Effects of turbulence structure and urbanization on the heavy haze pollution process

In this paper, an automated algorithm is developed, which is used to identify the spectral gap during the heavy haze pollution process, reconstruct acquired data, and obtain pure turbulence data. Comparisons of the reconstructed turbulent flux and eddy covariance (EC) flux show that there are overestimations regarding the exchange between the surface and the atmosphere during heavy haze pollution episodes. After reconstruction via the automated algorithm, pure turbulence data can be obtained. We introduce a definition to characterize the local intermittent strength of turbulence (LIST). The trend in the LIST during pollution episodes shows that when pollution is more intense, the LIST is smaller, and intermittency is stronger; when pollution is weaker, the LIST is larger, and intermittency is weaker. At the same time, the LIST at the city site is greater than at the suburban site, which means that intermittency over the complex city area is weaker than over the flat terrain area. Urbanization seems to reduce intermittency during heavy haze pollution episodes, which means that urbanization reduces the degree of weakening in turbulent exchange during pollution episodes. This result is confirmed by comparing the average diurnal variations in turbulent fluxes at urban and suburban sites during polluted and clean periods. The sensible heat flux, latent heat flux, momentum flux, and turbulent kinetic energy (TKE) in urban and suburban areas are all affected when pollution occurs. Material and energy exchanges between the surface and the atmosphere are inhibited. Moreover, the impact of the pollution process on suburban areas is much greater than on urban areas. The turbulent effects caused by urbanization seem to help reduce the consequences of pollution under the same weather and pollution source condition, because the turbulence intermittency is weaker, and the reduction in turbulence exchange is smaller over the urban underlying surface.

pollution diffusion model, it is very important to accurately calculate pure turbulent transport during the heavy pollution process.
Therefore, the purpose of this study is to separate classic turbulent motions from submesoscale motions. At the same time, intermittency can be defined by the turbulent and nonturbulent portions of a signal once the criteria for identifying the boundary between them have been established (Salmond, 2005). Only in this way can we identify the classic turbulence 5 exchange during pollution periods, regardless of whether turbulence occurs over a flat underlying surface or a complex underlying surface. In a previous study, Muschinski (2004) found a plateau in the intermittency spectra. Vickers and Mahrt (2003) showed the existence of a cospectral gap, which separated turbulent and mesoscale contributions to calculate the fluxes in heat, moisture, and momentum used the multiresolution decomposition technique. Salmond (2005) used the wavelet analysis to objectively isolate intermittent turbulent bursts within vertical velocity time series. Wei et al. (2017) 10 found that the spectral gap separates fine-scale turbulence from large-scale motions using the arbitrary-order Hilbert spectral analysis during intermittency in the Cooperative Atmosphere-Surface Exchange Study-1999 (CASE-99). Because of the nonstationarity and nonlinearity of intermittent turbulence, analytical techniques have significant impacts on the results. A new technique, named the arbitrary-order Hilbert spectral analysis (Huang et al., 2011), showed its advantage and validity in the application of turbulent flow and intermittency (Wei et al. 2016(Wei et al. , 2017 In this paper, we use the arbitrary-order Hilbert spectral method to analyze turbulence data observed from several severe haze pollution episodes that occurred in Beijing and its nearby suburbs from 16 December 2016 to 8 January 2017 to identify the spectral gap and separate classic turbulence from the original signal. After obtaining the pure turbulence signal and the strength of the submesoscale motions, the classic turbulent flux and intermittency strength can be calculated. Then, we can obtain the macrostatistical characteristics of turbulence over a flat terrain in suburban Beijing and discover the difference 20 between clean and polluted days. This analysis is helpful for improving the current understanding of the transport and diffusion of PM2.5 and the intermittency strength of turbulence when heavy haze pollution occurs. The effect of urbanization on the heavy pollution process is simultaneously studied by comparing the results of the urban and suburban sites.

