Understanding haze pollution over the southern Hebei area of China using the CMAQ model

Atmospheric Environment 56 (2012) 69e79

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Understanding haze pollution over the southern Hebei area of China using the CMAQ model Litao Wang a, *, Jing Xu b, Jing Yang a, Xiujuan Zhao a, Wei Wei a, Dandan Cheng a, Xuemei Pan a, Jie Su a a b

Department of Environmental Engineering, Hebei University of Engineering, Handan, Hebei 056038, China Department of Physics, Hebei University of Engineering, Handan, Hebei 056038, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 December 2011 Received in revised form 26 March 2012 Accepted 7 April 2012

Haze has been one of the major air pollution problems in Chinese cities, and the southern Hebei area has attracted particular attention because of its high frequency of haze weather and the rapid deterioration in visibility in recent years. This study is aimed at understanding the characteristics and sources of the serious haze pollution in the southern Hebei area using the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality Model (CMAQ). The haze frequencies in the cities of southern Hebei, including Shijiazhuang, Xingtai, and five other urban centers in the surrounding regions, are analyzed for a ten-year period from 2001 to 2010, which shows a very similar and clear seasonal variation. The contributions of the local and regional anthropogenic emissions to the concentration of fine particulate matter (PM2.5, particles with an aerodiameter of less than or equal to 2.5 mm) and the light extinction coefficient (Bext) are estimated by conducting simulations of seven scenarios. The results show that approximately 65% of the PM2.5 in Shijiazhuang and Xingtai originated from the local emissions of the southern Hebei area, followed by Shanxi Province and the northern area of Hebei (13.8% and 7.3% to Shijiazhuang and 10.4% and 5.2% to Xingtai, respectively). The contributions of the emissions from the local area, Shanxi Province and the northern Hebei area to Bext are approximately 59.4%, 13.8% and 6.8% for Shijiazhuang and 58.2%, 10.1% and 5.0% for Xingtai, respectively. Moreover, an analysis of a typical pollution episode indicates that the contributions from the Shandong and Henan provinces are also significant. Further investigations are still required because of the complexity of the haze pollution over the southern Hebei area. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Haze Visibility PM2.5 Southern Hebei Models-3/CMAQ

1. Introduction In the observation standard released by the China Meteorological Administration (CMA), haze is defined as a pollution phenomenon characterized by deteriorated horizontal visibility of less than 10 km that is caused by fine particles suspended in the atmosphere (CMA, 2003). Haze occurs when sunlight is absorbed and scattered by high concentrations of atmospheric aerosols. It has a negative impact on human health and the environment (Wu et al., 2005; Tie et al., 2009), and it changes the climate on a regional or global scale by altering solar and infrared radiation in the atmosphere (Malm and Kreidenweis, 1997; Quinn and Bates, 2003). In recent years, regional haze has been one of the most disastrous weather events in Chinese cities (Wu et al., 2010). Excluding haze events associated with visibility degradation caused by

* Corresponding author. Tel.: þ86 310 8578749; fax: þ86 310 8578751. E-mail address: [email protected] (L. Wang). 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2012.04.013

natural sources, such as precipitation, sandstorm, dust, and fog, it has been reported that 71% of the 615 meteorological stations in Mainland China observed a notable deterioration in visibility from 1981 to 2005. This trend is more apparent after 1990 in which there was a speed of
2.1 km per every 10 years (Che et al., 2007). The highest frequency of haze events occurs in three areas: the North China area, the Yangtze River Delta (YRD) and the Pearl River Delta (PRD), while the regions with rapid increases in haze frequencies are the middle and southern areas of the North China Plain (NCP), the middle and lower reaches of the Yangtze River and South China. In these areas, the number of the haze days steadily rose by more than 3 days per every 10 years after 2000 (Hu and Zhou, 2009). The Hebei area is one of the regions with both the highest number of haze days and the most rapid growth speed in haze frequency. Statistics presented by Wei (2010), which were based on observations at the 81 stations in the BeijingeTianjineHebei area from 1971 to 2007, indicate that haze pollution is much more severe in the cities of southern Hebei, such as Shijiazhuang (the capital of Hebei, population of 10.2 million) and Xingtai (7.1

