Relationship between atmospheric pollution processes and synoptic pressure patterns in northern China

ARTICLE IN PRESS Atmospheric Environment 42 (2008) 6078– 6087

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Relationship between atmospheric pollution processes and synoptic pressure patterns in northern China Z.H. Chen a, S.Y. Cheng a,, J.B. Li b,,1, X.R. Guo a, W.H. Wang a, D.S. Chen a a b

College of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100022, China Environmental Engineering Program, University of Northern British Columbia, Prince George, British Columbia, Canada V2N 4Z9

a r t i c l e i n f o

abstract

Article history: Received 5 December 2007 Received in revised form 18 March 2008 Accepted 19 March 2008

The air pollution index (API) sequences in 10 cities in northern China and the synoptic pressure patterns during autumn and winter from 2002 to 2006 were analyzed with diagnostic and statistical methods. The results showed that the air qualities in northern China had a prominent correlation with the pressure systems. It revealed that (a) the increasing phase of API was associated with high pressure and the successive low pressure, (b) the preceding part of front (i.e. the retral part of low pressure) was associated with the maximum of API values during a cycle of air pollution process, (c) the pressure systems with high gradient led to the decrease of API, and (d) the synoptic pressure patterns and their evolvements were the main causes of regional air pollution processes. These relations can be used to analyze the variation characteristics and mechanism of regional atmosphere pollution process, and provide important basis for the qualitative prediction, control, and management of regional air pollution problems. & 2008 Published by Elsevier Ltd.

Keywords: Air pollution index (API) Air pollution process Northern China Synoptic pressure patterns

1. Introduction Air pollution is one of the major urban environmental problems in many Chinese cities as a consequence of industrialization and rapid urban growth. The ambient concentrations of pollutants such as total suspended particulates and sulfur dioxide (SO2) are quite frequently exceeding the recognized air quality standards. Particularly, the urban air pollution is much worse in northern China where coal is used to heat homes/buildings for several months of the winter season and where the industrial development heavily depends on coal combustion. Since the elevated urban air pollution levels would pose significant threats to human health and the environment (Kan and Chen, 2004), the accurate prediction of air quality in a region is of critical importance (Cheng et al.,

 Corresponding authors. Tel.: +86 10 67392176; fax: +86 10 67391983.

E-mail addresses: [email protected] (S.Y. Cheng), [email protected] (J.B. Li). 1 Tel.: +1 250 9606397; fax: +1 250 9605845. 1352-2310/$ - see front matter & 2008 Published by Elsevier Ltd. doi:10.1016/j.atmosenv.2008.03.043

2007). Generally, the air pollution over a region depends on both the pollutant emission quantity and the weather situations. In fact, the weather condition can help concentrate or disperse pollutants in a region or transport pollutants to regions distant from the emission sources (Davis and Kalkstein, 1990; Greene et al., 1999; Cheng et al., 2007, 2007a, b). A number of weather elements may influence the variations of air pollution levels. For example, the pollutant dispersion can be related to wind speed and direction, while the chemical reaction of air pollutants can be affected by the temperature and humidity conditions (Greene et al., 1999). As a result, the development of predictive relationship between air pollution levels and weather conditions is valuable for developing effective regional air quality management strategy and minimize the adverse impacts of air pollution. The relationships between the variations of atmospheric pollutant concentrations and weather conditions have received increasing research interests during the past years (McGregor and Bamzelis, 1995). Most of the previous investigations focused on using individual weather variable or numerical dispersion modeling

