Traffic-related Air Pollution Research Articles

Association of Tailpipe-Related and Nontailpipe-Related Air Pollution Exposure with Cognitive Decline in the Chicago Health and Aging Project.

Evidence suggests that long-term exposure to air pollution may increase the risk of dementia and related cognitive outcomes. A major source of air pollution is automotive traffic, which is modifiable by technological and regulatory interventions. We examined associations of four traffic-related air pollutants with rates of cognitive decline in a cohort of older adults. We analyzed data from the Chicago Health and Aging Project (CHAP), a longitudinal (1993-2012) community-based cohort study of older adults that included repeated assessments of participants' cognitive performance. Leveraging previously developed air pollution models, we predicted participant-level exposures to the tailpipe pollutants oxides of nitrogen () and nitrogen dioxide (), plus the nontailpipe pollutants copper and zinc found in coarse particulate matter [PM with aerodynamic diameter to () and , respectively], over the 3 y prior to each participant's baseline assessment. Using generalized estimating equations, we estimated covariate-adjusted associations of each pollutant with rates of cognitive decline. We probed the robustness of our results via several sensitivity analyses, including alterations to the length of the exposure assessment window and exploring the influence of pre- and post-baseline selection bias. Using data from 6,061 participants, estimated associations of these pollutant exposures with cognitive decline were largely inconsistent with large adverse effects. For example, a standard deviation () increment in corresponded to a slightly slower rate of cognitive decline [e.g., mean difference in change in global score, 0.010 standard unit/5 y, 95% confidence interval (CI): , 0.036]. The results of most of our sensitivity analyses were in generally similar to those of our main analyses, but our prebaseline selection bias results suggest that our analytic results may have been influenced by differential survivorship into our study sample. In this large prospective cohort study, we did not observe compelling evidence that long-term TRAP exposure is associated with cognitive decline. https://doi.org/10.1289/EHP14585.

Comparison of associations between proximity to major roads and all-cause mortality across a spectrum of cardiovascular diseases.

Global urbanization is leading to increased exposure to traffic-related air pollution (TRAP), which is associated with adverse health events. While individuals with cardiovascular disease (CVD) are known to have elevated susceptibility to air pollution exposure, no studies have evaluated how mortality risks associated with TRAP exposure differ based on the presence of CVD. We used three electronic health record-based cohorts to examine associations between proximity to major roadways and all-cause mortality. The three cohorts were a random sample of the hospital population, individuals with a prior myocardial infarction, and individuals with diagnosed heart failure (HF). We used Cox proportional hazards models to evaluate associations while adjusting for age, race, sex, and census block group socioeconomic status. Residing <250 m from a major roadway was associated with a hazard ratio (HR) of 1.13 (95% confidence interval = 1.05, 1.23) for individuals with HF, an HR of 1.07 (95% confidence interval = 0.96, 1.20) for those with a prior myocardial infarction, and an HR of 1.03 (95% confidence interval = 0.89, 1.20) for a random sample of hospital patients. This pattern persisted across several sensitivity analyses including alternative definitions of proximity to major roadways and matching the cohorts on demographics. These results highlight the differences in air quality-related health risks based on underlying CVD. Individuals with HF consistently had the highest environmental health risks. These results may better inform risks related to TRAP exposure in populations with differing underlying CVD.

Spatio-temporal quasi-experimental methods for rare disease outcomes: the impact of reformulated gasoline on childhood haematologic cancer

Abstract Although some pollutants emitted in vehicle exhaust, such as benzene, are known to cause leukaemia in adults with high exposure levels, less is known about the relationship between traffic-related air pollution (TRAP) and childhood haematologic cancer. In the 1990s, the US EPA enacted the reformulated gasoline program in select areas of the U.S., which drastically reduced ambient TRAP in affected areas. This created an ideal quasi-experiment to study the effects of TRAP on childhood haematologic cancers. However, existing methods for quasi-experimental analyses can perform poorly when outcomes are rare and unstable, as with childhood cancer incidence. We develop Bayesian spatio-temporal matrix completion methods to conduct causal inference in quasi-experimental settings with rare outcomes. Selective information sharing across space and time enables stable estimation, and the Bayesian approach facilitates uncertainty quantification. We evaluate the methods through simulations and apply them to estimate the causal effects of TRAP on childhood leukaemia and lymphoma.

