- Email: [email protected]
Modeling aerosol impacts on atmospheric visibility in Beijing with RAMS-CMAQ
Atmospheric Environment 72 (2013) 177e191
Contents lists available at SciVerse ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Modeling aerosol impacts on atmospheric visibility in Beijing with RAMS-CMAQ Xiao Han a,1, Meigen Zhang a, *, Jinhua Tao b, 2, Lili Wang a, 3, Jian Gao c, 4, Shulan Wang c, 5, Fahe Chai c, 6 a
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, HuaYanBeiLi 40#, Chaoyang District, Beijing 100029, China State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China c Chinese Research Academy of Environmental Sciences, Beijing 100012, China b
h i g h l i g h t s < A heavy pollution episode occurred in North China Plain is identified and simulated by RAMS-CMAQ. < The pollution processes and the relationship between visibility and aerosol are discussed. < Model simulations indicate the low atmospheric visibility was caused by high PM2.5 concentration.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 November 2012 Received in revised form 7 February 2013 Accepted 15 February 2013
A typical heavy air pollution episode occurred over the North China Plain (NCP) in December 2010. The air quality in Beijing and its surrounding regions worsened during the period December 17 to 22, and local visibility became significantly affected by the high pollution levels. The air quality modeling system RAMS-CMAQ coupled with an aerosol optical property scheme was applied to simulate the trace gases and major aerosol components in the NCP to obtain an in-depth understanding of the relationship between regional low visibility and aerosol particles. The model performance was evaluated using various observation data, such as meteorological factors (temperature, relative humidity, and wind field), gaseous pollutants (SO2, NO2, and O3), PM2.5, PM10, and visibility at several measurement stations. The modeled meteorological field and visibility were in good agreement with observations from December 2010. The modeled mass concentrations of gaseous pollutants and aerosol particles also suitably captured the magnitude and variation features of the observation data, especially during the air pollution episode. The simulated results showed that during this pollution episode, low visibility (lower than 10 km) occurred mainly in Beijing, Tianjin, Hebei, and Shandong. The analysis and sensitivity test indicated that the aerosol particles larger than PM2.5 and the water uptake effect of aerosol optical properties could not significantly influence visibility. Thus, the low visibility was primarily caused by the high mass burden of PM2.5as a result of the local pollutant accumulation and long-range transport. Statistics showed that the visibility variation was closely inversely related to the variation in PM2.5 in most regions in the NCP. Visibility decreased lower than 10 km when the mass concentration of PM2.5 exceeded 75 mg m3 to 85 mg m3 in the NCP. Sulfate and nitrate were the two major inorganic aerosol components of PM2.5 that evidently decreased visibility by contributing 40% to 45% to the total extinction coefficient value. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Visibility Aerosol CMAQ Haze North China Plain
* Corresponding author. Tel.: þ86 0 10 62379620; fax: þ86 0 10 62041393. E-mail addresses: [email protected] (X. Han), [email protected] (M. Zhang), [email protected] (J. Tao), [email protected] (L. Wang), [email protected] (J. Gao), [email protected] (S. Wang), [email protected] (F. Chai). 1 Tel.: þ86 0 15801475815. 2 Tel.: þ86 0 18601943282. 3 Tel.: þ86 0 13811716690. 4 Tel.: þ86 0 18911819868. 5 Tel.: þ86 0 10 84913904. 6 Tel.: þ86 0 10 84915164. 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.02.030
178
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
1. Introduction Beijing, the capital of P.R. China, is located at the northern tip of the North China Plain (NCP) and surrounded by high mountains in its northern (Yanshan Mountain) and western (Taihang Mountain) boundaries. In recent years, this megacity has suffered from the deterioration of air quality as a result of rapid population growth and economic development (Zhao et al., 2009a,b; Zheng et al., 2005). High aerosol loading induced severe regional climatic and environmental issues. Previous studies have suggested that the aerosol particles were mainly emitted by multiple local anthropogenic sources (Sun et al., 2006) and the pollution inflow from the surrounding regions (Zhang et al., 2012) of NCP. Aerosols have direct (Liao and Seinfeld, 2005; Liu et al., 2007) and indirect climatic effects (Ruckstuhl et al., 2008) on the earth’s energy balance. Atmospheric visibility and human health (Chuang et al., 2009; Che et al., 2007) can also be affected by aerosol particles. The frequently widespread haze cloud over the NCP (Tao et al., 2012) is a typical visibility deterioration phenomenon during the past decade. Aerosol particles cause visibility deterioration through light extinction. Several studies have focused on the complex features of local aerosols and analyzed the relationship between visibility and aerosol particles in Beijing and the NCP. Wang et al. (2006) analyzed the component of aerosol in hazy and clear days in Beijing using 4-year observation samples and suggested that sulfate and nitrate were the two major aerosol species in hazy days. Quan et al. (2011) summarized the hazy day occurrence trend over the NCP in the past 56 years. They also conducted an insitu field experiment and found a high concentration of fine aerosol particle during the hazy day. Li et al. (2010) analyzed the characteristic and impact of carbonous aerosol during hazy days in Beijing. Wang et al. (2012) simulated the most polluted months from 2001 to 2010 and analyzed the haze frequencies of seven urban centers in the NCP. The transboundary air pollution was found to play an important role in the haze formation over the NCP. Fei et al. (2009) used the Model-3/CMAQ to simulate a strong
Table 1 The air pollution index released by Ministry of Environment Protection of China at six cities in North China Plain. Date
Beijing
Tianjin
Shenyang
Taiyuan
Jinan
Shijiazhuang
2010-12-14 2010-12-15 2010-12-16 2010-12-17 2010-12-18 2010-12-19 2010-12-20 2010-12-21 2010-12-22 2010-12-23 2010-12-24
31 53 92 105 156 182 100 165 222 30 40
57 60 75 93 103 133 108 107 174 72 56
97 97 138 96 133 160 222 218 176 89 81
63 47 68 117 102 85 91 165 115 59 59
55 60 90 107 90 111 121 133 153 215 68
84 76 103 127 142 106 75 127 109 78 46
fog episode and analyzed its evolution in Beijing. They found that relative humidity and inorganic particles of PM2.5 were important factors for low visibility. All impact factors should be considered when investigating the detailed relationship between ambient visibility and aerosol particles because the aerosol extinction ability is related to various factors such as relative humidity, aerosol species, microphysics, and so on. The aerosol dynamic processes, size distribution, mixing state, and water uptake effect are especially important in improving the accuracy for estimating aerosol extinction. A typical heavy air pollution episode occurred over the NCP during the period December 17 to 22, 2010. The air pollution index (API, described in Appendix A), encompassing six cities (shown in Fig. 1) and released by the Ministry of Environment Protection of China, clearly showed the high pollution level of this episode over the NCP (Table 1). The API was relatively lower on December 14, 15, 16, 23, and 24, 2010, but higher from December 17 to 22, 2010,in these six cities. Thus, in the present study, the mass burden of major aerosol species (sulfate, nitrate, ammonium, black carbon, organic carbon, dust, and sea salt) and surface visibility in December 2010 are simulated by the air quality modeling system RAMS-CMAQ coupled with an aerosol optical property scheme. All
Fig. 1. Observation sites of Air Quality Network and Chinese National Meteorological Centre (CNMC) in the model domain. Also shown are geographic locations of Beijing, Tianjin, Hebei, Shanxi, Shangdong.
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
aerosol microphysical features that can influence aerosol optical properties considered in this modeling system. The temporal and spatial characteristics of aerosol mass burden and visibility during the pollution episode are analyzed. This study focused on and discussed the main factors that may cause low visibility to investigate the relationship between visibility and aerosol in Beijing and its surrounding regions in the NCP. 2. Methodology The major component of the RAMS-CMAQ modeling system is the air quality model CMAQ (version 4.7) developed by the US Environmental Protection Agency (US EPA) to simulate multiple air quality issues with multi-scale capabilities (Byun and Schere, 2006). The gas-phase chemistry mechanism is updated to the expanded version CB05 (Sarwar et al., 2008). The thermodynamic equilibrium between inorganic aerosol species and gas-phase concentrations is treated by ISORROPIA (Nenes et al., 1999). Particle nucleation, coagulation, condensation, and other dynamic processes are described using the Regional Particulate Model (Binkowski and Shankar, 1995). The aerosol size distribution is divided into three modes, namely, the Aitken, accumulation, and coarse modes. The aerosol components, geometric standard deviation, and geometric mean radius of each mode can be found in the study of Han et al. (2011). All modes are assumed to follow the log normal distribution. Internal mixing of the aerosol species is assumed within each mode, but the modes themselves are externally mixed. The summation of the aerosols in the Aitken and accumulation modes are used to represent the PM2.5 particles, and the total aerosols of all three modes are used to represent PM10 particles. The numerical prediction model RAMS is coupled with CMAQ in the offline method to provide meteorological fields to CMAQ. RAMS uses terrain-following sz coordinate system and can suitably simulate the boundary layer and the underlying surface (Cotton et al., 2003). To obtain the aerosol extinction coefficient, the scheme that could fully consider the major impact factors of aerosol optical properties is added in the modeling system. This scheme contains a parameterization (Ghan and Zaveri, 2007) that efficiently simplifies the calculation process of the Mie theory and also maintain sufficient accuracy. The effects of water uptake and internal mixture are treated using Kohler theory (Pruppacher and Klett, 1997) and MaxwelleGarnett mixing rule (Chuang et al., 2002) in this scheme, respectively. This scheme is described in detail in Han et al. (2011). After the aerosol extinction coefficient is obtained, the surface visibility is calculated using
VIS ¼ 3:912=b
(1)
where b is the aerosol extinction coefficient (Seinfeld and Pandis, 1998). Previous studies have demonstrated that the modeling system performs well on simulating the aerosol mass concentration and optical properties (Ge et al., 2011; Han et al., 2011, 2012; Zhang et al., 2005, 2006, 2007). The simulation has two layer grids. The larger one is 6654 5440 km2 with a 64 km grid cell on a rotated polar stereographic map projection centered at (116 E, 35 N) and covers the whole area of East Asia as shown in Han et al. (2011). The smaller one (Fig. 1) has 94 90 grid cells with a 16 km resolution on a rotated polar stereographic map projection centered at (116 E, 40 N). It covers all major regions in the NCP, including Beijing, Tianjin, Hebei, Shandong, and Shanxi. The model system has 15 vertical layers, unequally spaced from the ground to approximately 23 km, with nearly half of them concentrated in the lowest 2 km (vertical resolution of 100 me200 m from the ground to approximately 1.5 km) to
179
improve the simulation of the atmospheric boundary layer. The time step of the model simulation is 1 h. The emission inventories used by the modeling system are introduced as follows. The anthropogenic emissions of aerosols and their precursors (CO, NOx, SO2, volatile organic compounds, black carbon, organic carbon, PM2.5, and PM10) are obtained from Streets’ 2006 emission inventory (http://www.cgrer.uiowa.edu/EMISSIONDATA new/index16.html) of a 0.5 0.5 spatial resolution, which includes the following categories: power, industry, residential, and transport. The Regional Emission inventory for Asia domain (REAS, http://www.jamstec.go.jp/frsgc/research/d4/emission.htm) is used to provide NH3 emission. The biomass burning emissions from forest wildfires, savanna burning, and slash-and-burn agriculture are provided by the monthly mean inventory of Global Fire Emissions Database, Version 2 (GFEDv2.1) (Randerson et al., 2005). Nitrogen oxide emissions from the soil and natural hydrocarbon emissions are obtained from the Global Emissions Inventory Activity global monthly inventory (Benkovitz et al., 1996). The volcanic emissions, flight exhaust, and lightning nitrogen oxides are from MICS-Asia II and Emission Database for Global Atmospheric Research (Olivier et al., 1994), respectively.
