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Advances in Meteorology
Volume 2016 (2016), Article ID 6295878, 15 pages
http://dx.doi.org/10.1155/2016/6295878
Research Article

The Impacts of Different PBL Schemes on the Simulation of PM2.5 during Severe Haze Episodes in the Jing-Jin-Ji Region and Its Surroundings in China

1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
2State Key Laboratory of Severe Weather/Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
3Yantai Meteorological Bureau, Yantai 264003, China

Received 7 December 2015; Revised 12 February 2016; Accepted 26 April 2016

Academic Editor: Tareq Hussein

Copyright © 2016 Tian Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In this study, three schemes [Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), and Bougeault-Lacarrère (Boulac)] were employed in the Weather Research and Forecasting/Chemistry (WRF-Chem) model to simulate the severe haze that occurred in February 2014 in the Jing-Jin-Ji region and its surroundings. The PM2.5 concentration simulated using the three schemes, together with the meteorological factors closely related to PM2.5 (wind speed, local vertical diffusivity, and PBL height), was evaluated through comparison with observations. The results indicated that the eastern plain cities produced better simulation results than the western cities, and the cities under the eastern root of Taihang Mountain produced the worst results in simulating high PM2.5 concentration in haze. All three schemes simulated very similar variation trends of the surface PM2.5 concentration compared with observations. The diurnal variations of simulated surface PM2.5 were not as reasonable as their reflection of daily averaged variation. The simulated concentrations of surface PM2.5 using the YSU, MYJ, and Boulac schemes all showed large negative errors during daytime in polluted days due to their inefficient descriptions of local atmospheric stability or diffusion processes in haze. The lower ability of PBL schemes in distinguishing the diffusion between haze and clean days in the complex topography areas in China is an important problem for PM2.5 forecasting, which is worthy of being studied in detail.

1. Introduction

Owing to its population explosion, accelerated urbanization, and globalization, China—the country with the fastest growing economy in the world—has been suffering from increasingly severe air pollution since the 1980s. Related to this, haze occurrence in China, on the whole, has continued to grow during the past several decades, especially after 1980 [1]. Today, haze is a frequent phenomenon in most areas of eastern China, leading to adverse economic as well as human health impacts [2]. Broadly, four severe haze regions in China are recognized: Beijing, Tianjin, Hebei province (abbreviated to Jing-Jin-Ji) and its surroundings [35], Yangtze River Delta, Pearl River Delta, and the Sichuan Basin. As one of the most important urban agglomerations in China, the Jing-Jin-Ji region and its surroundings have attracted considerable attention recently, because of the serious pollution episodes it has experienced since 2013. Multisource observations that can characterize the haze process in Jing-Jin-Ji and its surrounding areas have been used to study the temporal and spatial variation of haze, meteorological conditions, and the chemical components of haze [612]. Based on these extensive observational studies, continuous studies of the resultant pollution emissions inventory have also been conducted [1315]. In addition, a number of simulation studies using atmospheric models have been carried out to study haze and pollutions processes in China; these studies involve the interactions between meteorological conditions, particle concentrations, and the variation in the transport characteristics of pollutants during the pollution process [1620]. There are two key factors involved in the formation and persistence of haze: one is fine particulate matter (PM2.5) and gas pollutants (O3, SO2, NOx, etc.) and the other is meteorological conditions. Moreover, when modeling haze, there are uncertainties related to the planetary boundary layer (PBL), which mainly derive from the particular PBL scheme used; and, therein, the PBL height (PBLH), turbulent mixing process, and wind fields are major variables controlling the haze process in the PBL [2123]. Therefore, the PBL scheme is a vital impacting factor in terms of modeling the formation and maintenance of haze and air pollution [24, 25]. A lower PBLH and weaker PBL turbulence diffusion are regarded as key meteorological aspects for haze formation [26]. Studies on different PBL parameterization schemes have shown that an accurate depiction of the meteorological conditions within the PBL via an appropriate PBL parameterization scheme is important for air pollution modeling [2729]. Some studies have also discussed the importance of the PBL scheme in the modeling of O3 concentrations, specifically, in the USA and using Weather Research and Forecasting/Chemistry model (WRF-Chem) [3032]. These studies also touched upon the possible effects of the PBL scheme on the modeling of PM2.5; however, little is known about whether current PBL schemes are efficient in modeling extremely high PM2.5 concentrations and haze events over the Chinese mainland.

