Reductions of PM 2.5 in Beijing Tianjin Hebei Urban Agglomerations during the 2008 Olympic Games

Transcription

1 ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 29, NO. 6, 2012, Reductions of PM 2.5 in Beijing Tianjin Hebei Urban Agglomerations during the 2008 Olympic Games XIN Jinyuan ( ), WANG Yuesi ( ), WANG Lili ( ), TANG Guiqian ( ), SUN Yang ( ), PAN Yuepeng ( ), and JI Dongsheng ( ) The State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing (Received 10 November 2011; revised 10 February 2012) ABSTRACT The Atmospheric Environmental Monitoring Network successfully undertook the task of monitoring the atmospheric quality of Beijing and its surrounding area during the 2008 Olympics. The results of this monitoring show that high concentrations of PM 2.5 pollution exhibited a regional pattern during the monitoring period (1 June 30 October 2008). The PM 2.5 mass concentrations were 53 µg m 3, 66 µg m 3, and 82 µg m 3 at the background site, in Beijing, and in the Beijing Tianjin Hebei urban agglomerations, respectively. The PM 2.5 levels were lowest during the 2008 Olympic Games (8 24 August): 35 µg m 3 at the background site, 42 µg m 3 in Beijing and 57 µg m 3 in the region. These levels represent decreases of 49%, 48%, and 56%, respectively, compared to the prophase mean concentration before the Olympic Games. Emission control measures contributed 62% 82% of the declines observed in Beijing, and meteorological conditions represented 18% 38%. The concentration of fine particles met the goals set for a Green Olympics. Key words: PM 2.5, the 2008 Olympic Games, the urban agglomerations, the network Citation: Xin, J. Y., Y. S. Wang, L. L. Wang, G. Q. Tang, Y. Sun, Y. P. Pan, and D. S. Ji, 2012: Reductions of PM 2.5 in Beijing Tianjin Hebei urban agglomerations during the 2008 Olympic Games. Adv. Atmos. Sci., 29(6), , doi: /s Introduction The adverse health effects related to air pollution are mainly associated with particulate matter (PM). Short-term exposure to PM has frequently been associated with increased human morbidity and mortality (Brunekreef and Holgate, 2002; Curtis et al., 2006). The effects of long-term exposure to PM are much less certain than the short-term effects, but long-term exposure is believed to have serious adverse health consequences (Dockery et al., 1993; Pope et al., 1995; Kappos et al., 2004). To assess exposure to particulate matter, the determination of aerosol masses and their composition is mandatory. The Beijing Tianjin Hebei economic band is located in the center of Northeast Asia; it is one of three major rapidly developing economic zones in China. However, regional economic and social development is still unbalanced in the Beijing Tianjin Hebei urban agglomeration. Differences in the structure of industry and technology, the effectiveness of energy utilization, and the goals of sustainable development and urban planning have resulted in a complex pattern of atmospheric pollution in the region (Xu et al., 2005a; Tang et al., 2006; Cheng et al., 2007; Chan and Yao, 2008). Given that stable weather is typical of the region, atmospheric pollutants accumulate easily, leading to reduced visibility and serious pollution-related accidents in the region (Chan et al., 2005; Streets et al., 2007). Atmospheric pollution threatens the health and wellbeing of Beijing residents as well as the economic development and international image of Beijing (West et al., 2007; Kampa and Castanas, 2008). Beijing hosted the 2008 Summer Games of the 29th Olympiad from 8 August to 24 August The air quality of Beijing became a topic of particularly intense interest (Stone, 2008). Streets et al. (2007) and Chen et al. (2007) asserted the urgent need for regional air quality man- Corresponding author: WANG Yuesi, wys@mail.iap.ac.cn China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2012

2 NO. 6 XIN ET AL agement studies and new emission control strategies to ensure that air quality goals would be met. The State Council of the People s Republic of China and municipal governments of the Beijing Municipality, Tianjin Municipality, and Hebei Province cooperated to improve air quality, sparing no effort or expense to protect the Green Olympics and implementing the Beijing Air Quality Protection Measures of the 29th Olympic Games. During the time period surrounding the Olympic Games, all levels of the government stringently reformed or shut down hundreds of polluting enterprises. All motor vehicles that failed to meet the European No. 1 standard for exhaust emissions ( en.htm), as well as trucks registered outside of Beijing that did not have special permits, were banned from July onward in Beijing. Additionally, vehicles with even- or odd-numbered license plates were banned on alternate days in Beijing and many other cities (e.g., Tianjin City, Tangshan City, and Qinhuangdao City) during the Olympic Games. These measures provided a good opportunity for an in-depth comprehensive field study of atmospheric pollution in Beijing and the surrounding region (Xin et al., 2010). Many reports have discussed the contributions of emission control