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1.
Environ Res ; 211: 113111, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35300962

RESUMO

The turbulent transport of particles is normally assumed to be similar to the momentum (or heat) transport, both in observations and simulations. However, observations from the boundary layer reinforcement experiment conducted at the Pingyuan County Meteorological Bureau, Shandong Province, China, showed dissimilar turbulent transports for momentum, heat, and particles. Our results reveal the prevalence of ejection and sweep motions in the transport of momentum and heat but not in that of particles. For momentum transport, sweep motion is more efficient, and the contribution of ejection (sweeps) motion is higher during the day (night) for heat transport. Momentum transport may be affected by pollutants during heavy pollution episodes (HPEs), whereas heat transport is affected by pollutants at night during HPEs. The sink/source differences lead to differences in particle transport for different HPEs. Furthermore, the momentum motion does not transport heat and particles in the same manner, particularly during HPEs. Compared to heat and momentum transport, the transport of particles is not significantly affected by stability. The turbulent transport of momentum is often smaller than that of particles and heat. Therefore, certain dissimilarities exist in the turbulent transport of momentum, heat, and particles. Overall, these findings found by the observations shed some light on the turbulent transport of particles in mesoscale models, and the turbulent transport dissimilarities between momentum, heat, and particles have an important impact on correcting and obtaining an accurate particle flux.


Assuntos
Poluição do Ar , Poluentes Ambientais , Poluição do Ar/análise , Poluição Ambiental , Temperatura Alta , Meteorologia
2.
Atmos Environ (1994) ; 244: 117986, 2021 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-33052190

RESUMO

The Yangtze-Huaihe (YH) region experiences heavy aerosol pollution, characterized by high PM2.5 concentration. To unravel the pollutant evolution mechanism during the heavy pollution episodes (HPEs), this study combined observational data analysis and three-dimensional WRF-Chem simulations. From December 2, 2016 to January 15, 2017, YH region experienced 4 HPEs under the control by synoptic system, normally associated with a transport stage (TS) and a cumulative stage (CS). During the TS, pollutants are transported to the north of YH region through the near-surface, and then transported to the "mountain corridor" through the residual layer (RL) under the influence of prevailing wind. For the RL transport mechanism, the change of pollutant concentration cannot only consider the net flux in the horizontal direction, but also the role of the vertical movement is extremely important and cannot be ignored. By analyzing the mass conservation equation of pollutant, the results show that the advection transport and turbulent diffusion have a synergistic effect on the change of pollutant in the CS of three HPEs. The change of turbulence characteristics also affected by topography. For the "mountain corridors", which is accompanied by variable wind direction and turbulence diffusion is easily affected by wind shear. In addition, the turbulence characteristics are different during the TS and CS, especially the strong stable conditions in the CS at nighttime. The turbulence is intermittent, and the model has insufficient performance for turbulence, which will lead to differences for the simulation of pollutant concentration. In short, as the PM2.5 concentration linearly increases, the friction velocity (turbulent diffusion coefficient) decreases 63% (80%), 61% (78%) and 45% (68%), respectively. Therefore, the change of pollutants is less sensitive to the change of turbulence during the HPEs. The contribution of regional transport (local emissions) reaches 43% (47%), thus we need pay attention to the contribution of each part during the HPEs, which will help us to build a certain foundation for the emission reduction work in the future.

3.
Sci Total Environ ; 925: 171582, 2024 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-38494029

RESUMO

The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 µg m-3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.

4.
Sci Total Environ ; 792: 148393, 2021 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-34465048

RESUMO

The ambient atmospheric PM2.5 concentrations in Anhui Province, China, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by local emissions, but also by changes in regional circulation. During the period 2013-2017, when China adopted a series of pollution abatement measures, there were still occasional pollution episodes with significant increases in PM2.5 concentrations. PM2.5 rise instead during the period 2013-2017 in Anhui (the Center of the Yangtze-Huaihe, YH), when pollution emissions continued to decrease? What is the controlling mechanism behind these? By analyzing elements such as ground-based PM2.5 concentration and the planetary boundary layer (PBL) structure affecting it as well as larger scale circulation, combined with the analysis of a parameterized index, one can find that aerosol pollution in the YH region can usually be classified into three types. (1) There is a short-term transport stage (TS) in the initial stage of pollution, then as the pollutant concentrations increase, the PBL height decreases, the temperature inversion is gradually formed or strengthened, the wind speed decreases and the relative humidity of the lower layer increases, forming a two-way feedback mechanism in the cumulative stage (CS). (2) Pollutant concentrations will not drop rapidly in the later stage of CS, while a short-term TS will occur again. (3) The explosive rise (ER) events are mainly affected by transportation in the YH. The first of these types tends to be accompanied by the emergence and maintenance of heavy pollution periods (HPEs), and some phases is accompanied by explosive rises (ERs) in PM2.5 that at least double in a short period of time. To sum up, deterioration of meteorological conditions explaining approximately 68% to the increase in PM2.5 in the ER.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , China , Monitoramento Ambiental , Material Particulado/análise , Tempo (Meteorologia)
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