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Zapata-Marin S, Schmidt AM, Weichenthal S, et al. Within city spatiotemporal variation of pollen concentration in the city of Toronto, Canada. Environ Res. 2021;206:112566doi: 10.1016/j.envres.2021.112566.
Zapata-Marin, S., Schmidt, A. M., Weichenthal, S., Katz, D. S. W., Takaro, T., Brook, J., & Lavigne, E. (2021). Within city spatiotemporal variation of pollen concentration in the city of Toronto, Canada. Environmental research, 206112566. https://doi.org/10.1016/j.envres.2021.112566
Zapata-Marin, Sara, et al. "Within city spatiotemporal variation of pollen concentration in the city of Toronto, Canada." Environmental research vol. 206 (2021): 112566. doi: https://doi.org/10.1016/j.envres.2021.112566
Zapata-Marin S, Schmidt AM, Weichenthal S, Katz DSW, Takaro T, Brook J, Lavigne E. Within city spatiotemporal variation of pollen concentration in the city of Toronto, Canada. Environ Res. 2021 Dec 17;206:112566. doi: 10.1016/j.envres.2021.112566. Epub 2021 Dec 17. PMID: 34922985.
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Environ Res. 2021 Dec 17;206:112566. doi: 10.1016/j.envres.2021.112566. Epub 2021 Dec 17.

Within city spatiotemporal variation of pollen concentration in the city of Toronto, Canada.

Environmental research

Sara Zapata-Marin, Alexandra M Schmidt, Scott Weichenthal, Daniel S W Katz, Tim Takaro, Jeffrey Brook, Eric Lavigne

Affiliations

  1. Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada. Electronic address: [email protected].
  2. Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
  3. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
  4. Dell Medical School, University of Texas at Austin, Austin, TX, USA.
  5. Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada.
  6. Department of Public Health Sciences, University of Toronto, Toronto, ON, Canada.
  7. Air Health Science Division and Population Studies Division, Health Canada, Ottawa, ON, Canada.

PMID: 34922985 DOI: 10.1016/j.envres.2021.112566

Abstract

BACKGROUND: The exacerbation of asthma and respiratory allergies has been associated with exposure to aeroallergens such as pollen. Within an urban area, tree cover, level of urbanization, atmospheric conditions, and the number of source plants can influence spatiotemporal variations in outdoor pollen concentrations.

OBJECTIVE: We analyze weekly pollen measurements made between March and October 2018 over 17 sites in Toronto, Canada. The main goals are: to estimate the concentration of different types of pollen across the season; estimate the association, if any, between pollen concentration and environmental variables, and provide a spatiotemporal surface of concentration of different types of pollen across the weeks in the studied period.

METHODS: We propose an extension of the land-use regression model to account for the temporal variation of pollen levels and the high number of measurements equal to zero. Inference is performed under the Bayesian framework, and uncertainty of predicted values is naturally obtained through the posterior predictive distribution.

RESULTS: Tree pollen was positively associated with commercial areas and tree cover, and negatively associated with grass cover. Both grass and weed pollen were positively associated with industrial areas and TC brightness and negatively associated with the northing coordinate. The total pollen was associated with a combination of these environmental factors. Predicted surfaces of pollen concentration are shown at some sampled weeks for all pollen types.

SIGNIFICANCE: The predicted surfaces obtained here can help future epidemiological studies to find possible associations between pollen levels and some health outcome like respiratory allergies at different locations within the study area.

Copyright © 2021. Published by Elsevier Inc.

Keywords: Bayesian inference; Land-use regression; Spatial distribution; Temporal variation

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