This proposal requests support for research on the development of a system to assimilate surface measurements of PM2.5, ozone (O3) and nitrogen dioxide (NO2) simultaneously to obtain better spatio-temporal variations of PM2.5 and O3 concentrations, to reduce uncertainties in health exposure assessment, and to improve the accuracy of air quality forecasting in South China. Chemical data assimilation with advanced coupled meteorology-chemistry models has recently been developed and applied as a useful approach to reduce model errors in both aerosols and meteorological variables, due to the nature of coupled models that treats aerosol-weather interactions. Aerosol data assimilation with coupled models over China has been investigated by several studies, while the exploration on O3 assimilation is still limited. However, O3 pollution is also a prominent environmental issue in China, especially in South China, posing a great threat to human health and vegetation growth. Estimating the impacts of air pollution on health, climate and ecosystem, and implementation of emergency responses to air pollution episodes require better representations of the spatio-temporal variations of air pollutants and accurate air quality forecasts. Based on our previous extension to assimilate PM2.5 measurements, two additional control variables (O3 and NO2) are proposed to be included in the Gridpoint Statistical Interpolation (GSI) data assimilation system. The developed system will be applied in South China with the WRF- Chem model (Weather Research and Forecasting model coupled with Chemistry) and CNEMC (China National Environmental Modeling Center) surface measurements to obtain spatio-temporal variations of PM2.5 and O3 concentrations, to estimate health exposure, and to demonstrate the improvements in air quality forecasts. The methodology and findings from this project can be applied easily to other regions.
|Effective start/end date||1/01/21 → 31/12/23|
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.