Levels of OH-PAHs and markers of oxidative stress in urine of taxi drivers and controls

Polycyclic aromatic hydrocarbons are pervasive in the atmosphere, originating from sources like vehicle emissions and incomplete combustion. Exposure to PAHs occurs through diet, tobacco smoke, and air pollutants, and they are recognized as carcinogens. This study, conducted from July to October 2021 in Seoul, Gyeonggi, and Ulsan regions, focused on taxi drivers, a group with elevated PAH exposure due to prolonged vehicle use. The study involved 19 male taxi drivers and 46 control participants (18 male, 28 female). LC-MS/MS analysis was employed to quantify urinary levels of 18 hydroxy-PAHs, oxidative damage markers (MDA, 8-OHdG), and cotinine. The detection rates of OH-PAHs were 1-naphthol (96.9 %), 2-naphthol (90.8 %), 2-hydroxyfluorene (86.2 %), and 1-hydroxypyrene (80.0 %). Compared to the male controls, taxi drivers showed higher median concentrations of 2-OH-Na (1.698 ng/mL), 1-OH-Na (0.666 ng/mL), 2-OH-Flu (0.067 ng/mL), and 1-OHP (0.045 ng/mL). Similarly, significant differences were observed between taxi drivers and female controls for 1-OH-Na, 2-OH-Na, 2-OH-Flu, 3-OH-Phe, and 1-OHP. MDA and 8-OHdG were detected in over 90% of all groups, with significant differences between taxi drivers. Strong positive correlations were revealed between urinary OH-PAHs, MDA, and 8-OHdG (r ranging from 0.589 to 0.770, p&lt;0.01). The findings suggest that taxi drivers, due to prolonged exposure to traffic-related air pollutants, have elevated levels of PAH metabolites and oxidative stress, especially among smokers. Further studies with larger sample sizes are recommended to validate these results and explore the long-term health implications of occupational PAH exposure in urban transportation workers..

Assessment of long-term exposure to traffic-related air pollution: An exposure framework.

Exposure to ambient air pollution is associated with morbidity and mortality, making it an important public health concern. Emissions from motorized traffic are a common source of air pollution but evaluating the contribution of traffic-related air pollution (TRAP) emissions to health risks is challenging because it is difficult to disentangle the contribution of individual air pollution sources to exposure contrasts in an epidemiological study. This paper describes a new framework to identify whether air pollution differences reflect contrasts in TRAP exposures. Because no commonly measured pollutant is entirely specific to on-road motor vehicles, this exposure framework combined information on pollutants, spatial scale (i.e., geographic extent), and exposure assessment methods and their spatial scale to determine whether the estimated effect of air pollution in a given study was related to differences in TRAP. The exposure framework extended beyond the near-road environment to include differences in exposure to TRAP at neighborhood resolution ( ≤ 5 km) across urban, regional, and national scales. It also embedded a stricter set of criteria to identify studies that provided the strongest evidence that exposure contrasts were related to differences in traffic emissions. Application of the framework to the transparent selection of epidemiological studies for a systematic review produced insights on assessing and improving comparability of TRAP exposure measures, particularly for indirect measures such as distances from roads. It also highlighted study design challenges related to the duration of measurements and the structure of epidemiological models. This manuscript describes a new exposure framework to identify studies of traffic-related air pollution, a case study of its application in an HEI systematic review, and its implications for exposure science and air pollution epidemiology experts. It identifies challenges and provides recommendations for the field going forward. It is important to bring this information to the attention of researchers in air pollution exposure science and epidemiology because applying the broader lessons learned will improve the conduct and reporting of studies going forward.

Investigating the Association Between Traffic-related Air Pollution (PM2.5 and Benzene) and the Risk of Asthma: A Systematic Review and Meta-analysis

Background and aims: Asthma is a chronic disease that causes respiratory system inflammation. Recently, traffic-related air pollution (TRAP), especially particulate matter (PM2.5) and benzene, has been considered a factor that may increase the risk of asthma. This study investigated the association between TRAP (PM2.5 and benzene) and asthma risk. Methods: In this systematic review and meta-analysis, the relevant published data were collected by searching the Cochrane Library, Web of Science, ScienceDirect, Scopus, PubMed, and Google Scholar databases up to November 2022. The study quality was evaluated using the Newcastle-Ottawa Scale checklist. The data were analyzed using Stata software (version 14), and the significance level in this meta-analysis study was considered to be&lt;0.05. Results: In the first search, 4,909 and 4,825 studies were extracted for PM2.5 and benzene, respectively. After evaluating and considering the search criteria, 25 and 4 studies remained for PM2.5 and benzene, respectively. For PM2.5, the odds ratio (OR) for developing asthma in the TRAP-exposed group compared to the unexposed group was 1.11 (95% confidence interval [CI]: 1.04-1.19, P=0.002). For benzene, the OR of developing asthma in the exposed group was 1.19 when compared to the unexposed group (95% CI: 1.10-1.29, P&lt;0.001). Conclusion: Based on this review study, there was a positive association between TRAP exposure and the development of asthma. The results confirmed that PM2.5 and benzene increase the risk of asthma.