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million), than in Beijing (19.6 million) and Tianjin (12.9 million). The locations of these cities are shown in Fig. 1(a). In comparison with other cities all over China, the haze frequency in Xingtai ranks second on average from 1951 to 2005, and first tops out after the mid 1990s (Wu et al., 2010). The southern Hebei area, located in the middle of NCP, is heavily populated, urbanized and industrialized and is surrounded by three provinces, Shanxi, Henan and Shandong, which are also populated and industrialized. Also shown in Fig. 1(a) are the five major urban centers of Beijing, Tianjin, Taiyuan (the capital of Shanxi, population of 4.2 million), Zhengzhou (the capital of Henan, 8.6 million) and Jinan (the capital of Shandong, 6.8 million). However, few studies have been conducted to investigate the nature of the haze pollution over southern Hebei. Previous research has focused on statistical analyses of haze frequencies (Fan et al., 2005; Wei, 2010), its qualitative relationship with meteorological

factors such as wind speed and terrain (Wei et al., 2010), and its semi-quantitative relationship with the concentrations of coarse particulate matter (PM10, particles with an aerodiameter of less than or equal to 10 mm) (Zhang, 2009). We still lack a sufficient understanding of the sources and the formation mechanism of the serious haze pollution in this area. Therefore, the purpose of this study is to investigate the characteristics and sources of the haze pollution over the cities of South Hebei. The Models-3/CMAQ modeling system developed by the U.S. Environmental Protection Agency (U.S. EPA) is applied to simulate the air quality over the North China area. The CMAQ system has been broadly used to study the formation and transport of multiple air pollutants (Byun and Schere, 2006) and has been extensively evaluated by several modeling studies in Asia by Zhang et al. (2006), Uno et al. (2007), Fu et al. (2008), Wang et al. (2008a,b, 2010) and Liu et al. (2010a,b). In this study, the characteristics of the haze appearance in two cities of southern Hebei, Shijiazhuang and Xingtai, and five surrounding capital cities, Beijing, Tianjin, Taiyuan, Zhengzhou and Jinan, were analyzed for ten years from 2001 to 2010. Then, the CMAQ model was employed for Dec. 2007, which is the most polluted month during the ten-year period. The contributions by local sources and the surrounding regions to the concentrations of PM2.5 and its major components and to Bext in Shijiazhuang and Xingtai were evaluated by conducting simulations of seven scenarios. Moreover, a typical heavy pollution episode was analyzed to further understand the formation of the regional haze over NCP. 2. Method 2.1. Domain Two-domain, one-way nesting was used in the CMAQ modeling, as shown in Fig. 1(b). The projection is a Lambert projection with the two true latitudes of 25 N and 40 N. The domain origin was 34  N, 110  E; the coordinates of the bottom left corner of Domain 1 were (x ¼
2934 km, y ¼
1728 km). Domain 1 covered most of East Asia with a 36  36 km grid resolution to generate the boundary conditions for Domain 2. The southern area of Hebei Province was set to be the center of Domain 2. To assess the regional contributions of the surrounding regions, the entire areas of the cities of Beijing and Tianjin, and the Henan, Shandong and Shanxi provinces were included in Domain 2 using a 12  12 km grid resolution. We chose two typical cities to analyze the haze pollution in southern Hebei: Shijiazhuang City, the capital of Hebei Province, and the city of Xingtai, which is mentioned above. As a comparison, five capital cities in the surrounding regions were analyzed as well, including Beijing, Tianjin, Taiyuan, Zhengzhou and Jinan, as labeled in Fig. 1(a). 2.2. Identification of haze days and the modeling period All of the haze days during the ten years from 2001 to 2010 were identified for the seven representative cities according to the surface meteorological observations from the urban sites. As for Beijing and Tianjin, which have more than one station in their urban areas, the averaging data of all of the urban sites were applied. We used four basic rules to determine whether a day was considered a haze day:

Fig. 1. (a) Locations of the seven typical cities in Hebei and the surrounding regions and the range of Domain 2. (b) Domains used in CMAQ modeling. CMAQ Domain 1: size ¼ 164  97 cells with 36 km resolution. CMAQ Domain 2: size ¼ 93  111 cells with 12 km resolution.