measures and meteorological conditions to the reduction of particulate pollutants in Beijing during the Olympic Games (e.g., Wang and Xie, 2009; Cermak and Knutti, 2009; Wang et al., 2009; Wang et al., 2010a, b; Xin et al., 2010; Zhou et al., 2010; Huang et al., 2010; Xing et al., 2011). Xin et al. (2010) reported that the PM 2.5 concentration decreased by 43.7% during the Olympic Games based on data from the Beijing Tianjin Hebei Atmospheric Environment Monitoring Network. Wang and Xie (2009) reported that the 28% reduction in PM 10 was due to the 32.3% reduction in traffic flow during the 2008 Olympic Games. Zhou et al. (2010) reported that the urban traffic emission of PM 10 was reduced by 51.6% in Beijing during the 2008 Olympics. Huang et al. (2010) investigated the PM 1.0 pollution level associated with the southerly back trajectory in the Olympic campaign and reported that it decreased by 31%, suggesting possible pollution control effects during the Olympic Games. Wang et al. (2010a) reported that >60% of the decrease in PM 2.5 during the Games could be attributed to emission reduction by comparing the levels to those 1 month prior to the Olympic Games (i.e., July 2008). Wang et al. (2010b) also reported that sulfate and nitrate concentrations in PM 2.5 increased by 64% and 37% in the first two weeks after full control measures were implemented, compared to the prior uncontrolled period. Xing et al. (2011) deduced that impact from meteorologic-driven decreases in PM 2.5 reached as much as 30% in Beijing in August 2008 and that PM 2.5 concentration was reduced more by the emission-driven effect ( 0% 55%) in the south. On the other hand, some studies indicated that atypical meteorological conditions had a significant effect on air quality during the Olympic Games. Using the 1 1 MODIS AOD product, Cermak and Knutti (2009) estimated that the magnitude of the aerosol load reduction was rather low compared to meteorological variability (10% 15%). Wang et al. (2009) statistically accounted for 56% of the daily PM 2.5 concentration variation as the contribution of source control measures (40%) and meteorology (precipitation and air mass back trajectory; 16%) using the observation data of one site. In the present study, we investigated the temporal and spatial variation of PM 2.5 mass concentrations in Beijing and its surrounding areas and analyzed the variability in and the reduction of fine particulate matter in the region during the Olympic Games based on real-time online data from the Beijing Tianjin Hebei Atmospheric Environment Monitoring Network. The network provided the first comprehensive data with a wide spatial coverage in the region, which has yielded valuable insights into PM pollution in Beijing and its regional transport.the results of this study provide a scientific basis for determining the characteristics of fine particulate pollution and the effects of comprehensive pollution management in the region. 2. Material and methods The Beijing Tianjin Hebei Atmospheric Environment Monitoring Network, funded under Beijing s Olympic technological projects of the Chinese Academy of Sciences and the Beijing Municipal Environmental Protection Bureau, was established by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP CAS). The network monitored atmospheric pollutants (i.e., PM 2.5, O 3, NO, NO 2, SO 2, CO, and VOCs) in real time and provided early warnings of atmospheric pollution in the region from 1 June to 30 October The network included 17 sites across the main cities in the Beijing Tianjin Hebei urban region. Figure 1 shows the distribution of the network sites. The Mt. Xinglong station was a background site located at Xinglong Mountain to the north. There were five data-collection sites in Beijing: the Olympic Village, Shuangqing Road, Longtan Lake, Yangfang Town, and the 325 m mast of IAP. Additionally, there were four sites near Beijing: Yanjiao Town, Xianghe Town, Langfang City, and Zhouzhou City, as well as seven sites far from Beijing: the 255 m mast of Tianjin, Baoding City, Shijiazhuang City, Yucheng

3 1332 CONTROLLING AIR POLLUTION IN THE GROWING MEGACITIES VOL. 29 Fig. 1. The location of the Beijing Tianjin Hebei atmospheric environment monitoring network established by IAP, CAS. The red star indicates the background site. Table 1. The geography information of the network, the valid observational days and the mean concentration of PM 2.5 during the monitoring period (1 June 30 October 2008). Area Latitude Longitude Altitude Valid PM 2.5 Region Site categories ( E) ( N) (m.a.s.l.) days (µg m 3 ) Background Xinglong Mountain ±44 Beijing area IAP Urban ±44 Olympic Village Urban ±50 Shuangqing Road Urban ±43 Longtan Lake Urban ±51 Yangfang Suburban ±49 Nearby area Yanjiao Suburban ±50 Xianghe Suburban ±54 Langfang Suburban ±55 Zhouzhou Suburban ±69 Tianjin Urban ±47 Tangshan Urban ±48 Qinhuangdao Urban ±42 Far area Shijiazhuang Urban ±49 Baoding Urban ±58 Cangzhou Urban ±49 Yucheng Town ±56 Town, Cangzhou City, Tangshan City, and Qinhuangdao City. Table 1 lists the geographic information about the network and provides a description of how the studied region was defined. The network was equipped with a TEOM RP1400- PM 2.5 (Thermo Scientific: with a 0.1 µg m 3 resolution, precisions of ±1.5 µg m 3 (1-h average) and ±0.5 µg m 3 (24-h average), and a 0.06 µg m 3 (1-h average) minimum detectable limit. The filters were exchanged every 2 weeks, and the flow rates were monitored and calibrated every month. However, standard TEOMs are well known to underestimate ambient PM concentrations due to the evaporation of semi-volatile components from the microbalance, which is conditioned at higher than ambient temperatures (Grover et al., 2005,

