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"The Olympics and air pollution: a “particulate” experience Dane Westerdahl, Xing Wang, K. Max Zhang* Sibley School of Mechanical and Aerospace Engineering, Cornell University 1 Introduction Beijing will be hosting the 29th Olympics Games in August, 2008. Transportation is critical to the success of the Olympics. The transportation sector is not only responsible for moving athletes and spectators from various parts of the city to the Olympic venues in a reasonable amount of time, but also a dominant source of air pollution in metropolitan areas like Beijing (Hao et al., 2007). Given the notorious air pollution and traffic congestion problems in present-day Beijing, the Olympic organizers will have to resort to measures such as restricting on-road private vehicles during the Olympics and replacing a large fraction of the current fleet of public transit with clean-burning vehicles. From a scientific perspective, these stringent emission control measures will create a unique air quality episode over a month long period, which previously could be tested only by numerical simulation on a hypothetical basis. In other words, the 2008 Olympics can be regarded as a large scale experiment where scientists can actually quantify the effectiveness of the traffic and emission control measures on mitigating air pollution and protecting public health. Epidemiological studies have linked transportation-related air pollution to the adverse human health effects, such as lung cancer and heart attacks (Heinrich et al., 2004; McConnell et al., 2006). Researchers have correlated the decrease in automobile use, especially during the weekday morning rush hour, with improved air quality and decrease in asthma attacks among children living in the Atlanta area during the summer Olympics in 1996 (Friedman et al., 2001). With much more aggressive measures than those taken in any of the previous Olympics, Beijing Olympics will provide a valuable opportunity for studying the impact of transportation on air quality and human health. In this paper, we report the results and analysis from the field campaign we conducted in August, 2007 in Beijing. The goal for this study is to characterize transportation-related air pollution, especially, the particulate matter (PM), under different types of microenvironments. The results will serve as a pre-Olympics baseline for comparisons with those during the Olympics (2008), and after the Olympics (2009). 2 Experiments The filed campaign was conducted under three different environments: on-road, roadside and ambient. The on-road measurements were conducted on a one mobile laboratory along the Fourth Ring Road and the Badaling Expressway in Beijing. The roadside (RS) sampling location was about 20 m from the centerline of the Fourth Ring Road near the North Gate of Peking University Health Science Center (PKUHSC). The ambient measurements were conducted in the athletic field (AF) of PKUHSC campus. In addition, pollutant concentrations were also measured on the second floor of the student dormitory, which is ~ 95 m away from the 4th Ring Road center line. The location of PKUHSC and the route of the mobile laboratory are shown in Fig. 1. Fig. 1 Measurement sites in Beijing (left: Entire Beijing Map; right: Measurement sites) The same sets of instruments were employed in the two fixed monitoring sites (ambient and roadside) and on the mobile laboratory. The instruments included a Fast Mobility Particle Sizer (FMPS, TSI, Model 3091) for ultrafine particle (UFP) number concentrations and size distributions in range 6-560nm; an Aethalometer (Magee Scientific, Model AE42) for the concentration of Black Carbon (BC); a DustTrak (TSI, Model 8520) for the mass concentration of PM2.5; a Qtrak (TSI, Model 7655) for CO and CO2 concentration; a Nanoparticle Surface Area Monitor (NSAM, TSI, Model 3550) for nanoparticle lung-deposited surface area. Data were recorded at 1s interval by FMPS, 10s intervals by DustTrak, Qtrak, and NSAM, and two minutes intervals by Aethalometer. A laptop was used to control the FMPS and NSAM. In addition, the environmental parameters including the temperature and relative humidity were also recorded during the monitoring days. During the on-road measurements using the mobile laboratory, a portable GPS (Garmin) was also used to record the locations, routes and vehicle speed. A digital camcorder (SONY) was held by the staff in the front seat of the van and recorded the view ahead continuously in order to characterize the traffic conditions during the on-road measurements. The measurements were performed during two weeks in August, 2007: Sunday Aug 12th 2007- Friday Aug 24th 2007. Between August 17 and 20, the city government enforced an odd-even traffic control experiment, effectively removing 1.3 millions cars off the road each day. The ambient and on-road measurements were conducted on both normal traffic days and traffic control days while the roadside measurements were only done on two normal days due to limited instruments. A decade long regulation in Beijing forbids trucks from entering the city before 11:00pm and after 6:00am. To capture the impact of this regulation on air quality, we conducted the on-road and ambient measurements both in the daytime and at nighttime. 3. Results and Discussion 3.1 On-road emission factors of gasoline and diesel vehicles in Beijing In this study, the fuel based emission factors were calculated based on the following equation (Kirchstetter et al., 1999): ∆[ P ] EFp = × wc , MWC MWC MWC ∆[CO2 ] × MWCO + ∆[CO ] × MWCO + ∆[ BC ] × MWBC 2 where ∆[i ] = [i ] − [i ]0 , i= P, CO2, CO and BC; subscription 0 denotes the baseline value; EFp is the emission factor of pollutant P in grams of pollutant emitted per kilogram of fuel consumed; Δ[P] is the concentration of pollutant P above the baseline;Δ[CO2], Δ[CO] and Δ[BC] represent the increase of CO2, CO and BC; MWi is the molecular weight of species i; wc is the mass fraction of carbon in the fuel. Fig. 2 EF of CO, BC, UFP and PM2.5 Figure 2 compares the emission factors of CO, BC, UFP number and PM2.5 for gasoline engine- and diesel engine-powered vehicles. Similar to what have been found in U.S., the emission factors of BC and UFP for diesel vehicles are much higher than those of gasoline vehicles, and the CO emission factor of gasoline vehicles is much higher than diesels. It is worth noting that the CO emission factors from this study and from the on-road remote sensing sampling by Chinese colleagues (Zhou et al., 2007) suggest a clear downward trend since mid 1990s, which is likely a result of increasingly stringent emission standards and adoption of advanced emission control technologies by the Beijing government. 