In order to investigate the abilities of PBL schemes in modeling PM2.5 over the Jing-Jin-Ji region during serious haze events with high PM2.5 values and to provide instructive guidance regarding PM2.5 prediction over this region, separate WRF-Chem model simulations using three popular PBL schemes [Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), and Bougeault-Lacarrère (Boulac)] were run for haze episodes that occurred in February 2014. After first introducing the methodology, model configuration, and data used, we then evaluate the PM2.5 simulation results from the three PBL schemes by comparing with observations and analyze the related meteorological fields. Finally, conclusions are drawn regarding the impacts of the PBL on PM2.5 simulation, along with a discussion on the possible underlying physical mechanisms involved.

2. Methodology

2.1. Model Introduction and Configuration

The WRF-Chem model is a fully coupled “online” model, with its air quality component fully consistent with the meteorological component [33, 34]. Version 3.5 of WRF-Chem was employed in this study. Two nested domains (Figure 1) were used in the simulation with grid spacing of 27 km and 9 km, respectively. The inner domain was centered at 115°E, 35.5°N on a Lambert map projection. Considering the regional transmission of PM2.5 during haze processes, the main research area of domain 2 ranged over 111°E–120.5°E, 34.5°N–42.5°N, containing the whole Jing-Jin-Ji area and its upstream region including most areas of Shanxi and Shandong provinces and part of Henan province—both regarded as contributors to Jing-Jin-Ji’s pollution. The research area is abbreviated as 3JNS hereafter. The two domains used the same 35 vertical levels extending from the surface to 10 hPa, and the layer heights within PBL are shown in Table 3. The simulation period ranged from 00:00 UTC January 28, 2014, to 00:00 UTC March 1, 2014. The simulation outputs from February 1 to 28 were used to obtain the chemical component balance from pollutant emissions.

Figure 1: Nested modeling domains (a), the distribution of observation sites within domain 2 ((b) filled circles: PM2.5 observation sites; open circles: surface meteorological sites; open triangles: upper-air meteorological stations; the dashed-line square area represents the research area (3JNS)), and the topography of the research area (c).

The CBM-Z chemistry mechanism [35] combined with MADE/SORGAM (Modal Aerosol Dynamics Model for Europe and Secondary Organic Aerosol Model) was applied in each domain, and the Fast-J photolysis scheme [36] coupled with hydrometeors, aerosols, and convective parameterizations was chosen. All domains used the RRTM scheme [37] for longwave radiation, the Goddard scheme for shortwave radiation, the Lin (Purdue) microphysics scheme [38], and the New Grell scheme for cumulus parameterization (Table 1). Three PBL schemes—YSU, MYJ, and Boulac—were adopted in the model runs to compare the modeling results of PM2.5.

Table 1: Main physical schemes used in WRF-Chem.
2.2. Emissions Instruction

The anthropogenic emissions of chemical species, with resolution of 0.1°  ×  0.1°, came from the Multiresolution Emissions Inventory for China (MEIC) for 2010 (http://www.meicmodel.org/), which was developed in 2006 for the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) mission [13]. The inventory includes 10 major kinds of pollutants and greenhouse gases and more than 700 kinds of anthropogenic emissions, which can be divided into five sources: transportation, residency, industry, power, and agriculture. According to the INTEX-B inventory, the main pollutants in China that year were SO2, NOx, CO, NMVOC, PM10, PM2.5, BC, and OC. This 2010 emissions inventory has been validated as credible and widely used in studies of pollution in China [14, 15, 39].

2.3. Data Descriptions

The National Centers for Environmental Prediction (NCEP) reanalysis data (resolution: 1°  ×  1°) were used for the model’s initial and boundary conditions. The hourly surface PM2.5 observational data for February 2014 from the China National Environmental Monitoring Center were used to evaluate the model results. There are 109 PM2.5 sites in domain 2 and 48 sites in 3JNS. The results presented in this paper focus mainly on the sites evenly distributed in 3JNS. Surface and vertical sounding balloon observations of the Meteorological Information Comprehensive Analysis and Process System (MICAPS) from the China Meteorological Administration were also used for evaluating and analyzing the model. The locations of all of these observational sites in domain 2 are marked in Figure 1. There are 88 MICAPS stations in 3JNS.

In order to explore the PBL schemes performance in different areas, five stations, Beijing (under the Yan Mountain), Taiyuan (on the west side of Taihang Mountain), Zhangjiakou (in the northwest of 3JNS), Cangzhou (the coastal station), and Xingtai (the east foot of Taihang Mountain) were picked up to represent five different categories of topography and land surface in the 3JNS. The location of their abbreviations is displayed in Figure 1. The topographic basemap of Figure 1 was downloaded from http://www.noaa.gov/.

