Comparison of NO2 and BC Predictions Estimated Using Google Street View-Based and Conventional European-Wide LUR Models in Copenhagen, Denmark
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Comparison of NO2 and BC Predictions Estimated Using Google Street View-Based and Conventional European-Wide LUR Models in Copenhagen, Denmark. / Tayebi, Shali; Kerckhoffs, Jules; Khan, Jibran; de Hoogh, Kees; Chen, Jie; Taghavi-Shahri, Seyed Mahmood; Bergmann, Marie L.; Cole-Hunter, Thomas; Lim, Youn Hee; Mortensen, Laust H.; Hertel, Ole; Reeh, Rasmus; Schwartz, Joel; Hoek, Gerard; Vermeulen, Roel; Jovanovic Andersen, Zorana; Loft, Steffen; Amini, Heresh.
I: ATMOSPHERE, Bind 14, Nr. 11, 1602, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Comparison of NO2 and BC Predictions Estimated Using Google Street View-Based and Conventional European-Wide LUR Models in Copenhagen, Denmark
AU - Tayebi, Shali
AU - Kerckhoffs, Jules
AU - Khan, Jibran
AU - de Hoogh, Kees
AU - Chen, Jie
AU - Taghavi-Shahri, Seyed Mahmood
AU - Bergmann, Marie L.
AU - Cole-Hunter, Thomas
AU - Lim, Youn Hee
AU - Mortensen, Laust H.
AU - Hertel, Ole
AU - Reeh, Rasmus
AU - Schwartz, Joel
AU - Hoek, Gerard
AU - Vermeulen, Roel
AU - Jovanovic Andersen, Zorana
AU - Loft, Steffen
AU - Amini, Heresh
N1 - Funding Information: This work was supported by Health Effects Institute (HEI) (#4982-RFA19-2/21-5) and Novo Nordisk Foundation Challenge Programme (NNF17OC0027812). HEI is an organization jointly funded by the United States Environmental Protection Agency (EPA) (Assistance Award CR 83998101) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers. Heresh Amini was supported by the US National Institute of Health (grant numbers: P30ES023515 and UL1TR004419). Publisher Copyright: © 2023 by the authors.
PY - 2023
Y1 - 2023
N2 - A widely used method for estimating fine scale long-term spatial variation in air pollution, especially for epidemiology studies, is land use regression (LUR) modeling using fixed off-road monitors. More recently, LUR models have been developed using data from mobile monitors that repeatedly measure road pollutants and mixed-effects modeling. Here, nitrogen dioxide (NO2) and black carbon (BC) predictions from two independent models were compared across streets (defined as 30–60 m road segments) (N = 30,312) and residences (N = 76,752) in Copenhagen, Denmark. The first model was Google Street View (GSV)-based mixed-effects LUR models (Google-MM) that predicted 2019 mean NO2 and BC levels, and the second was European-wide (EUW) LUR models that predicted annual mean 2010 levels at 100 m spatial resolution. Across street segments, the Spearman correlation coefficient between the 2019 NO2 from Google-MM-LUR and 2010 NO2 from EUW-LUR was 0.66, while at residences, this was 0.60. For BC, these were 0.51 across street segments and 0.40 at the residential level. The ratio of percentile 97.5 to 2.5 for NO2 across the study area streets using Google-MM NO2 was 4.5, while using EUW-LUR, this was 2.1. These NO2 ratios at residences were 3.1 using Google-MM LUR, and 1.7 using EUW-LUR. Such ratios for BC across street segments were 3.4 using Google-MM LUR and 2.3 using EUW-LUR, while at the residential level, they were 2.4 and 1.9, respectively. In conclusion, Google-MM-LUR NO2 for 2019 was moderately correlated with EUW-LUR NO2 developed in 2010 across Copenhagen street segments and residences. For BC, while Google-MM-LUR was moderately correlated with EUW-LUR across Copenhagen streets, the correlation was lower at the residential level. Overall, Google-MM-LUR revealed larger spatial contrasts than EUW-LUR.
AB - A widely used method for estimating fine scale long-term spatial variation in air pollution, especially for epidemiology studies, is land use regression (LUR) modeling using fixed off-road monitors. More recently, LUR models have been developed using data from mobile monitors that repeatedly measure road pollutants and mixed-effects modeling. Here, nitrogen dioxide (NO2) and black carbon (BC) predictions from two independent models were compared across streets (defined as 30–60 m road segments) (N = 30,312) and residences (N = 76,752) in Copenhagen, Denmark. The first model was Google Street View (GSV)-based mixed-effects LUR models (Google-MM) that predicted 2019 mean NO2 and BC levels, and the second was European-wide (EUW) LUR models that predicted annual mean 2010 levels at 100 m spatial resolution. Across street segments, the Spearman correlation coefficient between the 2019 NO2 from Google-MM-LUR and 2010 NO2 from EUW-LUR was 0.66, while at residences, this was 0.60. For BC, these were 0.51 across street segments and 0.40 at the residential level. The ratio of percentile 97.5 to 2.5 for NO2 across the study area streets using Google-MM NO2 was 4.5, while using EUW-LUR, this was 2.1. These NO2 ratios at residences were 3.1 using Google-MM LUR, and 1.7 using EUW-LUR. Such ratios for BC across street segments were 3.4 using Google-MM LUR and 2.3 using EUW-LUR, while at the residential level, they were 2.4 and 1.9, respectively. In conclusion, Google-MM-LUR NO2 for 2019 was moderately correlated with EUW-LUR NO2 developed in 2010 across Copenhagen street segments and residences. For BC, while Google-MM-LUR was moderately correlated with EUW-LUR across Copenhagen streets, the correlation was lower at the residential level. Overall, Google-MM-LUR revealed larger spatial contrasts than EUW-LUR.
KW - air pollution
KW - BC
KW - Google Street View
KW - land use regression (LUR)
KW - NO
U2 - 10.3390/atmos14111602
DO - 10.3390/atmos14111602
M3 - Journal article
AN - SCOPUS:85178128226
VL - 14
JO - ATMOSPHERE
JF - ATMOSPHERE
SN - 2073-4433
IS - 11
M1 - 1602
ER -
ID: 375801045