Experimental site 25
The data used in this paper are from two measurement sites ( Fig. 1 shows the location of the two sites). Data over a flat terrain were collected at a continuous measurement site (40°16′N, 116°28′E) in a Beijing suburb. The observational site was set up in the middle of a vast and horizontal farmland. A 10-m tower was erected for the eddy covariance (EC) and meteorological measurements. The EC system was mounted at a height of 8.3-m above ground. The system was equipped with an integrated CO2/H2O open-path gas analyzer and three-dimensional sonic anemometer (IRGASON, Campbell 30 Scientific, Inc., USA). The IRGASON was leveled and pointed north. The turbulence data were collected using a data logger Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-881 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 28 September 2018 c Author(s) 2018. CC BY 4.0 License.
(CR3000, Campbell Scientific, Inc., USA) at a frequency of 10 Hz. For convenience of description, this site is described as the suburban site.
The second dataset was collected over urban terrain at a continuous measurement site at Peking University (39°99′N, 116°31′E) in Beijing, which is the capital of China. The instruments are situated on top of a building at the School of Physics (Peking University) and extend 25-m above ground. There are teaching and residential buildings and public transit facilities 5 that surround the station. The site is located in a typical urban landscape. Details on the site introduction and data background have been shown in Ren et al. (2018). The concentrations of PM2.5 were collected using a Thermo-Fisher Sci.
Co. instrument (series FH-62-C14), and 30-min averaging time series were performed to remove outliers. Wind vectors and temperature fluctuations were logged at 10 Hz using a 3D sonic anemometer (Campbell Co., USA). For convenience of description, this site is described as the urban site. 10

Data processing
In this study, we select a 24-day pollution period from 16 December 2016 to 8 January 2017; the number of heavy pollution days with PM2.5 concentrations exceeding 200 μg m -3 reached 15 (17-21, 24-25, 28, and 30-31 December 2016 and 1-7 January 2017). The remaining days were identified as clear days. The PM2.5 mass concentration, horizontal wind speed, virtual temperature and water vapor mixing ratio are shown in Fig. 2 during the days of concern. The trends in 5 meteorological elements over time are consistent at both sites. The horizontal wind speed is larger during clear days and smaller during pollution days; the virtual temperature seems to be greater during pollution days, but this result is not obvious.
The water-vapor content is significantly higher during pollution days than that during clear days. The characteristics of these meteorological elements during the pollution process are similar to those in previous studies Sun et al., 2014;Tai et.al, 2010). The horizontal wind speed, virtual temperature and water vapor at the suburban site are greater, lower, 10 and higher than those at the city site, respectively. These differences between the two sites are caused by the difference in surface type. High-rise buildings across the city landscape weaken the horizontal wind speed.
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-881 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 28 September 2018 c Author(s) 2018. CC BY 4.0 License. The raw turbulence data were preprocessed over an averaging interval of 30 min by the Eddy Pro software (Advanced 4.2.1, LI-COR Biosciences, Inc., USA). Preprocessing included procedures involving error flags, despiking (following Vickers and 5 Mahrt, 1997), double rotations (following Wiczak et al., 2001), and detrending, which are implemented using the block averaging method. In this paper, the EC method is used to calculate the flux for comparison, where the average time is 30 min. At the same time, strict quality control is performed on the data and data groups to remove data meeting any of the following conditions: (1) the EC method runs with more than ±120° between the wind direction and direction of the sonic anemometer, (2) the included angle between the wind direction and horizontal plane is greater than 3°, (3) the wind speed is 10 less than 0.5 m s -1 , (4) the friction speed is less than 0.05 m s -1 , (5) the sensible heat flux is less than 5 W m -2 , and (6) data groups that are obviously incorrect. The physical quantities used in this paper are turbulent kinetic energy (TKE), several variances ( , , , and ), friction speed ( * ), and fluxes (− ′ ′ ̅̅̅̅̅̅ , ′ ′ ̅̅̅̅̅̅ and ′ ′ ̅̅̅̅̅̅ ). Among these, the TKE is calculated as: and the friction speed is calculated as: 3 Reconstructed Signals 20

