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. 2017 Jan;125(1):38-46.
doi: 10.1289/EHP131. Epub 2016 Jun 24.

Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM2.5 in Cohort Studies before the 1999 Implementation of Widespread Monitoring

Sun-Young Kim  1 , Casey Olives, Lianne Sheppard, Paul D Sampson, Timothy V Larson, Joshua P Keller, Joel D Kaufman
Affiliations

Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM2.5 in Cohort Studies before the 1999 Implementation of Widespread Monitoring

Sun-Young Kim et al. Environ Health Perspect. 2017 Jan.

Abstract

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.

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Conflict of interest statement

Although this publication was developed under a Science to Achieve Results (STAR) research assistance agreement (RD831697) awarded by the U.S. EPA, it has not been formally reviewed by the U.S. EPA. The views expressed in this document are solely those of the University of Washington, and the U.S. EPA does not endorse any products or commercial services mentioned in this publication. The authors declare they have no actual or potential competing financial interests.

Figures

Figure 1
Figure 1
Maps of (A) FRM and IMPROVE sites for 1999–2010 used in model development and trend estimation, (B) CASTNet and WBAN sites used for trend estimation, and (C) IMPROVE sites for 1990–1998, CHS, CARB dichot, and IPN sites used in model evaluation (blue, green, and red symbols represent West, Mountain West, and East regions, respectively); Maps generated using locations of regulatory monitoring sites downloaded from the U.S. Environmental Protection Agency (EPA) website (http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html#Daily) and boundaries in the R package (version 3.2.5; R Project for Statistical Computing). CARB dichot, California Air Resources Board dichotomous sampler monitoring; CASTNet, Clean Air Status and Trends Network; CHS, Children’s Health Study; FRM, Federal Reference Method; IMPROVE, Interagency Monitoring of Protected Visual Environment; IPN, Inhalable Particulate Network; WBAN, Weather Bureau Army Navy.
Figure 2
Figure 2
Estimated temporal trends based on fine particulate matter (PM2.5) annual averages in FRM and IMPROVE, PM2.5 sulfate annual averages in CASTNet, and visibility annual averages in WBAN. Notes: CASTNet, Clean Air Status and Trends Network; FRM, Federal Reference Method; IMPROVE, Interagency Monitoring of Protected Visual Environment; WBAN, Weather Bureau Army Navy.
Figure 3
Figure 3
Scatter plots of observed and predicted fine particulate matter (PM2.5) annual averages from the PM2.5 historical model using the Federal Reference Method/Interagency Monitoring of Protected Visual Environment (FRM/IMPROVE) PM2.5 trend across IMPROVE sites for 1990–1998.
Figure 4
Figure 4
Predicted fine particulate matter (PM2.5) annual averages in 1980, 1990, 2000, and 2010 from the 31-year PM2.5 model using the extrapolated temporal trend based on PM2.5 data for 1999–2010; Maps generated using model outputs discussed in the “Development of the PM2.5 model for 1980–2010” in “Methods” and boundaries for the year 2000 U.S. Census. Source: ArcUSA; U.S. Census; ESRI (Pop2010 fields); and ESRI, derived from Tele Atlas. Maps were created using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights reserved. For more information about Esri® software, please visit www.esri.com.
Figure 5
Figure 5
Scatter plots of predicted fine particulate matter (PM2.5) annual averages from the 31-year PM2.5 model using the extrapolated temporal trend based on PM2.5 data for 1999–2010 for 2000 versus long-term averages for 1980–2000 weighted by times of residence across home addresses of 5,086 participants who never moved during 1980–2000 and 2,466 Multi-Ethnic Study of Atherosclerosis (MESA)/MESA Air participants who moved at least once.
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