2.4. Three PBL Schemes’ Introduction

PBL schemes can be classified as local or nonlocal closure schemes [40], with the former obtaining the turbulent fluxes of each grid from mean variables and the latter by considering the grid and its surroundings. Additionally, nonlocal schemes are able to simulate the fluxes and profiles of the convective boundary layer. The YSU PBL scheme—an improved version of the Medium-Range Forecast (MRF) scheme, with a critical bulk Richardson number of 0.25 over land—is a revised vertical diffusion package with a nonlocal coefficient in the PBL. Compared with the MRF scheme, it increases boundary layer mixing in the thermally induced free convection regime and decreases it in the mechanically induced forced convection regime. In addition, this scheme is also a relatively mature scheme that is able to simulate a realistic structure of the PBL in the WRF model [41, 42]. The MYJ PBL scheme is a turbulent kinetic energy (TKE) local closure scheme that defines the eddy diffusion coefficients by forecasting the TKE. This scheme is suitable for all stable and weakly unstable boundary layers [43]. The Boulac scheme, regarded as a local closure scheme, has long been regarded as satisfactory in terms of its performance in orography-induced events [44]. These three PBL schemes are widely used in mesoscale or weather-scale modeling, and their respective merits or shortcomings have been reported in previous studies. They were also selected for use in the present reported study.

3. Results and Discussion

3.1. Evaluation of Surface PM2.5

To validate the efficiencies of the three PBL schemes in simulating PM2.5 in the Jing-Jin-Ji region, the spatial distribution of the modeled PM2.5 values is compared with observations for a severe and long-lasting haze episode in this region. Figure 2 displays the averaged PM2.5 distribution from 00:00 UTC February 21 to 00:00 UTC February 25, together with the observed values during the same period. The period-averaged PM2.5 values reached 300–500 μg m−3 at observation sites over this region (marked with circles in Figure 2), and the instantaneous values were even higher; the PM2.5 concentration in some cities (e.g., Beijing, Xingtai, and Tangshan) even reached above 500 μg m−3 (Figure 3). Furthermore, as shown in Figures 2 and 3, cities in southern Hebei province endured more severe pollution than northern areas (e.g., Chengde and Zhangjiakou). For this haze period, the model results using the three PBL schemes were all reasonable; the observed and simulated distributions of PM2.5 showed reasonable consistency. The differences in distributions between the YSU, MYJ, and Boulac schemes were very small. To evaluate the accuracies of the three PBL schemes in modeling the variation in PM2.5, 10 representative cities in 3JNS were selected (locations displayed in Figure 2), and their hourly variations in PM2.5 concentration, as modeled using the three PBL schemes, were compared with observations for the period from 00:00 UTC February 1 to 00:00 UTC March 1 (Figure 3). The results show that all three PBL schemes produced similar representations of the real variation in PM2.5 for the whole of February, and the differences in modeling values by these three schemes were very little. As the concentration of PM2.5 is the primary indicators in haze periods, it can be seen from Figure 3 that there were two main haze events in February: one from February 13 to 15 and the other from February 21 to 25. The start and end points of these two events were each modeled well using the three PBL schemes. However, as the simulated conditions of the second event (February 21 to 25) were more accurate, this one was chosen as the research period in this study. In terms of the simulations at individual stations, eastern cities (e.g., Hengshui, Cangzhou, and Chengde) produced better simulation results than western cities (e.g., Zhangjiakou and Baoding) for this event overall, suggesting that the PBL schemes possess properties that are more suited to simulating the PM2.5 concentration in particular localities. As for how model behaves for particular localities (plains, mountains, or coastal areas, etc.) by using these 3 PBL schemes, we will discuss this below.

Figure 2: Mean simulated (shaded) and observed (circles) PM2.5 values during the haze period (February 21 to 25).
Figure 3: Simulated and observed hourly PM2.5 concentration at 10 sites ((a)–(j)) in February 2014.

Four statistical indicators [mean bias (MB), normalized mean bias (NMB), normalized mean error (NME), and root mean square error (RMSE)] of the haze episode, clean days, and whole month averaged over 3JNS were calculated to evaluate the abilities of the three PBL schemes in simulating PM2.5 (Table 2). The mean and extreme values of haze and clean periods using each PBL scheme are also displayed in Table 2. The results show that the PM2.5 modeled during the haze episode was better than that for the whole month. NB and NMB values of less than zero indicate that the model results were an underestimation of the actual situation. The YSU, MYJ, and Boulac schemes underestimated the PM2.5 concentration during daytime but overestimated it at night, the reason which will be discussed later. On the whole, the Boulac scheme produced the least bias for haze episode compared with the other three schemes, followed by the YSU scheme and MYJ scheme. The MB, NMB, NME, and RMSE values further illustrate that the YSU, MYJ, and Boulac schemes differed little in terms of their simulation of the PM2.5 concentration during haze.

