Dataset Information

Estimating ambient-origin PM_2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.

ABSTRACT:

Objectives

We aim to characterize the qualities of estimation approaches for individual exposure to ambient-origin fine particulate matter (PM_2.5), for use in epidemiological studies.

Methods

The analysis incorporates personal, home indoor, and home outdoor air monitoring data and spatio-temporal model predictions for 60 participants from the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). We compared measurement-based personal PM_2.5 exposure with several measured or predicted estimates of outdoor, indoor, and personal exposures.

Results

The mean personal 2-week exposure was 7.6 (standard deviation 3.7) µg/m³. Outdoor model predictions performed far better than outdoor concentrations estimated using a nearest-monitor approach (R = 0.63 versus R = 0.43). Incorporating infiltration indoors of ambient-derived PM_2.5 provided better estimates of the measurement-based personal exposures than outdoor concentration predictions (R = 0.81 versus R = 0.63) and better scaling of estimated exposure (mean difference 0.4 versus 5.4 µg/m³ higher than measurements), suggesting there is value to collecting data regarding home infiltration. Incorporating individual-level time-location information into exposure predictions did not increase correlations with measurement-based personal exposures (R = 0.80) in our sample consisting primarily of retired persons.

Conclusions

This analysis demonstrates the importance of incorporating infiltration when estimating individual exposure to ambient air pollution. Spatio-temporal models provide substantial improvement in exposure estimation over a nearest monitor approach.

SUBMITTER: Miller KA

PROVIDER: S-EPMC6380932 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Estimating ambient-origin PM<sub>2.5</sub> exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.

Miller Kristin A KA Spalt Elizabeth W EW Gassett Amanda J AJ Curl Cynthia L CL Larson Timothy V TV Avol Ed E Allen Ryan W RW Vedal Sverre S Szpiro Adam A AA Kaufman Joel D JD

Journal of exposure science & environmental epidemiology 20180830 2

<h4>Objectives</h4>We aim to characterize the qualities of estimation approaches for individual exposure to ambient-origin fine particulate matter (PM<sub>2.5</sub>), for use in epidemiological studies.<h4>Methods</h4>The analysis incorporates personal, home indoor, and home outdoor air monitoring data and spatio-temporal model predictions for 60 participants from the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). We compared measurement-based personal PM<sub>2.5</sub> expos ...[more]

PMID: 30166581

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Dataset Information

Estimating ambient-origin PM_2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.

Objectives

Methods

Results

Conclusions

Publications

Estimating ambient-origin PM<sub>2.5</sub> exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

Estimating ambient-origin PM2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.

Objectives

Methods

Results

Conclusions

Publications

Estimating ambient-origin PM<sub>2.5</sub> exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Estimating ambient-origin PM_2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.