Elevated dust layers inhibit dissipation of heavy anthropogenic surface air pollution

Persistent winter–time heavy haze incidents caused by anthropogenic aerosols have repeatedly shrouded North China in recent years, while natural dust from west and northwest of China also frequently affects air quality in this region. Through continuous observation by a multi–wavelength Raman lidar, here we found that aerosols in North China are typically 25 characterized by a pronounced vertical stratification, where scattering non–spherical particles (dust or mixtures of dust and anthropogenic aerosols) dominated above the planetary boundary layer (PBL), and absorbing spherical particles (anthropogenic aerosols) prevailed within the PBL. This stratification is governed by meteorological conditions that strong northwesterly winds usually prevailed in the lower free troposphere, and southerly winds are dominated in the PBL, producing persistent and intense haze pollution. With the accumulation of elevated dust, the proportion of aerosol and trace gas at the 30 surface in the whole column increased. Model results show that, besides directly deteriorating air quality, the key role of the elevated dust is to depress the development of PBL and weaken the turbulence exchange, mostly by lower–level cooling and upper–level heating, and it is more obvious during dissipation stage, thus inhibiting the dissipation of heavy surface anthropogenic aerosols. The interactions of natural dust and anthropogenic aerosols under the unique topography of North https://doi.org/10.5194/acp-2020-379 Preprint. Discussion started: 26 May 2020 c © Author(s) 2020. CC BY 4.0 License.


Introduction
Booming industrialization and urbanization in China is releasing large amounts of atmospheric anthropogenic pollutants, especially in the Beijing-Tianjing-Heibei (BTH) and surrounding regions, where the air pollution is the highest in the country 5 (Zhang et al., 2019a;Zhang et al., 2019b). Accumulation of air pollutants from stationary and transportation sources, accompanied by the explosive increase of new particles under stagnant weather conditions (Guo et al., 2014;Huang et al., 2014), cause PM2.5 (particle mass less than 2.5 μm in diameter) concentrations to increase several-fold within a few hours.
Recent studies have shown that the radiative effect of aerosols reduces solar shortwave radiation, increases the strength of the capping inversion, and enhances the stability of the planetary boundary layer (PBL) (Zhong et al., 2018). Such unfavorable 10 meteorological conditions will enhance the explosive growth of surface air pollutants. Simulation results from atmospheric chemical transport models have also led to similar conclusions (Ding et al., 2016;Huang et al., 2018), i.e., that absorbing aerosols, particularly black carbon (BC), will increase the temperature at the top of PBL and induce a cooling effect near the surface, thereby inhibiting the dispersion of air pollutants.
In addition to BC, dust is also an important source of air pollution. Besides directly acting as an important component of PM10 (particle mass less than 10 μm in diameter) and PM2.5, it scatters solar shortwave radiation and absorbs longwave radiation and thus leads to a cooling at the earth surface (Xia and Zong, 2009). Compared with the impact of other aerosol types, such as nitrates and sulfates, the effect of dust on decreasing radiation is more serious (Sokolik and Toon, 1996). Recent studies have also shown that dust can function as a reactant or a catalyst affecting atmospheric chemical reactions (Cwiertny et al., 2008). However, the current understanding of the effects of dust on meteorology and air pollution in North China remains 20 insufficient.
To elucidate the role of dust during heavy air pollution, multi-wavelength Raman lidar (RL) was deployed to monitor the vertical structure of atmospheric aerosols with high spatial and temporal resolution. RL can provide several optical parameters of aerosols to distinguish anthropogenic aerosols, dust, and other aerosol types (de Foy et al., 2011;Freudenthaler et al., 2009;Groß et al., 2013;Müller et al., 2007;Tesche et al., 2009), including the aerosol extinction coefficient (EXT), linear volume 25 depolarization ratio (VDR), and lidar ratio (LR). The RL measurements were performed at the Central Weather Bureau Farm (CWBF) since 17 December 2016 (Fig. 1). The CWBF (39.15 o N,115.73 o E) is located 120 km southwest of Beijing and approximately 40 km away from the Baoding urban district. It is surrounded by wheat fields, and there are no nearby stationary pollution sources. Combined with Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem) simulations and multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations, the mechanism of dust's impact on meteorology and air pollution was explored.