Table 2: Comparisons of statistical indicators of PM2.5. (Haze: February 21 to 25, daytime, 00:00–11:00 UTC; night, 12:00–24:00 UTC. Clean days: February 3 to 5. Unit: μg m−3.)
Table 3: Model levels and their corresponding heights.
3.2. Relationship between PBL Meteorology and PM2.5

The daily averaged values of PM2.5 concentration, surface wind speed, PBLH, and vertical diffusivity at level 8 (Table 3) in 3JNS for the whole of February are shown in Figure 4. The PM2.5 values were determined by averaging the PM2.5 data of 48 observation stations (Figure 1), and the wind speed values were the average of data of 88 CMA surface monitoring stations in the same area. The three PBL schemes all showed similar trends as those observed. As indicated by the results in Figure 4, the PM2.5 modeled using the YSU, MYJ, and Boulac schemes also showed very little difference. The three schemes all simulated similar trends for surface wind speed, which were in agreement with the observed trend, though they were all higher than observed. All three schemes showed that the PM2.5 concentration possessed an accurate inverse relationship with wind speed in terms of the daily averaged variation trend. The daily variation in PM2.5 concentration also possessed a good inverse relationship with the PBLH (averaged over 48 sites, the same for vertical diffusion), which suggested that a lower PBLH is an essential prerequisite for haze episodes; but when the PBLH is lower than a certain value, such as 400 m, its relationship with PM2.5 is not so close. Considering their different diagnoses, the specific values between different PBL schemes are not comparable, so the focus here is the relationships between PM2.5 and PBL meteorology. The anticorrelation between the daily PM2.5 and vertical diffusivity of the YSU, MYJ, and Boulac schemes was even weaker than that between PM2.5 and the PBLH, indicating that the impact of local vertical diffusivity on the time scale of the daily averaged change trend of PM2.5 is limited. Nevertheless, its impact on the hourly change of PM2.5 during the daytime is clearer and more important, which will be discussed in Section 3.4.

Figure 4: Variations of the daily averaged PM2.5 concentration (a), wind speed near-surface (b), PBLH (c), and vertical diffusivity (d) of the area mean in February.

To illustrate the modeling performance by using three PBL schemes in different topographies, Figure 5 displays the simulated and observed daily averaged PM2.5 concentration and wind speed in the whole of February of five stations which can represent five topographies in the 3JNS (Figure 1). As to PM2.5 concentrations and wind speed, PBL schemes can depict appropriate variation trends compared with observation, and they showed a good negative correlation with each other. The difference of PM2.5 concentration in YSU, MYJ, and Boulac schemes is still little in separate stations; meanwhile, the modeling results in different terrain contain certain differences. In this haze process, the modeling PM2.5 concentrations in some stations are slightly higher than the observations (Beijing, Taiyuan), while some are lower (Zhangjiakou, Xingtai), and the eastern coastal city (Cangzhou) performed well in this simulation. It is worth mentioning that the Xingtai station, representing the eastern foot of Taihang Mountain, has obviously lower simulating PM2.5 concentration than the observation by the three schemes, which can be mainly owing to the extremely higher simulation of wind speed (Figure 5). Compared with near stations on the eastern Taihang Mountain, Shijiazhuang and Handan also have similar phenomena (lower simulated PM2.5 and higher simulated wind speed). It should be noted here that the higher simulated wind speed is one main but probably not the only reason contributing to the higher simulated PM2.5. In conclusion, the performance of schemes in the eastern root of Taihang Mountain, the most polluted region by haze in China, was relatively poor due to its specific terrain and complex PBL meteorology. The modeling results in the eastern plain stations (Cangzhou, etc.) of the 3JNS were better than the west (Zhangjiakou, Xingtai, etc.) as mentioned in Section 3.1.

Figure 5: Variations of the daily averaged PM2.5 concentration ((a), (c), (e), (g), and (i)) and wind speed near-surface ((b), (d), (f), (h), and (j)) at 5 sites of different terrain in February.