3. Model evaluation The modeled meteorological field is an important factor in aerosol simulation. The transport, scavenging processes, and particle water uptake of aerosols are intuitively related to wind vector, temperature, and relative humidity in a modeling system. Thus, in this paper, the observed meteorological data from surface stations of the Chinese National Meteorological Center (CNMC; described in Appendix B) are collected to evaluate the performance of the model. Fig. 1 demonstrates the positions of the measurement sites in the model domain. The comparative results are shown from Figs. 2e5. The modeled temperature can suitably reflect the trend of observation data, which are very close in most sites (Fig. 2). An overestimation of the modeled temperature is found at Wutaishan and Taishan primarily because the observation height is higher than that of the first layer of the model simulation for these two stations located on the mountains with more than 1 km height. The modeled relative humidity has generally good performance as shown in the comparative results in Fig. 3. However, the observed daily minimum relative humidity is mostly smaller than that of simulation. This deviation may have been caused by the different time resolutions between the measurements and the model output. The modeled wind speed shown in Fig. 4 is obtained by converting the output at the first layer (90 me200 m) of the model simulation of near surface wind (w10 m) using the MonineObukhov similarity theory (Ding et al., 2001). The trends of modeled and observed wind speed are evidently similar. The observed wind direction could also be well captured by the modeling system most of the time (Fig. 5). However, the modeled wind speed is broadly larger than the observed results from Baotou, Yiyuan, and Wuzhai, but smaller than the observed results from Wutaishan and Taishan. All deviations are primarily systematic errors at these five sites, probably caused by the errors originating from the conversion process of modeled wind speed from the first layer to near surface. The large deviation between the modeled and observed maximum wind speed presented in Fig. 4 is possibly due to the different time resolutions between measurement (10 min average) and model output (1 h), which is one of the main reasons that caused the deviation of wind direction between observed and simulated results (Fig. 5).
180
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 2. Observed (circle) and modeled (solid line) daily averaged temperature (K) at 12 stations in December 2010. Also shown are simulated and observed daily maximum and minimum in December 2010.
Modeled hourly mass concentrations of gases and particles (including SO2, NO2, O3, PM2.5, and PM10) in December 2010 are compared with the observation data at the Air Quality Network, which monitors air pollutants over the NCP in real-time (Tang et al., 2011). Figs. 6 and 7 demonstrate the comparative results of SO2, O3, NO2, PM2.5, and PM10. The site positions in the model domain are given in Fig. 1. Table 2 provides the statistical parameters, including averaged values, standard deviations, and correlation coefficients. The model can broadly reproduce the trend and magnitude of several air pollutants at each site, especially during the heavily pollution episode (Figs. 6 and 7).
Table 2 shows that most of the correlation coefficients are larger than 0.6, which indicates that the model performs relatively well on capturing the temporal trend of pollutant concentrations. The low correlation coefficients of SO2 at Hengshui are probably caused by the overestimation at the beginning of December, which may also be the same factor that caused the overestimation of the monthly mean of the modeled SO2 concentration at Hengshui. The modeled NO2 is systematically underestimated at Caofeidian as shown in Fig. 7(d), and the modeled monthly mean is evidently lower than the observed one, probably because of the bias of the emission inventory used
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
181
Fig. 3. Same as Fig. 2 but for relative humidity (%). Also shown are daily minimum of simulated and observed relative humidity.
in this simulation work. The monthly mean of PM2.5 concentration at Tanggu is obviously overestimated (higher than a factor of 2 as shown in Table 2). Given that Thanggu is located in the Beijing-Tianjin-Tangshan megacity cluster area, the overestimation may be caused by the heavy anthropogenic emission from the surrounding region. The modeled PM10 concentration monthly mean was generally underestimated by the modeling system as shown in Table 2. Several previous studies have also reported a similar deviation (Li et al., 2008; Song et al., 2002; Zhang et al., 2006). This condition is mainly due to the fact that the emission inventory used to construct the model omits the urban fugitive dust from building material, road dust, and other similar anthropogenic sources in China (Streets et al., 2003).
However, these kinds of sources are not the primary PM2.5 emissions. Thus, this deviation should not significantly affect the accuracy of the modeled PM2.5. Comparative results of the daily surface visibilities between the simulated result and surface measurement data in Beijing during the whole month of December 2010 are shown in Fig. 8. The observation data is obtained by a visibility sensor (Belfort Model 6000), which is regularly maintained, and the accuracies of the measurement are ensured (Tao et al., 2009). Fig. 8 illustrates that the modeled visibility trend and magnitude are quite similar to the features of the observed visibility, especially during December 17 to 22, when bad visibility occurred. This similarity indicates that the model could well capture surface visibility. The deviation between
182
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 4. Same as Fig. 2 but for wind speed (m s1). Also shown are daily maximum of simulated and observed wind speed.
simulated and observed results is mainly caused by interpretation of the model results as the average of each grid cell. 4. Results and discussions 4.1. Distribution features of simulated aerosol concentration and visibility Fig. 9 shows the modeled daily average mass concentration of surface PM2.5, wind field, visibility, and relative humidity from December 17 to 22. Fig. 10 shows the regional average vertical temperature, virtual potential temperature, and surface wind
speed variation in Beijing. On December 17, finding visibility below 10 km was difficult in Beijing. The mass concentration of PM2.5 in this megacity was generally lower than 100 mg m3. The temperature inversion was not obvious near the surface in the morning, and the wind speed exceeded 6 m s1 as shown by Fig. 10. These features indicated that the diffusion condition was beneficial for the scavenging of pollutants. In addition, the dominating northwest flux also brought clean air to the entire NCP region. However, the virtual potential temperature still evidently increased with an increase in height in the afternoon, and the wind speed also became smaller (lower than 6 m s1). This condition indicates that the meteorological pattern started
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
183
Fig. 5. Observed (circle) and modeled (solid line) wind direction of daily maximum wind at 12 stations in December 2010.
to be unfavorable for pollution diffusion in Beijing during the afternoon. On December 18, the visibility deteriorated significantly (lower than 10 km) in Beijing, Tianjin, Hebei, and Shandong, and was even less than 3 km in the southern part of Beijing. The mass concentration of PM2.5 increased by 100 mg m3 to 200 mg m3 in the regions east of Taihang Mountain. In the southern part of Beijing, the mass concentration exceeded 300 mg m3. Fig. 10 shows that the temperature inversion appeared near the surface during the whole day, and that the wind speed also became smaller (w3 m s1 to 5 m s1) in Beijing. These features indicated the planetary boundary layer (PBL) was stable, and the local pollution more easily
accumulated in this megacity. Additionally, the south wind could bring pollutants from the south polluted regions of the NCP and East China (Huang et al., 2010; Zhang et al., 2009a,b), apparently enhancing the PM2.5 mass burden in Beijing and its surrounding regions. The geographical feature of Beijing (as introduced in the first paragraph) is favorable for pollution accumulation when the south wind also dominates. On December 19, the visibility and air quality became better in Beijing, Tianjin, and Hebei. The mass burden of PM2.5 in Beijing generally returned to the magnitude similar to that on December 17. The strong surface wind (approximately 6 m s1 to 10 m s1, as shown in Fig. 10) and the domination of the northwest wind field
184
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 6. Observed (circle) and modeled (line) hourly mass concentrations (m gm3) of SO2 (aec), O3 (def), and PM2.5 (gei) in December 2010.
should be the main reasons of the improved visibility and air quality. In addition, the northwest wind carried the heavy PM2.5 mass burden to Shandong province, which increased the local mass concentration by approximately 100 mg m3, whereas the visibility deteriorated to less than 5 km in the middle and eastern part of Shandong. On December 20, the visibility slightly decreased and the mass concentration of PM2.5 increased by w50 mg m3 in Beijing. Fig. 10 shows that an obvious temperature inversion appeared near the surface during the morning in Beijing, and that the wind speed decreased to w3 m s1 in the afternoon. Considering that the west wind did not bring much pollutants to Beijing, the deterioration of air quality should have been mainly caused by the local emission. On December 21, the diffusion condition was still not favorable for the scavenging of pollutants in Beijing because of the temperature inversion and weak wind speed, as shown in Fig. 10. The visibility became worse (3 kme8 km), and the mass concentration of PM2.5 increased to 200 mg m3 to 300 mg m3 in southern Beijing. In addition, the visibility and air quality also deteriorated in the entire NCP region, which was dominated by the south wind field. On December 22, the PM2.5 visibility and mass concentration distribution patterns were very similar to that of December 19 because of their similar local diffusion conditions and large-scale wind fields. The daily average mass concentration of the modeled PM10 from December 17 to 22 at the surface is shown in Fig. 11. The PM10 distribution patterns in Beijing, Tianjin, Heibei, and Shangdong were similar with those of PM2.5. Therefore, the detailed analysis on PM10 variation feature will no longer be given. However, compared with PM2.5, a high concentration of PM10 appeared in the western Inner Mongolia province and northern Shaanxi province on December 19, as shown in Fig. 11(c). However, the heavy mass burden that exceeded 150 mg m3 did not evidently reduce visibility
(Fig. 9(i)). This phenomenon clearly indicates that aerosol particles larger than the size of PM2.5 could not efficiently enhance the extinction coefficient and cause visibility deterioration. 4.2. Sensitivity test of relative humidity on visibility variation Wu et al. (2007) described that a day with atmospheric haze occurrence has a daily mean visibility below 10 km and daily mean relative humidity of less than 90%. Fig. 9(gel) shows that the relative humidity was rarely higher than 90% in the model domain during the pollution episode, which suggests that the atmospheric haze occurred in all regions where visibility was less than 10 km. Most parts of Beijing, Tianjin, Hebei, and Shandong were covered by haze on December 18 and 21, when the air quality deteriorated. We conducted seven additional simulations to investigate the relationship between relative humidity and visibility. In each simulation case, the constant relative humidity values of 60%, 70%, 75%, 80%, 85%, 88%, and 90% were assumed over the whole model domain. Beijing and its surrounding regions, including Hebei, Shanxi, Shandong, and Tianjin were selected to show their regional daily average mass concentrations of PM2.5, ratio of PM2.5 to PM10, and the visibility under different relative humidity, as shown in Fig. 12. Visibility decreased with increasing relative humidity in all cases, and the declining trend became faster when relative humidity exceeded 80%. This phenomeno is due to the efficiently enhanced extinction coefficient of aerosol particles with the relative humidity increase in these regions. However, the larger decreasing range of visibility with increasing relative humidity can be observed when good visibility occurs. For example, the visibility, which was better than 40 km, could decrease to approximately 20 km, while relative humidity increased from 60% to 90% on December 22 in Beijing. Conversely, the variation in the visibility was just a few kilometers when it deteriorated (lower than 10 km) on December 21 in Beijing. This feature reflects that the impact of
Fig. 7. Same as Fig. 6 but for NO2 (aee) and PM10 (fel).
