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Atmosphere
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Transport Phenomena in the Atmospheric Boundary Layer
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Transport Phenomena in the Atmospheric Boundary Layer

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 22895

Special Issue Editor

Dr. Amir A. Aliabadi

E-Mail Website
Guest Editor
School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: air quality and pollution; thermal comfort; thermofluids of air; urban environment; building energy systems; heating ventilation, air conditioning, and refrigeration (HVAC-R) systems; turbulent flows; numerical weather prediction (NWP); computational fluid dynamics (CFD); experimental, analytical, and numerical thermofluids

Special Issue Information

Dear Colleagues,

The atmospheric boundary layer (ABL) of the earth is a thin layer of the atmosphere near the surface that responds to surface forcing within a time scale of 1 hour or less. There is growing interest in ABL studies specifically in the context of the two-way interaction between the ABL and anthropogenic activities. Many issues pertaining to extreme weather, climate change, and excess pollution require a deep understanding of transport phenomena within the ABL. Transport phenomena refer to the exchanges of momentum (wind velocity components), energy (temperature), and mass (water vapor, natural atmospheric constituents such as ozone, carbon dioxide, and inert gases, as well as gaseous and particulate pollutants) within the ABL. Meanwhile, the transport phenomena within ABL are not well understood due to complex physical and chemical processes that are ever-present in the ABL. Transport phenomena can be studied in either the Eulerian or Lagrangian frameworks. Currently, the transport phenomena are understood in terms of numerous processes such as storage, advective transport, molecular diffusion, turbulent transport, surface emission–deposition, chemical reactions, phase change, and conductive, convective, and radiative heat transfer. This Special Issue of Atmosphere is centered around understanding transport phenomena within the ABL from experimental, analytical, and numerical points of view. Articles are invited that aim to advance the understanding of transport phenomena in ABL in areas of 1) discovery (e.g., new or previously unstudied phenomena); 2) measurement techniques (e.g., in situ or remote sensing); 3) data processing (e.g., instrumentation, data collection, algorithm design, statistical analysis, etc.); 4) analytical modeling (e.g., closed-form solutions); and 5) numerical modeling (e.g., numerical weather prediction (NWP), computational fluid dynamics (CFD), etc.) across the scales from micro to meso scales. The proposed articles may study ABLs in rural, agricultural, urban, industrial, or remote environments. For instance, rural ABLs may be concerned with exchange processes over natural land types (e.g., forest, tundra, grasslands, oceans, etc.). Agricultural ABLs may be concerned with exchange processes over croplands or animal production farms. Urban ABLs may be concerned with exchange processes involved with the built environment (e.g., buildings, roads, vehicles, etc.). Industrial ABLs may be concerned with exchange processes over mines, power generation, or production facilities. Finally, remote ABLs may be concerned with exchange processes over largely uninhabited lands such as the Arctic, Antarctic, or deserts of the earth. Preference will be given to articles that investigate transport phenomena within the ABL at some level of fundamental and mathematical depth.

Dr. Amir A. Aliabadi
Guest Editor

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

  • atmospheric boundary layer
  • transport phenomena
  • analytical methods
  • experimental methods
  • numerical methods
  • rural, agricultural, urban, industrial, or remote environments

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Published Papers (6 papers)

