What you see is what you breathe? Estimating air pollution spatial variation using street level imagery
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to high input data needs of existing estimation approaches. Here we introduce a computer vision method to estimate annual means for air pollution levels from street level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250k images for each city). Our experimental setup is designed to quantify intra and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing on images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Like LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities i.e., London, New York, and Vancouver, which have similar pollution source profiles were moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on these cities with very different source profiles i.e., Accra in Ghana and Hong Kong were lower (R2 between zero and 0.21) suggesting the need for local calibration with local calibration using additional measurement data from cities that share similar source profiles.
Date Issued
2022-07-17
Date Acceptance
2022-07-05
Citation
Remote Sensing, 2022, 14 (14)
ISSN
2072-4292
Publisher
MDPI AG
Journal / Book Title
Remote Sensing
Volume
14
Issue
14
Copyright Statement
© 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/)
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/)
License URL
Sponsor
Medical Research Council (MRC)
Wellcome Trust
Grant Number
MR/S003983/1
209376/Z/17/Z
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Technology
Environmental Sciences
Geosciences, Multidisciplinary
Remote Sensing
Imaging Science & Photographic Technology
Environmental Sciences & Ecology
Geology
computer vision
deep learning
street images
air pollution
data science
transferability
urban pollution
LAND-USE REGRESSION
GLOBAL BURDEN
EXPOSURE
MODELS
DISEASES
NO2
0203 Classical Physics
0406 Physical Geography and Environmental Geoscience
0909 Geomatic Engineering
Publication Status
Published
Article Number
ARTN 3429