4 NO. 6 XIN ET AL ). The data used in this study were collected from the network data using the analogue acquisition module and were transported in real time to the data center through wireless communication technology. The central server was an integrated and visualized system that performed quality control procedures and monitored the network results in real time. The concentration of PM 2.5 was averaged for the sites in the different study areas. The pollutant standards were acquired from the World Health Organization (WHO, 2006; interim targets 1(IT.1): 75 µg m 3 and interim targets 2 (IT.2): 50 µg m 3 ). Backward trajectories computed using the NOAA HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) model were used to trace air-mass movement. In this study, two-day backward trajectories following the three-dimensional (3D) wind components were calculated using the HYbrid Single- Particle Lagrangian Integrated Trajectory, version 4.7 model (HYSPLIT4; LIT.php) with isentropic coordinates and 1-h time resolution. Gridded NCEP Final Analyses Operational Global Analysis data were used in HYSPLIT. The location of the backward trajectory end point was the Olympic Village station (altitude 100 m above ground level). Cluster analysis was used to classify backward air trajectories into groups of similar history. Our results show that the trajectory clusters are associated with the features of large-scale atmospheric circulation (Dorling et al., 1992; Cape et al., 2000; Rozwadowska et al., 2010). To generate clustered trajectories, a nonhierarchical clustering algorithm (k-means algorithm in SPSS v.13, a software of Statistical Product and Service Solutions, was applied to the principal component scores for each hour. Statistical tests revealed that the optimal number of clusters was seven; they were named from I to VII (Tang et al., 2009). 3. Results and discussion 3.1 Variability of PM 2.5 in the different areas Table 1 shows the valid observational days, the average concentration, and the standard deviation of PM 2.5 for the network sites during the monitoring period (1 June 30 October 2008; 152 days). The differences in concentration from area to area and from time period to time period were small (< ±2 µg m 3 ) because the valid observation days at the sites were >118 of 152 days, or 78% of the monitoring period. (The mean among all stations was 137 of 152 days, or 90% for the network.) In this study, the mean concentrations in the different areas and stages (Table 2) were averaged using the daily means, and the daily means were averaged across the sites on the same day for an overall mean. Figures 2 and 3 show the distribution and variability of PM 2.5 in the Beijing Tianjin Hebei region during the monitoring period. The mean concentration of PM 2.5 (66±44 µg m 3 ) in Beijing was lower than that in nearby areas (87±50 µg m 3 ) and in distant areas (91±41 µg m 3 ) during the monitoring period. These concentrations differed for many reasons. The standards of exhaust emissions for vehicles, industries, and buildings are higher in Beijing than in surrounding cities, as are the surface-area rates of vegetation and water coverage. In particular, the control measures, known as the Beijing Staged 14th Air Quality Controls ( 151/info15394.htm), were more severe in Beijing during the period surrounding the Olympic Games. During the monitoring period, the PM 2.5 mass concentrations were 53±44 µg m 3 at the background station at Mt. Xinglong, 66±44 µg m 3 in Beijing, and 82±40 µg m 3 in the surrounding region. The background (Mt. Xinglong station) PM 2.5 value of the Table 2. The mean concentrations of PM 2.5 and the mean precipitation during several stages of the source control period and the monitoring period in Beijing and surrounding areas [units: PM 2.5, µg m 3 ; precipitation (Preci), mm]. Xinglong Beijing Near area Distant area The region Stage PM 2.5 Preci PM 2.5 Preci PM 2.5 Preci PM 2.5 Preci PM 2.5 Preci (1) ± ± ± ± ± (2) ± ± ± ± ± (3) ± ± ± ± ± (4) ± ± ± ± ± (5) ± ± ± ± ± (6) ± ± ± ± ± Note: (1) No control. (2) Traffic control, a ban all motor vehicles that failed to meet the European No. 1 standard for exhaust emissions as well as trucks registered outside Beijing without special permits. (3) Severe control, a ban vehicles with even and odd-numbered license plates on alternate days in Beijing and other cities. (4) Olympic Games period. (5) Later control, a ban on vehicles with tail-numbered license plates on alternate days in Beijing until now. (6) Overall period.