(1) Only the data for 6:00 GMT (14:00 in Beijing time) are used because radiation fog and high humidity may appear in the early morning, thereby inducing low visibility in the local area,

L. Wang et al. / Atmospheric Environment 56 (2012) 69e79

and then disappear by midday. Therefore, the data obtained during midday are considered to be more representative (Lee, 1990; Che et al., 2009). (2) The horizontal visibility is less than 10 km (CMA, 2003). (3) Other types of weather influencing the visibility are screened out, such as precipitation, dust, gale, fog, mist, and sandstorm (Lee, 1990). (4) The relative humidity is less than 90% to ensure that the fog or mist weather that were forecasted as “haze” were excluded (Doyle and Dorling, 2002; Che et al., 2009).

a different statistical number of haze days and different rankings when comparing cities. They also concluded that the 6:00 GMT data might be more applicable to assess the evolution of regionalscale haze pollution (Zhao et al., 2011). Considering the statistical results, we chose Dec. 2007 as the modeling period; it had the highest total number of haze days out of the 120 months from 2001 to 2010 regardless of how the numbers were counted in the seven-city total or in the two-city total for Shijiazhuang and Xingtai. During this month, the haze frequencies were 22 days in Taiyuan, 18 days in Shijiazhuang and Xingtai, 17 days in Zhengzhou, and 9, 7, and 5 days in Beijing, Tianjin and Jinan, respectively. To eliminate the influence of initial conditions, a spin-up period of 5 days beginning on Nov. 26 was applied in the modeling.

Fig. 2 presents the statistics of the number of the haze days from 2001 to 2010. It can be observed from Fig. 2(a) that the seven cities had similar yearly variations in haze appearance. On average, for the seven cities, the haze frequencies did not show explicit increasing or decreasing trends during the ten years. The most polluted year was 2007, when the average number of haze days reached 90. The best year was 2005, during which the average haze frequency was 54 days. From city to city, we can find visible decreases in the number of haze days in Xingtai, while slight increases can be observed in Zhengzhou and Tianjin. The other four cities of Beijing, Shijiazhuang, Taiyuan and Jinan did not show clear changes in 2001e2010. Fig. 2(b) indicates a very clear and similar seasonal variation in the haze appearance in the seven cities. Winter was the worst season, and on average, over the ten-year period, the haze frequencies in January, November, and December were 9, 8, and 8 days, respectively. Autumn and summer presented better haze frequencies. The best month is May, which had only 2 haze days on average. By comparing the seven cities, it can be found that Taiyuan had the highest haze frequency, rather than Xingtai, which was previously reported by Wu et al. (2010), with 142 days on average for the ten years. The second highest frequency occurred in Zhengzhou with 85 days, followed by Xingtai, Shijiazhuang and Jinan. Beijing and Tianjin are the best of the seven cities with 45 and 23 days, respectively. This rank is consistent with the results presented for 2001e2005 by Che et al. (2009) on the haze pollution in the capital cities of 31 provinces in China. The above mentioned difference between this study and Wu et al. (2010)’s study can be attributed to the different statistical years and possibly the different screening rules that defined a haze day. In our study, only the meteorological data from 6:00 GMT were applied, as opposed to the daily-average used in Wu et al. (2010). Zhao et al. (2011) compared the two methods, indicating that they are both applicable for evaluating long-term haze trends for a single city but they will generate

2.3. Model configurations and inputs MM5 model version 3.7 was used to generate the meteorological fields for the CMAQ simulations in this study. Twenty-three sigma levels were selected for the vertical grid structure with a top surface of 100 mb. Of the MM5 input data, the terrain and land-use data was obtained from the U.S. Geological Survey database (ftp://ftp.ucar.edu/mesouser/MM5V3/TERRAIN_DATA/). Firstguess fields and the initial conditions were obtained from the National Center for Environmental Prediction (NCEP) Final (FNL) Operational Global Analysis datasets. We used a four-dimensional data assimilation (FDDA) technique for the objective analysis, for which the observations were from NCEP Automated Data Processing (ADP) surface and upper air data. The analysis-nudged parameters included wind, temperature, and the water vapor mixing ratio using the following nudging coefficients: 3.0 10
4 s
1 and 10
4 s
1 for Domains 1 and 2, respectively, for both wind and temperature and 10
5 s
1 for moisture for both domains. Two-way nesting was applied in this simulation. The major physics options include the KaineFritsch cumulus scheme (Kain and Fritsch, 1993), the high resolution Blackadar planetary boundary layer (PBL) scheme (Zhang and Anthes, 1982), the mixed phase (Reisner 1) explicit moisture scheme for cloud microphysics (Reisner et al., 1998), the cloud atmospheric radiation scheme (Dudhia, 1993) and the force/restore (Blackadar) surface scheme (Blackadar, 1976; Deardorff, 1978). CMAQ version 4.7.1 that was officially released in July 2010 was applied in this study. The vertical resolution of CMAQ includes 14 layers from the surface to the tropopause with denser layers at lower altitudes. The corresponding sigma levels are 1.000, 0.995, 0.988, 0.980, 0.970, 0.956, 0.938, 0.893, 0.839, 0.777, 0.702, 0.582,