Abstract 4129036: Air pollution and Cardiovascular Disease Incidence in a Pooled Analysis of 6 U.S. Cohorts

Introduction/Background: Long term air pollution is associated with cardiovascular disease (CVD) incidence, however the effects at low levels in the US are unknown. Research Questions/Hypothesis: To assess the effect of long-term exposure to ambient air pollution CVD incidence (myocardial infarction [MI], stroke, and all CVD) in a pooled analysis of epidemiological cohorts. Materials and Methods: We harmonized data from six cohorts (Cardiovascular Health Study, Nurses’ Health Study II, Multi-Ethnic Study of Atherosclerosis, and Reasons for Geographic And Racial Differences in Stroke Study, Sister Study, and Women’s Health Initiative) with both CVD incidence data and extensive covariate information. We predicted time-varying two-year average concentrations of particulate matter &lt;2.5 microns (PM 2.5 ) and nitrogen dioxide (NO 2 ) from 1990-2019 at participant residential addresses using validated regionalized spatio-temporal models, combining spatio-temporal universal kriging and partial least squares regression with inputs from monitoring data from regulatory and researcher-deployed networks, satellite remote-sensing data and chemical transport modeling. Cox proportional hazards regression models adjusted for individual and area-level information, including smoking status, socio-economic factors, anthropometry, blood pressure, cholesterol, and other clinical variables. Results: Our analysis included over 300,000 participants and 10,229 incident CVD events. The median residential concentration of PM 2.5 was 10.1 µg/m 3 and NO 2 was 9.3 ppb. A 5 µg/m 3 increase in PM 2.5 was not significantly associated with a higher hazard ratio for all outcomes: 1.03 (95%CI: 0.96, 1.10) for CVD incidence, 1.03 (95%CI 0.93, 1.15) for MI, and 1.00 (95%CI 0.90, 1.10) for stroke. We found that a 10ppb increase in NO 2 was associated with a 1.06 (95%CI: 1.01, 1.11) higher hazard ratio for CVD, 1.07 (95%CI: 1.01, 1.11) for MI, and 1.02 (95%CI 0.96, 1.09) for stroke. Conclusions: In this analysis of a large, pooled cohort with excellent exposure, outcome, and covariate information, we found an association of NO 2 exposure—considered a surrogate for traffic-related air pollution—with CVD incidence and MI. Associations with particulate matter were not statistically significant, but could not exclude a small increased risk. We are extending this analysis to include 6 additional cohorts and an additional 700,000 participants to allow for generation of a highly resolved concentration-response function.

Multidimensional structural racism and estimated cancer risk from traffic-related air pollution.

Traffic-related air pollutants have been associated with a variety of adverse human health impacts, including cancers. In the United States, numerous studies have documented racial inequities in neighborhood exposures to traffic-related air pollution. Emerging evidence suggests that structural racism may influence neighborhood exposures to air pollutants. However, existing research has largely focused on residential racial segregation, one indicator of structural racism. This study developed a multidimensional measure of structural racism to examine the relationship between structural racism and estimated cancer risk from air pollutants in Georgia. Carcinogenic air toxics data were obtained from the US Environmental Protection Agency's 2019 Air Toxics Screening Assessment and sociodemographic data from the American Community Survey. Guided by stakeholder input, county-level data on residential segregation, education, employment, incarceration, economic status, political participation, and homeownership were used to create a multidimensional county-level structural racism index. Relative risks (RRs) were estimated for associations between structural racism and elevated (top 10% in Georgia) estimated cancer risk from air toxics. Multilevel analyses revealed a significant association between multidimensional structural racism and exposure to carcinogenic traffic-related air pollutants. Neighborhoods in the highest quartile of structural racism exhibited an elevated cancer risk from traffic-related air pollutants (RR,7.84; 95% CI,5.11-12.05) compared to neighborhoods with lower levels of structural racism. Multidimensional structural racism was associated with estimated cancer risk from traffic-related air pollution in Georgia. Findings can inform future studies and policy interventions that address racial inequalities in exposure to traffic-related air pollution.

Defining VOC signatures of Airway Epithelial Cells with PM2.5 Exposure.