Figure 6 displays the hourly variations of area mean PM2.5, wind speed at 10 m, the PBLH, and vertical diffusivity at level 8 (Table 3) of the three PBL schemes during the haze episode (total duration: 120 hours). The YSU scheme simulated the lowest concentration, followed by the Boulac and MYJ schemes. It can be seen from this figure that the PM2.5 concentrations simulated using the YSU, MYJ, and Boulac schemes all possessed good inverse relationships with wind speed at 10 m, the PBLH, and vertical diffusivity. After sunrise, with the strengthening of solar radiation, the turbulent diffusivity within the PBL continued to improve, the PBLH and wind speed also increased, and all three variables reached their maximum at about 07:00 UTC (local 3 o’clock in the afternoon). After then, all three variables weakened with solar radiation and remained stable at night (after sunset). For the reasons outlined above, the concentration of simulated PM2.5 during daytime was lower than at night. In summary, in the model, the effects of vertical diffusion on the hourly change trend of PM2.5 during daytime are much more important compared with the effects on daily averaged PM2.5.

Figure 6: Hourly variations of the area-averaged PM2.5 concentration (a), wind speed at 10 m (b), PBLH (c), and vertical diffusivity (d) during the haze process.
3.3. Vertical Profiles of PM2.5 and Meteorology within the PBL

The structure of PBL vertical meteorology is very important to particle diffusion, vertical and horizontal transportation, and thus the simulation of PM2.5. Air sounding observations are only carried out by the CMA at 00:00 UTC (8 o’clock, local time) and 12:00 UTC (nightfall), meaning observational data in terms of the vertical profiles of meteorological parameters during local daytime—local noon (06:00 UTC) especially—are not available and therefore cannot be used for model validation in China at present. Accordingly, Figure 7 only compares the modeled PM2.5 concentration, wind speed, and vertical diffusion using the three PBL schemes. Each value of the profile was first averaged over the stations in 3JNS and then averaged over the duration of the haze process (120 hours). The model levels and their corresponding heights are displayed in Table 3. It can be seen from Figure 7 that the differences among the profiles of the YSU, MYJ, and Boulac schemes were small, ranging from 160 μg m−3 to 175 μg m−3. The discrepancies in the PM2.5 concentrations and PBL variables among different PBL schemes were mainly apparent beneath level 11 (height of approximately 402 m). Just under this height local diffusion was the strongest, indicating that local vertical diffusion occurring mainly from 100 m to 400 m and heights below 400 m were important for the PM2.5 simulation. The results also showed that the surface PM2.5 concentration was affected by the wind speed and diffusion collectively throughout the whole PBL (especially under 400 m), rather than the surface only.

Figure 7: Vertical profiles of wind speed (a), vertical diffusivity (b), and PM2.5 concentration (c).
3.4. Diurnal Variation of Surface PM2.5 and Vertical Diffusion

The diurnal variations of vertical diffusion and PM2.5 concentration of haze and clean days using the three PBL schemes are displayed in Figures 8(a)8(d). These figures show exactly how diffusion affected the PM2.5 trend during the course of one day, from 00:00 UTC to 23:00 UTC. The values of PM2.5 and diffusion were both averaged over 48 PM2.5 stations in the 3JNS, and each hour was also averaged during haze (February 21 to 25) and clean periods (February 3 to 5) separately. Most stations’ daily averaged PM2.5 of observations were above 200 μg m−3 in the “haze” and under the 50 μg m−3 in the “clean” days. The diurnal variation produced by the three PBL schemes was exactly the same as shown in Figure 6. The PM2.5 simulated using the YSU, MYJ, and Boulac schemes was obviously lower than observed during daytime, and their diurnal variation of PM2.5 disagreed with and even contrasted with the observation especially for haze days. It can be concluded that the three PBL schemes might overestimate the vertical diffusion process in the 3JNS region, leading to lower simulated surface PM2.5 and negative errors during daytime, particularly when severe haze occurred. There are two reasons for this probably strong diffusion and lower PM2.5 during daytime. The direct radiative feedback of aerosols may lead to weaker diffusion, a more stable atmosphere, and higher surface PM2.5 when the PM2.5 concentration is higher than a certain threshold [26, 45]. However, this feedback was not calculated in the present study. Besides, it was the calculation methods with respect to vertical diffusion by the three PBL schemes that led to stronger particle diffusion and lower surface PM2.5 than was actually the case in the real atmosphere.

Figure 8: Diurnal variations of PM2.5 ((a)-(b)) and vertical diffusivity ((c)-(d)).

It should be noted that the different representations of vertical diffusion in these PBL schemes might have different impacts on PM2.5 simulation under different conditions of atmospheric stability in different regions. So here, the same five stations mentioned above were picked up again to illustrate the modeling result of diurnal variations over different topography (Figure 9). For the small difference of vertical diffusivity between haze and clean period at the five stations, the figures of diffusion were ignored here. Though there is no significant pattern in the diurnal variations of observation, this figure also indicated that the simulated diurnal variations of PM2.5 of specific stations were not as well as daily averaged variations in Figure 5. Despite this, the modeled trends of Taiyuan and the eastern city Cangzhou were better. By the influence of Taihang Mountains, Xingtai simulated lower PM2.5 in haze days and higher PM2.5 in clean days compared with observations. Moreover, when the model performed well in haze with high PM2.5 concentrations (Taiyuan and Cangzhou), it simulated apparently higher PM2.5 in the clean days with lower PM2.5 concentrations and vice versa for Zhangjiakou. It seems to be that the little difference of diffusivity calculation between haze and clean days by the PBL schemes calculation might lead to this interesting phenomenon, which is probably the main way to improve PM2.5 forecasting in complex topography.