a

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Fig. 2. Statistics of the number of haze days in the seven typical cities in 2001e2010. (a) Yearly variations in the number of haze days; (b) ten-year-average monthly variations in the number of haze days.

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0.400, 0.200 and 0.000. The SAPRC-99 chemical mechanism (Carter, 1990, 2000) with aqueous and aerosol extensions and the AERO4 model derived from the Regional Particulate Model (RPM) (Binkowski and Shankar, 1995) were chosen for the gas-phase chemistry and aerosol modules, respectively. The emissions were derived from the Asian emission inventory for the National Aeronautics and Space Administration’s (NASA) Intercontinental Chemical Transport Experiment-Phase B (INTEXB) emission established by Zhang et al. (2009). The inventory includes the emissions of sulfur dioxide (SO2), nitrogen oxides (NOX), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), PM10, PM2.5, black carbon (BC) and organic carbon (OC) for Asian countries in 2006. All emissions have been gridded into a 30 min  30 min resolution (http://mic. greenresource.cn/intex-b2006). We regridded this inventory into the resolutions in this study (36 km and 12 km) using the gridding technique described in Streets et al. (2003) and Woo et al. (2003). Then, the inventory was distributed into hourly emissions using the monthly, weekly and hourly temporal profiles established by Tsinghua University based on various research and investigations (Tsinghua University, 2006). The MeteorologyeChemistry Interface Processor (MCIP) version 3.6 was applied to process the meteorological data into the format required by CMAQ. The default clean profile in CMAQ was applied as the initial conditions (ICON), and a spin-up period of 5 days was used to eliminate its influence. The boundary conditions (BCON) for Domain 1 were maintained as the model’s default profile, and those for Domain 2 were extracted from the outer domain. The total ozone column data from the Ozone Measurement Instrument (OMI) (http://toms.gsfc.nasa.gov/ozone/ozone_v8.html) on the Aura satellite were used in the photolysis rates processor (JPROC) to calculate the photolysis rate. The model performance in this study has been evaluated and is discussed in the Supplementary information. 2.4. Scenarios and contribution analysis Seven modeling scenarios were conducted for Domain 2 to evaluate the pollution contributions by region, which is summarized in Table 1. The regions that were analyzed are denoted in Fig. 3. Hebei Province is separated into the southern Hebei area (SHB) and the northern Hebei area (NHB). The southern Hebei area includes three cities: Shijiazhuang, Xingtai and Handan (located in the southern edge of Hebei Province); all of the other areas are Table 1 Modeling scenarios in this study. Scenario name

Emission scenario

Remarks

Base

Base case

SHB-0

Anthropogenic emissions in the southern Hebei area are turned off Anthropogenic emissions in the northern Hebei area are turned off Anthropogenic emissions in Beijing and Tianjin are turned off Anthropogenic emissions in Shanxi Province are turned off Anthropogenic emissions in Henan Province are turned off Anthropogenic emissions in Shandong Province are turned off

Baseline for contribution analysis To estimate contribution of the southern Hebei area

NHB-0

BJTJ-0

SX-0

HN-0

SD-0

To estimate contribution of the northern Hebei area To estimate contribution of Beijing and Tianjin To estimate contribution of Shanxi To estimate contribution of Henan To estimate contribution of Shandong

Fig. 3. Regions in the contribution analysis. The southern Hebei area includes three cities: Shijiazhuang, Xingtai and Handan. The other areas in Hebei Province are considered to be northern Hebei.

considered to be northern Hebei. Beijing and Tianjin are grouped together. The contributions from one region are determined by calculating the difference between the base case and the case when all of the anthropogenic emission in that region is turned off using the equations:

