Respiratory Effects of Exposure to Traffic-Related Air Pollutants During Exercise
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ABSTRACT:
SUBMITTER: Morici G
PROVIDER: S-EPMC7793908 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:We examined traffic-related pollution (TRP) exposure and respiratory health effects in Hispanic white (HW) children, both compared with non-Hispanic white (NHW) children and according to genetically determined Native American (NA) ancestry. The sample included over 5,000 children from the Children's Health Study in California, followed during 1993-2014. HW children were 1.47 (95% confidence interval (CI): 1.24, 1.73) times more likely to live close (<500 m) to a freeway and 1.54 (95% CI: 1.26, 1.87) times more likely to live close (<75 m) to a major nonfreeway road compared with NHW children. Among HW children, those with >50% NA ancestry were >40% more likely to live close to a freeway or to a major nonfreeway road, compared with those with ≤50% NA ancestry. The association of TRP with ever having been diagnosed by a doctor as having asthma differed between HW and NHW children (P < 0.05), with the strongest association among HW children with >50% NA ancestry. Within this subgroup, those close to a major nonfreeway road were 2.16 (95% CI: 1.26, 3.69) times more likely to have ever reported asthma compared with those living further away. This paper provides evidence that HW children in southern California, especially those with greater NA ancestry, are more exposed to TRP and are potentially at greater risk for TRP-related respiratory health effects.
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Project description:Urban populations are often simultaneously exposed to air pollution and environmental noise, which are independently associated with cardiovascular disease. Few studies have examined acute physiologic responses to both air and noise pollution using personal exposure measures. We conducted a repeated measures panel study of air pollution and noise in 46 non-smoking adults in Toronto, Canada. Data were analyzed using linear mixed-effects models and weighted cumulative exposure modeling of recent exposure. We examined acute changes in cardiovascular health effects of personal (ultrafine particles, black carbon) and regional (PM2.5, NO2, O3, Ox) measurements of air pollution and the role of personal noise exposure as a confounder of these associations. We observed adverse changes in subclinical cardiovascular outcomes in response to both air pollution and noise, including changes in endothelial function and heart rate variability (HRV). Our findings show that personal noise exposures can confound associations for air pollutants, particularly with HRV, and that impacts of air pollution and noise on HRV occur soon after exposure. Thus, both noise and air pollution have a measurable impact on cardiovascular physiology. Noise should be considered alongside air pollution in future studies to elucidate the combined impacts of these exposures in urban environments.
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Project description:Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into "low", "medium" or "high" traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments.
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Project description:Human exposure to air pollution in many studies is represented by ambient concentrations from space-time kriging of observed values. Space-time kriging techniques based on a limited number of ambient monitors may fail to capture the concentration from local sources. Further, because people spend more time indoors, using ambient concentration to represent exposure may cause error. To quantify the associated exposure error, we computed a series of six different hourly-based exposure metrics at 16,095 Census blocks of three Counties in North Carolina for CO, NO(x), PM(2.5), and elemental carbon (EC) during 2012. These metrics include ambient background concentration from space-time ordinary kriging (STOK), ambient on-road concentration from the Research LINE source dispersion model (R-LINE), a hybrid concentration combining STOK and R-LINE, and their associated indoor concentrations from an indoor infiltration mass balance model. Using a hybrid-based indoor concentration as the standard, the comparison showed that outdoor STOK metrics yielded large error at both population (67% to 93%) and individual level (average bias between -10% to 95%). For pollutants with significant contribution from on-road emission (EC and NO(x)), the on-road based indoor metric performs the best at the population level (error less than 52%). At the individual level, however, the STOK-based indoor concentration performs the best (average bias below 30%). For PM(2.5), due to the relatively low contribution from on-road emission (7%), STOK-based indoor metric performs the best at both population (error below 40%) and individual level (error below 25%). The results of the study will help future epidemiology studies to select appropriate exposure metric and reduce potential bias in exposure characterization.
| S-EPMC4690943 | biostudies-literature
Project description:BackgroundPrevious studies have shown inconsistent results regarding the impact of traffic pollution on the prevalence of chronic obstructive pulmonary disease (COPD). Therefore, using frequency matching and propensity scores, we explored the association between traffic pollution and COPD in a cohort of 8284 residents in a major agricultural county in Taiwan.MethodsAll subjects completed a structured questionnaire interview and health checkups. Subjects with COPD were identified using Taiwan National Health Insurance Research Databases. A hybrid kriging/LUR model was used to identify levels of traffic-related air pollutants (PM2.5 and O3). Multiple logistic regression models were used to calculate the prevalence ratios (PRs) of COPD and evaluate the role played by traffic-related indices between air pollutants and COPD. The distributed lag nonlinear model was applied in the analysis; we excluded current or ever smokers to perform the sensitivity analysis.ResultsIncreased PRs of COPD per SD increment of PM2.5 were 1.10 (95% CI 1.05-1.15) and 1.25 (95% CI 1.13-1.40) in the population with age and sex matching as well as propensity-score matching, respectively. The results of the sensitivity analysis were similar between the single and two pollutant models. PM2.5 concentrations were significantly associated with traffic flow including sedans, buses, and trucks (p < 0.01). The higher road area and the higher PM2.5 concentrations near the subject's residence correlated with a greater risk of developing COPD (p for interaction < 0.01).ConclusionsOur results suggest that long-term exposure to traffic-related air pollution may be positively associated with the prevalence of COPD.