Table 2 Statistical summary of modeled mass concentrations (mg m3) of SO2, O3, NO2, PM2.5, and PM10 compared with observation data. Component
Site
Na
Cobsb
Cmodc
sobsd
smode
Rf
SO2
Hengshui Xianghe Yangfang Shuangqinglu Xianghe Yangfang Shuangqinglu Hengshui Tangshan Caofeidian Yangfang Tianjin Tanggu Tangshan Shuangqinglu Hengshui Tianjin Tanggu Tangshan Caofeidian Yangfang
729 707 738 739 586 678 744 736 744 744 718 614 684 708 657 626 698 720 741 744 738
22.99 21.68 10.42 17.80 11.39 19.71 21.40 22.27 32.19 29.94 21.90 103.91 58.91 94.38 68.69 170.06 195.40 154.13 171.32 101.13 101.61
38.93 27.67 12.48 16.02 18.72 18.94 27.60 27.16 25.81 10.64 16.06 139.86 112.92 106.14 101.44 134.94 140.49 121.90 113.75 94.53 47.58
17.29 21.17 11.06 12.69 10.73 12.32 15.92 10.99 15.81 14.14 21.62 101.87 51.83 83.30 74.41 104.03 136.17 110.40 138.75 94.77 98.49
19.66 23.75 15.82 14.56 16.95 16.35 15.58 13.11 16.28 7.42 15.91 90.74 82.99 85.25 74.17 78.37 98.00 92.07 95.26 80.81 53.42
0.35 0.73 0.40 0.73 0.76 0.64 0.77 0.66 0.74 0.63 0.76 0.58 0.73 0.71 0.81 0.62 0.67 0.64 0.57 0.69 0.63
O3
NO2
PM2.5
PM10
a b c d e f
Number of samples. Total mean of observations. Total mean of simulations. Standard deviation of observations. Standard deviation of simulations. Correlation coefficient between hourly observation and simulation.
relative humidity on visibility should not be the major reason for the occurrence of low visibility during this episode. 4.3. The relationship between PM2.5 and visibility Fig. 13 shows the regional daily average mass concentrations of PM2.5 and PM10, visibility, and relative humidity in Beijing, Tianjin,
Fig. 8. Observed and modeled daily averaged atmospheric visibility (km) at Beijing in December 2010.
186
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 9. The horizontal distributions of daily averaged PM2.5 concentration (aef) (m gm3) and visibility (gel) (km) in the period 17e22 December 2010 at the surface. Also shown is wind field (arrows) in (aef). The contour lines in (gel) represent the horizontal distribution of relative humidity (%).
Hebei, Shandong, and Shanxi from December 17 to 22. The averaged relative humidity did not evidently vary Fig. 13(d) clearly demonstrates that the relative humidity was rarely higher than 40% in Beijing, Tianjin, Hebei, and Shandong, and generally ranged from 40% to 60% in Shanxi. The similar tends of the PM2.5 and PM10 mass
concentration are shown in Fig. 13(a) and (b). Relatively high mass loading of aerosol mainly appeared in Beijing, Tianjin, and Hebei on December 18 and 21, and appeared in Shandong one day later. The condition on December 19was analyzed to elucidate this feature. The mass loading of aerosol in Shanxi was the lowest in the
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 10. The regional averaged vertical temperature and virtual potential temperature (K) variation (aeb) from surface to w1000 m in Beijing in the period 17e22 December 2010. Also shown is the regional averaged surface wind speed (m s1) variation (c).
aforementioned five regions, and the local visibility which remained around 40 km also reflected good air quality. The visibility in Beijing and Hebei changed significantly and are inversely correlated with the variation of aerosol mass burden. The visibility
187
in Shandong and Tianjin, with the highest aerosol mass loading, had the lowest value, as shown in Fig. 13(c). The correlation coefficients between regional hourly averaged mass concentration of PM2.5 and visibility in Beijing, Tianjin, Hebei, Shandong, and Shanxi are also calculated, and the correlation coefficient values were all negative. Even though the magnitude of PM2.5 mass concentration and visibility were apparently different among these five regions, the correlation coefficients still remained within the range of 0.7 to 0.8. The clearly inverse correlation between PM2.5 and visibility can be observed in the entire region of Beijing and its surrounding NCP regions. Sulfate and nitrate are two major PM2.5 components having significant extinction efficiency and are mainly concentrated in the regions of high anthropogenic emissions in China. Most sulfate and nitrate aerosols are secondary aerosols that are converted from their precursor gases (SO2 and NOx). In the previous section, the modeled SO2 and NO2 have been evaluated by several surface measurement data, and the relatively good agreement between simulated and observed results generally indicated that the modeling system could relatively well predict the mass concentration of sulfate and nitrate. Here, the contribution of these two aerosol species to the total extinction coefficient is analyzed to investigate the detailed relationship between aerosol particles of PM2.5 and visibility. Fig. 14 shows the daily averaged mass concentrations and the contribution ratios (CR) of sulfate and nitrate at the surface from December 17 to 22. The CR was obtained by subtracting the extinction coefficient with and without sulfate (or nitrate). The distribution patterns of sulfate and nitrate mass concentrations were generally similar to that of PM2.5. The heavy mass burden was mainly concentrated in Beijing, Tianjin, Hebei, and Shandong. The mass burden of these two PM2.5 components reached the highest value (ranging from 50 mg m3 to 75 mg m3) on December 18 to 22. However, the high sulfate and nitrate CR values were mainly concentrated in the regions with heavy mass
Fig. 11. The horizontal distributions of daily averaged PM10 concentrations (mg m3) in the period 17e22 December 2010 at the surface.
188
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 12. The variation of daily averaged visibility with relative humidity in Beijing, Tianjin, Hebei, Shandong, and Shanxi in the period 17e22 December 2010. Also shown are mass concentration of PM2.5 (mg m3; the first number in front of each curve) and ratio of PM2.5 to PM10 (the second number in front of each curve).
burden, and could reach approximately 20%e25% and 15%e20%, respectively. This result indicates that sulfate and nitrate could evidently reduce the visibility during the pollution episode. Evidently, 20% of the sulfate and nitrate CR areas became smaller
on December 18 and 21, compared with those on December 17 and 20. This result indicated that the extinction coefficient of other aerosol components increased when the heavy pollution appeared over NCP. However, sulfate CR increased in the urban
Fig. 13. The daily averaged mass concentration of PM2.5 (a) (mg m3), PM10 (b) (mg m3), visibility (c) (km), and relative humidity (d) (%) in Beijing, Tianjin, Hebei, Shandong, Shanxi in the period 17e22 December 2010.
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
189
Fig. 14. The horizontal distributions of daily averaged sulfate (aef) and nitrate (gel) mass concentrations (mg m3) in the period 17e22 December 2010 at the surface. Contour lines represent the contributions (%) of sulfate and nitrate aerosol to the total extinction coefficient.
regions on December 18 and 21 and exceed 25% in the south part of Beijing and Tianjin. This phenomenon indicates that the impact of sulfate on visibility should become more significant when heavy pollution occurs in these two megacities. Fig. 15 shows the hourly mass concentration of regional averaged PM2.5, sulfate, and nitrate when the visibility values decreased to 10 km in Beijing, Tianjin, Hebei, and Shandong on December 17 to 22. The change in the concentrations of these three pollutants along with time was not apparent, and the mass
burdens in these four regions were broadly concentrated within similar numerical intervals. Fig. 15 shows that the visibility in Beijing and its surrounding regions of NCP probably decreased to 10 km when PM2.5, sulfate, and nitrate reached 75 mg m3 to 85 mg m3, 20 mg m3 to 30 mg m3, and 15 mg m3 to 25 mg m3, respectively. Given that 10 km is the threshold of haze occurrence, the value ranges mentioned above could be a reference condition for distinguishing the haze occurrence in Beijing and NCP.
190
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 15. Regional average mass concentration (mg m3) of PM2.5, sulfate, and nitrate when the visibility was 10 km in Beijing, Tianjin, Hebei, and Shandong in the period 17e22 December 2010.
5. Conclusions In this study, RAMS-CMAQ air quality modeling system coupled with an aerosol optical properties scheme was applied to investigate the relationship between atmospheric aerosol and visibility during a heavy air pollution episode from December 17 to 22, 2010 in Beijing and NCP. The mass concentration and extinction coefficient of major aerosol species, including sulfate, nitrate, ammonium, black carbon, organic carbon, dust, and sea salt based on three modes (Aitken, accumulation, and coarse mode) were simulated, and then the surface visibility was calculated with full consideration of related aerosol microphysical properties. The simulated results were compared with the ground-based measurements collected from surface meteorological stations of CNMC, Air Quality Network, and the surface visibility measurement to evaluate the modeling system. The daily distribution features of aerosol mass concentration and visibility were shown and discussed. The possible reasons that could have caused the heavy pollution episode were analyzed in detail. Simulated results show that the visibility in Beijing and its surrounding regions in the NCP significantly decreased to lower than 10 km when heavy pollution occurred. During this episode, the south part of Beijing became one of the most seriously polluted regions in the NCP, and the minimum daily average visibility in this region was lower than 3 km. The highest daily average PM2.5 concentration exceeded 300 mg m3. Simulation results indicated that the local accumulation and long-range transport of pollutants both provided important contributions to the heavy pollution in Beijing. The daily average PM2.5 concentration increased by w50 mg m3 to 100 mg m3 when stable PBL and weak wind prevailed on December 20. The heavy PM2.5 loading also appeared in Beijing because of the transport of pollutants by the south wind on December 18 and 21, and the increasing range reached w50 mg m3 to 100 mg m3. In addition, the simulated results showed that aerosol particles larger than PM2.5 could not efficiently influence visibility. The relative humidity is an important factor that influences visibility by changing the extinction coefficient of aerosol caused by the water uptake behavior of soluble particles. The sensitivity tests in this study indicate that the visibility could be exponentially decreased with an increase in the relative humidity, and the declining trend becomes more obvious as relative humidity exceeds 80%. However, sensitivity tests also show that the variation in relative humidity cannot significantly change visibility when the visibility decreases around 10 km. The aerosol analysis indicated that the particle extinction of PM2.5 should be the primary reason that caused the visibility deterioration during this pollution episode. The variation in PM2.5 was closely inversely correlated with that of visibility in Beijing and