Figure 9: Diurnal variations of PM2.5 in polluted ((a), (c), (e), (g), and (i)) and clean process ((b), (d), (f), (h), and (j)) at 5 sites of different terrains.

4. Conclusion

To explore the impacts of different PBL schemes on PM2.5 simulation, three PBL schemes (YSU, MYJ, and Boulac) were applied in the WRF-Chem model to simulate haze episodes that occurred in the Jing-Jin-Ji region and its surroundings of China. The research area is abbreviated to 3JNS in this paper.

The results of the three PBL schemes in simulating the PM2.5 concentration over 3JNS showed that all these schemes performed similarly with respect to the PM2.5 trend during a month that included haze episodes. However, among them, the Boulac scheme produced the least bias for haze period, followed by the YSU and MYJ scheme, and these three schemes showed negligible difference in simulating the PM2.5 concentration. All three PBL schemes simulated similar daily averaged trends in PM2.5 concentration, which was in agreement with the observation and possessed a good inverse relationship with the PBLH and wind speed—better than with vertical diffusion. All the PBL schemes behave diversely in different terrains. On the whole, eastern plain cities such as Cangzhou and Chengde produced better simulation results than the western cities such as Zhangjiakou and Baoding which are near mountains; the cities under the eastern root of Taihang Mountain produced the worst results in simulating high PM2.5; the modeling results of plain cities were better than the cities under the mountain (e.g., Beijing under the Yan Mountain). The heights under or near the 400 m were found to be very important for PM2.5 simulation. The effects of vertical diffusion on the hourly change trend of PM2.5 simulation during daytime were far more important than those on the simulation of daily averaged PM2.5. The three PBL schemes might all overestimate the vertical diffusion process in the 3JNS, leading to a lower simulation of surface PM2.5 and negative errors during daytime—especially when severe haze occurred. In addition, the small gap of diffusivity between haze and clean days by PBL schemes may lead to the errors in simulating PM2.5 concentrations. It can also be said that the three PBL schemes had not enough ability to distinguish the diffusion between haze and clean days in the complex topography area in China, which may be regarded as an important direction for the improving of PM2.5 simulation.

Since the differences in PM2.5 concentration among the PBL schemes were small, the exact reasons related to these differences were not discussed in this study. The reasons for the poor reflection of diurnal variation in the PBL schemes, resulting in PM2.5 errors in numerical models, need to be studied in detail and then adjustments need to be made to improve results for different regions.

Competing Interests

The authors declare that there are no competing interests regarding the publication of this paper.

Acknowledgments

This work is supported by 973 Program of Ministry of Science and Technology of China (2014CB441201), National Natural Scientific Foundation of China (41275007), and National Science and Technology Project of China (2014BAC22B04).