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methods, such as using the phenomena of dew, frost, fog, and smog as indicators of predicting pollution episodes (Cheng and Lam, 2000). Research studies were also conducted to consider multiple weather variables. For example, by comparing six meteorological elements with their past scenarios, Lam and Cheng (1998) developed an automated synoptic climatological procedure to forecast high air pollution concentrations in the most polluted synoptic categories in Hong Kong; Tanner and Law (2002) investigated the relationships between the occurrence frequency of high-level pollution days and the meteorological variables such as wind speed, wind direction, relative humidity, temperature, and solar radiation intensity. Generally, the relationship between air pollution levels and meteorological conditions in a region is of complexity. Liu et al. (1994) found that no clear linear relationship existed between the meteorological components (i.e. wind speed, ultraviolet radiation, or temperature) and pollution levels (i.e. NOX and NMHC). The complexity of the interactions between meteorological conditions and ambient air quality can be further upset in urban areas due to the modified aerodynamic roughness and urban heat island effect as well as the variation of emission sources. Such complexity often poses difficulties to establish direct links between weather parameters and air pollutant concentrations. The synoptic climatological approaches have been applied for addressing the related difficulties (Heidorn and Yap, 1986; Greene et al., 1999; Tanner and Law, 2002; Tsunematsu et al., 2005; Cheng et al., 2007, 2007a, b). Synoptic events represent holistic units of atmospheric conditions, which commonly occur at a given locale, and possess specific weather and pollution characteristics. The synoptic climatology analysis usually involves summarizing a complex series of weather conditions into a catalog containing a small number of typical modes of atmospheric circulation (Barry and Perry, 1973; Yarnal, 1993; Leighton and Spark, 1997; Tanner and Law, 2002). Previous studies have shown that there is a substantial difference in atmospheric pollution loading capacities under different synoptic patterns and different regional conditions (Greene et al., 1999). The advantage of synoptic approach for studying air pollution is that the analysis is not merely considering variations in individual weather elements (or a group of selected weather elements), but also examining the totality of the complex weather. Recent studies have shown that regional orography and synoptic patterns greatly affect the atmospheric pollutant concentrations. When the orography of certain locations is unfavorable for pollutant dispersion, the specific synoptic patterns in these locations would lead to serious air pollution episodes. For example, Liu et al. (1994) found that two synoptic patterns, an approaching cold front and a high-pressure system emanating from mainland China, were responsible for high ozone levels in Taipei. In a similar study, Cheng et al. (2001) observed that high ozone levels occurred with two types of synoptic patterns including a continental anticyclone and a tropical lowpressure system moving northwards closer to Taiwan. Some other studies have indicated that high ozone concentrations often occurred with slow-moving anti-

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cyclones (Hanna, 1991; Xu et al., 1997). The research by Eshel and Bernstein (2006) indicated that when a highpressure system was centered over south-eastern Canada, a higher ground-level ozone concentration was observed in Illinois of USA which is in the south-west of this pressure system. Ulas et al. (2006) found that high levels of ozone concentrations were observed mostly under anticyclonic conditions with relatively low wind speeds, and the low wind speeds played a major role in the increase of pollution levels in a certain region. The synoptic approaches were also used to study the atmospheric CO pollution issues. For example, Shahgedanova et al. (1998) showed that high CO concentrations were mainly associated with anticyclonic conditions in winter, while the anticyclonic conditions in spring, summer and autumn have also been identified as the causes of producing high air pollutant concentrations. The synoptic patterns during each year can be highly variable, but the research conducted in Sydney by Leighton and Spark (1997) indicated that the synoptic situations more favorable for air pollution can be expected to re-occur in the future. The weather types favoring air pollution can be attributed to the presence of many symptoms, such as anticyclonic and windy situations, low relative humidity, unstable condition, as well as cool and damp situations (Karine, 2001). The relationship between weather phenomena and atmospheric particulate matter (PM) concentrations has also been examined previously (Spieksma and Tonkelaar, 1986; Triantafyllou, 2001; Wai and Tanner, 2005). In addition, there have been previous attempts to incorporate synoptic climatological methodology within the analysis of pollution episodes. For example, a synoptic classification has been developed for Wilmington, Delaware and used to analyze changes in atmospheric SO2 concentrations (Kalkstein and Corrigan, 1986). The study found that the highest SO2 concentration were shown to be related to slow-moving anticyclones and transitions from continental polar to maritime categories, while the low SO2 levels were associated with synoptic situations of southerly winds and precipitation as well as strong cold front passages (Kalkstein and Corrigan, 1986). The previous studies indicated that there exists a predictive relationship between regional air pollution levels and weather conditions, and the investigation of synoptic patterns would provide a valuable tool to help identify the mechanisms of air pollution problems. Although many research studies have been conducted on examining the impact of synoptic patterns on the regional air pollution, few attempts have been made to reveal the relationships between synoptic pressure patterns and the evolutionary process of air pollution within an entire region. This paper will focus on examining this relationship through examination of the air pollution issues in 10 major cities in northern China. Northern China is a region associated with serious air pollution problems, and the weather situations have great impacts on the evolution process of air pollution. The convergence system of air pollution in boundary layer above northern China has been previously studied (Su et al., 2004a, b), and the results showed that the air pollutant transport convergence was a major cause of forming a serious pollution