its surrounding NCP regions. Sulfate and nitrate were the two major compositions of PM2.5 over the NCP. They provided approximately 40%e45% CR to the total extinction coefficient in Beijing, Tianjin, Hebei, and Shandong, which signifies that the visibility could be significantly reduced. The extinction effect of sulfate was even stronger (25% CR to the total extinction coefficient) in the urban area (Beijing and Tianjin) than those in other regions (approximately 20% CR to the total extinction coefficient) when the heavy pollution occurred on December 18 and 21. The model results also indicated that the value range of PM2.5 concentration between 75 mg m3 to 85 mg m3 could be a parameter for haze occurrence in Beijing and its surrounding regions in the NCP. Acknowledgments This work was supported by the “Strategic Priority Research Program (B)” of the Chinese Academy of Sciences (XDB05030105, XDB05030102, XDB05030103), National Department Public Benefit Research Foundation (Ministry of Environmental Protection of the People’s Republic of China) (No. 201009001), the National Natural Science Foundation of China (41105106, 20937001 and 41205123). Appendix A API represents the air pollution level in Chinese cities (available at http://datacenter.mep.gov.cn) and is linearly related to the daily mean PM10 concentration of hourly observations. When API (I) lies between the breakpoints Ii and Ij, the mass concentration of PM10 can be calculated by
C ¼ ci cj
Ii Ij I Ij þ Cj
(A.1)
where C is the PM10 concentration, and Ci and Cj are the PM10 concentrations corresponding to Ii and Ij listed in Table A.1. Appendix B The meteorological dataset provided by CNMC (http://cdc.cma. gov.cn/home.do) includes surface observations of several variables such as mean temperature, maximum and minimum temperature, mean relative humidity, minimum relative humidity, mean wind speed, maximum wind speed, the wind direction of maximum wind, and total precipitation. Except for wind direction of maximum wind, all other variables are on a daily basis. All variables are measured from 1 January 1951 to present. A total of 726 CNMC measurement stations are evenly distributed in mainland China (Feng et al., 2004). In the present study, the observed temperature, relative humidity, wind speed, and wind direction in
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
December 2010 at 12 observation sites in North China Plain are used to evaluate the modeled meteorological results. Table A.1 The breakpoints of air pollution index and corresponding PM10 concentrations. API PM10 (mg m3)
0 0
50 50
100 150
200 350
300 420
400 500
500 600
References Benkovitz, C., Schultz, M., Pacyna, J., Tarrason, L., Dignon, J., Voldner, E., Spiro, P., Logan, J., Graedel, T., 1996. Global gridded inventories of anthropogenic emissions of sulfur and nitrogen. Journal of Geophysical Research 101 (D22), 29239e29254. Binkowski, F., Shankar, U., 1995. The regional particulate model, 1, model description and preliminary results. Journal of Geophysical Research 100, 26191e26209. Byun, D., Schere, K., 2006. Review of the governing equations, computation algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51e77. Che, H., Zhang, X., Li, Y., Zhou, Z., Qu, J., 2007. Horizontal visibility trends in China 1981e2005. Geophysical Research Letters 34, L24706. http://dx.doi.org/ 10.1029/2007GL031450. Chuang, C., Penner, J., Prospero, J., Grant, K., Rau, G., Kawamoto, K., 2002. Cloud susceptibility and the first aerosol indirect forcing: sensitivity to black carbon and aerosol concentrations. Journal of Geophysical Research 107. http:// dx.doi.org/10.1029/2000JD000215. Chuang, D., Song, Y., Liu, B., 2009. Visibility trends in six mega cities in China 1973e 2007. Atmospheric Research 94 (2), 161e167. Cotton, W., Pielke, R., Walko, G., Liston, G., Tremback, C., Jiang, H., McAnelly, R., Harrington, J., Nicholls, M., Carrio, G., McFadden, J., 2003. RAMS 2001: current status and future directions. Meteorology Atmospheric Physics 82, 5e29. Ding, F., Pal Arya, S., Lin, Y., 2001. Large-eddy simulations of the atmospheric boundary layer using a new subgrid-scale model-II. Weakly and moderately stable cases. Environmental Fluid Mechanics 1 (1), 49e69. Fei, J., Wang, R., Wang, Y., Huang, X., 2009. A numerical analysis of secondary aerosol inorganic particles of PM2.5 in a strong fog (in Chinese). Transactions of Atmospheric Sciences 32, 360e366. Feng, S., Hu, Q., Qian, W., 2004. Quality control of daily meteorological data in China, 1951e2000: a new dataset. International Journal of Climatology 24 (7), 853e870. Ge, C., Zhang, M., Han, Z., Liu, Y., 2011. Episode simulation of Asian dust storms with an air quality modeling system. Advances in Atmospheric Sciences 28 (3), 511e520. Ghan, S., Zaveri, R., 2007. Parameterization of optical properties for hydrated internally mixed aerosol. Journal of Geophysical Research 112, D10201. http:// dx.doi.org/10.1029/2006JD007927. Han, X., Zhang, M., Han, Z., Xin, J., Liu, X., 2011. Simulation of aerosol direct radiative forcing with RAMS-CMAQ in East Asia. Atmospheric Environment 45 (36), 6576e6592. Han, X., Ge, C., Tao, J., Zhang, M., Zhang, R., 2012. Air quality modeling for a strong dust event in East Asia in March 2010. Aerosol and Air Quality Research 12, 615e628. Huang, X., He, L., Hu, M., Canagaratna, M., Sun, Y., Zhang, Q., Zhu, T., Xue, L., Zeng, L., Liu, X., Zhang, Y., Jayne, J., Ng, N., Worsnop, D., 2010. Highly time-resolved chemical characterization of atmospheric submicron particles during 2008 Beijing Olympic Games using an aerodyne high-resolution aerosol mass spectrometer. Atmospheric Chemistry and Physics 10 (18), 8933e8945. Li, L., Chen, C., Huang, H., Li, Z., Fu, J., Jang, C., Streets, D., 2008. Regional air pollution characteristics simulation of O3 and PM10 over Yangtze River Delta region (in Chinese). Environmental Sciences 29, 237e245. Li, W., Shao, L., Buseck, P., 2010. Haze types in Beijing and the influence of agricultural biomass burning. Atmospheric Chemistry and Physics 10, 8119e8130. Liao, H., Seinfeld, J.H., 2005. Global impacts of gas-phase chemistry-aerosol interactions on direct radiative forcing by anthropogenic aerosols and ozone. Journal of Geophysical Research 110, D18208. http://dx.doi.org/10.1029/ 2005JD005907. Liu, X., Pernner, J., Das, B., Bergmann, D., Rodriguez, J., Strahan, S., Wang, M., Feng, Y., 2007. Uncertainty in global aerosol simulation: assessment using three meteorological data sets. Journal of Geophysical Research 112, D11212. http:// dx.doi.org/10.1029/2006JD008216.
191
Nenes, A., Pandis, S., Pilinis, C., 1999. Continued development and testing of a new thermodynamic aerosol module for urban and regional air quality models. Atmospheric Environment 33 (10), 1553e1560. Olivier, J., Bouwman, A., Maas, C., Berdowski, J., 1994. Emission database for global atmospheric research. Environmental Monitoring and Assessment 31, 93e106. Pruppacher, H., Klett, J., 1997. Microphysics of Clouds and Precipitation. Springer, New York. p. 954. Quan, J., Zhang, Q., He, H., Liu, J., Huang, M., Jin, H., 2011. Analysis of the formation of fog and haze in North China Plain (NCP). Atmospheric Chemistry and Physics 11 (4), 8214e8250. Randerson, J.T., Van der Werf, G., Giglio, L., Collatz, G., Kasibhatla, P., 2005. Global Fire Emissions Database, Version 2 (GFEDv2. 1). OakRidge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennesse, USA. Available at: http://daac.ornl.gov/ http://dx.doi.org/10.3334/ORNLDAAC/849. from. Ruckstuhl, C., Philipona, R., Behrens, K., Collaud, C., Durr, B., Heimo, A., Matzler, C., Nyeki, S., Ohmura, A., Vuilleumier, L., Weller, M., Wehrli, C., Zelenka, A., 2008. Aerosol and cloud effects on solar brightening and recent rapid warming. Geophysical Research Letters 35 (12). http://dx.doi.org/10.1029/2008GL034228. Sarwar, G., Luecken, D., Yarwood, G., Whitten, G., Carter, W., 2008. Impact of an updated carbon bond mechanism on predictions from the CMAQ modeling system: preliminary assessment. Journal of Applied Meteorology and Climatology 47, 3e14. Seinfeld, J., Pandis, S., 1998. Atmospheric Chemistry and Physics. Wiley, New York, USA. Song, Y., Chen, J., Cai, X., 2002. The modeling of PM10 contribution to Beijing by the Shijingshan industrial area. Chinese Journal of Environmental Science 23, 65e 68 (in Chinese). Streets, D., Bond, T., Carmichael, G., Fernandes, S., Fu, Q., He, D., 2003. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. Journal of Geophysical Research 108. http://dx.doi.org/10.1029/2002JD003093. Sun, Y., Zhuang, G., Tang, A., Wang, Y., An, Z., 2006. Chemical characteristics of PM2.5 and PM10 in Haze-Fog episodes in Beijing. Environmental Science & Technology 40, 3148e3155. Tang, G., Wang, Y., Li, X., Ji, D., Gao, X., 2011. Spatial-temporal variations of surface ozone and ozone control strategy for Northern China. Atmospheric Chemistry and Physics Discussions 11 (9), 26057e26109. Tao, J., Ho, K., Chen, L., Zhu, L., Han, J., Xu, Z., 2009. Effect of chemical composition of PM2.5 on visibility in Guangzhou, China, 2007 Spring. Particuology 7 (1), 68e75. Tao, M., Chen, L., Su, L., Tao, J., 2012. Satellite observation of regional haze pollution over the North China Plain. Journal of Geophysical Research 117. http:// dx.doi.org/10.1029/2012JD017915. Wang, Y., Zhuang, G., Sun, Y., An, Z., 2006. The variation of characteristics and formation mechanisms of aerosols in dust, haze, and clear days in Beijing. Atmospheric Environment 40, 6579e6591. Wang, L., Xu, J., Yang, J., Zhao, X., Wei, W., Cheng, D., Pan, X., Su, J., 2012. Understanding haze pollution over the southern Hebei area of China using the CMAQ model. Atmospheric Environment 56, 69e79. Wu, D., Bi, X., Deng, X., Li, F., Tan, H., 2007. Effect of atmospheric haze on the deterioration of visibility over the Pearl River Delta. Acta Geographica Sinica 21, 215e223 (in Chinese). Zhang, M., Xu, Y., Zhang, R., Han, Z., 2005. Emission and concentration distribution of black carbon aerosol in East Asia during springtime. Chinese Journal of Geophysics 48, 55e61. Zhang, M., Uno, I., Zhang, R., Han, Z., Wang, Z., Pu, Y., 2006. Evaluation of theModels3 Community Multi-scale Air Quality (CMAQ) modeling system with observations obtained during the TRACE-P experiment: comparison of ozone and its related species. Atmospheric Environment 40 (26), 4874e4882. Zhang, Q., Klimont, Z., Streets, D., Huo, H., He, K., 2006. An anthropogenic PM emission model for China and emission inventory for the year 2001. Progress in Natural Science 16, 223e231 (in Chinese). Zhang, M., Gao, L., Ge, C., Xu, Y., 2007. Simulation of nitrate aerosol concentrations over East Asia with the model system RAMS-CMAQ. Tellus B 59, 372e380. Zhao, X., Zhang, X., Xu, X., Xu, J., Meng, W., Pu, W., 2009a. Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing. Atmospheric Environment 43, 2893e2900. Zhang, Q., Streets, D., Carmichael, G., He, K., Huo, H., Kannari, A., et al., 2009b. Asian emissions in 2006 for the NASA INTEX-B mission. Atmospheric Chemistry and Physics 9, 5131e5153. Zhang, J., Zhu, T., Zhang, Q., Li, C., Shu, H., Ying, Y., Dai, Z., Liu, X., Liang, A., Shen, H., 2012. The impact of circulation patterns on regional transport pathways and air quality over Beijing and its surroundings. Atmospheric Chemistry and Physics 11 (12), 5031e5053. Zheng, M., Salmon, L., Schauer, J., Zeng, L., Kiang, C., Zhang, Y., Cass, G., 2005. Seasonal trends in PM2.5 source contributions in Beijing, China. Atmospheric Environment 39 (22), 3967e3976.
