Effects of aerosol-radiation feedback and topography during an air pollution event over the North China Plain during December 2017

Atmospheric Pollution Research 10 (2019) 587–596 HOSTED BY

Contents lists available at ScienceDirect

Atmospheric Pollution Research journal homepage: www.elsevier.com/locate/apr

Effects of aerosol-radiation feedback and topography during an air pollution event over the North China Plain during December 2017

T

Dongdong Wanga, Baolin Jianga,∗, Wenshi Lina,b,∗∗, Feng Guc a

School of Atmospheric Sciences, Sun Yat-sen University, Xingang West Road 135, Guangzhou, 510275, PR China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou, 510275, PR China c Industrial and Commercial College, Hebei University, Qiyi East Road 2666, Baoding, 071000, China b

ARTICLE INFO

ABSTRACT

Keywords: WRF-Chem model Haze event Aerosol-radiation feedback Topography

The online coupled Weather Research and Forecasting-Chemistry (WRF-Chem) model was used to investigate a regional haze event, which occurred over the North China Plain (NCP) from 27 to 30 December 2017. Modeling scenarios with and without aerosol-radiation feedback within the planetary boundary layer (PBL) were investigated. By adding aerosol-radiation feedback to the model, we captured the spatial and temporal characteristics of the observed temperature and relative humidity (RH), as well as surface PM2.5, SO2, and NO2 concentrations during this event. The primary meteorological driver of this event was stable meteorological conditions, namely, a low PBL, strong temperature inversion, high RH, and a weak wind field. Aerosol-radiation feedback mechanisms affected these meteorological fields, causing reductions in maximum surface solar radiation, the surface energy budget, PBL height, surface 2-m temperature and middle atmosphere RH, while increasing surface 2-m RH, middle atmosphere temperature, and atmospheric stability. Changes in meteorological variables in turn affected air pollutant distributions and concentrations, with PM2.5 increasing by more than 20 μg/m3 over the NCP during this event. Another sensitivity experiment was carried out over the Taihang and Yanshan mountain areas, in which topography was flattened to a 30-m height to explore the impacts of topography on air pollution in the NCP region. Modeling revealed various topographic effects on meteorological variables related to these two mountains, including a lowered PBL height, reduced wind speed, blocked uniform surface winds, and more intense temperature inversion. Consequently, surface air pollutant concentrations increased unevenly over the NCP in this experiment. Implementation of more detailed PBL processes, as well as radiosonde and satellite data products into the WRF-Chem model would improve simulations of haze formation over the NCP.

1. Introduction Over the past 40 years, air quality in China has become very serious as result of rapid urbanization and industrialization, especially over the North China Plain (NCP), which is one of the most densely populated areas in the world (Miao et al., 2015a). In this region, high PM2.5 (particulate matter with aerodynamic diameters less than 2.5 μm) mass concentrations substantially reduce visibility and have adverse impacts on the human health (Adams et al., 2001; Elias et al., 2009; Jacobson, 2001; WHO, 2014). Furthermore, these high concentrations of aerosols influence global climate and ecosystems via aerosol-cloud-radiation interactions (Gao et al., 2016; Liu et al., 2013; Zhang et al., 2010). Therefore, air pollution episodes in China have attracted worldwide attention.

Numerous studies in recent years have focused on the sources and formation mechanisms of air pollution episodes over the NCP (An et al., 2007; Huang et al., 2014; Liu et al., 2015; Sun et al., 2016; Tian et al., 2014; Wang et al., 2012; Zhao et al., 2009). These studies indicate that the contaminants are mainly composed of multiple pollution sources, such as emissions from vehicle exhausts, power plants, domestic sources, and industry (Guo et al., 2014; Liu et al., 2016; Wang et al., 2017a, 2017b; Wu et al., 2016). Chemical compositional analyses show they are generally secondary inorganic aerosols (SIA) (Gao et al., 2016). Researchers have found that formation of severe haze is closely related to stagnant weather conditions, such as calm or weak wind fields, temperature inversions, and a low planetary boundary layer height (PBLH) (He et al., 2015; Wang et al., 2014; Zhang et al., 2015a,b; Zhao

Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control. ∗ Corresponding author. ∗∗ Corresponding author. School of Atmospheric Sciences, Sun Yat-sen University, Xingang West Road 135, Guangzhou, 510275, PR China. E-mail addresses: [email protected] (B. Jiang), [email protected] (W. Lin). https://doi.org/10.1016/j.apr.2018.10.006 Received 25 June 2018; Received in revised form 30 September 2018; Accepted 17 October 2018 Available online 19 October 2018 1309-1042/ © 2019 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V.

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al.

Podgorny and Ramanathan, 2001; Sekiguchi et al., 2018; Wu and Han, 2011; Zhuang et al., 2010; Ramanathan and Carmichael, 2017). Instead, we were concerned with the feedback of aerosol-induced weather conditions on regional to local aerosol concentrations, and their effects on meteorological factors over shorter time scales. In addition, because both PBLH and structure over the NCP can be affected by topography in complex ways (Chen et al., 2009; Miao et al., 2015a, 2015b, 2016; Sun et al., 2013), the potential of the neighboring mountains to exacerbate poor air quality also was explored in our modeling. In this study, we provide a comprehensive and systematic analysis of a specific severe haze event, which occurred over the NCP from 28 to 29 December 2017, based on simulations of this event carried out using the online fully coupled WRF-Chem model. The formation mechanisms of this haze event, assumed to be triggered by changes in temperature, PBLH, wind speed, and RH, were investigated. The effects of feedback between aerosol and radiation, as well as key meteorological variables are discussed herein. Impacts of regional topography on the PBLH, wind speed, RH, temperature, air pollutant concentration also are discussed. Our results provide valuable information on the links between aerosols, radiation, and topography for forecasting of air pollution episodes over the NCP.

et al., 2013). All these conditions are unfavorable to diffusion, causing accumulation of air pollutants. Haze episodes also are often associated with high relative humidity (RH), which is conducive to the formation of secondary aerosols (Gao et al., 2016; Yang et al., 2015; Wang et al., 2015). Aerosol particles are known to affect the radiation balance and their feedback mechanisms can change meteorological conditions. Scattering and absorption of solar radiation (direct effects) can lead to large decreases in solar radiation reaching the surface, causing cooling of the ground surface (Atwater, 1970; Gao et al., 2015; Sokolik and Toon, 1996; Yu et al., 2006). Meanwhile, absorption of solar radiation by some aerosols, such as black carbon (BC) (Ding et al., 2016; Gao et al., 2016; Wang, 2004), can increase solar heating of the atmosphere, reducing cloud formation (Gao et al., 2016; Twomey, 1991). Such changes alter the distribution of RH, wind fields, temperature, and affect atmospheric stability (via semi-direct effects) (Forkel et al., 2012; Gao et al., 2015). Extensive previous modeling has concentrated on analyzing changes in average radiation levels at the surface, induced by aerosols over China and its neighboring regions (Giorgi et al., 2002; Huang et al., 2006; Podgorny and Ramanathan, 2001; Sekiguchi et al., 2018; Wu and Han, 2011; Zhuang et al., 2010) or globally (Ramanathan and Carmichael, 2017). For instance, Sekiguchi et al. (2018) used the Weather Research and Forecasting-Community Multiscale Air Quality model to simulate impacts of direct effects of aerosols from January to March 2014 over East Asia. They showed that the surface shortwave (SW) radiation level decreased by 15% over northeast and central China related to these direct effects. The NCP is bounded by the Yanshan Mountains to the north, Taihang Mountains to the west, and the Bohai Sea to the east (Fig. 1b). The region is prone to regional transport of air pollutants (Chen et al., 2009; Miao et al., 2015a, 2015b, 2016; Sun et al., 2013). Miao et al. (2015a) used idealized simulations to investigate how the Taihang Mountains, Yanshan Mountains, and Bohai Sea impact on the seasonal variation of the PBLH over the Beijing-Tianjin-Hebei region using the Weather Research and Forecasting Model with Chemistry (WRF-Chem) model. They illustrated that mountain-plain circulation is prominent in fall; sea-breeze circulation is strongest in summer, when the mountainplain circulation is suppressed; sea-breeze circulation is confined to coastal areas in spring and winter; and a relatively low PBLH may exacerbate air pollution within this region. This suggests that the basinlike topography of the NCP and its weather systems are contributing to the degradation in air quality of this region. Given this background, we considered the mechanisms underlying a specific air pollution event to improve our understanding of the formation of severe haze over the NCP region. To date, most studies have concentrated on the change of aerosol-induced meteorological conditions on a climate scale (Giorgi et al., 2002; Huang et al., 2006;