Raman lidar system
Ground based RL measurements were performed at CWBF during Jan to Mar 2017. The RL was placed in an airconditioned room to monitor air pollution through the roof skylight in a continuous mode (7 min for data collection with 15minute intervals). A schematic of the multi-wavelength RL system is shown in Fig. S1 in the supplement. The light source of 5 the RL system uses an Nd:YAG laser (QSmart850) with a pulse repetition rate of 10 Hz, producing two wavelengths: second harmonic generation 532 nm and third harmonic generation 355 nm, with an output energy of 300 mJ and 230 mJ, respectively.
The backscatter signals of the Raman, Rayleigh, and Mie scattering were received by a Cassegrain telescope with a diameter of 400 mm and field of view of 0.2 mrad. In addition, the 532 nm return signal was divided into parallel (532p) and vertical (532s) polarization components. Thus, the receiver had 5 channels: 532p, 355 nm Mie scattering channel, nitrogen (387 nm), water vapor (408 nm) Raman scattering channel, and polarization channel 532s. The data collector was a transient recorder (LICEL, TR20-160) with five acquisition channels. For each channel, the signal was acquired in both analog and photon counting modes with a spatial resolution of 7.5 m. Signals from 4000 laser shots were accumulated to produce a single sampled signal profile (approximately 7 min). More details of the RL system can be found in Table 1.
The RL used in this study can provide various aerosol optical parameters, including EXT, VDR, LR, and relative humidity 15 (RH). The VDR distinguishes between non-spherical and spherical particles (Freudenthaler et al., 2009;Tesche et al., 2009), and non-spherical particles are identified by a high VDR (over 15%). The LR is related to the absorption (>70 sr) and scattering (<40 sr) of particles (Müller et al., 2007). The input signal of aerosol optical parameters are provided in Table 1. The relative error was calculated in accordance with the law of error propagation, and primarily depends on the signal-to-noise ratio (Heese et al., 2010) of the input signal given in Table 1. Data with a signal-to-noise ratio of the input signal less than 1 were discarded.

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Given that the uncertainty of the overlap correction (Wandinger and Ansmann, 2002) was too high below 400 m, data below 400 m were not used for subsequent analysis. The details of data inversion and data validation can be found in Supplementary materials section S1 and our previous studies (Ji et al., 2019).

Multi-axis differential optical absorption spectroscopy
MAX-DOAS was performed at CWBF since Jan 2017. The instruments used for MAX-DOAS include a telescope, two 25 spectrometers [ultraviolet (303-370 nm) and visible (390-550 nm)] with the temperature stabilized at 20 °C, and a computer that acts as a control and data acquisition unit. The elevation angle (1°-6°, 8°, 10°, 15°, 30°, and 90°) of the telescope is controlled by a stepping motor. The scattered sunlight collected by the telescope is redirected by a prism reflector and a quartz filter to the spectrometer for data analysis. MAX-DOAS can retrieve aerosol profiles with the corresponding aerosol properties and trace gas profiles using the measured spectrum information. Further data screening was conducted using the root mean 08:00 to 16:00 local time) with a temporal resolution of 15 min and a spatial resolution of 100 m, respectively. The complete description of the MAX-DOAS system and retrieval algorithm can be found in our previous studies (Xing et al., 2017;Xing et al., 2019).
To explore the effects of upper-level dust on low-level anthropogenic aerosols, the percentage of bottom EXT360 in total EXT360 and percentage of bottom NO2 VMR in total NO2 VMR measured via MAX-DOAS were used to represent the low- 10 VMR, respectively.

