Input	4.29	6.03	2.590	4.630	0.648	0.872	16.84	30.08
w/ Cube Embedding ∗	4.25	6.13	2.558	4.425	0.619	0.845	16.76	30.00
w/ Zonal Release Attention ∗	4.28	6.03	2.543	4.420	0.627	0.864	16.80	30.05
w/ Dual-branch Fusion ∗	4.12	5.79	2.540	4.361	0.622	0.826	16.28	28.95

Table 3: Ablation results of key architectural components in AirMPA (lower is better). PM 2.5 is in

\mathrm{\mu g}/\mathrm{m}^{3}

, O 3 is in ppb, and TC NO 2 is in

\mathrm{mg}/\mathrm{m}^{2}

. Best values are shown in bold, and second-best values are underlined. ∗ indicates models built on the version with emission input.

Appendix figures & tables14 assets

Supplementary material from the paper’s appendix.

Appendix

Data Type	Source	Variables	Spatial Res.	Temporal Res.	Period
Pollutants	CAMS-RA	pm1, pm2p5, pm10, tcco, tcno2, tc_no, gtco3, tcso2, co_1000, no_1000, no2_1000, so2_1000, go3_1000	$0.75^{\circ}\to 0.4^{\circ}$	12 h	2010.01–2021.12
	CAMS-FC	Same as above	$0.4^{\circ}$	12 h	2017.10–2022.05
Meteorology	ERA5	U10m, v10m, d2m, t2m, msl, tcc, tp; z, u, v, t (at 13 levels: 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000 hPa)	$0.25^{\circ}\to 0.4^{\circ}$	12 h	2010.01–2023.12
	FuXi	U10m, v10m, d2m, t2m, msl, tcc, tp; z, u, v, t (at 13 levels)	$0.25^{\circ}\to 0.4^{\circ}$	12 h	2022.06–2022.11
Emissions	CAMS emission	ammonia, black carbon, carbon monoxide, nitrogen oxides, NMVOCs, sulfur dioxide	$0.1^{\circ}\to 0.4^{\circ}$	12 h	2010.01–2023.12

Appendix

Table 4: Model Input Data

Stage / Split	Dataset	Purpose	Period
Stage 1	CAMS-RA	Global pre-training	2010.01–2021.12
Stage 2	CAMS-FC	Operational fine-tuning	2017.10–2022.05
Test	CAMS-FC	Final evaluation	2022.06–2022.11

Appendix

Table 5: Dataset partitioning and time ranges for different training stages.

Stage	Objective	Learning rate	Loss settings
Stage 1	Global pre-training on CAMS reanalysis	$2\times 10^{-4}\rightarrow 10^{-6}$	Hybrid loss: $\alpha=1.0$ , $\beta=0.25$ , $\epsilon=10^{-3}$
Stage 2	Operational fine-tuning on CAMS forecast data	$5\times 10^{-5}\rightarrow 10^{-6}$	Hybrid loss: $\alpha=1.0$ , $\beta=0.75$ , $\epsilon=10^{-3}$
Stage 3	Extreme-event optimization	continued from Stage 2	Weighted RMSE loss: overall RMSE $=0.5$ , extreme-event RMSE $=0.5$

Appendix

Table 6: Training objectives and optimization settings for the three training stages of AirMPA.

Variable	PM 1	PM 2.5	PM 10	TC CO	TC NO	TC NO 2	TC SO 2	TC O 3
Training extreme threshold	8.0	25.0	50.0	900	1.5	3.5	3.0	7500
Threshold (90%)	5.2323	20.8496	42.1983	811.4433	1.4372	3.2538	1.7426	7227.6772
Variable (at 1000 hpa)	CO	NO	NO 2	SO 2	O 3
Training extreme threshold	135	0.05	0.8	0.5	45
Threshold (90%)	110.1245	0.0265	0.4042	0.2312	38.8720

Appendix

Table 7: Extreme thresholds and 90th percentile values for different variables.

Model	Zonal	Cube	Dual-Branch	Emission
	Release Attention	Embedding	Fusion	Input
Swin-Transformer (backbone)	$\times$	$\times$	$\times$	$\times$
w/ Emission	$\times$	$\times$	$\times$	✓
w/ Zonal Release Attention	✓	$\times$	$\times$	✓
w/ Cube Embedding	$\times$	✓	$\times$	✓
w/ Dual-Branch Fusion	$\times$	$\times$	✓	✓

Appendix

Table 8: Ablation study of key architectural components in AirMPA.

Variable	Day 1	Day 3	Day 5
PM 1	19.36	13.88	11.79
PM 2.5	21.89	15.17	8.10
PM 10	33.46	30.39	24.90
TC CO	15.38	13.58	18.14
TC NO 2	20.18	13.84	8.10
TC NO	29.33	24.76	18.13

Appendix

Table 9: Relative RMSE reduction (%) of AirMPA compared with the offline meteorology–pollution model of Fan et al. [ 34 ] over June–November 2022. Positive values (in bold) indicate lower RMSE for AirMPA, while negative values indicate degradation. The comparison covers eight shared pollutant variables at lead times of 1, 3, and 5 days.

	Day 1		Day 3		Day 5
Variable	ERA5	HRES	ERA5	HRES	ERA5	HRES
PM 1	1.53	-0.08	3.79	0.17	6.94	0.38
PM 2.5	2.46	0.13	8.29	0.48	16.12	0.82
PM 10	2.99	0.32	10.44	0.48	19.66	1.42
TC CO	2.15	-0.25	9.22	-0.53	17.06	-1.57
TC NO 2	3.40	0.16	11.52	1.90	19.92	3.56

Appendix

Table 10: Pollutant-specific comparison of meteorological forcing at lead times of 1, 3, and 5 days. Values are relative RMSE reductions (%) compared with FuXi forcing. Positive values (in bold) indicate lower RMSE, while negative values indicate higher RMSE. TC denotes total column. The mean is the unweighted average across the 13 variables. ERA5 provides an offline reanalysis reference.

Figure 5: Comparison of forecasting performance across AirMPA (with FuXi), CAMS, and Aurora.

Figure 6: Same as Figure 3 , but for other pollutants. The three columns in each subplot correspond to lead times of 1, 3, and 5 days, while the rows correspond to AirMPA, CAMS – AirMPA, and Aurora – AirMPA, respectively.

Figure 7: Comparison of extreme forecasting performance(90% RMSE during June 2022 – November 2022) across AirMPA (with FuXi), CAMS, and Aurora.

Figure 8: Comparison of extreme forecasting performance(90% CSI during June 2022 – November 2022) across AirMPA (with FuXi), CAMS, and Aurora.

Figure 9: Comparison of the AirMPA model in extreme value forecasting metrics relative to CAMS and Aurora.

Figure 10: Comparison of forecasting performance of AirMPA, Aurora, and CAMS at monitoring stations in the United States (EPA), Europe (EEA) and China (CNEMC).

Figure 11: Distribution of monitoring stations in the United States (EPA), Europe (EEA) and China (CNEMC).

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