Automated algorithm to identify the spectral gap
In the traditional turbulence theory, Van der Hoven (1957) presented an analysis on the large spectrum of horizontal wind speeds, which showed a notable spectral gap of approximately 1 cycle h -1 between the macroscale (synoptic) and microscale (turbulent) parts of the spectrum. Many other studies have generally proven the existence of the spectral gap (Panofsky, 1969;Fiedler and Panofsky, 1970;Smedman and Hogstrom, 1974). As observations progress, many studies on the stable boundary 25 layer have indicated that the turbulent part of the signal decreases, the time scale for turbulence decreases, and the time scale for the spectral gap also decreases, as mentioned above. These studies used the multiresolution decomposition method to choose a variable averaging time to remove the effects of mesoscale motions. The method we used here to separate pure turbulence and submesoscale motion is based on half-hour data (i.e., the fixed averaging time). The multiresolution basis utilized is a wavelet set, as it is the only wavelet set that satisfies Reynolds averaging (Howell and Mahrt, 1997;Mallat, 1998;30 Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-881 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 28 September 2018 c Author(s) 2018. CC BY 4.0 License. Mahrt, 2003Mahrt, , 2006. In this study, we adopt the Hilbert-Huang transform (Huang et al., 1998(Huang et al., , 1999, which is fully data-driven and local in both the physical and frequency domains (Huang et al., 1998;Flandrin et al., 2004). The advantages of researching turbulence data that are nonstationary and nonlinear have been proven previously (Huang et al., 2008;Schmitt et al., 2009;Huang et al., 2013;Wei et al., 2013Wei et al., , 2016Wei et al., , 2017.

Vicers and
The spectral gap was examined by studying the second-order Hilbert spectra (u ′ , v ′ , w ′ , θ ′ and q ′ ) for each 30-min period. 5 The calculation method for the second-order Hilbert spectra comes from the arbitrary-order Hilbert spectral analysis (Huang et al., 2008), which is an extended version of the Hilbert-Huang transform. A joint probability density function (PDF), p(ω, A), can be extracted from and for all intrinsic mode functions. Then, the arbitrary-order Hilbert marginal spectrum is defined as an amplitude-frequency space by the marginal integration of the joint PDF p(ω, A) where ω represents the instantaneous frequency, A represents the instantaneous amplitude, q ≥ 0 represents the arbitrary moment, and p(ω, A) represents the PDF. Specifically, h(ω) corresponds to the second-order Hilbert marginal spectrum(h( ) = ℒ 2 (ω) = ∫ p( , ) 2 ).
An automated algorithm was developed to objectively find the frequency location of the spectral gap. This method is based on the characteristics in Fig. 4b in Wei et al. (2017). The spectral gap between turbulence and the submesoscale motions is 15 identified as the frequency interval in which the second-order Hilbert spectral values are approximately constant, or the slope is approximately equal to 0. Figure 3 shows the second-order Hilbert spectra from the newly reconstructed data and raw data.
As shown in Fig. 3a, the lower frequency limit of the spectral gap is ω = 0.008 Hz, and the upper frequency limit is not used to reconstruct the data. The part of the frequency larger than ω indicates the turbulence signal, and the part of the frequency smaller than ω indicates nonstationary motion, which has a scale larger than that for turbulence. Then, we can search for the 20 mode of the intrinsic mode function (IMF) (N=9 in this case), whose mean frequency is closest to ω. Therefore, modes 1-N are chosen to reconstruct the pure turbulence signal, and the remaining modes are chosen to reconstruct the signal of the submesoscale motion.
As shown in Figs. 3a-c, it is obvious that the reconstruction successfully eliminated the energy contained by large-scale motion while retaining turbulent energy. The new spectrum, which is shown by the black dotted lines in Fig. 3, is consistent 25 with the structure of the turbulent energy spectrum in the classic theory. We reconstructed the data for all time periods when the spectral gap existed in our dataset. The results of the total reconstructed data spectrum showed that the whole process removed the effects of large-scale motion and retained classic turbulence, while simultaneously obtaining large-scale motions.

