Lobdell et al. This approach can be applied at a much smaller geographical or urban scale if the necessary health, air pollution, demographic, and other explanatory data can be accessed or estimated at fine-scale spatial resolution. In the present study, we used both conventional sample-size– based approaches and the strategy presented by Pope et al. (2009) to assess the feasibility of conducting a study on the impact that cumula- tive air pollution reduction programs may have on environmental public health within a small geographic area, New Haven, Connecticut (USA). We then evaluated the strengths and limitations of these approaches in the context of urban scale assessments. We chose New Haven for this assessment because it was des- ignated as one of two Connecticut counties in nonattainment of the PM2.5 standard in 1997 [U.S. Environmental Protection Agency (EPA) 2010], and because a variety of air pol- lution reduction activities have subsequently been implemented at multiple jurisdiction lev- els by various federal, state, local, and volun- tary actions, including the CARE (Community Action for a Renewed Environment) program (U.S. EPA 2011). National and regional initia- tives have resulted in large reductions in ambi- ent nitrogen oxides (NOx) from mobile sources in New Haven, and the Northeast region also adopted more stringent vehicle emission stan- dards earlier than did other parts of the United States and had faster fleet turnover. In addi- tion, New Haven has implemented a number of voluntary air pollution reduction activities such as promoting smoke-free homes, use of ultra-low-sulfur diesel fuel, school bus retrofits, solvent reduction workshops, and Tools for Schools (City of New Haven 2004). Materials and Methods The New Haven Study Area is centered in the City of New Haven, Connecticut (population ~ 127,000), and extends to a 20-km radius, encompassing 318 census block groups in New Haven County with an estimated popu- lation in 2007 of more than 367,000 people. The City of New Haven is located on the southern coast of Connecticut on New Haven Harbor, which is fed by three rivers (the West, Mill, and Quinnipiac) that discharge into northern Long Island Sound. New Haven lies at the intersection of interstates I-91 and I-95, both major regional expressways that are often congested. In addition, several surface arteries pass through or around New Haven, includ- ing Routes 1, 10, 17, 34, and 63. Seaborne traffic passes through the Port of New Haven, a deep-water seaport that attracts a consider- able number of barges and associated truck and rail traffic. In addition to several insti- tutional power plants, one power generation facility serves the community. This wide range of emission source categories allows for testing of multipollutant emission control strategies. 488 We evaluated the overall feasibility of assessing the public health impact of air pol- lution reduction programs in the City of New Haven by linking projected emissions reduc- tions from overall regulatory actions to esti- mated detectable health outcome changes. We began by identifying pollutants of interest for New Haven based on the local emissions inventory for the baseline year of 2001 (Weil 2004) and criteria air pollutants. For the pres- ent study, we focused on two air pollutants: NOx and PM2.5. We also identified health out- comes that have been associated with these pollutants: cardiovascular disease hospitaliza- tion and mortality; respiratory disease hospi- talization and mortality; chronic obstructive pulmonary disease mortality and hospitaliza- tion; and asthma prevalence, diagnosis, and hospitalization. We then evaluated existing data on ambi- ent level air pollution, emission data, per- sonal exposure data, and health outcome data for the New Haven area. As part of this data inventory evaluation, we assessed the relevance and completeness of data, as well as verification of locations and quantities of emissions from local sources. We then gener- ated emission estimates for NOx and PM2.5 based on local emissions sources and the pro- jected impacts of federal, state, and local regu- latory reduction activities. We also applied an improved methodology to predict mobile source emissions (Cook et al. 2008). We first estimated pollutant specific local- scale air concentrations using the U.S. EPA’s AERMOD dispersion model (Cimorelli et al. 2005). This model used information on local emission sources and local meteorological con- ditions to provide hourly and annual average concentrations at multiple locations corre- sponding to the weighted centroids of each of the 318 census block groups in the study area. We estimated total NOx and PM2.5 concen- trations by combining regional background levels, chemically reactive pollutant estimates from the CMAQ (Community Multiscale Air Quality) model, and the AERMOD estimates. We estimated emissions using the baseline year (2001) emissions rates and projected emissions in 2010, 2020, and 2030 based on planned and anticipated pollution control programs. To assess feasibility using a sample size approach, we first determined the minimum detectable decrease in each outcome relative to its baseline incidence rate [tests of two inde- pendent proportions for a (one-sided) likeli- hood ratio chi-square test with an α of 0.05 and power of 0.80] for a study population of 367,173 (i.e., the 2007 Census estimate for the New Haven population within the 318 block groups included in the study area). For some of the health outcomes, we made additional study area subpopulation calculations for different age groups (< 18 years, ≥ 18 years). There is general consensus that RRs asso- ciated with air pollution exposure for a wide variety of health outcomes are typically less than 1.50, and usually within the range of 1.01–1.20, often for a 10-μg/m 3 change in PM2.5 or an interquartile range change in gas- eous pollutant concentrations. Jerrett et al. (2008) and Wellenius et al. (2005) found risk ratios or a RR in this range for NO2. RRs for PM2.5 and various outcomes in this range were found by Pope et al. (2002) and Sheppard et al. (1999), whereas Laden et al. (2006) found higher PM2.5 and mortality RRs of 1.16–1.28, and Peters et al. (2001) found an RR of 1.69 for PM2.5 and acute myocardial infarction. Next we determined the percent reduction in exposure that would be required to produce a given reduction in the outcome assuming a range of possible effect sizes for concentra- tion–outcome associations. Specifically, we considered air pollution RR values of 1.01, 1.05, 1.10, 1.15, and 1.20 representing the increase in outcome (y) associated with an incremental increase in a given pollutant exposure equal to the level of the average value of the ambient pollution concentration (c) in the study population. The change in the outcome (Δy) associated with a change in exposure (Δc) is a function of the baseline incidence rate (y) and the risk coefficient (β) for a one-unit increase in exposure: Δy = y[e βΔc – 1] [1] where β = [ln(RR)]/c. The percent decrease in exposure (Δcreq) required to produce a par- ticular reduction in the outcome for a given RR is calculated as Dcreq = -> ln^1 - D y yh ln^RRh H # 100 . [2] Values of Δcreq < 100 indicate the percent reduction in exposure that would be required to produce a specific reduction in the out- come (Δy) assuming a given RR for the expo- sure–outcome association. Values of Δcreq ≥ 100 indicate that the corresponding value for Δy is not feasible, because exposure would have to be reduced by more than 100% to achieve it. Finally, we combined data on projected changes in mean annual ambient concentra- tions of air pollutants for 2010, 2020, and 2030 with the information on minimum detectable effect estimates and the percent reduction in exposure required to produce a given effect estimate to identify which air pollutant–health outcome associations (out of 26 possible combinations) would be most feasible for assessment. For pollutants such as PM where projected reductions were relatively modest (~ 8%), we ovlume 119 | number 4 | April 2011 • Environmental Health Perspectives