conference paper
conference paper
published journal article
Background Epidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents. Objectives To assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents. Methods Detailed clinical data were obtained for 395,927 pregnancies in southern California (2008–2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter < 2.5 μm (PM2.5), <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) were estimated using kriging interpolation of Environmental Protection Agency’s routine monitoring station data, while PM2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon) were estimated using a fine-resolution geoscience-derived model. A multilevel logistic regression was used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of air pollution and PM component mixtures. Main analyses adjusted for maternal age, race/ethnicity, education, median family household income, pre-pregnancy BMI, smoking during pregnancy, insurance type, season of conception and year of delivery. Results The incidence of GDM was 10.9% in the study population. In single-pollutant models, we observed an increased odds for GDM associated with exposures to PM2.5, PM10, NO2 and PM2.5 constituents. The association was strongest for NO2 [adjusted odds ratio (OR) per interquartile range: 1.176, 95% confidence interval (CI): 1.147–1.205)]. In multi-pollutant models, increased ORs for GDM in association with one quartile increase in air pollution mixtures were found for both kriging-based regional air pollutants (NO2, PM2.5, and PM10, OR = 1.095, 95% CI: 1.082–1.108) and PM2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon, OR = 1.258, 95% CI: 1.206–1.314); NO2 (78%) and black carbon (48%) contributed the most to the overall mixture effects among all krigged air pollutants and all PM2.5 constituents, respectively. The risk of GDM associated with air pollution exposure were significantly higher among Hispanic mothers, and overweight/obese mothers. Conclusion This study found that exposure to a mixture of ambient PM2.5, PM10, NO2, and PM2.5 chemical constituents was associated with an increased risk of GDM. NO2 and black carbon PM2.5 contributed most to GDM risk.
published journal article
published journal article
BACKGROUND AND AIM[|]Previous studies have suggested that smokers may have higher susceptibility to air pollution-related morbidity and mortality. However, the biological mechanisms are poorly understood. We sought to elucidate whether air pollution impacts health outcomes via epigenetic modification in a racially and ethnically diverse population of smokers.[¤]METHOD[|]We conducted an epigenome-wide association study (EWAS) of air pollution among 843 Los Angeles-based smokers from the Multiethnic Cohort Study (361 African American, 82 Japanese American, 395 Latino, and 5 White participants). Blood DNA methylation was measured using the MethylationEPICv1 array. Exposures to kriging-interpolated PM2.5, PM10, NO2, and NOx averaged twelve months prior to blood draw (1994-2006) were estimated based on participants’ addresses. Linear regression was conducted to assess the association between each pollutant and DNA methylation, adjusting for race and ethnicity, age, sex, neighborhood SES, and cell type proportions. Race- and ethnicity-specific EWAS were conducted for African Americans and Latinos.[¤]RESULTS[|]Average levels of all pollutants were significantly higher among Latinos compared to the other races and ethnicities (p<0.0001). Among all participants, there were no statistically significant associations between any pollutant and DNA methylation (genome-wide p<9×10⁻⁸). In the African American-specific EWAS, the highest quartile (Q4 vs Q1) of NO2 was associated with differential methylation at 42 CpG sites (p<9×10⁻⁸). The most significant hits were in genes associated with cancer risk/survival, including ATP9B (cg14071758), PEMT (cg10633334), WIZ (cg01387972) and TRIM27 (cg22355525). In the Latino-specific EWAS, the highest quartile (Q4 vs Q1) of NO2 was associated with decreased DNA methylation at cg03020635 in chromosome 14 (p<9×10⁻⁸). The effect estimates for these CpG sites were heterogeneous between African Americans and Latinos (p-heterogeneity<0.0001).[¤]CONCLUSIONS[|]Among African Americans, NO2 was associated with differential methylation at several CpG sites in cancer-related genes. The heterogeneous associations by race and ethnicity suggest potential racial- and ethnic-specific epigenetic changes due to air pollution among smokers.[¤]
published journal article
Exposure estimation of fine particulate matter with diameter ¡ 2.5 mu m (PM2.5) at high spatiotemporal resolution is crucial to epidemiological studies that examine acute or sub-chronic health outcomes of PM2.5. However, exposure assessment of PM2.5 has been negatively affected by sparsely distributed monitoring stations. In addition, several limitations exist among the existing methods for high spatiotemporal resolution PM2.5 estimation, including ignorance or limited use of spatial autocorrelation, single-model methods, and use of aerosol optical depth data with non-random missingness. These limitations probably introduce bias or high uncertainty in model estimation. In this paper, we proposed an approach of constrained mixed-effect bagging models to leverage advanced algorithm of the high-resolution AOD retrieved by Multi-Angle Implementation of Atmospheric Correction (MAIAC), with other spatiotemporal predictors and spatial autocorrelation to reliably estimate PM2.5 at a high spatiotemporal resolution. Our base model was a daily mixed-effect spatial model that accounted for spatial autocorrelation using embedded structured and unstructured spatial random effects. Point estimates from the base models were then averaged based on the bootstrap aggregating (bagging) to reduce variance in prediction. Then, constrained optimization was developed to minimize the impact of missing AOD and to capture a full time-series of PM2.5 concentration. Our daily-level bagging allowed AOD-PM2.5 association and spatial autocorrelation to vary daily, which substantially improved the model performance. As a case study of daily PM2.5 predictions in 2014 in Shandong Province, China, our approach achieved R-2 of 0.87 (RMSE: 18.6 mu g/m(3)) in cross validation, and R-2 of 0.75 (RMSE: 20.6 mu g/m(3)) in an independent test, similar to or better than most existing methods. We further extended the 2014 models to simulate 2014-2016 full time-series of biweekly average PM2.5 concentrations with no use of covariates in 2015-2016 but constrained optimization over 2014 daily point estimates; the results showed well-captured temporal trend with a total correlation of 0.81 between the simulated and observed values from 2015 to 2016. Our approach can be applied for other regions for exposure estimation of PM2.5 when measurements alone are not able to capture the desirable spatial and temporal resolutions.
conference paper
conference paper
published journal article
conference paper
Two existing techniques are integrated and a methodology for evaluating transportation projects, especially complex projects that involve new technologies, is created. While integrating the multiattribute value function technique with the analytic hierarchy process, a new scaling approach is introduced through use of a linear scaling proxy. Additionally, the approach identifies an overall worth for a project. This overall worth provides decision makers with a quantitative value they can use to compare different projects or to estimate and compare hypothetical results. The methodology is demonstrated in a sample problem. The methodology’s application to the Anaheim field operational test is then described. Often, evaluations fall to look at all the potential areas a project may affect. This methodology simplifies the process for including institutional issues in the final results of an evaluation.