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Frontiers of Environmental Science & Engineering

Front Envir Sci Eng Chin    2011, Vol. 5 Issue (3) : 331-341     https://doi.org/10.1007/s11783-011-0307-5
RESEARCH ARTICLE
Estimating the effects of meteorology on PM2.5 reduction during the 2008 Summer Olympic Games in Beijing, China
Liu YANG1, Ye WU1, Jerry M. DAVIS2, Jiming HAO1()
1. School of Environment, Tsinghua University, Beijing 100084, China; 2. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division, US Environmental Protection Agency, Raleigh, NC 27513, USA
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Abstract

Particulate pollution was a critical challenge to the promise of good air quality during the 2008 Beijing Olympic Games, which took place from August 8th to 24th. To ensure good air quality for the Games, several temporary emission control measures were implemented in Beijing and surrounding areas. Ambient particulate matter concentration decreased significantly during the Olympic period; however, it is difficult to distinguish the effectiveness of those control measures since meteorology also affects ambient PM2.5 concentration. In this work, a multiple linear regression model based on continuous field monitoring at a roadside site was conducted to evaluate the effects of meteorology and emission control measures on the reduction of PM2.5 during the 2008 Olympic Games. The hourly data set was divided into two time periods, the no control period, June 22nd to July 4th, and the control period, July 28th to August 21st. The response variable was PM2.5 and the meteorology covariates used in the model were hourly temperature, dew point temperature, wind speed and precipitation. Wind direction was not a significant predictor of PM2.5 levels in either the control or the no control period. Using the meteorologically-based regression coefficients from the two time periods, meteorology was found to contribute to at least a 16% reduction in PM2.5 levels in the roadside microenvironment; while the pollution control measures contributed to at least a 43% reduction in PM2.5 levels.

Keywords meteorology      emission control measures      Beijing Olympic Games      PM2.5      linear statistical models     
Corresponding Author(s): HAO Jiming,Email:hjm-den@tsinghua.edu.cn   
Issue Date: 05 September 2011
 Cite this article:   
Liu YANG,Ye WU,Jerry M. DAVIS, et al. Estimating the effects of meteorology on PM2.5 reduction during the 2008 Summer Olympic Games in Beijing, China[J]. Front Envir Sci Eng Chin, 2011, 5(3): 331-341.
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http://journal.hep.com.cn/fese/EN/10.1007/s11783-011-0307-5
http://journal.hep.com.cn/fese/EN/Y2011/V5/I3/331
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Liu YANG
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Jerry M. DAVIS
Jiming HAO
Fig.1  Location and surroundings of the monitoring site and meteorology site: (a) location of monitoring site, (b) surrounding of monitoring site
sampling periodtemperature/°Cwind speed/( m·s-1)relative humidity/%dew point temperature/°CPM2.5/(μg·m-3)
no control period: June 22nd 23:00–July 4th 08:0024.05±3.360.52±0.4476.71±12.4419.19±1.6476.0±32.2
control period: July 28th 12:00–July 31st 14:0026.09±3.521.00±0.6865.80±15.9119.44±3.0940.3±32.9
August 1st 10:00–August 3rd 22:00
August 4th 09:00–August 21st 08:00
Tab.1  Characteristic meteorology data and PM concentration in no traffic control and control periods (mean value±standard deviation). Monitoring for no traffic control period and control period lasted for 274 h and 544 h, respectively
Fig.2  Hourly averaged meteorological data collected during the no control and control periods: (a) no control period, (b) control period
Fig.3  Marginal effects of temperature on PM mass concentration. The dotted lines show the confidence interval for each regression model
Fig.4  Marginal effects of dew point temperature on PM mass concentration. The dotted lines showed the confidence interval for each regression model
Fig.5  Marginal effects of wind speed on PM mass concentration. The dotted lines showed the confidence interval for each regression model
wind directionno control periodcontrol period
frequencyPM2.5/(μg·m-3)frequencyPM2.5/(μg·m-3)
blowing from the road40.3% (106)1)70.0 (63.5, 76.5)2)34.0% (178)15.0 (11.4, 18.6)
blowing to the road38.0% (100)78.3 (69.1, 87.5)38.9% (204)40.4 (35.4, 45.4)
blowing parallel to the road21.7% (57)63.8 (53.2, 74.4)27.1% (142)35.3 (27.5, 43.1)
Tab.2  Median values of PM concentrations under different wind conditions
Fig.6  Relationship between wind direction and PM concentrations measured at the monitoring site: (a) distribution of wind direction, (b) wind direction v.s. median value of PM
time periodestimated1) PM2.5/(μg·m-3)estimated2) PM2.5/(μg·m-3)reduction/%
June 24, 05:00-19:0066.5 (±6.1)30.1(±8.4)54.7(±13.3)
June 29, 0:00-14:0081.2(±6.5)32.0(±11.3)60.6(±14.3)
July 2, 09:00-23:0076.3(±8.8)51.3(±6.7)32.8(±11.7)
Tab.3  Effects of applying the control period regression coefficients to the no control period data
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