WRF-Chem simulations
The air pollution and meteorology parameters from 20 Jan to 5 Feb 2017 were simulated by WRF-Chem version 3.6.1.
The model domain was centered at 110.68° E, 39.34° N with a 20 km × 20 km grid resolution, encompassing North China, especially the Mongolia region and its surrounding areas. There are 44 vertical layers from the ground level to the top pressure 15 of 50 hPa, in which 17 layers were located below 2 km to well describe the vertical structure of the air pollutants below PBL.
The simulation was conducted from 15 Jan to 5 Feb 2017. Each run covered 48 hours and the last 24-hour results were used for the analysis. The initial and boundary conditions of meteorological fields for simulation were adopted from the 6-hour  Table 2 and our previous studies (Liu et al., 2016a).
In addition, to explore the role of dust in aerosol-meteorology interactions and its impact on surface air pollution during the dissipation stage, the simulation period of each heavy pollution incident dissipation stage was performed five simulations https://doi.org/10.5194/acp-2020-379 Preprint. Discussion started: 26 May 2020 c Author(s) 2020. CC BY 4.0 License.
Particularly, four-dimensional data assimilation (FDDA) for wind, temperature, and water vapor mixing ratio was not adopted in the five simulations. The average of five simulations of each simulation period was used for the final analysis. The validation of WRF-Chem simulations can be found in Supplementary materials section S2.

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In this study, we selected the MOSAIC aerosol scheme (Zaveri and Peters, 1999;Zaveri et al., 2008), and the analysis variables here were the 3D mass mixing ratios of the 32 MOSAIC aerosol variables at each grid point. We conducted two parallel experiments using WRF-Chem to investigate the mechanism of the elevated dust layer enhancing the pollution near the ground: 1. without considering the influence of dust (dust_off), that is, the effects of dust on radiation transfer and meteorology were ignored; 2. with consideration of the effect of dust (dust_on), that is, the effects of dust on radiation transfer and meteorology were assimilated into the model. The dust concentrations is calculated as Where the idust_on OIN and idust_off OIN represent the other inorganic compounds in each bin when the influence of dust 20 was considered and ignored, respectively. The non-dust particles concentration is defined as Where the Tur_exch is average turbulent exchange coefficient within the PBL, exchi is the turbulent exchange coefficient of each model layer. Tur_exchon and Tur_exchoff are the average turbulent exchange coefficients for the two experiments dust_on and dust_off, respectively.

Characteristics of dust, ice clouds and anthropogenic aerosols
Based on RL measurements, the VDR at 532 nm and LR at 355 nm were derived to represent the characteristics of different aerosol types. The VDR can distinguish between non-spherical and spherical particles (Tesche et al., 2009), which is useful to identify ice clouds (Sassen, 1991) and dust layers (Murayama et al., 1999) (the value is typically greater than 20%).
Many researchers have reported the VDR of dust and ice clouds (see Table 3). The typical VDR of Asian dust is between 20% and 33%, which can be distributed at different heights. In addition, the different height distributions of Asian dust may be related to the different origins. The dust from Mongolia generally accumulates between 0 and 3,000 m (Sun et al., 2001), and the dust from the Taklimakan Desert is distributed above 5,000 m (Liu et al., 2008;Sun et al., 2001). Unlike dust, ice clouds have a wider VDR distribution between 20% and 60%, and are usually located above 4,000 m. Therefore, distinguishing Asian dust via high VDR is difficult due to the wide height distribution of dust and a VDR comparable to ice clouds (Sakai et al., RL can provide independent measurements of backscatter and extinction profiles (Ansmann et al., 1990;Ferrare et al., 1998) to compute LR. As LR is related to the absorption and scattering of particles (Müller et al., 2007;Omar et al., 2009), a higher LR indicates that the particles tend to be more absorbing. The typical value of LR for Asian dust is 40-60 sr (Omar et al., 2009). The LR of Asian dust observed in Beijing is smaller, from 30 sr to 47 sr, and it is usually located below 3,000 m 20 (see Table 3). By contrast, the LR for ice clouds is lower, less than 30 sr. Therefore, a threshold of 30 sr can be set to distinguish between dust and ice clouds (Sakai et al., 2003). The combined VDR and LR can distinguish between dust and ice clouds.
Asian dust has a higher VDR (20%-33%) and the LR is usually greater than 30 sr. The VDR of ice clouds is even higher (20%-60%), but the LR is typically less than 30 sr. In addition to Asian dust and ice clouds, the LR and VDR of anthropogenic aerosols also summarized in Table 3. The low VDR of anthropogenic aerosols (less than 10%) indicates spherical particles 25 (Tesche et al., 2009). The high LR of anthropogenic aerosols is very distinct compared with Asian dust and ice clouds, and the values range from 40 sr to 80 sr.