Contents lists available at SciVerse ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Modeling aerosol impacts on atmospheric visibility in Beijing with RAMS-CMAQ Xiao Han a,1, Meigen Zhang a, *, Jinhua Tao b, 2, Lili Wang a, 3, Jian Gao c, 4, Shulan Wang c, 5, Fahe Chai c, 6 a
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, HuaYanBeiLi 40#, Chaoyang District, Beijing 100029, China State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China c Chinese Research Academy of Environmental Sciences, Beijing 100012, China b
h i g h l i g h t s < A heavy pollution episode occurred in North China Plain is identified and simulated by RAMS-CMAQ. < The pollution processes and the relationship between visibility and aerosol are discussed. < Model simulations indicate the low atmospheric visibility was caused by high PM2.5 concentration.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 November 2012 Received in revised form 7 February 2013 Accepted 15 February 2013
A typical heavy air pollution episode occurred over the North China Plain (NCP) in December 2010. The air quality in Beijing and its surrounding regions worsened during the period December 17 to 22, and local visibility became significantly affected by the high pollution levels. The air quality modeling system RAMS-CMAQ coupled with an aerosol optical property scheme was applied to simulate the trace gases and major aerosol components in the NCP to obtain an in-depth understanding of the relationship between regional low visibility and aerosol particles. The model performance was evaluated using various observation data, such as meteorological factors (temperature, relative humidity, and wind field), gaseous pollutants (SO2, NO2, and O3), PM2.5, PM10, and visibility at several measurement stations. The modeled meteorological field and visibility were in good agreement with observations from December 2010. The modeled mass concentrations of gaseous pollutants and aerosol particles also suitably captured the magnitude and variation features of the observation data, especially during the air pollution episode. The simulated results showed that during this pollution episode, low visibility (lower than 10 km) occurred mainly in Beijing, Tianjin, Hebei, and Shandong. The analysis and sensitivity test indicated that the aerosol particles larger than PM2.5 and the water uptake effect of aerosol optical properties could not significantly influence visibility. Thus, the low visibility was primarily caused by the high mass burden of PM2.5as a result of the local pollutant accumulation and long-range transport. Statistics showed that the visibility variation was closely inversely related to the variation in PM2.5 in most regions in the NCP. Visibility decreased lower than 10 km when the mass concentration of PM2.5 exceeded 75 mg m3 to 85 mg m3 in the NCP. Sulfate and nitrate were the two major inorganic aerosol components of PM2.5 that evidently decreased visibility by contributing 40% to 45% to the total extinction coefficient value. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Visibility Aerosol CMAQ Haze North China Plain
* Corresponding author. Tel.: þ86 0 10 62379620; fax: þ86 0 10 62041393. E-mail addresses: [email protected] (X. Han), [email protected] (M. Zhang), [email protected] (J. Tao), [email protected] (L. Wang), [email protected] (J. Gao), [email protected] (S. Wang), [email protected] (F. Chai). 1 Tel.: þ86 0 15801475815. 2 Tel.: þ86 0 18601943282. 3 Tel.: þ86 0 13811716690. 4 Tel.: þ86 0 18911819868. 5 Tel.: þ86 0 10 84913904. 6 Tel.: þ86 0 10 84915164. 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.02.030
178
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
1. Introduction Beijing, the capital of P.R. China, is located at the northern tip of the North China Plain (NCP) and surrounded by high mountains in its northern (Yanshan Mountain) and western (Taihang Mountain) boundaries. In recent years, this megacity has suffered from the deterioration of air quality as a result of rapid population growth and economic development (Zhao et al., 2009a,b; Zheng et al., 2005). High aerosol loading induced severe regional climatic and environmental issues. Previous studies have suggested that the aerosol particles were mainly emitted by multiple local anthropogenic sources (Sun et al., 2006) and the pollution inflow from the surrounding regions (Zhang et al., 2012) of NCP. Aerosols have direct (Liao and Seinfeld, 2005; Liu et al., 2007) and indirect climatic effects (Ruckstuhl et al., 2008) on the earth’s energy balance. Atmospheric visibility and human health (Chuang et al., 2009; Che et al., 2007) can also be affected by aerosol particles. The frequently widespread haze cloud over the NCP (Tao et al., 2012) is a typical visibility deterioration phenomenon during the past decade. Aerosol particles cause visibility deterioration through light extinction. Several studies have focused on the complex features of local aerosols and analyzed the relationship between visibility and aerosol particles in Beijing and the NCP. Wang et al. (2006) analyzed the component of aerosol in hazy and clear days in Beijing using 4-year observation samples and suggested that sulfate and nitrate were the two major aerosol species in hazy days. Quan et al. (2011) summarized the hazy day occurrence trend over the NCP in the past 56 years. They also conducted an insitu field experiment and found a high concentration of fine aerosol particle during the hazy day. Li et al. (2010) analyzed the characteristic and impact of carbonous aerosol during hazy days in Beijing. Wang et al. (2012) simulated the most polluted months from 2001 to 2010 and analyzed the haze frequencies of seven urban centers in the NCP. The transboundary air pollution was found to play an important role in the haze formation over the NCP. Fei et al. (2009) used the Model-3/CMAQ to simulate a strong
Table 1 The air pollution index released by Ministry of Environment Protection of China at six cities in North China Plain. Date
Beijing
Tianjin
Shenyang
Taiyuan
Jinan
Shijiazhuang
2010-12-14 2010-12-15 2010-12-16 2010-12-17 2010-12-18 2010-12-19 2010-12-20 2010-12-21 2010-12-22 2010-12-23 2010-12-24
31 53 92 105 156 182 100 165 222 30 40
57 60 75 93 103 133 108 107 174 72 56
97 97 138 96 133 160 222 218 176 89 81
63 47 68 117 102 85 91 165 115 59 59
55 60 90 107 90 111 121 133 153 215 68
84 76 103 127 142 106 75 127 109 78 46
fog episode and analyzed its evolution in Beijing. They found that relative humidity and inorganic particles of PM2.5 were important factors for low visibility. All impact factors should be considered when investigating the detailed relationship between ambient visibility and aerosol particles because the aerosol extinction ability is related to various factors such as relative humidity, aerosol species, microphysics, and so on. The aerosol dynamic processes, size distribution, mixing state, and water uptake effect are especially important in improving the accuracy for estimating aerosol extinction. A typical heavy air pollution episode occurred over the NCP during the period December 17 to 22, 2010. The air pollution index (API, described in Appendix A), encompassing six cities (shown in Fig. 1) and released by the Ministry of Environment Protection of China, clearly showed the high pollution level of this episode over the NCP (Table 1). The API was relatively lower on December 14, 15, 16, 23, and 24, 2010, but higher from December 17 to 22, 2010,in these six cities. Thus, in the present study, the mass burden of major aerosol species (sulfate, nitrate, ammonium, black carbon, organic carbon, dust, and sea salt) and surface visibility in December 2010 are simulated by the air quality modeling system RAMS-CMAQ coupled with an aerosol optical property scheme. All
Fig. 1. Observation sites of Air Quality Network and Chinese National Meteorological Centre (CNMC) in the model domain. Also shown are geographic locations of Beijing, Tianjin, Hebei, Shanxi, Shangdong.
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
aerosol microphysical features that can influence aerosol optical properties considered in this modeling system. The temporal and spatial characteristics of aerosol mass burden and visibility during the pollution episode are analyzed. This study focused on and discussed the main factors that may cause low visibility to investigate the relationship between visibility and aerosol in Beijing and its surrounding regions in the NCP. 2. Methodology The major component of the RAMS-CMAQ modeling system is the air quality model CMAQ (version 4.7) developed by the US Environmental Protection Agency (US EPA) to simulate multiple air quality issues with multi-scale capabilities (Byun and Schere, 2006). The gas-phase chemistry mechanism is updated to the expanded version CB05 (Sarwar et al., 2008). The thermodynamic equilibrium between inorganic aerosol species and gas-phase concentrations is treated by ISORROPIA (Nenes et al., 1999). Particle nucleation, coagulation, condensation, and other dynamic processes are described using the Regional Particulate Model (Binkowski and Shankar, 1995). The aerosol size distribution is divided into three modes, namely, the Aitken, accumulation, and coarse modes. The aerosol components, geometric standard deviation, and geometric mean radius of each mode can be found in the study of Han et al. (2011). All modes are assumed to follow the log normal distribution. Internal mixing of the aerosol species is assumed within each mode, but the modes themselves are externally mixed. The summation of the aerosols in the Aitken and accumulation modes are used to represent the PM2.5 particles, and the total aerosols of all three modes are used to represent PM10 particles. The numerical prediction model RAMS is coupled with CMAQ in the offline method to provide meteorological fields to CMAQ. RAMS uses terrain-following sz coordinate system and can suitably simulate the boundary layer and the underlying surface (Cotton et al., 2003). To obtain the aerosol extinction coefficient, the scheme that could fully consider the major impact factors of aerosol optical properties is added in the modeling system. This scheme contains a parameterization (Ghan and Zaveri, 2007) that efficiently simplifies the calculation process of the Mie theory and also maintain sufficient accuracy. The effects of water uptake and internal mixture are treated using Kohler theory (Pruppacher and Klett, 1997) and MaxwelleGarnett mixing rule (Chuang et al., 2002) in this scheme, respectively. This scheme is described in detail in Han et al. (2011). After the aerosol extinction coefficient is obtained, the surface visibility is calculated using
VIS ¼ 3:912=b
(1)
where b is the aerosol extinction coefficient (Seinfeld and Pandis, 1998). Previous studies have demonstrated that the modeling system performs well on simulating the aerosol mass concentration and optical properties (Ge et al., 2011; Han et al., 2011, 2012; Zhang et al., 2005, 2006, 2007). The simulation has two layer grids. The larger one is 6654 5440 km2 with a 64 km grid cell on a rotated polar stereographic map projection centered at (116 E, 35 N) and covers the whole area of East Asia as shown in Han et al. (2011). The smaller one (Fig. 1) has 94 90 grid cells with a 16 km resolution on a rotated polar stereographic map projection centered at (116 E, 40 N). It covers all major regions in the NCP, including Beijing, Tianjin, Hebei, Shandong, and Shanxi. The model system has 15 vertical layers, unequally spaced from the ground to approximately 23 km, with nearly half of them concentrated in the lowest 2 km (vertical resolution of 100 me200 m from the ground to approximately 1.5 km) to
179
improve the simulation of the atmospheric boundary layer. The time step of the model simulation is 1 h. The emission inventories used by the modeling system are introduced as follows. The anthropogenic emissions of aerosols and their precursors (CO, NOx, SO2, volatile organic compounds, black carbon, organic carbon, PM2.5, and PM10) are obtained from Streets’ 2006 emission inventory (http://www.cgrer.uiowa.edu/EMISSIONDATA new/index16.html) of a 0.5 0.5 spatial resolution, which includes the following categories: power, industry, residential, and transport. The Regional Emission inventory for Asia domain (REAS, http://www.jamstec.go.jp/frsgc/research/d4/emission.htm) is used to provide NH3 emission. The biomass burning emissions from forest wildfires, savanna burning, and slash-and-burn agriculture are provided by the monthly mean inventory of Global Fire Emissions Database, Version 2 (GFEDv2.1) (Randerson et al., 2005). Nitrogen oxide emissions from the soil and natural hydrocarbon emissions are obtained from the Global Emissions Inventory Activity global monthly inventory (Benkovitz et al., 1996). The volcanic emissions, flight exhaust, and lightning nitrogen oxides are from MICS-Asia II and Emission Database for Global Atmospheric Research (Olivier et al., 1994), respectively.