2. Model configuration and experimental design 2.1. Model description The WRF-Chem is designed for both operational and research applications. It is a fully compressible Euler nonhydrostatic model, with a hydrostatic option available. There are two major parts of the model, namely, a dynamical model (WRF), and an online chemical model. In the coupled version, the chemical components are consistent with the meteorological components. A variety of coupled physical and chemical processes, such as aerosol-radiation interactions, deposition/emission, transformation, photolysis, and chemical transportation of gases, as well as various meteorological fields, are considered in the WRF-Chem model (Chapman et al., 2009; Fast et al., 2006; Grell et al., 2005). Anthropogenic emissions (such as SO2, NOx, primary particles, and volatile organic compounds) are transformed into secondary aerosols through physical and chemical processes that include transportation, deposition, gas-phase chemistry, aqueous chemistry, and photolysis. A broader description of the model can be found at http://www.wrfmodel.org/index.php. Therefore, this model has all the components required to realistically simulate the formation of severe haze over the NCP. 2.2. Model configuration The WRF-Chem model version 3.6.1 was used in this study. Simulations were conducted from 00:00 on 26 December to 00:00 UTC on 30 December 2017. To overcome the impacts of initial conditions, 36 h were simulated and considered as the spin-up period. Two domains with two-way nesting were used, with grid resolutions of 18 km and 6 km (Fig. 1a). The larger domain (D01) covers most of China, including parts of Mongolia, Japan, as well as North and South Korea. The NCP was located at the center of the inner nested domain (D02). Correspondingly, the time steps were set to 60 s and 20 s in each domain. There were 42 unevenly spaced vertical layers in the model, ranging up to 50 hPa. Twenty layers were defined below 1200 m (above ground) to better resolve the processes within the PBL. Some of the physical configuration options selected in this study include Lin cloud-microphysics (Lin et al., 1983), RRTMG long-wave/ shortwave radiation scheme (Iacono et al., 2000; Mlawer et al., 1997), the Yonsei University planetary boundary layer scheme (Hong et al., 2004), and the Noah land surface model. The chemical and aerosol mechanisms used include gas-phase chemistry and carbon-bond mechanism version Z (CBMZ) (Zaveri and Peters, 1999), coupled with the

Fig. 1. Simulation domains of the study region: (a) largest domain of the simulation (D01), and (b) topographic map of domain D02 (unit: m). Shading indicates terrain height, except for the dark blue area representing the Bohai Sea. Dotted circles indicate the main Chinese cities. The solid black line indicates the location of the vertical cross-sections of Fig. 12. The long-dashed black box indicates the area flattened in the FLAT experiment, while the shortdashed black box indicates the area modeled in aerosol-radiation feedback experiments of this study. 588

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al.

8-bin sectional MOSAIC model with aqueous chemistry (Zaveri et al., 2008). MOSAIC treats all the important aerosol species, including sulfate, nitrate, chloride, ammonium, sodium, BC, primary organic matter, liquid water, and other inorganic species (Zaveri et al., 2008). Both the initial and boundary chemical conditions are from MOZART's (Model for OZone and Related chemical Tracers) chemical boundary conditions, which are specific to the period being studied (Emmons et al., 2010). Anthropogenic emissions of carbon monoxide, nitrogen oxides, sulfur dioxide, volatile organic compounds, BC, organic carbon, PM2.5 and PM10 from several sectors (power generation, industry, residential, transportation) are based on Tsinghua University's 2010 monthly emission inventory (http://www.meicmodel.org). These emissions undergo chemical reactions, wet and dry deposition, and diffusion (advection and convection), which can lead to the formation of secondary aerosols, in the WRF-Chem model. The initial and boundary weather conditions for the simulations were obtained from the 1° × 1°National Center for Environmental Prediction Final (NCEP-FNL) global tropospheric analysis dataset (http://rda.ucar.edu/datasets/ds083.2). The MODIS 2010 land use dataset was also used in our simulations.

Fig. 2. Time series of simulated (red line) and observed (blue stars) hourly 2-m relative humidity (RH2; %), 2-m temperature (T2; °C), as well as surface PM2.5, NO2, and SO2 concentrations (μg/m3) from 20:00 on 27 December to 08:00 on 30 December 2017 (Beijing Time).

2.3. Experimental design Three numerical experiments were conducted to investigate the feedback between aerosols and meteorological variables, as well as the topographic impacts of local mountains. The control experiment (CTL) was conducted using the WRF-Chem model including aerosol-radiation feedbacks and standard topography, with full coupling of aerosols and meteorology in the simulation. The second sensitivity experiment (NF) was conducted without feedback between aerosols and meteorological variables for standard topography. The feedback discussed in this paper only includes direct and semi-direct aerosol effects. The third sensitivity experiment (FLAT) was conducted by flattening the Taihang Mountains and half of Yanshan Mountains to a height of 30 m to explore the impacts of regional topography over the NCP. Aerosol-radiation feedback was also included in this experiment.

site, yielding R values of 0.71–0.92 and mean bias (MB) values of −1.15–1.69 °C for all five sites. The mean T2 values increased from Beijing, Shijiazhuang, Handan, Jinan to Zhengzhou, namely from high to low latitude. Fig. 2 also presents chemical variations in aerosols at all five sites. The magnitudes and changes of the simulated PM2.5, NO2 and SO2 levels were generally consistent with measurements. Values for the model mean (CMOD), observational mean (COBS), MB, R, root-meansquare error (RMSE), NMB, and the normalized mean error (NME) were calculated for simulated and observed PM2.5, SO2 and NO2 levels at all five sites; these are given in Table 1. Typically, the model underestimated PM2.5 concentrations at all five sites, with MB values of −7.95 to −73.18 μg/m3. Both simulated and observed SO2 levels were very low, compared to previous years (Ji et al., 2014; Li and Han, 2016; Wang et al., 2017b; Zhang et al., 2015a,b), with observed mean values ranging from 9 to 47 μg/m3. This could be related to the outcome of policies of the Chinese government urging the NCP region to transform its energy structure, close factories with high energy consumption and high pollution emissions, and introduce desulfurization and denitrification (http://www.hebqg.com/newsv.asp?nid=2025). Simulated NO2 levels match well with observations, with NMB and NME values ranging from −0.04 to 0.04 and from 0.12 to 0.26, respectively. These results indicate that the simulated meteorological variables (R = 0.66–0.92) outperform the simulated chemical variables (R = 0.33–0.71). This may be attributed to uncertainties in the emission inventory based on Tsinghua University's 2010 product, which is now out of date. In addition, uncertainties in meteorological fields could influence modeling of aerosol transport and secondary organic aerosol processes in the atmosphere. Typically, RH simulation is sensitive to surface temperature (Ruosteenoja and Räisänen, 2013, Ruosteenoja et al., 2001; Bei et al., 2017), leading to underestimation of RH when surface temperatures are overestimated. This also may cause underestimation of PM2.5 concentrations in our simulation. There may also be chemical processes occurring that are not yet incorporated into the WRF-Chem model (Gao et al., 2016). Despite these areas of uncertainty, our simulations largely captured the meteorological and chemical characteristics of this heavy air pollution event, establishing both the reliability of the model and a baseline for further sensitivity experiments.