5 1334 CONTROLLING AIR POLLUTION IN THE GROWING MEGACITIES VOL. 29 Fig. 2. The spatial distribution of the PM 2.5 mean in the Beijing Tianjin Hebei region during the monitoring period. region was close to the WHO IT.2 standard (50 µg m 3 ). The PM 2.5 mean of Beijing was within the WHO IT.1 standard (75 µg m 3 ), while the mean of the region exceeded it. The averaged daily minimum of the network sites was 51±36 µg m 3, which was similar to the background mean of 53±44 µg m 3 at Mt. Xinglong station, which was above the WHO IT.2 standard. Fine particle pollution is a serious problem in the Beijing Tianjin Hebei region (Xu et al., 2005b; Wen et al., 2007; Streets et al., 2007). The temporal and spatial variation of PM 2.5 levels appear to have been controlled by slow cyclical accumulation ( 66% of the time) and quick removal processes ( 33% of the time; Ding et al., 2005), and oscillating cycles were a general feature of populated midlatitude areas crossed regularly by polar fronts (Jia et al., 2008). The variation of the maximum and minimum PM 2.5 values was consistent between Beijing and its surrounding area, which was mainly a result of the meteorological dynamics in the region (Ding et al., 2005). For example, two accumulation cycles related to the regional pollution were significant before the Olympic Games. The first slow accumulative process occurred from July 18 to 28: the maximum levels of PM 2.5 reached 155 µg m 3 at the background Mt. Xinglong station, 187 µg m 3 in Beijing, 200 µg m 3 in nearby areas, and 181 µg m 3 in more distant areas. A rapid removal process followed from July 28 to August 1: the minimum levels of PM 2.5 reached 10 µg m 3 at Mt. Xinglong station, 12 µg m 3 in Beijing, 32 µg m 3 in nearby areas, and 60 µg m 3 in more distant areas. The second accumulative period (1 8 August) was followed by a removal process (8 11 August). Thereafter, accumulative processes occurred during the Olympic Games, but the PM 2.5 levels remained low because strict atmospheric pollution control measures continued in the region. The results also show that the PM 2.5 levels and day-to-day changes were lower during the control period than the no control period, not only in Beijing but also across the entire region. Table 2 shows the mean concentration of PM 2.5 during several stages of the source control period and the monitoring period in Beijing and its surrounding area. The stages of the source control period could be divided into five periods: (1) no control (1 30 June); (2) traffic control (1 19 July), a ban on all motor vehicles that failed to meet the European No. 1 standard for exhaust emissions as well as trucks registered outside Beijing without special permits; (3) severe control (20 July 20 September), a ban on vehicles with even- and odd-numbered license plates on alternate days in Beijing and other cities; (4) control during the Olympic Games (8 24 August); (5) later control (21 September 30 October), a ban on vehicles with tail-numbered license plates on alternate days (a car laying off one day every week) in Beijing until now. In the summer of 2008 (1 June 31

6 NO. 6 XIN ET AL Fig. 3. The daily mean, maximum and minimum levels of PM 2.5 and the daily precipitation in Beijing and its surrounding area during the monitoring period.

7 1336 CONTROLLING AIR POLLUTION IN THE GROWING MEGACITIES VOL. 29 Fig. 4. Correlation of the daily PM 2.5 concentrations among the background Mt. Xinglong Station and Beijing, the near area, the far area and the region during the monitoring period. The asterisk was the daily PM 2.5 concentration as the categories (I, II, III) of air mass backward trajectories; the blank circle represented the categories IV and V; the triangle represented the categories VI and VII. The square was the mean PM2.5 concentration of the seven categories. August), the PM 2.5 concentration decreased markedly in the region, which was likely a result of controlling industrial pollutant emissions in Beijing and its surrounding area. The PM 2.5 levels were the lowest during the Olympic Games (8 24 August), exhibiting values of 35±43 µg m 3 at Mt. Xinglong station, 42±31 µg m 3 in Beijing, and 57±26 µg m 3 in the surrounding region. Compared with the mean levels of PM 2.5 before the Olympic Games (1 June 7 August), the PM 2.5 mass concentration decreased by 51% at Mt. Xinglong station, 52% in Beijing, and 44% in the surrounding region. North China was controlled by the clear and cool weather in autumn. An extended air-pollution-removal process after the Olympic Games was continued. The background PM 2.5 mass concentration reached its lowest level at Mt. Xinglong station. The PM 2.5 concentration slowly increased to the WHO IT.2 standard, despite the fact that some measures to control atmospheric pollution continued in Beijing. However, PM 2.5 levels quickly increased to no-control levels (i.e., June), when temporary control measures for atmospheric pollution that had been implemented in the surrounding area ceased after the Olympic Games. The prevention and control measures for atmospheric pollution that were implemented jointly by the Beijing, Tianjin, and Hebei municipal governments were successful, which indicates that these measures represent scientifically validated, effective countermeasures that temporarily controlled the atmospheric pollution in the region (Chen et al., 2007; Streets et al., 2007). Figures 4a d and Table 3 show the significant correlation (p <0.001, at 0.01 level) of the daily PM 2.5 concentration among Mt. Xinglong station and Beijing, the nearby area, the distant area, and the surrounding region during the monitoring period. In addition, significant correlations (p<0.01, at 0.01 level) of the PM 2.5 concentration under the different airmass categories were revealed. These correlations imply that the levels of particulate matter pollution were highly consistent in Beijing and its surrounding area. The average correlation coefficient from statistical ana-

8 NO. 6 XIN ET AL Table 3. Pearson correlation coefficients (R and p value) between the daily PM2.5 concentrations, precipitation, and air-mass clusters among Mt. Xinglong station, Beijing, the nearby area, the distant area, and the overall region. PM2.5 (µg m 3 ) Precipitation (mm) Cluster Variables Beijing Xinglong Near area Far area The region Beijing Xinglong Near area Far area The region Category PM2.5 Beijing (µg m 3 ) Xinglong Nearby area Distant area Overall region Precipitation Beijing (mm) Xinglong Nearby area Distant area Overall region Cluster category Note: Correlation is significant at the 0.01 level, *Correlation is significant at the 0.05 level.

