hypoxia, in the venous cord blood of 41 new- borns (Ziaei et al. 2005). The levels of COHb may interfere with the oxygen levels in the fetus, possibly leading to fetal death, as demonstrated in an animal model (Singh and Scott 1984). Perera et al. (1999) found that ambient air pollution was significantly associated with the amount of polycyclic aromatic hydro- carbon (PAH) bound to DNA (PAH–DNA adducts) in both maternal and infant cord white blood cells. Previous studies have found associations between ambient air pollution and birth defects (Gilboa et al. 2005; Ritz et al. 2002). In our study, the effect of traf- fic was greater, albeit not significant, among women with SABs at ≤ 10 weeks than among those with later losses. This would be con- sistent with genetic damage as a mechanism, given that early SABs are more likely to have chromosomal abnormalities or birth defects (Byrne et al. 1985). The effect of air pollution on SAB may also be mediated by reproductive effects in the male (Slama et al. 2005). Studies have shown that exposure to air pollution is associated with decrements in several measures of semen quality (De Rosa et al. 2003; Selevan et al. 2000). The SAB rate is higher if a man has > 30% of sperm showing DNA fragmentation (Evenson and Wixon 2005). More than 90% of the women in this study were either mar- ried or living with a partner, so traffic expo- sure at the maternal residence may be serving as a proxy for paternal exposure to traffic. Effect modification. Our finding of a stron- ger effect of traffic exposure among African- American women than among women of other racial/ethnic groups is based on a relatively small sample size. However, it is possible that African-American women are more sensitive to the effects of air pollution than women of other ethnicities. A study of live births in the northeastern United States found that the asso- ciation between CO and term low birth weight was more consistent and stronger for African- American than for white or Hispanic infants (Maisonet et al. 2001). A study of women resid- ing in New York City found that PAHs had a significant adverse effect on birth weight and head circumference among African-American but not among Dominican women (Perera et al. 2003). Other unmeasured confounders such as nutrition may also account for increased susceptibility to air pollution among African Americans, as they had lower mean intakes of several vitamins than other groups in the Third National Health and Nutrition Examination Survey (Arab et al. 2003). Kannan et al. (2006) postulated several mechanisms by which nutri- tion could modify the effect of particulate mat- ter on birth outcomes. Regarding the effect modification by smoking, Samoli et al. (2003) found that the effect of NO2 on mortality was greater in cities where the smoking prevalence was lower. In a cohort study of the effect of air pollution on lung development, Gauderman et al. (2004) found that air pollutants had no effect on growth of lung function among those who had ever smoked. Furthermore, the reduction in lung function growth asso- ciated with living within 500 m of a free- way was greater in nonsmokers than smokers (Gauderman et al. 2007). Like these studies, we found an increased risk in nonsmokers, which may be attributable, in addition to the explanations offered by other researchers, to chance or unmeasured confounders. We have no explanation for the protective effect seen in smokers, perhaps because of unmeasured confounders or chance. Limitations. Some women may have had SABs before they could be interviewed, and they would not be included in the cohort. This would lead to left truncation of the data, which could bias the study results based on logistic regression if the exposure was related to the week of entry into the cohort (Howards et al. 2007). In our study, the average number of weeks of gestation at interview did not dif- fer for those women with high versus low traffic exposure (8.08 weeks and 8.02 weeks, respectively). However, bias toward the may still have occurred if women with very Stratum Race/ethnicity African American Hispanic Asian Non-Hispanic white Smoking status Nonsmokers at time of LMP and interview Smokers, either at LMP or interview Percentile of traffic exposure 0–74th 75–89th ≥ 90th 0–74th 75–89th ≥ 90th 0–74th 75–89th ≥ 90th 0–74th 75–89th ≥ 90th 0–74th 75–89th ≥ 90th 0–74th 75–89th ≥ 90th No. 314 925 461 3,275 4,056 907 Traffic exposure and spontaneous abortion high traffic exposure had SABs that were so early as to not be detected. Conversely, if women with low traffic exposure were more likely to have undetected SABs, that could lead to a bias of finding an association. The GIS methods we used have some error in assigning exposure because the street line work layer used to geocode the residential addresses did not align perfectly with the street line work from CalTrans, which contains the traf- fic volume data. A recent analysis of traffic exposure at child care facilities in Los Angeles County suggested that errors resulting from unreconciled data produced a modest bias in terms of aggregated number of facilities at risk (Ong et al. 2006). We assumed that these misalignment errors would be nondifferential with respect to pregnancy outcome. Although we were able to construct a vari- ety of traffic metrics, there were some limita- tions to the traffic database we used in this study. For example, we were unable to con- struct a metric for traffic density (sum of traf- fic on all roads within a buffer) because the CalTrans AADT data is reported for road segments, not complete roads. Therefore, if we had summed the traffic on all segments within a specified buffer, we would have over- counted traffic on some streets that comprised multiple segments. However, in a study of AORb after traffic scaled using NO2 0–29 days after LMP (95% CI) AORb after traffic scaled using NO2 30–59 days after LMP (95% CI) AORb (95% CI) 1 0.92 (0.31–2.74) 3.11 (1.26–7.66) 1 1.02 (0.53–1.98) 0.87 (0.40–1.91) 1 0.98 (0.39–2.47) 0.73 (0.27–1.94) 1 0.86 (0.61–1.25) 1.16 (0.79–1.71) 1 1.09 (0.80–1.49) 1.47 (1.07–2.04) 1 0.41 (0.19–0.86) 0.36 (0.14–0.93) 1 1.06 (0.38–2.95) 3.74 (1.53–9.12) 1 1.11 (0.59–2.12) 0.80 (0.33–1.93) 1 0.90 (0.33–2.42) 0.91 (0.34–2.48) 1 1.08 (0.67–1.52) 1.04 (0.69–1.57) 1 1.26 (0.93–1.72) 1.49 (1.06–2.10) 1 0.43 (0.20–0.93) 0.35 (0.14–0.89) Table 4. Effects of traffic exposurea on SAB (using maximum annual daily traffic within 50 m), stratified by race and smoking exposure. 1 0.82 (0.26–2.57) 3.32 (1.30–8.49) 1 1.22 (0.65–2.29) 0.50 (0.18–1.44) 1 0.97 (0.36–2.64) 0.91 (0.34–2.48) 1 1.00 (0.70–1.44) 1.19 (0.80–1.77) 1 1.24 (0.91–1.70) 1.51 (1.07–2.13) 1 0.44 (0.21–0.94) 0.36 (0.14–0.92) 1943 aPercentiles of traffic exposure based on entire study population and use same cut points as those listed in Table 3 for maximum traffic within 50 m. bAdjusted for SES, stressful life events (0–1 vs. ≥ 2 in the preceding 6 months), employment status, maternal age, maternal smoking (at LMP or since), and/or race/ethnicity (as appropriate). Environmental Health Perspectives • volume 117 | number 12 | December 2009