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China Plain, Beijing is topographically characterized as a semi-basin region surrounded by TaiHang Mountains on its southwest and YanShan Mountains on its northwest; Baotou lies in the western region of Inner Mongolia, and is surrounded by Yin Mountains; Xianyang is located in the center of Guanzhong Plain, and its north is surrounded by Ziwuling Mountains; Lanzhou is surrounded by Qilian Ranges at its south and north, and the Yellow River flows through the city from west to east. These cities possess unique atmospheric environmental characteristics since the large-scale synoptic patterns (such as high pressure, low pressure, and front) often control this region and cover all of these cities. Due to influence of local orography and synoptic systems, the evolution of air pollution episodes in the study region occurs frequently.

region. The transport convergence was focused on various short and shallow wind belts of the boundary layer in autumn and winter (such as forming local valley wind and the heat island effects). The thermal and dynamic circulation of the low-pressure system under the background of weak pressure and average pressure fields were found to be the major causes of forming the transport convergence, and the phenomenon of regional synchronous air pollution in the entire northern China plain was affected by persistently steady zephyr at high altitude and various steady synoptic patterns at the low altitude (Su et al., 2004a, b). Thus, the application of synoptic patterns approach in this study for investigating the regional air pollution problems in northern China is of great importance. Particularly, the evaluation of the impacts of synoptic pressure patterns on regional air pollution would provide important scientific basis for developing regional air quality management strategies.

2.2. Analysis of air pollution index (API) data API is a quantitative measure of describing air pollution levels in China, and it converts air pollution data from several types of pollutants (including PM10, SO2, and NO2) into a single value ranging from 0 to 500 (Yu, 2000). The API value in the range of 0–50 indicates excellent air quality (i.e. grade 1), 51–100 good air quality (i.e. grade 2), 101–150 slightly polluted (i.e. grade 3A), 151–200 lightly polluted (i.e. grade 3B), 201–250 moderately polluted (i.e. grade 4A), 251–300 moderate-heavily polluted (i.e. grade 4B), and o300 heavily polluted (i.e. grade 5). Table 1 lists the break-point air pollutant concentrations corresponding to different API index values (SEPA, 1996). The API subindex value of each pollutant (Ij) in a city is firstly calculated using Eq. (1), and the maximum sub-index value among all the pollutants is then selected as the API of the city as described by Eq. (2):

2. Data investigations 2.1. Description of study region The impact of synoptic pressure patterns on regional air pollution problems is studied for10 major cities in northern China, including Beijing, Datong, Baotou, Shijiazhuang, Taiyuan, Linfen, Luoyang, Xianyang, Lanzhou, and Changzhi. These cities are selected because of their generally complete datasets of air pollution. All of the study cities are located in an easily polluted region (latitude: 32–421N, longitude: 102–1181E) (Fig. 1). For example, Datong is located in the north of Shanxi province, close to Sanggan River region; Taiyuan and Linfen are located in the south of Shanxi province, nearby the Fen River basin that is between Taihang Mountains and Luliang Mountains, easily facilitating the convergence of air pollutants; Shijiazhuang, Luoyang, and Changzhi are located in the piedmont which is the convergence location of air pollutants; located in the northern part of North

102° 

104° 

106° 

108° 

41°Ν

Ij ¼

Ij;high  Ij;low ðC  BPj;low Þ þ Ij;low BPj;high  BPj;low j

API ¼ maxðI1 ; I2 ; . . . ; In Þ

110° 

112° 

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datong beijing

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taiyuan

shijiazhuang

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linfen changzhi

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luoyang

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(2)

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baotou

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106° 

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Fig. 1. The study cities in northern China region.