3.2 Micro-environmental Air Quality in Beijing Fig. 3 BC, Surface Area and UFP concentrations in micro-environments Figure 3 illustrates the results from the continuous measurements of BC, Surface Area and UFP concentration under different microenvironments from August 13 to 15. The impact of truck activities is clearly demonstrated. First, data collected 100 m from the Fourth Ring Road clearly suggest that pollutant concentrations rose up at about 11:00 pm and fell back around 6:00 am in the morning, coincide with the period when trucks were allowed to enter the city. Second, the on-road concentrations measured during daytime when gasoline vehicles dominated the traffic were much lower than those measured during the nighttime when diesel vehicles dominated the traffic. Similarly higher BC concentrations at night were also observed in the ambient measurements the next day, indicating the significant diesel effects at community level. Thus, drivers on the road and residents living near the roadways are exposed to elevated diesel particulate matter at night. Table. 1 Concentrations in different microenvironments and periods (ND: Normal Daytime; TCD: Traffic Control Daytime; PM: In the afternoon; EVE: at night) BC Ambient (ND) Ambient (TCD) Roadside Daytime On-road (ND PM) On-road (TCD PM) On-road (ND EVE) On-road (TCD EVE) μg m 6.1(3.7-11.6) 7.4(5.1-8.8) 4.6(2.6-7.0) 21.9(16.7-32.5) 18.3(14.5-22.0) 60.1(46.6-82.8) 46.0(26.7-49.2) -3 CO ppm 0.3(0.1-0.7) 1.0(0.8-1.4) 1.4(0.9-2.6) 4.0(3.0-5.2) 3.7(3.3-4.0) 2.6(2.0-3.1) 2.8(2.4-3.2) UFP 104 particles cm-3 2.59(2.16-3.11) 1.77(1.40-2.09) 3.84(2.26-4.96) 8.17(5.65-12.7) 7.82(6.12-10.9) 29.3(15.6-50.7) 24.3(16.8-35.9) Table 1 summarizes the typical concentrations under different microenvironments measured from August 15 to 24. Fig. 4 UFP size distributions in different environments UFP number size distributions are also shown in Figure 4. The UFP size distributions present a sharp gradient from on-road to roadside to ambient conditions, indicates that vehicle emissions on the road make a great contribution to the UFP pollution, especially the particles around 10 nm. Different from what has been observed in Los Angeles (Zhang et al., 2004), there are no significant size shifts from on-road to ambient conditions. 3.3 The impacts of traffic reduction experiment in summer 2007 The comparison of pollutant levels on normal days and traffic control days are shown in Fig. 5 and also in Table 1. These results do not suggest a clear impact of the traffic reduction experiment on ambient air quality. The median values of on-road concentrations were not reduced very much either, probably due to the short test periods including a Saturday and a Sunday. However, the on-road measurements conducted during traffic control days did not record the extreme BC and CO concentrations, which were observed during normal days. This phenomenon was also observed in the nighttime UFP concentrations. This suggests that traffic control measure did have a positive effect on reducing the number of high emission vehicles, such as heavy smoky trucks or malfunctioning gasoline cars. Human exposure to such extreme concentrations should have decreased accordingly. Fig. 5 Concentrations in normal days and traffic control days Upper-left: BC; Upper-right: CO; Bottom-left: UFP. 4 Conclusions Our pre-Olympics study has characterized the air quality in Beijing under different microenvironments, i.e., on-road, roadside and ambient. The preliminary results demonstrate a strong traffic impact on the BC, UFP, CO concentrations. The real-world emission factors for gasoline and diesel vehicles were derived. Some favorable impacts on reducing the high values of pollutants were observed during the traffic control days, but the effectiveness of the traffic control was not clear for reducing the median values of pollutant levels, probably owing to the short test period. Acknowledgement This study was supported by supported Lehman Fund for Scholarly Exchange with China and College of Engineering at Cornell University. The authors also appreciate the helps form their Chinese collaborators, Prof. Tong Zhu and Dr. Wei Huang at College of Environment, and Prof. Xiaochuan Pan at School of Public Health, Peking University. References Hao, J., Y. Wu, et al. (2007). "Motor vehicle source contributions to air pollutants in Beijing." Huan Jing Ke Xue 22(5): 1-6. Zhou, Y., et al. (2007). “Characterization of In-Use Light-Duty Gasoline Vehicle Emission by Remote Sensing in Beijing: Impact of Recent Control Measures.” Journal of Air & Waste Management Association 57: 1071-1077. Heinrich J, Wichmann HE. Traffic related pollutants in Europe and their effect on allergic disease. Curr Opin Allergy Clin Immunol. 2004; 4(5):341-8. Review. McConnell, R., K. Berhane, et al. (2006). "Traffic, susceptibility, and childhood asthma." Environmental Health Perspectives 114(5): 766-772. Zhang, K. M., A. S. Wexler, et al. (2004). "Evolution of particle number distribution near roadways. Part II: the 'Road-to-Ambient' process." Atmospheric Environment 38(38): 6655-6665. Friedman, M. S., K. E. Powell, et al. (2001). "Impact of changes in transportation and commuting behaviors during the 1996 Summer Olympic Games in Atlanta on air quality and childhood asthma." Journal of the American Medical Association 285(7): 897-905 ..."

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