Traffic PM2 Research Articles

Chronic maternal exposure to low-dose PM2.5 impacts cognitive outcomes in a sex-dependent manner

There is no safe level of air pollution for human health. Traffic-related particulate matter (PM2.5) is a major in-utero toxin, mechanisms of action of which are not fully understood. BALB/c dams were exposed to an Australian level of traffic PM2.5 (5 µg/mouse/day, intranasal, 6 weeks before mating, during gestation and lactation). Male offspring had reduced memory in adulthood, whereas memory was normal in female littermates, similar to human responses. Maternal PM2.5 exposure resulted in oxidative stress and abnormal mitochondria in male, but not female, brains. RNA-sequencing analysis showed unique sex-related changes in newborn brains. Two X-chromosome-linked histone lysine demethylases, Kdm6a and Kdm5c, demonstrated higher expression in female compared to male littermates, in addition to upregulated genes with known functions to support mitochondrial function, synapse growth and maturation, cognitive function, and neuroprotection. No significant changes in Kdm6a and Kdm5c were found in male littermates, nor other genes, albeit significantly impaired memory function after birth. In primary foetal cortical neurons, PM2.5 exposure suppressed neuron and synaptic numbers and induced oxidative stress, which was prevented by upregulation of Kdm6a or Kdm5c. Therefore, timely epigenetic adaptation by histone demethylation to open DNA for translation before birth may be the key to protecting females against prenatal PM2.5 exposure-induced neurological disorders, which fail to occur in males associated with their poor cognitive outcomes.

Applying principal component pursuit to investigate the association between source-specific fine particulate matter and myocardial infarction hospitalizations in New York City.

We adapted principal component pursuit (PCP), a dimensionality-reduction technique previously used in computer vision, as a novel pattern recognition method for environmental mixtures to apportion speciated PM2.5 to its sources. We used data from the NY Department of Health Statewide Planning and Research Cooperative System of daily city-wide counts of MI admissions (2007-2015). We examined associations between same-day, lag 1, and lag 2 source-specific PM2.5 exposure and MI admissions in a time-series analysis, using a quasi-Poisson regression model adjusting for potential confounders. We identified four sources of PM2.5 pollution: crustal, salt, traffic, and regional and detected three single-species factors: cadmium, chromium, and barium. In adjusted models, we observed a 0.40% (95% confidence interval [CI]: -0.21, 1.01%) increase in MI admission rates per 1 μg/m3 increase in traffic PM2.5, a 0.44% (95% CI: -0.04, 0.93%) increase per 1 μg/m3 increase in crustal PM2.5, and a 1.34% (95% CI: -0.46, 3.17%) increase per 1 μg/m3 increase in chromium-related PM2.5, on average. In our NYC study, we identified traffic, crustal dust, and chromium PM2.5 as potentially relevant sources for cardiovascular disease. We also demonstrated the potential utility of PCP as a pattern recognition method for environmental mixtures.

A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents

Although PM2.5 mass varies in source and composition over time and space, most health effects assessment have made the inherent assumption that all PM2.5 mass has the same health implications, irrespective of composition. Nationwide estimates of source-specific PM2.5 mass and constituents at local-scale would allow for epidemiological studies and health effects assessments that consider the variability in PM2.5 characteristics in their health impact assessments. In response, we developed US models of annual exposures at the census tract level for five major PM2.5 sources (traffic, soil, coal, oil, and biomass combustion) and six trace elements (elemental carbon, sulfur, silicon, selenium, nickel, and non-soil potassium) for 2001 through 2014. We employed Absolute Factor Analysis (APCA) to derive the source-specific PM2.5 impacts at monitoring stations. Random forest algorithms that incorporated predictors derived from satellite, chemical transport model, and census tract resolution land-use data on traffic, meteorology, and emissions, which were rigorously tested by 10-fold cross-validation (CV), were then employed to estimate elemental and source-specific PM2.5 levels at non-monitoring site census-tracts over the study years. Model performances were moderate to good, with CV R2 ranging from 0.41 to 0.95. For PM2.5 sources, the highest CV R2 was attained for traffic PM2.5 (CV R2 = 0.73), followed by coal (CV R2 = 0.65), oil (CV R2 = 0.62), soil (CV R2 = 0.60), and biomass (CV R2 = 0.41). Among constituents, the CV was highest for sulfur (CV R2 = 0.95). Our analyses provided highly resolved spatial estimates of annual elemental and source-specific PM2.5 concentrations at the census-tract level, for 2001 through 2014. This dataset offers exposure estimates in support of future nationwide long-term health effects studies of source-specific PM2.5 mass and constituents, enabling epidemiological research that addresses the fact that not all particles are the same.

