Lobdell et al. were attenuated somewhat depending on the outcome chosen (data not shown). Discussion We used detailed information on local health and exposure-related data to assess the fea- sibility of identifying an impact of cumula- tive air pollution programs on environmental public health in New Haven for 26 different pollutant–health outcome linkages. Combined regional (CMAQ) and local-scale (AERMOD) air quality modeling analysis showed a small overall decrease for PM2.5 (~ 8–9%) in mean pollutant concentrations mostly from local sources and between 2001 and 2010; in con- trast, we projected that NOx would decrease by > 60%. Most NOx reductions can be attributed to mobile source emission reduction programs. Thus, it is important to accurately character- ize near-road impacts. Local reductions in PM2.5 are modest relative to high background PM concentrations. Statistical power calcula- tions suggest that projected decreases in NOx may result in statistically significant improve- ments in health outcomes, including all-cause mortality, asthma prevalence in children and adults, and cardiovascular and respiratory hos- pitalizations. For other pollutants with more modest reductions, including PM, we deter- mined the likelihood of performing a success- ful traditional air pollution reduction–health reduction analysis in New Haven to be poor. Alternative epidemiological study designs that use spatially and temporally resolved air quality and exposure models to characterize intraur- ban gradients were promising based on explor- atory epidemiological simulations. However, health outcomes with low baseline rates would have to be strongly associated with air pollu- tion exposures in order for exposure reduc- tions to result in identifiable improvements and thus would not be ideal for examining risk management decisions. This study illustrates the advantages of using air quality models over traditional epi- demiological approaches using ambient mea- surements. For example, central-site data are especially problematic for certain PM com- ponents and species (e.g., elemental carbon, organic carbon, coarse and ultrafine PM) that Table 3. Distribution of estimated changes in CHD and asthma hospitalizations among 318 New Haven census block groups, 2001–2010.a Outcomeb Mean ± SDc Interquartile range Range CHD hospitalizations for the population ≥ 65 years of age –0.52 ± 0.56 –0.77 to –0.13 –4.11 to 1.07 Asthma hospitalizations for all ages 0.23 ± 0.27 0.04 to 0.31 0.003 to 1.25 Asthma hospitalizations for the population < 24 years 0.13 ± 0.18 –0.003 to 0.22 –0.03 to 1.20 of age aHospitalizations in 2010 are represented by 2007 hospitalization data. bNumber of hospitalizations in 2010 minus number of hospitalizations in 2001 for each census block group. cNegative values indicate a decrease in the average number of hospitalizations across census block groups from 2001 to 2010; positive values, an increase. Table 4. Associations of predicted reductions in PM2.5 with changes in CHD hospitalizations and in asthma hospitalization among 30 New Haven census block groups with > 4-μg/m3 decrease in average PM2.5 concentrations, 2001–2010.a Outcome R 2 β (95% CI)b Change in CHD hospitalizations among those ≥ 65 years of age 0.08 –0.06 (–0.13 to 0.01)* Change in asthma hospitalization (all ages) 0.03 –0.04 (–0.11 to 0.02) Change in asthma hospitalization for children < 24 years of age 0.01 –0.02 (–0.05 to 0.01) CI, confidence interval. aHospitalizations in 2010 are represented by 2007 hospitalization data for 30 census block groups with a predicted decline in PM2.5 of 4 μg/m3 or more between 2001 and 2010. bLinear regression coefficient (95% confidence interval) for the association between hospitalizations and the decline in PM2.5 between 2001 and 2010. *p < 0.1. 1.4 10 1.2 0 tahm 2100– 01..08 sa ,2s 0.6 isn ion 0.4 e tza 0.2 g i hanC itlspa–00..02 ho–0.4 –0.6 0.2 100 0.0 2 inCHD ,001s2–––00..24 on–0.6 seg it an iza–0.8 hC ltia–1.0 p s ho–1.2 –1.4 492 2 4 6 8 10 12 14 Reduction in PM2.5, 2001–2010 (µg/m3) 2 4 6 8 10 12 14 Reduction in PM2.5, 2001–2010 (µg/m3) Figure 3. Changes in hospitalizations from 2001 to 2010 according to predicted reductions in PM2.5 concen- trations based on linear regression (with 95% confidence intervals) among 30 census block groups with a predicted PM2.5 reduction > 4 μg/m3. (A) CHD hospitalizations among those > 65 years of age. (B) Asthma hospitalizations (all ages). Dots represent observed data for individual census block groups. exhibit significant spatial heterogeneity. Also, for many pollutants (e.g., toxic pollutants), ambient monitoring data are often nonexis- tent or limited. Appropriately verified air qual- ity models, on the other hand, can provide the needed spatial and temporal resolution for multiple air pollutant concentrations at many locations. These same models can also be used to estimate the projected air quality and inputs for exposure models for future years, depen- dent on air pollution reduction activities, or due to the addition of new sources in a com- munity (Isakov et al. 2006). For example, this model can address what happens if emis- sions from some specific stationary or mobile sources are reduced by certain amounts and what the associated impacts of these local controls versus regional controls may be. This model application helps determine which control options are most effective in reducing ambient concentrations. Both the air quality modeling and feasi- bility analysis methodologies we used in this research have certain shortcomings. For instance, despite their advantages of being able to provide temporal (hourly) and spatial (at hundreds of locations) estimates, and having a long history of use by regulatory agencies in multipollutant mitigation strategies, mod- els have uncertainties due to model inputs, algorithms, and model parameters (Sax and Isakov 2003). Therefore, in order to reduce uncertainty due to model inputs, detailed emis- sions and meteorological information should be provided for each model application. In the simulation-based epidemiological feasibility analyses we considered only single-pollutant models and did not include ecological covari- ates (e.g., income, poverty status, smoking) typically used in cross-sectional, ecological analysis (Özkaynak and Thurston 1987; Pope et al. 2009), because of a lack of complete information. Moreover, it is possible that some of the covariates may change over time, but presumably this may be less of an issue in local- scale assessments than in national-scale analy- ses. We did not perform joint optimizations with NOx and PM, which could be used to examine more complicated alternative study designs such as census block groups with low reduction levels in NOx but intermediate to high reductions in PM. Clearly, accounting for multipollutant strategies in future assessments will be important in implementing enhanced air pollution–health outcome risk management studies (Mauderly et al. 2010). The linkages between air quality and expo- sure models (e.g., with the Stochastic Human Exposure and Dose Simulation Model and Hazardous Air Pollution Exposure Model) in the context of the New Haven study have been examined elsewhere (Isakov et al. 2009). Our biggest challenge has been with accessing geo- graphically and temporally resolved health data ovlume 119 | number 4 | April 2011 • Environmental Health Perspectives