The Influence of Urban Form and Vegetation on Near-Source Dispersion in a Realistic Urban Canopy
Bibliographic record
Abstract
The atmospheric boundary layer controls many interactions within the troposphere from larger scale atmospheric features to land-atmospheric interactions. The urban boundary layer is the layer above an urban area that is heavily impacted by what happens below, while the urban canopy layer is impacted by the immediate surroundings within the urban area. Understanding flow within the urban canopy layer is crucial for determining the distribution of particulate matter in urban areas and implications for air quality and human health. \n \nPrevious experiments have examined downtown urban domains by releasing tracers to understand dispersion and dissipation of tracer plumes and how turbulence and urban geometry can affect them. Gaussian plume models have been used in the past to model dispersion in urban areas, especially for scalar transport. While multiple studies have been conducted to understand plume characteristics in urban environments, plume behavior close to point sources (< 1 km) and the effects of buildings and foliage on plume characteristics are not well understood. \n \nA field campaign, Tracer Release in an Urban Canopy (TRUC), was conducted in the Sunset Neighborhood of Vancouver, British Columbia in June 2017. This location is well-documented by previous field campaigns. The instrument configuration used during TRUC consisted of fifty spinning impaction traps, a mobile tower with 3-D sonic anemometers at two levels (16.6 m and 1.5 m), and five 2-D sonic anemometers deployed at 1.5 m. A mobile source at 2.4 m released 35 micron yellow/green and violet fluorescent microspheres from 3-D ultrasonic atomizer nozzles. Fourteen successful releases were conducted, each for twenty minutes, at four different locations throughout the neighborhood. \n \nAn equation consisting of the Superposition of two Orthogonally-oriented Gaussian plume distributions (SOG; Miller et al., 2018) was utilized to fit the concentration data collected. The SOG was compared to the TRUC data to determine suitability for interpolating between collection points. While the SOG did characterize the pattern of the concentration behavior well for both the near and far fields, the magnitude of the concentrations was often misrepresented. Channeling of the plume was observed during the TRUC campaign, which was characterized by the SOG most of the time. Comparison of the mean wind angle relative to the street network and various plume parameters were utilized to visualize the channeling of the plume. \n \nResults from this data and usage of the SOG equation were utilized to determine higher-order plume moment statistics. Turbulence, building, and vegetation statistics were also calculated to describe the plume characteristics and behaviors. Evidence of a relationship was seen between the mean wind direction and first order moment, as well as between the mean wind angle and the second order plume moment. While turbulence does affect the plume, especially with mean wind direction, the urban geometry proved to affect the plume characteristics more so in the urban domain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".