The local intermittent strength of turbulence 30
We use the ratio of turbulent intensity compared to all signals to indicate the intermittent strength. To obtain the strength of submesoscale motion, we introduce the definition of the velocity scale for submesoscale motion from 2009; Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-881 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 28 September 2018 c Author(s) 2018. CC BY 4.0 License.
2010; 2011). The velocity scale for submesoscale motion represents the kinetic energy of submesoscale motions and is defined as: where ′ , ′ , and ′ , represent the deviations reconstructed from the IMF corresponding to the submesoscale motions during each 30-min period. Similarly, the turbulent velocity scale is defined as: 5 where ′ , ′ , and ′ , represent the deviations reconstructed from the IMF corresponding to the turbulent motions during each 30-min period. Then, we can define the local intermittent strength from an energy point of view as The value of the LIST is equal to the ratio of turbulent intensity in the acquired signal. When the LIST is large and close to 1, 10 there are more turbulent components in the acquired signal, the influence of the submesoscale motion is weaker, and the intermittency is weaker. When the LIST is small and far from 1, there are fewer turbulent components in the collected signal, the influence of submesoscale motion is stronger, and the intermittency is stronger.  We use the automated algorithm to identify the spectral gaps in 1152 groups of 10-Hz high-frequency data from 16 December 2016 to 8 January 2017 at the urban and suburban sites. There are 440 groups of data in the along-wind direction, 5 519 groups of data in the cross-wind direction, 390 groups of data in the vertical direction, 467 groups of data in the scalar θ, and 501 groups of data in the scalar q, which is where the spectral gap occurs at the suburban site. The datasets encompassing the spectral gap account for 38%, 45%, 34%,41%, and 43% of the total data at the suburban site, respectively.
The proportions are 41% (481), 37% (425), 35% (402), 41% (476), and 45% (520) for the u, v, w, t, and q components at the urban site, respectively. The occurrence of a spectral gap is common throughout the pollution process, which means that the 10 effects of nonstationary motion on the collected signals are common throughout the entire process. Under these conditions, the usual half-hour length is not suitable for stationary conditions. The eddy-correlation flux calculated using a conventional averaging time of 30 min to define the perturbations is severely contaminated by poorly sampled mesoscale motions.
During the half-hour period, we refer to the variance in the pure turbulent fluctuations as the new results, and that calculated via the classic EC system is referred to as the old variance results. A comparison of the TKE and variance parameters 15 ( , , , and )   All of the fitted results show that there are certain degrees of overestimated variance in Fig. 4. The slope of the fitted line is 5 0.81 for the TKE, as Fig. 4f shows, which means that the TKE originally calculated by the conventional method is overestimated by approximately 19%. The comparison of and shows the same pattern as that for the TKE. The traditional method for the half-hour eddy correlation overestimates by approximately 27% (slope of 0.73) and 21% (slope of 0.79) for and , respectively. The overestimation of is not as obvious as those for and . The slope of the fitted line in Fig. 4 c is 0.99, which indicates that the appearance of the spectral gap has fewer effects on the variance in vertical 10 velocity. The overestimation is even more pronounced when the TKE and , and are small, which indicates that the overestimation can be more significant when the turbulence is weak. Similarly, we examine the variance in potential temperature and moisture content in Figs. 4d and 4e, respectively. The variations in potential temperature and moisture content are also overestimated; the difference is that the overestimation percentages are larger. addition, the overestimation in the horizontal direction is more significant than that in the vertical direction. However, the overestimation of these quantities cannot be ignored.