Ci; contrib: ¼ CBase
Ci
0 Pi; contrib: ¼

(1)

Ci; contrib: CBase

(2)

Where Ci,contrib. and Pi,contrib. denote the pollution contributions from region i in terms of concentration and percentage, respectively. CBase and Ci-0 are the predicted concentrations of the base case and the case with zero man-made emissions in region i, respectively. It should be noted that uncertainties may be introduced due to the non-linear atmospheric response to the emissions. This methodology has been applied in North China in several studies, particularly for the policy-making and evaluation for the 2008 Beijing summer Olympics (Streets et al., 2007; Chen et al., 2007a; Wang et al., 2008b; Xing et al., 2011). Wang et al. (2008b) discussed the effects of the non-linear response in the contribution analysis for PM10 for the case of Beijing and found the results to be acceptable, especially in the wintertime. The light extinction coefficient Bext (km
1) was used as the indicator of visibility. In this study, we use the reconstructed Bext calculated by CMAQ according to the equation (Byun and Ching, 1999; Malm et al., 1994; Sisler and Malm, 2000):

Bext ¼ 0:003  f ðRHÞ 

i h i h io nh þ þ NO
þ 0:004 SO2
4 3 þ NH4

 ½OC þ 0:01  ½EC þ 0:001  ½Soil þ 0:0006  ½CM (3)

L. Wang et al. / Atmospheric Environment 56 (2012) 69e79
þ Where [SO2
4 ], [NO3 ], [NH4 ], [OC], [EC] and [Soil] are the concentrations (mg m
3) of sulfate, nitrate, ammonia, organic carbon, elemental carbon and the soils in PM2.5, respectively. [CM] is the concentration of coarse particulate matter, which is not yet added in the present CMAQ because of the large uncertainties in the coarse particulate matter emission inventory. f(RH) is a dimensionless relative humidity adjustment factor. The reconstructed Bext is a linear combination of the PM2.5 species for a specific f(RH); thus, it can also be considered to be a special “concentration” directly reflecting the capacity of atmospheric aerosols in visibility deterioration. Therefore, a similar analysis was pursued for Bext as for PM2.5 concentrations (Equations

3. Results and discussions 3.1. PM2.5 concentrations in haze days The simulated urban daily-average PM2.5 concentrations in the seven typical cities are shown in Fig. 4. The gray bars represent the observed haze days. By averaging the concentrations in the haze and non-haze days separately, we can find that the largest increase occurs in Beijing, where the haze-day-average (337 mg m
3) is 3.0

Xingtai

500

-3 PM2.5 Conc., µg m

-3 PM2.5 Conc., µg m

(1) and (2)) to estimate the regional contributions in terms of visibility deterioration.

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Fig. 4. The simulated daily-average PM2.5 concentrations in haze days and non-haze days in the seven typical cities.

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L. Wang et al. / Atmospheric Environment 56 (2012) 69e79

times that of the non-haze days (111 mg m
3). The second largest increase is in Shijiazhuang with a ratio of 2.6, followed by Tianjin (1.8), Taiyuan (1.8), Xingtai (1.7) and Zhengzhou (1.5). Jinan has the smallest ratio of 1.3, which can partially be attributed to the more frequent fog during this month, e.g., heavy fog appeared in Dec. 20e23 during which the highest PM2.5 concentrations were predicted, but those days cannot be categorized as haze days. From Fig. 4, we can observe different pollution patterns in these cities. The concentrations in Beijing and Tianjin show much more apparent temporal variations (note the Y-axes for Beijing and Tianjin are different than the others). In Beijing, the highest predicted daily concentration can reach 632 mg m
3, which is also the highest of the seven cities, and the lowest is only 21.5 mg m
3. This is because Beijing is located at the northwestern edge of the NCP (Fig. 1(a)). When the prevailing northwest wind blows to Beijing from the clean area, where the Yanshan Mountain and the Taihang Mountain meet, the pollutants in the atmosphere are washed out and this induces relatively good air quality. However, if calm winds or light winds blowing from the south dominate for a couple of days, which often occurs in wintertime, heavy pollution will appear because of the rapid accumulation of the local pollutants (Wang et al., 2008b). Although the largest daily-average concentration occurs in Beijing (632 mg m
3), the number of days with a relatively higher concentration that can possibly induce haze pollution, e.g.,