3. Model evaluation and analysis of planetary boundary layer processes 3.1. Model evaluation Herein, an evaluation of the WRF-Chem model was carried out, comparing simulated aerosol and meteorological variables with real observations for the December 2017 haze event. Differences in their temporal variation and spatial distribution are described. Fig. 2 shows the observed and simulated hourly-averaged 2-m temperature (T2), 2m relative humidity (RH2), and surface PM2.5, NO2, and SO2 levels at five sites: Beijing, Handan, Shijiazhuang, Zhengzhou, and Jinan from 20:00 on 27 December to 08:00 on 30 December 2017 (Beijing Time). All times given are in Beijing Time. Observational data were provided by China's Ministry of Environmental Protection; they can be downloaded from https://www.zq12369.com/index.php. A statistical analysis of our model's performance is presented in Table 1. Generally, the simulated meteorological variables agreed well with observations. The spatial distribution of RH2 from WRF-Chem was reasonably well matched with observations, with correlation coefficients (R) of 0.66–0.87 and normalized mean bias (NMB) values of −0.15–0.10. During the haze event, both the simulated and observed RH2 had high values, ranging from 50% to 80% approximately. The RH2 value at the Beijing site had an average value of 81%, which may have been influenced by water vapor from moist sea air (Wang et al., 2014; Wu et al., 2017). The simulated RH2 at Zhengzhou was 11.44% lower than the observed value; this may indicate that the resolution of the NCEP-FNL initial field was too coarse. This suggests that initial and boundary conditions slightly influenced our simulations. The model captured T2 temporal variation but slightly overestimated their values, except at the Beijing

3.2. Analysis of planetary boundary layer processes Haze is easily formed under conditions of weak mixing and dispersion, such as those characterized by cold high-pressure systems, 589

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al.

Table 1 Performance statistics for simulated meteorological and chemical variables for the haze event from 20:00 on 27 December to 08:00 on 30 December 2017 (Beijing Time). Variable

Site Name

N

COBS

CMOD

MB

R

RMSE

NMB

NME

RH2

Beijing Shijiazhuang Handan Zhengzhou Jinan Beijing Shijiazhuang Handan Zhengzhou Jinan Beijing Shijiazhuang Handan Zhengzhou Jinan Beijing Shijiazhuang Handan Zhengzhou Jinan Beijing Shijiazhuang Handan Zhengzhou Jinan

62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62

81.00 70.48 70.88 65.69 69.50 −2.00 −1.90 −0.72 2.52 2.40 146.58 242.74 238.84 252.76 201.06 9.06 32.21 47.19 25.52 36.61 80.24 98.42 93.15 95.39 88.02

79.32 62.20 59.97 54.25 76.88 −3.15 −1.60 0.65 4.21 3.30 138.63 203.70 166.00 179.58 145.23 8.02 31.79 44.18 30.03 30.96 83.31 96.73 93.18 91.73 89.90

−1.67 −8.29 −10.91 −11.44 7.38 −1.15 0.30 1.37 1.69 0.90 −7.95 −39.04 −72.84 −73.18 −55.84 −1.04 −0.42 −3.02 4.51 −5.66 3.07 −1.69 0.04 −3.66 1.89

0.80 0.87 0.70 0.66 0.83 0.74 0.92 0.71 0.80 0.92 0.33 0.36 0.37 0.45 0.39 0.55 0.59 0.47 0.44 0.43 0.47 0.59 0.58 0.71 0.53

8.89 10.50 14.69 16.49 9.99 1.91 1.16 1.55 2.44 2.38 78.94 93.19 94.27 86.46 86.70 4.79 12.57 20.40 14.85 19.50 22.00 15.37 16.10 21.41 21.46

−0.02 −0.11 −0.14 −0.15 0.10 0.66 −0.03 −1.56 0.49 0.36 −0.05 −0.16 −0.30 −0.29 −0.28 −0.11 −0.01 −0.06 0.18 −0.15 0.04 −0.02 0.004 −0.04 0.02

0.09 0.14 0.17 0.22 0.11 −0.78 −0.47 −1.79 0.83 0.84 0.44 0.37 0.39 0.31 0.39 0.40 0.33 0.3 0.52 0.42 0.26 0.12 0.15 0.17 0.20

T2

PM2.5

SO2

NO2

NB: RH2 is 2-m relative humidity, T2 is 2-m temperature, N is the number of paired samples, COBS and CMOD are the mean values for observations and model results during this period, MB is the mean bias between observations and model results, RMSE is the root-mean-square error of observations and model results, NMB is the normalized mean bias between observations and model results, and NME is the normalized mean error between observations and model results.

surface temperature inversions, and weak surface winds (Wu et al., 2017; Zhao et al., 2013). The surface weather patterns during the air pollution event, obtained from the China Meteorological Administration, indicate that there was a weak high-pressure system from 28 to 30 December 2017 over the NCP region (Fig. 3). During this period, the surface pressure field initially increased to 1028 hPa at 14:00 on 28 December (Fig. 3a), remaining high from 14:00 on 29 December (Fig. 3b) until 08:00 on 30 December (Fig. 3c), before weakening to 1025 hPa by 17:00 on 31 December 2017 (Fig. 3d). When the highpressure system weakened, the haze subsided.

To determine the underlying reasons for the formation of haze during this specific air pollution event, Fig. 4 shows the simulated average temporal variations along vertical profiles of PM2.5 concentration, temperature, RH, wind speed, and PBLH over the NCP region from 00:00 on 28 December to 00:00 on 30 December 2017. The selected study area (34.35–40.2°N, 113.35–117.3°E; shown as the shortdashed black box in Fig. 1b) has a topography below 300 m above sea level. It is characterized by a relatively high aerosol concentration during the haze event, suggesting there may be strong feedback between aerosols and meteorological variables within the PBL. The PM2.5 concentrations were concentrated below 600 m (Fig. 4a), with peak

Fig. 4. Simulated temporal variations in vertical profiles of (a) PM2.5 (μg/m3), (b) temperature (°C), (c) relative humidity (RH; %), (d) wind speed (m/s), and (e) planetary boundary layer height (PBLH; m) from 00:00 on 28 December 28 to 00:00 on 30 December 2017 (Beijing Time). Values are averaged over the study region shown as a short-dashed box in Fig. 1(b).

