- Email: [email protected]
Diurnal, seasonal, and spatial variation of PM2.5 in Beijing
Sci. Bull. (2015) 60(3):387–395 DOI 10.1007/s11434-014-0607-9
www.scibull.com www.springer.com/scp
Article
Earth Sciences
Diurnal, seasonal, and spatial variation of PM2.5 in Beijing Runkui Li • Zhipeng Li • Wenju Gao • Wenjun Ding • Qun Xu • Xianfeng Song
Received: 12 April 2014 / Accepted: 25 July 2014 / Published online: 30 December 2014 Ó Science China Press and Springer-Verlag Berlin Heidelberg 2014
Abstract PM2.5 pollution in Beijing has attracted extensive attention in recent years, but research on the detailed spatiotemporal characteristics of PM2.5 is critically lacking for effective pollution control. In our study, hourly PM2.5 concentration data of 35 fixed monitoring sites in Beijing were collected continuously from October 2012 to September 2013, for exploring the diurnal and seasonal characteristics of PM2.5 at traffic, urban, and background environments. Spatial trend and regional contribution of PM2.5 under different pollution levels were also investigated. Results show that the average PM2.5 concentration of all the 35 sites (including 5 traffic sites) was 88.6 lg/m3. Although PM2.5 varied largely with the site location and seasons, a clear spatial trend could be observed with the PM2.5 concentration decreasing linearly from south to north, with a gradient of -0.46 lg/m3/km for average days, -0.83 lg/m3/km for heavily–severely polluted days, -0.52 lg/m3/km at lightly–moderately polluted days, and -0.26 lg/m3/km for excellent–good days. PM2.5 at traffic R. Li Z. Li X. Song (&) College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China e-mail: [email protected] W. Gao Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo 454000, China W. Ding College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China Q. Xu Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine of Peking Union Medical College, Beijing 100005, China
sites was varied, but was generally over 10 % higher than at the nearby urban assessment sites. Keywords Fine particulate matter Spatiotemporal variation Trend Traffic site Regional transmission Beijing
1 Introduction PM2.5 is fine particles suspended in the atmosphere with a diameter less than 2.5 lm and may have damaging effects on human health, especially the cardiovascular and respiratory systems [1–6]. Due to this, various governments and international groups have launched regulations and standards to control ambient particulate concentrations [7]. Extremely polluted weather related to high PM2.5 concentrations, such as haze and fog, has frequently affected Beijing severely over the past few years [8–13], causing wide public concern. In October 2011, severe air pollution in Beijing resulted in rapid administrative measures. This included PM2.5 being added to the real-time air quality monitoring system of the Ministry of Environmental Protection of China, and integration into the Chinese National Ambient Air Quality (CNAAQ) standard. Despite initial improvements in air quality, long-lasting haze and fog occurred in January 2013. Therefore, it is necessary to comprehensively investigate the source, distribution, fluctuation pattern, and other characteristics of the PM2.5 pollution in Beijing and to provide a more reliable research basis for valid control measures. There is a complex contribution of different emission sources to PM2.5 in Beijing. Given the rapid growth in traffic during the past two decades, vehicles in Beijing reached 5.2 million by the end of 2012, while coal consumption and industrial emissions decreased at the same time [14]. On-road
123
388
traffic emission has become a main concern for public health and new pollution control targets in Beijing. Meanwhile, pollutants from neighboring regions, such as the Hebei Province and Tianjin Municipality, have contributed to the air pollution in Beijing. Although scientific research has provided some useful data, temporal variations in the proportion of PM2.5 sources have made it difficult to determine the exact composition of PM2.5 in Beijing [14–17]. In addition, the source of air pollution varies across multiple spatial scales, e.g., on local scales associated with immediate sources to larger spatial areas with secondary reactions and transport mechanisms [18]. High traffic emissions and regional changes also increase the difficulty of exploring detailed spatiotemporal patterns of PM2.5 concentration. Only with long-term monitoring across both space and time can the variations of PM2.5 in Beijing be quantitatively characterized. In this paper, to comprehensively describe the spatiotemporal characteristics of PM2.5, a large amount of PM2.5 data were collected over a 1-year time span and analyzed with the new technique of geographic information system (GIS), which greatly facilitates the understanding of PM2.5 pollution at spatial perspectives [19]. To evaluate the contribution of traffic toward pollution control in Beijing, data from traffic sites were specifically collected and compared with data from other sites. The aim of this study was to explore the spatiotemporal variation of PM2.5 concentration in Beijing through analyzing the diurnal, seasonal, and spatial patterns based on 1-year hourly collected data from 35 monitoring stations. PM2.5 pollution status and spatial trends in Beijing were investigated to provide information for exposure assessment and theoretical support for taking appropriate control strategies on PM2.5 pollution in this region.
Sci. Bull. (2015) 60(3):387–395
and at least one air quality monitoring site was contained in each district. 2.2 Data collection
2 Materials and methods
Hourly PM2.5 data from October 2012 to September 2013 for 35 fixed monitoring sites were obtained from the real-time air quality system (AQS) of Beijing Municipal Environmental Protection Bureau (BJEPB). These data were collected since the stations started to publish PM2.5 data and covered a total of more than 280,000 site-hour records. Among the 35 monitoring sites, 23 are urban environmental assessment sites, which are mainly used to assess regional environmental air quality and its overall variation; 1 is an urban background site to reflect the air quality unaffected by urban pollution; 6 are cross-region transmission sites close to Beijing municipal boundary in six directions to characterize regional background levels and monitor the transmission of pollutants between regions; and 5 are traffic sites at the edges of busy roads to monitor on-road traffic pollution on ambient air quality (Fig. 1). These sites scatter from the very south to the north end of Beijing, covering most of the spatial regions and typical land types in Beijing, e.g., from the center of most developed urban areas to faraway countryside. Six regional transmission sites are set in separate directions: Two sites in the north are called the Transmission-North in the study, two in the east and southeast near Tianjin refer to the Transmission-Southeast, and another two sites in the south and southwest near Hebei the Transmission-Southwest. Meteorological data, including daily maximum 10-min averaged wind speed and observed sunshine hours, were acquired from China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/). For the missing data in September 2013 for Beijing, we took that of Tianjin station in view of the high similarity between the two sites.
2.1 Study area
2.3 Data preparation
Beijing covers about 16,410 km2 and consists of six urban districts, eight suburban districts, and two rural counties and possessed 21.148 million people at the end of 2013, making it one of the most populous cities in the world. Mountainous at northern, northwestern, and western regions, the terrain of Beijing gets flatter toward the southeast. Plains cover most of the central urban area and the vast suburban districts in south and east directions (Fig. 1). The main urban area, on the plains in the southcenter of the municipality, spreads out in concentric ring roads with Tian’anmen roughly as its center. Beijing is surrounded almost entirely by Hebei Province except for the Tianjin Municipality, which is neighboring to the southeast. This study covered all the 16 municipal districts,
Because the hourly real-time data were probably published before official auditing, the data were checked manually. Due to equipment failure or internet error, some data were missing. Some data were also rejected due to anomalous measurements. Daily average concentration was obtained by averaging everyday hourly data from 00:00 to 23:00. According to the national standard GB 3095-2012, observation for at least 20 h is required to obtain daily average concentrations for each site to ensure the representativeness of the daily average value when missing data appear. Otherwise, data of the day was invalid and had to be excluded from this study (Table 1). The spatial pattern of pollution may vary with climate conditions and pollution levels. To investigate such effects,
123
Sci. Bull. (2015) 60(3):387–395
389
Fig. 1 Spatial distribution of the 35 PM2.5 monitoring sites with different purposes in Beijing Table 1 Overall description for daily mean PM2.5 concentration of 35 sites Pollution level (lg/m3)
Days
Median (lg/m3)
Range (lg/m3)
Low (\75)
175
43.7
7.7–74.6
Medium (75–150)
106
104.5
75.1–149.9
53
202.1
150.3–411.7
334
71.4
7.7–411.7
High ([150) All period
experiments were conducted repeatedly for three pollution levels. This was implemented by the following four steps: (1) For each day, daily concentration of all sites was averaged to get an overall mean concentration for the whole study area and used for pollution-level classification. (2) The overall mean concentration was labeled as high, medium, and low pollution level when PM2.5 concentration were [150, 75–150, and \75 lg/m3, respectively. These levels correspond to heavily–severely polluted, lightly– moderately polluted, and excellent–good days according to the latest Air Quality Index (AQI) in China. (3) Based on the overall daily mean concentration and the above
classification criteria, days falling into each pollution level were labeled and grouped. (4) For days in a specific pollution level, the daily concentration was averaged to get the mean concentration of a given site under that level. As a result, the mean concentration of each site during high, medium, and low pollution condition was derived. PNi Ck Mi ¼ k¼1 i ; ð1Þ Ni where Mi is mean concentration of one of the 35 sites under pollution level i (i = 1, 2, and 3, which corresponds to three levels of air pollution) (lg/m3); Ni is the number of days under pollution level i; Cki is the daily concentration of the given site on day k (k = 1 to Ni, which corresponds to the days under pollution level i) in pollution level i (lg/m3).
