Traffic-related air pollution and obesity formation in children: a longitudinal, multilevel analysis.

BackgroundBiologically plausible mechanisms link traffic-related air pollution to metabolic disorders and potentially to obesity. Here we sought to determine whether traffic density and traffic-related air pollution were positively associated with growth in body mass index (BMI = kg/m2) in children aged 5-11 years.MethodsParticipants were drawn from a prospective cohort of children who lived in 13 communities across Southern California (N = 4550). Children were enrolled while attending kindergarten and first grade and followed for 4 years, with height and weight measured annually. Dispersion models were used to estimate exposure to traffic-related air pollution. Multilevel models were used to estimate and test traffic density and traffic pollution related to BMI growth. Data were collected between 2002-2010 and analyzed in 2011-12.ResultsTraffic pollution was positively associated with growth in BMI and was robust to adjustment for many confounders. The effect size in the adjusted model indicated about a 13.6% increase in annual BMI growth when comparing the lowest to the highest tenth percentile of air pollution exposure, which resulted in an increase of nearly 0.4 BMI units on attained BMI at age 10. Traffic density also had a positive association with BMI growth, but this effect was less robust in multivariate models.ConclusionsTraffic pollution was positively associated with growth in BMI in children aged 5-11 years. Traffic pollution may be controlled via emission restrictions; changes in land use that promote jobs-housing balance and use of public transit and hence reduce vehicle miles traveled; promotion of zero emissions vehicles; transit and car-sharing programs; or by limiting high pollution traffic, such as diesel trucks, from residential areas or places where children play outdoors, such as schools and parks. These measures may have beneficial effects in terms of reduced obesity formation in children

Air Pollution Exposure Assessment for Epidemiologic Studies of Pregnant Women and Children: Lessons Learned from the Centers for Children’s Environmental Health and Disease Prevention Research

The National Children’s Study is considering a wide spectrum of airborne pollutants that are hypothesized to potentially influence pregnancy outcomes, neurodevelopment, asthma, atopy, immune development, obesity, and pubertal development. In this article we summarize six applicable exposure assessment lessons learned from the Centers for Children’s Environmental Health and Disease Prevention Research that may enhance the National Children’s Study: a) Selecting individual study subjects with a wide range of pollution exposure profiles maximizes spatial-scale exposure contrasts for key pollutants of study interest. b) In studies with large sample sizes, long duration, and diverse outcomes and exposures, exposure assessment efforts should rely on modeling to provide estimates for the entire cohort, supported by subject-derived questionnaire data. c) Assessment of some exposures of interest requires individual measurements of exposures using snapshots of personal and microenvironmental exposures over short periods and/or in selected microenvironments. d) Understanding issues of spatial–temporal correlations of air pollutants, the surrogacy of specific pollutants for components of the complex mixture, and the exposure misclassification inherent in exposure estimates is critical in analysis and interpretation. e) “Usual” temporal, spatial, and physical patterns of activity can be used as modifiers of the exposure/outcome relationships. f) Biomarkers of exposure are useful for evaluation of specific exposures that have multiple routes of exposure. If these lessons are applied, the National Children’s Study offers a unique opportunity to assess the adverse effects of air pollution on interrelated health outcomes during the critical early life period

Traffic, Susceptibility, and Childhood Asthma

Results from studies of traffic and childhood asthma have been inconsistent, but there has been little systematic evaluation of susceptible subgroups. In this study, we examined the relationship of local traffic-related exposure and asthma and wheeze in southern California school children (5–7 years of age). Lifetime history of doctor-diagnosed asthma and prevalent asthma and wheeze were evaluated by questionnaire. Parental history of asthma and child’s history of allergic symptoms, sex, and early-life exposure (residence at the same home since 2 years of age) were examined as susceptibility factors. Residential exposure was assessed by proximity to a major road and by modeling exposure to local traffic-related pollutants. Residence within 75 m of a major road was associated with an increased risk of lifetime asthma [odds ratio (OR) = 1.29; 95% confidence interval (CI), 1.01–1.86], prevalent asthma (OR = 1.50; 95% CI, 1.16–1.95), and wheeze (OR = 1.40; 95% CI, 1.09–1.78). Susceptibility increased in long-term residents with no parental history of asthma for lifetime asthma (OR = 1.85; 95% CI, 1.11–3.09), prevalent asthma (OR = 2.46; 95% CI, 0.48–4.09), and recent wheeze (OR = 2.74; 95% CI, 1.71–4.39). The higher risk of asthma near a major road decreased to background rates at 150–200 m from the road. In children with a parental history of asthma and in children moving to the residence after 2 years of age, there was no increased risk associated with exposure. Effect of residential proximity to roadways was also larger in girls. A similar pattern of effects was observed with traffic-modeled exposure. These results indicate that residence near a major road is associated with asthma. The reason for larger effects in those with no parental history of asthma merits further investigation

Near-Roadway Air Pollution and Coronary Heart Disease: Burden of Disease and Potential Impact of a Greenhouse Gas Reduction Strategy in Southern California

