An assessment of urbanization impact in China by using WRF-Chem and configuration optimization

摘要

Urbanization is an inevitable process for every developing and developed country. In China this process accelerated after the reform and open-door policies initialed in 1980s. Urbanization can bring tremendous influences on air quality and in turn adverse health effects. Therefore it is of importance to access and evaluate urbanization process. In this Thesis, we focus on three major impacts in China: land-cover change (from nature land type to urban land type), anthropogenic heat emission (due to human activity), and pollutant emission increase (mainly from industry, power, transportation and residential). The model tool used in this paper is called WRF-Chem (the fully coupled Weather Research and Forecast Model with Chemistry Module). After designing and performing three different sensitivity runs, it turns out that all of these three impacts from urbanization tend to worsen air quality conditions in Beijing, especially for ozone and PM2.5 concentrations. The first impact from land-cover change in Chapter 2 increases temperature by 2.4 C; for Beijing and ozone by 20 ppb. Adding human heat release (the second impact) also increases surface temperature by 0.8 C; at daytime and 1.2 C; at nighttime (Chapter 4). Consequently, model outputs a more polluted scenario in Beijing, with 18 ppb more ozone during nocturnal time. When exploring the third impact from emission change, we found out that the governmentu27s mitigation regulations on emissions in Beijing has in effect. Around Beijing area, the emissions for CO and SO2 remains the same level from 2006 to 2010, while other cities inside North China Plain are experiencing rapid growths in anthropogenic emissions. Results show a slightly increase in surface temperature and ozone concentrations. Meanwhile, the concentration of particulate matters tends to increase near surface and decrease in the upper atmosphere. For future study, it is highly recommended to include these impacts into model configurations. Additional sensitivity runs were conducted to optimize forecast computing in China, concerning both spatial and vertical resolutions. This sensitivity studies represented 4 different grid resolutions and three different vertical meshes. Regards to the analysis with available observation data, a resolution of 9 km and 27 vertical layers is determined to be the best option for future efficient and accurate forecasts in China. For horizontal aspects, both 81-km and 27-km resolutions are not able to capture pollutant distributions and no significant discrepancy is found out between 9-km and 3-km case. In vertical resolution sensitivity runs, we use 9 layers, 27 layers, and 54 layers mesh with same top and bottom staggers. Analysis reveals totally different vertical profile between 9 layers and 27 layers cases and similar profile between 27 layers and 54 layers. Therefore, we recommend spatial settings with 9-km resolution and a vertical mesh with 27 layers. Finally, the updated 3-d model, involving three urbanization impacts and using recommended resolution settings is used to support a field campaign in summer 2013 for North China Plain. Some preliminary results show a confidence using our model, by capturing both meteorological and chemical trends in Beijing.