Asanjan, A

Brauer, M

Deardorff, E

Ezzati, M

Lingenfelter, V

Moise, I

Oza, N

Sahasrabhojanee, A

Sorek-Hamer, M

Suel, E

Von Pohle, M

Spiral - Imperial College Digital Repository

Predicting air pollution spatial variation withstreet-level imageryEsra Suel∗Imperial College Londonesra.suel@imperial.ac.ukMeytar Sorek-HamerUSRA at NASA Ames Research Centermeytar.sorekhamer@nasa.govIzabela MoiseETH Zurichizabela.moise@sdsc.ethz.chMichael von PohleNASA Ames Research Centermichael.vonpohle@nasa.govAdwait SahasrabhojaneeNASA Ames Research Centeradwait.sahasrabhojanee@nasa.govAta Akbari AsanjanNASA Ames Research Centerata.akbariasanjan@nasa.govEmily DeardorffNASA Ames Research Centeremily.deardorff@gmail.comViolet LingenfelterNASA Ames Research Centerviolet.l.lingenfelter@nasa.govNikunj OzaNASA Ames Research Centernikunj.c.oza@nasa.govMajid EzzatiImperial College Londonmajid.ezzati@imperial.ac.ukMichael BrauerUniversity of British Columbiamichael.brauer@ubc.caAir pollution has important and widely accepted implications on health outcomes [1, 2, 3, 4].Advances in spatial and temporal estimation have been instrumental both in establishing healtheffects [2] and estimating impact on the burden of disease [1, 5]. Yet, we currently lack informationon air pollution levels in many areas globally [1], especially at high spatial resolution. Limited spatialand temporal coverage of air quality models is partially due to input data requirements of existingestimation approaches including physically-based e.g., dispersion models, and geostatistical e.g.,land use regression (LUR) models [6]. Advances in deep learning methods and their success incomputer vision applications led to a growing interest in using images for estimating air pollutionlevels [6, 7, 8, 9, 10, 11, 12, 13]. The rationale behind this interest is that information on commoninputs to traditional approaches (e.g. land use, traffic, and built environment features) is, at leastpartially, visible from street-level and satellite images. If utilized effectively, this approach has thepotential for scaling up to global coverage at high spatial and temporal resolution with increasingavailability of imagery at low cost.Building on existing work, for this study, we obtained LUR based mean annual NO2 (200m resolutionraster) and PM2.5 (100m resolution raster) estimates for 2010 from four cities: London, New York,Los Angeles, and Vancouver (BC) [14, 15, 16, 17, 18]. To obtain images, a 100m grid for each citywas created using the boundary shape files covering LUR model outputs. For each of the grid centroidpoints, we obtained the nearest street-level panorama image available from Google Street View that∗Corresponding Author34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.was captured from years 2008 to 2012 (+/- 2 years from the LUR model year). Street-level images(two million in total), were then matched with PM2.5 and NO2 values as outcome labels.In addition to the novel use of street-level imagery an innovative aspect of our approach is that we usemodelled output data rather than measurements for training, allowing us to compile a much largerdataset of image-output pairs for training deep learning models. We used a slightly modified ResNet[19] architecture where the outputs are continuous pollutant levels (as opposed to discrete classes)to train two separate deep learning models for estimating PM2.5 and NO2 concentrations. ResNetwas used as it has a global average pooling at the end, and it allowed us to generate post-hoc modelvisualisations highlighting most relevant areas in the image for predictions using class activationmaps [20]. In the first set of experiments, we trained separate models for each city using four-foldcross validation. In each fold, 75% of data (i.e., image-outcome pairs for 75% of grid points) wereused for training the network and the remaining 25% were withheld. We then measured how wellthe trained network uses images to predict outcomes at locations that were not used in trainingfrom the same city. We repeated this process four times holding out a different 25% of data eachtime. The root-mean-squared-error (RMSE) values were all below 2µg/m3 for each of the cities.In the second set of experiments, we used combined data from three cities holding out one city fortraining, and tested the model performance on the held-out city. We repeated this process three timesholding out a different city each time. The results from the second set of experiments allow us toevaluate capabilities of trained networks to generalize to different geographies i.e. transferability. Asexpected, RMSE values were higher but remained low, mostly below 2µg/m3. Lastly, and crucially,we calculated annual average values from available ground monitoring stations from study regions[16, 21, 22, 23, 24]. This allows us to evaluate how well deep learning models trained on modelledpollution values perform on measured pollution values.References[1] Philip J Landrigan, Richard Fuller, Nereus JR Acosta, Olusoji Adeyi, Robert Arnold, Abdoulaye BibiBaldé, Roberto Bertollini, Stephan Bose-O’Reilly, Jo Ivey Boufford, Patrick N Breysse, et al. The lancetcommission on pollution and health. The lancet, 391(10119):462–512, 2018.[2] C. Arden Pope, Jacob S. Lefler, Majid Ezzati, Joshua D. Higbee, Julian D. Marshall, Sun-Young Kim,Matthew Bechle, Kurtis S. Gilliat, Spencer E. Vernon, Allen L. Robinson, and Richard T. Burnett. Mortalityrisk and fine particulate air pollution in a large, representative cohort of u.s. adults. 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A new method of estimating global pm2. 5 concentrations using satelliteimages. Environmental Epidemiology, 3:432, 2019.2[10] Arman Ganji, Laura Minet, Scott Weichenthal, and Marianne Hatzopoulou. Predicting traffic-related airpollution using feature extraction from built environment images. Environmental Science & Technology,54(17):10688–10699, 2020.[11] Kris Y Hong, Pedro O Pinheiro, and Scott Weichenthal. Predicting outdoor ultrafine particle numberconcentrations, particle size, and noise using street-level images and audio data. Environment International,144:106044, 2020.[12] Meytar Sorek-Hamer, Allan C Just, and Itai Kloog. The use of satellite remote sensing in epidemiologicalstudies. Current opinion in pediatrics, 28(2):228, 2016.[13] Esra Suel, John W Polak, James E Bennett, and Majid Ezzati. Measuring social, environmental and healthinequalities using deep learning and street imagery. 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Atmospheric Environment, 72:10–23, 2013.[16] I Kheirbek, S Johnson, K Ito, K Anan, C Huskey, T Matte, D Kass, H Eisl, J Gorczynski, and S Markowitz.The new york city community air survey: Neighborhood air quality 2008–2014, 2017.[17] Sarah B Henderson, Bernardo Beckerman, Michael Jerrett, and Michael Brauer. Application of land useregression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter.Environmental science & technology, 41(7):2422–2428, 2007.[18] Matthew J Bechle, Dylan B Millet, and Julian D Marshall. National spatiotemporal exposure surface forno2: monthly scaling of a satellite-derived land-use regression, 2000–2010. Environmental Science &Technology, 49(20):12297–12305, 2015.[19] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition.In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.[20] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep featuresfor discriminative localization. In Proceedings of the IEEE conference on computer vision and patternrecognition, pages 2921–2929, 2016.[21] US EPA. Us epa air quality download data, 2020. Last accessed 15 June 2020.[22] Government of Canada. Environment and climate change canada data, 2020. Last accessed 1 June 2020.[23] Kings College. London air quality network, 2020. Last accessed 15 June 2020.[24] Polly E. Lang, David C. Carslaw, and Sarah J. Moller. A trend analysis approach for air quality networkdata. Atmospheric Environment: X, 2:100030, Apr 2019.3

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Predicting air pollution spatial variation with street-level imagery

Predicting air pollution spatial variation with street-level imagery

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