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In this study, the characteristics of turbulence transport and intermittency and the evolutionary mechanisms were studied in different pollution stages of heavy haze weather from December 2016 to January 2017 in the Beijing area using the method developed by Ren et al. (2019) as the automatic identification of atmospheric spectral gaps and the reconstruction of atmospheric turbulence sequences. The results reveal that turbulence intermittency is the strongest in the cumulative stage (CS) of heavy haze weather, followed by in the transport stage (TS), and it is the weakest in the dissipation stage (DS). During the development and accumulation of haze pollution, buoyancy contributes negatively to turbulent kinetic energy (TKE), and horizontal wind speed is low. The classical turbulent motion is often affected by submesoscale motion. As a result, the calculation results of turbulence parameters are affected by submesoscale motion, which causes intensified turbulence intermittency. During the dissipation of pollution, the downward momentum transfer induced by low-level jets provides kinetic energy for turbulent motion in the near surface layer. The turbulent mixing effect is enhanced, and intermittency is weakened. Due to the intermittency of atmospheric turbulence, turbulence parameters calculated from the original fluctuation of meteorological elements may be overestimated. The overestimation of turbulence parameters in the CS is the strongest, followed by the TS, and the DS is the weakest. The overestimation of turbulent fluxes results in an overestimation of atmospheric dissipation capability that may cause an underestimation of pollutant concentrations in the numerical simulations of air quality. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyses a framework of food safety regulation system in agro-food wholesale markets as revealed by a recent investigation in Beijing, which consists of government regulation and market regulation. The results show that it hasn’t formed a standardized and uniform food safety regulation system for the agro-food wholesale market, and the government regulation permeated in all aspects of the market, which make the food safety management at higher cost and lower efficiency. Therefore, the study intends to develop a co-regulation model for food safety governance in agro-food wholesale markets. Future research is needed to empirically test this model. [ABSTRACT FROM AUTHOR]

In order to clarify the influence of primary emission and secondary formation on the organic compositions of fine aerosols during the atmospheric pollution process in summer in Beijing, an intensive field measurement were carried out at an urban site in Beijing from July 1 to July 30, 2017. Inorganic ions (SO 4 2−, NO 3 −, and NH 4 +) and organic compounds (OM) were the most important components of PM 2.5 throughout the whole sampling period, with inorganic ions having the highest concentrations during the polluted period and OM having the highest concentration during the clean period. The apportioned quantitatively primary and secondary sources accounted for an average 86 ± 12% of the measured organic carbon (OC) at the urban site, based on positive matrix factorization modeling and the tracer-based method. Coal combustion was an important primary source for OC in the polluted episode, and biomass burning was the main contributor during the clean period. β -Caryophyllene SOC (secondary OC) was the main secondary contributor for OC during summertime in Beijing. The significant amount of SOC enhanced with an increased ozone and acidification indicated that the atmospheric oxidants and the in-situ acidity of particles were important factors in the formation of SOC during period of high pollution in Beijing. Image 1 • Changes in chemical composition of aerosols was quantified. • Coal burning and traffic emissions were the main contributors of organic carbon in polluted period. • Atmospheric oxidants and particle acidity were important factors for the formation of SOC. [ABSTRACT FROM AUTHOR]

In this paper, we developed an automated algorithm to identify the spectral gap to separate pure turbulence and sub-mesoscale 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. The results of the urban site reflect are consistent with the conclusions of the suburban site. The calculations of the fluxes were also overestimated. 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. We introduce a definition to characterize the local intermittent strength of turbulence (LIST). 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. 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 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 sensible heat flux, latent heat flux, momentum flux, and TKE over the urban and suburban areas are all affected when pollution occurs. Due to the close distance between the two stations, the large-scale weather background is consistent, and the suburban site is locally flat, so the turbulent structure over the flat surface of the suburban area is considered as the normal state in this area under such weather and pollution source conditions, then the urban turbulent structure is influenced by urbanization. More importantly, the reduction of turbulence exchange during the pollution period in suburban sites is greater, compared to urban sites. In other words, the impact of the pollution process on the suburbs is much greater than that on the urban area. The change in the underlying surface of the urban site (the dynamic effect of complex terrain and the heat island effect caused by human activities) leads to a greater resistance to the weakening effects of turbulent exchange caused by pollution. [ABSTRACT FROM AUTHOR]