## Materials and Methods
### Statistical Methods  
We conducted multivariate Poisson regression analysis to obtain adjusted risk ratios (RR) with their 95% confidence intervals (95% CI) as the measure of effect of exposure to air pollution during the entire pregnancy on the risk of LBW. We compared the risk of LBW among babies with the highest quartile (Q4) of exposure to the reference categories of exposure, i.e., those with exposure in the lower exposure quartiles (Q1-Q3). We fitted both single- and two-pollutant models.  
We studied both the independent and joint effects of different air pollutants during the entire pregnancy on the risk of LBW. Potential interaction was estimated on an additive scale because it has the most relevance for public health [37]. The statistical methods for estimating joint effects have been described in detail in our previous article [15]. In short, for example, we compared the risk of LBW in three different exposure categories, defined as (1) high CO and low O\({}_{3}\) (A), (2) low CO and high O\({}_{3}\) (B), and (3) high CO and high O\({}_{3}\) (AB), and these were compared to the reference category of \"low CO and low O\({}_{3}\)\" exposure. Here, high and low refer to the exposure levels that were above or below the Q4 value, respectively. Similar categorization was carried out for the analysis of the joint effect between PM\({}_{2.5}\) and O\({}_{3}\). Estimates for the independent and joint effects were derived from the modified Poisson regression analysis by fitting both crude and adjusted models [38]. We assessed the excess relative risk (ERR) for the independent and joint effects of the air pollutants of interest. The relative risk due to interaction (RERI) was quantified on an additive scale by calculating the risk that is more than expected based on summing the independent effects related to these exposures. This can be expressed in terms of ERRs as follows:  
\[\text{RERI}=\text{ERR (AB)}-\text{ERR (A)}-\text{ERR (B)}\]  
We estimated the 95% CI for RERI using the method of variance estimates recovery [38]. For RERI, the null value corresponded to a statistical significance level of \(p=0.05\). Applying a similar procedure, we also studied the independent and joint effects of other pollutant exposures and their combinations with O\({}_{3}\) (i.e., between PM\({}_{2.5}\) and O\({}_{3}\) and between CO and O\({}_{3}\)) on the risk of LBW. For the joint-effects analyses, we chose those pollutants that were not strongly correlated with each other but that showed the highest increases in effect estimates in the multipollutant models  
To examine the potential modifying effect by season, we performed analyses stratified by two birth seasons, i.e., warm season and cold season. The warm season included spring and summer (March to August) and the cold season included autumn and winter (September to February).  
We have conducted several sensitivity analyses (i) by applying the air pollution data as continuous variables, (ii) by providing the effect estimates per a 10-unit increase in each pollutant, and (iii) by comparing the air pollution levels below and above the median values.  
Analyses were carried out using PROC GENMOD procedure in SAS 9.4 statistical software (SAS Institute Inc., Cary, NC, USA).