9 1338 CONTROLLING AIR POLLUTION IN THE GROWING MEGACITIES VOL. 29 lyses of different areas in the Beijing Tianjin Hebei region was The daily variation of PM 2.5 is usually controlled by the similar weather in the region. Figures 4a d also shows the similar variation of PM 2.5 in different backward trajectories, which implies that the measurements of Mt. Xinglong station are able to logically represent the background PM 2.5 level of the region. Given that the background value of PM 2.5 was zero (x=0) in the correlation function (Figs. 4a d), the contribution of anthropogenic emissions (y) can be estimated in different areas of the region. The anthropogenic emissions level was 20 µg m 3 in Beijing (Fig. 4a, the correlation function: y=0.84x+19.63), 37 µg m 3 in nearby areas (Fig. 4b, the correlation function: y=0.88x+36.73), 59 µg m 3 in distant areas (Fig. 4c, the correlation function: y=0.60x+58.69), and 42 µg m 3 in the surrounding region (Fig. 4d, the correlation function: y=0.73x+42.02). When the anthropogenic emissions levels were divided by the mean PM 2.5 concentrations, the ratio of anthropogenic emissions was 29.8% in Beijing and 51.2% in the region during the monitoring period. The remaining ratios, 70.2% for Beijing and 48.8% for the region, were the contribution of background concentration and transportation. 3.2 Precipitation and PM 2.5 Figure 3 showed the daily precipitation and PM 2.5 concentration during the monitoring period in the region. The monitoring period (1 June 30 October, 152 days) of the network included the rainy season of the region. The average frequency of the precipitation (>1.0 mm) was 50% in the network sites, 18% for the precipitation >10 mm, and 10% for the precipitation >20 mm, respectively. A significant negative correlation (p <0.01, in Table 3) between the PM 2.5 concentration and the precipitation on a daily basis was revealed. Rainfall was able to efficiently wash away the particle pollutant in a short time. When there was no rain and the precipitation was <10 mm, the averaged concentrations of PM 2.5 were 58 µg m 3 at Mt. Xinglong station, 70 µg m 3 at Beijing, 88 µg m 3 in nearby areas, 93 µg m 3 in distant areas, and 86 µg m 3 overall. When the precipitation was between 10 mm and 20 mm, the PM 2.5 concentrations were 14 µg m 3 at Mt. Xinglong station, 52 µg m 3 at Beijing, 112 µg m 3 at the nearby area, 91 µg m 3 at the distant area, and 72 µg m 3 overall. When precipitation was >20 mm, the PM 2.5 concentrations were 12 µg m 3 at Mt. Xinglong station, 57 µg m 3 at Beijing, 62 µg m 3 at the nearby area, 58 µg m 3 at the distant area and 56 µg m 3 overall. Moderate-to-heavy rain over a few days cleared the pollution across the region. But the PM 2.5 concentration at the region was also high and very close to the average concentration of 53 µg m 3 at Mt. Xinglong station during the observation period. This level indicates that the regional background concentration of PM 2.5 was the lowest in the region, µg m 3. Table 2 lists the averaged concentration of PM 2.5 and the precipitation in different stages. At Beijing, the precipitation during the no-control period (7.9 mm) was almost equal to the precipitation during severe-control period (8.1 mm), but the PM 2.5 concentration of the no-control period (94 µg m 3 ) was higher than the PM 2.5 concentration during the severecontrol period (61 µg m 3 ). Near Beijing, the PM 2.5 concentration during no control (105 µg m 3 ), with 6.4 mm precipitation, also was higher than the PM 2.5 concentration during severe control (75 µg m 3 ), with 5.9 mm precipitation. The PM 2.5 concentration decreased by 33 µg m 3 in Beijing and 30 µg m 3 in the near area, with a mean of 32 µg m 3, which accounted for 62% of the total decline during the Olympic Games (52 µg m 3 in Beijing and 53 µg in the nearby area, the difference between the no-control period (item 1, Table 2) and the Olympic Games period (item 4, Table 2). These decreases were almost equal in Beijing and the nearby area during the severe-control period and the Olympic Games, indicating that Beijing and its nearby area were basically effected by the same emission control measures. This implies that the emission control measures reduced 32 µg m 3 in Beijing during the severe control stage, but the decline included the contribution of intense precipitation. On the other hand, the clean weather conditions and rainfall reduced 20 µg m 3 from the total decrease (52 µg m 3 ) in Beijing during the Olympic Games, which was responsible for 38% of the total decline. The contributions of meteorological conditions was responsible for 38% of the decrease in PM 2.5 concentration observed in Beijing during the Olympic Games, while the contribution of emission control measures was responsible for 62% of the decrease. The results are similar to those of Zhou et al. (2010) and Wang et al. (2010a). m Comparison of PM 2.5 concentration from different air mass clusters The rainfalls are short clean processes (Fig. 3) that can efficiently wash away much particle matter from the local air in a short time. There was significant negative correlation (p<0.01) between the daily PM 2.5 concentration and the precipitation of the monitoring day (Table 3); the correlation was not significant between the daily PM 2.5 concentration and the precipitation of the prior day, or between the mean precipitation and the mean PM 2.5 concentration at the different