Fig. 3. Surface weather patterns for (a) 14:00 on 28 December, (b) 14:00 on 29 December, (c) 08:00 on 30 December, and (d) 17:00 on 31 December 2017 (Beijing Time). Data were download from the China Meteorological Administration (http:// www.nmc.cn/publish/observations/china/dm/weatherchart-h000.htm). 590

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al.

values occurring in three intervals, defined from 00:00 to 11:00 on 28 December, from 18:00 on 28 December to 13:00 on 29 December 29, and from 16:00 on 29 December to 00:00 on 30 December, respectively. Air pollutants were suppressed below 150 m, because of a strong temperature inversion over the NCP (Fig. 4b), which inhibited vertical diffusion. Both temperature and RH showed marked diurnal variation linked to solar radiation during the study period. The temperature inversion typically disappeared during the afternoon, reappearing at night; RH was low in the afternoon and high at night (Fig. 4c). Typically, high RH was associated with high concentrations of air pollutants. Therefore, PM2.5 growth was likely enhanced by the moist atmospheric conditions, accumulating below 150 m when there were weak horizontal winds (Fig. 4d). This conclusion is consistent with previous studies (Gao et al., 2016; He et al., 2015; Yang et al., 2015; Wang et al., 2014, 2015; Zhang et al., 2015a,b; Zhao et al., 2013). Correspondingly, the PBLHs were very low, mostly below 500 m during the haze event, forcing pollutants closer to the surface (Fig. 4e). This suggests there was a negative correlation between the change in PBLH and changes in air pollutant concentrations. For instance, when the PBLH rose to 300–460 m from 12:00 to 16:00 on 28 December, diffusion helped dilute air pollutants, lowering concentrations during this period. Meanwhile, when the PBLH was below 150 m from 17:00 on 28 December to 10:00 on 29 December, air pollutant levels increased markedly. Moreover, the PBLH rose again between 12:00 and 16:00 on 29 December, diffusing air pollutant concentration once again. These correlations are consistent with aerosol observations from previous studies (He et al., 2015; Wang et al., 2014; Zhang et al., 2015a,b; Zhao et al., 2013).

Fig. 5. Simulated time series of maximum surface solar radiation for scenarios: (a) with aerosol-radiation feedback (control, CTL) and without feedback (nofeedback, NF); the simulated planetary boundary layer height (PBLH) (b) in CTL and NF scenarios from 00:00 on 28 December to 00:00 on 30 December 2017 (Beijing Time). Values are averaged for the study region shown as a shortdashed box in Fig. 1(b).

4. Effects of aerosol feedback on meteorological fields and air pollutant concentration Aerosol-radiation effects linked to absorption and scattering can change contaminant concentrations, temperatures, RH, solar radiation levels, surface energy, atmospheric stability, and PBLH during haze events (Ding et al., 2016; Forkel et al., 2012; Gao et al., 2015, 2016; Ramanathan et al., 2001). To quantify these effects during this specific haze event, we focused on evaluating aerosol feedback mechanisms on surface energy, meteorological variables, and atmospheric stability. Simulated maximum surface solar radiation and PBLH for scenarios with aerosol-radiation feedback (control, CTL) and without feedback (no-feedback, NF) from 00:00 on 28 December to 00:00 on 30 December 2017 are shown in Fig. 5. These variable were averaged over the selected study region, defined by the short-dashed box in Fig. 1b. Clearly, maximum solar radiation values of the NF scenario were higher than those of the CTL scenario. The NF scenario showed that surface energy increased by 59.62 W/m2 and 88.15 W/m2 at 12:00 on 28 and 29 December, suggesting that aerosols reduced SW radiation values through scattering and absorption. Lowering of the PBLH over the study region also decreased SW radiation levels (Fig. 5b). In the CTL scenarios, the average PBLH was 168 m and 24 m lower than for the NF scenario on 28 and 29 December (Fig. 5b), which would suppress diffusion of pollutants, increasing their scattering and absorption effects. Aerosol-radiation effects on the surface energy budget influenced surface temperature and RH levels. Fig. 6 presents times series illustrating the difference between CTL and NF scenarios in the aerosolinduced diurnal variations of the surface budget, including SW, longwave radiation (LW), sensible heat (SH), latent heat (LH), and the net energy flux (NET; defined as LH+LW+SH+SW), as well as meteorological variables (T2 and RH2) over the study region. To better depict diurnal variation, we calculated average values of these variables for 28 and 29 December. These averages show that SW fluxes decreased by from 1.99 to 75.42 W/m2 at the surface between 08:00 and 17:00, with the minimal value occurring at 13:00, because of aerosol scattering and absorption. In contrast, there is no notable diurnal variation in LW fluxes. Given the large number of aerosols measured during this haze

event, the ground surface likely underwent cooling, with SH and LH fluxes at the surface decreasing by from 0.77 to 31.26 W/m2 (minimal value occurring at 13:00) and from 0.12 to 1.84 W/m2 (minimal value occurring at 14:00) between 08:00 and 17:00, respectively. This infers that the NET decreased by from 3.53 to 108.26 W/m2 between 08:00 and 17:00 (minimal value occurring at 13:00). Fig. 6b shows that the T2 dropped between 0.54 °C and 1.32 °C during this 24-h period, related to aerosol-radiation feedback. Most of the time periods show an increase in RH2, although decreases in RH2 occurred during 02:00–08:00 and 10:00–11:00, which require further study to explain. Overall, the average RH2 value increased by 1.12%, related to the reduction in SW solar radiation. The effects of aerosols on surface energy and meteorological variables suggests they can also influence the stability of the atmosphere during air pollution events. Here, we characterize the stability of the atmosphere using profiles of equivalent potential temperature (EPT). Fig. 7 shows the difference between CTL and NF (CTL−NF) scenario values, averaged for 28 and 29 December, as EPT profiles over the study region at time points: 00:00, 04:00, 08:00, 12:00, 16:00 and 20:00. These profiles show that aerosol-radiation effects reduced EPT values by as much as 0.58 K above 875 hPa and 0.67 K above 950 hPa at midnight (00:00) on both 28 and 29 December, respectively. In contrast, EPT values increased by as much as 0.09 K between 700 and 875 hPa and 0.08 K between 700 and 950 hPa at 00:00 on 28 and 29 December, respectively. At midday (12:00) on 28 and 29 December, aerosol-radiation feedback effects decreased EPT values by as much as 0.37 K above 925 hPa and 0.58 K above 950 hPa, but increased them between 700 and 925 hPa by as much as 0.09 K and 0.10 K. On both 28 and 29 December, a more stable atmosphere formed at around 16:00 in the afternoon, related to aerosol-radiation feedback effects decreasing EPT values by as much as 0.75 K above 950 hPa and 0.86 K above 591

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al.

Fig. 6. Time series showing aerosol-induced diurnal changes in (a) the surface energy budget (SW, LW, SH, LH, and NET; W/m2), and (b) meteorological variables T2 (°C) and RH2 (%). Values are averaged for the study region shown as a short-dashed box in Fig. 1(b) for the period 28–29 December 2017. SW is shortwave radiation, LW is long-wave radiation, SH is sensible heat, LH is latent heat, NET is the sum of the total energy fluxes, T2 is 2-m temperature, and RH2 is 2-m relative humidity.