Near-road air quality modelling that incorporates input variability and model uncertainty

Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM2.5 concentrations at twelve out of the eighteen locations.

Impact of Long-term Exposures to PM2.5 Sources on Cause-Specific Mortality in U.S. Medicare Beneficiaries

Background: While studies have demonstrated associations between long-term PM2.5 exposures and mortality, our understanding of the component(s) or sources responsible for observed mortality risks is poor. Methods: We examined the impacts of long-term PM2.5 source and component exposures on cause-specific mortality for 15.4 million Medicare beneficiaries living within the conterminous United States between 2000 to 2008. We linked Medicare beneficiary and mortality data to daily PM2.5 component concentrations from 323 stationary monitoring sites and to source-specific PM2.5 estimated using factor analysis. We analyzed these data in age-, race-, and gender-stratified log-linear regression models controlling for potential confounding by other PM2.5 components and area-level demographic and behavioral covariates. We assessed sensitivity of the mortality risk estimates to (1) variation in source estimates by urbanicity and geographical region and (2) to exposure error. Findings: We identified eight major PM2.5 source categories (and key elements): soil (Si, Ca), traffic (EC, OC, NO3 -), metals (Pb and Zn), biomass combustion (K and Cu), salt (Cl, Na), coal combustion (SO42-, Se), residual oil combustion (Ni, V) and steel (Mn, Fe). For all causes of death, we found the highest and most consistent RRs (per IQR increase) for coal combustion (MRRall-cause : 1.052 (95% CI: 1.048-1.056)) and its key tracer elements, with no evidence of confounding by behavioral covariates. Traffic PM2.5 was also significantly associated with increased risk of all-cause (MRRall-cause : 1.010; 95% CI: 1.008, 1.012), CVD- and respiratory-related, and lung cancer mortality but not all cancer-related mortality. Associations were generally null for other examined PM2.5 sources and causes of death. Conclusions: We found long-term PM2.5 exposures from coal combustion and traffic-related sources to be most strongly associated with increased mortality for all causes of death.

Exposure to traffic-related particle matter and effects on lung function and potential interactions in a cross-sectional analysis of a cohort study in west Sweden

ObjectivesTo investigate the long-term effects of source-specific particle matter (PM) on lung function, effects of Surfactant Protein A (SP-A) and glutathione S-transferase (GST) genes GSTP1 and GSTT1 gene variants and...

The impact of measurement error in modeled ambient particles exposures on health effect estimates in multilevel analysis

We sampled 1,000 small administrative areas in London, United Kingdom, and simulated the "true" underlying daily exposure surfaces for PM10 and PM2.5 for 2009-2013 incorporating temporal variation and spatial covariance informed by the extensive London monitoring network. We added measurement error assessed by comparing measurements at fixed sites and predictions from spatiotemporal land-use regression (LUR) models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multilevel Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation. For long-term exposure to particles, we observed bias toward the null, except for traffic PM2.5 for which only LUR underestimated the effect. For short-term exposure, results were variable between exposure models and bias ranged from -11% (underestimate) to 20% (overestimate) for PM10 and of -20% to 17% for PM2.5. Integration of models performed best in almost all cases. No single exposure model performed optimally across scenarios. In most cases, measurement error resulted in attenuation of the effect estimate.

Morin Protects Human Respiratory Cells from PM2.5 Induced Genotoxicity by Mitigating ROS and Reverting Altered miRNA Expression.

Chronic fine particulate matter (PM2.5) exposure causes oxidative stress and leads to many diseases in human like respiratory and cardiovascular disorders, and lung cancer. It is known that toxic responses elicited by PM2.5 particles depend on its physical and chemical characteristics that are greatly influenced by the source. Dietary polyphenolic compounds that possess antioxidant and free radical scavenging properties could be used for therapeutic or preventive approaches against air pollution related health hazards. This study evaluates characteristics and toxicity of PM2.5 collected from rural, urban, industrial, and traffic regions in and around Coimbatore City, Tamilnadu, India. Traffic PM2.5 particles contained higher amounts of metals and polycyclic aromatic hydrocarbons (PAHs). It also possessed higher levels of oxidative potential, induced more intracellular reactive oxygen species (ROS), and caused more levels of cell death and DNA damage in human respiratory cells. Its exposure up regulated DNA damage response related miR222, miR210, miR101, miR34a, and miR93 and MycN and suppressed Rad52. Pre-treatment with morin significantly decreased the PM2.5 induced toxicity and conferred protection against PM2.5 induced altered miRNA expression. Results of this study showed that cytoprotective effect of morin is due to its antioxidative and free radical scavenging activity.

