Quebec, was associated with exposure to intraurban concentrations of nitrogen dioxide (NO2), a marker for traffic-related pollution. Materials and Methods Montreal is the second largest city in Canada, with the greater Montreal area having a pop- ulation > 3.6 million people. Our study is restricted to the Island of Montreal and Nun’s Island, which have a population of approxi- mately 1.8 million people (Statistics Canada 2006). Design of the case–control study. We con- ducted a hospital-based case–control study of incident, invasive cases of postmenopausal breast cancer (Labrèche et al. 2003, 2010). The target population comprised postmeno- pausal women, 50–75 years of age at the time of diagnosis, who in 1996 and 1997 were resi- dents of the greater Montreal area. Eligible case subjects were diagnosed with primary, invasive breast cancer [International Classification of Diseases, 9th Revision, code 174 (World Health Organization 1975)] that was confirmed histo- logically. Cases were identified from all 18 hos- pitals in the region that treated breast cancer, thus ensuring almost complete coverage of the target population. To minimize the potential for recall bias, control subjects had one of 32 other selected sites of incident, histologically confirmed cancers. The controls were matched to the cases by hospital and approximately frequency matched by age. The data were col- lected originally for a study examining risk of breast cancer associated with occupational exposures to selected substances; thus, selected sites of cancer (i.e., liver and intrahepatic bile duct, pancreas, lung, bronchus and trachea, brain and central nervous system, leukemias, and lymphomas) were excluded because of their possible association with occupational exposures. The controls were approximately frequency matched to the cases by age. At 1–3 months after diagnosis, participants completed a structured questionnaire with con- tent related to occupational history and other personal risk factors, including reproductive history, educational attainment, family history of breast cancer, age at menarche, smoking and alcohol consumption, body mass index, and home address (and duration of residence at that address) at time of diagnosis. Proxy respondents, who were close family members, completed a total of 75 of the questionnaires. Ethics committees at all participating hospitals and affiliated universities approved the proto- col, and signed informed consent was obtained from participating subjects. Occupational exposures were estimated using a standard methodology (Gérin et al. 1985; Siemiatycki 1990). Briefly, interviewers used a structured set of questionnaires and probed for details regarding each occupation that the subject had ever had, and a team of industrial hygienists and chemists attributed exposure to about 300 substances. For each substance, the team coded physical aspect, average duration of exposure in a working day, percentage of working days exposed during the period, confidence that there was actual expo- sure to each agent using a 4-point ordinal scale (probably no exposure, and “low,” “medium,” and “high” confidence of exposure), and level of intensity. Occupational exposures to four agents that may be associated with breast cancer, from the results of other analyses of our study (Labrèche et al. 2003, 2010), were included in the present analysis: organic sol- vents with reactive metabolites, extremely low- frequency magnetic fields, carbon monoxide, and PAHs from petroleum. Exposure indices were computed for exposures before 36 years of age (the period during which breast tis- sue may be more susceptible to exogenous insults), because female breast cells continue to develop until that age (as described in detail by Labrèche and Goldberg 1997). Neighborhood deprivation may be a con- founding factor in the association between breast cancer and air pollution because deprived populations often live in areas that are characterized by higher concentrations of air pollution (Crouse et al. 2009b; Jerrett et al. 2001). Thus, census data from 1996 were aggregated to the census tract level to describe socioeconomic characteristics of Montreal’s neighborhoods for assigning indicators of deprivation to subjects (Statistics Canada 2006). Variables describing median household income and percentage of adults who did not complete high school were compiled for the 350 census tracts that included addresses of subjects (heretofore referred to as neighbor- hood ecologic covariates). Assessment of exposure to traffic-related air pollution. A dense sampling of ambient NO2 was conducted in 2005 and 2006 (Crouse et al. 2009a, 2009b). NO2 is recognized as a marker of traffic-related pollution because of its collocational association with other pollut- ants (Beckerman et al. 2008). The locations for the samplers were selected using a location- allocation model that placed samplers in areas likely to have high spatial variability in traffic- related pollution and high population densi- ties (Kanaroglou et al. 2005). Samplers were deployed at 133 locations across the Island of Montreal on three occasions (spring, summer, winter) for 2-week periods each time. The devices were Ogawa passive diffusion samplers (Ogawa and Co., Pompano Beach, FL, USA) that make use of triethanolamine-impregnated filters as an absorbent. Valid observations at 129 locations were obtained from all three sampling periods. We used these observations to develop a land-use regression model to predict concen- trations of mean annual NO2 for 2005–2006, Environmental Health Perspectives • volume 118 | number 11 | November 2010 Breast cancer and air pollution in Montreal at a resolution of 5 m across the island (Crouse et al. 2009a). We modeled the natu- ral logarithm of NO2 on land-use and traffic- related variables to generate an exposure surface. The model explained 80% of the variability in concentrations of NO2. The res- idential addresses of cases and controls ascer- tained at the time of diagnosis (i.e., 1996) were linked to the exposure surface. Ninety- eight percent of subjects were geocoded to the x- and y-coordinates of their home address, and 2% were geocoded to the centroid of the area represented by the six-character postal code, which in Montreal refers usually to a block face or to a large apartment complex. Historical estimates of exposure. To account for the possibility that the spatial patterns have changed over time, we devel- oped two separate but related methods (Chen et al. 2009, 2010) to extrapolate our exposure surface back to 1996 and to 1985 (i.e., approximately the time of diagnosis and 10 years before this, respectively) using measurements of NO2 from Environment Canada’s National Air Pollution Surveillance network [see Supplemental Material, Figure 1 (doi:10.1289/ehp.1002221)]. The network in Montreal included 13 fixed-site stations that were used to meas- ure hourly concentrations of several criteria pollutants. Our goal was to use the spatial patterns of concentrations of NO2 collected at these stations to adjust our land-use regres- sion surface to reflect the spatial patterns of the past. Because of incomplete information, however, observations of annual mean con- centrations of NO2 were available at only nine stations for both 2006 and 1985 and at only 10 stations for both 2006 and 1996. We used inverse distance weighting on the mean annual concentrations at these stations, in each year, to interpolate spatial surfaces. Two approaches were used. First, we divided the interpolated surface describing concentrations of NO2 in 1985 by the interpolated surface for 2006. We then multiplied our original land-use regression model by this ratio to produce an extrapolated surface of estimated annual mean concentrations of NO2 for 1985. This process was repeated with the data for 1996. Second, we used the predicted val- ues from our land-use regression surface in 2006 at the locations of the stations for which there were observations in 1985 (n = 10) and in 1996 (n = 12), respectively, and used these data to create new interpolated surfaces. Similar to the first method, we divided the new interpolated surface of the observed con- centrations of NO2 in 1985 by the interpo- lated surface that made use of predicted values (from the land-use regression of 2006), which was then multiplied by the land-use regres- sion surface of 2006. Again, the process was repeated with the data for 1996. 1579