Optimization And Application Of Data Fusion Between Air Quality Modeling Data And Monitoring Data

As the public is increasingly concerned about air quality,the epidemiological study of pollutant exposure concentration on human health is a typical representative of the air quality related research,which will require more to capture the concentration of pollutants in the whole study area.Field observation is an effective way to carry out air quality research.However,due to the representation and coverage of the observation point are limited,it just only represents the local pollution condition around the sampling point.The air quality model can reproduce the migration and transformation process of pollutants in the atmosphere,and then obtain the spatial and temporal distribution characteristics of regional air pollutants.However,the results are influenced by some factors such as air pollutant emission inventory,meteorological field and terrain.The data fusion is based on the statistical calculation of the actual observation data and the model simulation data.At the same time,by using the advantages of high reliability of the monitoring point data and good temporal and spatial continuity of the simulated data,it can betterly capture the spatiotemporal distribution characteristics of the pollutant concentration in the study area,which is of high practical value.Commissioned by the US Environmental Protection Agency(USEPA),the paper make algorithm analysis and verification,optimize the calculation speed and evaluate the application effect to the data fusion of VNA,eVNA,Downscaler provided by the USEPA.On this basis,we developed the air quality simulation and monitoring data fusion tool(Data Fusion),which can quickly provide different model data input based on data fusion method results for the air pollution and health benefits assessment.Finally,the paper explores the application of data fusion to air quality input for air quality correlation analysis and human health benefit assessment through practical case analysis of PM2.5 and O3 pollutants.The results of the application analysis show that the data accuracy of the data fusion results are significantly higher than the original air quality model simulation data at the independent verification site.To the VNA,eVNA,Downscaler,the R-square values of PM2.5 pollutants were increased by 30.05%,51.76% and 55.14%,respectively,and the RMSE values were reduced by 30.16%,29.05% and 30.64%,respectively.The R-square values of O3 pollutants were increased by 34.45%,34.27%,36.80% and the RMSE values were reduced by 65.70%,63.51% and 65.79%,respectively.The results show that the air quality input obtained by different data fusion algorithms has a significant effect on the health impact assessment.The PM2.5 and O3 air quality concentrations in Pennsylvania have improved monetary health benefits(The environmental concentrations of PM2.5 to CMAQ,VNA,eVNA,Downscaler obtained the economic benefits of about 5.11 billion US dollars,7.16 billion US dollars,7.05 billion US dollars,6.98 billion US dollars;and O3 got the economic benefits of about 70.7 billion US dollars,8.39 billion US dollars,8.05 billion $ 8.5 billion).