## 4 Discussion
### Validity of Results  
Selection bias was minimized in this study, as it was a population-based study with a good response rate at 80.3%. Potential for selection bias from excluding mothers who did not receive any prenatal care was also minimal, as practically all pregnant women in Finland receive prenatal care [39]. Ideally, exposure assessment should be based on personal monitoring of pregnant women throughout the pregnancy, but this was not feasible in a large epidemiologic study with current methods. Misclassification of prenatal exposure to air pollution was minimized, as we were able to use maternal residential addresses throughout the pregnancy period to get an excellent coverage for the exposure assessment. In addition, the exposure assessment method used was independent of the outcome assessment, which reduced the likelihood of any major bias. Air pollution constitutes a complex mixture of pollutants, which may have strong correlations between each other especially when emitted from the same types of sources, such as traffic and heating. We present results from single-pollutant and two-pollutant models to quantify the independent effects of the main air pollutants. The potential problem of collinearity was reduced by fitting only one pollutant from each main source in the model, traffic (PM\({}_{2.5}\), NO\({}_{2}\), or CO), heating (PM\({}_{10}\) or SO\({}_{2}\)), and O\({}_{3}\) as a secondary pollutant. Both weight and gestational age of the infant, that are relevant for the outcome assessment, were based primarily on self-report. For accuracy, any information missing was retrieved from the baseline data collection at the Birth Clinic and the Finnish Medical Birth Registry. The weight and gestational age of any child was taken and recorded in the hospital immediately after delivery, and this was reported into the database of the Finnish Medical Birth Registry and marked on the card that each mother receives in Finland during their pregnancy. The gestational age was estimated using the last menstrual cycle in combination with the findings in the ultrasound examination. Low birth weight can be considered an indicator of disturbance in fetal growth and development that is caused by genetic factors and/or environmental exposures during pregnancy. The WHO has used a birth weight of less than 2500 g as an indicator of prematurity since 1948 [4]. This cut point was introduced in Finland by professor of pediatrics [PERSON] already in 1919, because gestational age-based measurements were considered inaccurate. He presented that babies with low birth weight should receive special care. There is evidence that, in populations with good nutritional status, the traditional cut point may not be a sufficiently sensitive indicator for identifying fetal growth disturbance and related health risks at birth or later in life. In a study of 1,372,092 singleton births inseven western countries including Finland, Sweden, Norway, Denmark, Scotland, the Netherlands, and Flanders in Belgium, the risk of perinatal death increased substantially with birth weights from 2500 g to 3000 g [40]. Our choice of birth weight less than 3000 g as a cut point for low birth weight in these populations is a more sensitive and thus better indicator of health risk than the traditional cut point of 2500 g. Therefore, we have presented the primary results using LBW\({}_{3000}\), but for comparison, we also conducted sensitivity analyses using LBW\({}_{2500}\) as the outcome. Our study has several strengths. First, we used GIS-based exposure assessment data to estimate the weather conditions and air pollutant concentrations for each participant mother. We were able to geocode all respondents' residential addresses using the home coordinates obtained from the Population Register Center over the study period, and we were able to use this information in the exposure assessment as it was utilized in the modelling.