Fig. 8. Simulated differences between the control (CTL) and no-feedback (NF) scenarios in temporal variations of (a) temperature (T; °C), (b) relative humidity (RH; %), and (c) PM2.5 (μg/m3) along vertical profiles from 00:00 on 28 December to 00:00 on 30 December 2017 (Beijing Time). Values are averaged over the study region shown as a short-dashed box in Fig. 1(b).

the PBL appears to have been more stable on 29 December, leading to a greater increase in air pollutant concentrations (Fig. 8c). Because a more stable atmosphere developed in the afternoon around 16:00 (relative to the NF scenario), large changes in the values of T2, RH2, and PBLH occurred from 14:00 to 18:00 (Figs. 5b and 6b). Aerosol effects induced decreases in T2 from 15:00 to 17:00, with values dropping by 1.08–1.31 °C (Fig. 6b). Meanwhile, the largest increase in RH2 occurred from 16:00 to 17:00, with values increasing by 3.49%–3.62%. The maximum reduction in PBLH occurred from 14:00 to 17:00, with heights falling by 74.75–111.34 m and 85.07–114.43 m on 28 and 29 December, respectively (Fig. 5b). Large decreases in PBLH were detrimental to the diffusion of air pollutants, leading to increases in their surface concentrations. However, the variation in PM2.5 concentrations was only slight from 14:00 to 18:00 on both 28 and 29 December, possibly because of low total PM2.5 levels (Figs. 4a and 8c). Clearly, changes in meteorological variables related to the aerosolradiation effects do not only occur near the surface, but continue into mid-atmospheric levels. Fig. 8 illustrates the difference between CTL and NF simulated temporal variations in temperature, RH, and PM2.5 as vertical profiles for the period from 00:00 on 28 December to 00:00 on 29 December 2017. Values are averaged over the study region shown as a short-dashed box in Fig. 1b. Temperatures near the surface decreased (Fig. 8a), reflecting reduced solar radiation reaching the surface

Fig. 7. Aerosol-radiation feedback effects on equivalent potential temperature (EPT; K) profiles between 00:00 and 20:00 (Beijing Time) on (a) 28 and (b) 29 December 2017. Values are averaged for the study region shown as a shortdashed box in Fig. 1(b).

930 hPa and increasing them by as much as 0.13 K and 0.23 K below 930 hPa, respectively. In the evening (20:00), aerosol-radiation feedbacks decreased EPT values around 0.70 K and 0.69 K above 940 hPa on 28 and 29 December, respectively. Overall, the magnitude of change in EPT on 28 December was less than that on 29 December (Fig. 7). Thus, 592

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al.

(Figs. 5a and 6b), as aerosol scattering and absorption took place. Some suspended aerosols, like BC, absorb radiation, heating the upper PBL and causing temperatures to rise (Fig. 8a). This increases the intensity of the temperature inversion, producing a more stable atmospheric stratification in the CTL scenario, which negatively affected air quality. Moreover, in the CTL scenario, aerosols reduced the levels of solar radiation reaching the ground, producing an increase in RH near the surface during most of the haze event. Decreased levels of RH only occurred between 22:00 on 28 December and 11:00 on 29 December. The processes underlying this decrease require further study. Decreased RH in the middle atmosphere (Fig. 8b) could be due to increased temperature at these altitudes. Our preliminary simulations of the haze event show that average of the PM2.5 concentrations near the surface increased more than 20 μg/m3 on 28 and 29 December in the CTL compared to the NF scenario (Fig. 8c). In this study, we also evaluated changes in chemical constituents using the WRF-Chem. Fig. 9 presents simulated levels of various chemical species comprising PM2.5 over the study region shown as the short-dashed box in Fig. 1b. Primary organic carbon, BC, sulfate, nitrate, and ammonium account for the major part of simulated PM2.5 during the haze event. Table 2 summarizes average concentrations of primary aerosols (primary organic carbon and BC) and SIA (sulfate, nitrate, and ammonium) on 28 and 29 December 2017. The SIA account for 86% of the increase in PM2.5 (Table 2). Sulfate, nitrate and ammonium are prone to aqueous-phase chemical reactions under high RH (Gao et al., 2016; Yang et al., 2015; Wang et al., 2015; Zhang et al., 2015a,b). Therefore, we believe that some of the SIA may be derived from aerosol aqueous-phase chemical reactions, although we cannot define any specific aqueous-phase pathway. Regrettably, we lack information on the observed chemical species of PM2.5 in the study region, which could reduce credibility to our simulation results. Heterogeneous reactions of aerosols during this event are yet to be investigated.

Table 2 Average values of primary and secondary inorganic aerosols (SIA) on 28 and 29 December 2017 over the study region shown as a short-dashed box in Fig. 1(b). Primary (μg/m3) Date CTL NF

28 50.36 47.83

SIA (μg/m3) 29 64.41 61.47

28 82.21 47.83

29 94.12 61.47

Fig. 10. Simulated (CTL, red line; FLAT, green line) and observed (blue stars) hourly 2-m temperature (T2), surface PM2.5, and NO2 concentrations from 00:00 on 28 December to 00:00 on 30 December 2017 (Beijing Time). CTL, control scenario; FLAT, flattening experiment.

conducted a FLAT experiment, in which the terrain height was flattened to a 30-m height within a selected area (35.5–42.8°N, 110.8–116°E; shown as the long-dashed box in Fig. 1b). Fig. 10 compares time series of observations and results for two simulated scenarios (CTL and FLAT), showing hourly-averaged PM2.5, NO2, T2, and PBLH values at five sites from 28 to 29 December 2017. Observed concentrations of PM2.5 and NO2 were much higher than the FLAT experimental values at all five sites. For example, observed average PM2.5 and NO2 concentrations were 130 μg/m3 and 35 μg/m3 higher compared with FLAT simulated values at the Handan site. This indicates that topographic effects of the mountains contributed to poor air quality in the study region. In contrast, T2 values decreased markedly when mountains were included in the simulations, better approximating observed values. This may reflect that more air pollution occurred near the surface in the CTL scenario, causing less solar radiation to reach the surface. In the FLAT scenario, the PBLH was 25 m lower on average than in the CTL scenario. Generally, the PBLH is determined by the vertical shear strength of horizontal winds and the thermal condition of the ground surface. However, during this haze event, vertical mixing was very weak, whether mountains were included or not (Fig. 12). Therefore, we attribute the reduced PBLH to wind blocking effects of the mountains, which caused more aerosols to accumulate, in turn reducing solar radiation levels and temperatures near the surface. Fig. 11 (panels a, c) shows the averaged wind field for the D02 region (33–41°N, 112–122°E) on 28 and 29 December 2017 for the CTL scenario, while Fig. 11 (panels b, d) shows daily averages of the difference between wind fields simulated by the CTL and FLAT scenarios on 28 and 29 December, respectively. In Fig. 11a, weak winds occur over most of the study region, especially over Beijing, and northwestern parts of Hebei and Shandong. Thus, a large amount of air pollution accumulated within this region. However, strong winds are modeled over the sea areas. In this case, moist air may have been transported to the NCP region from coastal regions, increasing RH. This would exacerbate pollutant concentrations, as hygroscopic growth and aqueousphase chemical reactions took place. In contrast, a more uniform and stronger wind field was simulated in the FLAT scenario (Fig. 11b). Given the lack of mountains, better diffusion (and less accumulation) of

5. Effects of topography on meteorological fields and air pollutant concentrations Clearly, PBL processes are affected by topography, which in turn influence air pollutant concentrations. To explore this link, we

Fig. 9. Simulated mass concentrations of various chemical species of PM2.5 in the (a) control scenario (CTL) and (b) flattening experiment (FLAT) on 28 and 29 December 2017. Values are averaged over the study region shown as a shortdashed box in Fig. 1(b). BC, black carbon; and OC, organic carbon. 593

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al.

Fig. 11. Simulated and observed 10-m wind fields, (a) averaged for 28 December and (c) 29 December 2017. Differences between the control (CTL) scenario and flattening (FLAT) experiment, (b) averaged for 28 December and (d) 29 December 2017. The shading indicates wind speed. Black arrows indicate wind field directions.