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Research

19 pages, 2568 KiB  
Article
Parametrization of Horizontal and Vertical Transfers for the Street-Network Model MUNICH Using the CFD Model Code_Saturne
by Alice Maison, Cédric Flageul, Bertrand Carissimo, Andrée Tuzet and Karine Sartelet
Atmosphere 2022, 13(4), 527; https://doi.org/10.3390/atmos13040527 - 25 Mar 2022
Cited by 8 | Viewed by 2344
Abstract
Cities are heterogeneous environments, and pollutant concentrations are often higher in streets compared with in the upper roughness sublayer (urban background) and cannot be represented using chemical-transport models that have a spatial resolution on the order of kilometers. Computational Fluid Dynamics (CFD) models [...] Read more.
Cities are heterogeneous environments, and pollutant concentrations are often higher in streets compared with in the upper roughness sublayer (urban background) and cannot be represented using chemical-transport models that have a spatial resolution on the order of kilometers. Computational Fluid Dynamics (CFD) models coupled to chemistry/aerosol models may be used to compute the pollutant concentrations at high resolution over limited areas of cities; however, they are too expensive to use over a whole city. Hence, simplified street-network models, such as the Model of Urban Network of Intersecting Canyons and Highways (MUNICH), have been developed. These include the main physico-chemical processes that influence pollutant concentrations: emissions, transport, deposition, chemistry and aerosol dynamics. However, the streets are not discretized precisely, and concentrations are assumed to be homogeneous in each street segment. The complex street micro-meteorology is simplified by considering only the vertical transfer between the street and the upper roughness sublayer as well as the horizontal transfer between the streets. This study presents a new parametrization of a horizontal wind profile and vertical/horizontal transfer coefficients. This was developed based on a flow parametrization in a sparse vegetated canopy and adapted to street canyons using local-scale simulations performed with the CFD model Code_Saturne. CFD simulations were performed in a 2D infinite street canyon, and three streets of various aspect ratios ranging from 0.3 to 1.0 were studied with different incoming wind directions. The quantities of interest (wind speed in the street direction and passive tracer concentration) were spatially averaged in the street to compare with MUNICH. The developed parametrization depends on the street characteristics and wind direction. This effectively represents the average wind profile in a street canyon and the vertical transfer between the street and the urban roughness sublayer for a wide range of street aspect ratios while maintaining a simple formulation. Full article
(This article belongs to the Special Issue Transport Phenomena in the Atmospheric Boundary Layer)
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18 pages, 9503 KiB  
Article
Wind Speed Statistics from a Small UAS and Its Sensitivity to Sensor Location
by Trevor C. Wilson, James Brenner, Zachary Morrison, Jamey D. Jacob and Brian R. Elbing
Atmosphere 2022, 13(3), 443; https://doi.org/10.3390/atmos13030443 - 9 Mar 2022
Cited by 10 | Viewed by 3237
Abstract
With the increase in the use of small uncrewed aircraft systems (UAS) there is a growing need for real-time weather forecasting to improve the safety of low-altitude aircraft operations. This will require integration of measurements with autonomous systems since current available sampling lack [...] Read more.
With the increase in the use of small uncrewed aircraft systems (UAS) there is a growing need for real-time weather forecasting to improve the safety of low-altitude aircraft operations. This will require integration of measurements with autonomous systems since current available sampling lack sufficient resolution within the atmospheric boundary layer (ABL). Thus, the current work aims to assess the ability to measure wind speeds from a quad-copter UAS and compare the performance with that of a fixed mast. Two laboratory tests were initially performed to assess the spatial variation in the vertically induced flow from the rotors. The horizontal distribution above the rotors was examined in a water tunnel at speeds and rotation rates to simulate nominally full throttle with a relative air speed of 0 or 8 m/s. These results showed that the sensor should be placed between rotor pairs. The vertical distribution was examined from a single rotor test in a large chamber, which suggested that at full throttle the sensor should be about 400 mm above the rotor plane. Field testing was then performed with the sensor positioned in between both pairs of rotors at 406, 508, and 610 mm above the rotor plane. The mean velocity over the given period was within 5.5% of the that measured from a fixed mast over the same period. The variation between the UAS and mast sensors were better correlated with the local mean shear than separation distance, which suggests height mismatch could be the source of error. The fluctuating velocity was quantified with the comparison of higher order statistics as well as the power spectral density, which the mast and UAS spectra were in good agreement regardless of the separation distance. This implies that for the current configuration a separation distance of 5.3 rotor diameters was sufficient to minimize the influence of the rotors. Full article
(This article belongs to the Special Issue Transport Phenomena in the Atmospheric Boundary Layer)
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17 pages, 7568 KiB  
Article
Estimating Urban Wind Speeds and Wind Power Potentials Based on Machine Learning with City Fast Fluid Dynamics Training Data
by Mohammad Mortezazadeh, Jiwei Zou, Mirata Hosseini, Senwen Yang and Liangzhu Wang
Atmosphere 2022, 13(2), 214; https://doi.org/10.3390/atmos13020214 - 28 Jan 2022
Cited by 16 | Viewed by 4411
Abstract
Wind power is known as a major renewable and eco-friendly power generation source. As a clean and cost-effective energy source, wind power utilization has grown rapidly worldwide. A roof-mounted wind turbine is a wind power system that lowers energy transmission costs and benefits [...] Read more.
Wind power is known as a major renewable and eco-friendly power generation source. As a clean and cost-effective energy source, wind power utilization has grown rapidly worldwide. A roof-mounted wind turbine is a wind power system that lowers energy transmission costs and benefits from wind power potential in urban areas. However, predicting wind power potential is a complex problem because of unpredictable wind patterns, particularly in urban areas. In this study, by using computational fluid dynamics (CFD) and the concept of nondimensionality, with the help of machine learning techniques, we demonstrate a new method for predicting the wind power potential of a cluster of roof-mounted wind turbines over an actual urban area in Montreal, Canada. CFD simulations are achieved using city fast fluid dynamics (CityFFD), developed for urban microclimate simulations. The random forest model trains data generated by CityFFD for wind prediction. The accuracy of CityFFD is investigated by modeling an actual urban area and comparing the numerical data with measured data from a local weather station. The proposed technique is demonstrated by estimating the wind power potential in the downtown area with more than 250 buildings for a long-term period (2020–2049). Full article
(This article belongs to the Special Issue Transport Phenomena in the Atmospheric Boundary Layer)
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22 pages, 4936 KiB  
Article
Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines
by Seyedahmad Kia, Manoj K. Nambiar, Jesse Thé, Bahram Gharabaghi and Amir A. Aliabadi
Atmosphere 2022, 13(2), 210; https://doi.org/10.3390/atmos13020210 - 27 Jan 2022
Cited by 6 | Viewed by 3847
Abstract
Greenhouse gas (GHG) emissions from open-pit mines pose a global climate challenge, which necessitates appropriate quantification to support effective mitigation measures. This study considers the area-fugitive methane advective flux (as a proxy for emission flux) released from a tailings pond and two open-pit [...] Read more.
Greenhouse gas (GHG) emissions from open-pit mines pose a global climate challenge, which necessitates appropriate quantification to support effective mitigation measures. This study considers the area-fugitive methane advective flux (as a proxy for emission flux) released from a tailings pond and two open-pit mines, denominated “old” and “new”, within a facility in northern Canada. To estimate the emission fluxes of methane from these sources, this research employed near-surface observations and modeling using the weather research and forecasting (WRF) passive tracer dispersion method. Various machine learning (ML) methods were trained and tested on these data for the operational forecasting of emissions. Predicted emission fluxes and meteorological variables from the WRF model were used as training and input datasets for ML algorithms. A series of 10 ML algorithms were evaluated. The four models that generated the most accurate forecasts were selected. These ML models are the multi-layer perception (MLP) artificial neural network, the gradient boosting (GBR), XGBOOST (XGB), and support vector machines (SVM). Overall, the simulations predicted the emission fluxes with R2 (-) values higher than 0.8 (-). Considering the bias (Tonnes h−1), the ML predicted the emission fluxes within 6.3%, 3.3%, and 0.3% of WRF predictions for the old mine, new mine, and the pond, respectively. Full article