Volatile organic compounds (VOCs) produced by the lung in response to exposure to environmental pollutants can be utilized to study their impact on lung health and function. Previously, we developed a method to measure VOCs emitted from well-differentiated tracheobronchial epithelial cells in vitro. Using this method, we exposed well-differentiated proximal (PECs) and distal airway epithelial cells (DECs) to varying doses of traffic-related air pollutants (TRAP) and wildfire particulates to determine specific VOC signatures after exposure. We utilized PM2.5 TRAP collected from the Caldecott tunnel in Oakland, CA and the 2018 Camp Fire to model "real-life" exposures. The VOCs were collected and extracted from Twisters and analyzed using gas chromatography-mass spectrometry (GC-MS). Exposure to both types of particulate matter (PM) resulted in specific VOC responses grouped by individual subjects with little overlap. Interestingly the VOCs produced by the PECs and DECs were also differentiated from each other. Our studies suggest that PM exposure induces a specific compartmentalized cellular response that can be exploited for future studies. This response is cell-type specific and potentially related to a phenotype we have yet to uncover.

Determining the Effectiveness of Interventions for the Reduction of Child Exposure to Traffic-Related Air Pollution at Schools in England

Traffic-related air pollution (TRAP) is a significant risk to human health and is particularly damaging to children as a vulnerable group. TRAP exposure near schools and on the school commute is linked to a growing number of adverse health effects, including respiratory and cardiovascular disease and can lead to (and exacerbate existing) respiratory conditions. The current study aimed to assess the effectiveness of interventions for the reduction of potential child exposure to TRAP at the school gates and on the school commute. This study employed dispersion modelling to assess the effects of interventions for reducing TRAP concentrations in the vicinity of five schools in England. The results revealed that all interventions led to reductions in nitrogen dioxide (NO2) concentrations. Improved travel routes were the most effective intervention for reducing concentrations along travel routes, while the introduction of low-emission zones (LEZs) proved most effective in reducing NO2 concentrations at schools, with greater effectiveness observed at shorter distances. Active travel also demonstrated effectiveness, particularly in areas with heavy traffic. When considering all receptors, LEZ implementation, active travel, and rideshare interventions exhibited effectiveness, with greater distance providing greater reductions in NO2 concentrations. Anti-idling was found to be more effective in sparsely populated areas. Combined with improved travel routes, anti-idling showed the greatest percentage difference in concentrations, followed by active travel, and rideshare.

Health impact assessment of exposure to road traffic noise and air pollution according to pre- and post-densification scenarios in Helsingborg, Sweden

A prevalent solution to accommodate population growth due to urbanization is densification. However, this often pushes new residential housing closer to roads, increasing exposure to both noise and air pollution. The present study’s aim was to estimate the health impacts of road traffic-related noise and air pollution for a low-income area (Drottninghög) in Helsingborg, Sweden, according to pre-densification (2012) and post-densification (2030) scenarios.Road traffic noise was simulated at the façade of residential buildings using the Nordic prediction method, and exposure was assessed using SoundPLAN. Exposure-response functions (ERF) from the WHO were utilized for the following health outcomes associated with noise: annoyance, adverse sleep disturbance, ischemic heart disease (IHD) incidence and IHD mortality. Air pollution (nitrogen dioxide, NO2) was assessed using a Gaussian dispersion model (AERMODE). Health outcomes associated with NO2 included natural cause mortality, pediatric asthma, respiratory hospitalizations and low birth weight (LBW). ERFs were derived from meta-analyses. Health impact assessments were then performed for both scenarios.Densifying Drottninghög according to the municipality’s planned strategy would lead to a 15 % unit increase in the proportion of residents exposed to road traffic noise above the WHO’s health-based guideline value (53 dB(A) Lden). This was estimated to markedly increase the proportion of residents highly annoyed by traffic noise (7.4–13.9 %) as well as those highly sleep disturbed (3.0–4.9 %). IHD incidence and IHD mortality attributed to noise would increase by an estimated 49 % and 44 %, respectively, post-densification. NO2 exposure was estimated to increase slightly (0.7 µg/m3) post-densification, which would contribute to an estimated 4–6 % increase in natural cause mortality, pediatric asthma, respiratory hospitalization and LBW.Urban planning initiatives need to consider these prevalent urban environmental exposures and integrate a public health perspective into densification strategies. Doing so can create synergies in the built environment that promote healthy, sustainable cities.