2.4 Statistical methods A simple linear regression method was adopted for spatial trend detection. To examine the north–south trend, latitude
123
390
Sci. Bull. (2015) 60(3):387–395
Wind speed (m/s)
0 15
9 6 3
350 300 250
200 150 100
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
2013 Jan
0
Dec
50
Nov
Daily mean PM2.5 concentration (µg/m3)
400
ð3Þ
Urban assessment
210
Traffic site Transmission-North
180
Transmission-Southeast 150
Transmission-Southwest
120 90
Sep
Aug
Jul
Jun
May
Apr
30
Mar
60
Feb
Daily mean PM2.5 concentration rose and dropped rapidly, with sharp peaks and deep valleys appearing alternately (Fig. 2). Peak values tended to be many times higher than that of the neighboring valleys. Very good consistency could be observed that days with more sunshine hours (a clear sky) generally appeared during or shortly after a strong wind, and PM2.5 concentrations were generally lower in accordance with these weather conditions (Fig. 2). PM2.5 concentration rose rapidly after a windy day and thereafter would reduce the sunshine hours, which in turn decreased dispersion and accelerated accumulation of pollutants. Figure 2 shows that heavy PM2.5 pollution usually occurred under stagnant weather conditions, with little or no sunshine together with gentle wind. Thus, meteorological conditions were one of the key influencing factors on variation of Beijing PM2.5 concentration during the study period. It could also be seen that PM2.5 concentration in April, August, and November was relatively lower, while that in January and June was significantly higher (Figs. 2, 3). Heavy pollution in winter was probably caused by
Urban background
2013 Jan
3.1 Temporal variation of PM2.5
240
Dec
3 Results
Fig. 2 Daily mean PM2.5 concentration from October 2012 to September 2013 and its relationship with 10-min maximum wind speed and daily sunshine hours
Nov
where Ymin is Y coordinate of the extremely south boundary of Beijing (m).
123
12
0 450
Monthly mean PM2.5 concentration (µg/m3)
DFS ¼ Y Ymin ;
6 3
3
where Mi is mean concentration of pollution level i (lg/m ); Y is coordinate at south–north direction of the monitoring sites (m); a and b are the coefficients. For computational convenience and physical clarity, distance to the extreme south boundary line of Beijing [hereinafter, called ‘‘Distance from South’’ (DFS)] was used as a surrogate of the original Y coordinate as shown in Eq. (3). As a result, mean PM2.5 concentration from 35 monitoring sites was regressed with their DFS for each pollution level.
9
2012 Oct
ð2Þ
12
2012 Oct
Mi ¼ aY þ b;
15
Sunshine hours (h)
of the monitoring sites (with degree unit) under the latitude–longitude coordinate system indicating relative north and south positions was derived and used as a predictor variable. For convenience, geographic coordinates of all the 35 sites were projected onto a rectangular coordinate system, and latitude was then transferred to a projected geographic Y coordinate with meter units, and mean PM2.5 concentration of each pollution level, as derived from Eq. (1), was used as response variable to explore the spatial trend from north to south.
Fig. 3 Monthly mean PM2.5 concentration of different types of sites
increased emissions (such as coal burning for heating), combined with low vertical dispersion due to reduced solar radiation. Lower PM2.5 concentrations in April could be attributed to strong wind and cessation of winter heating. Relatively low concentrations in autumn would be the result of the air clearing effect of rain and strong dispersion.
Sci. Bull. (2015) 60(3):387–395
140 120
100 80 60 40
Spring
Summer
Autumn
Winter
22:00
20:00
18:00
16:00
14:00
12:00
10:00
08:00
06:00
04:00
02:00
0
00:00
20
140 120 100 80 60
22:00
20:00
18:00
16:00
Urban assessment Traffic sites Urban background
14:00
12:00
10:00
08:00
0
06:00
20
04:00
Transmission-Southwest Transmission-Southeast Transmission-North
40
02:00
(b)
160
00:00
Mean PM2.5 concentration (µg/m3)
(a)
Mean PM2.5 concentration (µg/m3)
PM2.5 concentration differences among sites were significant and also varied with seasons (Fig. 3). Traffic sites were always higher than urban assessment sites, while the urban background was much lower. This fact verified our understanding that sites closer to intensive emission sources would have higher values, and vice versa. Transmission sites at different directions varied more distinctly. For example, transmission sites in the southwest (close to Hebei) and southeast (close to Tianjin) were much higher than those in the north (close to the mountains). This could be explained by the regional characteristics that sites at central and south urbanized plain areas would probably be more polluted than sites in the northern mountainous area that were less affected by human activities [20]. Concentrations in the southwest was generally higher than in the southeast from October 2012 to June 2013, yet was lower from July to September 2013. This would be caused by the change of pollution sources and wind directions during the summer and requires further investigation. The classified pollution level based on daily mean concentration of all sites is shown in Table 1. Median PM2.5 concentration of the 35 sites during the study period is 71.4 lg/m3 (with 88.6 lg/m3 for the mean), and median concentration during low, medium, and high pollution level is 43.7, 104.5, and 202.1 lg/m3, respectively. Although most days of the study were under low and medium pollution level, highly polluted days also occupied a considerable part of the period, to which special attention should be given. Because all sites (including traffic sites) were included in this study, the classification of low, medium, and high pollution level of a day here would probably be different from the AQI published by BJEPB, which is based on only part of sites in this study.
391
Fig. 4 Diurnal variation of PM2.5 concentration in different seasons and among different types of site. a Seasonal variation of PM2.5 concentration of 35 sites for Spring (March–May), Summer (June– August), Autumn (September–November), and Winter (December– February), b spatial variation across 24 h among urban assessment sites, traffic sites, urban background site, and transmission sites in southwest, southeast, and north directions
3.2 Diurnal variation among seasons and sites Diurnal characteristics of PM2.5 concentration varied with seasons and sites (Fig. 4). The overall PM2.5 concentration had significantly higher values in winter than the other three seasons (Fig. 4a). Spring was slightly but consistently higher than summer, while autumn showed stronger diurnal fluctuations with lower values during daytime and higher values at night. The seasonal variations were probably caused by changes in meteorological conditions and sources of particulate matter. For example, dry climate and heavy wind in spring generated more soil dust, while wet and hot summer enhanced photochemistry and generated more secondary pollution, and cold winter induced more primary pollution from coal burning for heating [21–25]. Diurnal variation was weak in spring and summer, but was much stronger in autumn and winter (Fig. 4a). The
daily variation in autumn and winter seemed like a flat ‘‘W’’ shape, with lowest values generally appearing at 06:00–07:00 or 14:00–16:00. Diurnal concentrations varied largely among sites (Fig. 4b). Transmission sites in the north had the lowest values, while sites in the southwest had the highest values. The concentrations at southwest sites were nearly double that of the north sites, which revealed the remarkable spatial variation from south to north. Traffic sites were generally higher than urban assessment sites. Concentration at most sites rose from 06:00 until 11:00 to reach the peak and then decreased reaching the lowest point of a day at around 16:00, except for urban background sites and transmission sites in the north. Concentration at urban background sites rose steadily during daytime and showed a distinct pattern.