3. Model evaluation The modeled meteorological field is an important factor in aerosol simulation. The transport, scavenging processes, and particle water uptake of aerosols are intuitively related to wind vector, temperature, and relative humidity in a modeling system. Thus, in this paper, the observed meteorological data from surface stations of the Chinese National Meteorological Center (CNMC; described in Appendix B) are collected to evaluate the performance of the model. Fig. 1 demonstrates the positions of the measurement sites in the model domain. The comparative results are shown from Figs. 2e5. The modeled temperature can suitably reflect the trend of observation data, which are very close in most sites (Fig. 2). An overestimation of the modeled temperature is found at Wutaishan and Taishan primarily because the observation height is higher than that of the first layer of the model simulation for these two stations located on the mountains with more than 1 km height. The modeled relative humidity has generally good performance as shown in the comparative results in Fig. 3. However, the observed daily minimum relative humidity is mostly smaller than that of simulation. This deviation may have been caused by the different time resolutions between the measurements and the model output. The modeled wind speed shown in Fig. 4 is obtained by converting the output at the first layer (90 me200 m) of the model simulation of near surface wind (w10 m) using the MonineObukhov similarity theory (Ding et al., 2001). The trends of modeled and observed wind speed are evidently similar. The observed wind direction could also be well captured by the modeling system most of the time (Fig. 5). However, the modeled wind speed is broadly larger than the observed results from Baotou, Yiyuan, and Wuzhai, but smaller than the observed results from Wutaishan and Taishan. All deviations are primarily systematic errors at these five sites, probably caused by the errors originating from the conversion process of modeled wind speed from the first layer to near surface. The large deviation between the modeled and observed maximum wind speed presented in Fig. 4 is possibly due to the different time resolutions between measurement (10 min average) and model output (1 h), which is one of the main reasons that caused the deviation of wind direction between observed and simulated results (Fig. 5).
180
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 2. Observed (circle) and modeled (solid line) daily averaged temperature (K) at 12 stations in December 2010. Also shown are simulated and observed daily maximum and minimum in December 2010.
Modeled hourly mass concentrations of gases and particles (including SO2, NO2, O3, PM2.5, and PM10) in December 2010 are compared with the observation data at the Air Quality Network, which monitors air pollutants over the NCP in real-time (Tang et al., 2011). Figs. 6 and 7 demonstrate the comparative results of SO2, O3, NO2, PM2.5, and PM10. The site positions in the model domain are given in Fig. 1. Table 2 provides the statistical parameters, including averaged values, standard deviations, and correlation coefficients. The model can broadly reproduce the trend and magnitude of several air pollutants at each site, especially during the heavily pollution episode (Figs. 6 and 7).
Table 2 shows that most of the correlation coefficients are larger than 0.6, which indicates that the model performs relatively well on capturing the temporal trend of pollutant concentrations. The low correlation coefficients of SO2 at Hengshui are probably caused by the overestimation at the beginning of December, which may also be the same factor that caused the overestimation of the monthly mean of the modeled SO2 concentration at Hengshui. The modeled NO2 is systematically underestimated at Caofeidian as shown in Fig. 7(d), and the modeled monthly mean is evidently lower than the observed one, probably because of the bias of the emission inventory used
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
181
Fig. 3. Same as Fig. 2 but for relative humidity (%). Also shown are daily minimum of simulated and observed relative humidity.
in this simulation work. The monthly mean of PM2.5 concentration at Tanggu is obviously overestimated (higher than a factor of 2 as shown in Table 2). Given that Thanggu is located in the Beijing-Tianjin-Tangshan megacity cluster area, the overestimation may be caused by the heavy anthropogenic emission from the surrounding region. The modeled PM10 concentration monthly mean was generally underestimated by the modeling system as shown in Table 2. Several previous studies have also reported a similar deviation (Li et al., 2008; Song et al., 2002; Zhang et al., 2006). This condition is mainly due to the fact that the emission inventory used to construct the model omits the urban fugitive dust from building material, road dust, and other similar anthropogenic sources in China (Streets et al., 2003).
However, these kinds of sources are not the primary PM2.5 emissions. Thus, this deviation should not significantly affect the accuracy of the modeled PM2.5. Comparative results of the daily surface visibilities between the simulated result and surface measurement data in Beijing during the whole month of December 2010 are shown in Fig. 8. The observation data is obtained by a visibility sensor (Belfort Model 6000), which is regularly maintained, and the accuracies of the measurement are ensured (Tao et al., 2009). Fig. 8 illustrates that the modeled visibility trend and magnitude are quite similar to the features of the observed visibility, especially during December 17 to 22, when bad visibility occurred. This similarity indicates that the model could well capture surface visibility. The deviation between
182
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 4. Same as Fig. 2 but for wind speed (m s1). Also shown are daily maximum of simulated and observed wind speed.
simulated and observed results is mainly caused by interpretation of the model results as the average of each grid cell. 4. Results and discussions 4.1. Distribution features of simulated aerosol concentration and visibility Fig. 9 shows the modeled daily average mass concentration of surface PM2.5, wind field, visibility, and relative humidity from December 17 to 22. Fig. 10 shows the regional average vertical temperature, virtual potential temperature, and surface wind
speed variation in Beijing. On December 17, finding visibility below 10 km was difficult in Beijing. The mass concentration of PM2.5 in this megacity was generally lower than 100 mg m3. The temperature inversion was not obvious near the surface in the morning, and the wind speed exceeded 6 m s1 as shown by Fig. 10. These features indicated that the diffusion condition was beneficial for the scavenging of pollutants. In addition, the dominating northwest flux also brought clean air to the entire NCP region. However, the virtual potential temperature still evidently increased with an increase in height in the afternoon, and the wind speed also became smaller (lower than 6 m s1). This condition indicates that the meteorological pattern started
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
183
Fig. 5. Observed (circle) and modeled (solid line) wind direction of daily maximum wind at 12 stations in December 2010.
to be unfavorable for pollution diffusion in Beijing during the afternoon. On December 18, the visibility deteriorated significantly (lower than 10 km) in Beijing, Tianjin, Hebei, and Shandong, and was even less than 3 km in the southern part of Beijing. The mass concentration of PM2.5 increased by 100 mg m3 to 200 mg m3 in the regions east of Taihang Mountain. In the southern part of Beijing, the mass concentration exceeded 300 mg m3. Fig. 10 shows that the temperature inversion appeared near the surface during the whole day, and that the wind speed also became smaller (w3 m s1 to 5 m s1) in Beijing. These features indicated the planetary boundary layer (PBL) was stable, and the local pollution more easily
accumulated in this megacity. Additionally, the south wind could bring pollutants from the south polluted regions of the NCP and East China (Huang et al., 2010; Zhang et al., 2009a,b), apparently enhancing the PM2.5 mass burden in Beijing and its surrounding regions. The geographical feature of Beijing (as introduced in the first paragraph) is favorable for pollution accumulation when the south wind also dominates. On December 19, the visibility and air quality became better in Beijing, Tianjin, and Hebei. The mass burden of PM2.5 in Beijing generally returned to the magnitude similar to that on December 17. The strong surface wind (approximately 6 m s1 to 10 m s1, as shown in Fig. 10) and the domination of the northwest wind field
184
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 6. Observed (circle) and modeled (line) hourly mass concentrations (m gm3) of SO2 (aec), O3 (def), and PM2.5 (gei) in December 2010.
should be the main reasons of the improved visibility and air quality. In addition, the northwest wind carried the heavy PM2.5 mass burden to Shandong province, which increased the local mass concentration by approximately 100 mg m3, whereas the visibility deteriorated to less than 5 km in the middle and eastern part of Shandong. On December 20, the visibility slightly decreased and the mass concentration of PM2.5 increased by w50 mg m3 in Beijing. Fig. 10 shows that an obvious temperature inversion appeared near the surface during the morning in Beijing, and that the wind speed decreased to w3 m s1 in the afternoon. Considering that the west wind did not bring much pollutants to Beijing, the deterioration of air quality should have been mainly caused by the local emission. On December 21, the diffusion condition was still not favorable for the scavenging of pollutants in Beijing because of the temperature inversion and weak wind speed, as shown in Fig. 10. The visibility became worse (3 kme8 km), and the mass concentration of PM2.5 increased to 200 mg m3 to 300 mg m3 in southern Beijing. In addition, the visibility and air quality also deteriorated in the entire NCP region, which was dominated by the south wind field. On December 22, the PM2.5 visibility and mass concentration distribution patterns were very similar to that of December 19 because of their similar local diffusion conditions and large-scale wind fields. The daily average mass concentration of the modeled PM10 from December 17 to 22 at the surface is shown in Fig. 11. The PM10 distribution patterns in Beijing, Tianjin, Heibei, and Shangdong were similar with those of PM2.5. Therefore, the detailed analysis on PM10 variation feature will no longer be given. However, compared with PM2.5, a high concentration of PM10 appeared in the western Inner Mongolia province and northern Shaanxi province on December 19, as shown in Fig. 11(c). However, the heavy mass burden that exceeded 150 mg m3 did not evidently reduce visibility
(Fig. 9(i)). This phenomenon clearly indicates that aerosol particles larger than the size of PM2.5 could not efficiently enhance the extinction coefficient and cause visibility deterioration. 4.2. Sensitivity test of relative humidity on visibility variation Wu et al. (2007) described that a day with atmospheric haze occurrence has a daily mean visibility below 10 km and daily mean relative humidity of less than 90%. Fig. 9(gel) shows that the relative humidity was rarely higher than 90% in the model domain during the pollution episode, which suggests that the atmospheric haze occurred in all regions where visibility was less than 10 km. Most parts of Beijing, Tianjin, Hebei, and Shandong were covered by haze on December 18 and 21, when the air quality deteriorated. We conducted seven additional simulations to investigate the relationship between relative humidity and visibility. In each simulation case, the constant relative humidity values of 60%, 70%, 75%, 80%, 85%, 88%, and 90% were assumed over the whole model domain. Beijing and its surrounding regions, including Hebei, Shanxi, Shandong, and Tianjin were selected to show their regional daily average mass concentrations of PM2.5, ratio of PM2.5 to PM10, and the visibility under different relative humidity, as shown in Fig. 12. Visibility decreased with increasing relative humidity in all cases, and the declining trend became faster when relative humidity exceeded 80%. This phenomeno is due to the efficiently enhanced extinction coefficient of aerosol particles with the relative humidity increase in these regions. However, the larger decreasing range of visibility with increasing relative humidity can be observed when good visibility occurs. For example, the visibility, which was better than 40 km, could decrease to approximately 20 km, while relative humidity increased from 60% to 90% on December 22 in Beijing. Conversely, the variation in the visibility was just a few kilometers when it deteriorated (lower than 10 km) on December 21 in Beijing. This feature reflects that the impact of
Fig. 7. Same as Fig. 6 but for NO2 (aee) and PM10 (fel).
