A Coupled MM5-CMAQ Modeling System for Assessing Effects of Restriction Measures on PM10 Pollution in Olympic City of Beijing, China

In this paper, a coupled MM5–CMAQ modeling system was employed to investigate the PM10 pollution issue in Beijing, China, with a focus on assessing the effects of different restriction policies implemented during and after the 2008 Olympic Games. The simulations under designed scenarios were implemented over a 2-level nested grid domain for comparing the difference of PM10 concentrations under restriction and no-restrictions situations. The restriction measures include alternate-day vehicle driving, construction activities, trans-boundary emissions from neighboring provinces, and vehicle restrictions during the post-Olympic period. Meteorological contributions to the air quality improvement were also examined. The results show that significant improvement of air quality in Beijing during the 2008 Games was attributed largely to these restriction measures, although favorable weather conditions play an important role. Also, during the post-Olympic period, daily vehicle restrictions implemented temporarily under extreme weather conditions played a crucial role in alleviating Beijing’s air pollution. Beijing not only needs to take continuing efforts to addresses its own PM10 problem, but also has a clear self-interest in demanding better environmental performance from neighboring provinces. It is suggested that Beijing would work collectively with neighboring provinces to develop a long-term multi-region initiative and strategy aimed at emission reduction for providing the citizens in this region a healthy and clean air in the long run.

[1]  Zhiwei Han,et al.  Simulation of sulfur transport and transformation in East Asia with a comprehensive chemical transport model , 2006, Environ. Model. Softw..

[2]  Stefan Emeis,et al.  Application of a multiscale, coupled MM5/chemistry model to the complex terrain of the VOTALP valley campaign , 2000 .

[3]  Zhong Liu-ju,et al.  Vehicle Exhaust Emission Characteristics and Contributions in the Pearl River Delta Region , 2009 .

[4]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[5]  Yong Li,et al.  Optimizing emission inventory for chemical transport models by using genetic algorithm , 2010 .

[6]  Shuiyuan Cheng,et al.  Assessment of the Integrated ARPS–CMAQ Modeling System through Simulating PM10 Concentration in Beijing, China , 2008 .

[7]  Jeffrey M. Vukovich,et al.  Updates to the Sparse Matrix Operator Kernel Emissions ( SMOKE ) Modeling System and Integration with Models-3 , 1999 .

[8]  Richard A. Anthes,et al.  Development of Hydrodynamic Models Suitable for Air Pollution and Other Mesometerological Studies , 1978 .

[9]  Jianbing Li,et al.  Identification of regional atmospheric PM10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis , 2010, Environ. Model. Softw..

[10]  Shuiyuan Cheng,et al.  An integrated MM5–CMAQ modeling approach for assessing trans-boundary PM10 contribution to the host city of 2008 Olympic summer games—Beijing, China , 2007 .

[11]  Haiyan Wang,et al.  The assessment of emission-source contributions to air quality by using a coupled MM5-ARPS-CMAQ modeling system: A case study in the Beijing metropolitan region, China , 2007, Environ. Model. Softw..

[12]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation , 2001 .

[13]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .