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Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New...
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Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New Perspectives

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (21 July 2023) | Viewed by 8625

Special Issue Editors

Dr. Sumei Liu

E-Mail Website
Guest Editor
Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
Interests: urban airflow; ventilation; CFD; pollutant control
Prof. Dr. Junjie Liu

E-Mail
Guest Editor
Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
Interests: air science; indoor air quality; sustainable urban and buildings; ventilation and air cleaning technology
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Liu

E-Mail Website
Guest Editor
Division of Sustainable Buildings, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Brinellvägen 23, 100 44 Stockholm, Sweden
Interests: indoor air quality (IAQ); air distribution; inverse design and control; data-driven/AI-based smart buildings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban airflow and pollutant dispersion are closely related to people's lives and climate change. High wind speeds introduced at the pedestrian level by high-rise buildings can cause wind nuisance and safety issues, and also affect outdoor thermal comfort. High concentrations of pollutants emitted from civil and industrial processes harm pedestrian health and contaminate indoor air through building intakes. The basic characteristics of the urban underlying surface are heterogeneous and rough, and its complexity makes the urban wind environment and pollutant dispersion very complicated. It is of great significance to explore the mechanism and law of urban airflow and pollutant dispersion under complex underlying surface conditions to improve people's living environment and contribute to a healthy, sustainable urban climate in the future.

This Special Issue aims to be an international forum for researchers to summarize the most important developments, findings, challenges, and new perspectives in the field of urban airflow and pollutant dispersion. Original results from experimental measurements, modeling, models, and review papers related to urban airflow and pollutant dispersion are all welcome contributions. 

Dr. Sumei Liu
Prof. Dr. Junjie Liu
Dr. Wei Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • air dispersion
  • outdoor thermal comfort
  • urban airflow
  • modeling
  • numerical simulation
  • measurement

Published Papers (6 papers)

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Research

16 pages, 3283 KiB  
Article
MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas
by Hongqing Wang, Lifu Zhang and Rong Wu
Atmosphere 2023, 14(8), 1294; https://doi.org/10.3390/atmos14081294 - 16 Aug 2023
Viewed by 933
Abstract
The accurate prediction of PM2.5 concentration, a matter of paramount importance in environmental science and public health, has remained a substantial challenge. Conventional methodologies for predicting PM2.5 concentration often grapple with capturing complex dynamics and nonlinear relationships inherent in multi-station meteorological [...] Read more.
The accurate prediction of PM2.5 concentration, a matter of paramount importance in environmental science and public health, has remained a substantial challenge. Conventional methodologies for predicting PM2.5 concentration often grapple with capturing complex dynamics and nonlinear relationships inherent in multi-station meteorological data. To address this issue, we have devised a novel deep learning model, named the Meteorological Sparse Autoencoding Transformer (MSAFormer). The MSAFormer leverages the strengths of the Transformer architecture, effectively incorporating a Meteorological Sparse Autoencoding module, a Meteorological Positional Embedding Module, and a PM2.5 Prediction Transformer Module. The Sparse Autoencoding Module serves to extract salient features from high-dimensional, multi-station meteorological data. Subsequently, the Positional Embedding Module applies a one-dimensional Convolutional Neural Network to flatten the sparse-encoded features, facilitating data processing in the subsequent Transformer module. Finally, the PM2.5 Prediction Transformer Module utilizes a self-attention mechanism to handle temporal dependencies in the input data, predicting future PM2.5 concentrations. Experimental results underscore that the MSAFormer model achieves a significant improvement in predicting PM2.5 concentrations in the Haidian district compared to traditional methods. This research offers a novel predictive tool for the field of environmental science and illustrates the potential of deep learning in the analysis of environmental meteorological data. Full article
(This article belongs to the Special Issue Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New Perspectives)
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17 pages, 1789 KiB  
Article
Influence of Urban Spatial Structure on the Spatial Distribution of Gaseous Pollutants
by Qixin Ren, Baoyan Shan, Qiao Zhang and Changkuan Shui
Atmosphere 2023, 14(8), 1231; https://doi.org/10.3390/atmos14081231 - 31 Jul 2023
Viewed by 847
Abstract
The spatial distribution pattern of urban spatial structure affects air flow and local meteorological conditions, which in turn influence the diffusion of air pollutants. This study built the urban spatial structure index system based on DEM, urban road networks, and big data. The [...] Read more.
The spatial distribution pattern of urban spatial structure affects air flow and local meteorological conditions, which in turn influence the diffusion of air pollutants. This study built the urban spatial structure index system based on DEM, urban road networks, and big data. The ordinary kriging interpolation method was used to analyze the spatial distribution of gaseous pollutant concentrations in Jinan City. Correlation analysis, stepwise regression analysis, and bivariate global spatial autocorrelation analysis were used to study the influence of the urban spatial structure index on the spatial distribution of gaseous pollutant concentration. The main conclusions were as follows: (1) Evident spatial and temporal differences were observed in the concentration distribution of gaseous pollutants in Jinan. The spatial distribution of NO2 and CO concentrations showed a gradual decrease from north to south. Spatial heterogeneity was observed in the distribution of SO2 and O3 concentrations. (2) The urban spatial structure indicators had varying effects on the spatial distribution of different gaseous pollutant concentrations. The important factors that influenced the spatial distribution of urban gaseous pollutant concentrations included terrain elevation, building density, building volume, and floor area ratio. The greater the terrain undulation, the denser the building distribution, the greater the difference in building volume, and the greater the plot ratio, the greater the impact on the diffusion and spatial distribution of urban gaseous pollutants. (3) The spatial distribution of urban gaseous pollutant concentrations was significantly affected by the urban spatial structure indicators in the surrounding areas. Furthermore, the spatial distributions of NO2, SO2, CO, and O3 concentrations had a significant negative spatial correlation with the average DEM and standard deviation of the surrounding adjacent areas and a significant positive spatial correlation with the average and standard deviation of building height, standard deviation of building area, and building density in the surrounding adjacent areas (in June). Full article
(This article belongs to the Special Issue Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New Perspectives)
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19 pages, 6201 KiB  
Article
City Wind Impact on Air Pollution Control for Urban Planning with Different Time-Scale Considerations: A Case Study in Chengdu, China
by Jianwu Xiong, Jin Li, Fei Gao and Yin Zhang
Atmosphere 2023, 14(7), 1068; https://doi.org/10.3390/atmos14071068 - 24 Jun 2023
Cited by 1 | Viewed by 1066
Abstract
Economic development and fast growing urbanization in China have caused severe air pollution, with frequent pollution episodes endangering the health of inhabitants and disturbing social activities, and as an expanding metropolis, Chengdu has suffered ever since. The concentration variations of main air pollutants, [...] Read more.
Economic development and fast growing urbanization in China have caused severe air pollution, with frequent pollution episodes endangering the health of inhabitants and disturbing social activities, and as an expanding metropolis, Chengdu has suffered ever since. The concentration variations of main air pollutants, such as PM10, PM2.5 and NO2, often show periodicity because of meteorological impact and anthropic activities, and display orientation discrepancies due to influences of wind speed (WS), frequency and pollutant sources. These features have complicated the mechanisms of pollution episodes and deepened the difficulty in pollution control evaluation. The WS has significant influences on the periodicity and orientation variations in pollutant concentrations, and quantifying the influence of which is of high significance and provides sustainable foundations for pollution alleviation strategies. Different time-scale cycles (i.e., Diurnal, weekly, seasonal and annual), along with the WS, wind frequency, wind and spatial orientations in urban areas, were analyzed in this paper. Results show that the periodicity of diurnal, seasonal and annual cycles is remarkable, and weekly cycle is obvious by adding the influence of the WS in 16 orientations. The WS has direct impacts on pollutants varying in the range of 1.5–2.5 m/s, and has a remarkable diffusion effect on pollutants once above 2.5 m/s. Over heavy pollution hours in diurnal, weekly, annual cycles and transitional seasons, the WS had more significant influences on pollutants, and whereas the wind frequency is not the main impact factor for orientation variations. For Chengdu, the northeast orientation is suitable to construct a wind panel with a remarkable diffusion effect on pollutants, while air pollutions in the northwest and southwest orientations were severe with the WS below 1.5 m/s, and pollution diffusion in the north-northwest orientation was the worst. This work can provide guidance and reference for urban planning optimization and air environment protection in cities with air quality control considerations impacted by city wind. Full article
