*2.1. Study Area*

The study area is the city of Paris which counts about 2,250,000 inhabitants. We used the smallest census unit area whose aggregate data can be used on a routine basis: the Ilots Regroupés pour l'Information Statistique (IRIS: the French acronym for 'blocks for incorporating statistical information'). The IRIs is a sub-municipal French census block defined by the National Institute of Statistics and Economic Studies (INSEE). This statistical unit averages 2000 inhabitants and is constructed to be as homogenous as possible in terms of socioeconomic and demographic characteristics and land use. Paris is subdivided into 992 census blocks with a mean population of about 2199 inhabitants and a mean area of 0.11 km2.

### *2.2. Health Data: Preterm Birth*

The preterm birth case has be defined according to the definition of World Health Organization (WHO) [42,43]: it corresponds to a neonate born before 37 weeks of pregnancy (gestational age ≤36 weeks). The preterm birth cases were identified from the first birth certificate information registered over the period 2008–2011 by the Maternal and Child Care department of Paris (named PMI, for Protection Maternelle et Infantile). This certificate is completed by parents and health professional before exit of the maternity and then sent to the PMI unit of the department of residence.

All the postal addresses of mothers' residency were geocoded at the census block level. For confidential concerns, to be in agreemen<sup>t</sup> with the ethical authorization provided for this study, it was not possible to keep individual localization of the newborn. The number of cases was aggregated at census blocks level for the statistical analysis.

### *2.3. Air pollution: Nitrogen Dioxide (NO2)*

Annual NO2 concentrations were modelled from a grid of 25 × 25 m resolution throughout the study period (2008–2011) by the local air quality monitoring networks corresponding to the Ile de France region (AirParif: http://www.airparif.asso.fr/). The ESMERALDA inter-regional platform for air quality mapping and forecasting (www.esmeralda-web.fr) provided background pollution data, while the STREET dispersion model [44] was used for traffic-related pollution.

AirParif used a deterministic model which integrates various input parameters including linear (main roads), surface (diffuse road sources, residential and tertiary emissions) or industrial point sources and meteorological data (temperature, wind speed and direction, relative humidity, barometric pressure). More than 200 points sources were selected from the regional emission inventory. Emissions for traffic roads were estimated using the regional traffic network and the COPERT III European database for the 2002–2006 period, and COPERT IV for the 2007–2012 period. Concerning meteorological data, the Mesoscale Meteorological model (MM5: www.mmm.ucar.edu/mm5) developed by the Division of the NCAR Earth System Laboratory (NESL) was used. The NO2 background concentrations were determined by combining monitored NO2 concentrations from monitoring stations and those modeled at a regional scale from the ESMERALDA. The NO2 road traffic concentrations estimated from the STREET software model were added to NO2 background concentrations. Air pollutant concentrations were then aggregated at census block scale in order to obtain annual mean NO2 concentration for each census block (for more detail see Kihal-Talantikite et al. [45]; Deguen et al. [46]) over the study period.

NO2 pollutant was chosen for several reasons: while data of PM10, PM2.5 and NO2 were available at the time of the study, we privileged the NO2 because this pollutant is recognized to be a good tracer of traffic and other combustion sources (major problems in Paris) [47]. It is also well known that the spatial variability of NO2 concentrations is higher than that of particulate matter [48]; a crucial point especially for spatial analysis; previous studies already revealed that exposure to NO2 may be related to adverse birth outcomes [11,12,49–51]. NO2 has been shown to be the best available indicators of local traffic emissions [48]. Finally, the correlation coefficients between NO2 and, PM10 and PM2.5 are very high: *r* = 0.95 and 0.93, respectively (see Figure S1 and Table S1 in Supplementary Materials).