116° 

(1)

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Table 1 Break-point air pollutant concentrations corresponding to API index values API

50 100 200 300 400 500

Pollutant concentrations (daily average) (mg m3) SO2

NO2

PM10

0.050 0.150 0.800 1.600 2.100 2.620

0.080 0.120 0.280 0.565 0.750 0.940

0.050 0.150 0.350 0.420 0.500 0.600

where C j is the mean daily average concentration of pollutant j in the city (which is calculated as the average of concentrations measured at all the monitoring sites in the city); BPj, low and BPj, high represent the breakpoint concentrations of pollutant j at the lower and upper limits (most approaching to the value of C j ) of the API category listed in Table 1, respectively; Ij, low and Ij,high are the index values at the lower and upper limits of that API category, respectively. For example, if the mean daily average concentration of PM10 in a city is 0.30 mg m3, then according to Table 1, its BPlow ¼ 0.15 mg m3, BPhigh ¼ 0.35 mg m3, Ilow ¼ 100, and Ihigh ¼ 200. Thus, the API sub-index value of PM10 is calculated as 175 according to Eq. (1). The API data of the study region were downloaded from the website of State Environmental Protection Administration of China. A synoptic approach, which combines a number of synoptic charts, will be used to better identify the relationships between atmospheric pollution and climatological conditions in the study region. All synoptic charts were obtained from the China Meteorological Administration (CMA). The statistic analysis of the atmospheric pollution in autumn and winter periods from 2002 to 2006 was conducted based on the obtained datasets, and it indicated that the air qualities within the study region showed consistent variations. For example, Fig. 2(a)–(d) presents the API series from 1 to 31 January in 2005, where Fig. 2d illustrates the average API values of the 10 cities. During this period, there were obviously several air pollution processes. The first minimum of API values appeared on 4 January after several days of increase and decrease, minimum values of API appeared on 8 January, and the third minimum values appeared on 14 January. The air qualities of these cities showed improvement at regular intervals. Most cities encountered synchronous pollution processes, such as from 4 to 7, 8 to 14, and 18 to 25 January.

3. Air pollution process The air pollution process during 3–7 January 2005 is analyzed as an example in this paper to examine the relations between regional air qualities and synoptic pressure systems. It is found that the API values in the study cities decreased from 3 to 4 January while its minimum value appeared on 4 January, then increased