over 200 mg m
3, is not larger in Beijing. Tianjin city shows similar results. In Shijiazhuang and Xingtai, the temporal variations are smaller. The concentrations are relatively stable at approximately 200 mg m
3, which leads to, on the contrary, more days with concentrations of over 200 mg m
3. This finding may be one of the reasons why, compared to Beijing, Shijiazhuang and Xingtai have lower particulate matter (PM) concentrations but more haze days. Taiyuan and Zhengzhou have similar patterns. It should be noted that during this month, Taiyuan had the most frequent number of haze days according to meteorological observations, but the predicted PM2.5 concentrations are visibly lower than those for the other cities. This observation may indicate the possibility of an underestimation in the PM2.5 concentrations in Taiyuan, which do not appear to be consistent with the air quality observations (Fig. S2 in the supplementary information). Taiyuan may have relatively different PM compositions, which are more effective in inducing visibility deterioration. More data or evidence is required to understand this conflict. 3.2. Contributions by region to PM2.5 concentrations The contributions by region to the urban daily-average concentration of PM2.5 and its composition in Shijiazhuang are summarized in Fig. 5. On average, 65.3% of PM2.5 originates from


þ Fig. 5. Contributions by region to the daily-average PM2.5, SO2
4 , NO3 , NH4 , OC and EC concentrations in Shijiazhuang. Diamonds denote the mean, and bars mark the range.

L. Wang et al. / Atmospheric Environment 56 (2012) 69e79

the local emissions in the SHB. The largest regional contributor is Shanxi Province (SX) with an average contribution of 13.8%, and the maximum contribution can be as high as 39.6%. The contribution is even higher than that by the NHB (7.3% on average and a maximum of 28.6%). Shandong (SD) Province only provides 1.6% on average, but a maximum of 10.3% to the PM2.5 concentrations in Shijiazhuang, followed by Henan Province (HN) and Beijing and Tianjin (BJTJ). Regarding SO2
4 , the rank of the average contribution by region is similar: the largest contributor is SHB with 63.0% on average, followed by SX and NHB. SD, BJTJ and HN only offer approximately 1% of the concentrations, but the maximum contributions can reach 13.4%, 17.4% and 5.7%, respectively. NO
3 is special because of its more apparent non-linear response to the emission changes. The negative contributions indicate that the absence of the anthropogenic emission in the SHB, NHB, BJTJ and HN areas may induce an increase in the NO
3 concentrations in Shijiazhuang because of the semi-volatility of NO
3 and the complex equilibrium in the sulfate-nitrate-ammonia aerosol system. According to statistics, SX is the largest contributor, and the absent of SX emissions may decrease an average of 27.4% and a maximum of 65.0% of the NO
3 concentrations in Shijiazhuang. The second largest contributor is SHB, followed by NHB, SD, HN and BJTJ.

75

The three largest contributors to the NHþ 4 concentrations are SHB, SX, and NHB, whose maximum contributions can reach 75.8%, 48.5% and 27.2%, respectively, followed by SD, HN and BJTJ. OC and EC are more local compared with other species. The SHB area contributes an average of 71.2% of OC and 71.6% of EC. On the day in which the maximum can be observed, as high as 85.4% of OC and 86.5% of EC originate from SHB. The next two largest contributors are SX Province and NHB. The other regions contribute only approximately 1% to the OC and EC concentrations in Shijiazhuang. Fig. 6 presents the contributions by region to Xingtai. Because Xingtai is located in the center of the SHB area, its contributions are more distinct compared with Shijiazhuang. On average 64.7% of
þ PM2.5, 59.5% of SO2
4 , 28.3% of NO3 , 46.9% of NH4 , 74.4% of OC and 73.3% of EC originate from SHB. The contributions of SX to Xingtai are lower than that of Shijiazhuang with an average of 8.9%e17.4% for each species. However, the maximum can still reach 23.6% for PM2.5, and 18.0%e53.3% for the PM2.5 species. Compared with Shijiazhuang, Xingtai receives more pollutants from HN Province. At a maximum, 19.3% of PM2.5 and 12.2%e25.0% of the PM2.5 species are from HN. The contributions of NHB decrease to an average of 4.9%e7.6% for each species. SD Province provides a maximum 12.6% in PM2.5 concentration. BJTJ presents


þ Fig. 6. Contributions by region to the daily-average PM2.5, SO2
4 , NO3 , NH4 , OC and EC concentrations in Xingtai. Diamonds denote the mean, and bars mark the range.

