Abstract

Geostatistical interpolation methods to estimate individual exposure to outdoor air pollutants can be used in pregnancy cohorts where personal exposure data are not collected. Our objectives were to a) develop four assessment methods (citywide average (CWA); nearest monitor (NM); inverse distance weighting (IDW); and ordinary Kriging (OK)), and b) compare daily metrics and cross-validations of interpolation models. We obtained 2008 hourly data from Mexico City’s outdoor air monitoring network for PM10, PM2.5, O3, CO, NO2, and SO2 and constructed daily exposure metrics for 1,000 simulated individual locations across five populated geographic zones. Descriptive statistics from all methods were calculated for dry and wet seasons, and by zone. We also evaluated IDW and OK methods’ ability to predict measured concentrations at monitors using cross validation and a coefficient of variation (COV). All methods were performed using SAS 9.3, except ordinary Kriging which was modeled using R’s gstat package. Overall, mean concentrations and standard deviations were similar among the different methods for each pollutant. Correlations between methods were generally high (r = 0.77 to 0.99). However, ranges of estimated concentrations determined by NM, IDW, and OK were wider than the ranges for CWA. Root mean square errors for OK were consistently equal to or lower than for the IDW method. OK standard errors varied considerably between pollutants and the computed COVs ranged from 0.46 (least error) for SO2 and PM10 to 3.91 (most error) for PM2.5. OK predicted concentrations measured at the monitors better than IDW and NM. Given the similarity in results for the exposure methods, OK is preferred because this method alone provides predicted standard errors which can be incorporated in statistical models. The daily estimated exposures calculated using these different exposure methods provide flexibility to evaluate multiple windows of exposure during pregnancy, not just trimester or pregnancy-long exposures.Implications: Many studies evaluating associations between outdoor air pollution and adverse pregnancy outcomes rely on outdoor air pollution monitoring data linked to information gathered from large birth registries, and often lack residence location information needed to estimate individual exposure. This study simulated 1,000 residential locations to evaluate four air pollution exposure assessment methods, and describes possible exposure misclassification from using spatial averaging versus geostatistical interpolation models. An implication of this work is that policies to reduce air pollution and exposure among pregnant women based on epidemiologic literature should take into account possible error in estimates of effect when spatial averages alone are evaluated.

Highlights

  • Air pollution is a major public health problem, with growing numbers of urban residents living in areas with air pollution levels exceeding the World Health Organization (WHO) air quality guidelines (Cohen, Ross Anderson et al 2005)

  • The ranges of estimated concentrations determined by Nearest monitor (NM), Inverse distance weighting (IDW), and Ordinary Kriging (OK) were always wider than the ranges for Citywide averaging (CWA)

  • A study comparing kriging and nearest monitor methods in estimating health effects associated with long-term pollution exposures concluded that kriging was generally better due to lower bias and better coverage of health effect parameter estimates. (Kim, Sheppard et al 2009) In addition, researchers found that different interpolation methods produced similar estimates when monitor density was low, whereas when monitor density was high the differences were much greater (Kim, Sheppard et al 2009; Wong, Yuan et al 2004)

Read more

Summary

Introduction

Air pollution is a major public health problem, with growing numbers of urban residents living in areas with air pollution levels exceeding the World Health Organization (WHO) air quality guidelines (Cohen, Ross Anderson et al 2005). Many previous epidemiological studies of air pollution have used data collected from central monitoring sites (Ozkaynak, Baxter et al 2013). Recent advances in the availability of fineresolution remote sensing data and application of methods that incorporate temporal and spatial variability have demonstrated that exposure measurement error from the use of central sites can influence pollutant-specific health effects estimates (Ozkaynak, Baxter et al 2013) Several publications on outdoor air pollution and perinatal outcomes have used birth registries and air pollution data from government-run air quality monitors to create exposure estimates (Bell, Ebisu et al 2007; Chang, Reich et al 2012; Sagiv, Mendola et al 2005). Other exposure assessment approaches (Jerrett, Arain et al 2004) include methods that account for the mother’s residential location (Nethery, Leckie et al 2008), proximity to traffic or other sources of emission (Wilhelm and Ritz 2003; Yorifuji, Naruse et al 2011), land-use regression models (Brauer, Lencar et al 2008; Slama, Morgenstern et al 2007), models to reduce exposure measurement error (Zou, Wilson et al 2009), and even personal monitoring of mothers during pregnancy (Jedrychowski, Galas et al 2005; Perera, Tang et al 2005; Perera, Li et al 2009)

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call