Exposure to fine particulate matter from traffic in early life and childhood pneumonia

Introduction: Exposure to air pollution in early life may increase susceptibility to childhood pneumonia. We examined the association between exposure to fine particulate matter (PM2.5) from traffic and pneumonia in the first two years of life. Methods: Cox proportional hazards regression was used to assess the association between primary PM2.5 from traffic emissions, modeled using A Research LINE-source dispersion model (RLINE) at 250 meter resolution, and pneumonia by age two among 22,517 children from the Kaiser Air Pollution and Pediatric Asthma Study (KAPPA). KAPPA is a historical birth cohort of children born between 2000 and 2010 and insured by Kaiser Permanente Georgia. Average ambient PM2.5 from traffic at a child’s residence was estimated for the first year of life, or until the time of pneumonia diagnosis or insurance enrollment attrition if that occurred before the first birthday. Pneumonia was defined as one diagnosis of pneumonia (ICD-9 codes 480-486) in the medical record after the first 28 days of life (excluding neonatal pneumonia). Results: 2,188 children were diagnosed with pneumonia during follow-up. Average traffic PM2.5 exposure ranged from 0.31 to 2.66 micrograms per cubic meter (ug/m3). We estimated a modest positive association between PM2.5 exposure in early life and pneumonia incidence (hazard ratio (95% confidence interval): 1.17 (0.93, 1.47) for a change of 1 ug/m3 PM2.5) in a model controlling for sex, race, maternal asthma, prenatal smoking, maternal age, maternal education, city region, and neighborhood socioeconomic status. Low maternal education was the strongest risk factor for childhood pneumonia (adjusted model HR (95% CI): 1.57 (1.11, 2.22) comparing a child whose mother did not finish high school to a child whose mother attended at least some college). Conclusions: These preliminary results provide limited evidence for an association between traffic-related PM2.5 and pneumonia in the first two years of life.

Contribution of traffic and tobacco smoke in the distribution of polycyclic aromatic hydrocarbons on outdoor and indoor PM2.5

Traffic emissions and tobacco smoke are considered two main sources of polycyclic aromatic hydrocarbons (PAHs) in indoor and outdoor air. In this study, the impact of these sources on the level of fine particulate matter (PM2.5) and on the distribution of 15 PAHs regarded as priority pollutants by the US-EPA on PM2.5 were evaluated and compared. Outdoor and indoor PM2.5 samples were collected during winter 2008 in Oporto city in Portugal, for sampling periods of 12 and 24 hours, respectively. The outdoor PM2.5 were sampled at one site directly influenced by traffic emissions and the indoor PM2.5 samples were collected at one home directly influenced by tobacco smoke and another one without smoke. A methodology based on microwave-assisted extraction and liquid chromatography with fluorescence detection was applied for the efficient PAHs determination in indoor and outdoor PM2.5. PAHs in indoor PM2.5 concentrations were significantly influenced by the presence of traffic and tobacco smoking emissions. The mean of ΣPAHs in the outdoor traffic PM2.5 was not significantly different from the value attained in the indoor without smoking site. The tobacco smoke increased significantly PAHs concentrations on average about 1000 times more, when compared with the outdoor profile samples suggesting that tobacco smoking may be the most important source of indoor PAHs pollution.

A source apportionment of U.S. fine particulate matter air pollution

Using daily fine particulate matter (PM2.5) composition data from the 2000-2005 U.S. EPA Chemical Speciation Network (CSN) for over 200 sites, we applied multivariate methods to identify and quantify the major fine particulate matter (PM2.5) source components in the U.S. Novel aspects of this work were: (1) the application of factor analysis (FA) to multi-city daily data, drawing upon both spatial and temporal variations of chemical species; and, (2) the exclusion of secondary components (sulfates, nitrates and organic carbon) from the source identification FA to more clearly discern and apportion the PM2.5 mass to primary emission source categories. For the quantification of source-related mass, we considered two approaches based upon the FA results: 1) using single key tracers for sources identified by FA in a mass regression; and, 2) applying Absolute Principal Component Analysis (APCA). In each case, we followed a two-stage mass regression approach, in which secondary components were first apportioned among the identified sources, and then mass was apportioned to the sources and to other secondary mass not explained by the individual sources. The major U.S. PM2.5 source categories identified via FA (and their key elements) were: Metals Industry (Pb, Zn); Crustal/Soil Particles (Ca, Si); Motor Vehicle Traffic (EC, NO2); Steel Industry (Fe, Mn); Coal Combustion (As, Se); Oil Combustion (V, Ni); Salt Particles (Na, Cl) and Biomass Burning (K). Nationwide spatial plots of the source-related PM2.5 impacts were confirmatory of the factor interpretations: ubiquitous sources, such as Traffic and Soil, were found to be spread across the nation, more unique sources (such as Steel and Metals Processing) being highest in select industrialized cities, Biomass Burning was highest in the U.S. Northwest, while Residual Oil combustion was highest in cities in the Northeastern U.S. and in cities with major seaports. The sum of these source contributions and the secondary PM2.5 components agreed well with the U.S. PM2.5 observed during the study period (mean=14.3 ug/m3; R2= 0.91). Apportionment regression analyses using single-element tracers for each source category gave results consistent with the APCA estimates. Comparisons of nearby sites indicated that the PM2.5 mass and the secondary aerosols were most homogenous spatially, while traffic PM2.5 and its tracer, EC, were among the most spatially representative of the source-related components. Comparison of apportionment results to a previous analysis of the 1979-1982 IP Network revealed similar and correlated major U.S. source category factors, albeit at lower levels than in the earlier period, suggesting a consistency in the U.S. spatial patterns of these source-related exposures over time, as well. These results indicate that applying source apportionment methods to the nationwide CSN can be an informative avenue for identifying and quantifying source components for the subsequent estimation of source-specific health effects, potentially contributing to more efficient regulation of PM2.5.