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from 4 to 6 January, and then decreased after 6 January. Most of the 10 cities showed simultaneous API variation characteristics, and the evolvement of synoptic patterns during this period would help explain this phenomenon. For example, the consistent API decline phase for the10 cities from 3 to 4 January was mainly controlled by the front (Fig. 3a). During this period, the strong northerly winds appeared in this region, which facilitated the easy dispersion of pollutants; the center of high pressure lay in the north to Inner Mongolia was moving to northern China. At 20:00 on 4 January, most parts of the northern China region were controlled by high pressure (Fig. 3b), and the center of high pressure lay in Beijing. The highpressure system was the dominating pattern that led to the accumulation of air pollutants. When high pressure was stationary for a relatively long time in the region, the API values of all cities in this region would keep increasing. In addition, the vertical structure of the highpressure system posed difficulty for pollutant dispersion and was beneficial for the increase of pollutant concentrations. During this period, the local circulation appeared under the influence of strong high pressure, and this led to an increasing phase of API from 4 to 5 January for most cities in the study region (i.e. the API value increased by 50 in Beijing within 1 day). At 20:00 on 5 January, the low pressure controlled northern China region (Fig. 3c), which led to the accumulation of air pollutants and the formation of heavy pollution. The long-time existence of the stationary trough of low pressure played an important role for the regional pollution in northern China during this period (5–6 January). The south-west warm-wet air stream was easily formed in the foreside of low pressure, which led to warm air and inversion lid. The more damp the ground, the denser the inversion layer. The curve of temperature and dew-point variations with height showed a trumpetlike distribution under the control of these low-pressure systems, indicating significant temperature difference from top layer to ground which was very conducive to the storage of air pollutants. Under this vertical structure of the low-pressure system, the API values reached maximum (January 6) before the front came. There was an API decline phase for the10 cities from 6 to 7 January, and this was mainly controlled by the front again (Fig. 3d). The strong northerly winds appeared in the region during this period, and the lower layer was with neutral stratification, which facilitated easy dispersion of pollutants. Generally, the air pollutants were accumulated in the phase of high pressure, and stored in the phase of low pressure to reach maximum API values, and then the pollutants were dispersed when front came, completing a cycle of air pollution process. Another cycle of pollution process would begin again with the iterative appearance of high pressure and would finish until the appearing of the front again.

4. Simulation of synoptic patterns A more detailed examination of the synoptic pressure system in the study region would help clearly evaluate the

1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 1-9 1-10 1-11 1-12 1-13 1-14 1-15 1-16 1-17 1-18 1-19 1-20 1-21 1-22 1-23 1-24 1-25 1-26 1-27 1-28 1-29 1-30 1-31

API

1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 1-9 1-10 1-11 1-12 1-13 1-14 1-15 1-16 1-17 1-18 1-19 1-20 1-21 1-22 1-23 1-24 1-25 1-26 1-27 1-28 1-29 1-30 1-31

API

1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 1-9 1-10 1-11 1-12 1-13 1-14 1-15 1-16 1-17 1-18 1-19 1-20 1-21 1-22 1-23 1-24 1-25 1-26 1-27 1-28 1-29 1-30 1-31

API

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500

400

1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 1-9 1-10 1-11 1-12 1-13 1-14 1-15 1-16 1-17 1-18 1-19 1-20 1-21 1-22 1-23 1-24 1-25 1-26 1-27 1-28 1-29 1-30 1-31

API

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600 lanzhou taiyuan datong beijing

300

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0

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300

250 xianyang luoyang baotou

200

150

100

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0

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200 180 160 140 120 100 80 60 40 20 0 average

date (m−d)

Fig. 2. API sequence for the study cities (2005.01.01–2005.01.31).

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20

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Fig. 3. Sea-level atmospheric pressure: (a) at 08:00 on 3 January 2005, (b) at 20:00 on 4 January 2005, (c) at 20:00 on 5 January 2005, and (d) at 20:00 on 6 January 2005.

relationship between the atmospheric pollution process and synoptic patterns. This is facilitated through the simulation of synoptic pressure patterns in northern China using the Weather Research and Forecasting Model (WRF) (Michalakes et al., 2001). The modeling domain has a horizontal resolution of 30 km with 99 grids in the west–east direction and 74 grids latitudinally, centered at (38.441N, 104.121E). In the vertical direction the terrainfollowing coordinates (s-coordinates) were applied and the simulation domain was divided into 31 layers. The numerical integrations were advanced with the time-step of 60 s, and the physical schemes adopted in WRF included Lin et al. microphysics (Lin et al., 1983), Kain–Fritsch cumulus parameterizations (Kain, 2004), Dudhia cloud radiation parameterization (Dudhia, 1989), and four-layer soil scheme. The three-dimensional (3-D) first-guess meteorological fields for the WRF simulation were obtained from the global tropospheric analysis datasets provided by the US National Center for Environmental Prediction, and were available every 6 h with