Fig. 12. (a, c, e, g) Vertical cross-sections showing PM2.5 diffusion for control scenario (CTL) and (b, d, f, h) flattening experiment (FLAT). Time points shown are 02:00 and 14:00 on 28 and 29 December 2017 (Beijing Time), respectively. Shading indicates PM2.5 concentrations (μg/m3), contours show temperature (°C), and black arrows depict wind fields (m/s).

contaminants occurred. Obviously, the wind direction in the Taihang Mountain region was most affected in the FLAT scenario, where northeast winds prevailed when the mountains were flattened. The CTL scenario shown in Fig. 11c shows a weak wind field over the NCP region on 29 December, although a stronger wind field developed over the two mountain regions. In contrast, there is a uniform wind field in the FLAT scenario for 29 December. FLAT scenario results in Fig. 11d indicate that wind speeds over mountain regions were weaker compared with the CTL scenario and wind direction was basically consistent with adjacent NCP regions. To investigate effects explicitly related to changes in topography, Fig. 12 provides vertical cross-sections of air pollutant concentrations, wind fields, and temperatures at time points of 02:00 and 14:00 on 28 and 29 December, respectively. Cross-sections were constructed along the 10-m wind direction, shown as the black solid line in Fig. 1b, depicting the transition from plain to mountain topography. Vertical wind speed was exaggerated by 100 times to show variation in the vertical wind field. However, cross-sections show that almost all wind directions were horizontal (Fig. 12), suggesting that the atmospheric stratification was very stable. Large amounts of contaminants accumulated below 1.2 km at 02:00 on 28 December, especially where the two mountains are situated in the CTL scenario (Fig. 12a). In contrast, air pollutant levels were distinctly lower, when the two mountains were not considered (FLAT scenario; Fig. 12b). A strong temperature inversion appeared in the CTL scenario at 02:00, which contrasts with results of the FLAT scenario. Therefore, diffusion of air pollutants was not suppressed in the FLAT scenario, although air pollutants accumulated below 150 m in the region between 113°E and 116°E. Without the mountains, wind speeds below 0.4 km were higher compared to those in the CTL scenario, facilitating advective transport of contaminants towards the southwest. At 14:00 on 28 December, air pollutants were confined to below 1.2 km in the CTL scenario (Fig. 12c). However, contaminant concentrations remained low, because of a weaker temperature inversion. The mountain-plain circulation pattern appeared, when mountains were considered, causing contaminants to accumulate at the foot of the mountains. This reflects wind blocking effects on near surface winds. However, it is important to stress that the mountain-

plain circulation was very weak during the haze event, only occurring at 14:00 on 28 December. In the FLAT scenario, air pollutant concentrations were lower, and pollutants were concentrated below 500 m (Fig. 12d). This distribution reflects the absence of the temperature inversion and wind blocking effects, and presence of stronger near surface winds. At 02:00 on 29 December, the increased southwesterly wind speed above 500 m caused much of the air pollution to be advected towards the northeast in the CTL scenario (Fig. 12e). In the FLAT scenario, because no temperature inversion developed, the air pollutants diffused upwards, while increased wind speeds around 500–900 m resulted in air pollutants being transported away from the study region (Fig. 12f). In the afternoon of 29 December, a northeast wind prevailed on the eastern side of the mountains, while a southwest wind occurred above the mountains in the CTL scenario (Fig. 12g). This caused air pollutants to accumulate at the foot of the mountains, because of the blocking effects. In the FLAT scenario, near surface wind were chaotic, while southwesterly winds dominated above 600 m (Fig. 12h), concentrating pollutants below 600 m. Typically, as seen in Fig. 12a–h, temperatures near the surface of the CTL scenario were lower than in the FLAT scenario, which is attributed to feedback effects of the higher concentrations of aerosols on solar radiation. 6. Discussion and conclusions In this study, numerical simulations using the online coupled WRFChem model were carried out to explore spatial and temporal characteristics of T2 and RH2, as well as surface concentrations of PM2.5, NO2, and SO2 during a severe haze event in December 2017. Comparison of simulation results with observations of the haze event from 20:00 on 27 December 27 to 08:00 on 30 December 2017 showed that these characteristics were basically reproduced by the model. This indicates the reliability of the WRF-Chem to model haze events over the NCP region under stable weather conditions. Typically, low PBLH, high RH, weak surface wind speed, low surface temperature, and temperature inversions reduced the diffusion of atmospheric pollutants, producing haze conditions. 594

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al.

Impacts of high aerosol concentrations during haze on solar radiation, the surface energy budget, PBLH, RH, temperature, and surface PM2.5 concentrations were demonstrated in this study. The results of our numerical experiments showed that, when aerosol-radiation feedbacks were included, average maximum surface SW radiation and net surface energy were reduced by as much as 73.71 W/m2 and 108.26 W/ m2 over the NCP study region in daytime periods during the haze event. These reductions were related to decreases in the solar radiation linked to aerosol scattering and absorption. In addition, the calculation of the aerosol-induced EPT profiles for 28 and 29 December 2017 over the NCP study region showed that the atmosphere was much more stable, when aerosol-radiation feedbacks were considered. Aerosol-radiation feedbacks caused the average PBLH to be lowered by 24 m, decreased surface temperatures but increased middle atmosphere temperatures, and increased surface RH but decreased middle atmosphere RH during the haze event. For instance, the average maximum T2 value decreased by as much as 1.32 °C, while the average maximum RH2 value increased by as much as 3.62%. Because of a more stable atmospheric stratification, surface PM2.5 concentrations increased more than 20 μg/ m3 during this period over the study region. Therefore, positive feedback clearly occurred between aerosol concentrations and meteorological variables, contributing to haze formation. In addition, effects of the Taihang and Yanshan Mountains located in the western and northern parts of the NCP on PBL processes and air quality were illustrated using the model. To evaluate these impacts, a numerical experiment (FLAT) was conducted, in which these regions were flattened and compared with the CTL scenario. Our results showed that blocking effects of the mountains aggravated air pollution extremely. The averaged PM2.5 and NO2 concentration increased by 130 μg/m3 and 35 μg/m3 at the Handan site, respectively. Furthermore, a more uniform wind field developed, when the two mountains were flattened. The increased air pollutant concentrations related to the blocking effects of the mountains as well as reduced wind speeds enhanced the intensity of the temperature inversion in the CTL scenario. Because vertical mixing was very weak in both scenarios, the average of PBLH reduced by 25 m on the five sites in the CTL scenario caused decreased surface solar radiation levels and temperatures, related to aerosol-radiation feedback effects. There were some uncertainties in this study that need to be addressed. The model underestimated PM2.5 concentrations at the Beijing, Handan, Shijiazhuang, Zhengzhou and Jinan sites during the haze event (Table 1). This may reflect the model's PBLH setting or inaccurate emission inputs. Because PM2.5 concentrations were underestimated, this may have led to an underestimation of the aerosol-radiation feedback effects during the haze event. A more recent emission inventory with better resolution may improve the model's performance. Our evaluation of the impacts of topography on air pollutant concentrations and meteorological fields is a preliminary investigation; further work is needed to reveal the physical mechanisms underlying the topographic effects on meteorological fields and pollutant concentrations. However, higher resolution land use data and grid spacing need to be used to consider topographic effects in the model in more detail. In this research, the observational data are primarily from surface stations. This may reduce the reliability of the simulations. Implementation of detailed PBL processes as well as radiosonde and satellite data products will improve simulation of the NCP using the WRF-Chem model.