Vertical layering of particles in North China
We focused on the transmission, explosive growth, and dissipation of air pollution along with the interactions between aerosol and meteorology in North China. The EXT355 (EXT at 355 nm wavelength) measured via RL shows a periodic cycle of 2-5 days, rising rapidly from less than 0.5 km -1 in the early stage of each heavy pollution incident (HPI) to 3-5 km -1 within 5 1-2 days (Fig. 2). For the subsequent discussion, the whole observation set was classified into clean stages, cumulative growth stages (CS), and dissipation stages (DS) based on the surface EXT360 (EXT at 360 nm wavelength) measured via MAX-DOAS and the surface winds from model simulations. Clean stages are defined as the times when the surface EXT360 is less than 0.5 km -1 . The surface EXT360 during CS and DS is typically greater than 0.5 km -1 , and the surface winds during CS were dominated by southerly weak winds or a static atmosphere, while much stronger northwesterly surface winds were most prevalent in the

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DS. An entire HPI includes a clean period plus the subsequent CS and DS. One of these HPIs lasted for less than 2 days during the whole observation period (Fig. 2), whereas the other two HPIs persisted for more than 4 days and had peak mass concentrations greater than 500 µg m -3 . Thus, two HPIs (Table 4) We evaluated the aerosol optical parameters, including the VDR and LR provided by RL during the HPI 1 and HPI 2 in the upper lidar layer (700-1,300 m) and lower lidar layer (400-600 m). Aerosols that accumulated in the upper lidar layer had a relatively broad VDR value (4%-34% in most cases) and LR range of 32-72 sr (34-60 sr in more than 90% of the cases) during HPI 1 and HPI 2 ( Fig. 5a and 5b). Therefore, aerosols accumulated in the upper lidar layer are mainly scattering nonspherical particles. We also selected several RH profiles to identify the aerosol types in the upper lidar layer (see Fig. S3 in the 20 supplement). All of the available RH values of aerosols in the upper lidar layer was less than 80%, whereas the RH of ice clouds were usually greater than 100% (Ferrare et al., 1998;Sakai et al., 2003). Furthermore, the non-spherical scattering particles in the upper lidar layer during the two HPIs had the same origin ( Fig. 1) and also had similar distribution heights (700-1,300 m). Because anthropogenic aerosols also occurred in the upper lidar layer due to the southerly industrial transport, the non-spherical particles in the upper lidar layer during the HPI 1 and HPI 2 are mainly dust or mixtures of dust and 25 anthropogenic aerosols (polluted dust).
By contrast, a low VDR of less than 10% (2%-8% in most cases) in the lower lidar layer was always found during HPI 1 and HPI 2, and a much higher LR (53-85 sr) in the lower lidar layer ( Fig. 5c and 5d) indicating the aerosol's trend to be more absorbing (Müller et al., 2007). The RH of pollutants in the lower lidar layer varied from 25% to 85% and increased as the pollution grew more severe (see Fig. S3 in the supplement). Moreover, aerosols accumulated in the lower lidar layer came 30 from the polluted industrial regions (Zhang et al., 2019a;Zhang et al., 2019b) (Fig. 1). Therefore, these spherical absorbing particles were mainly anthropogenic aerosols. Based on these measured lidar parameters, we conclude that the aerosols in the During the period from 20 Jan to 5 Feb 2017, weak southerly winds (47%) typically prevailed in the lower lidar layer between the polluted periods ( Fig. 1), carrying polluted air masses from industrial areas and resulting in a sharp increase in EXT355. The strong northwesterly winds in the lower lidar layer from the Gobi desert (37%) and sparsely populated northern 5 mountain areas (16%) were most prevalent in the dissipation stage and clean period, causing EXT355 to drop distinctly (Fig.   2c). The average EXT355 in the lower lidar layer during the weak southerly wind conditions was 1.76 km -1 , followed by winds from Gobi desert (1.35 km -1 ) and sparsely populated northern mountain areas (0.62 km -1 ). The measured VDR in the lower lidar layer was relatively low, and fluctuated with the VDR in the upper lidar layer. In the upper lidar layer, strong northwesterly winds (66%) from the Gobi desert prevailed, carrying dust to the CWBF, leading to a significant increment in VDR. The strong 10 northwesterly winds (7%) in the upper lidar layer from the sparsely populated northern mountain areas usually occurred during the period of VDR decline (Fig. 2d). The EXT355 in the upper lidar layer is less than 1.5 km -1 in the most cases, except that during the period of southerly wind (27%) transmission, EXT355 increased considerably. The average EXT355 in the upper lidar layer during the weak southerly wind conditions was 1.00 km -1 , which is clearly higher than that during the winds from Gobi desert (0.66 km -1 ) and sparsely populated northern mountain areas (0.38 km -1 ).