10 NO. 6 XIN ET AL Table 4. The mean PM 2.5 concentration, the mean precipitation and the occurrence frequencies of different air-mass clusters during several stages of the monitoring period in Beijing [units: PM 2.5, µg m 3 ; frequency (Freq), %; precipitation(preci), mm]. Category Term/Time I/II/III PM 2.5 (µg m 3 ) 64±55 37±23 26±14 21±9 27±21 31±25 Freq (%) Preci (mm) IV/V PM 2.5 (µg m 3 ) 76±12 87±39 51±25 36±8 91±41 68±36 Freq (%) Preci (mm) VI/VII PM 2.5 (µg m 3 ) 101±37 83±42 81±47 57±40 90±23 89±43 Freq (%) Preci (mm) stages (Table 4). It was difficult to estimate the quantitative relation between the precipitation and the decrease in PM 2.5 as the emission control measures were implemented. For example, the precipitation and rainfall frequency were not the largest in each monitored region during the Olympic Games, but the concentration of PM 2.5 was the lowest during this period (Fig. 3 and Tables 2 and 4). The decrease in PM 2.5 emission is affected by precipitation, and this effect must be calculated. Rainfall often occurs at the beginning or end of a certain kind of air masses. Air mass processes, particularly rainfall, have the effect of eliminating pollution or cleaning the air. Statistically, certain kinds of air masses have certain effects on PM 2.5 variation. The change in emissions can be estimated by comparing the PM 2.5 concentration in similar airmass groups. The effect of severe emission control in a relatively large region can cause a decrease in PM 2.5 that is nearly equivalent to that of certain categories of air-mass transport, in theory. Wang et al. (2009) statistically analyzed the effect of air masses on the decrease of PM 2.5 during the Olympic Games using a multivariable linear regression model which assumed the indicator variable (0 or 1) for the source control period only in the southerly wind direction. However, the model was not suitable for this study because of the large differences in PM 2.5 concentration among the five periods of source control and seven main air-mass categories (Table 2 and Table 4). Seven main categories of air masses occurred in Beijing, based on cluster analysis of backward trajectories of air mass. Figure 5 shows the occurrence frequencies and the PM 2.5 means of seven air-mass categories (I VII) during the monitoring period. The occurrence frequencies of seven categories of back trajectories of air mass were 5.9% (category I), 7.2% (cat- Fig. 5. Cluster analysis of air mass backward trajectories in Beijing during the monitoring period (from 1 June to 30 October, 2008). The occurrence frequencies and the PM 2.5 means for seven categories, I: 5.9%, 20.1 µg m 3 ; II: 7.2%, 24.5 µg m 3 ; III: 17.8%, 37.5 µg m 3 ; IV: 8.6%, 64.4 µg m 3 ; V: 16.4%, 69.7 µg m 3 ; VI: 25.7%, 84.9 µg m 3 ; VII: 18.4%, 94.2 µg m 3.

11 1340 CONTROLLING AIR POLLUTION IN THE GROWING MEGACITIES VOL. 29 egory II), 17.8% (category III), 8.6% (category IV), 16.4% (category V), 25.7% (category VI), and 18.4% (category VII). Significant correlations (p <0.01, Table 3) occured between the PM 2.5 concentration and the cluster category on a daily basis, indicating that there were the similar effects on the PM 2.5 concentration from the air masses. Table 4 gives the PM 2.5 means and the occurrence frequencies of different cluster categories during several stages of the monitoring period in Beijing. Cluster categories I, II, and III represent the processes of rapid cold air moving from the far northwest, northeast, and north in two days; these clean, strong air masses transport particulate matter pollution away from Beijing (Jia et al., 2008; Li and Shao, 2009; Tang et al., 2009). The occurrence frequencies of category I, II, and III air masses were 5.9%, 7.2%, and 17.8%, respectively. The occurrence frequency of clean air masses was 30.9%, while the PM 2.5 mean was 31±25 µg m 3. The relatively weaker air masses (categories IV and V) from the west and the east, respectively, did not clearly affect the concentration of PM 2.5. The frequency of 25.0% and mean value of 68±36 µg m 3 associated with these categories of air masses matched the mean concentration during the monitoring period. However, the relatively strong air masses (categories VI and VII) from the south and southeast often transferred high levels of particulate matter pollution to Beijing. The frequencies of the category VI and VII air masses were 25.7% and 18.4%, and the mean PM 2.5 concentrations were 85±44 µg m 3 and 94±41 µg m 3, respectively. These results indicate that the days on which air masses from the south and east occurred; the local accumulation of PM 2.5 was enhanced during 69.1% of the overall monitoring period. The days associated with clean air masses from the northwest and north reflect decreased particulate matter pollution in the region, and they represent 30.9% of the entire monitoring period. The mean PM 2.5 concentration was high (97 µg m 3 ) on 8 10 August during the Olympic Games because of the transport of air pollution masses from the south. Subsequently, the particulate matter pollution markedly decreased over the remaining 14 days of the Olympic Games. The daily mean of PM 2.5 was 31 µg m 3, which was far lower than the WHO IT2 standard and the PM 2.5 mean during the monitoring period. During the monitoring period, the occurrence frequency of northerly clean air masses was 23.6%, and the mean PM 2.5 concentration was 21 