11 11 resolution. Fig. 4 shows the simulated pressure of sea surface and wind field at 10 m above ground. It is noted that at 06:00 on 3 January (Fig. 4a), high pressure controlled the west of Shanxi province, and the wind speed was large under the influence of front while the north wind field dominated northern China. This synoptic pattern is beneficial for the dispersion of air pollutants. At 20:00 on 4 January (Fig. 4b), northern China was controlled by high pressure, and the wind speed was low due to the influence of homogeneous high pressure, which was suitable for the accumulation of pollutants. At 20:00 on 5 January (Fig. 4c), northern china was influenced by low pressure, and the west-south wind field was dominating in this region, which represents a synoptic pattern for facilitating the transport and storage of pollutants. The API in the study region reached maximum values during this period. At 20:00 on 6 January (Fig. 4d), north wind dominated this region again, and API values decreased accordingly, completing an air pollution process cycle. The pollution process evolved

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2005−01−03 06

2005−01−04 20

42° N

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32 102

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10m/s

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10m/s

Fig. 4. Simulated sea-level atmospheric pressure and wind field at 10 m above ground.

according to the evolution of synoptic pressure patterns. The simulated pressure and wind fields are consistent with the observed meteorological data, and they are useful for predicting the variation trend of the regional air quality.

5. Statistics analysis of relationship between air pollution process and pressure systems To examine the relationship between the regional air pollution process and the evolution of pressure system, a comprehensive investigation of the pressure system in the study region in autumn and winter from 2002 to 2006 was conducted. For example, Table 2 lists the statistical analysis results of the total number of days with increasing or decreasing API values due to various pressure systems during the period of 2002.09.02–2003.02.28. It is found from Table 2 that the number of days with increasing and decreasing API values is approximately equal. Since the number of non-APIincreasing and non-API-decreasing days during the time period varied among the study cities, the total number of API-increasing and API-decreasing days was different for

different cities. The increase of API values was mainly due to high pressure and low pressure, while the low-pressure systems were divided into four types including trough, topographic troughs, saddle-shaped field, and inverted V-patterns. The decrease of API was mainly caused by the front. Thus, the air pollution process was affected by the evolution of pressure system. These characteristics are similar during the four periods of 2003.09.02–2004.02.28, 2004.09.01–2005.02.28 and 2005.09.01–2006.02.28. The average API values of the 10 study cities during the four periods are shown in Fig. 5, and it is found that many cycles of air pollution process were existing in the study region. Each air pollution process experienced the evolution of three pressure patterns including high pressure, low pressure, and front. Table 3 lists the occurrence time of different pressure systems during each of 16 selected air pollution processes in the study region. The statistical analysis results showed that the air pollution processes occurred repeatedly at different levels while the pressure systems evolved around different time and spatial scales. According to the atmospheric long-wave theory, obvious high and low pressure fluctuate at 500 h Pa (Fig. 6), the surface low pressure occurs in corresponding to the foreside of trough in the upper layer, and the surface

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Table 2 Days of increasing and decreasing API and synoptic patterns of 10 cities (2002.09.02– 2003.02.28) Cities

Shijiazhuang Taiyuan Linfen Xianyang Changzhi Datong Luoyang Lanzhou Baotou Beijing

Total number of API-increasing days

94 99 88 90 94 92 93 92 97 95

Number of API-increasing days influenced by synoptic patterns High pressure

34 35 36 37 38 38 37 39 37 34

Low pressure

Other patterns

Trough

Topographic trough

Saddle

Inverted V-pattern

18 19 11 18 16 15 16 15 19 24

10 9 12 8 9 13 8 9 9 8

2 3 2 3 3 2 3 2 3 3

9 9 8 7 8 7 7 7 9 8

21 24 19 17 20 17 22 20 20 18

Total number of API-decreasing days (front)

83 80 73 83 84 84 85 88 82 84

400 2002.09-2003.02 2003.09-2004.02

350 300 API

250 200 150 100 50 9-1 9-8 9-15 9-22 9-29 10-6 10-13 10-20 10-27 11-3 11-10 11-17 11-24 12-1 12-8 12-15 12-22 12-29 1-5 1-12 1-19 1-26 2-2 2-9 2-16 2-23

