Using Atmospheric Simulation And Data Assimilation, For Improving The Understanding Of Haze Emissions In Chinese Region

Abstract

Both the general people and academics have taken note of the increasing frequency and severity of haze outbreaks on the North China Plain (NCP). The very high aerosol loadings seen during these haze events have a significant impact on visibility, human health, and temperature. Improving our scientific understanding of air pollution is therefore of the utmost importance. Air quality modelling is challenging to do due to large inaccuracies in emission data and poor parameterizations in the current model. In order to gain a better understanding of haze pollution in the NCP, this thesis employs a regional model called WRF-Chem. To improve the model's performance, it employs a number of strategies, including finding the right model settings, adding missing mechanisms, and using data assimilation techniques. Research indicates that the haze event in January 2010 was caused by a combination of factors, including low wind speeds and a severe temperature inversion, which contributed to the accumulation of air contaminants. The significance of cloud chemistry and secondary aerosol formation in winter haze was further demonstrated by quantifying the increase in PM2.5 concentrations caused by cloud chemistry and by calculating the increasing ratios of primary aerosols, secondary inorganic aerosols, and secondary organic aerosols from days without haze to days with haze. A study examined the impact of local mobility on Beijing pollution. The provinces of south Hebei, Shandong, and Henan were the main contributors to the 47.8% of PM2.5 concentrations found in Beijing on hazy days, which came from beyond the city boundaries. In certain areas, the positive feedback from heavy aerosol loadings caused a rise in PM2.5 of about 20 g/m3 and a decrease of more than 100 metres in boundary layer heights.