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A Functional Data Analysis of Spatiotemporal Trends and Variation in Fine Particulate Matter.

ABSTRACT: In this paper we illustrate the application of modern functional data analysis methods to study the spatiotemporal variability of particulate matter components across the United States. The approach models the pollutant annual profiles in a way that describes the dynamic behavior over time and space. This new technique allows us to predict yearly profiles for locations and years at which data are not available and also offers dimension reduction for easier visualization of the data. Additionally it allows us to study changes of pollutant levels annually or for a particular season. We apply our method to daily concentrations of two particular components of PM_2.5 measured by two networks of monitoring sites across the United States from 2003 to 2015. Our analysis confirms existing findings and additionally reveals new trends in the change of the pollutants across seasons and years that may not be as easily determined from other common approaches such as Kriging.

SUBMITTER: King MC

PROVIDER: S-EPMC7948063 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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A Functional Data Analysis of Spatiotemporal Trends and Variation in Fine Particulate Matter.

King Meredith C MC Staicu Ana-Maria AM Davis Jerry M JM Reich Brian J BJ Eder Brian B

Atmospheric environment (Oxford, England : 1994) 20180407

In this paper we illustrate the application of modern functional data analysis methods to study the spatiotemporal variability of particulate matter components across the United States. The approach models the pollutant annual profiles in a way that describes the dynamic behavior over time and space. This new technique allows us to predict yearly profiles for locations and years at which data are not available and also offers dimension reduction for easier visualization of the data. Additionally ...[more]

PMID: 33716545

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Project description:BackgroundPrevious research has identified an association between fine particulate matter (PM2.5) air pollution and lung cancer. Most of the evidence for this association, however, is based on research using lung cancer mortality, not incidence. Research that examines potential associations between PM2.5 and incidence of non-lung cancers is limited.ObjectivesThe primary purpose of this study was to evaluate the association between the incidence of cancer and exposure to PM2.5 using >8.5 million cases of cancer incidences from U.S. registries. Secondary objectives include evaluating the sensitivity of the associations to model selection, spatial control, and latency period as well as estimating the exposure-response relationship for several cancer types.MethodsSurveillance, Epidemiology, and End Results (SEER) program data were used to calculate incidence rates for various cancer types in 607 U.S. counties. County-level PM2.5 concentrations were estimated using integrated empirical geographic regression models. Flexible semi-nonparametric regression models were used to estimate associations between PM2.5 and cancer incidence for selected cancers while controlling for important county-level covariates. Primary time-independent models using average incidence rates from 1992-2016 and average PM2.5 from 1988-2015 were estimated. In addition, time-varying models using annual incidence rates from 2002-2011 and lagged moving averages of annual estimates for PM2.5 were also estimated.ResultsThe incidences of all cancer and lung cancer were consistently associated with PM2.5. The incident rate ratios (IRRs), per 10-μg/m3 increase in PM2.5, for all and lung cancer were 1.09 (95% CI: 1.03, 1.14) and 1.19 (95% CI: 1.09, 1.30), respectively. Less robust associations were observed with oral, rectal, liver, skin, breast, and kidney cancers.DiscussionExposure to PM2.5 air pollution contributes to lung cancer incidence and is potentially associated with non-lung cancer incidence. https://doi.org/10.1289/EHP7246.

Dataset Information

A Functional Data Analysis of Spatiotemporal Trends and Variation in Fine Particulate Matter.

Publications

A Functional Data Analysis of Spatiotemporal Trends and Variation in Fine Particulate Matter.

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