0

date (m−d) 300 2004.09-2005.02 2005.09-2006.02

250

API

200 150 100 50 9-1 9-8 9-15 9-22 9-29 10-6 10-13 10-20 10-27 11-3 11-10 11-17 11-24 12-1 12-8 12-15 12-22 12-29 1-5 1-12 1-19 1-26 2-2 2-9 2-16 2-23

0

date (m−d) Fig. 5. API average values of the study cities during four periods.

high pressure occurs in corresponding to the foreside of ridge in the upper layer (Fig. 7). Thus the surface fluctuation of the high and low pressure as well as the front systems takes place repeatedly. Such cyclic fluctuations of air pressure system lead to the cycles of air pollution processes. The relations between air pollution processes and pressure systems can be summarized as (a) the increasing phase of API is associated with high pressure and the succedent low pressure, (b) the preceding part of front (i.e. the retral part

of low pressure) is associated with the maximum of API values, and (c) the pressure systems with high gradient lead to the decrease of API (Fig. 8). It should be noted that the API variations could be affected by other factors, such as the temporal variations in pollutant emissions in the study region (i.e. lower emissions in a city may lead to lower API value in that city). However, on a regional scale, the relationship between the air pollution processes and pressure patterns should still hold.

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Table 3 Air pollution processes and occurrence time of different pressure patterns Pollution process (start time– end time)

High pressure

Low pressure

Front

2005.01.04– 2005.01.08 2005.01.18– 2005.01.25 2005.01.25– 2005.01-30 2005.02.10– 2005.02.19 2005.09.14– 2005.09.21 2005.10.07– 2005.10.14 2005.10.28– 2005.11.08 2005.11.14– 2005.11.23 2005.11.23– 2005.11.29 2005.12.05– 2005.12.14 2005.12.17– 2005.12.21 2005.12.21– 2006.01.01 2006.01.01– 2006.01.06 2006.01.06– 2006.01.20 2006.02.07– 2006.02.16 2006.02.16– 2006.02.23

01.04 01.19– 01.21 01.26– 01.27 02.10– 02.13 09.14– 09.15 10.08– 10.11 10.29– 11.01 11.15– 11.18 11.23– 11.24 12.07– 12.08 12.17– 12.18 12.24– 12.29 01.01 01.06– 01.07 02.07– 02.12 02.17– 02.19

01.05 01.22– 01.23 01.28 02.14 09.16– 09.18 10.12 11.02– 11.05 11.19– 11.20 11.25– 11.27 12.09 12.19 12.30 01.02 01.08– 01.10 02.13 02.20

01.06– 01.08 01.24 01.29– 01.30 02.15– 02.18 09.19– 09.20 10.13– 10.14 11.05 11.21– 11.23 11.28 12.09– 12.14 12.20– 12.21 05.12.31– 06.01.01 01.03– 01.06 01.16– 01.19 02.14– 02.15 02.21

Fig. 8. Sketch of air pollution process and pressure systems.

Fig. 6. Long-wave systems at 500 h Pa (Ahrens, 1982).

Fig. 7. Systems allocation of upper layer and surface (Ahrens, 1982).

through examination of the air pollution issues in 10 major cities. The air qualities in the study cities were represented using API. It was found that the synoptic pressure patterns and their evolvements were the main causes of regional air pollution processes. The API values of regional cities in northern China increase and decrease coincidently, and the air pollution processes repeat constantly. Pressure patterns can objectively and accurately reflect the evolution of atmospheric pollution process and regional characteristics. The pressure patterns of high pressure, the succedent lowpressure system and front zone were accordant with the increasing phase, maximum values, and decreasing phase of API in the study region. Such relations between synoptic pressure patterns and air pollution process can be used to analyze the variation characteristics and mechanism of regional atmosphere pollution problems. Since the spatial and temporal changes of pressure systems are easy to forecast, the understanding of such synoptic patterns can then be important for the qualitative prediction, control and management of regional air pollution issues.