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The shift of the origin of the air mass from northerly to southerly, together with a considerable decrease in wind speed, promotes the southerly transport of industrial pollutants and a stagnant condition (Guo et al., 2014), which is conducive to the accumulation and explosive growth of aerosols in the lower and upper lidar layers. The air mass origin in the upper lidar layer shifts from industrial areas to the Gobi desert with a substantially increasing wind speed, driving the accumulation of dust in the upper lidar layer. As a consequence of these shifts, aerosols are stratified in distinct layers, with anthropogenic aerosols in 20 the lower lidar layer and dust or polluted dust in the upper lidar layer. Thus, the meteorological conditions not only regulate the transmission, accumulation, and dissipation of aerosols, but also control the stratification of air pollutants, which is one of the most powerful factors that promote haze pollution in North China.

Correlation between elevated dust and surface haze pollution
Stratified aerosol typically shrouded CWBF during 20 Jan to 5 Feb 2017. The maximum value of VDR in the upper lidar 25 layer usually appeared during the DS. Moreover, the percentage of EXT355 of total EXT355 in the lower lidar layer during the DS is considerably higher than during the CS and clean periods (Fig. 6). To further investigate the relationship between elevated dust and surface anthropogenic aerosols, HPI 1 and HPI 2 were examined in detail. During HPI 1, the upper dust layer formed slightly later than the accumulation of the anthropogenic aerosols in the lower lidar layer (Fig.3). At the end of the CS during HPI 1, the air mass in the upper lidar layer was mainly from the northwest, and the wind speed increased significantly. while the percentage of bottom EXT355 rose at first, and then declined. The upper dust layer during HPI 2 appeared earlier than the anthropogenic aerosols in the lower lidar layer (Fig. 4). Similar to HPI 1, the northwesterly winds in the lower lidar layer increased significantly during DS in HPI 2, and both upper VDR and the percentage of bottom EXT355 reached a maximum.
We selected hourly and spatially (950 m-1,050 m) average VDR as an indicator of dust in the upper lidar layer. Also, the percentage of bottom EXT360 in total EXT360 and the percentage of bottom NO2 volume mixing ratio (VMR) in total NO2 VMR 5 measured via MAX-DOAS were used to represent the air pollution near the ground. We find that the hourly and spatially average VDR roughly correlates with the hourly average percentage of bottom EXT360 and percentage of bottom NO2 VMR during HPI 1 and HPI 2 (Fig. 7). This positive correlation suggests that the increase in upper-level VDR is related to the aggravation of the proportion of aerosol and trace gas at the surface in the whole layer.

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We conducted two parallel experiments using WRF-Chem to investigate the mechanism by which the elevated dust layer enhances air pollution near the ground, especially during DS: 1. without considering the influence of dust (dust_off); 2.
with consideration of the effect of dust (dust_on). In the MOSAIC aerosol scheme, dust is represented by the difference of "other inorganic compounds" (OIN) between dust_on and dust_off, and non-dust particles include nitrate, sulfate, ammonium, organic compounds, and BC.