123
392
Sci. Bull. (2015) 60(3):387–395
3.3 The spatial trend
4 Discussion This study presented the overall spatiotemporal characteristics of PM2.5 in Beijing based on the latest hourly data from 35 sites including 5 traffic sites. Diurnal, seasonal, Table 2 Regression of PM2.5 concentration to distance from south (DFS) of Beijing under different pollution levels Pollution levels Low Medium High
(a) 150
(b) 300 PM2.5 concentration (µg/m 3)
All days
PM2.5 concentration (µg/m3)
To investigate the general spatial trend across the study area, regression analysis of PM2.5 concentration with distance from the site to the south was conducted for each pollution level (Table 2, Fig. 5). Analysis was conducted with and without traffic sites separately. Traffic sites were included to demonstrate the effect of local automobile emission sources on the overall pollution pattern. PM2.5 concentration showed a very good relationship with distance from south, with R2 larger than 0.70 when all 35 sites were used, and larger than 0.80 without traffic sites (Table 2). Relative ranking of R2 for different pollution levels was All days (R2 = 0.84) [ Medium (R2 = 0.83) [ Low (R2 = 0.77) [ High (R2 = 0.73) for all 35 sites. The relative rank of R2 kept the same when traffic sites were excluded, and an improved relationship was obtained with R2 = 0.89 for All days and R2 = 0.80 for High pollution level. A clear spatial trend could be found with the PM2.5 concentration decreasing steadily from the south to north of Beijing (Fig. 5). Without traffic sites, the gradient was about 0.46 lg/m3/km for all of the study period, 0.83 lg/m3/km for highly polluted days, 0.52 lg/m3/km at medium pollution level, and 0.26 lg/m3/km at low pollution levels (Table 2).
Therefore, gradual change of the PM2.5 concentration from south to north of Beijing, with the distance exceeding 100 km, caused significant regional differences.
120
90
60
PM2.5 = – 0.4741DSF + 116.65 R 2 = 0.84
30
Urban background
Regional transmission
Urban assessment
Traffic pollution
Regression function
Number of sites
PM2.5 = -0.2685DFS ? 58.13
35
0.77
PM2.5 = -0.2588DFS ? 56.94
30
0.81
PM2.5 = -0.5468DFS ? 138.66 PM2.5 = -0.5237DFS ? 135.87
35 30
0.83 0.88
PM2.5 = -0.8736DFS ? 258.26
35
0.73
PM2.5 = -0.8336DFS ? 252.99
30
0.80
PM2.5 = -0.4741DFS ? 116.65
35
0.84
PM2.5 = -0.4553DFS ? 114.31
30
0.89
240
180
120
PM2.5 = –0.8736DSF + 258.26 R 2 = 0.74
60
Urban background
Regional transmission
Urban assessment
Traffic pollution
0
0 0
20
40
60
80
100
0
120
20
(d) PM2.5 concentration (µg/m3)
(c) 150 120
90
60
PM2.5 = –0.5468DSF + 138.66 R 2 = 0.83
30
40
60
80
100
120
Distance from south (km)
Distance from south (km)
PM2.5 concentration (µg/m3)
R2
Urban background
Regional transmission
Urban assessment
Traffic pollution
70 60 50 40 30 PM2.5 = – 0.2685DSF + 58.13 R 2 = 0.77
20 10
Urban background
Regional transmission
Urban assessment
Traffic pollution
0
0 0
20
40
60
80
Distance from south (km)
100
120
0
20
40
60
80
100
120
Distance from south (km)
Fig. 5 Regression plot of PM2.5 concentration to distance from south (DFS) of Beijing of 35 sites, with labeled site types and ±10 % enveloping area of the trend line. a Data of entire research period, b high polluted days, c medium polluted days, d low polluted days
123
Sci. Bull. (2015) 60(3):387–395
PM2.5 concentrations at Transmission sites in the north were even lower than urban background sites (Fig. 3–5). This could be explained by the spatial trend shown in Fig. 5. The urban background site was closer to the central urban area and also closer to the south of Beijing, therefore was closer to emission sources or more influenced by pollution transported from the south. The concentrations of regional transmission sites in the south were also higher than the central urban area, especially during periods with low pollution levels (Fig. 5d). Therefore, except for traffic emissions, there should be extra emissions generated from the south suburban area or transported from the neighboring Hebei Province in the south or Tianjin Municipality in the southeast. This could also be verified by the inconsistent behavior of sites neighboring Hebei and Tianjin with the inner sites that far from the southeastern or southwestern boundary (Fig. 6). PM2.5 concentrations generally decreased when distance to the core urban area (taking Tian’anmen as the center) increased, except for the abnormal sites located close to Hebei and Tianjin, which were obvious outliers. The outliers had much higher concentrations and partly indicated a large amount of cross-boundary transmission from outside. Previous studies have also shown that contribution from the outside would be significant [15–17]. However, the quantitative contribution of each part, e.g., locally generated and transmission across large spatial scales, had not been clearly separated here in this study. Another issue requiring explanation is the daily AQI of Beijing. AQI is usually published based on 11 sites in the
120 Close to Hebei
Mean PM2.5 concentration (µg/m 3)
and spatial variations of the PM2.5 concentrations were demonstrated. Daily fluctuations varied among sites and seasons, mainly due to diversification in local emissions, secondary reactions, regional transmissions, and meteorological conditions (such as wind speed, wind direction, and solar radiation). Variation between heavily polluted days and good days followed the cycles of quick accumulation and rapid removal processes, which was similar to previous study [20]. Daily mean PM2.5 concentrations fluctuated markedly, and heavily polluted days were several times higher than the neighboring days. Fog and haze days not only appeared in winter, but also occurred during the summer and autumn. Highly polluted days mostly occurred during stagnant weather conditions, and the good days were usually accompanied with heavy wind (Fig. 2). Thus, the rapidly raised PM2.5 concentrations were probably the result of large emission, reaction, or transmission rate comparing with small dispersion rate, which induced fast regional accumulation of PM2.5. As for annual variations, higher PM2.5 concentration in winter was probably attributable to increased emissions due to coal burning for heating, superimposed with lower vertical dispersion, which adjusted with solar radiation. Decreasing PM2.5 concentrations from south to north represented the overall spatial pattern across the study area. The pattern was simple yet steady and clear regardless of the pollution level, which confirmed the public perceptual recognition of air quality in Beijing. The spatial gradient from south to north varied with pollution levels and was much larger during highly polluted days, indicating larger difference of pollution accumulation rate among regions under such conditions (Table 2, Fig. 5). Pollutants at or surrounding the south part of Beijing were even higher than at traffic sites (Figs. 3–5). However, transmission sites in the north part retained a consistently lower level around the year, representing the closeness to regional background with much less anthropogenic emissions nearby. Behind the general spatial trend, local variation could also be observed (Fig. 5). Some sites faraway from emission sources were 10 % lower than the trend line, and some traffic sites were more than 10 % higher (Fig. 5a). This indicated that local variation of annual mean PM2.5 could be more than roughly 20 % in the urban area and that traffic emission was an important influencing factor at the intra-urban scale. However, the overall spatial trend overwhelmed the traffic effect, and we may arbitrarily deduce that restricting traffic on roads would improve air quality, but might have limited effects due to high regional pollution. This could also be partly verified by results from other studies that traffic contributed relatively small portion to total air pollution in Beijing [22, 23].
