Air pollution in a microclimate; the impact of different green barriers on the dispersion

Taleghani, Mohammad; Clark, Adele; Swan, William; Mohegh, Arash

doi:10.1016/j.scitotenv.2019.134649

Cited by 30 publications

(10 citation statements)

References 39 publications

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“…Reductions in ozone of 52 percent and nitrogen dioxide of 27 percent have been demonstrated with the application of non-productive green roofs [42]. Factors influencing efficacy include configuration, wind flows, and orientation and geometry of the streetscape [40,[48][49][50][51].…”

Section: Literature Reviewmentioning

confidence: 99%

Nature-Based Resilience: A Multi-Type Evaluation of Productive Green Infrastructure in Agricultural Settings in Ontario, Canada

Anderson

Gough

2021

Atmosphere

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Nature-based solutions such as green infrastructure present an opportunity to reduce air pollutant concentrations and greenhouse gas emissions. This paper presents new findings from a controlled field study in Ontario, Canada, evaluating the impact of productive applications of green infrastructure on air pollution and carbon dioxide concentrations across different agricultural morphologies compared to other non-productive applications. This study demonstrates that productive green infrastructure applications are as beneficial as non-productive applications in reducing ozone, nitrogen dioxide, and carbon dioxide concentrations. Nature-based solutions present an opportunity to build climate resilience into agricultural systems through supply-side mitigation and adaptation. The implementation of productive green infrastructure could be a viable agricultural practice to address multiple climate change impacts.

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Section: Literature Reviewmentioning

confidence: 99%

Nature-Based Resilience: A Multi-Type Evaluation of Productive Green Infrastructure in Agricultural Settings in Ontario, Canada

Anderson

Gough

2021

Atmosphere

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“…Localization: Maximizing the usefulness of collected information requires following stringent protocols that determine where to collect measurements as this ensures the measurements can be used for modeling purposes, e.g., for understanding dispersion patterns and how they are affected by weather, buildings, green areas and other factors [11]. This requires precise horizontal and vertical localization.…”

Section: Challenges and Enablersmentioning

confidence: 99%

Toward Blue Skies: City-Scale Air Pollution Monitoring Using UAVs

Motlagh

Irjala²,

Zuniga

et al. 2023

IEEE Consumer Electron. Mag.

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Dangers associated with poor air quality are driving deployments of air quality monitoring technology worldwide. Having a comprehensive understanding of the health effects of pollutants requires understanding both the distribution and dispersion of pollutants in the environments, but currently this information is highly difficult to capture. This article presents a vision for city-scale air pollution monitoring that uses unmanned aerial vehicles (UAVs) to complement current ground and infrastructure-based measurements with a vertical profile of pollutants. We highlight the key requirements and research challenges, demonstrate the benefits UAVs bring through measurements from an industrial and a residential location, and establish a research roadmap for the path forward.

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“…The model description is detailed in Bruse and Fleer [31]. It has been used to study the atmospheric dispersion of air pollutants included in urban environments [32,33]. Particle sedimentation due to gravity and particle deposition to different surfaces by considering the aerodynamic and sub-layer surface resistances [34] were simulated.…”

Section: Computational Fluid Dynamics (Cfd) Modelmentioning

confidence: 99%

Application of a Machine Learning Method for Prediction of Urban Neighborhood-Scale Air Pollution

Wai

2023

IJERPH

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Urban air pollution has aroused growing attention due to its associated adverse health effects. A model which could promptly predict urban air quality with considerable accuracy is, therefore, important and will benefit the development of smart cities. However, only a computational fluid dynamics (CFD) model could better resolve the dispersion behavior within an urban canyon layer. A machine learning (ML) model using the Artificial Neural Network (ANN) approach was formulated in the current study to investigate vehicle-derived airborne particulate (PM10) dispersion within a compact high-rise-built environment. Various measured meteorological parameters and PM10 concentrations were adopted as the model inputs to train the ANN model. A building-resolved CFD model under the same environmental settings was also set up to compare its model performance with the ANN model. Our results showed that the ANN model exhibited promising performance (r = 0.82, fractional bias = 0.002) when comparing the > 1000 h PM10 measurements. When comparing the diurnal hourly measured PM10 variations in a clear-sky day, both the ANN and CFD models performed well (r > 0.8). The good performance of the CFD model relied on the knowledge of the in situ diurnal traffic profile, the adoption of suitable mobile source emission factor(s) (e.g., from MOBILE 6 and COPERT4), and the use of urban thermal and dynamical variables to capture PM10 variations in both neutral and unstable atmospheric conditions. These requirements/constraints make it impractical for daily operation. On the contrary, the ML (ANN) model adopted here is free from these constraints and is fast (less than 0.1% computational time relative to the CFD model). These results demonstrate that the ANN model is a superior option for a smart city application.

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Air pollution in a microclimate; the impact of different green barriers on the dispersion

Cited by 30 publications

References 39 publications

Nature-Based Resilience: A Multi-Type Evaluation of Productive Green Infrastructure in Agricultural Settings in Ontario, Canada

Nature-Based Resilience: A Multi-Type Evaluation of Productive Green Infrastructure in Agricultural Settings in Ontario, Canada

Toward Blue Skies: City-Scale Air Pollution Monitoring Using UAVs

Application of a Machine Learning Method for Prediction of Urban Neighborhood-Scale Air Pollution

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