A nationwide evaluation of crowd-sourced ambient temperature data

Growing concerns about heat in urban areas paired with the sparsity of weather stations have resulted in individuals drawing on data from citizen science sensor networks to fill in data gaps. In the past decade, a proliferation of crowd-sourced sensors has provided low-cost local air quality and tem...

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Main Authors: Priyanka deSouza, Peter C. Ibsen, Daniel M. Westervelt, Ralph Kahn, Benjamin F. Zaitchik, Patrick L. Kinney
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Environmental Science
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Online Access:https://www.frontiersin.org/articles/10.3389/fenvs.2025.1527855/full
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author Priyanka deSouza
Priyanka deSouza
Peter C. Ibsen
Daniel M. Westervelt
Daniel M. Westervelt
Ralph Kahn
Benjamin F. Zaitchik
Patrick L. Kinney
author_facet Priyanka deSouza
Priyanka deSouza
Peter C. Ibsen
Daniel M. Westervelt
Daniel M. Westervelt
Ralph Kahn
Benjamin F. Zaitchik
Patrick L. Kinney
author_sort Priyanka deSouza
collection DOAJ
description Growing concerns about heat in urban areas paired with the sparsity of weather stations have resulted in individuals drawing on data from citizen science sensor networks to fill in data gaps. In the past decade, a proliferation of crowd-sourced sensors has provided low-cost local air quality and temperature, with one brand having over 14,000 sensors deployed in the United States between 1 January 2017 and 20 July 2021. Although the air quality data from PurpleAir sensors have been widely studied, less attention has been paid to reported temperature. Gridded modeled temperature datasets are widely used in epidemiologic studies. The spatial granularity of the crowd-sourced sensor data captures local temperature variation which existing gridded datasets cannot, and can potentially be used to generate exposure assessments for health research. We compare temperature metrics reported by the dominantly used crowd-sourced sensor in the United States with a gridded temperature product, the North American Land Data Assimilation System (NLDAS)-2, which although not a gold-standard measure of temperature, is widely used in epidemiologic research. We evaluate the lag between indoor and outdoor sensor temperatures. We report associations of the difference between outdoor sensor temperatures and NLDAS-2 temperatures, an indicator of degradation, and the duration of sensor operation. Finally, based on the temperature range recorded by the outdoor sensors vis-a-vis NLDAS-2 temperatures, we provide a list of 271 (2.5%) sensors potentially misclassified as outdoor and likely located indoors. We observed that the outdoor sensors agreed well with NLDAS-2 (R2 > 0.82). This association broke down under warm conditions (daily average NLDAS ≥21.1oC). Our comparison suggests that a radiative-correction needs to be applied to use crowd-sourced data reliably. However, the spatial granularity of the continental sensor network can reduce the measurement error in exposure assignment compared to the NLDAS-2. Indoor sensor temperatures lagged hourly NLDAS temperatures by 2 hours across almost all climate zones. The mean difference in hourly sensor and NLDAS-2 temperatures increased by 0.57oC for every operational year, suggesting that careful attention must be paid to degradation. Overall, we found that researchers should be aware of the limitations in crowd-sourced sensor air temperatures when examining extreme heat, or when aggregating sensor data across multiple years.
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spelling doaj-art-539e4c879b084daf80fa05d93c13cbc92025-08-20T02:08:24ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-04-011310.3389/fenvs.2025.15278551527855A nationwide evaluation of crowd-sourced ambient temperature dataPriyanka deSouza0Priyanka deSouza1Peter C. Ibsen2Daniel M. Westervelt3Daniel M. Westervelt4Ralph Kahn5Benjamin F. Zaitchik6Patrick L. Kinney7Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, United StatesCU Population Center, University of Colorado Boulder, Boulder, CO, United StatesU. S. Geological Survey, Geosciences and Environmental Change Science Center, Lakewood, CO, United StatesDivision of Ocean and Climate Physics, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, United StatesNational Aeronautics and Space Administration Goddard Institute for Space Studies, New York, NY, United StatesLaboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, CO, United StatesDepartment of Earth and Planetary Sciences, The Johns Hopkins University, Baltimore, MD, United StatesBoston University School of Public Health, Boston, MA, United StatesGrowing concerns about heat in urban areas paired with the sparsity of weather stations have resulted in individuals drawing on data from citizen science sensor networks to fill in data gaps. In the past decade, a proliferation of crowd-sourced sensors has provided low-cost local air quality and temperature, with one brand having over 14,000 sensors deployed in the United States between 1 January 2017 and 20 July 2021. Although the air quality data from PurpleAir sensors have been widely studied, less attention has been paid to reported temperature. Gridded modeled temperature datasets are widely used in epidemiologic studies. The spatial granularity of the crowd-sourced sensor data captures local temperature variation which existing gridded datasets cannot, and can potentially be used to generate exposure assessments for health research. We compare temperature metrics reported by the dominantly used crowd-sourced sensor in the United States with a gridded temperature product, the North American Land Data Assimilation System (NLDAS)-2, which although not a gold-standard measure of temperature, is widely used in epidemiologic research. We evaluate the lag between indoor and outdoor sensor temperatures. We report associations of the difference between outdoor sensor temperatures and NLDAS-2 temperatures, an indicator of degradation, and the duration of sensor operation. Finally, based on the temperature range recorded by the outdoor sensors vis-a-vis NLDAS-2 temperatures, we provide a list of 271 (2.5%) sensors potentially misclassified as outdoor and likely located indoors. We observed that the outdoor sensors agreed well with NLDAS-2 (R2 > 0.82). This association broke down under warm conditions (daily average NLDAS ≥21.1oC). Our comparison suggests that a radiative-correction needs to be applied to use crowd-sourced data reliably. However, the spatial granularity of the continental sensor network can reduce the measurement error in exposure assignment compared to the NLDAS-2. Indoor sensor temperatures lagged hourly NLDAS temperatures by 2 hours across almost all climate zones. The mean difference in hourly sensor and NLDAS-2 temperatures increased by 0.57oC for every operational year, suggesting that careful attention must be paid to degradation. Overall, we found that researchers should be aware of the limitations in crowd-sourced sensor air temperatures when examining extreme heat, or when aggregating sensor data across multiple years.https://www.frontiersin.org/articles/10.3389/fenvs.2025.1527855/fulltemperaturecitizen sciencesensor degradationpurpleairEpidemiology
spellingShingle Priyanka deSouza
Priyanka deSouza
Peter C. Ibsen
Daniel M. Westervelt
Daniel M. Westervelt
Ralph Kahn
Benjamin F. Zaitchik
Patrick L. Kinney
A nationwide evaluation of crowd-sourced ambient temperature data
Frontiers in Environmental Science
temperature
citizen science
sensor degradation
purpleair
Epidemiology
title A nationwide evaluation of crowd-sourced ambient temperature data
title_full A nationwide evaluation of crowd-sourced ambient temperature data
title_fullStr A nationwide evaluation of crowd-sourced ambient temperature data
title_full_unstemmed A nationwide evaluation of crowd-sourced ambient temperature data
title_short A nationwide evaluation of crowd-sourced ambient temperature data
title_sort nationwide evaluation of crowd sourced ambient temperature data
topic temperature
citizen science
sensor degradation
purpleair
Epidemiology
url https://www.frontiersin.org/articles/10.3389/fenvs.2025.1527855/full
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