393
100 Close to Tianjin 80
60
40
20
0
0
20
40
60
80
Distance to Tian'anmen (km) Fig. 6 Plot of PM2.5 concentration to the distance of site to center of Beijing core area, Tian’anmen. Outliers in the circles were close to Hebei Province and Tianjin Municipality
123
394
National network within the 23 urban assessment sites that are close to or located in areas with people intensively living or working. These sites are adopted to assess the overall health risk to air pollution inside Beijing. Figure 5 clearly shows that PM2.5 concentrations of urban assessment sites varied largely from south to north. Therefore, the daily AQI of Beijing is used to represent the overall air condition of a certain day and could not reflect the individual conditions at a specific location. This is probably the reason for disagreement about the published AQI [9]. As is shown in Fig. 5, the transmission site in the north has the lowest PM2.5 concentration of entire Beijing, while the transmission site in the south region tended to have the highest. We could then arbitrarily infer that the difference of PM2.5 concentration between regional transmission sites at north and urban assessment sites, to some extent, indicated the maximum potential magnitude of improvements that would be achieved by environmental management practices in a predictable future. Yet, PM2.5 concentration in Beijing was generally high, with the lowest concentration of monitoring sites in the north (about 60 lg/m3) still much higher than developed countries [18, 26–33]. Further pollution reduction may occur after the launch of Beijing 2013–2017 Clean Air Action Plan.
5 Conclusions Spatiotemporal variation of PM2.5 concentration in Beijing was investigated in this study. The median of PM2.5 concentration of all the 35 sites (including 5 traffic sites) was 71.4 lg/m3, with the range of 7.7–411.7 lg/m3, and mean of 88.6 lg/m3 during the study period. The time series of PM2.5 showed a typical accumulation–removal circle, with heavy wind as an important influencing factor. PM2.5 concentration varied largely between seasons, with winter significantly higher than the other three seasons, and more distinct diurnal variation could be observed in winter and autumn. PM2.5 concentration decreased linearly from south to north, with a gradient of -0.46 lg/m3/km in average. The spatial gradient of PM2.5 concentration was small at low polluted days (excellent-good days) with the value of -0.26 lg/m3/km, but would be larger at lightly-moderately polluted and heavily-severely polluted days, with -0.52 and -0.83 lg/m3/km, respectively. PM2.5 concentrations at traffic sites which floated with different site locations, were generally 10 % higher than nearby urban assessment sites. In future study, a detailed land use regression (LUR) model including various geographic covariates can be adopted to explore the spatial distribution of PM2.5 concentration and the potential contributors in Beijing. Also, examination of the spatiotemporal evolutionary process of haze and fog days using hourly data from spatially
123
Sci. Bull. (2015) 60(3):387–395
scattered sites may partly reveal the origin and transmission of pollutants. Acknowledgments We thank Mingsi Xie from Research Laboratory for Conservation and Archaeology of Shanghai Museum who contributed instructive discussions. This study was supported by the Key Research Program of Chinese Academy of Sciences (KZZDEW-13), the Gong-Yi Program of Chinese Ministry of Environmental Protection (200909016, 201209008), the National Natural Science Foundation of China (21377127, 41201038), and the President Fund of University of Chinese Academy of Sciences (UCAS). Conflict of interest of interest.
The authors declare that they have no conflict
References 1. Cao JJ, Xu HM, Xu Q et al (2012) Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city. Environ Health Perspect 120:373–378 2. Du X, Kong Q, Ge W et al (2010) Characterization of personal exposure concentration of fine particles for adults and children exposed to high ambient concentrations in Beijing, China. J Environ Sci 22:1757–1764 3. Pope CA III, Ezzati M, Dockery DW (2009) Fine-particulate air pollution and life expectancy in the United States. N Engl J Med 360:376–386 4. Dockery DW, Stone PH (2007) Cardiovascular risks from fine particulate air pollution. N Engl J Med 356:511–513 5. Miller KA, Siscovick DS, Sheppard L et al (2007) Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med 356:447–458 6. Jerrett M, Burnett RT, Beckerman BS et al (2013) Spatial analysis of air pollution and mortality in California. Am J Respir Crit Care Med 188:593–599 7. Cao JJ, Chow JC, Lee FSC et al (2013) Evolution of PM2.5 measurements and standards in the U.S. and future perspectives for China. Aerosol Air Qual Res 13:1197–1211 8. Han X, Zhang M, Tao J et al (2013) Modeling aerosol impacts on atmospheric visibility in Beijing with RAMS-CMAQ. Atmos Environ 72:177–191 9. Wang JF, Hu MG, Xu CD et al (2013) Estimation of citywide air pollution in Beijing. PLoS One 8:e53400 10. Yang Y, Li RK, Li WJ et al (2013) The association between ambient air pollution and daily mortality in Beijing after the 2008 Olympics: a time series study. PLoS One 8:e76759 11. Zhang A, Qi QW, Jiang LL et al (2013) Population exposure to PM2.5 in the urban area of Beijing. PLoS One 8:e63486 12. Zhang FY, Krafft T, Ye BX et al (2013) The lag effects and seasonal differences of air pollutants on allergic rhinitis in Beijing. Sci Total Environ 442:172–176 13. Zhang FY, Li LP, Krafft T et al (2011) Study on the association between ambient air pollution and daily mortality of cardiovascular disease and respiratory disease in a district of Beijing. Int J Environ Res Publ Health 8:2109–2123 14. Wang SX, Zhao M, Xing J et al (2010) Quantifying the air pollutants emission reduction during the 2008 Olympic Games in Beijing. Environ Sci Technol 44:2490–2496 15. Chen DS, Cheng SY, Liu L et al (2007) An integrated MM5CMAQ modeling approach for assessing trans-boundary PM10 contribution to the host city of 2008 Olympic summer games— Beijing, China. Atmos Environ 41:1237–1250
Sci. Bull. (2015) 60(3):387–395 16. Streets DG, Fu JS, Jang CJ et al (2007) Air quality during the 2008 Beijing Olympic Games. Atmos Environ 41:480–492 17. Wang LT, Hao JM, He KB et al (2008) A modeling study of coarse particulate matter pollution in beijing: regional source contributions and control implications for the 2008 Summer Olympics. J Air Waste Manage Assoc 58:1057–1069 18. Beckerman BS, Jerrett M, Serre M et al (2013) A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States. Environ Sci Technol 47:7233–7241 19. Richardson DB, Volkow ND, Kwan MP et al (2013) Spatial turn in health research. Science 339:1390–1392 20. Xin JY, Wang YS, Tang GQ et al (2010) Variability and reduction of atmospheric pollutants in Beijing and its surrounding area during the Beijing 2008 Olympic Games. Chin Sci Bull 55:1937–1944 21. Yao Q, Li SQ, Xu HW et al (2009) Studies on formation and control of combustion particulate matter in China: a review. Energy 34:1296–1309 22. Zhang R, Jing J, Tao J et al (2013) Chemical characterization and source apportionment of PM2.5 in Beijing: seasonal perspective. Atmos Chem Phys 13:7053–7074 23. Yu L, Wang G, Zhang R et al (2013) Characterization and source apportionment of PM2.5 in an urban environment in Beijing. Aero Air Qual Res 13:574–583 24. Zhang K, Wang YS, Wen TX et al (2007) Properties of nitrate, sulfate and ammonium in typical polluted atmospheric aerosols (PM10) in Beijing. Atmos Res 84:67–77 25. Meng Z, Dabdub D, Seinfeld JH (1997) Chemical coupling between atmospheric ozone and particulate matter. Science 277:116–119
395 26. Henderson SB, Beckerman B, Jerrett M et al (2007) Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ Sci Technol 41:2422–2428 27. Moore DK, Jerrett M, Mack WJ et al (2007) A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA. J Environ Monit 9:246–252 28. Ross Z, Jerrett M, Ito K et al (2007) A land use regression for predicting fine particulate matter concentrations in the New York City region. Atmos Environ 41:2255–2269 29. Kashima S, Yorifuji T, Tsuda T et al (2009) Application of land use regression to regulatory air quality data in Japan. Sci Total Environ 407:3055–3062 30. Su JG, Jerrett M, Beckerman B et al (2009) Predicting trafficrelated air pollution in Los Angeles using a distance decay regression selection strategy. Environ Res 109:657–670 31. Hoek G, Beelen R, Kos G et al (2011) Land use regression model for ultrafine particles in Amsterdam. Environ Sci Technol 45:622–628 32. Beckerman BS, Jerrett M, Martin RV et al (2013) Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California. Atmos Environ 77:172–177 33. Sampson PD, Richards M, Szpiro AA et al (2013) A regionalized national universal kriging model using partial least squares regression for estimating annual PM2.5 concentrations in epidemiology. Atmos Environ 75:383–392
123
www.scibull.com www.springer.com/scp
Article
Earth Sciences
Diurnal, seasonal, and spatial variation of PM2.5 in Beijing Runkui Li • Zhipeng Li • Wenju Gao • Wenjun Ding • Qun Xu • Xianfeng Song
Received: 12 April 2014 / Accepted: 25 July 2014 / Published online: 30 December 2014 Ó Science China Press and Springer-Verlag Berlin Heidelberg 2014
