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Xu J, Zhang M, Ganji A, Mallinen K, Wang A, Lloyd M, Venuta A, Simon L, Kang J, Gong J, Zamel Y, Weichenthal S, Hatzopoulou M. Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements. Environ Sci Technol 2022; 56:12886-12897. [PMID: 36044680 DOI: 10.1021/acs.est.2c03193] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Within-city ultrafine particle (UFP) concentrations vary sharply since they are influenced by various factors. We developed prediction models for short-term UFP exposures using street-level images collected by a camera installed on a vehicle rooftop, paired with air quality measurements conducted during a large-scale mobile monitoring campaign in Toronto, Canada. Convolutional neural network models were trained to extract traffic and built environment features from images. These features, along with regional air quality and meteorology data were used to predict short-term UFP concentration as a continuous and categorical variable. A gradient boost model for UFP as a continuous variable achieved R2 = 0.66 and RMSE = 9391.8#/cm3 (mean values for 10-fold cross-validation). The model predicting categorical UFP achieved accuracies for "Low" and "High" UFP of 77 and 70%, respectively. The presence of trucks and other traffic parameters were associated with higher UFPs, and the spatial distribution of elevated short-term UFP followed the distribution of single-unit trucks. This study demonstrates that pictures captured on urban streets, associated with regional air quality and meteorology, can adequately predict short-term UFP exposure. Capturing the spatial distribution of high-frequency short-term UFP spikes in urban areas provides crucial information for the management of near-road air pollution hot spots.
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Affiliation(s)
- Junshi Xu
- Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
| | - Mingqian Zhang
- Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
| | - Arman Ganji
- Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
| | - Keni Mallinen
- Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
| | - An Wang
- Urban Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Marshall Lloyd
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec H3A 1A2, Canada
| | - Alessya Venuta
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec H3A 1A2, Canada
| | - Leora Simon
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec H3A 1A2, Canada
| | - Junwon Kang
- Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
| | - James Gong
- Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
| | - Yazan Zamel
- Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec H3A 1A2, Canada
| | - Marianne Hatzopoulou
- Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
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