Seamless Fine Simulation And Forecast Of Real-Time PM_2.5 Concentration Using Mutlimodal Data Fusion

Although China’s air quality has been gradually improved in recent years,heavy air pollution events still occur in some local regions,and pollution incidents with PM_2.5 as the primary pollutant are particularly frequent.It is still very important to clarify the spatiotemporal evolution rules of the generation,elimination and diffusion of PM_2.5 concentration in key regions,and to accurately predict the spatiotemporal distribution of PM_2.5 concentration in the future.Therefore,it is the real needs of current air pollution prevention and control to realize the seamless fine simulation and forecast of real-time PM_2.5 Concentration.In this regard,this paper intends to take the Beijing-Tianjin-Hebei urban agglomeration with surrounding areas as the research area,comprehensively consider the complementary characteristics of multi-modal data,and couple ground-based monitoring data with high accuracy,polar-orbiting and geostationary satellite inversion data with high spatial or temporal resolution,numerical model data with real-time seamless simulation and forecast in all periods of day and night,and carry out the research on seamless fine simulation and forecast of real-time PM_2.5 concentration using mutli-modal data fusion.Specifically,after fully evaluating the accuracy of multi-modal AOD/PM_2.5 products,this paper fuses the multi-modal data to realize seamless fine model of real-time AOD in all periods of day and night,and develops a LMESTKF model to support efficient estimation of PM_2.5concentration at hourly scale,and couples numerical model forecasting results to achieve the spatiotemporal fine forecast of PM_2.5 concentration distribution with high accuracy.The research summary is as follows:（1）Validate the accuracy of multi-modal data such as satellite-based inversion and numerical models using high-accurate ground-based monitoring data.Select 1 km/1 day MAIAC and 750 m/1 day VIIRS＿IP AOD products,5 km/1 h Himawari-8 and 6 km/1 h GOCI AOD products,0.25°/3 h GEOS-FP and 0.625°×0.5°/1 h MERRA2 AOD/PM_2.5reanalysis/forecast product of numerical model as multi-modal data fusion source,and take the ground-based PM_2.5 concentration and AERONET AOD data as the true value,evaluate the accuracy of multi-modal data using the unified validation criterion.The main results show that the MAIAC and GOCI AOD products achieve the highest accuracy（R²/RMSE is around 0.87/0.15）,the Himawari-8 AOD product obtains the second highest accuracy（R²/RMSE is 0.67/0.20）,and the VIIRS＿IP AOD has poor accuracy（R²/RMSE is only 0.43/0.35）.The AOD reanalysis accuracy of GEOS-FP/MERRA2（R²/RMSE is 0.6/0.4）products is slightly lower than the satellite-based inversion results,and the PM_2.5 reanalysis accuracy of GEOS-FP/MERRA2 products is poor（R²/RMSE is only 0.15/50 g/m³）.Compared with the reanalysis products,the AOD/PM_2.5 forecast results of GEOS-FP/MERRA2 products have similar accuracy to the reanalysis products at the initial forecast time,but the accuracy decreases with the increase of the forecast time.（2）Construct a real-time seamless AOD optimization model for all periods of day and night by filling the missing data in satellite-based products with numerical model results.Taking the seamless simulation advantage of in all periods of day and night in the numerical model AOD product,and using the accuracy validation results to weight the multi-modal data,an autoencoder-based deep residual network and random forest model is employed to fuse multi-modal data which successfully solves the problem of large missing gaps in satellite-based AOD products and generates seamless AOD data with 1 km/1 h resolution for the whole period of day and night.The validation results based on ground-based AERONET AOD find that the accuracy of AOD fusion results in areas without satellite data is slightly lower than that those areas with satellite data,but the validated R²,RMSE,Within＿EE and Bias in areas without satellite data can still reach about 0.83,0.21,62.50%and 0.03 respectively.The correlation between AOD fusion results and ground-based PM_2.5concentration shows no obvious discrepancy between daytime and nighttime which indicates that the explanatory power on PM_2.5concentration of AOD fusion results at nighttime keeps the same level with the fusion results at daytime.Compared with previous studies on filling remote sensing AOD with numerical models,this study not only considers the retrieval products of multiple satellites,but also realizes the fine fusion of hourly AOD at all period of the day and night.