Abstract PM2.5 pollution in Beijing has attracted extensive attention in recent years, but research on the detailed spatiotemporal characteristics of PM2.5 is critically lacking for effective pollution control. In our study, hourly PM2.5 concentration data of 35 fixed monitoring sites in Beijing were collected continuously from October 2012 to September 2013, for exploring the diurnal and seasonal characteristics of PM2.5 at traffic, urban, and background environments. Spatial trend and regional contribution of PM2.5 under different pollution levels were also investigated. Results show that the average PM2.5 concentration of all the 35 sites (including 5 traffic sites) was 88.6 lg/m3. Although PM2.5 varied largely with the site location and seasons, a clear spatial trend could be observed with the PM2.5 concentration decreasing linearly from south to north, with a gradient of -0.46 lg/m3/km for average days, -0.83 lg/m3/km for heavily–severely polluted days, -0.52 lg/m3/km at lightly–moderately polluted days, and -0.26 lg/m3/km for excellent–good days. PM2.5 at traffic R. Li Z. Li X. Song (&) College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China e-mail: [email protected] W. Gao Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo 454000, China W. Ding College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China Q. Xu Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine of Peking Union Medical College, Beijing 100005, China
sites was varied, but was generally over 10 % higher than at the nearby urban assessment sites. Keywords Fine particulate matter Spatiotemporal variation Trend Traffic site Regional transmission Beijing
1 Introduction PM2.5 is fine particles suspended in the atmosphere with a diameter less than 2.5 lm and may have damaging effects on human health, especially the cardiovascular and respiratory systems [1–6]. Due to this, various governments and international groups have launched regulations and standards to control ambient particulate concentrations [7]. Extremely polluted weather related to high PM2.5 concentrations, such as haze and fog, has frequently affected Beijing severely over the past few years [8–13], causing wide public concern. In October 2011, severe air pollution in Beijing resulted in rapid administrative measures. This included PM2.5 being added to the real-time air quality monitoring system of the Ministry of Environmental Protection of China, and integration into the Chinese National Ambient Air Quality (CNAAQ) standard. Despite initial improvements in air quality, long-lasting haze and fog occurred in January 2013. Therefore, it is necessary to comprehensively investigate the source, distribution, fluctuation pattern, and other characteristics of the PM2.5 pollution in Beijing and to provide a more reliable research basis for valid control measures. There is a complex contribution of different emission sources to PM2.5 in Beijing. Given the rapid growth in traffic during the past two decades, vehicles in Beijing reached 5.2 million by the end of 2012, while coal consumption and industrial emissions decreased at the same time [14]. On-road
123
388
traffic emission has become a main concern for public health and new pollution control targets in Beijing. Meanwhile, pollutants from neighboring regions, such as the Hebei Province and Tianjin Municipality, have contributed to the air pollution in Beijing. Although scientific research has provided some useful data, temporal variations in the proportion of PM2.5 sources have made it difficult to determine the exact composition of PM2.5 in Beijing [14–17]. In addition, the source of air pollution varies across multiple spatial scales, e.g., on local scales associated with immediate sources to larger spatial areas with secondary reactions and transport mechanisms [18]. High traffic emissions and regional changes also increase the difficulty of exploring detailed spatiotemporal patterns of PM2.5 concentration. Only with long-term monitoring across both space and time can the variations of PM2.5 in Beijing be quantitatively characterized. In this paper, to comprehensively describe the spatiotemporal characteristics of PM2.5, a large amount of PM2.5 data were collected over a 1-year time span and analyzed with the new technique of geographic information system (GIS), which greatly facilitates the understanding of PM2.5 pollution at spatial perspectives [19]. To evaluate the contribution of traffic toward pollution control in Beijing, data from traffic sites were specifically collected and compared with data from other sites. The aim of this study was to explore the spatiotemporal variation of PM2.5 concentration in Beijing through analyzing the diurnal, seasonal, and spatial patterns based on 1-year hourly collected data from 35 monitoring stations. PM2.5 pollution status and spatial trends in Beijing were investigated to provide information for exposure assessment and theoretical support for taking appropriate control strategies on PM2.5 pollution in this region.
Sci. Bull. (2015) 60(3):387–395
and at least one air quality monitoring site was contained in each district. 2.2 Data collection
2 Materials and methods
Hourly PM2.5 data from October 2012 to September 2013 for 35 fixed monitoring sites were obtained from the real-time air quality system (AQS) of Beijing Municipal Environmental Protection Bureau (BJEPB). These data were collected since the stations started to publish PM2.5 data and covered a total of more than 280,000 site-hour records. Among the 35 monitoring sites, 23 are urban environmental assessment sites, which are mainly used to assess regional environmental air quality and its overall variation; 1 is an urban background site to reflect the air quality unaffected by urban pollution; 6 are cross-region transmission sites close to Beijing municipal boundary in six directions to characterize regional background levels and monitor the transmission of pollutants between regions; and 5 are traffic sites at the edges of busy roads to monitor on-road traffic pollution on ambient air quality (Fig. 1). These sites scatter from the very south to the north end of Beijing, covering most of the spatial regions and typical land types in Beijing, e.g., from the center of most developed urban areas to faraway countryside. Six regional transmission sites are set in separate directions: Two sites in the north are called the Transmission-North in the study, two in the east and southeast near Tianjin refer to the Transmission-Southeast, and another two sites in the south and southwest near Hebei the Transmission-Southwest. Meteorological data, including daily maximum 10-min averaged wind speed and observed sunshine hours, were acquired from China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/). For the missing data in September 2013 for Beijing, we took that of Tianjin station in view of the high similarity between the two sites.
2.1 Study area
2.3 Data preparation
Beijing covers about 16,410 km2 and consists of six urban districts, eight suburban districts, and two rural counties and possessed 21.148 million people at the end of 2013, making it one of the most populous cities in the world. Mountainous at northern, northwestern, and western regions, the terrain of Beijing gets flatter toward the southeast. Plains cover most of the central urban area and the vast suburban districts in south and east directions (Fig. 1). The main urban area, on the plains in the southcenter of the municipality, spreads out in concentric ring roads with Tian’anmen roughly as its center. Beijing is surrounded almost entirely by Hebei Province except for the Tianjin Municipality, which is neighboring to the southeast. This study covered all the 16 municipal districts,
Because the hourly real-time data were probably published before official auditing, the data were checked manually. Due to equipment failure or internet error, some data were missing. Some data were also rejected due to anomalous measurements. Daily average concentration was obtained by averaging everyday hourly data from 00:00 to 23:00. According to the national standard GB 3095-2012, observation for at least 20 h is required to obtain daily average concentrations for each site to ensure the representativeness of the daily average value when missing data appear. Otherwise, data of the day was invalid and had to be excluded from this study (Table 1). The spatial pattern of pollution may vary with climate conditions and pollution levels. To investigate such effects,
123
Sci. Bull. (2015) 60(3):387–395
389
Fig. 1 Spatial distribution of the 35 PM2.5 monitoring sites with different purposes in Beijing Table 1 Overall description for daily mean PM2.5 concentration of 35 sites Pollution level (lg/m3)
Days
Median (lg/m3)
Range (lg/m3)
Low (\75)
175
43.7
7.7–74.6
Medium (75–150)
106
104.5
75.1–149.9
53
202.1
150.3–411.7
334
71.4
7.7–411.7
High ([150) All period
experiments were conducted repeatedly for three pollution levels. This was implemented by the following four steps: (1) For each day, daily concentration of all sites was averaged to get an overall mean concentration for the whole study area and used for pollution-level classification. (2) The overall mean concentration was labeled as high, medium, and low pollution level when PM2.5 concentration were [150, 75–150, and \75 lg/m3, respectively. These levels correspond to heavily–severely polluted, lightly– moderately polluted, and excellent–good days according to the latest Air Quality Index (AQI) in China. (3) Based on the overall daily mean concentration and the above
classification criteria, days falling into each pollution level were labeled and grouped. (4) For days in a specific pollution level, the daily concentration was averaged to get the mean concentration of a given site under that level. As a result, the mean concentration of each site during high, medium, and low pollution condition was derived. PNi Ck Mi ¼ k¼1 i ; ð1Þ Ni where Mi is mean concentration of one of the 35 sites under pollution level i (i = 1, 2, and 3, which corresponds to three levels of air pollution) (lg/m3); Ni is the number of days under pollution level i; Cki is the daily concentration of the given site on day k (k = 1 to Ni, which corresponds to the days under pollution level i) in pollution level i (lg/m3).