After separating pure turbulence and submesoscale motion from the signal, the pure turbulent flux can be calculated during heavy haze pollution periods. The comparison of the pure turbulent flux with the flux calculated by the eddy-correlation method at a fixed averaging time is presented in Fig. 5. The momentum flux, − ′ ′ ̅̅̅̅̅̅ , is overestimated by approximately 13%, 5 and the slope of the fitted line in Fig. 5c is approximately equal to 0.87. The effect of nonstationary motion on heat transfer and water-vapor transfer is similar to that on momentum, as seen in Fig. 5. Overestimation can reach 12% (slope of 0.88) and 15% (slope of 0.85) for ′ ′ ̅̅̅̅̅̅ and ′ ′ ̅̅̅̅̅̅ in Figs. 5a and b, respectively. The reconstructed turbulence data results at the urban site show that the traditional half-hour eddy-correlation results have a certain degree of overestimation regarding the turbulent fluxes and variances during the pollution period. However, the degree of overestimation in urban areas is less than 10 that in suburban areas. A comparison of the macrostatistical parameters for turbulence from the new half-hour results with those from the old results at the city site is not shown here. In the majority of recent research, the EC technique has become the most frequently used method to estimate the flux between land surfaces and the atmosphere (Baldocchi et al., 2003). Under weak stability conditions, eddy-correlation fluxes calculated using a conventional averaging time of 5 min or longer to define the perturbations are severely contaminated by poorly sampled mesoscale motions (Vickers and Mahrt, 2005). The average block time that has always been used is 30 min, 20 which has been used in practice since Kaimal and Finnigan (1994), who proposed that a 30-min averaging time was a reasonable compromise for daytime analyses. From the above analysis, we can see that turbulent fluxes are overestimated due to the effects of submesoscale motions when there is a spectral gap. During the heavy haze pollution process, turbulence is weak, which causes this overestimation to become even more apparent. Zhong et al. (2017Zhong et al. ( , 2018 proposed that heavy pollution events in Beijing are characterized by the transport stage (TS), whose formation of aerosol pollution is primarily 25

The relationship between the local intermittent strength and pollution
After reconstruction via the automated algorithm, pure turbulent data can be obtained. The LIST from 16 December 2016 to 8 January 2017 is discussed in this section. We calculated the LIST of the urban and suburban sites separately and discussed 5 the relationship between the LIST and degree of pollution. value of the IF is smaller, the intensity of the submesoscale motion is weaker than that during the clean period, and the energy of the turbulent flow is also weaker. Correspondingly, the local intermittent intensity is strong, as the LIST is farther from 1. Specifically, for the period of explosive growth in pollutant concentration (CS) during the two pollution processes of Case I and Case II in Fig. 7, the intermittent intensity (LIST) is significantly stronger (weaker) than that during the clean period and other weak pollution events, with a concentration of approximately 200 μg m -3 . The two processes Case I and 20 Case II are similar in that both experience a rapid decline in the concentration of PM2.5 (from a heavy pollution level greater than 500 μg m -3 to one less than 30 μg m -3 ) on a time scale of several hours. The difference is that there is a clean period approximately 1 day after the rapid decline in the Case I process, but in the Case II process, the PM2.5 concentration decreases sharply in just a few hours and then rapidly increases to the 600 μg m -3 level. By comparing the rapid reduction in the PM2.5 concentrations during the Case I and Case II processes, we find that there is no significant difference in 25 submesoscale motion strength, whereas turbulent intensity during the Case I process is significantly greater than that during the Case II process, which leads to a weaker (stronger) LIST during the Case I (Case II). During the Case II process, although the LIST (intermittent intensity) increases (decreases) when the PM2.5 concentration begins to decrease, the larger(weaker) LIST (intermittent intensity) does not maintain but decreases (increases) rapidly, which indicates that the pollutant concentration does not fully diffuse and rapidly increases again to 600 μg m -3 within a few hours. The difference 30 between these two processes also indicates that the diffusion of pollutants directly depends on the diffusion of turbulent flow during heavy pollution processes. Submesoscale motion may be an important source of turbulent energy under stable weather conditions, but it is not directly involved in the dissipation of pollutants. From the analysis of the clean and polluted periods, we can see that the submesoscale motion persists throughout the 30-min acquired signal. During heavily polluted periods, the intensity of turbulence is weak, and the effects of submesoscale motion on weak turbulence begin to appear. During clean periods, the intensity of turbulence is strong, and the effect of submesoscale motion is relatively small. Meanwhile, the submesoscale motion is a source of turbulent energy when the turbulence is very weak under heavy pollution and stable weather conditions. It can be said that turbulence derives energy 5 from nonstationary, large-scale motion and has a very large effect on the concentration of pollutants over time scales of hours (e.g., Case I and Case II). We also note the relationship between the LIST and IF. Wei et al. (2018) mentioned that the larger the absolute value of the IF is, the stronger the intermittent mixing. From the perspective of intermittent mixing strength, the IF tends to be consistent with the LIST defined in this paper. When the pollutant concentrations decreased sharply, intermittent mixing increased, the IF absolute value increased, the LIST (intermittent intensity) increased(decreased), 10 and turbulent exchange was enhanced.