Table 2 Statistical summary of modeled mass concentrations (mg m3) of SO2, O3, NO2, PM2.5, and PM10 compared with observation data. Component
Site
Na
Cobsb
Cmodc
sobsd
smode
Rf
SO2
Hengshui Xianghe Yangfang Shuangqinglu Xianghe Yangfang Shuangqinglu Hengshui Tangshan Caofeidian Yangfang Tianjin Tanggu Tangshan Shuangqinglu Hengshui Tianjin Tanggu Tangshan Caofeidian Yangfang
729 707 738 739 586 678 744 736 744 744 718 614 684 708 657 626 698 720 741 744 738
22.99 21.68 10.42 17.80 11.39 19.71 21.40 22.27 32.19 29.94 21.90 103.91 58.91 94.38 68.69 170.06 195.40 154.13 171.32 101.13 101.61
38.93 27.67 12.48 16.02 18.72 18.94 27.60 27.16 25.81 10.64 16.06 139.86 112.92 106.14 101.44 134.94 140.49 121.90 113.75 94.53 47.58
17.29 21.17 11.06 12.69 10.73 12.32 15.92 10.99 15.81 14.14 21.62 101.87 51.83 83.30 74.41 104.03 136.17 110.40 138.75 94.77 98.49
19.66 23.75 15.82 14.56 16.95 16.35 15.58 13.11 16.28 7.42 15.91 90.74 82.99 85.25 74.17 78.37 98.00 92.07 95.26 80.81 53.42
0.35 0.73 0.40 0.73 0.76 0.64 0.77 0.66 0.74 0.63 0.76 0.58 0.73 0.71 0.81 0.62 0.67 0.64 0.57 0.69 0.63
O3
NO2
PM2.5
PM10
a b c d e f
Number of samples. Total mean of observations. Total mean of simulations. Standard deviation of observations. Standard deviation of simulations. Correlation coefficient between hourly observation and simulation.
relative humidity on visibility should not be the major reason for the occurrence of low visibility during this episode. 4.3. The relationship between PM2.5 and visibility Fig. 13 shows the regional daily average mass concentrations of PM2.5 and PM10, visibility, and relative humidity in Beijing, Tianjin,
Fig. 8. Observed and modeled daily averaged atmospheric visibility (km) at Beijing in December 2010.
186
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 9. The horizontal distributions of daily averaged PM2.5 concentration (aef) (m gm3) and visibility (gel) (km) in the period 17e22 December 2010 at the surface. Also shown is wind field (arrows) in (aef). The contour lines in (gel) represent the horizontal distribution of relative humidity (%).
Hebei, Shandong, and Shanxi from December 17 to 22. The averaged relative humidity did not evidently vary Fig. 13(d) clearly demonstrates that the relative humidity was rarely higher than 40% in Beijing, Tianjin, Hebei, and Shandong, and generally ranged from 40% to 60% in Shanxi. The similar tends of the PM2.5 and PM10 mass
concentration are shown in Fig. 13(a) and (b). Relatively high mass loading of aerosol mainly appeared in Beijing, Tianjin, and Hebei on December 18 and 21, and appeared in Shandong one day later. The condition on December 19was analyzed to elucidate this feature. The mass loading of aerosol in Shanxi was the lowest in the
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 10. The regional averaged vertical temperature and virtual potential temperature (K) variation (aeb) from surface to w1000 m in Beijing in the period 17e22 December 2010. Also shown is the regional averaged surface wind speed (m s1) variation (c).
aforementioned five regions, and the local visibility which remained around 40 km also reflected good air quality. The visibility in Beijing and Hebei changed significantly and are inversely correlated with the variation of aerosol mass burden. The visibility
187
in Shandong and Tianjin, with the highest aerosol mass loading, had the lowest value, as shown in Fig. 13(c). The correlation coefficients between regional hourly averaged mass concentration of PM2.5 and visibility in Beijing, Tianjin, Hebei, Shandong, and Shanxi are also calculated, and the correlation coefficient values were all negative. Even though the magnitude of PM2.5 mass concentration and visibility were apparently different among these five regions, the correlation coefficients still remained within the range of 0.7 to 0.8. The clearly inverse correlation between PM2.5 and visibility can be observed in the entire region of Beijing and its surrounding NCP regions. Sulfate and nitrate are two major PM2.5 components having significant extinction efficiency and are mainly concentrated in the regions of high anthropogenic emissions in China. Most sulfate and nitrate aerosols are secondary aerosols that are converted from their precursor gases (SO2 and NOx). In the previous section, the modeled SO2 and NO2 have been evaluated by several surface measurement data, and the relatively good agreement between simulated and observed results generally indicated that the modeling system could relatively well predict the mass concentration of sulfate and nitrate. Here, the contribution of these two aerosol species to the total extinction coefficient is analyzed to investigate the detailed relationship between aerosol particles of PM2.5 and visibility. Fig. 14 shows the daily averaged mass concentrations and the contribution ratios (CR) of sulfate and nitrate at the surface from December 17 to 22. The CR was obtained by subtracting the extinction coefficient with and without sulfate (or nitrate). The distribution patterns of sulfate and nitrate mass concentrations were generally similar to that of PM2.5. The heavy mass burden was mainly concentrated in Beijing, Tianjin, Hebei, and Shandong. The mass burden of these two PM2.5 components reached the highest value (ranging from 50 mg m3 to 75 mg m3) on December 18 to 22. However, the high sulfate and nitrate CR values were mainly concentrated in the regions with heavy mass
Fig. 11. The horizontal distributions of daily averaged PM10 concentrations (mg m3) in the period 17e22 December 2010 at the surface.
188
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 12. The variation of daily averaged visibility with relative humidity in Beijing, Tianjin, Hebei, Shandong, and Shanxi in the period 17e22 December 2010. Also shown are mass concentration of PM2.5 (mg m3; the first number in front of each curve) and ratio of PM2.5 to PM10 (the second number in front of each curve).
burden, and could reach approximately 20%e25% and 15%e20%, respectively. This result indicates that sulfate and nitrate could evidently reduce the visibility during the pollution episode. Evidently, 20% of the sulfate and nitrate CR areas became smaller
on December 18 and 21, compared with those on December 17 and 20. This result indicated that the extinction coefficient of other aerosol components increased when the heavy pollution appeared over NCP. However, sulfate CR increased in the urban
Fig. 13. The daily averaged mass concentration of PM2.5 (a) (mg m3), PM10 (b) (mg m3), visibility (c) (km), and relative humidity (d) (%) in Beijing, Tianjin, Hebei, Shandong, Shanxi in the period 17e22 December 2010.
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
189
Fig. 14. The horizontal distributions of daily averaged sulfate (aef) and nitrate (gel) mass concentrations (mg m3) in the period 17e22 December 2010 at the surface. Contour lines represent the contributions (%) of sulfate and nitrate aerosol to the total extinction coefficient.
regions on December 18 and 21 and exceed 25% in the south part of Beijing and Tianjin. This phenomenon indicates that the impact of sulfate on visibility should become more significant when heavy pollution occurs in these two megacities. Fig. 15 shows the hourly mass concentration of regional averaged PM2.5, sulfate, and nitrate when the visibility values decreased to 10 km in Beijing, Tianjin, Hebei, and Shandong on December 17 to 22. The change in the concentrations of these three pollutants along with time was not apparent, and the mass
burdens in these four regions were broadly concentrated within similar numerical intervals. Fig. 15 shows that the visibility in Beijing and its surrounding regions of NCP probably decreased to 10 km when PM2.5, sulfate, and nitrate reached 75 mg m3 to 85 mg m3, 20 mg m3 to 30 mg m3, and 15 mg m3 to 25 mg m3, respectively. Given that 10 km is the threshold of haze occurrence, the value ranges mentioned above could be a reference condition for distinguishing the haze occurrence in Beijing and NCP.
190
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
Fig. 15. Regional average mass concentration (mg m3) of PM2.5, sulfate, and nitrate when the visibility was 10 km in Beijing, Tianjin, Hebei, and Shandong in the period 17e22 December 2010.
5. Conclusions In this study, RAMS-CMAQ air quality modeling system coupled with an aerosol optical properties scheme was applied to investigate the relationship between atmospheric aerosol and visibility during a heavy air pollution episode from December 17 to 22, 2010 in Beijing and NCP. The mass concentration and extinction coefficient of major aerosol species, including sulfate, nitrate, ammonium, black carbon, organic carbon, dust, and sea salt based on three modes (Aitken, accumulation, and coarse mode) were simulated, and then the surface visibility was calculated with full consideration of related aerosol microphysical properties. The simulated results were compared with the ground-based measurements collected from surface meteorological stations of CNMC, Air Quality Network, and the surface visibility measurement to evaluate the modeling system. The daily distribution features of aerosol mass concentration and visibility were shown and discussed. The possible reasons that could have caused the heavy pollution episode were analyzed in detail. Simulated results show that the visibility in Beijing and its surrounding regions in the NCP significantly decreased to lower than 10 km when heavy pollution occurred. During this episode, the south part of Beijing became one of the most seriously polluted regions in the NCP, and the minimum daily average visibility in this region was lower than 3 km. The highest daily average PM2.5 concentration exceeded 300 mg m3. Simulation results indicated that the local accumulation and long-range transport of pollutants both provided important contributions to the heavy pollution in Beijing. The daily average PM2.5 concentration increased by w50 mg m3 to 100 mg m3 when stable PBL and weak wind prevailed on December 20. The heavy PM2.5 loading also appeared in Beijing because of the transport of pollutants by the south wind on December 18 and 21, and the increasing range reached w50 mg m3 to 100 mg m3. In addition, the simulated results showed that aerosol particles larger than PM2.5 could not efficiently influence visibility. The relative humidity is an important factor that influences visibility by changing the extinction coefficient of aerosol caused by the water uptake behavior of soluble particles. The sensitivity tests in this study indicate that the visibility could be exponentially decreased with an increase in the relative humidity, and the declining trend becomes more obvious as relative humidity exceeds 80%. However, sensitivity tests also show that the variation in relative humidity cannot significantly change visibility when the visibility decreases around 10 km. The aerosol analysis indicated that the particle extinction of PM2.5 should be the primary reason that caused the visibility deterioration during this pollution episode. The variation in PM2.5 was closely inversely correlated with that of visibility in Beijing and