2.4 Statistical methods A simple linear regression method was adopted for spatial trend detection. To examine the north–south trend, latitude
123
390
Sci. Bull. (2015) 60(3):387–395
Wind speed (m/s)
0 15
9 6 3
350 300 250
200 150 100
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
2013 Jan
0
Dec
50
Nov
Daily mean PM2.5 concentration (µg/m3)
400
ð3Þ
Urban assessment
210
Traffic site Transmission-North
180
Transmission-Southeast 150
Transmission-Southwest
120 90
Sep
Aug
Jul
Jun
May
Apr
30
Mar
60
Feb
Daily mean PM2.5 concentration rose and dropped rapidly, with sharp peaks and deep valleys appearing alternately (Fig. 2). Peak values tended to be many times higher than that of the neighboring valleys. Very good consistency could be observed that days with more sunshine hours (a clear sky) generally appeared during or shortly after a strong wind, and PM2.5 concentrations were generally lower in accordance with these weather conditions (Fig. 2). PM2.5 concentration rose rapidly after a windy day and thereafter would reduce the sunshine hours, which in turn decreased dispersion and accelerated accumulation of pollutants. Figure 2 shows that heavy PM2.5 pollution usually occurred under stagnant weather conditions, with little or no sunshine together with gentle wind. Thus, meteorological conditions were one of the key influencing factors on variation of Beijing PM2.5 concentration during the study period. It could also be seen that PM2.5 concentration in April, August, and November was relatively lower, while that in January and June was significantly higher (Figs. 2, 3). Heavy pollution in winter was probably caused by
Urban background
2013 Jan
3.1 Temporal variation of PM2.5
240
Dec
3 Results
Fig. 2 Daily mean PM2.5 concentration from October 2012 to September 2013 and its relationship with 10-min maximum wind speed and daily sunshine hours
Nov
where Ymin is Y coordinate of the extremely south boundary of Beijing (m).
123
12
0 450
Monthly mean PM2.5 concentration (µg/m3)
DFS ¼ Y Ymin ;
6 3
3
where Mi is mean concentration of pollution level i (lg/m ); Y is coordinate at south–north direction of the monitoring sites (m); a and b are the coefficients. For computational convenience and physical clarity, distance to the extreme south boundary line of Beijing [hereinafter, called ‘‘Distance from South’’ (DFS)] was used as a surrogate of the original Y coordinate as shown in Eq. (3). As a result, mean PM2.5 concentration from 35 monitoring sites was regressed with their DFS for each pollution level.
9
2012 Oct
ð2Þ
12
2012 Oct
Mi ¼ aY þ b;
15
Sunshine hours (h)
of the monitoring sites (with degree unit) under the latitude–longitude coordinate system indicating relative north and south positions was derived and used as a predictor variable. For convenience, geographic coordinates of all the 35 sites were projected onto a rectangular coordinate system, and latitude was then transferred to a projected geographic Y coordinate with meter units, and mean PM2.5 concentration of each pollution level, as derived from Eq. (1), was used as response variable to explore the spatial trend from north to south.
Fig. 3 Monthly mean PM2.5 concentration of different types of sites
increased emissions (such as coal burning for heating), combined with low vertical dispersion due to reduced solar radiation. Lower PM2.5 concentrations in April could be attributed to strong wind and cessation of winter heating. Relatively low concentrations in autumn would be the result of the air clearing effect of rain and strong dispersion.
Sci. Bull. (2015) 60(3):387–395
140 120
100 80 60 40
Spring
Summer
Autumn
Winter
22:00
20:00
18:00
16:00
14:00
12:00
10:00
08:00
06:00
04:00
02:00
0
00:00
20
140 120 100 80 60
22:00
20:00
18:00
16:00
Urban assessment Traffic sites Urban background
14:00
12:00
10:00
08:00
0
06:00
20
04:00
Transmission-Southwest Transmission-Southeast Transmission-North
40
02:00
(b)
160
00:00
Mean PM2.5 concentration (µg/m3)
(a)
Mean PM2.5 concentration (µg/m3)
PM2.5 concentration differences among sites were significant and also varied with seasons (Fig. 3). Traffic sites were always higher than urban assessment sites, while the urban background was much lower. This fact verified our understanding that sites closer to intensive emission sources would have higher values, and vice versa. Transmission sites at different directions varied more distinctly. For example, transmission sites in the southwest (close to Hebei) and southeast (close to Tianjin) were much higher than those in the north (close to the mountains). This could be explained by the regional characteristics that sites at central and south urbanized plain areas would probably be more polluted than sites in the northern mountainous area that were less affected by human activities [20]. Concentrations in the southwest was generally higher than in the southeast from October 2012 to June 2013, yet was lower from July to September 2013. This would be caused by the change of pollution sources and wind directions during the summer and requires further investigation. The classified pollution level based on daily mean concentration of all sites is shown in Table 1. Median PM2.5 concentration of the 35 sites during the study period is 71.4 lg/m3 (with 88.6 lg/m3 for the mean), and median concentration during low, medium, and high pollution level is 43.7, 104.5, and 202.1 lg/m3, respectively. Although most days of the study were under low and medium pollution level, highly polluted days also occupied a considerable part of the period, to which special attention should be given. Because all sites (including traffic sites) were included in this study, the classification of low, medium, and high pollution level of a day here would probably be different from the AQI published by BJEPB, which is based on only part of sites in this study.
391
Fig. 4 Diurnal variation of PM2.5 concentration in different seasons and among different types of site. a Seasonal variation of PM2.5 concentration of 35 sites for Spring (March–May), Summer (June– August), Autumn (September–November), and Winter (December– February), b spatial variation across 24 h among urban assessment sites, traffic sites, urban background site, and transmission sites in southwest, southeast, and north directions
3.2 Diurnal variation among seasons and sites Diurnal characteristics of PM2.5 concentration varied with seasons and sites (Fig. 4). The overall PM2.5 concentration had significantly higher values in winter than the other three seasons (Fig. 4a). Spring was slightly but consistently higher than summer, while autumn showed stronger diurnal fluctuations with lower values during daytime and higher values at night. The seasonal variations were probably caused by changes in meteorological conditions and sources of particulate matter. For example, dry climate and heavy wind in spring generated more soil dust, while wet and hot summer enhanced photochemistry and generated more secondary pollution, and cold winter induced more primary pollution from coal burning for heating [21–25]. Diurnal variation was weak in spring and summer, but was much stronger in autumn and winter (Fig. 4a). The
daily variation in autumn and winter seemed like a flat ‘‘W’’ shape, with lowest values generally appearing at 06:00–07:00 or 14:00–16:00. Diurnal concentrations varied largely among sites (Fig. 4b). Transmission sites in the north had the lowest values, while sites in the southwest had the highest values. The concentrations at southwest sites were nearly double that of the north sites, which revealed the remarkable spatial variation from south to north. Traffic sites were generally higher than urban assessment sites. Concentration at most sites rose from 06:00 until 11:00 to reach the peak and then decreased reaching the lowest point of a day at around 16:00, except for urban background sites and transmission sites in the north. Concentration at urban background sites rose steadily during daytime and showed a distinct pattern.
