Abstract
Abstract. The MM5-SMOKE-CMAQ model system, which was developed by the United States Environmental Protection Agency (US EPA) as the MODELS-3 system, has been used for daily air quality forecasts in the Beijing Municipal Environmental Monitoring Center (Beijing MEMC), as a part of the Ensemble air quality Modeling forecast System for Beijing (EMS-Beijing) since the 2008 Olympic Games. According to the daily forecast results for the entire duration of 2010, the model shows good performance in the PM10 forecast on most days but clearly underestimates PM10 concentration during some air pollution episodes. A typical air pollution episode from 11–20 January 2010 was chosen, in which the observed air pollution index of particulate matter (PM10-API) reached 180 while the forecast PM10-API was about 100. In this study, three numerical methods are used for model improvement: first, by enhancing the inner domain with 3 km resolution grids, and expanding the coverage from only Beijing to an area including Beijing and its surrounding cities; second, by adding more regional point source emissions located at Baoding, Landfang and Tangshan, to the south and east of Beijing; third, by updating the area source emissions, including the regional area source emissions in Baoding and Tangshan and the local village/town-level area source emissions in Beijing. The last two methods are combined as the updated emissions method. According to the model sensitivity testing results by the CMAQ model, the updated emissions method and expanded model domain method can both improve the model performance separately. But the expanded model domain method has better ability to capture the peak values of PM10 than the updated emissions method due to better reproduction of the pollution transport process in this episode. As a result, the hindcast results ("New(CMAQ)"), which are driven by the updated emissions in the expanded model domain, show a much better model performance in the national standard station-averaged PM10-API. The daily hindcast PM10-API reaches 180 and is much closer to the observed value, and has a high correlation coefficient of 0.93. The correlation coefficient of the PM10-API in all Beijing MEMC stations between the hindcast and observation is 0.82, clearly higher than the forecast 0.54. The FAC2 increases from 56% in the forecast to 84% in the hindcast, and the NMSE decreases from 0.886 to 0.196. The hindcast also has better model performance in PM10 hourly concentrations during the typical air pollution episode. The updated emissions method accompanied by a suitable domain in this study improved the model performance for the Beijing area significantly.
Highlights
In the last 10 years, air quality problems have caused particular concern in most of China, especially after the extreme air pollution episode that happened in multiple cities of North China in January 2013
The framework of the model system is shown in Fig. 1: the MM5 model is used to generate the meteorological field, the Sparse Matrix Operator Kernel Emissions (SMOKE) model is applied to deal with the emissions inventory and provides 3-D gridded emission data for the air quality model, and the Community Multiscale Air Quality (CMAQ) model provides the concentration of the gas- and particlespecies for daily air quality forecasts
The spatial distribution of area source emissions in the inner domain (D4) of the forecast system is presented in Fig. 3 – it can be seen that the high-PM10 emissions correspond to high-population density, with the highest emission being located in urban Beijing
Summary
In the last 10 years, air quality problems have caused particular concern in most of China, especially after the extreme air pollution episode that happened in multiple cities of North China in January 2013 In such a heavily air-polluted environment, people want access to reasonable air quality predictions, so as to have advance notice of future air pollution events with potential adverse health effects, and so that the government can take necessary short-term emissions reduction measures to improve air quality, as was done during the Beijing Olympic Games. 3.4 presents the model evaluation and discussion of PM10 in the model sensitivity testings when the model domain is expanded, when the emissions are updated, and in the hindcast simulation (including all the improvement methods).
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