µg m 3 below the average level. Therefore, the prevention and control measures for atmospheric pollution that were implemented jointly by the Beijing, Tianjin, and Hebei municipal governments made distinct successful contributions to the reduction of air pollution during this time. Compared to the PM 2.5 concentration during the no-control period, PM 2.5 levels decreased by total 52 µg m 3 during the Olympic Games in Beijing. Comparing the PM 2.5 concentration between the Olympic Games (8 24 August) and the no-control period (1 30 June) for different air-mass groups (Table 4), the decreases of PM 2.5 were µg m 3 (mean: 43 µg m 3 ) and similarities occurred not only in clean air masses from the north and west but also in pollutant air masses from the south and east. The similarities in decreases imply that the emission controls were severe in the region during the period. The effect of large or small rainfall amounts on the PM 2.5 concentration in different air masses was a decrease ( 43 µg m 3 ) representing the largest decline of PM 2.5, attributable to the jointly implemented control measures and favorable atmospheric conditions. Anthropogenic emission control contributed 82% of the total decrease (52 µg m 3 ). Therefore, the remaining decrease (52 µg m 3 43 µg m 3 = 9 µg m 3 ) was due to clean air-mass transport. Favorable meteorological conditions contributed 18% of the total decrease, contributing 38% of the decline in Beijing during the Olympic Games, while the contribution of emission control measures contributed 62% of the decrease. Some results (Wang et al., 2010a; Zhou et al., 2010; Xing et al., 2011) were near or within the contribution ranges of emission control and meteorological conditions. The results of this analysis indicate that the jointly implemented control measures for atmospheric pollution made a major contribution to decrease in PM 2.5. At the same time, the occurrence of natural clean air masses increased, also making a relevant contribution to the decrease in pollution. 4. Conclusions There was considerable particulate matter pollution in the Beijing Tianjin Hebei urban agglomeration in summer and autumn The pollution was controlled by cyclical slow accumulation 65% of the time and by rapid removal processes 35% of the time. The near and distant areas around Beijing were associated with the potential to transport particulate matter pollution because the concentration of PM 2.5 was higher in the surrounding areas than in Beijing. Under the influence of the southerly and easterly air masses, which occurred 35% of the time, high particulate-matter pollution was observed to easily be transferred, thus contaminating Beijing. During the Olympic Games, jointly implemented control measures for atmospheric pollution effectively decreased the regional pollution of particulate matter. In particular, PM 2.5 declined significantly during the Olympic

12 NO. 6 XIN ET AL Games. When the PM 2.5 concentrations from different air-mass clusters and the precipitation were compared, it was discovered that 60% 80% of the total decline was due to the contribution of anthropogenic measures, while 20% 40% of the total decline was associated with the contribution of weather conditions favorable for clean air transport. As the control measures were terminated in the surrounding areas, PM 2.5 levels significantly rebounded in the region after the Olympic Games. Meanwhile, PM 2.5 levels remained low in Beijing because some atmospheric pollution control measures continued to be implemented. The prevention and control measures for atmospheric pollution that were implemented jointly by the Beijing, Tianjin, and Hebei municipal governments were successful, thus representing effective countermeasures that temporarily controlled the complex atmospheric pollution in the region. However, the pattern of particulate matter pollution was highly regional and greatly associated with anthropogenic causes. Developing long-term controls for the particulate matter pollution of the region represents a very serious and difficult challenge for the Beijing Tianjin Hebei region. Acknowledgements. This study was supported by the Beijing Municipal Science & Technology Commission (Grant No. D ), the National Natural Science Foundation of China Grant Nos and and supported by the CAS Strategic Priority Research Program Grant No. XDA The establishment and running of the Beijing Tianjin Hebei Atmospheric Environment Monitoring Network was supported by local colleagues and institutes. The authors are also grateful to the monitoring staff for their contribution to this research. REFERENCES Brunekreef, B., and S. T. Holgate, 2002: Air pollution and health. The Lancet, 360, Cape, J. N., J. Methven, and L. E. Hudson, 2000: The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland. Atmos. Environ., 34, Cermak, J., and R. Knutti, 2009: Beijing Olympics as an aerosol field experiment. Geophys. Res. Lett., 36(L10806), doi: /2009GL Chan, C. K., and X. Yao, 2008: Atmospheric pollution in mega cities in China. Atmos. Environ., 42, Chan, C. Y., X. D. Xu, Y. S. Li, K. H. Wong, G. A. Ding, L. Y. Chan, and X. H. Cheng, 2005: Characteristics of vertical profiles and sources of PM 2.5, PM 10 and carbonaceous species in Beijing. Atmos. Environ., 39, Chen, D. S., S. Y. Cheng, L. Liu, T. Chen, and X. R. Guo, 2007: An integrated MM5-CMAQ modeling approach for assessing trans-boundary PM 10 