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Project description:BackgroundExposure to particulate matter has been shown to increase the adhesion of bacteria to human airway epithelial cells. However, the impact of traffic-related air pollution (TRAP) on the respiratory microbiome is unknown.MethodsForty children were recruited through the Cincinnati Childhood Allergy and Air Pollution Study, a longitudinal cohort followed from birth through early adolescence. Saliva and induced sputum were collected at age 14 years. Exposure to TRAP was characterized from birth through the time of sample collection using a previously validated land-use regression model. Sequencing of the bacterial 16S and ITS fungal rRNA genes was performed on sputum and saliva samples. The relative abundance of bacterial taxa and diversity indices were compared in children with exposure to high and low TRAP. We also used multiple linear regression to assess the effect of TRAP exposure, gender, asthma status, and socioeconomic status on the alpha diversity of bacteria in sputum.ResultsWe observed higher bacterial alpha diversity indices in sputum than in saliva. The diversity indices for bacteria were greater in the high TRAP exposure group than the low exposure group. These differences remained after adjusting for asthma status, gender, and mother's education. No differences were observed in the fungal microbiome between TRAP exposure groups.ConclusionOur findings indicate that exposure to TRAP in early childhood and adolescence may be associated with greater bacterial diversity in the lower respiratory tract. Asthma status does not appear to confound the observed differences in diversity. These results demonstrate that there may be a TRAP-exposure related change in the lower respiratory microbiota that is independent of asthma status.
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Project description:In large epidemiological studies, many researchers use surrogates of air pollution exposure such as geographic information system (GIS)-based characterizations of traffic or simple housing characteristics. It is important to evaluate quantitatively these surrogates against measured pollutant concentrations to determine how their use affects the interpretation of epidemiological study results. In this study, we quantified the implications of using exposure models derived from validation studies, and other alternative surrogate models with varying amounts of measurement error on epidemiological study findings. We compared previously developed multiple regression models characterizing residential indoor nitrogen dioxide (NO(2)), fine particulate matter (PM(2.5)), and elemental carbon (EC) concentrations to models with less explanatory power that may be applied in the absence of validation studies. We constructed a hypothetical epidemiological study, under a range of odds ratios, and determined the bias and uncertainty caused by the use of various exposure models predicting residential indoor exposure levels. Our simulations illustrated that exposure models with fairly modest R(2) (0.3 to 0.4 for the previously developed multiple regression models for PM(2.5) and NO(2)) yielded substantial improvements in epidemiological study performance, relative to the application of regression models created in the absence of validation studies or poorer-performing validation study models (e.g., EC). In many studies, models based on validation data may not be possible, so it may be necessary to use a surrogate model with more measurement error. This analysis provides a technique to quantify the implications of applying various exposure models with different degrees of measurement error in epidemiological research.
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Project description:BACKGROUND:Some studies have supported an association between traffic-related air pollution exposure and breast cancer risk. However, few studies have considered exposures in early life, which may be a period of increased susceptibility. OBJECTIVES:To examine the association of childhood residential exposure to traffic-related air pollution with breast cancer development. METHODS:The Sister Study is a prospective cohort of 50,884 initially breast cancer-free women, of whom 42,934 provided information at enrollment about roads and traffic near their primary childhood residence before age 14 as well as relevant covariates. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for the association between traffic-related measures at childhood residence and adult incident breast cancer were estimated using Cox regression. RESULTS:During follow-up (mean = 6.3 years), 2,028 breast cancers were diagnosed. Traffic-related characteristics were not consistently associated with breast cancer risk. However, incidence was elevated among women who reported a median/barrier dividing either their primary childhood residential road (aHR = 1.2; 95% CI: 0.9-1.7) or the nearest cross-street (aHR = 1.3; 95% CI: 0.9-1.8, if the cross-street was within 100ft.), and among women whose nearest cross-street had the highest traffic, ?3 lanes, and/or a median/barrier (aHR = 1.4; 95% CI: 1.0-1.9). CONCLUSIONS:Measures of potential exposure to vehicular traffic were not consistently associated with breast cancer risk. However, living during childhood on or near a road with a median or other barrier, which may be a more easily remembered road characteristic than the others assessed, was associated with increased breast cancer risk.
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Project description:Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km2) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NOx) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9,700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 hr, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic-related pollutants accurately, data should be geocoded or estimated at the most-resolved spatial level; census tract and larger zones have little if any ability to represent intraurban variation in traffic-related air pollutant concentrations. These results are based on one of the most comprehensive intraurban modeling studies in the literature and results are robust. Recommendations address the value of dispersion models to portray spatial and temporal variation of air pollutants in epidemiology and other studies; techniques to improve accuracy and reduce the computational burden in urban scale modeling; the necessary spatial resolution for health surveillance, demographic, and pollution data; and the consequences of low resolution data in terms of exposure misclassification.
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