The effects of lateral entrainment on pollutant dispersion inside a street canyon and the corresponding optimal urban design strategies

Li, Zhengtong; Zhang, H.; Wen, Chih Yung; Yang, An-Shik; Yu, Jiang

doi:10.1016/j.buildenv.2021.107740

Cited by 25 publications

(5 citation statements)

References 56 publications

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“…This phenomenon is perhaps influenced by the changes in near-surface air momentum caused by friction and perturbation of the horizontal wind field of the atmosphere, which is accompanied by a decrease in BLH and a weakening of air mixing, diffusion, and transmission, leading to an increase in PM 2.5 concentration [43]. In cities other than Tianjin, U10 is negatively correlated with PM 2.5 concentrations, possibly because lateral winds, carrying pollutants out of the city and introducing fresh air, reduce the accumulation of pollutants within the city [44]. However, in Tianjin, lateral winds, carrying pollutants to the center of the city, contribute to higher PM 2.5 concentrations.…”

Section: Correlation Analysis Of Pm 25 With Various Factorsmentioning

confidence: 99%

Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area

Su,

Li,

Liu

et al. 2023

Atmosphere

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Prolonged exposure to high concentrations of suspended particulate matter (SPM), especially aerodynamic fine particulate matter that is ≤2.5 μm in diameter (PM2.5), can cause serious harm to human health and life via the induction of respiratory diseases and lung cancer. Therefore, accurate prediction of PM2.5 concentrations is important for human health management and governmental environmental management decisions. However, the time-series processing of PM2.5 concentration based only on a single region and a special time period is less explanatory, and thus, the spatial-temporal applicability of the model is more restricted. To address this problem, this paper constructs a PM2.5 concentration prediction optimization model based on Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM). Hourly data of atmospheric pollutants, meteorological parameters, and Precipitable Water Vapor (PWV) of 10 cities in the Beijing-Tianjin-Hebei metropolitan area during the period of 1–30 September 2021/2022 were used as the training set, and the PM2.5 data of 1–7 October 2021/2022 were used for validation. The experimental results show that the CNN-LSTM model optimizes the average root mean square error (RMSE) by 25.52% and 14.30%, the average mean absolute error (MAE) by 26.23% and 15.01%, and the average mean absolute percentage error (MAPE) by 35.64% and 16.98%, as compared to the widely used Back Propagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) models. In summary, the CNN-LSTM model is superior in terms of applicability and has the highest prediction accuracy in the Beijing-Tianjin-Hebei metropolitan area. The results of this study can provide a reference for the relevant departments in the Beijing-Tianjin-Hebei metropolitan area to predict PM2.5 concentration and its trend in specific time periods.

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Section: Correlation Analysis Of Pm 25 With Various Factorsmentioning

confidence: 99%

Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area

Su,

Li,

Liu

et al. 2023

Atmosphere

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“…Most of the investigated articles do not provide any information on the type of wall treatment used in their studies. Among those that do, however, wall functions are the preferred option, e.g., standard wall function (ANSYS Fluent), fully rough wall function (PHOENICS), nutkRoughWallFunction (OpenFOAM) (e.g., [53,84,86,[105][106][107]). Few studies have used the all-y + wall treatment, of which the most popular is the enhanced wall treatment used in ANSYS Fluent (e.g., [100,[108][109][110]).…”

Section: Wall Treatmentmentioning

confidence: 99%

Air Pollution Dispersion Modelling in Urban Environment Using CFD: A Systematic Review

et al. 2022

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Air pollution is a global problem, which needs to be understood and controlled to ensure a healthy environment and inform sustainable development. Urban areas have been established as one of the main contributors to air pollution, and, as such, urban air quality is the subject of an increasing volume of research. One of the principal means of studying air pollution dispersion is to use computational fluid dynamics (CFD) models. Subject to careful verification and validation, these models allow for analysts to predict air flow and pollution concentration for various urban morphologies under different environmental conditions. This article presents a detailed review of the use of CFD to model air pollution dispersion in an urban environment over the last decade. The review extracts and summarises information from nearly 90 pieces of published research, categorising it according to over 190 modelling features, which are thematically systemised into 7 groups. The findings from across the field are critically compared to available urban air pollution modelling guidelines and standards. Among the various quantitative trends and statistics from the review, two key findings stand out. The first is that, despite the existence of best practice guidelines for pollution dispersion modelling, anywhere between 12% and 34% of the papers do not specify one or more aspects of the utilised models, which are required to reproduce the study. The second is that none of the articles perform verification and validation according to accepted standards. The results of this review can, therefore, be used by practitioners in the field of pollution dispersion modelling to understand the general trends in current research and to identify open problems to be addressed in the future.

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“…Kumar et al [41] studied the use of the venturi effect to intensify the air pressure in the vertical void to enhance the lateral ventilation on the leeward side of a double-load apartment building. Li et al [42] studied the venturi effect on the effect of transverse entrains on pollutant diffusion in a street canyon. Some studies have been devoted to finding the best window opening to effectively regulate indoor airflow intensity by using the venturi effect [43][44][45][46].…”

Section: Research Into Application Of the Venturi Effectmentioning

confidence: 99%

Study on the Influence and Optimization of the Venturi Effect on the Natural Ventilation of Buildings in the Xichang Area

et al. 2021

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Natural ventilation is a way to reduce the energy consumption of building operations and improve the indoor living environment comfort. The venturi cap is designed with a roof, grille and wind deflector to intensify the natural ventilation of buildings. The structural parameters of the venturi cap were designed using an orthogonal design. Fluid analysis software was used for numerical simulation, and variance analysis was used to study the importance of seven influence factors: the width of the roof opening, the roof slope, the height of the wind deflector, the horizontal width of the wind deflector, the angle of the wind deflector, the angle of the grille, and the spacing of the grille slices. The results show that the most significant influencing factor is the width of the roof opening, while significant influence factors include the angle of the grille and the horizontal width of the wind deflector. Additionally, the optimum parameter combination for ventilation performance at the research level was put forward, with the proposed combination achieving a volume flow rate of 5.507 m3/s. The average temperature of the horizontal plane at a height of 1.2 m above the ground was 3.002 K lower than that without a venturi cap, which provides a reference for the optimization of indoor ventilation design in buildings in the Xichang area.

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The effects of lateral entrainment on pollutant dispersion inside a street canyon and the corresponding optimal urban design strategies

Cited by 25 publications

References 56 publications

Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area

Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area

Air Pollution Dispersion Modelling in Urban Environment Using CFD: A Systematic Review

Study on the Influence and Optimization of the Venturi Effect on the Natural Ventilation of Buildings in the Xichang Area

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