123
392
Sci. Bull. (2015) 60(3):387–395
3.3 The spatial trend
4 Discussion This study presented the overall spatiotemporal characteristics of PM2.5 in Beijing based on the latest hourly data from 35 sites including 5 traffic sites. Diurnal, seasonal, Table 2 Regression of PM2.5 concentration to distance from south (DFS) of Beijing under different pollution levels Pollution levels Low Medium High
(a) 150
(b) 300 PM2.5 concentration (µg/m 3)
All days
PM2.5 concentration (µg/m3)
To investigate the general spatial trend across the study area, regression analysis of PM2.5 concentration with distance from the site to the south was conducted for each pollution level (Table 2, Fig. 5). Analysis was conducted with and without traffic sites separately. Traffic sites were included to demonstrate the effect of local automobile emission sources on the overall pollution pattern. PM2.5 concentration showed a very good relationship with distance from south, with R2 larger than 0.70 when all 35 sites were used, and larger than 0.80 without traffic sites (Table 2). Relative ranking of R2 for different pollution levels was All days (R2 = 0.84) [ Medium (R2 = 0.83) [ Low (R2 = 0.77) [ High (R2 = 0.73) for all 35 sites. The relative rank of R2 kept the same when traffic sites were excluded, and an improved relationship was obtained with R2 = 0.89 for All days and R2 = 0.80 for High pollution level. A clear spatial trend could be found with the PM2.5 concentration decreasing steadily from the south to north of Beijing (Fig. 5). Without traffic sites, the gradient was about 0.46 lg/m3/km for all of the study period, 0.83 lg/m3/km for highly polluted days, 0.52 lg/m3/km at medium pollution level, and 0.26 lg/m3/km at low pollution levels (Table 2).
Therefore, gradual change of the PM2.5 concentration from south to north of Beijing, with the distance exceeding 100 km, caused significant regional differences.
120
90
60
PM2.5 = – 0.4741DSF + 116.65 R 2 = 0.84
30
Urban background
Regional transmission
Urban assessment
Traffic pollution
Regression function
Number of sites
PM2.5 = -0.2685DFS ? 58.13
35
0.77
PM2.5 = -0.2588DFS ? 56.94
30
0.81
PM2.5 = -0.5468DFS ? 138.66 PM2.5 = -0.5237DFS ? 135.87
35 30
0.83 0.88
PM2.5 = -0.8736DFS ? 258.26
35
0.73
PM2.5 = -0.8336DFS ? 252.99
30
0.80
PM2.5 = -0.4741DFS ? 116.65
35
0.84
PM2.5 = -0.4553DFS ? 114.31
30
0.89
240
180
120
PM2.5 = –0.8736DSF + 258.26 R 2 = 0.74
60
Urban background
Regional transmission
Urban assessment
Traffic pollution
0
0 0
20
40
60
80
100
0
120
20
(d) PM2.5 concentration (µg/m3)
(c) 150 120
90
60
PM2.5 = –0.5468DSF + 138.66 R 2 = 0.83
30
40
60
80
100
120
Distance from south (km)
Distance from south (km)
PM2.5 concentration (µg/m3)
R2
Urban background
Regional transmission
Urban assessment
Traffic pollution
70 60 50 40 30 PM2.5 = – 0.2685DSF + 58.13 R 2 = 0.77
20 10
Urban background
Regional transmission
Urban assessment
Traffic pollution
0
0 0
20
40
60
80
Distance from south (km)
100
120
0
20
40
60
80
100
120
Distance from south (km)
Fig. 5 Regression plot of PM2.5 concentration to distance from south (DFS) of Beijing of 35 sites, with labeled site types and ±10 % enveloping area of the trend line. a Data of entire research period, b high polluted days, c medium polluted days, d low polluted days
123
Sci. Bull. (2015) 60(3):387–395
PM2.5 concentrations at Transmission sites in the north were even lower than urban background sites (Fig. 3–5). This could be explained by the spatial trend shown in Fig. 5. The urban background site was closer to the central urban area and also closer to the south of Beijing, therefore was closer to emission sources or more influenced by pollution transported from the south. The concentrations of regional transmission sites in the south were also higher than the central urban area, especially during periods with low pollution levels (Fig. 5d). Therefore, except for traffic emissions, there should be extra emissions generated from the south suburban area or transported from the neighboring Hebei Province in the south or Tianjin Municipality in the southeast. This could also be verified by the inconsistent behavior of sites neighboring Hebei and Tianjin with the inner sites that far from the southeastern or southwestern boundary (Fig. 6). PM2.5 concentrations generally decreased when distance to the core urban area (taking Tian’anmen as the center) increased, except for the abnormal sites located close to Hebei and Tianjin, which were obvious outliers. The outliers had much higher concentrations and partly indicated a large amount of cross-boundary transmission from outside. Previous studies have also shown that contribution from the outside would be significant [15–17]. However, the quantitative contribution of each part, e.g., locally generated and transmission across large spatial scales, had not been clearly separated here in this study. Another issue requiring explanation is the daily AQI of Beijing. AQI is usually published based on 11 sites in the
120 Close to Hebei
Mean PM2.5 concentration (µg/m 3)
and spatial variations of the PM2.5 concentrations were demonstrated. Daily fluctuations varied among sites and seasons, mainly due to diversification in local emissions, secondary reactions, regional transmissions, and meteorological conditions (such as wind speed, wind direction, and solar radiation). Variation between heavily polluted days and good days followed the cycles of quick accumulation and rapid removal processes, which was similar to previous study [20]. Daily mean PM2.5 concentrations fluctuated markedly, and heavily polluted days were several times higher than the neighboring days. Fog and haze days not only appeared in winter, but also occurred during the summer and autumn. Highly polluted days mostly occurred during stagnant weather conditions, and the good days were usually accompanied with heavy wind (Fig. 2). Thus, the rapidly raised PM2.5 concentrations were probably the result of large emission, reaction, or transmission rate comparing with small dispersion rate, which induced fast regional accumulation of PM2.5. As for annual variations, higher PM2.5 concentration in winter was probably attributable to increased emissions due to coal burning for heating, superimposed with lower vertical dispersion, which adjusted with solar radiation. Decreasing PM2.5 concentrations from south to north represented the overall spatial pattern across the study area. The pattern was simple yet steady and clear regardless of the pollution level, which confirmed the public perceptual recognition of air quality in Beijing. The spatial gradient from south to north varied with pollution levels and was much larger during highly polluted days, indicating larger difference of pollution accumulation rate among regions under such conditions (Table 2, Fig. 5). Pollutants at or surrounding the south part of Beijing were even higher than at traffic sites (Figs. 3–5). However, transmission sites in the north part retained a consistently lower level around the year, representing the closeness to regional background with much less anthropogenic emissions nearby. Behind the general spatial trend, local variation could also be observed (Fig. 5). Some sites faraway from emission sources were 10 % lower than the trend line, and some traffic sites were more than 10 % higher (Fig. 5a). This indicated that local variation of annual mean PM2.5 could be more than roughly 20 % in the urban area and that traffic emission was an important influencing factor at the intra-urban scale. However, the overall spatial trend overwhelmed the traffic effect, and we may arbitrarily deduce that restricting traffic on roads would improve air quality, but might have limited effects due to high regional pollution. This could also be partly verified by results from other studies that traffic contributed relatively small portion to total air pollution in Beijing [22, 23].
393
100 Close to Tianjin 80
60
40
20
0
0
20
40
60
80
Distance to Tian'anmen (km) Fig. 6 Plot of PM2.5 concentration to the distance of site to center of Beijing core area, Tian’anmen. Outliers in the circles were close to Hebei Province and Tianjin Municipality
123
394
National network within the 23 urban assessment sites that are close to or located in areas with people intensively living or working. These sites are adopted to assess the overall health risk to air pollution inside Beijing. Figure 5 clearly shows that PM2.5 concentrations of urban assessment sites varied largely from south to north. Therefore, the daily AQI of Beijing is used to represent the overall air condition of a certain day and could not reflect the individual conditions at a specific location. This is probably the reason for disagreement about the published AQI [9]. As is shown in Fig. 5, the transmission site in the north has the lowest PM2.5 concentration of entire Beijing, while the transmission site in the south region tended to have the highest. We could then arbitrarily infer that the difference of PM2.5 concentration between regional transmission sites at north and urban assessment sites, to some extent, indicated the maximum potential magnitude of improvements that would be achieved by environmental management practices in a predictable future. Yet, PM2.5 concentration in Beijing was generally high, with the lowest concentration of monitoring sites in the north (about 60 lg/m3) still much higher than developed countries [18, 26–33]. Further pollution reduction may occur after the launch of Beijing 2013–2017 Clean Air Action Plan.
