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Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM(2.5) in Cohort Studies before the 1999 Implementation of Widespread Monitoring
INTRODUCTION: Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spati...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Institute of Environmental Health Sciences
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226688/ https://www.ncbi.nlm.nih.gov/pubmed/27340825 http://dx.doi.org/10.1289/EHP131 |
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author | Kim, Sun-Young Olives, Casey Sheppard, Lianne Sampson, Paul D. Larson, Timothy V. Keller, Joshua P. Kaufman, Joel D. |
author_facet | Kim, Sun-Young Olives, Casey Sheppard, Lianne Sampson, Paul D. Larson, Timothy V. Keller, Joshua P. Kaufman, Joel D. |
author_sort | Kim, Sun-Young |
collection | PubMed |
description | INTRODUCTION: Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The U.S. Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. OBJECTIVES: We evaluated a novel statistical approach to produce high-quality exposure predictions from 1980 through 2010 in the continental United States for epidemiological applications. METHODS: We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. Temporal trends before 1999 were estimated by using a) extrapolation based on PM2.5 data in FRM/IMPROVE, b) PM2.5 sulfate data in the Clean Air Status and Trends Network, and c) visibility data across the Weather Bureau Army Navy network. We validated the models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Children’s Health Study (CHS), and the Inhalable Particulate Network (IPN). RESULTS: In our validation using pre-1999 data, the prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2 = 0.84–0.91) with lower R2 values in early years. Model performance using CARB dichot and IPN data was worse (R2 = 0.00–0.85) most likely because of fewer monitoring sites and inconsistent sampling methods. CONCLUSIONS: Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods ≤ 30 years. CITATION: Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, Kaufman JD. 2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125:38–46; http://dx.doi.org/10.1289/EHP131 |
format | Online Article Text |
id | pubmed-5226688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | National Institute of Environmental Health Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-52266882017-01-15 Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM(2.5) in Cohort Studies before the 1999 Implementation of Widespread Monitoring Kim, Sun-Young Olives, Casey Sheppard, Lianne Sampson, Paul D. Larson, Timothy V. Keller, Joshua P. Kaufman, Joel D. Environ Health Perspect Research INTRODUCTION: Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The U.S. Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. OBJECTIVES: We evaluated a novel statistical approach to produce high-quality exposure predictions from 1980 through 2010 in the continental United States for epidemiological applications. METHODS: We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. Temporal trends before 1999 were estimated by using a) extrapolation based on PM2.5 data in FRM/IMPROVE, b) PM2.5 sulfate data in the Clean Air Status and Trends Network, and c) visibility data across the Weather Bureau Army Navy network. We validated the models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Children’s Health Study (CHS), and the Inhalable Particulate Network (IPN). RESULTS: In our validation using pre-1999 data, the prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2 = 0.84–0.91) with lower R2 values in early years. Model performance using CARB dichot and IPN data was worse (R2 = 0.00–0.85) most likely because of fewer monitoring sites and inconsistent sampling methods. CONCLUSIONS: Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods ≤ 30 years. CITATION: Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, Kaufman JD. 2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125:38–46; http://dx.doi.org/10.1289/EHP131 National Institute of Environmental Health Sciences 2016-06-24 2017-01 /pmc/articles/PMC5226688/ /pubmed/27340825 http://dx.doi.org/10.1289/EHP131 Text en http://creativecommons.org/publicdomain/mark/1.0/ Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, “Reproduced with permission from Environmental Health Perspectives”); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. |
spellingShingle | Research Kim, Sun-Young Olives, Casey Sheppard, Lianne Sampson, Paul D. Larson, Timothy V. Keller, Joshua P. Kaufman, Joel D. Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM(2.5) in Cohort Studies before the 1999 Implementation of Widespread Monitoring |
title | Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM(2.5) in Cohort Studies before the 1999 Implementation of Widespread Monitoring |
title_full | Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM(2.5) in Cohort Studies before the 1999 Implementation of Widespread Monitoring |
title_fullStr | Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM(2.5) in Cohort Studies before the 1999 Implementation of Widespread Monitoring |
title_full_unstemmed | Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM(2.5) in Cohort Studies before the 1999 Implementation of Widespread Monitoring |
title_short | Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM(2.5) in Cohort Studies before the 1999 Implementation of Widespread Monitoring |
title_sort | historical prediction modeling approach for estimating long-term concentrations of pm(2.5) in cohort studies before the 1999 implementation of widespread monitoring |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226688/ https://www.ncbi.nlm.nih.gov/pubmed/27340825 http://dx.doi.org/10.1289/EHP131 |
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