What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Land Use Regression and Dispersion Model Outputs
2.2. Street-Level Images
2.3. Ground Monitoring Stations
2.4. Modelling Approach
3. Results and Discussion
3.1. Intracity Performance for London, Vancouver, and New York
3.2. Intracity Performance for London, Vancouver, and New York
3.3. Evaluation of Transferability to Accra and Hong Kong
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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City-Wide LUR Based Estimates of Air Pollution | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
London | New York | Vancouver | ||||||||||||||
r | RMSE | NRMSE | R2 | ME | r | RMSE | NRMSE | R2 | ME | r | RMSE | NRMSE | R2 | ME | ||
NO2 | N = 94,714 | N = 49,575 | N = 60,562 | |||||||||||||
Intracity | 0.79 | 7.31 | 0.20 | 0.62 | −0.19 | 0.87 | 3.83 | 0.18 | 0.75 | 0.34 | 0.73 | 1.81 | 0.19 | 0.52 | 0.11 | |
Intercity | 0.42 | 21.38 | 0.58 | 0.18 | 18.80 | 0.60 | 12.83 | 0.59 | 0.36 | −8.66 | 0.28 | 17.57 | 1.81 | 0.08 | −16.69 | |
Adj. intercity | 12.07 | 0.32 | 0.00 | 6.89 | 0.31 | 0.00 | 3.12 | 0.32 | 0.00 | |||||||
PM2.5 | ||||||||||||||||
Intracity | 0.79 | 0.88 | 0.06 | 0.59 | 0.04 | 0.85 | 0.61 | 0.06 | 0.71 | 0.069 | 0.60 | 1.63 | 0.49 | 0.33 | 0.14 | |
Intercity | 0.37 | 5.77 | 0.40 | 0.14 | 5.52 | 0.47 | 4.06 | 0.42 | 0.22 | −2.21 | 0.06 | 9.10 | 2.73 | 0.00 | −8.80 | |
Adj. intercity | 1.55 | 0.11 | 0.00 | 1.17 | 0.12 | 0.00 | 2.75 | 0.82 | 0.00 | |||||||
Ground Monitoring Stations | ||||||||||||||||
London | New York | Vancouver | ||||||||||||||
r | RMSE | NRMSE | R2 | ME | r | RMSE | NRMSE | R2 | ME | r | RMSE | NRMSE | R2 | ME | ||
NO2 | N = 8 | N = 47 | N = 4 | |||||||||||||
Intracity | 0.90 | 17.71 | 0.27 | 0.81 | 13.47 | 0.76 | 27.97 | 0.49 | 0.58 | 26.29 | 0.97 | 2.63 | 0.24 | 0.95 | 1.65 | |
Intercity | 0.82 | 46.22 | 0.71 | 0.67 | 41.91 | 0.51 | 22.30 | 0.39 | 0.26 | 17.90 | 0.51 | 13.70 | 1.23 | 0.00 | −12.14 | |
Adj. intercity | 13.52 | 0.21 | 0.00 | 14.38 | 0.25 | 0.00 | 4.81 | 0.43 | 0.00 | |||||||
PM2.5 | N = 9 | N = 4 | N = 2 | |||||||||||||
Intracity | 0.72 | 2.19 | 0.13 | 0.51 | −0.34 | 0.86 | 1.15 | 0.10 | 0.74 | 1.56 | - | 1.04 | 0.25 | - | 0.70 | |
Intercity | 0.46 | 8.03 | 0.48 | 0.22 | 7.54 | 0.29 | 5.26 | 0.47 | 0.09 | −4.41 | - | 8.81 | 2.15 | - | −8.7 | |
Adj. intercity | 2.72 | 0.16 | 0.00 | 0.65 | 0.06 | 0.00 | 0.78 | 0.19 | 0.00 | |||||||
Hong Kong | Accra | |||||||||||||||
r | RMSE | NRMSE | R2 | ME | r | RMSE | NRMSE | R2 | ME | |||||||
NO2 | N = 97 | N = 35 | ||||||||||||||
Intercity | 0.46 | 63.30 | 0.58 | 0.21 | 51.63 | 0.25 | 38.55 | 0.63 | 0.06 | 30.19 | ||||||
Adj. intercity | 42.42 | 0.39 | 0.00 | 28.65 | 0.47 | 0.00 | ||||||||||
PM2.5 | N = 52 | N = 32 | ||||||||||||||
Intercity | 0.02 | 8.35 | 0.36 | 0.00 | 3.90 | −0.12 | 13.02 | 0.61 | 0.01 | 8.09 | ||||||
Adj. intercity | 9.76 | 0.42 | 0.00 | 11.91 | 0.56 | 0.00 |
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Suel, E.; Sorek-Hamer, M.; Moise, I.; von Pohle, M.; Sahasrabhojanee, A.; Asanjan, A.A.; Arku, R.E.; Alli, A.S.; Barratt, B.; Clark, S.N.; et al. What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery. Remote Sens. 2022, 14, 3429. https://doi.org/10.3390/rs14143429
Suel E, Sorek-Hamer M, Moise I, von Pohle M, Sahasrabhojanee A, Asanjan AA, Arku RE, Alli AS, Barratt B, Clark SN, et al. What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery. Remote Sensing. 2022; 14(14):3429. https://doi.org/10.3390/rs14143429
Chicago/Turabian StyleSuel, Esra, Meytar Sorek-Hamer, Izabela Moise, Michael von Pohle, Adwait Sahasrabhojanee, Ata Akbari Asanjan, Raphael E. Arku, Abosede S. Alli, Benjamin Barratt, Sierra N. Clark, and et al. 2022. "What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery" Remote Sensing 14, no. 14: 3429. https://doi.org/10.3390/rs14143429