6. Conclusions

Acknowledgments

The relationship between the synoptic pressure patterns and regional air pollution in northern China was investigated

This research was supported by the ‘‘National Basic Research (973) Program’’ Project (No. 2005CB724201) and

ARTICLE IN PRESS Z.H. Chen et al. / Atmospheric Environment 42 (2008) 6078–6087

High Technology Project (863) (No. 2006AA06A305, 6, 7) of the Ministry of Science and Technology of China. The authors would like to thank the Natural Sciences Foundation of China (No. 50578002) as well as the Natural Science Foundation of Beijing (No. 8061001) for supporting the research work. References Ahrens, C.D., 1982. Meteorology Today: An Introduction to Weather, Climate, and the Environment. West Publishing, St. Paul, Minnesota. Barry, R.G., Perry, A.H., 1973. Synoptic Climatology: Methods and Applications. Methuen, London. Cheng, S., Lam, K.C., 2000. Synoptic typing and its application to the assessment of climatic impact on concentrations of sulfur dioxide and nitrogen oxides in Hong Kong. Atmospheric Environment 34, 585–594. Cheng, W.L., Pai, J.L., Tsuang, B.J., Chen, C.L., 2001. Synoptic patterns in relation to ozone concentrations in west-central Taiwan. Meteorology and Atmospheric Physics 78, 11–21. Cheng, S.Y., Chen, D.S., Li, J.B., Wang, H.Y., Guo, X.R., 2007. The assessment of emission-source contributions to air quality by using a coupled MM5-ARPS-CMAQ modeling system: a case study in the Beijing metropolitan region, China. Environmental Modelling and Software 22, 1601–1616. Cheng, C.S., Campbell, M., Li, Q., Li, G., Auld, H., Day, N., Pengelly, D., Gingrich, S., Yap, D., 2007a. A synoptic climatological approach to assess climatic impact on air quality in south-central Canada, Part I: historical analysis. Water, Air, and Soil Pollution 182, 131–148. Cheng, C.S., Campbell, M., Li, Q., Li, G., Auld, H., Day, N., Pengelly, D., Gingrich, S., Yap, D., 2007b. A synoptic climatological approach to assess climatic impact on air quality in south-central Canada, Part II: future estimates. Water, Air, and Soil Pollution 182, 117–130. Davis, R.E., Kalkstein, L.S., 1990. Using a spatial synoptic climatological classification to assess changes in atmospheric pollution concentration. Physical Geography 11, 320–342. Dudhia, J., 1989. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of the Atmospheric Sciences 46, 3077–3107. Eshel, G., Bernstein, J., 2006. Relationship between large-scale atmospheric states, subsidence, static stability and ground-level ozone in Illinois, USA. Water, Air, and Soil Pollution 171, 111–133. Greene, J.S., Kalkstein, L.S., Ye, H., Smoyer, K., 1999. Relationships between synoptic climatology and atmospheric pollution at 4 US cities. Theoretical and Applied Climatology 62, 163–174. Hanna, S.R., 1991. Characteristic of ozone episodes during SCCCAMP 1985. Journal of Applied Meteorology 30, 534–550. Heidorn, K.C., Yap, D., 1986. A synoptic climatology for surface ozone concentrations in Southern Ontario 1976–1981. Atmospheric Environment 20, 695–703. Kain, J.S., 2004. The Kain–Fritsch convective parameterization: an update. Journal of Applied Meteorology 43, 170–181. Kalkstein, L.S., Corrigan, P., 1986. A synoptic climatological approach for geographical analysis: assessment of sulphur dioxide concentrations. Annals of the Association of American Geographers 76, 381–395. Kan, H., Chen, B., 2004. Particulate air pollution in urban areas of Shanghai, China: health-based economic assessment. Science of the Total Environment 322, 71–79.

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