References

  1. Y. H. Ding and Y. J. Liu, “Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity,” Science China Earth Sciences, vol. 57, no. 1, pp. 36–46, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Li and L. Zhang, “Haze in China: current and future challenges,” Environmental Pollution, vol. 189, pp. 85–86, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Che, X. Zhang, Y. Li, Z. Zhou, J. J. Qu, and X. Hao, “Haze trends over the capital cities of 31 provinces in China, 1981–2005,” Theoretical and Applied Climatology, vol. 97, no. 3-4, pp. 235–242, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Zhao, X. Zhang, X. Xu, and X. Zhao, “Long-term visibility trends and characteristics in the region of Beijing, Tianjin, and Hebei, China,” Atmospheric Research, vol. 101, no. 3, pp. 711–718, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Chai, J. Gao, Z. Chen et al., “Spatial and temporal variation of particulate matter and gaseous pollutants in 26 cities in China,” Journal of Environmental Sciences, vol. 26, no. 1, pp. 75–82, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. X. Y. Zhang, Y. Q. Wang, T. Niu et al., “Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols,” Atmospheric Chemistry and Physics, vol. 12, no. 2, pp. 779–799, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. Q. Zhang, Z. Shen, J. Cao et al., “Variations in PM2.5, TSP, BC, and trace gases (NO2, SO2, and O3) between haze and non-haze episodes in winter over Xi'an, China,” Atmospheric Environment, vol. 112, pp. 64–71, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. X. Fu, S. X. Wang, Z. Cheng et al., “Source, transport and impacts of a heavy dust event in the Yangtze River Delta, China, in 2011,” Atmospheric Chemistry and Physics, vol. 14, no. 3, pp. 1239–1254, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Wang, J. Chen, J. Sun et al., “Severe haze episodes and seriously polluted fog water in Ji'nan, China,” The Science of the Total Environment, vol. 493, pp. 133–137, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. P. S. Zhao, F. Dong, D. He et al., “Characteristics of concentrations and chemical compositions for PM2.5 in the region of Beijing, Tianjin, and Hebei, China,” Atmospheric Chemistry and Physics, vol. 13, no. 9, pp. 4631–4644, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Tao, L. Chen, Z. Wang, P. Ma, J. Tao, and S. Jia, “A study of urban pollution and haze clouds over northern China during the dusty season based on satellite and surface observations,” Atmospheric Environment, vol. 82, pp. 183–192, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Wang, S.-C. Tan, Y. Wang et al., “A multisource observation study of the severe prolonged regional haze episode over eastern China in January 2013,” Atmospheric Environment, vol. 89, pp. 807–815, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. Q. Zhang, D. G. Streets, G. R. Carmichael et al., “Asian emissions in 2006 for the NASA INTEX-B mission,” Atmospheric Chemistry and Physics, vol. 9, no. 14, pp. 5131–5153, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Wang, D. G. Streets, Q. Zhang et al., “Satellite detection and model verification of NOxemissions from power plants in Northern China,” Environmental Research Letters, vol. 5, no. 4, Article ID 044007, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Li, Q. Zhang, D. G. Streets et al., “Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical mechanisms,” Atmospheric Chemistry and Physics, vol. 14, no. 11, pp. 5617–5638, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. F. Wang, J. Li, Z. Wang et al., “Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control,” Science China Earth Sciences, vol. 57, no. 1, pp. 3–13, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Gao, M. Zhang, Z. Liu et al., “Modeling the feedback between aerosol and meteorological variables in the atmospheric boundary layer during a severe fog–haze event over the North China Plain,” Atmospheric Chemistry and Physics, vol. 15, no. 8, pp. 4279–4295, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. X. Tie, Q. Zhang, H. He et al., “A budget analysis of the formation of haze in Beijing,” Atmospheric Environment, vol. 100, pp. 25–36, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. Q. Zhang, C. Zhao, X. Tie et al., “Characterizations of aerosols over the Beijing region: a case study of aircraft measurements,” Atmospheric Environment, vol. 40, no. 24, pp. 4513–4527, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. G. J. Zheng, F. K. Duan, H. Su et al., “Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions,” Atmospheric Chemistry and Physics, vol. 15, no. 6, pp. 2969–2983, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Quan, X. Tie, Q. Zhang et al., “Characteristics of heavy aerosol pollution during the 2012–2013 winter in Beijing, China,” Atmospheric Environment, vol. 88, pp. 83–89, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. C.