Heavy particulate pollution events have frequently occurred in the North China Plain over the past decades. Due to high emissions and poor dispersion conditions, issues are becoming increasingly serious during cold seasons. Although early studies have explored some potential reasons for air pollution, there are few works focusing on the effects of intermittent turbulence. This paper draws upon two typical PM_2.5 (particulate matter with diameter less than 2.5 mm) pollution cases from the winter of 2016–2017. After several days of gradual accumulation, the concentration of PM_2.5 near the surface reached the maximum as a combined result of strong inversion layer, stagnant wind, and high ambient humidity and then sharply decreased to a very low level within a few hours. In order to identify the strength of turbulent intermittency, an effective index, called the intermittency factor (IF), was proposed by this work. The results show that the turbulence is very weak during the cumulative stage due to the suppression by strongly stratified layers, while for the stage of dispersion, the turbulence is highly intermittent and not locally generated. The vertical structure of turbulence and wind profiles confirms the generation and downward transport of intermittent turbulence associated with low-level jets. The intermittent turbulent fluxes contribute positively to the vertical transport of particulate matter and improve the air quality near the surface. This work has demonstrated a possible mechanism of how intermittent turbulence affects the dispersion of particulate matter.

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.

From November 2016 to January 2017, there were large-scale dense fog processes in Tianjin area on the west coast of Bohai Bay, China, even strong dense fog with visibility less than 50 m occurred. Based on the observation data of fog droplet spectrum monitor, visibility sensor, environmental particle monitoring equipment and meteorological automatic station, the characteristics of fog droplet size distribution and the interaction between the fog droplets and fine particles during dense fog events were analyzed. The results show following characteristics: (1) The average concentration of fog droplets (N_a), the average liquid water content (L_a) and the maximum liquid water content (L_max) in the strong dense fog process are larger than those in the dense fog. The average spectrum of fog droplet size distribution conforms to Junge distribution, and they are all broad-spectrum fog with a spectrum width of about 45 μm. The average spectrum is similar to the dense fog of heavily industrialized inland in the world. (2) The maximum of fog droplet diameter during the formation stage have a good indication for the outbreak of strong dense fog. (3) The mass concentration of PM_2.5 (C_PM2.5) is ranged from 121–375 μg/m³, and the interaction between fog droplets and fine particles is analyzed. During the formation, development and maturity stages, fog process can scavenge atmospheric fine particles, and the scavenging efficiency of PM_2.5 is more remarkable than PM₁₀. When C_PM2.5 does not exceed 350 μg/m³, the increase in the concentration of fine particles is conducive to the rapid growth of fog droplets and the sharp drop of visibility. However, when C_PM2.5 exceeds the critical value, the increase has a negative feedback effect on the development of the fog process. More investigations and cases are necessary to fully assess the mechanisms related to the dense fog events in Tianjin area and further analysis will be done.

Different methods have been developed to estimate the fog-top height of radiation fog and evaluated using the measurements obtained from a 255-m meteorological tower located in Tianjin in 2016. Different indicators of turbulence intensity, friction velocity (u_*), turbulence kinetic energy (TKE), and variance of vertical velocity (σ_w²) were used to estimate the fog-top height, respectively. Positive correlations between the fog-top height and u_*, TKE, and σ_w² were observed, with empirical parameterization schemes H = 583.35 × $u_{*}^{1.12}$ , H = 205.4 $\times {\left(TKE\right)}^{0.68}$ , and H = 420.10 $\times {\left({\sigma }_w^2\right)}^{0.51}$ being obtained. Among them, σ_w² is the most appropriate indicators of turbulence intensity to estimate the fog-top height. Compared with sensible flux and condensation rate, the new form of convective velocity scale (w_*) was the most appropriate indicator of buoyancy induced by radiative cooling, and the relationship H = 328.33 × $w_{*}^{1.34}$ was obtained. σ_w² and with w_*, which represents the intensity of turbulence and buoyancy, were used to estimate the fog-top height. The relationship H = 396.26 $\times$ (σ_w + 0.1 $\times$ w_*) − 16 was obtained, which can be used to accurately estimate the fog-top height. Moreover, the temperature convergence (TC) method was used to estimate the fog-top height; however, the results strongly rely on the threshold value.

Low visibility, associated with fog, severely affects land, marine, and air transportation. Visibility is an important indicator to identify different intensities of fog; therefore, improving the ability to forecast visibility in fog is an urgent need for social and economic development. Establishing a proper visibility parameterization scheme is crucial to improving the accuracy of fog forecast operation. Considering various visibility impact factors, including RH, N_d, D, LWC, the parameterization formula of visibility in fog, as well as their performance in meteorology operation, are reviewed. Moreover, the estimated ability of the visibility parameterization formulas combined with the numerical model is briefly described, and their advantages and shortcomings are pointed out.