Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO₂) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO₂ concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO₂ emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO₂ forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO₂ emission optimization methodology is computationally cost-effective.

Accurate emission inventories are essential for effective air quality prediction and management. Traditional bottom‐up methods and top‐down data assimilation approaches struggle to balance accuracy, timeliness, and usability. This study presents a novel inversion method combining deep learning (DL) and 3D‐Variational (3DVar) assimilation, termed DL‐3DVar. Based on the prior emissions and SO ₂ concentration observations, this method was applied to invert China's grided hourly SO ₂ emission inventory during the 2023 Chinese New Year (CNY). The results show that DL‐3DVar estimated an hourly SO ₂ emission increment of 1.95 × 10 ⁶ kg at midnight on CNY's Eve relative to the prior emissions, primarily due to extensive fireworks. Forecast experiments showed that using DL‐3DVar inverted emissions reduced the root‐mean‐square error (RMSE) by 52.9% and increased the correlation coefficient (CORR) by 331% compared to the prior emissions. This high‐temporal‐resolution emission inventory can improve pollutant forecasting and provide robust support for air pollution control policymaking.

Nuclear explosions and accidents release large amounts of radionuclides that harm human health and the environment. Accurate forecasting of nuclide pollutants and assessment of the ramifications of nuclear incidents are necessary for the emergency response and disaster assessment of nuclide pollution. In this study, we developed a three-dimensional variational (3Dvar) system to assimilate ¹³⁷Cs based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. The distribution of ¹³⁷Cs after the Fukushima nuclear accident in Japan on 15 March 2011 was analysed. The ¹³⁷Cs background field at 06:00 UTC was assimilated using a 3Dvar system and surface observational data to optimise the ¹³⁷Cs analysis field. Compared with the background field, the root mean square error (RMSE) and mean bias in the ¹³⁷Cs analysis field decreased by 98% and 94%, respectively. The average fraction of predictions within factors of 2 (FAC2), 5 (FAC5), and 10 (FAC10) increased from 0.67, 0.72, and 0.72 to 0.90, 1.00, and 1.00, respectively. This substantial enhancement indicated the effectiveness of the 3DVar system in mitigating the uncertainty associated with the background field. Two 12 h forecast experiments were conducted to gauge the advancement in ¹³⁷Cs forecasting facilitated by data assimilation (DA). The control experiment was conducted without DA, whereas the assimilation experiment was conducted with DA. Compared with the control experiment, the average FAC2, FAC5, and FAC10 in the assimilation experiment increased by 28%, 30%, and 29%, respectively. The average RMSE decreased by 33%. The mean bias and correlation coefficient increased by 41% and 36%, respectively. These results indicated that the 3Dvar method improves the forecast accuracy of ¹³⁷Cs concentration.

Data assimilation (DA) integrates the latest observational data into initial background field, producing an optimal analysis field in continuous time and space within the realm of numerical model forecasting to improve forecasting. However, its computational efficiency remains a concern due to its relatively slow processing speed. Alternatively, deep learning (DL) methods train assimilation models using observational or forecast data as labels, but its effectiveness is often constrained by the accuracy of these labels. In this paper, we proposed a novel approach that combined three-dimensional variational assimilation (3D-Var) with DL method, which integrated the Hadamard attention mechanism and Transformer modules into the Unet framework (HT-Unet) to assimilate sulfur dioxide (SO2) and improve the forecast skill. This hybrid method not only significantly improved the forecast accuracy but also accelerated computational processes. Three forecast experiments were conducted with background filed, 3D-Var, and HT-Unet analysis fields to assess forecast improvements in SO2 concentration. The results showed that the correlation with HT-Unet analysis field was nearly 50% higher than the background field, which was comparable to those from 3D-Var analysis field. The RMSEs of the SO2 concentration in the HT-Unet and 3D-Var DA experiments were reduced by 2.59 µg/m³ (29.50%), and 2.60 µg/m³ (29.61%). Additionally, the HT-Unet method achieved computational efficiency far surpassing traditional methods, which was 34 times faster than traditional approaches for assimilating a single analysis. The new method demonstrated the potential to replace the traditional 3D-Var method, overcoming the high-cost limitations of conventional variational assimilation and significantly enhancing air pollution forecasting accuracy.

During the 2023 Chinese New Year (CNY), many city governments relaxed restrictions on residents setting off fireworks. Fireworks contain large amounts of sulfur powder, which releases the toxic air pollutant sulfur dioxide (SO₂) when combusted rapidly. In this study, we employed a four-dimensional variational (4DVar) assimilation system to examine variations in SO₂ emissions in China by assimilating hourly ground-based observation data. Two sets of experiments were conducted during the CNY in 2022 and 2023 to quantify the variations in SO₂ emissions. On CNY's Eve in 2022, nationwide SO₂ emissions increased by 4.701 Gg compared to the preceding day. During the CNY in 2023, following the relaxation of the fireworks ban, there was a significant upsurge of 8.22 Gg in SO₂ emissions on CNY's Eve compared to the previous day. This increase included a rise of 2.037 Gg in the Energy Golden Triangle region, 1.709 Gg in the North China Plain and 0.945 Gg in northeastern China. After reaching the peak on CNY's Eve, the overall national SO₂ emissions during the 2023 CNY showed a rapid decline in the following two days. However, the emission levels remained elevated compared to the pre-CNY period, indicating the lingering effects of fireworks burning. Compared to the forecasts using the prior emissions, the optimized emissions markedly improved the model forecasts of SO₂ during the 2023 CNY period, evidenced by an increase in the correlation coefficient (R) from 0.12 to 0.63 and a reduction in the root-mean-square error (RMSE) by 49.9%, demonstrating the effectiveness of the optimized emissions. These findings will be useful for local governments in formulating strategies for fireworks burning during the CNY period.