Environmental Factors and Lung Cancer: Polycyclic Aromatic Hydrocarbon Content of Traffic Airborne PM2.5 and AhR Gene Expression

PP-31-010 Background/Aims: Recent studies have provided increasing evidence that lung cancer incidence in different countries is associated with exposure to airborne PM2.5 of environmental motorcycle emitted particulates. Traffic-related airborne pollutants include polycyclic aromatic hydrocarbons (PAHs), are known activators for nuclear receptors like AhR and PPAR. The aim of this work is to investigate if exposure to PAHs from traffic PM2.5 would induce changes in expressions of related genes. Methods: We collected blood from traffic police officers in Northern Taiwan before and after they went on duty at the road cross-sections. We asked them to carry personal air samplers during the day of work, to collect PM2.5 particles to which they would be exposed. Afterwards, PAHs were extracted from these particles and analyzed with Gas Chromatograph-Mass Spectrometer. mRNA was extracted and converted into cDNA for gene expression analysis with Taqman systems. Expression of AhR, CYP1A1, CYP1A2, CYP1B1, TNF, IL-1, IL-6, IL-8, PPARα, and IL-10 were analyzed. Results: Ambient air PM2.5 concentrations ranged from 39.5 to 244.8 μg/m3. Most abundant PAH were acenaphthene, dibenzo(a,e)pyrene, naphthalene, and acenaphthylene, among 20 PAH species. AhR mRNA level either decreased or increased after exposure in 75% of the participants, and most of the subjects also had their IL-1, IL-6, IL-8, IL-10 gene levels altered in their post-shift levels compared to pre-shift levels (P < 0.05). Conclusion: PAHs from traffic PM2.5 may induce changes in expressions of AhR and related gene levels.

Influence of traffic emissions on the composition of atmospheric particles of different sizes—Part 2: SEM–EDS characterization

In urban areas traffic is the major contributor to atmospheric particulate matter and exposure to these particles currently represents a serious risk to human health. The attention has been recently focused more on the particles of smaller sizes (PM2.5) which penetrate deeper in respiratory system causing severe health effects. Therefore, more information on PM2.5 should be provided, namely concerning morphological and chemical characterization. Aiming further evaluation of the impact of traffic emissions on public health, this work evaluated the influence of traffic on the chemical and morphological characteristics of PM10 and PM2.5, collected at one site influenced by traffic emissions and at one reference site. Chemical and morphological characteristics of 1,000 individual particles were determined by scanning electron microscopy combined with energy dispersive spectrometer (SEM–EDS). Cluster analysis (CA) was used to identify different types of particles that occurred in PM, aiming the identification of the respective emission sources. Traffic PM2.5 were dominated by particles composed of Fe oxides and alloys (67%) which were related to traffic emissions (this percentage was 3.7 times higher than at the background site); in PM2.5–10 the abundance of Fe oxides and alloys were 20% and 0% for the traffic and background sites, respectively. Background PM2.5 were mainly constituted by aluminum silicates (63%) related to natural sources (this percentage was 2.5 times higher than at the traffic site); the abundances of aluminum silicates in PM2.5–10 were 74% and 73% for traffic and background sites, respectively. It was concluded that traffic emissions were mainly present in PM2.5 (the percentage of particles associated to these emissions was 3.4 times higher than in PM2.5–10), while coarse particles were dominated by material of natural origin (the percentage of particles associated was 1.2 and 3.0 times higher than in PM2.5 for traffic and background sites, respectively). Previous results obtained by proton induced X-ray emission (PIXE) were consistent with SEM–EDS analysis that showed to be very useful to complement elemental analysis of different PM2.5 and PM2.5–10.