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Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment

Journal of Exposure Science & Environmental Epidemiology volume 33, pages 465–473 (2023)Cite this article

Abstract

Background

Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment.

Objective

We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces.

Methods

We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design’s land use regression prediction model.

Results

The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results.

Significance

A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts.

Impact statement

Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

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Fig. 1: Best fit lines of cross-validated short-term predictions for 30 campaigns vs the gold standard predictions for NOx.
Fig. 2: Site-specific NOx prediction errors for short-term designs (N = 30 campaigns) as compared to the gold standard predictions (long-term Balanced Design Version 1).
Fig. 3: Model performances.

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Data availability

Air pollution data are available through the EPA (https://www.epa.gov/outdoor-air-quality-data). The covariates used in this analysis for regulatory sites are freely available through various online sources and may be available from the authors upon request.

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Funding

This work was funded by the Adult Changes in Thought – Air Pollution (ACT-AP) Study (National Institute of Environmental Health Sciences [NIEHS], National Institute on Aging [NIA], R01ES026187), and BEBTEH: Biostatistics, Epidemiologic & Bioinformatic Training in Environmental Health (NIEHS, T32ES015459). Research described in this article was conducted under contract to the Health Effects Institute (HEI), an organization jointly funded by the United States Environmental Protection Agency (EPA) (Assistance Award No. 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.

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Authors and Affiliations

  1. Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA

    Magali N. Blanco, Annie Doubleday, Elena Austin, Edmund Seto, Timothy V. Larson & Lianne Sheppard

  2. Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA, 98195, USA

    Julian D. Marshall & Timothy V. Larson

  3. Department of Biostatistics, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA

    Lianne Sheppard

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  1. Magali N. Blanco
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  2. Annie Doubleday
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  3. Elena Austin
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Contributions

MNB: data curation, formal analysis, software, investigation, methodology, software, visualization, writing – original draft and editing. AD: formal analysis, validation, visualization, writing – review, and editing. EA: writing – review, and editing. JDM: Conceptualization, writing – review, and editing. ES: writing – review, and editing. TL: Writing – review, and editing. LS: Conceptualization, methodology, funding acquisition, resources, supervision, writing – original draft, review, and editing.

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Correspondence to Magali N. Blanco or Lianne Sheppard.

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Blanco, M.N., Doubleday, A., Austin, E. et al. Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment. J Expo Sci Environ Epidemiol 33, 465–473 (2023). https://doi.org/10.1038/s41370-022-00470-5

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