Bai, Z. and Peng, C. orcid.org/0000-0001-8199-0955 (2023) Convolutional Neural Network (CNN) supported urban design to reduce particle air pollutant concentrations. In: Koh, I., Reinhardt, D., Makki, M., Khakhar, M. and Bao, N., (eds.) Human-Centric: Proceedings of the 28th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA 2023). 28th International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2023), 21-23 Mar 2023, CEPT University Ahmedabad, India. CAADRIA , pp. 505-514. ISBN 9789887891796
Abstract
PM2.5 has become a significant factor contributing to the haze outbreak in mainland China, which has negative impacts for public health. The current agility of CFD-based modelling to reveal in real-time the changes in PM2.5 concentrations in response to (proposed) changes in urban form limits its practical applications in the design processes. To support urban design for better air quality (AQ), this study presents a machine learning approach to test: (1) that the spatial distribution of PM2.5 concentrations measured in an urban area reflects the area’s capacity to disperse particle air pollution; (2) that the PM2.5 concentration measurements can be linked to certain urban form attributes of that area. A Convolutional Neural Network algorithm called Residual Neural Network (ResNet) was trained and tested using the ChinaHighPM2.5 and urban form datasets. The result is a ResNet-AQ predictor for the city centre area in Beijing which had one of the highest air pollution levels within the Beijing-Tianjin-Hebei region. The urban area covered by the ResNet-AQ predictor contains 4,000 grid cells (approx. 25.3 km x 25.3 km), of which 1,200 (30%) cells were selected randomly for testing. The ResNet-AQ prediction accuracy achieved 87.3% after 100 iterations. An end-use scenario is presented to show how a social housing project can be supported by the AQ predictor to achieve better urban air quality performance.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong |
Keywords: | Built Environment and Design; Architecture; Sustainable Cities and Communities |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Architecture and Landscape |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Mar 2025 11:22 |
Last Modified: | 07 Mar 2025 11:36 |
Published Version: | https://caadria.org/downloads/ |
Status: | Published |
Publisher: | CAADRIA |
Refereed: | Yes |
Identification Number: | 10.52842/conf.caadria.2023.1.505 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224105 |