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Xingtai 90%

80%

80%

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70%

Contribution by Region

Contribution by Region

Shijiazhuang 90%

60% 50% 40% 30% 20% 10%

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0%

0%

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-10% SHB

NHB

BJTJ

SX

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SX

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SD

Fig. 7. Contributions by region to Bext in Shijiazhuang and Xingtai. Diamonds denote the mean, and bars mark the range.

the lowest numbers out of these regions, contributing 1.1% on average and a maximum of 9.4% to the PM2.5 concentration. 3.3. Contributions by region to Bext The responses of Bext to the absence of the man-made emissions from each region are summarized in Fig. 7. The contributions of SHB to the Bext, in comparison with those to the PM2.5 concentration, visibly decrease to 59.4% in Shijiazhuang and 58.2% in Xingtai (65.3% and 64.7% for PM2.5, respectively). This finding may indicate that regional sources are more important in visibility deterioration than in the PM2.5 concentrations for the two cities. The contributions from SX, NHB and BJTJ slightly decrease as well, with averages of 13.8%, 6.8%, and 0.9% for Shijiazhuang and 10.1%, 5.0% and 1.0% for Xingtai, respectively. The average contributions of SD and HN to Bext slightly increase compared with those to PM2.5, but the maximums decrease to 7.8% and 7.5% for Shijiazhuang and to 11.2% and 14.9% for Xingtai. 3.4. A heavy pollution episode To obtain a better understanding of the pollution formation over the southern Hebei cities, we plot the temporal variations in the regional contributions to the PM2.5 concentrations in Shijiazhuang and Xingtai in Fig. 8, along with the simulated urban daily-average PM2.5 concentration and the observed visibility. The visibility is denoted using its reverse (Mm
1). According to the PM2.5 concentrations, there are two heavy pollution episodes during this month: Dec. 8e12 and 23e29. During the two episodes, the PM2.5 concentrations are higher than 200 mg m
3, and the corresponding visibilities decrease to less than 5 km. Particularly in the first episode, the temporal variations in the visibility are quite

consistent with the trend of the PM2.5 concentrations. Another interesting phenomenon is that when the visibility decreases while the PM2.5 concentrations increase, the contributions of SX decrease while those from SD, HN and NHB present a visible increase. This may indicate that, although SX Province is the top regional contributor to the PM2.5 concentrations in Shijiazhuang and Xingtai, as discussed above, it may not be as significant during heavy pollution episodes. Moreover, we plot the PM2.5 spatial distributions in SHB every six hours for the period of Dec. 8e12 in Fig. 9. At the beginning of this episode, three directions of wind coming from the northwest, the west and the southwest met over Shijiazhuang and Xingtai, which resulted in a period of light and calm wind. Pollutants began accumulating, and the concentrations increased. Over the Beijing area, the northwest wind was dominant; therefore, the PM2.5 concentrations were still maintained at a low level. At the end of Dec. 8, the wind over the Beijing area began changing to the northeast and east directions, resulting in approximately a one-day period of light wind, which led to a rapid increase in the PM2.5 concentration. On this day (Dec. 9), the wind directions over SHB varied rapidly at low wind speeds so that pollutants kept accumulating. For several hours, e.g., 12:00 to 18:00, the southeast wind was dominant, which may be the reason for the increase in the SD and HN contributions. Following Dec. 10, the north wind dominated in Shijiazhuang and Xingtai, and the Beijing area was controlled by the northwest wind, which resulted in the transportation of pollutants from the northeast to the south along the Taihang Mountain. Therefore, the contributions from the NHB area increased. By the end of Dec. 10, a strong wind from the northwest began blowing away the pollutants over the Beijing area and rapidly reduced its concentrations to lower than 150 mg m
3 at the end of Dec. 11. It required

Fig. 8. Temporal variations in the contributions by region to the PM2.5 concentrations, the observed visibilities in haze days and the simulated PM2.5 concentrations in Shijiazhuang and Xingtai. The observed visibilities are from the data of 6:00 GMT and are denoted using visibility
1 (Mm
1).