The tendency of the LIST under pollution conditions in Fig. 8 is consistent with that in Fig. 7, which indicates that the above analysis conclusions apply not only to suburban sites but also to urban sites. However, there is a significant difference between the urban and suburban sites: the LIST (intermittent intensity) at suburban sites is weaker (stronger) than that at urban sites. The value of at the city site is slightly smaller than that at the suburban site, and the value of at the 15 city site is larger than that at the suburban site. Correspondingly, the value of the LIST at the city site is significantly greater than that at the suburban site, which means that the dynamic effect of the underlying surface of the urban area is stronger than that of the flat underlying surface of the suburban area. Next, in   ) and moisture content ( | * | ⁄ ) as functions of the stability parameter ζ at the suburban site, respectively. It should be noted that the reconstructions of the data have two benefits. One is that the discrete situation of the fitted line has been significantly improved after reconstruction. The other is that the statistical characteristics of turbulence are more consistent with the classic statistical pattern for turbulence, and the 10 normalized standard deviations in potential temperature and water vapor under near-neutral conditions are closer to those of Panofsky et al. 1984 andWyngaard (1971). The figures regarding the macrostatistical characteristics of turbulence before reconstruction are not shown here.    horizontal and vertical wind speeds with the stability parameter ζ follows the 1/3 power law well under stable and unstable stratification conditions. The normalized standard deviation was more dispersed in the horizontal direction than that in the vertical direction, which is consistent with the results presented in Zhang et al. (2004) and Ma et al. (2002) and shows that the physical characteristics of the land surface (e.g., topography and roughness) affect the statistical properties of the vertical wind speed less than those of the horizontal wind speed. The normalized standard deviations in the horizontal and vertical 10 wind speeds are nearly constant under near-neutral conditions (i.e., -0.01 < ζ < 0.01), and the constants are slightly larger than those from Panofsky et al. (1984), which is similar to the results from other suburban works (i.e., Zhang et al. (1991), Roth et al. (1993) and Su et al. (1994)). Figure 9 shows that the values during clean periods (circle) are slightly larger than those during haze periods (asterisk) under unstable conditions. Ren et al. 2018 (Fig. 4) shows the significant difference in the normalized standard deviations in the horizontal and vertical wind speeds between clear and haze periods. The reconstructed 15 data from the city site also show the same conclusion (the figures are not shown in this paper), which means that the dynamic effect of turbulence during pollution episodes is reduced significantly at both the city and suburban sites. At the same time, the normalized standard deviations in wind speed in three directions over the suburban area are smaller than those Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-881 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 28 September 2018 c Author(s) 2018. CC BY 4.0 License. over urban areas under near-neutral conditions, which shows that the dynamic effect of turbulence at the urban site is stronger than that at the suburban site. Figure 10 shows the relationships of the normalized standard deviations in potential temperature and moisture with the stability parameter ζ. Given unstable stratification, | * | ⁄ and | * | ⁄ fit the function −1 3 ⁄ . The fitting coefficients are larger than the typical value of 0.95 presented in Wyngaard (1971), which used data from a grassland area in Kansas (United 5 States). Similar to the conclusion for the normalized standard deviations in the three-directional wind speeds, Ren et al. 2018 ( Fig. 5) shows the slight differences in the normalized standard deviations in potential temperature between clear and haze periods, while Fig. 10 in this paper shows a significant difference between clear and haze periods. Since the time periods studied are the same, the difference in the performance of these turbulence statistics during polluted and clean periods over city and suburban sites indicates the impact of urbanization. In other words, the pollution process has a more significant 10 influence on the turbulent statistical characteristics of potential temperature and moisture in the suburbs and a less significant impact on those in the urban area.  