(This article belongs to the Special Issue Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New Perspectives)
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15 pages, 6994 KiB  
Article
Spatial Analysis of SO2, PM10, CO, NO2, and O3 Pollutants: The Case of Konya Province, Turkey
by Ilkay Bugdayci, Oguz Ugurlu and Fatma Kunt
Atmosphere 2023, 14(3), 462; https://doi.org/10.3390/atmos14030462 - 26 Feb 2023
Cited by 2 | Viewed by 2389
Abstract
Geographical information systems are frequently used in analyses of air quality based on location and time. They are also used in the creation of pollution distribution maps to determine the parameters related to air pollutants. In this study, a spatial analysis of SO [...] Read more.
Geographical information systems are frequently used in analyses of air quality based on location and time. They are also used in the creation of pollution distribution maps to determine the parameters related to air pollutants. In this study, a spatial analysis of SO2, PM10, CO, NO2 and O3 pollutants, which cause air pollution within the borders of the municipal urban areas of Konya province, was carried out for the years 2019–2020. In this context, air pollution maps were produced using the IDW interpolation method with data obtained from the National Air Quality Monitoring Network stations, which belong to the Ministry of Environment and Urbanization, in the Konya region. The results obtained were examined with maps and graphics based on the limit values found in the Air Quality Assessment and Management Regulation published by the Ministry of Environment and Urbanization. In this context, the periods of lockdown experienced during the COVID-19 pandemic were also evaluated in terms of air pollution. From the evaluation made on the values taken from the air quality stations, it can be observed that the air pollution did not violate the national limit value much in 2019 and 2020. Full article
(This article belongs to the Special Issue Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New Perspectives)
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14 pages, 5011 KiB  
Article
Field and Wind Tunnel Experiments of Wind Field Simulation in the Neutral Atmospheric Boundary Layer
by Dong Xie, Peilin Xiao, Ninghua Cai, Lixin Sang, Xiumin Dou and Hanqing Wang
Atmosphere 2022, 13(12), 2065; https://doi.org/10.3390/atmos13122065 - 08 Dec 2022
Cited by 2 | Viewed by 1327
Abstract
To investigate the pollutant dispersion of a nuclear power plant, a field tracing experiment was carried out in neutral stratification weather with the main wind direction SSW. On this basis, a wind speed profile and turbulence intensity profile consistent with the site were [...] Read more.
To investigate the pollutant dispersion of a nuclear power plant, a field tracing experiment was carried out in neutral stratification weather with the main wind direction SSW. On this basis, a wind speed profile and turbulence intensity profile consistent with the site were created in the wind tunnel. Meanwhile, how to generate a wind field of neutral stratification in a wind tunnel was studied in detail. Finally, a 1:1000 nuclear power area model was made to conduct tracing experiments in the wind tunnel. The results show that when the horizontal and vertical distances of the spire are 300 mm and 500 mm, and the horizontal and vertical distances of the rough element are 250 mm and 500 mm. A wind speed profile with a wind profile index of 0.321 was generated in the wind tunnel (0.334 in the field test), and the wind tunnel tracer experiment had the same diffusion trend as the field, which verified the accuracy of the flow field. Full article
(This article belongs to the Special Issue Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New Perspectives)
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16 pages, 4967 KiB  
Article
Investigation of a Gaussian Plume in the Vicinity of an Urban Cyclotron Using Helium as a Tracer Gas
by Philippe Laguionie, Olivier Connan, Thinh Lai Tien, Sophie Vecchiola, Johann Chardeur, Olivier Cazimajou, Luc Solier, Perrine Charvolin-Volta, Liying Chen, Irène Korsakissok, Malo Le Guellec, Lionel Soulhac, Amita Tripathi and Denis Maro
Atmosphere 2022, 13(8), 1223; https://doi.org/10.3390/atmos13081223 - 02 Aug 2022
Viewed by 1333
Abstract
Studies focusing on the radiological impact of fluorine 18 on populations living near to cyclotrons (<200 m) frequently assume normal distribution of atmospheric concentration for simplification purposes. On this basis, Gaussian models are used, despite their limits, as deployment requires little input data [...] Read more.
Studies focusing on the radiological impact of fluorine 18 on populations living near to cyclotrons (<200 m) frequently assume normal distribution of atmospheric concentration for simplification purposes. On this basis, Gaussian models are used, despite their limits, as deployment requires little input data and computing resources. To estimate the ability of a Gaussian model to predict atmospheric dispersion in an urban environment, we used helium as a new passive tracer of atmospheric dispersion in the near-field range (<500 m) of the Beuvry hospital cyclotron (France). The atmospheric transfer coefficients measured in the field were compared with those modeled using a Gaussian equation. According to the results, helium is an effective tracer of atmospheric dispersion when attempting to determine atmospheric transfer coefficients ( downwind of a discharge point. The Briggs-rural, Briggs-urban and Doury Gaussian models underestimate and sometimes maximum in the prevailing weather conditions during the experiments. By compiling the results of this study with data from the literature, it appears that the maximum observed obey a power law as a function of the distance from the discharge point, for distances from the discharge point in excess of 20 m. Full article
(This article belongs to the Special Issue Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New Perspectives)
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