Original Article | Published:

Use of mobile and passive badge air monitoring data for NOX and ozone air pollution spatial exposure prediction models

Journal of Exposure Science and Environmental Epidemiology volume 27, pages 184192 (2017) | Download Citation

Abstract

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NOX) and ozone (O3) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NOX and O3, with LOOCV R2s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NOX had LOOCV R2s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O3. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NOX and O3 and are a better source of data for these models than 2-week passive badge data.

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Acknowledgements

Wei Xu was supported by a scholarship awarded by the Chinese Scholarship Council. This publication was made possible by USEPA grant (RD-83479601-0). Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication.

Author information

Affiliations

  1. Department of Environmental Engineering, East China University of Science and Technology, Shanghai, China

    • Wei Xu
    •  & Guangli Xiu
  2. Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA

    • Wei Xu
    • , Erin A Riley
    • , Elena Austin
    • , Miyoko Sasakura
    • , Kris Hartin
    • , Christopher D Simpson
    • , Michael G Yost
    • , Timothy V Larson
    •  & Sverre Vedal
  3. Department of Statistics, University of Washington, Seattle, Washington, USA

    • Lanae Schaal
    •  & Paul D Sampson
  4. Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA

    • Timothy R Gould
    •  & Timothy V Larson

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Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to Guangli Xiu.

Supplementary information

Glossary

AQS

Air Quality System

LOOCV

leave-one-out cross-validation

LUR

land use regression

PLS

partial least squares

UK

universal kriging

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DOI

https://doi.org/10.1038/jes.2016.9

Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website (http://www.nature.com/jes)

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