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The two parallel simulations, dust_on and dust_off, well reproduced the spatial and temporal variations of dust concentration at CWBF (Fig. 8 and Fig. 9). The PBL height during the CS was usually below 800 m and decreased with the daily accumulation of air pollutants. Dust typically concentrated above the PBL and the fraction of dust in total PM10 concentrations increased with height. The lower PBL height led to a reduction of dust entrainment into the PBL from the upper levels, thereby promoting the stratification of aerosol at CWBF. The northwest wind strengthened during the DS, accompanied To explore the role of dust in aerosol-meteorology interactions and its impact on surface air pollution during the DS, we examined the dissipation process during HPI 1 and HPI 2. The suspended dust above the PBL is widely distributed in North China during HPI 1, whereas it is mainly located in the upper air over the BTH region during HPI2 (Fig. 10a). Surface dust 30 concentrations also increased but clearly less than those within the PBL (Fig. 10b). Unexpectedly, the concentration of surface non-dust particles increased by 0-11.4 µg/m3 after the upper-level suspended dust had passed across the downstream https://doi.org/10.5194/acp-2020-379 Preprint. Discussion started: 26 May 2020 c Author(s) 2020. CC BY 4.0 License.
urban/industrial regions in Northern China (Fig. 10c). In addition, the gaseous pollutants (NO2) exhibited the same variation (increases by 0-4.4 ppb) as the non-dust particle concentration (Fig. 10d). This indicates that, in addition to directly acting as an important component of air pollutants, suspended dust can also induce the enhancement of non-dust particles and precursor gases during DS, thus further increasing the surface anthropogenic aerosol concentrations.
The interaction between dust and meteorology appears to be responsible for the enhancement of surface air pollution 5 during DS. The dust layer during DS plays an important role in modifying the temperature vertical structure ( Fig. 11a and 11b).
The opposing effects of the dust on temperature, a net heating above the PBL and cooling within the PBL, favor formation of a capping inversion and thereby promote aerosol stratification. Consequently, dust-meteorology interactions result in more stagnant conditions, with the turbulent exchange coefficient within the PBL falling by over 60%. Similarly, a significant decrease in PBL height was also attributable to the stable stratification ( Fig. 11c and 11d). As a consequence, although the 10 strong northwesterly winds during DS increase the horizontal and vertical diffusion in the atmosphere considerably, the upperlevel dust brought in simultaneously by the northwesterly wind strengthens the temperature inversion due to both scattering and absorption of solar radiation, thereby weakening convective motions. Enhanced vertical atmospheric stability due to dust during DS hinders the air pollutants from being dispersed vertically and leads to a reduction of the dissipation rates of surface air pollution Liu et al., 2016b;Wilcox et al., 2016).

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The results demonstrate that dust aerosols during DS can substantially affect meteorological conditions by strong radiative feedbacks, and hence increase the surface air pollution (aerosols and precursor gases) by inhibiting the vertical diffusion of air pollutants. Evidently, such a deterioration of surface air quality is ultimately driven by the emission of pollutants, but is also strongly related to the reduced vertical diffusion capacity of the atmosphere. Surface dimming and upper-PBL warming by dust aerosols help strengthen the capping inversion and weaken turbulent mixing . Previous studies 20 have also found that the levels of gaseous pollutants, such as NO2 (Wallace and Kanaroglou, 2009), are closely related to temperature inversion. Changes of atmospheric stability, precursor gases, and solar radiation could significant modify new particle formation  and photochemical reactions (Zhou et al., 2007), which may also contribute to the surface air pollution. The decreasing upper-level dust concentration (usually less than 40 µg/m 3 in the model) during CS has an insignificant impact on low-level meteorological conditions, while its mixing with anthropogenic aerosols affects upper-level 25 aerosol optical properties. Moreover, the mixing of dust and anthropogenic aerosols will enhance the formation and growth rates of particles (Nie et al., 2014) to strengthen the particle concentrations in the upper lidar layer (Tao et al., 2014), which in turn further enhances the atmospheric stability and promotes the temperature inversion (Reichardt et al., 2002).

Summary
Our observations clearly show the stratification of aerosols over North China, especially during the DS. Absorbing   and the upper dust layers arrived mostly from Mongolia (Sun et al., 2001). Secondly, unfavorable vertical diffusion conditions, when strong northwesterly winds prevailed above the PBL with southerly air masses within the PBL, produced lengthy and intense temperature inversions and low PBL heights (Tao et al., 2014). The suppressed convection constrained dust into the PBL, which may also have contributed to higher surface relative

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Similar stratification and effects should be investigated in other parts of the world that also suffer from severe particulate pollution (Wu et al., 2017). Competing interests. The authors declare that they have no competing interests.