-M. Gan, Y. Wu, B. L. Madhavan, B. Gross, and F. Moshary, “Application of active optical sensors to probe the vertical structure of the urban boundary layer and assess anomalies in air quality model PM2.5 forecasts,” Atmospheric Environment, vol. 45, no. 37, pp. 6613–6621, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. Q. Zhang, J. Quan, X. Tie et al., “Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China,” The Science of the Total Environment, vol. 502, pp. 578–584, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. H. He, X. Tie, Q. Zhang et al., “Analysis of the causes of heavy aerosol pollution in Beijing, China: a case study with the WRF-Chem model,” Particuology, vol. 20, pp. 32–40, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Quan, Y. Gao, Q. Zhang et al., “Evolution of planetary boundary layer under different weather conditions, and its impact on aerosol concentrations,” Particuology, vol. 11, no. 1, pp. 34–40, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. H. Wang, M. Xue, X. Y. Zhang et al., “Mesoscale modeling study of the interactions between aerosols and PBL meteorology during a haze episode in Jing-Jin-Ji (China) and its nearby surrounding region—part 1: aerosol distributions and meteorological features,” Atmospheric Chemistry and Physics, vol. 15, no. 6, pp. 3257–3275, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. X.-M. Hu, P. M. Klein, and M. Xue, “Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments,” Journal of Geophysical Research: Atmospheres, vol. 118, no. 18, pp. 10,490–10,505, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. X.-M. Hu, J. W. Nielsen-Gammon, and F. Zhang, “Evaluation of three planetary boundary layer schemes in the WRF model,” Journal of Applied Meteorology and Climatology, vol. 49, no. 9, pp. 1831–1844, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. X.-M. Hu, F. Zhang, and J. W. Nielsen-Gammon, “Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error: a real-data study,” Geophysical Research Letters, vol. 37, no. 8, Article ID L08802, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. X.-M. Hu, P. M. Klein, M. Xue, J. K. Lundquist, F. Zhang, and Y. Qi, “Impact of low-level jets on the nocturnal urban heat island intensity in Oklahoma city,” Journal of Applied Meteorology and Climatology, vol. 52, no. 8, pp. 1779–1802, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. X.-M. Hu, P. M. Klein, M. Xue et al., “Impact of the vertical mixing induced by low-level jets on boundary layer ozone concentration,” Atmospheric Environment, vol. 70, pp. 123–130, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. X.-M. Hu, D. C. Doughty, K. J. Sanchez, E. Joseph, and J. D. Fuentes, “Ozone variability in the atmospheric boundary layer in Maryland and its implications for vertical transport model,” Atmospheric Environment, vol. 46, pp. 354–364, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. G. A. Grell, S. E. Peckham, R. Schmitz et al., “Fully coupled ‘online’ chemistry within the WRF model,” Atmospheric Environment, vol. 39, no. 37, pp. 6957–6975, 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. J. D. Fast, W. I. Gustafson Jr., R. C. Easter et al., “Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model,” Journal of Geophysical Research Atmospheres, vol. 111, no. 21, Article ID D21305, 2006. View at Publisher · View at Google Scholar · View at Scopus
  35. R. A. Zaveri and L. K. Peters, “A new lumped structure photochemical mechanism for large-scale applications,” Journal of Geophysical Research Atmospheres, vol. 104, no. 23, pp. 30387–30415, 1999. View at Publisher · View at Google Scholar · View at Scopus
  36. O. Wild, X. Zhu, and M. J. Prather, “Fast-J: accurate simulation of in- and below-cloud photolysis in tropospheric chemical models,” Journal of Atmospheric Chemistry, vol. 37, no. 3, pp. 245–282, 2000. View at Publisher · View at Google Scholar · View at Scopus
  37. E. J. Mlawer, S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, “Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave,” Journal of Geophysical Research D: Atmospheres, vol. 102, no. 14, pp. 16663–16682, 1997. View at Publisher · View at Google Scholar · View at Scopus
  38. Y.-L. Lin, R. D. Farley, and H. D. Orville, “Bulk parameterization of the snow field in a cloud model,” Journal of Climate and Applied Meteorology, vol. 22, no. 6, pp. 1065–1092, 1983. View at Publisher · View at Google Scholar · View at Scopus
  39. S. W. Wang, Q. Zhang, D. G. Streets et al., “Growth in NOx emissions from power plants in China: bottom-up estimates and satellite observations,” Atmospheric Chemistry and Physics, vol. 12, no. 10, pp. 4429–4447, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. B. Xie, J. C. H. Fung, A. Chan, and A. Lau, “Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model,” Journal of Geophysical Research: Atmospheres, vol. 117, no. 12, Article ID D12103, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. S.-Y. Hong, Y. Noh, and J. Dudhia, “A new vertical diffusion package with an explicit treatment of entrainment processes,” Monthly Weather Review, vol. 134, no. 9, pp. 2318–2341, 2006. View at Publisher · View at Google Scholar · View at Scopus
  42. S.-Y. Hong and H.-L. Pan, “Nonlocal boundary layer vertical diffusion in a medium-range forecast model,” Monthly Weather Review, vol. 124, no. 10, pp. 2322–2339, 1996. View at Publisher · View at Google Scholar · View at Scopus
  43. Z. I. Janjic, “The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes,” Monthly Weather Review, vol. 122, no. 5, pp. 927–945, 1994. View at Publisher · View at Google Scholar · View at Scopus
  44. P. Bougeault and P. Lacarrère, “Parameterization of orography-induced turbulence in a mesobeta-scale model,” Monthly Weather Review, vol. 117, no. 8, pp. 1872–1890, 1989. View at Publisher · View at Google Scholar · View at Scopus
  45. H. Wang, G. Y. Shi, X. Y. Zhang et al., “Mesoscale modelling study of the interactions between aerosols and PBL meteorology during a haze episode in China Jing-Jin-Ji and its near surrounding region—part 2: Aerosols' radiative feedback effects,” Atmospheric Chemistry and Physics, vol. 15, no. 6, pp. 3277–3287, 2015. View at Publisher · View at Google Scholar · View at Scopus