contribution to the host city of 2008 Olympic summer games- Beijing, China. Atmos. Environ., 41, Cheng, S., D. Chen, J. Li, X. Guo, and H. Wang, 2007: An ARPS-CMAQ modeling approach for assessing the atmospheric assimilative capacity of the Beijing metropolitan region. Water, Air, & Soil Pollution, 181, Curtis, L., W. Rea, P. Smith-Willis, E. Fenyves, and Y. Pan, 2006: Adverse health effects of outdoor air pollutants. Environment International, 32, Ding, G. A., and Coauthors, 2005: Vertical structures of PM 10 and PM 2.5 and their dynamical character in low atmosphere in Beijing urban areas. Science in China (D), 35 (Suppl. I), Dockery, D. W., C. A. Pope III, X. Xu, J. D. Spengler, J. H. Ware, M. E. Fay, B. G. Ferris, and F. E. Speizer, 1993: An association between air pollution and mortality in six US cities. The New England Journal of Medicine, 329, Dorling, S. R., T. D. Davies, and C. E. Pierce, 1992: Cluster analysis: A technique for estimating the synoptic meteorological controls on air and precipitation chemistry Method and applications. Atmos. Environ., 26A(14), Grover, B. D., and Coauthors, 2005: Measurement of total PM 2.5 mass (nonvolatile plus semi-volatile) with the Filter Dynamic Measurement System tapered element oscillating microbalance monitor. J. Geophys. Res., 110, D07S03, doi: /2004JD Grover, B. D., and Coathors, 2006: Measurement of both nonvolatile and semi-volatile fractions of fine particulate matter in Fresno, CA. Aerosol Science and Technology, 40, Huang, X. F., and Coauthors, 2010: Highly time-resolved chemical characterization of atmospheric submicron particles during 2008 Beijing Olympic Games using an Aerodyne High-Resolution Aerosol Mass Spectrometer. Atmospheric Chemistry and Physics, 10, Jia, Y., K. A. Rahn, K. He, T. Wen, and Y. Wang, 2008: A novel technique for quantifying the regional component of urban aerosol solely from its sawtooth cycles. J. Geophys. Res., 113, D21309, doi: /2008JD Kampa, M., and E. Castanas, 2008: Human health effects of atmospheric pollution. Environmental Pollution, 151, Kappos, A. D., and Coauthors, 2004: Health effects of particles in ambient air. International Journal of Hygiene and Environmental Health, 207, Li, W., and L. Shao, 2009: Transmission electron microscopy study of aerosol particles from the brown hazes in northern China. J. Geophys. Res., 114, D09302, doi: /2008JD Pope, C. A., D. W. Dockery, and J. Schwartz, 1995: Review of epidemiological evidence of health effects of particulate air pollution. Inhalation Toxicology, 7, 1

13 1342 CONTROLLING AIR POLLUTION IN THE GROWING MEGACITIES VOL Rozwadowska, A., T. Zielinski, T. Petelski, and P. Sobolewski, 2010: Cluster analysis of the impact of air back-trajectories on aerosol optical properties at Hornsund, Spitsbergen. Atmospheric Chemistry and Physics, 10, Stone, R., 2008: China s environmental challenges: Beijing s marathon run to clean foul air nears finish line. Science, 321, Streets, D. G., and Coauthors, 2007: Air quality during the 2008 Beijing Olympic Games. Atmos. Environ., 41, Tang, G., X. Li, Y. Wang, J. Xin, and X. Ren, 2009: Surface ozone trend details and interpretations in Beijing, Atmospheric Chemistry and Physics, 9, Tang, X. Y., Y. H. Zhang, and M. Shao, 2006: Chemistry of Atmospheric Environment. 2nd ed., Higher Education Press, Beijing, 739pp. (in Chinese) Wang, S. X., and Coauthors., 2010a: Quantifying the air pollutants emission reduction during the 2008 Olympic Games in Beijing. Environ. Sci. Technol., 44, Wang, T., and S. D. Xie, 2009: Assessment of trafficrelated air pollution in the urban streets before and during the 2008 Beijing Olympic Games traffic control period. Atmos. Environ., 43, Wang, T., and Coauthors, 2010b: Air quality during the 2008 Beijing Olympics: Secondary pollutants and regional impact. Atmospheric Chemistry and Physics, 10, Wang, W., T. Primbs, S. Tao, and S. L. Massey Simonich, 2009: Atmospheric particulate matter pollution during the 2008 Beijing Olympics. Environ. Sci. Technol., 43, , doi: /es Wen, T. X., Y. S. Wang, H. H. Xu, Z. Q. Ma, and D. S. Ji, 2007: Comparison of the characteristics of ambient pollutants in urban and background region in Beijing during August and September. Research of Environmental Sciences, 20, (in Chinese) West, J. J., S. Szopa, and D. A. Hauglustaine, 2007: Human mortality effects of future concentrations of tropospheric ozone. Comptes Rendus Geoscience, 339, Xin, J. Y., and Coauthors, 2010: Variability and reduction of atmospheric pollutants in Beijing and its surrounding area during the Beijing 2008 Olympic Games. Chinese Science Bulletin, 55, Xing, J., and Coauthors, 2011: Modeling study on the air quality impacts from emission reductions and atypical meteorological conditions during the 2008 Beijing Olympics. Atmos. Environ., 45(10), doi: /j.atmosenv Xu, X. D., X. J. Zhou, and X. H. Xiu, 2005a: Spatial structure and scale feature of the atmospheric pollution source impact of city agglomeration. Science in China (D), 35 (Suppl. I), Xu, X. D., X. H. Shi, L. A. Xie, G. A. Ding, Q. J. Miao, J. Z. Ma, and X. D. Zheng, 2005b: Spatial character of the gaseous and particulate state compound correction of urban atmospheric pollution in winter and summer. Science in China (D), 35(Suppl. I), Zhou, Y., and Coauthors, 2010: The impact of transportation control measures on emission reductions during the 2008 Olympic Games in Beijing, China. Atmos. Environ., 44,