L. Wang et al. / Atmospheric Environment 56 (2012) 69e79

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Fig. 9. The spatial distributions of the PM2.5 concentrations and the wind field during the pollution episode of Dec. 8e12. The time is local Beijing time.

one more day to dilute the concentrations over the southern Hebei cities. This is a typical weather process in the winter in the BeijingeTianjineHebei area (Chen et al., 2007b; Liu et al., 2010c). 4. Conclusions In this study, the MM5-Models-3/CMAQ modeling system was applied to simulate the air quality in NCP to understand the serious

haze pollution over the southern Hebei area. First, the number of haze days in seven typical cities, including Shijiazhuang, Xingtai, Beijing, Tianjin, Taiyuan, Zhengzhou and Jinan, were identified for ten years from 2001 to 2010. The result indicates that the seven cities had a similar and clear seasonal variation in the haze frequency. Winter is the worst season, followed by autumn and summer. Second, the PM2.5 concentrations on haze and non-haze days were analyzed for the seven cities. The PM2.5 concentrations

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in Beijing and Tianjin show a larger temporal variation that unexpectedly resulted in fewer days with higher concentrations that can induce haze pollution. This may be one of the reasons why Shijiazhuang and Xingtai have lower PM concentrations but more haze days than Beijing and Tianjin. Third, the contributions by region to the PM2.5 concentrations and Bext in Shijiazhuang and Xingtai were estimated. For our modeling period, approximately 65% of PM2.5 and 59% of Bext in Shijiazhuang and Xingtai originated from the local southern Hebei emissions. Shanxi Province and the northern area of Hebei are the two major regional contributors according to the statistics. Finally, a heavy pollution episode was analyzed using the modeling results, which indicates that the pollutant transportation from Shandong, Henan and northern Hebei play a more significant role during heavy pollution periods. This study has several limitations. First, uncertainties exist in the model predictions due to the lack of fugitive dust emissions. It should be mentioned that the most recently released version of CMAQ 5.0 contains an optional calculator of windblown dust. Better performance can be expected in the future. Second, the non-linear response of the atmospheric concentrations to the emissions is not considered, which may result in uncertainties in the regional contribution estimation. Third, air quality observations are very limited for the model evaluation, particularly for PM2.5, which is more important in haze pollution than the monitored PM10. Nevertheless, this study provides useful insight into the serious haze pollution in the southern Hebei area, which is not only heavily populated and industrialized but also surrounded by urbanized regions with high emission densities. Our model can be helpful in the policy-making in air pollution control in the southern Hebei area. However, additional observations and modeling studies are still required to further investigate the nature of the regional haze over NCP and to assist in the design and assessment of integrated control strategies. Acknowledgments This study was sponsored by the National Natural Science Foundation of China (No. 41105105) and the Natural Science Foundation of Hebei Province (No. D2011402019). Special thanks are given to Dr. Jia Xing for his kind help with the data gridding process. Appendix A. Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.atmosenv.2012.04.013. References Binkowski, F.S., Shankar, U., 1995. The regional particulate model. 1. Model description and preliminary results. Journal of Geophysical Research 100, 26191e26209. Blackadar, A.K., 1976. Modeling the nocturnal boundary layer. In: Proceedings of the Third Symposium on Atmospheric Turbulence, Diffusion and Air Quality. American Meteorological Society, Boston, MA, pp. 46e49. Byun, D.W., Ching, J.K.S., 1999. Science Algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) Modeling System. EPA/600/R-99/030. Office of Research and Development, U.S.EPA, Research Triangle Park, NC. Byun, D.W., Schere, L.K., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system. Applied Mechanics Reviews 59 (2), 51e77. Carter, W.P.L., 1990. A detailed mechanism for the gas-phase atmospheric reactions of organic compounds. Atmospheric Environment 24, 481e518. Carter, W.P.L., 2000. Implementation of the SAPRC-99 Chemical Mechanism into the Models-3 Framework. Report to the U.S. EPA, Prepared by Cater, W.P.L. Statewide Air Pollution Research Center, University of California, Riverside, CA. Che, H.Z., Zhang, X.Y., Li, Y., et al., 2007. Horizontal visibility trends in China 1981e2005. Geophysical Research Letters 34, L24706. doi:10.1029/ 2007GL031450.

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