(This article belongs to the Special Issue Transport Phenomena in the Atmospheric Boundary Layer)
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25 pages, 5467 KiB  
Article
A CFD Approach for Risk Assessment Based on Airborne Pathogen Transmission
by Hamid Motamedi Zoka, Mohammad Moshfeghi, Hadi Bordbar, Parham A. Mirzaei and Yahya Sheikhnejad
Atmosphere 2021, 12(8), 986; https://doi.org/10.3390/atmos12080986 - 30 Jul 2021
Cited by 11 | Viewed by 3923
Abstract
The outbreak of COVID-19 necessitates developing reliable tools to derive safety measures, including safe social distance and minimum exposure time under different circumstances. Transient Eulerian–Lagrangian computational fluid dynamics (CFD) models have emerged as a viably fast and economical option. Nonetheless, these CFD models [...] Read more.
The outbreak of COVID-19 necessitates developing reliable tools to derive safety measures, including safe social distance and minimum exposure time under different circumstances. Transient Eulerian–Lagrangian computational fluid dynamics (CFD) models have emerged as a viably fast and economical option. Nonetheless, these CFD models resolve the instantaneous distribution of droplets inside a computational domain, making them incapable of directly being used to assess the risk of infection as it depends on the total accumulated dosage of infecting viruses received by a new host within an exposure time. This study proposes a novel risk assessment model (RAM) to predict the temporal and spatial accumulative concentration of infectious exhaled droplets based on the bio-source’s exhalation profile and droplet distribution using the CFD results of respiratory events in various environmental conditions. Unlike the traditional approach in the bulk movement assessment of droplets’ outreach in a domain, every single droplet is traced inside the domain at each time step, and the total number of droplets passing through any arbitrary position of the domain is determined using a computational code. The performance of RAM is investigated for a series of case studies against various respiratory events where the horizontal and the lateral spread of risky zones are shown to temporarily vary rather than being fixed in space. The sensitivity of risky zones to ambient temperature and relative humidity was also addressed for sample cough and sneeze cases. This implies that the RAM provides crucial information required for defining safety measures such as safety distances or minimum exposure times in different environments. Full article
(This article belongs to the Special Issue Transport Phenomena in the Atmospheric Boundary Layer)
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33 pages, 2374 KiB  
Article
How Much Building Renewable Energy Is Enough? The Vertical City Weather Generator (VCWG v1.4.4)
by Amir A. Aliabadi, Mohsen Moradi, Rachel M. McLeod, David Calder and Robert Dernovsek
Atmosphere 2021, 12(7), 882; https://doi.org/10.3390/atmos12070882 - 7 Jul 2021
Cited by 14 | Viewed by 3237
Abstract
A challenge in the integration of renewable and alternative energy systems for buildings is the determination of the renewable energy ratio, which involves the selection and sizing of appropriate building systems. To address this need, a micro climate-weather software titled the Vertical City [...] Read more.
A challenge in the integration of renewable and alternative energy systems for buildings is the determination of the renewable energy ratio, which involves the selection and sizing of appropriate building systems. To address this need, a micro climate-weather software titled the Vertical City Weather Generator (VCWG) is further developed to include renewable and alternative energy systems and account for full two-way interaction between the building system and outdoor environment. VCWG is forced to simulate performance of a residential building in Guelph, Canada, for an entire year in 2015. Various energy options are considered and further optimized for the building to reduce natural gas consumption, electricity consumption, and cost. On an annual basis using the global cost method, and compared to a building with no such renewable or alternative energy systems, the optimized system resulted in 80.3% savings in natural gas consumption, 73.4% savings in electricity consumption, and 3% savings is annualized cost. According to this analysis, some technologies, such as photovoltaics are more favorable in the Canadian climate than other technologies. It is suggested that the building optimization process is not unique, and it depends on background climate, optimization weighing factors, and assumptions used in the economic analysis, which require further research. Full article
(This article belongs to the Special Issue Transport Phenomena in the Atmospheric Boundary Layer)
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