5 Conclusions Spatiotemporal variation of PM2.5 concentration in Beijing was investigated in this study. The median of PM2.5 concentration of all the 35 sites (including 5 traffic sites) was 71.4 lg/m3, with the range of 7.7–411.7 lg/m3, and mean of 88.6 lg/m3 during the study period. The time series of PM2.5 showed a typical accumulation–removal circle, with heavy wind as an important influencing factor. PM2.5 concentration varied largely between seasons, with winter significantly higher than the other three seasons, and more distinct diurnal variation could be observed in winter and autumn. PM2.5 concentration decreased linearly from south to north, with a gradient of -0.46 lg/m3/km in average. The spatial gradient of PM2.5 concentration was small at low polluted days (excellent-good days) with the value of -0.26 lg/m3/km, but would be larger at lightly-moderately polluted and heavily-severely polluted days, with -0.52 and -0.83 lg/m3/km, respectively. PM2.5 concentrations at traffic sites which floated with different site locations, were generally 10 % higher than nearby urban assessment sites. In future study, a detailed land use regression (LUR) model including various geographic covariates can be adopted to explore the spatial distribution of PM2.5 concentration and the potential contributors in Beijing. Also, examination of the spatiotemporal evolutionary process of haze and fog days using hourly data from spatially
123
Sci. Bull. (2015) 60(3):387–395
scattered sites may partly reveal the origin and transmission of pollutants. Acknowledgments We thank Mingsi Xie from Research Laboratory for Conservation and Archaeology of Shanghai Museum who contributed instructive discussions. This study was supported by the Key Research Program of Chinese Academy of Sciences (KZZDEW-13), the Gong-Yi Program of Chinese Ministry of Environmental Protection (200909016, 201209008), the National Natural Science Foundation of China (21377127, 41201038), and the President Fund of University of Chinese Academy of Sciences (UCAS). Conflict of interest of interest.
The authors declare that they have no conflict
References 1. Cao JJ, Xu HM, Xu Q et al (2012) Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city. Environ Health Perspect 120:373–378 2. Du X, Kong Q, Ge W et al (2010) Characterization of personal exposure concentration of fine particles for adults and children exposed to high ambient concentrations in Beijing, China. J Environ Sci 22:1757–1764 3. Pope CA III, Ezzati M, Dockery DW (2009) Fine-particulate air pollution and life expectancy in the United States. N Engl J Med 360:376–386 4. Dockery DW, Stone PH (2007) Cardiovascular risks from fine particulate air pollution. N Engl J Med 356:511–513 5. Miller KA, Siscovick DS, Sheppard L et al (2007) Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med 356:447–458 6. Jerrett M, Burnett RT, Beckerman BS et al (2013) Spatial analysis of air pollution and mortality in California. Am J Respir Crit Care Med 188:593–599 7. Cao JJ, Chow JC, Lee FSC et al (2013) Evolution of PM2.5 measurements and standards in the U.S. and future perspectives for China. Aerosol Air Qual Res 13:1197–1211 8. Han X, Zhang M, Tao J et al (2013) Modeling aerosol impacts on atmospheric visibility in Beijing with RAMS-CMAQ. Atmos Environ 72:177–191 9. Wang JF, Hu MG, Xu CD et al (2013) Estimation of citywide air pollution in Beijing. PLoS One 8:e53400 10. Yang Y, Li RK, Li WJ et al (2013) The association between ambient air pollution and daily mortality in Beijing after the 2008 Olympics: a time series study. PLoS One 8:e76759 11. Zhang A, Qi QW, Jiang LL et al (2013) Population exposure to PM2.5 in the urban area of Beijing. PLoS One 8:e63486 12. Zhang FY, Krafft T, Ye BX et al (2013) The lag effects and seasonal differences of air pollutants on allergic rhinitis in Beijing. Sci Total Environ 442:172–176 13. Zhang FY, Li LP, Krafft T et al (2011) Study on the association between ambient air pollution and daily mortality of cardiovascular disease and respiratory disease in a district of Beijing. Int J Environ Res Publ Health 8:2109–2123 14. Wang SX, Zhao M, Xing J et al (2010) Quantifying the air pollutants emission reduction during the 2008 Olympic Games in Beijing. Environ Sci Technol 44:2490–2496 15. Chen DS, Cheng SY, Liu L et al (2007) An integrated MM5CMAQ modeling approach for assessing trans-boundary PM10 contribution to the host city of 2008 Olympic summer games— Beijing, China. Atmos Environ 41:1237–1250
Sci. Bull. (2015) 60(3):387–395 16. Streets DG, Fu JS, Jang CJ et al (2007) Air quality during the 2008 Beijing Olympic Games. Atmos Environ 41:480–492 17. Wang LT, Hao JM, He KB et al (2008) A modeling study of coarse particulate matter pollution in beijing: regional source contributions and control implications for the 2008 Summer Olympics. J Air Waste Manage Assoc 58:1057–1069 18. Beckerman BS, Jerrett M, Serre M et al (2013) A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States. Environ Sci Technol 47:7233–7241 19. Richardson DB, Volkow ND, Kwan MP et al (2013) Spatial turn in health research. Science 339:1390–1392 20. Xin JY, Wang YS, Tang GQ et al (2010) Variability and reduction of atmospheric pollutants in Beijing and its surrounding area during the Beijing 2008 Olympic Games. Chin Sci Bull 55:1937–1944 21. Yao Q, Li SQ, Xu HW et al (2009) Studies on formation and control of combustion particulate matter in China: a review. Energy 34:1296–1309 22. Zhang R, Jing J, Tao J et al (2013) Chemical characterization and source apportionment of PM2.5 in Beijing: seasonal perspective. Atmos Chem Phys 13:7053–7074 23. Yu L, Wang G, Zhang R et al (2013) Characterization and source apportionment of PM2.5 in an urban environment in Beijing. Aero Air Qual Res 13:574–583 24. Zhang K, Wang YS, Wen TX et al (2007) Properties of nitrate, sulfate and ammonium in typical polluted atmospheric aerosols (PM10) in Beijing. Atmos Res 84:67–77 25. Meng Z, Dabdub D, Seinfeld JH (1997) Chemical coupling between atmospheric ozone and particulate matter. Science 277:116–119
395 26. Henderson SB, Beckerman B, Jerrett M et al (2007) Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ Sci Technol 41:2422–2428 27. Moore DK, Jerrett M, Mack WJ et al (2007) A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA. J Environ Monit 9:246–252 28. Ross Z, Jerrett M, Ito K et al (2007) A land use regression for predicting fine particulate matter concentrations in the New York City region. Atmos Environ 41:2255–2269 29. Kashima S, Yorifuji T, Tsuda T et al (2009) Application of land use regression to regulatory air quality data in Japan. Sci Total Environ 407:3055–3062 30. Su JG, Jerrett M, Beckerman B et al (2009) Predicting trafficrelated air pollution in Los Angeles using a distance decay regression selection strategy. Environ Res 109:657–670 31. Hoek G, Beelen R, Kos G et al (2011) Land use regression model for ultrafine particles in Amsterdam. Environ Sci Technol 45:622–628 32. Beckerman BS, Jerrett M, Martin RV et al (2013) Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California. Atmos Environ 77:172–177 33. Sampson PD, Richards M, Szpiro AA et al (2013) A regionalized national universal kriging model using partial least squares regression for estimating annual PM2.5 concentrations in epidemiology. Atmos Environ 75:383–392
123