its surrounding NCP regions. Sulfate and nitrate were the two major compositions of PM2.5 over the NCP. They provided approximately 40%e45% CR to the total extinction coefficient in Beijing, Tianjin, Hebei, and Shandong, which signifies that the visibility could be significantly reduced. The extinction effect of sulfate was even stronger (25% CR to the total extinction coefficient) in the urban area (Beijing and Tianjin) than those in other regions (approximately 20% CR to the total extinction coefficient) when the heavy pollution occurred on December 18 and 21. The model results also indicated that the value range of PM2.5 concentration between 75 mg m3 to 85 mg m3 could be a parameter for haze occurrence in Beijing and its surrounding regions in the NCP. Acknowledgments This work was supported by the “Strategic Priority Research Program (B)” of the Chinese Academy of Sciences (XDB05030105, XDB05030102, XDB05030103), National Department Public Benefit Research Foundation (Ministry of Environmental Protection of the People’s Republic of China) (No. 201009001), the National Natural Science Foundation of China (41105106, 20937001 and 41205123). Appendix A API represents the air pollution level in Chinese cities (available at http://datacenter.mep.gov.cn) and is linearly related to the daily mean PM10 concentration of hourly observations. When API (I) lies between the breakpoints Ii and Ij, the mass concentration of PM10 can be calculated by
C ¼ ci cj
Ii Ij I Ij þ Cj
(A.1)
where C is the PM10 concentration, and Ci and Cj are the PM10 concentrations corresponding to Ii and Ij listed in Table A.1. Appendix B The meteorological dataset provided by CNMC (http://cdc.cma. gov.cn/home.do) includes surface observations of several variables such as mean temperature, maximum and minimum temperature, mean relative humidity, minimum relative humidity, mean wind speed, maximum wind speed, the wind direction of maximum wind, and total precipitation. Except for wind direction of maximum wind, all other variables are on a daily basis. All variables are measured from 1 January 1951 to present. A total of 726 CNMC measurement stations are evenly distributed in mainland China (Feng et al., 2004). In the present study, the observed temperature, relative humidity, wind speed, and wind direction in
X. Han et al. / Atmospheric Environment 72 (2013) 177e191
December 2010 at 12 observation sites in North China Plain are used to evaluate the modeled meteorological results. Table A.1 The breakpoints of air pollution index and corresponding PM10 concentrations. API PM10 (mg m3)
0 0
50 50
100 150
200 350
300 420
400 500
500 600
References Benkovitz, C., Schultz, M., Pacyna, J., Tarrason, L., Dignon, J., Voldner, E., Spiro, P., Logan, J., Graedel, T., 1996. Global gridded inventories of anthropogenic emissions of sulfur and nitrogen. Journal of Geophysical Research 101 (D22), 29239e29254. Binkowski, F., Shankar, U., 1995. The regional particulate model, 1, model description and preliminary results. Journal of Geophysical Research 100, 26191e26209. Byun, D., Schere, K., 2006. Review of the governing equations, computation algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51e77. Che, H., Zhang, X., Li, Y., Zhou, Z., Qu, J., 2007. Horizontal visibility trends in China 1981e2005. Geophysical Research Letters 34, L24706. http://dx.doi.org/ 10.1029/2007GL031450. Chuang, C., Penner, J., Prospero, J., Grant, K., Rau, G., Kawamoto, K., 2002. Cloud susceptibility and the first aerosol indirect forcing: sensitivity to black carbon and aerosol concentrations. Journal of Geophysical Research 107. http:// dx.doi.org/10.1029/2000JD000215. Chuang, D., Song, Y., Liu, B., 2009. Visibility trends in six mega cities in China 1973e 2007. Atmospheric Research 94 (2), 161e167. Cotton, W., Pielke, R., Walko, G., Liston, G., Tremback, C., Jiang, H., McAnelly, R., Harrington, J., Nicholls, M., Carrio, G., McFadden, J., 2003. RAMS 2001: current status and future directions. Meteorology Atmospheric Physics 82, 5e29. Ding, F., Pal Arya, S., Lin, Y., 2001. Large-eddy simulations of the atmospheric boundary layer using a new subgrid-scale model-II. Weakly and moderately stable cases. Environmental Fluid Mechanics 1 (1), 49e69. Fei, J., Wang, R., Wang, Y., Huang, X., 2009. A numerical analysis of secondary aerosol inorganic particles of PM2.5 in a strong fog (in Chinese). Transactions of Atmospheric Sciences 32, 360e366. Feng, S., Hu, Q., Qian, W., 2004. Quality control of daily meteorological data in China, 1951e2000: a new dataset. International Journal of Climatology 24 (7), 853e870. Ge, C., Zhang, M., Han, Z., Liu, Y., 2011. Episode simulation of Asian dust storms with an air quality modeling system. Advances in Atmospheric Sciences 28 (3), 511e520. Ghan, S., Zaveri, R., 2007. Parameterization of optical properties for hydrated internally mixed aerosol. Journal of Geophysical Research 112, D10201. http:// dx.doi.org/10.1029/2006JD007927. Han, X., Zhang, M., Han, Z., Xin, J., Liu, X., 2011. Simulation of aerosol direct radiative forcing with RAMS-CMAQ in East Asia. Atmospheric Environment 45 (36), 6576e6592. Han, X., Ge, C., Tao, J., Zhang, M., Zhang, R., 2012. Air quality modeling for a strong dust event in East Asia in March 2010. Aerosol and Air Quality Research 12, 615e628. Huang, X., He, L., Hu, M., Canagaratna, M., Sun, Y., Zhang, Q., Zhu, T., Xue, L., Zeng, L., Liu, X., Zhang, Y., Jayne, J., Ng, N., Worsnop, D., 2010. Highly time-resolved chemical characterization of atmospheric submicron particles during 2008 Beijing Olympic Games using an aerodyne high-resolution aerosol mass spectrometer. Atmospheric Chemistry and Physics 10 (18), 8933e8945. Li, L., Chen, C., Huang, H., Li, Z., Fu, J., Jang, C., Streets, D., 2008. Regional air pollution characteristics simulation of O3 and PM10 over Yangtze River Delta region (in Chinese). Environmental Sciences 29, 237e245. Li, W., Shao, L., Buseck, P., 2010. Haze types in Beijing and the influence of agricultural biomass burning. Atmospheric Chemistry and Physics 10, 8119e8130. Liao, H., Seinfeld, J.H., 2005. Global impacts of gas-phase chemistry-aerosol interactions on direct radiative forcing by anthropogenic aerosols and ozone. Journal of Geophysical Research 110, D18208. http://dx.doi.org/10.1029/ 2005JD005907. Liu, X., Pernner, J., Das, B., Bergmann, D., Rodriguez, J., Strahan, S., Wang, M., Feng, Y., 2007. Uncertainty in global aerosol simulation: assessment using three meteorological data sets. Journal of Geophysical Research 112, D11212. http:// dx.doi.org/10.1029/2006JD008216.
191
Nenes, A., Pandis, S., Pilinis, C., 1999. Continued development and testing of a new thermodynamic aerosol module for urban and regional air quality models. Atmospheric Environment 33 (10), 1553e1560. Olivier, J., Bouwman, A., Maas, C., Berdowski, J., 1994. Emission database for global atmospheric research. Environmental Monitoring and Assessment 31, 93e106. Pruppacher, H., Klett, J., 1997. Microphysics of Clouds and Precipitation. Springer, New York. p. 954. Quan, J., Zhang, Q., He, H., Liu, J., Huang, M., Jin, H., 2011. Analysis of the formation of fog and haze in North China Plain (NCP). Atmospheric Chemistry and Physics 11 (4), 8214e8250. Randerson, J.T., Van der Werf, G., Giglio, L., Collatz, G., Kasibhatla, P., 2005. Global Fire Emissions Database, Version 2 (GFEDv2. 1). OakRidge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennesse, USA. Available at: http://daac.ornl.gov/ http://dx.doi.org/10.3334/ORNLDAAC/849. from. Ruckstuhl, C., Philipona, R., Behrens, K., Collaud, C., Durr, B., Heimo, A., Matzler, C., Nyeki, S., Ohmura, A., Vuilleumier, L., Weller, M., Wehrli, C., Zelenka, A., 2008. Aerosol and cloud effects on solar brightening and recent rapid warming. Geophysical Research Letters 35 (12). http://dx.doi.org/10.1029/2008GL034228. Sarwar, G., Luecken, D., Yarwood, G., Whitten, G., Carter, W., 2008. Impact of an updated carbon bond mechanism on predictions from the CMAQ modeling system: preliminary assessment. Journal of Applied Meteorology and Climatology 47, 3e14. Seinfeld, J., Pandis, S., 1998. Atmospheric Chemistry and Physics. Wiley, New York, USA. Song, Y., Chen, J., Cai, X., 2002. The modeling of PM10 contribution to Beijing by the Shijingshan industrial area. Chinese Journal of Environmental Science 23, 65e 68 (in Chinese). Streets, D., Bond, T., Carmichael, G., Fernandes, S., Fu, Q., He, D., 2003. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. Journal of Geophysical Research 108. http://dx.doi.org/10.1029/2002JD003093. Sun, Y., Zhuang, G., Tang, A., Wang, Y., An, Z., 2006. Chemical characteristics of PM2.5 and PM10 in Haze-Fog episodes in Beijing. Environmental Science & Technology 40, 3148e3155. Tang, G., Wang, Y., Li, X., Ji, D., Gao, X., 2011. Spatial-temporal variations of surface ozone and ozone control strategy for Northern China. Atmospheric Chemistry and Physics Discussions 11 (9), 26057e26109. Tao, J., Ho, K., Chen, L., Zhu, L., Han, J., Xu, Z., 2009. Effect of chemical composition of PM2.5 on visibility in Guangzhou, China, 2007 Spring. Particuology 7 (1), 68e75. Tao, M., Chen, L., Su, L., Tao, J., 2012. Satellite observation of regional haze pollution over the North China Plain. Journal of Geophysical Research 117. http:// dx.doi.org/10.1029/2012JD017915. Wang, Y., Zhuang, G., Sun, Y., An, Z., 2006. The variation of characteristics and formation mechanisms of aerosols in dust, haze, and clear days in Beijing. Atmospheric Environment 40, 6579e6591. Wang, L., Xu, J., Yang, J., Zhao, X., Wei, W., Cheng, D., Pan, X., Su, J., 2012. Understanding haze pollution over the southern Hebei area of China using the CMAQ model. Atmospheric Environment 56, 69e79. Wu, D., Bi, X., Deng, X., Li, F., Tan, H., 2007. Effect of atmospheric haze on the deterioration of visibility over the Pearl River Delta. Acta Geographica Sinica 21, 215e223 (in Chinese). Zhang, M., Xu, Y., Zhang, R., Han, Z., 2005. Emission and concentration distribution of black carbon aerosol in East Asia during springtime. Chinese Journal of Geophysics 48, 55e61. Zhang, M., Uno, I., Zhang, R., Han, Z., Wang, Z., Pu, Y., 2006. Evaluation of theModels3 Community Multi-scale Air Quality (CMAQ) modeling system with observations obtained during the TRACE-P experiment: comparison of ozone and its related species. Atmospheric Environment 40 (26), 4874e4882. Zhang, Q., Klimont, Z., Streets, D., Huo, H., He, K., 2006. An anthropogenic PM emission model for China and emission inventory for the year 2001. Progress in Natural Science 16, 223e231 (in Chinese). Zhang, M., Gao, L., Ge, C., Xu, Y., 2007. Simulation of nitrate aerosol concentrations over East Asia with the model system RAMS-CMAQ. Tellus B 59, 372e380. Zhao, X., Zhang, X., Xu, X., Xu, J., Meng, W., Pu, W., 2009a. Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing. Atmospheric Environment 43, 2893e2900. Zhang, Q., Streets, D., Carmichael, G., He, K., Huo, H., Kannari, A., et al., 2009b. Asian emissions in 2006 for the NASA INTEX-B mission. Atmospheric Chemistry and Physics 9, 5131e5153. Zhang, J., Zhu, T., Zhang, Q., Li, C., Shu, H., Ying, Y., Dai, Z., Liu, X., Liang, A., Shen, H., 2012. The impact of circulation patterns on regional transport pathways and air quality over Beijing and its surroundings. Atmospheric Chemistry and Physics 11 (12), 5031e5053. Zheng, M., Salmon, L., Schauer, J., Zeng, L., Kiang, C., Zhang, Y., Cass, G., 2005. Seasonal trends in PM2.5 source contributions in Beijing, China. Atmospheric Environment 39 (22), 3967e3976.