Berman et al. and morbidity) from changes in population exposure to air pollution. A log-linear function contains four components and has the basic form of Δy = y0(eβΔx – 1)Z, where y0 is the baseline incidence rate of the health outcome, β is the coefficient of associa- tion between ozone concentration and health outcome (i.e., the C-R function based on epi- demiological research), Δx is the estimated air pollution change (i.e., the difference between the current interpolated ozone level and the interpolated ozone level assuming attainment of the NAAQS), Z is the size of the exposed population, and Δy is the estimated change in the health outcomes due to the change in ozone exposure. This approach assumes a log- linear C-R function across the entire range of possible ozone concentrations, such that effects may be observed below NAAQS levels (Bell et al. 2006). We used morbidity and mortality C-R functions reported for daily 8-hr maximum ozone exposures, or adjusted the reported C-R functions to be consistent with daily 8-hr maximum exposures using national conversion factors (e.g., if based on 24-hr mean or 1-hr maximum ozone concentrations) (Thurston and Ito 2001). In addition, we used C-R func- tions derived using warm-season data (May– September). Using multicity mortality studies provided better control for the confounding effects of particulate matter and gaseous pollut- ants, including carbon monoxide (CO), nitro- gen dioxide (NO2), and sulfur dioxide (SO2) (Bell et al. 2004; Thurston and Ito 2001). U.S. Census data for 2000 were aggregated from census blocks to a 12 × 12-km grid and extrapolated to estimate population sizes in 2005, 2006, and 2007 using growth factors. [For estimated population sizes used for each outcome and year, see Supplemental Material Table S1 (national estimates) and Table S2 (MSAs used to derive regional estimates) (http://dx.doi.org/10.1289/ehp.1104851).] Morbidity incidence rates are U.S. EPA esti- mates from national and local data sources, including the Centers for Disease Control and Prevention (Atlanta, GA), National Center for Health Statistics (Hyattsville, MD), Health Care Cost and Utilization Project (Rockville, MD), National Center for Education Statistics (Washington, DC), and individual studies (U.S. EPA 2010a). We estimated exposures at the grid level and assumed that all individuals within a grid cell experienced the same changes 2007 45.51 6.93 1,183 in exposure levels. We estimated changes in health outcomes at the county level according to the fraction of population-level grids that fell within county boundaries. Because C-R functions for morbidities were derived from fewer cities and smaller data-year sample sizes, we pooled risk esti- mates for the same outcome across studies using a random-effects weighting procedure to derive a single C-R function. Specifically, we used a two-stage inverse variance weighting approach to account for both between-study variability and within-study individual effect estimates that minimize uncertainties based on spatial and temporal data availability (Hubbell et al. 2005). For respiratory-related ED vis- its, pooling increases the data by combining studies limited to younger individuals (Jaffe et al. 2003) with studies that cover all ages (Peel et al. 2005; Wilson et al. 2005). We used Monte Carlo simulation from the pooled risk estimates to generate 90% confidence intervals based on the C-R function variance. Data reporting and software. Expected changes in ozone concentrations from meeting the regulatory standards were mapped and used to estimate avoided morbidities and mortalities for the United States (excluding Alaska and Hawaii). In addition, we estimated reductions in nonaccidental mortality for the 15 most populous MSAs using the C-R function derived by Bell at al. (2004). The HIA was performed using the U.S. EPA’s publicly available BenMAP software (version 4.0.43; U.S. EPA 2012b). We created maps using ESRI® ArcMAP™ software (ver- sion 10.0; ESRI, Redlands, CA) and summa- rized results with the R Statistical Computing Table 2. Summary of 8-hr maximum ozone measurements (ppb) averaged across eligible AQS monitors. Summary value Mean Minimum Maximum SD Count 2005 45.13 6.35 1,170 Counties indicate locations of minimum and maximum observed values. 2006 44.90 6.07 1,168 20.31 (Alameda, CA) 62.11 (Maricopa, AZ) 26.23 (Sonoma, CA) 65.57 (El Dorado, CA) 24.70 (King, WA) 68.94 (San Bernardino, CA) 75-ppb standard 2005 ozone data 75-ppb standard 2006 ozone data 75-ppb standard 2007 ozone data 70-ppb standard 70-ppb standard 60-ppb standard 60-ppb standard Ozone exceedance (ppb) 0.00 0.00–2.00 2.01–4.00 4.01–6.00 6.01–9.00 9.01–14.00 14.01–23.63 70-ppb standard 60-ppb standard Figure 1. Estimated reductions in annual ozone (8-hr maximum) if regulatory attainments of 75, 70, and 60 ppb had been achieved (2005–2007). 1406 ovlume 120 | number 10 | October 2012 • Environmental Health Perspectives