A total of 657 women were enrolled in the study between June 2004 and July 2006. This sample was representative of the target popu- lation in terms of women’s attendance at pre- natal care in the public health system (used by 85% of the pregnant women in Sabadell), but the educational level of our sample was higher than the target population average. From the initial sample, we followed 619 (94%) women until the child’s birth. We excluded 44 chil- dren from the analysis because their mothers did not live in Sabadell during pregnancy but in nearby cities covered by the health service of the municipal hospital. We also excluded three children with no recorded birth weight and two with gestational duration of 28 and 32 weeks, respectively, because of missing data in the covariates obtained in the 32nd week interview. Finally, 570 (87%) children were included in the analysis. Air pollution exposure model. We used LUR modeling in the study area to estimate individual exposure to nitrogen dioxide and BTEX (benzene, toluene, ethylbenzene, m/p-xylene, and o-xylene) as markers of motor vehicle exhaust pollution. A complete descrip- tion of the methodology on exposure model- ing has been reported previously (Aguilera et al. 2008). Briefly, we measured NO2 and BTEX with passive samplers in four and three sampling campaigns of 1 week, respectively, between April 2005 and March 2006, con- ducted simultaneously at 57 sampling sites (29 urban background and 28 traffic sites) repre- senting the gradient of exposure in the study population. For each pollutant, we calculated average concentrations of all the sampling campaigns, assuming that they are representa- tive of annual mean levels of NO2 and BTEX (Lebret et al. 2000), and fitted linear regres- sion models using five groups of geographic data (land coverage, topography, population density, roads, and distance to local sources of pollution) as predictor variables. Geographic variables were stored and derived in ArcGIS, version 9.1 (ESRI, Redlands, CA, USA). The final model for NO2 (R2 = 0.75) included alti- tude, road type (major, secondary, or minor road), and a land cover factor within a 500-m buffer as predictor variables. Geographic vari- ables included in the final BTEX model (R2 = 0.74) were altitude and three source-proximity variables (distance to nearest major road, sec- ondary road, and parking lot). We used two cross-validation procedures to evaluate the precision of the regression models. We then applied models to predict outdoor air pollution levels at the cohort addresses. For women who changed their home address during pregnancy (n = 25, 4%), exposure was calculated using the estimated concentrations at both the old and new home address, weighted by the percentage of the pregnancy period spent in each of them. We adjusted models for temporal vari- ations to calculate term-specific individual exposures, as it has been done in other stud- ies relying on LUR exposure models (Brauer et al. 2008; Slama et al. 2007). To obtain an average exposure for the whole pregnancy period and for each trimester, we used tem- poral variations of air pollution measured in the only fixed monitoring station operating in Sabadell. The station is located on a traffic island in the middle of a main road, in a rela- tively open area with unobstructed air flow. Daily measurements of air pollution con- ducted simultaneously in this traffic site and in an urban background location during 1 month showed similar temporal variations in NO2 levels between the two sites, with a cor- relation coefficient of 0.96 (Rivas-Lara 2008). We averaged daily means of NO2 measured at the fixed station over the pregnancy period for each woman. The resulting value was divided by the average NO2 concentration corresponding to the whole sampling period (from April 2005 to March 2006) and mul- tiplied by the predicted value obtained in the LUR model. We applied the same procedure to estimate trimester-specific exposures for each woman. We defined the first trimester of pregnancy as weeks 1–13, the second trimes- ter as weeks 14–26, and the third trimester as the period from week 27 until birth. Regarding BTEX, the fixed monitoring station measures daily mean levels of benzene and toluene, but the high percentage of miss- ing data (65% of the sampling period) did not allow us to “seasonalize” the BTEX model with them. Because NO2 showed higher cor- relation with benzene and toluene in the fixed monitoring station than with other traffic- related pollutants such as carbon monoxide or particulate matter (PM), and given the high correlation between NO2 and BTEX levels measured with the passive samplers (r = 0.80 for the whole sampling period), we used NO2 daily levels to make the temporal adjustment, assuming that the temporal variations in both pollutants were similar. Birth weight and gestational age. Birth weight was recorded by specially trained mid- wives at delivery. We calculated gestational age from the date of the last menstrual period (LMP) reported at recruitment and confirmed using estimates based on ultrasound examina- tion in the 12th week of gestation. When the difference between the LMP reported at recruitment and estimated from the ultra- sound was ≥ 7 days (n = 91; 16%), we esti- mated LMP using a quadratic regression formula defined by Westerway et al. (2000). Statistical analysis. We examined the association between birth weight and prena- tal exposure to NO2 and BTEX by simple and multiple linear regression models. Other reproductive outcomes such as LBW or small Environmental Health Perspectives • volume 117 | number 8 | August 2009 Traffic-related air pollution and birth weight for gestational age were not considered for analysis because of the relatively low sample size. Given the high correlation among the five BTEX compounds (r > 0.75) and because the relative fetal toxicity of each of them is not well known (Agency for Toxic Substances and Disease Registry 2004), we used the LUR estimate of the sum of the five compounds to assess the relationship between BTEX expo- sure and birth weight. We chose covariates included in the anal- ysis based on previous knowledge on their influence on birth weight. We collected some through questionnaire in the two interviews carried out during pregnancy for each woman: Maternal age, maternal education, maternal ethnicity, parity, maternal height and prepreg- nancy weight, and paternal height and weight were obtained in the 12th-week interview; tobacco use and passive smoking information were collected in the 32nd-week interview. Birth date and child sex were collected from the child’s neonatal anthropometry record filled in by the midwives. We calculated sea- son of conception using date of LMP. Because season of birth is influenced by the duration of pregnancy, we used season of conception in the analysis rather than season of birth. With linear regression models, we esti- mated the change in birth weight for an interquartile range (IQR) increase in NO2 and BTEX exposure (micrograms per cubic meter), for each trimester and for the entire pregnancy. We retained as adjustment fac- tors only those covariates that modified the association between air pollution and birth weight by > 10%. Because fetal weight gain per week is not constant throughout preg- nancy, we examined the association between birth weight and gestational age by using frac- tional polynomial models to identify the best- fit transformation of gestational age and allow polynomial terms for gestational age in the linear regression models (Blair et al. 2005). We performed a sensitivity analysis consid- ering time–activity patterns during pregnancy. In the 32nd-week interview, women answered the following question for a typical weekday and for a typical weekend: “Since you have gotten pregnant, how much time have you typically spent daily in these environments?” The answer options were (a) home indoors, (b) work indoors, (c) in other people’s houses, (d) in other indoor environments, (e) home outdoors, (f) work outdoors, (g) in other outdoor environments, and (h) in means of transportation. The question was designed to obtain a 24-hr sum. We weighted the data to account for weekdays (5 of 7) and weekends (2 of 7) and then calculated time spent at home (answers a + e) and time spent in non- residential outdoor environments (answers f + g). We used the median (rounded to the nearest whole number) as a cutoff value to 1323