National Natural Science Foundation of China (NSFC; grant nos. 41875168 and 41705117), and Guangzhou Science and Technology Plan (201707010088). The authors are grateful to the NCAR Mesoscale and Microscale Meteorology Division for making the WRF-Chem model available at http://www.mmm.ucar.edu/wrf/users. The National Centers for Environmental Prediction Final Global Tropospheric Analysis (NCEP-FNL) data were taken from https://rda.ucar.edu/ datasets/ds083.2. References Adams, P.J., Seinfeld, J.H., Koch, D., Mickley, L., Jacob, D., 2001. General circulation assessment of direct radiative forcing by the sulphate-nitrate-ammonium-water inorganic aerosol system. J. Geophys. Res. 106 (D1), 1097–1111. An, X., Zhu, T., Wang, Z., Li, Z., Wang, Y., 2007. A modeling analysis of a heavy air pollution episode occurred in Beijing. Atmos. Chem. Phys. 7 (12), 3103–3114. Atwater, M.A., 1970. Planetary albedo changes due to aerosols. Science 170 (3953), 64–66. Bei, N., Wu, J., Elser, M., Feng, T., Cao, J., Elhaddad, I., et al., 2017. Impacts of meteorological uncertainties on the haze formation in Beijing-Tianjin-Hebei (BTH) during wintertime: a case study. Atmos. Chem. Phys. 17 (23), 1–32. Chapman, E.G., Jr, W.I.G., Easter, R.C., Barnard, J.C., Ghan, S.J., Pekour, M.S., et al., 2009. Coupling aerosol-cloud-radiative processes in the WRF-Chem model: investigating the radiative impact of elevated point sources. Atmos. Chem. Phys. 9 (3), 945–964. Chen, Y., Zhao, C., Zhang, Q., Deng, Z., Huang, M., Ma, X., 2009. Aircraft study of mountain chimney effect of Beijing, China. J. Geophys. Res. Atmos. 114 (D8) D08306. Ding, A.J., Huang, X., Nie, W., Sun, J.N., Kerminen, V.-M., Petäjä, T., Su, H., Cheng, Y.F., et al., 2016. Enhanced haze pollution by black carbon in megacities in China. Geophys. Res. Lett. 43 (6), 2873–2879. Elias, T., Haeffelin, M., Drobinski, P., Colomb, M., 2009. Particulate contribution to extinction of visible radiation: pollution, haze, and fog. Atmos. Res. 92 (4), 443–454. Emmons, L.K., Walters, S., Hess, P.G., Lamarque, J.F., Pfister, G.G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., et al., 2010. Description and evaluation of the model for ozone and related chemical tracers, version 4 (MOZART-4). Geosci. Model Dev. 3, 43–67. Fast, J.D., G Jr., W.I., Easter, R.C., Zaveri, R.A., Barnard, J.C., Chapman, E.G., et al., 2006. Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model. J. Geophys. Res. Atmos. 111 (D21) D21305. Forkel, R., Werhahn, J., Hansen, A.B., Mckeen, S., Peckham, S., Grell, G., Suppan, P., 2012. Effect of aerosol-radiation feedback on regional air quality – a case study with WRF/Chem. Atmos. Environ. 53, 202–211. Gao, Y., Zhang, M., Liu, Z., Wang, L., Wang, P., Xia, X., Tao, M., Zhu, L., 2015. Modeling the feedback between aerosol and meteorological variables in the atmospheric boundary layer during a severe fog-haze event over the North China Plain. Atmos. Chem. Phys. 15 (8), 4279–4295. Gao, M., Carmichael, G.R., Wang, Y., Saide, P.E., Yu, M., Xin, J., Liu, Z., Wang, Z., 2016. Modeling study of the 2010 regional haze event in the North China Plain. Atmos. Chem. Phys. 16 (3), 1673–1691. Giorgi, F., Bi, X., Qian, Y., 2002. Direct radiative forcing and regional climate effects of anthropogenic aerosols over East Asia: a regional coupled climate-chemistry/aerosol model study. J. Geophys. Res. Atmos. 107 (D20), 7–18. Grell, G.A., Pechham, S.E., Schmitz, R., Mckeen, S.A., Frost, G., Skamarock, W.C., Eder, B., 2005. Fully coupled 'online' chemistry within the WRF model. Atmos. Environ. 39 (37), 6957–6975. Guo, S., Hu, M., Zamora, M.L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu, Z., Shao, M., Zeng, L., et al., 2014. Elucidating severe urban haze formation in China. Proc. Natl. Acad. Sci. U. S. A. 111 (49), 17373–17378. He, H., Tie, X., Zhang, Q., Liu, X., Gao, Q., Li, X., Gao, Y., 2015. Analysis of the causes of heavy aerosol pollution in Beijing, China: a case study with the WRF-Chem model. Particuology 20, 32–40. Hong, S.Y., Dudhia, J., Chen, S.H., 2004. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Weather Rev. 132 (1), 103–120. Huang, Y., Dickinson, R.E., Chameides, W.L., 2006. Impact of aerosol indirect effect on surface temperature over East Asia. Proc. Natl. Acad. Sci. U. S. A. 103 (12), 4371–4376. Huang, R.J., Szidat, S., Haddad, I., Zhang, Y., Schwikowski, M., Bozzetti, C., Daellenbach, K., Slowik, J.G., Platt, S.M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S.M., Bruns, E.A., Ciarelli, G., Baltensperger, U., Prévôt, A.S.H., Ho, K.F., Cao, J.J., Han, Y., 2014. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514 (7521), 218–222. Iacono, M.J., Mlawer, E.J., Clough, S.A., Morcrette, J.J., 2000. Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM3. J. Geophys. Res. Atmos. 105 (D11), 14873–14890. Jacobson, M.Z., 2001. Global direct radiative forcing due to multicomponent anthropogenic and natural aerosols. J. Geophys. Res. 106 (D2), 1551–1568. Ji, D., Li, L., Wang, Y., Zhang, J., Cheng, M., Sun, Y., Liu, Z., Wang, L., Tang, G., Hu, B., Chao, N., Wen, T., Miao, H., 2014. The heaviest particulate air-pollution episodes

Conflict of interest No conflict of interest. Acknowledgements The authors were supported by the National Program for Key Basic Research Projects of China (973) (grant no. 2014CB953904), Natural Science Foundation of Guangdong Province (2015A030311026), 595

