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Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment



Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution.


Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model’s performance through cross-validation.


We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996–2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics.


The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination (\({R}^{2}\)) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV-\({R}^{2}\) = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and \({R}^{2}\) = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV-\({R}^{2}\) = 0.51 (with LCS).


We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington’s Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

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Data availability

Data used in this study were a mix of publicly available data sources (e.g. agency data), data available upon request (e.g. supplementary monitoring campaigns at non-confidential locations), and unavailable/protected information (e.g. ACT-AP participant geolocations).


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The authors thank Johan Lindström, Paul Sampson, Silas Bergen, Assaf Oron, Michael Young, and Victoria Knutson for their work updating and maintaining the SpatioTemporal package.


This research was funded by National Institute for Environmental Health Science (NIEHS), grant numbers R56ES026528 and P30ES007033, NIEHS and National Institute on Aging (NIA) grant number R01ES026187, STAR research assistance agreements RD831697 (MESA Air) and RD-83830001 (MESA Air Next Stage) awarded by the US Environmental Protection Agency (EPA), the University of Washington (UW) Interdisciplinary Center for Exposures, Diseases, Genomics, and Environment (EDGE) of the National Institutes of Health under grant number P30ES007033 and CR-83998101, from the (Health Effects Institute HEI), jointly funded by US EPA and the Automobile Manufacturers Association. CZ and NC were supported by the University of Washington’s Biostatistics, Epidemiology, and Bioinformatics Training in Environmental Health (BEBTEH), grant number T32ES015459, from the National Institute for Environmental Health Science (NIEHS). This research has not been formally reviewed by any of these funding sources or agencies; the views expressed in this document are solely those of the authors; none of these funding sources or agencies endorse any products or commercial services mentioned in this publication.

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Conceptualization, CZ, LS; Methodology, CZ, AJG, DLS, JB, EA, AAS; Software, CZ, DLS, CS, DB; Validation, DLS, JB, DB; Formal analysis, CZ, NC; Investigation, CZ, AJG, NC; Resources, LS; Data curation, CZ, AJG, CS, DLS, JB; Writing - original draft, CZ; Writing – review & editing, CZ, JB, DB, NC, AJG, DLS, CS, EA, ES, AAS, LS; Visualization, CZ; Supervision, LS; Project administration, AJG; Funding acquisition, LS.

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Correspondence to Lianne Sheppard.

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Zuidema, C., Bi, J., Burnham, D. et al. Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment. J Expo Sci Environ Epidemiol (2024).

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