Exposure to Particle Beta Radiation in Greater Massachusetts and Factors Influencing Its Spatial and Temporal Variability
Blomberg, Annelise J LU
; Li, Longxiang
; Schwartz, Joel D
; Coull, Brent A
and Koutrakis, Petros
(2020)
In Environmental Science & Technology
54(11).
p.6575-6583
- Abstract
Particle radioactivity is a property of airborne particles caused by the presence of naturally occurring or anthropogenic radionuclides. Recent studies have found associations between particle radioactivity and adverse health outcomes, including changes in blood pressure and lung function. However, the spatiotemporal distribution of particle radioactivity and factors influencing its variability have not been extensively studied. We address these knowledge gaps using measurements of gross beta activity, collected at seven Environmental Protection Agency (EPA) RadNet monitors located in and around Massachusetts. We apply back-trajectory analysis to identify prevailing air mass trajectories and find that these trajectories strongly... (More)
Particle radioactivity is a property of airborne particles caused by the presence of naturally occurring or anthropogenic radionuclides. Recent studies have found associations between particle radioactivity and adverse health outcomes, including changes in blood pressure and lung function. However, the spatiotemporal distribution of particle radioactivity and factors influencing its variability have not been extensively studied. We address these knowledge gaps using measurements of gross beta activity, collected at seven Environmental Protection Agency (EPA) RadNet monitors located in and around Massachusetts. We apply back-trajectory analysis to identify prevailing air mass trajectories and find that these trajectories strongly influence seasonal trends in beta activity. We also evaluate the effects of different meteorological predictors on daily beta activity concentrations using a mixed-effect model. Important predictors of beta activity include air mass trajectories, temperature, and relative humidity. Finally, we create a series of random forest models to impute missing beta activity concentrations at each RadNet monitor for use in future health studies. This is the first study to analyze spatiotemporal trends in particle radioactivity using measurements from the EPA RadNet system.
(Less)
- author
- Blomberg, Annelise J
LU
; Li, Longxiang
; Schwartz, Joel D
; Coull, Brent A
and Koutrakis, Petros
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- keywords
- Air Pollutants/analysis, Particulate matter, Beta Particles, Environmental Exposure/analysis, Environmental Monitoring, Massachusetts
- in
- Environmental Science & Technology
- volume
- 54
- issue
- 11
- pages
- 6575 - 6583
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- pmid:32363859
- scopus:85085193670
- ISSN
- 1520-5851
- DOI
- 10.1021/acs.est.0c00454
- language
- English
- LU publication?
- no
- id
- e2d0962e-a332-47ab-8702-42833818adcd
- date added to LUP
- 2021-09-09 10:26:26
- date last changed
- 2024-06-16 18:38:11
@article{e2d0962e-a332-47ab-8702-42833818adcd,
abstract = {{<p>Particle radioactivity is a property of airborne particles caused by the presence of naturally occurring or anthropogenic radionuclides. Recent studies have found associations between particle radioactivity and adverse health outcomes, including changes in blood pressure and lung function. However, the spatiotemporal distribution of particle radioactivity and factors influencing its variability have not been extensively studied. We address these knowledge gaps using measurements of gross beta activity, collected at seven Environmental Protection Agency (EPA) RadNet monitors located in and around Massachusetts. We apply back-trajectory analysis to identify prevailing air mass trajectories and find that these trajectories strongly influence seasonal trends in beta activity. We also evaluate the effects of different meteorological predictors on daily beta activity concentrations using a mixed-effect model. Important predictors of beta activity include air mass trajectories, temperature, and relative humidity. Finally, we create a series of random forest models to impute missing beta activity concentrations at each RadNet monitor for use in future health studies. This is the first study to analyze spatiotemporal trends in particle radioactivity using measurements from the EPA RadNet system.</p>}},
author = {{Blomberg, Annelise J and Li, Longxiang and Schwartz, Joel D and Coull, Brent A and Koutrakis, Petros}},
issn = {{1520-5851}},
keywords = {{Air Pollutants/analysis; Particulate matter; Beta Particles; Environmental Exposure/analysis; Environmental Monitoring; Massachusetts}},
language = {{eng}},
number = {{11}},
pages = {{6575--6583}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Environmental Science & Technology}},
title = {{Exposure to Particle Beta Radiation in Greater Massachusetts and Factors Influencing Its Spatial and Temporal Variability}},
url = {{http://dx.doi.org/10.1021/acs.est.0c00454}},
doi = {{10.1021/acs.est.0c00454}},
volume = {{54}},
year = {{2020}},
}