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dc.contributor.authorWalker, Erica D.
dc.contributor.authorHart, Jaime E.
dc.contributor.authorKoutrakis, Petros
dc.contributor.authorCavallari, Jennifer M.
dc.contributor.authorVoPham, Trang
dc.contributor.authorLuna, Marcos
dc.contributor.authorLaden, Francine
dc.creatorWalker, Erica D.
dc.creatorHart, Jaime E.
dc.creatorKoutrakis, Petros
dc.creatorCavallari, Jennifer M.
dc.creatorVoPham, Trang
dc.creatorLuna, Marcos
dc.creatorLaden, Francine
dc.date2021-11-24T14:05:37.000
dc.date.accessioned2021-11-29T11:27:52Z
dc.date.available2021-11-29T11:27:52Z
dc.date.issued2017-09-18T00:00:00-07:00
dc.date.submitted2017-10-03T10:13:25-07:00
dc.identifiergeography_facpub/3
dc.identifier.urihttp://hdl.handle.net/20.500.13013/532
dc.description.abstractBackground: Urban sound levels are a ubiquitous environmental stressor and have been shown to be associated with a wide variety of health outcomes. While much is known about the predictors of A-weighted sound pressure levels in the urban environment, far less is known about other frequencies. Objective: To develop a series of spatial-temporal sound models to predict A-weighted sound pressure levels, low, mid, and high frequency sound for Boston, Massachusetts. Methods: Short-term sound levels were gathered at n = 400 sites from February 2015 – February 2016. Spatial and meteorological attributes at or near the sound monitoring site were obtained using publicly available data and a portable weather station. An elastic net variable selection technique was used to select predictors of A-weighted, low, mid, and high frequency sound. Results: The final models for low, mid, high, and A-weighted sound levels explained 59 – 69% of the variability in each measure. Similar to other A-weighted models, our sound models included transportation related variables such as length of roads and bus lines in the surrounding area; distance to road and rail lines; traffic volume, vehicle mix, residential and commercial land use. However, frequency specific models highlighted additional predictors not included in the A-weighted model including temperature, vegetation, impervious surfaces, vehicle mix, and density of entertainment establishments and restaurants. Conclusions: Building spatial temporal models to characterize sound levels across the frequency spectrum using an elastic net approach can be a promising tool for noise exposure assessments within the urban soundscape. Models of sound's character may give us additional important sound exposure metrics to be utilized in epidemiological studies.
dc.titleSpatial And Temporal Determinants Of A-Weighted And Frequency Specific Sound Levels—An Elastic Net Approach
dc.typearticle
dc.legacy.embargo2017-10-03T00:00:00-07:00
dc.legacy.pubstatuspublished
dc.source.titleEnvironmental Research Volume 159, November 2017, Pages 491-499, http://dx.doi.org/10.1016/j.envres.2017.08.034
dc.legacy.pubtitleGeography Faculty Publications
dc.legacy.identifierhttps://digitalcommons.salemstate.edu/cgi/viewcontent.cgi?article=1002&context=geography_facpub&unstamped=1
dc.legacy.identifieritemhttps://digitalcommons.salemstate.edu/geography_facpub/3


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