K m s -1 ; the peak in the daily sensible heat flux at the urban site also appears around 12 noon, with a peak value close to 0.04 K m s -1 , which is less than that in the suburban area. One possible reason is that the buildings in the urban area are denser and have higher reflectivity, resulting in less net radiation and, consequently, a lower peak in the sensible heat flux. Another difference in the sensible heat flux between the city and suburban sites during the clean period is that the sensible heat flux over the suburbs is negative at night, with a downward heat transfer, but not in urban areas. This pattern may be due to the 5 emission of anthropogenic heat sources at nighttime in cities and the difficulty of dissipating heat over tall and dense buildings; therefore, there is not a vast temperature difference between the surface of the city and the upper air and, accordingly, the sensible heat flux is not completely below zero. Previous research on city and suburban sites in Helsinki has also found the same features (Nordbo et al., 2013). During the pollution period, the peak in sensible heat flux at the urban site dropped to approximately 0.02 K m s -1 . In addition, other trends were exactly the same as those during the clean period. 10 At the same time, the peak in sensible heat flux at the suburban site dropped to 0.02 K m s -1 , and the daily change in sensible heat flux was somewhat different than that during the clean period (i.e., the characteristic of downward sensible heat flux at night disappeared completely). In general, pollution caused a reduction in the upward transport of sensible heat flux, and it had a weaker impact on the city and a stronger impact on the suburbs. In addition, pollution can also hinder the downward transmission of nighttime sensible heat flux in the suburbs. 15

The impact of urbanization on turbulent transport during heavy pollution episodes
The daily changes in latent heat flux over the urban and suburban areas are completely different. During the clean period, the latent heat flux at the city site (Fig. 11b, dotted line) does not have obvious daily variations and fluctuates at approximately 0, which is related to low winter temperatures, the presence of water in the form of solid ice, and very dry air. However, the daily changes in latent heat in the suburbs during the clean period show a unimodal change, reaching a peak at approximately 12 noon. It seems that the pollution process has no significant effect on the transport of latent heat over the 20 urban area. As the solid line in Fig. 11b shows, the latent heat flux over the urban area during the pollution period remains low, fluctuating at approximately 0. However, the pollution process has a greater impact on the transfer of latent heat in the suburbs. As shown by the dotted line in Fig. 11f, the transfer of latent heat is reduced throughout the day.
The magnitude of the momentum flux is related to the roughness of the underlying surface, wind speed, and stability of the boundary layer. During the clean period, the momentum flux over the city site experiences a wide range of changes, and the 25 peak value is larger due to the high roughness of the city's underlying surface; the suburban momentum flux also has obvious daily changes, but the peak value is slightly lower than that over the city site. During the pollution period, the momentum fluxes at the urban and suburban sites are significantly lower than those during the clean period and remain low throughout the day. However, at this time, the momentum flux over the city is greater than that over the suburbs, as Figs. 11c and 11g show, respectively. The change in TKE is similar to that in momentum flux. During the clean period, the TKE values over 30 the urban and suburban sites all showed obvious unimodal diurnal variations. The TKE values over the urban area were greater than those over the suburban area, which is due to the strong turbulence in wake vortices caused by underlying surfaces, such as high buildings in urban areas. During the pollution process, the urban and suburban TKEs both decreased significantly and remained low throughout the day. However, the TKE over the city site was still greater than that over the suburban site.
