Estimation of personal NO2 exposure in a cohort of pregnant women
Introduction
Exposure to air pollution during pregnancy could be associated with sub-optimal foetal development (Bobak, 2000, Maisonet et al., 2001, Chen et al., 2002, Gouveia et al., 2004, Mannes et al., 2005, Salam et al., 2005) and adverse effects on pregnancy (Liu et al., 2003, Maisonet et al., 2004, Lacasaña et al., 2005, Srám et al., 2005, Leem et al., 2006, Ritz et al., 2006, Ritz et al., 2007). From an epidemiological point of view, the development of models that enable exposure to be evaluated more accurately has become a priority for future research (Lacasaña et al., 2005, Ritz and Wilhelm, 2008, Slama et al., 2008).
Individual exposure assignment based exclusively on data from the monitoring networks tends to be inaccurate, since the number of sampled locations is usually small and often biased towards specific sources of pollution (i.e. traffic, background, industry). In this context, the geographic information systems (GIS), capable of providing, managing and displaying spatial data have been shown to be a powerful tool for evaluating exposure to air pollution (Vine et al., 1997, Bellander et al., 2001, Briggs, 2005, Briggs, 2007).
The so called land use regression (LUR) has become a popular analysis technique to use when a GIS is available (Henderson et al., 2007, Aguilera et al., 2008, Nethery et al., 2008, Wheeler et al., 2008). This technique uses regression to map air pollution using geographical variables as predictors: land use, traffic intensity, population, etc (Briggs et al., 1997, Brauer et al., 2003). Obviously, the efficiency of LUR directly depends on the quality of available geographical information: if useful information is not available throughout the study area or the accuracy of the information is not consistent, LUR will probably not give good results (Diem and Comrie, 2002). This may easily happen in areas with heterogeneous typology or different administrative organization.
Spatial interpolation models like kriging have also been widely used to map air pollution (Finkelstein et al., 2003, Leem et al., 2006, Liao et al., 2006). The advantage of kriging is that it provides the best linear unbiased estimation (BLUE) and its standard error, enabling the uncertainty of the prediction to be quantified. However, mapping air pollution levels using spatial interpolation requires a network of sampling points that is dense enough to capture the spatial variability pattern. In urban areas, where marked variations can occur over very short distances, it may not be feasible to have a sufficient number of sampling points.
In some special cases where LUR and kriging fail when used separately, a combination of the two could result in a simple and realistic approach.
Whatever the method used for mapping outdoor air pollution, individual exposure is usually assigned as the estimated ambient level at the address coordinates of the residence (Bellander et al., 2001, Morgenstern et al., 2007). In order to achieve a precise individual exposure assignment, it is important to have information on time-activity patterns, thus several microenvironments, such as the work place, could be considered to create an integrated personal exposure estimate (Nethery et al., 2008, Slama et al., 2008).
Currently, there is a lack of toxicological information helping to select relevant exposure windows during pregnancy for most birth outcomes and although there are several epidemiologic studies suggesting specific periods for specific outcomes (Glinianaia et al., 2004, Slama et al., 2007), the results are not consistent. Characterizing the exposure during windows narrower than the entire pregnancy should help to identify critical exposure windows during pregnancy and also determine whether the effects are cumulative. For many birth outcomes, an appropriate scale may be months or trimesters (Ritz and Wilhelm, 2008).
The INMA project (environment and childhood) is a collaborative research network of seven prospective birth cohort studies in different areas of Spain, designed to explore the relationship between environmental exposures and child's health. (Ribas-Fitó et al., 2006). NO2 was chosen to be studied in INMA because it is a good marker of pollution from traffic and accurate determinations may be made at low cost (Esplugues et al., 2007).
The goal of this study is to estimate the personal air pollution exposure of women in the INMA cohort in Valencia throughout their whole pregnancy and during each trimester from ambient levels of NO2 (mapped using kriging and LUR) and time-activity information.
Section snippets
Study population
The cohort of INMA-Valencia is formed by 855 pregnant women who were recruited at the same hospital between February 2004 and June 2005. Participants had to meet several inclusion criteria and they all gave their written informed consent (Ramón et al., 2005). Thirty women were excluded from this sub-study due to problems in registering their postal address. The study area covered the home addresses of all participants. Approximately 9% lived in a typically urban zone (city), 50% lived in a
Results
All NO2 values were above the detection limit (1 μg/m3). A statistical summary of NO2 levels at the sampling sites is presented in Table 1. The lowest concentrations were found in June and the highest in November. By zone type, there was a positive gradient from rural to urban zones in all the sampling periods.
Kriging maps of NO2 showed a decreasing trend from urban to rural zones and the great influence of a highway crossing the area. Accuracy indicators (Table 2) showed that the predictions
Discussion
The aim of characterizing personal exposure to air pollution during pregnancy was fulfilled for the INMA cohort in Valencia. Individual exposure to NO2 in each trimester was evaluated, enabling the periods of greatest susceptibility during pregnancy to be identified (Ritz and Wilhelm, 2008, Slama et al., 2008).
In environmental epidemiology, an essential element is to ensure a sufficiently wide range of exposures (Navidi et al., 1999). In this respect, the Valencia cohort, distributed over an
Acknowledgements
We would like to thank other members of the INMA study group in Valencia involved in this sub-study: M Jose López-Espinosa, Maria Andreu, Amparo Cases, Virginia Fuentes, Francisco Garcia, Ana M García, M Carmen Gonzalez, Marina Lacasaña, Gemma Leon, ppppAlfredo Marco, Maria Monzonís, Alicia Moreno, Mario Murcia, Sandra Pérez, Amparo Quiles, Rosa Ramón, Marisa Rebagliato, M Paz Rodriguez, Elena Romero, Jesus Vioque. This study has been supported by “Fondo de Investigaciones Sanitarias”, ISCIII,
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