TY - JOUR
T1 - Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen
AU - Kerckhoffs, Jules
AU - Khan, Jibran
AU - Hoek, Gerard
AU - Yuan, Zhendong
AU - Hertel, Ole
AU - Ketzel, Matthias
AU - Jensen, Steen Solvang
AU - Al Hasan, Fares
AU - Meliefste, Kees
AU - Vermeulen, Roel
N1 - Funding Information:
The project received funding from the Environmental Defense Fund, Google, EXPOSOME-NL (NWO; project number 024.004.017 ) and EXPANSE (EU-H2020 Grant No 874627). Jibran Khan’s work is supported by the Danish Big Data Centre for Environment and Health (BERTHA), funded by the Novo Nordisk Foundation (NNF) Challenge Programme , grant number NNF170C0027864 .
Publisher Copyright:
© 2022 The Authors
PY - 2022/12
Y1 - 2022/12
N2 - Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO2), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 – March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables. We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO2. This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).
AB - Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO2), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 – March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables. We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO2. This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).
KW - Exposure
KW - Google Street View
KW - Hyperlocal air quality
KW - Mixed-effect model
KW - Ultrafine particles
UR - http://www.scopus.com/inward/record.url?scp=85140738358&partnerID=8YFLogxK
U2 - 10.1016/j.envint.2022.107575
DO - 10.1016/j.envint.2022.107575
M3 - Article
C2 - 36306551
AN - SCOPUS:85140738358
SN - 0160-4120
VL - 170
JO - Environment International
JF - Environment International
M1 - 107575
ER -