Based on the above analysis, we know that the sensible heat flux and latent heat flux of the urban site are less than those of the suburban site, and the momentum transport and TKE of the urban areas are greater than those of the suburban sites due to different underlying surfaces. The sensible heat flux, latent heat flux, momentum flux, and TKE in the urban and suburban 5 areas are all affected when pollution occurs. Pollution inhibits the material and energy exchanges between the surface and atmosphere. Moreover, the impact of the pollution process on the suburbs is much greater than that on the urban area. If the turbulent structure over the flat surface of the suburban area is considered the normal state for turbulent structures in this area during the studied period, then the urban turbulent structure is influenced by urbanization. From this perspective, urbanization has reduced the impact of pollution on water and heat exchanges between the surface and atmosphere. In 10 conjunction with Section 4.2, the value of the LIST in urban areas is greater than that in suburban areas, which means that the intermittent intensity of urban turbulence is less than that of suburban turbulence. Therefore, the turbulence signal over the urban site is stronger than that over the suburban site, and pollution in urban areas is weaker than in adjacent suburbs.

Conclusions and discussions
In this paper, we developed an automated algorithm to identify the spectral gap to separate pure turbulence and 15 submesoscale motions from a 30-min signal based on the arbitrary-order Hilbert spectral method. We used this automated algorithm to analyze turbulence data observed from several severe haze pollution episodes in Beijing and its nearby suburbs from 16 December 2016 to 8 January 2017. The datasets with a spectral gap accounted for approximately 30% of the total data, indicating that the eddy-correlation flux calculated using a conventional averaging time of 30 min to define perturbations is severely contaminated by poorly sampled mesoscale motions. Due to space limitations, only the detailed 20 suburban site results are listed in the text. The results of the urban site reflect are consistent with the conclusions of the suburban site. A comparison between the reconstructed variances of u ′ , v ′ , w ′ , θ ′ and q ′ and those calculated by the conventional method revealed overestimations of approximately 27%, 21%, 1%, 40%, and 46%, respectively, when there was a spectral gap. The vertical wind speed was overestimated less than the horizontal wind speed. The scalar, potential temperatures were overestimated more than the vector, wind speed. The calculations of the fluxes were also overestimated. 25 The momentum flux, heat flux and water-vapor flux were overestimated by approximately 13%, 12%, and 15%, respectively.
We can see from these comparisons that the overestimation of flux during haze events cannot be ignored.
After reconstruction via the automated algorithm, pure turbulent data can be obtained. Then, we explore the relationship between the LIST and pollution. The results indicate that when pollution is heavy, the LIST is smaller, and the intermittency is stronger; when pollution is lighter, the LIST is larger, and the intermittency is weaker. During the heavy pollution process, 30 air quality is determined by turbulent motion on the hourly scale. At the same time, the LIST at the city site is greater than that at the suburban site, which means that the intermittency over the complex city surface is weaker than that over the flat Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-881 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 28 September 2018 c Author(s) 2018. CC BY 4.0 License.
terrain of the suburbs. Urbanization seems to reduce the intermittency during heavy haze pollution episodes. The results were validated via the statistics on the impact of urbanization on turbulence and turbulent transport during polluted and clean periods.
The results for the statistical characteristics of turbulence show a significant difference in the normalized standard deviations in the horizontal and vertical wind speeds between clear and haze periods at the suburban site, which is the same as that at 5 the city site; significant differences in the normalized standard deviations in potential temperature and water-vapor content between clear and haze periods are also observed at the suburban site, which are substantially different from those at the city site. These conclusions are consistent with the diurnal variations in mean flux under both polluted weather and clear weather conditions at the city site and suburban site. The sensible heat flux, latent heat flux, momentum flux, and TKE over the urban and suburban areas are all affected when pollution occurs. Pollution inhibits material and energy exchanges between the 10 surface and atmosphere. Moreover, the impact of the pollution process on suburban areas is much greater than that on urban areas. Urbanization seems to help reduce the consequences of pollution.
Data availability. Data used in this study are available from the corresponding author upon request (hsdq@pku.edu.cn).