（3）Propose an efficient spatiotemporal dynamic simulation model（LME and Spatiotemporal Kalman Filter,LMESTKF）of hourly PM_2.5concentration.The model couples the traditional LME model and the efficient spatiotemporal Kalman model to solve the time-consuming problem of the traditional spatiotemporal statistical model for AOD-PM_2.5concentration estimation.Using the idea of cross-validation,a spatial dimensionality reduction algorithm of LMESTKF is also proposed.Based on the LMESTKF model,the statistical relationship between ground-based PM_2.5 concentration and seamless AOD data at 1 km/1 h resolution is established,the seamless PM_2.5 results at 1 km/1 h resolution are also generated.The results show that the R²,RMSE,Bias and MAPE of the LMESTKF model based on sample-based and site-based cross-validation are（0.91,14.37 g/m³,-0.41 g/m³,28.91%）and（0.87,16.98 g/m³,-0.32 g/m³,34.62%）,the accuracy of the proposed LMSETKF model is higher than the existing PM_2.5 concentration estimation model at Beijing-Tianjin-Hebei region.In addition,the time and memory consumption of the LMESTKF model for hourly PM_2.5 concentration mapping at the monthly scale is only about 13.85 minutes and 1.67 GB respectively,which shows that the LMESTKF model proposed in this study not only obtains higher accuracy but also achieves faster efficiency.Compared with traditional spatiotemporal simulation studies of PM_2.5 concentration,which are mostly conducted on the daily,monthly and annual scales,the LMESTKF model proposed in this study can achieve efficient spatiotemporal simulation of hourly PM_2.5 concentration.（4）Propose a high accurate statistical forecasting framework of real-time seamless and fine PM_2.5 concentration in the next 5 days by coupling with numerical model forecasting results.On the basis of coupled numerical model forecast,the framework firstly realizes ground-based PM_2.5 concentration time series forecast with high accuracy and AOD real-time forecast with fine resolution using RF model and autoencoder-based CNN residual network respectively.Then,considering the stability of the pollution cause mechanism,the framework also adopts the historical model migration forecasting strategy with site assimilation or the direct modeling forecasting strategy of future scenarios to achieve real-time seamless forecast of high-accurate fine PM_2.5 concentration with 1 km/1 h resolution in the next 5 days.The corresponding accuracy validation results show that the R²,R,RMSE,and Bias of the forecasting results at nowcasting time are 0.80,0.90,9.71 g/m³,and 0.76 g/m³,respectively.Compared with the traditional numerical model forecast,it is found that the forecast results based on the proposed forecast framework improves the R²,R and RMSE at 79.59%,48.12%and 64.50%ratio,respectively.However,the accuracy of the proposed forecasting framework showed a downward trend with the forecast time.On the fifth day,the R²,R,RMSE,and Bias of the forecast results drops to 0.30,0.57,18.11 g/m³,-0.05 g/m³.The information entropy evaluation results also point out that the forecasting results based on the proposed framework are more abundant in spatial information than the numerical model forecasting results,and the information entropy improvement ratio can reach up to 140.60%,with an average increase of36.92%.Compared with the traditional numerical prediction method of PM_2.5 concentration,the method proposed in this study has higher prediction accuracy and richer spatial information.In summary,regarding to the problems of the existing model which simulation results contain largely spatiotemporal missing gaps and forecast results are rough and inaccurate,the research results of this paper have realized the seamless hourly AOD/PM_2.5 estimation at kilometric scale in all periods of day and night,and the created LMESTKF model can improve the accuracy and efficiency of seamless fine simulation of real-time PM_2.5concentration,and the corresponding high-accurate PM_2.5 concentration fine forecast framework can further improve the level of air pollution prevention and control in China.