Background: Several studies have estimated the burden of coronary heart disease (CHD) mortality from ambient regional particulate matter ≤ 2.5 μm (PM2.5). The burden of near-roadway air pollution (NRAP) generally has not been examined, despite evidence of a causal link with CHD. Objective: We investigated the CHD burden from NRAP and compared it with the PM2.5 burden in the California South Coast Air Basin for 2008 and under a compact urban growth greenhouse gas reduction scenario for 2035. Methods: We estimated the population attributable fraction and number of CHD events attributable to residential traffic density, proximity to a major road, elemental carbon (EC), and PM2.5 compared with the expected disease burden if the population were exposed to background levels of air pollution. Results: In 2008, an estimated 1,300 CHD deaths (6.8% of the total) were attributable to traffic density, 430 deaths (2.4%) to residential proximity to a major road, and 690 (3.7%) to EC. There were 1,900 deaths (10.4%) attributable to PM2.5. Although reduced exposures in 2035 should result in smaller fractions of CHD attributable to traffic density, EC, and PM2.5, the numbers of estimated deaths attributable to each of these exposures are anticipated to increase to 2,500, 900, and 2,900, respectively, due to population aging. A similar pattern of increasing NRAP-attributable CHD hospitalizations was estimated to occur between 2008 and 2035. Conclusion: These results suggest that a large burden of preventable CHD mortality is attributable to NRAP and is likely to increase even with decreasing exposure by 2035 due to vulnerability of an aging population. Greenhouse gas reduction strategies developed to mitigate climate change offer unexploited opportunities for air pollution health co-benefits

Prospective Analysis of Traffic Exposure as a Risk Factor for Incident Coronary Heart Disease: The Atherosclerosis Risk in Communities (ARIC) Study

BackgroundFor people living close to busy roads, traffic is a major source of air pollution. Few prospective data have been published on the effects of long-term exposure to traffic on the incidence of coronary heart disease (CHD).ObjectivesIn this article, we examined the association between long-term traffic exposure and incidence of fatal and nonfatal CHD in a population-based prospective cohort study.MethodsWe studied 13,309 middle-age men and women in the Atherosclerosis Risk in Communities study, without previous CHD at enrollment, from 1987 to 1989 in four U.S. communities. Geographic information system–mapped traffic density and distance to major roads served as measures of traffic exposure. We examined the association between traffic exposure and incident CHD using proportional hazards regression models, with adjustment for background air pollution and a wide range of individual cardiovascular risk factors.ResultsOver an average of 13 years of follow-up, 976 subjects developed CHD. Relative to those in the lowest quartile of traffic density, the adjusted hazard ratio (HR) in the highest quartile was 1.32 [95% confidence interval (CI), 1.06–1.65; p-value for trend across quartiles = 0.042]. When we treated traffic density as a continuous variable, the adjusted HR per one unit increase of log-transformed density was 1.03 (95% CI, 1.01–1.05; p = 0.006). For residents living within 300 m of major roads compared with those living farther away, the adjusted HR was 1.12 (95% CI, 0.95–1.32; p = 0.189). We found little evidence of effect modification for sex, smoking status, obesity, low-density lipoprotein cholesterol level, hypertension, age, or education.ConclusionHigher long-term exposure to traffic is associated with incidence of CHD, independent of other risk factors. These prospective data support an effect of traffic-related air pollution on the development of CHD in middle-age persons

Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction

Crystalline silicate dust around evolved stars I. The sample stars

This is the first paper in a series of three where we present the first comprehensive inventory of solid state emission bands observed in a sample of 17 oxygen-rich circumstellar dust shells surrounding evolved stars. The data were taken with the Short and Long Wavelength Spectrographs on board of the Infrared Space Observatory (ISO) and cover the 2.4 to 195 micron wavelength range. The spectra show the presence of broad 10 and 18 micron bands that can be attributed to amorphous silicates. In addition, at least 49 narrow bands are found whose position and width indicate they can be attributed to crystalline silicates. Almost all of these bands were not known before ISO. We have measured the peak positions, widths and strengths of the individual, continuum subtracted bands. Based on these measurements, we were able to order the spectra in sequence of decreasing crystalline silicate band strength. We found that the strength of the emission bands correlates with the geometry of the circumstellar shell, as derived from direct imaging or inferred from the shape of the spectral energy distribution. This naturally divides the sample into objects that show a disk-like geometry (strong crystalline silicate bands), and objects whose dust shell is characteristic of an outflow (weak crystalline silicate bands). All stars with the 33.6 micron forsterite band stronger than 20 percent over continuum are disk sources. We define spectral regions (called complexes) where a concentration of emission bands is evident, at 10, 18, 23, 28, 33, 40 and 60 micron. We derive average shapes for these complexes and compare these to the individual band shapes of the programme stars.Comment: 41 pages, 20 figures, accepted by A&A. Tables 4 to 20 are only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A