Atmospheric Pollution Research 10 (2019) 587–596

D. Wang et al. occurred in northern China in January, 2013: insights gained from observation. Atmos. Environ. 92, 546–556. Li, J., Han, Z., 2016. A modeling study of severe winter haze events in Beijing and its neighboring regions. Atmos. Res. 170, 87–97. Lin, Y.L., Farley, R.D., Orvilie, H.D., 1983. Bulk parameterization of the snow field in a cloud model. J. Appl. Meteorol. 22 (6), 1065–1092. Liu, X.G., Li, J., Qu, Y., Han, T., Hou, L., Gu, J., Chen, C., Yang, Y., Liu, X., 2013. Formation and evolution mechanism of regional haze: a case study in the megacity Beijing, China. Atmos. Chem. Phys. 13 (9), 4501–4514. Liu, Z., Hu, B., Ji, D., Wang, Y., Wang, M., Wang, Y., 2015. Diurnal and seasonal variation of the PM2.5, apparent particle density in Beijing, China. Atmos. Environ. 120, 328–338. Liu, J., Mauzerall, D.L., Chen, Q., Zhang, Q., Song, Y., Peng, W., Klimont, Z., Qiu, X., Zhang, S., Hu, M., Lin, W., Smith, K.R., Zhu, T., 2016. Air pollutant emissions from Chinese households: a major and underappreciated ambient pollution source. Proc. Natl. Acad. Sci. U. S. A. 113 (28), 7756–7761. Miao, Y., Hu, X.M., Liu, S.H., Qian, T.T., Xue, M., Zheng, Y.J., Wang, S., 2015a. Seasonal variation of local atmospheric circulations and boundary layer structure in the Beijing‐Tianjin‐Hebei region and implications for air quality. J. Adv. Model. Earth Syst. 7 (4), 1602–1626. Miao, Y., Liu, S., Zheng, Y., Wang, S., Chen, B., Zheng, H., Zhao, J., 2015b. Numerical study of the effects of local atmospheric circulations on a pollution event over Beijing-Tianjin-Hebei, China. J. Environ. Sci. 30, 9–20. Miao, Y., Liu, S., Zheng, Y., Wang, S., 2016. Modeling the feedback between aerosol and boundary layer processes: a case study in Beijing, China. Environ. Sci. Pollut. Control Ser. 23 (4), 3342–3357. Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., Clough, S.A., 1997. Radiative transfer for inhomogeneous atmospheres: RRTM, A validated correlated-k model for the longwave. J. Geophys. Res. Atmos. 102 (D41), 16663–16682. Podgorny, I.A., Ramanathan, V., 2001. A modeling study of the direct effect of aerosols over the tropical Indian Ocean. J. Geophys. Res. Atmos. 106 (D20), 24097–24105. Ramanathan, V.C., Carmichael, G., 2017. Global and regional climate changes due to black carbon. Nat. Geosci. 36 (4), 335–358. Ruosteenoja, K., Räisänen, P., 2013. Seasonal changes in solar radiation and relative humidity in Europe in response to global warming*. J. Clim. 26 (8), 2467–2481. Ruosteenoja, V.C., Crutzen, P.J., Kiehl, J.T., Rosenfeld, D., 2001. Aerosols, climate, and the hydrological cycle. Science 294 (5549), 2119–2124. Sekiguchi, A., Shimadera, H., Kondo, A., 2018. Impact of aerosol direct effect on wintertime PM2.5 simulated by an online coupled meteorology-air quality model over East Asia. Aerosol Air Qual. Res. 18 (4), 1068–1079. Sokolik, I.N., Toon, O.B., 1996. Direct radiative forcing by airborne mineral aerosols. J. Aerosol Sci. 27, 11–12. Sun, Y., Song, T., Tang, G., Wang, Y., 2013. The vertical distribution of PM2.5, and boundary-layer structure during summer haze in Beijing. Atmos. Environ. 74, 413–421. Sun, Y., Chen, C., Zhang, Y., Xu, W., Zhou, L., Cheng, X., Zheng, H., Ji, D., Li, J., Tang, X., Fu, P., Wang, Z., 2016. Rapid formation and evolution of an extreme haze episode in northern China during winter 2015. Sci. Rep. 6, 27151–27159 2016. Tian, G., Qiao, Z., Xu, X., 2014. Characteristics of particulate matter (PM10) and its relationship with meteorological factors during 2001-2012 in Beijing. Environ. Pollut. 192, 266–274. Twomey, S., 1991. Aerosols, clouds and radiation. Atmos. Environ. Part A. General Topics. 25 (11), 2435–2442. Wang, C., 2004. A modeling study on the climate impacts of black carbon aerosols. J. Geophys. Res. Atmos. 109 (D3) D03106. 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. Atmos. Environ. 56, 69–79. Wang, J., Wang, S., Jiang, J., Ding, A., Zheng, M., Zhao, B., Wong, D.C., Zhou, W., Zheng, G., Wang, L., Pleim, J.E., Hao, J., 2014. Impact of aerosol-meteorology interactions on fine particle pollution during China's severe haze episode in January 2013. Environ. Res. Lett. 9 (9), 1–7. Wang, Y., Wang, L., Zhao, G., Wang, Y., An, J., Liu, Z., Tang, G., 2014. Analysis of different-scales circulation patterns and boundary layer structure of PM2.5 heavy pollution in Beijing during winter. Climatic Environ. Res. (in Chin) 19 (2), 173–184. Wang, M., Cao, C., Li, G., Singh, R.P., 2015. Analysis of a severe prolonged regional haze episode in the Yangtze River Delta, China. Atmos. Environ. 102, 112–121. Wang, G., Cheng, S., Wei, W., Yang, X., Wang, X., Jia, J., Lang, J., Lv, Z., 2017a. Characteristics and emission-reduction measures evaluation of PM2.5 during the two major events: APEC and Parade. Sci. Total Environ. 595, 81–92. Wang, Y., Xue, Y., Tian, H., Gao, J., Chen, Y., Zhu, C., Liu, H., Wang, K., Hua, S., Liu, S., Shao, P., 2017b. Effectiveness of temporary control measures for lowering PM2.5 pollution in Beijing and the implications. Atmos. Environ. 157, 75–83. The World Health Organization (WHO), 2014. 7 Million Premature Deaths Annually Linked to Air Pollution. Available at: http://www.who.int/mediacentre/news/ releases/2014/air-pollution/en/. Wu, P., Han, Z., 2011. Indirect radiative and climatic effects of sulfate and organic carbon aerosols over East Asia investigated by RIEMS. Atmos. Oceanic Sci. Lett. 4 (1), 7–11. Wu, R., Li, J., Hao, Y., Li, Y., Zeng, L., Xie, S., 2016. Evolution process and sources of ambient volatile organic compounds during a severe haze event in Beijing, China. Sci. Total Environ. 560, 62–72. Wu, P., Ding, Y., Liu, Y., 2017. Atmospheric circulation and dynamic mechanism for persistent haze events in the Beijing-Tianjin-Hebei region. Adv. Atmos. Sci. 34 (4), 429–440. Yang, Y., Liu, X., Qu, Y., Wang, J., An, J., Zhang, Y., Zhang, F., 2015. Formation mechanism of continuous extreme haze episodes in the megacity Beijing, China, in January 2013. Atmos. Res. 155, 192–203. Yu, H., Kaufman, Y.J., Chin, M., Feingold, G., 2006. A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmos. Chem. Phys. 6 (3), 613–666. Zaveri, R.A., Peters, L.K., 1999. A new lumped structure photochemical mechanism for large-scale applications. J. Geophys. Res. Atmos. 104 (D23), 30387–30415. Zaveri, R.A., Easter, R.C., Fast, J.D., Peters, L.K., 2008. Model for simulating aerosol interactions and chemistry (MOSAIC). J. Geophys. Res. Atmos. 113 (D13), D13204. Zhang, Y., Wen, X.Y., Jang, C.J., 2010. Simulating chemistry–aerosol–cloud–radiation–climate feedbacks over the continental US using the online-coupled Weather Research Forecasting Model with Chemistry (WRF/Chem). Atmos. Environ. 44 (29), 3568–3582. Zhang, L., Wang, T., Lv, M., Zhang, Q., 2015a. On the severe haze in Beijing during January 2013: unraveling the effects of meteorological anomalies with WRF-Chem. Atmos. Environ. 104, 11–22. Zhang, Q., Quan, J., Tie, X., Li, X., Liu, Q., Gao, Y., Zhao, D., 2015b. Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China. Sci. Total Environ. 502, 578–584. Zhao, X., Zhang, X., Xu, X., Xu, J., Meng, W., Pu, W., 2009. Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing. Atmos. Environ. 43 (18), 2893–2900. Zhao, X.J., Zhao, P.S., Xu, J., Meng, W., 2013. Analysis of a winter regional haze event and its formation mechanism in the North China Plain. Atmos. Chem. Phys. 13 (11), 5685–5696. Zhuang, B., Jiang, F., Wang, T., Li, S., Zhu, B., 2010. Investigation on the direct radiative effect of fossil fuel black-carbon aerosol over China. Theor. Appl. Climatol. 104 (3–4), 301–312.

596