Impact of childhood exposure to traffic related air pollution on adult cardiometabolic health: Exploring the role of perceived stress

BackgroundLittle is known about how childhood exposure to traffic-related air pollution (TRAP) and stress interact to affect adults' cardiometabolic health. We examined this interaction and assessed the impact of over 10 years of childhood TRAP exposure on cardiometabolic health. MethodsFrom 2018 to 2023, 313 young adults from the Southern California Children's Health Study were enrolled in a follow-up assessment. Using CALINE4 line source dispersion model, average childhood TRAP exposures (from pregnancy to age 13) were estimated for nitrogen oxides (NOx) from all roads. Traffic density was calculated within a 300-m residential buffer. Cardiometabolic health was assessed in adulthood (mean age 24 ± 1.7) based on blood lipids (total cholesterol, high- and low-density lipoprotein [HDL, LDL], triglycerides), glucose metabolism (fasting glucose, fasting insulin, HbA1c), body composition (BMI, android/gynoid ratio [AG ratio], percent body fat), and blood pressure. A PDAY (Pathobiologic Determinants of Atherosclerosis in Youth) score was generated to evaluate overall cardiometabolic health. Participants' perceived stress was assessed in childhood and adulthood (ages 13 and 24 years, respectively). ResultsResults of mixed effects linear models, adjusted for demographics and smoking status, suggested that each standard deviation increase in childhood exposure to traffic-related total NOx was associated with 0.62 increase in PDAY score (95% Confidence Interval [CI]:0.10,1.14), 0.09% increase in HbA1c (95%CI: 0.04, 0.15), 1.19% increase in percent body fat (95%CI: 0.18, 2.20), and 0.96 kg/m2 increase in BMI (0.11, 1.80) in adulthood. Among participants with higher childhood stress levels, we observed significant associations of traffic-related total NOx with total cholesterol, HDL, LDL, HbA1c, insulin, and BMI. None of these associations were significant among people with lower stress levels. We observed similar statistically significant associations of traffic density. ConclusionLong-term childhood exposure to TRAP in childhood may have lasting adverse impacts on cardiometabolic health, especially for children with higher stress levels.

Development of a metabolomic risk score for exposure to traffic-related air pollution: A multi-cohort study

To synthesize vast amounts of high-throughput biological information, omics-fields like epigenetics have applied risk scores to develop biomarkers for environmental exposures. Extending the risk score analytic tool to the metabolomic data would be highly beneficial. This research aimed to develop and evaluate metabolomic risk score (metRS) approaches reflecting the biological response to traffic-related air pollution (TRAP) exposure (fine particulate matter, black carbon, and nitrogen dioxide). A simulation study compared three metRS methodologies: elastic net regression, which uses penalized regression to select metabolites, and two variations of thresholding, where a p-value cutoff is used to select metabolites. The methods performance was compared to assess 1) ability to correctly select metabolites associated with daily TRAP and 2) ability of the risk score to predict daily TRAP exposure. Power calculations and false discovery rates (FDR) were calculated for each approach. This metRS was applied to two real cohorts, the Center for Health Discovery and Wellbeing (CHDWB, n = 180) and Environment and Reproductive Health (EARTH, n = 200). In simulations, elastic net regression consistently presented inflated FDR for both high and low effect sizes and across all three sample sizes (n = 200; 500; 1000). Power to detect correct metabolites exceeded 0.8 for all three sample sizes in all three methods. In the real data application assessing associations of metabolomics risk scores and TRAP, associations were largely null. While we did not identify strong associations between the risk scores and TRAP in the real data application, metabolites selected by the risk score approaches were enriched in pathways that are well-known for their association with TRAP. These results demonstrate that certain methodologies to construct metabolomics risk scores are statistically robust and valid; however, standardized metabolic profiling and large sample sizes are required.

Association between Long-Term Exposure to Traffic-Related Air Pollution and Cardio-Metabolic Phenotypes: An MRI Data-Based Analysis.

Long-term exposure to traffic-related air pollution (TRAP) is associated with cardiometabolic disease; however, its role in subclinical stages of disease development is unclear. Thus, we aimed to explore this association in a cross-sectional analysis, with cardiometabolic phenotypes derived from magnetic resonance imaging (MRI). Phenotypes of the left (LV) and right cardiac ventricle, whole-body adipose tissue (AT), and organ-specific AT were obtained by MRI in 400 participants of the KORA cohort. Land-use regression models were used to estimate residential long-term exposures to TRAP, e.g., nitrogen dioxides (NO2) or particle number concentration (PNC). Associations between TRAP and MRI phenotypes were modeled using linear regression. Participants' mean age was 56 ± 9 years, and 42% were female. Long-term exposure to TRAP was associated with decreased LV wall thickness; a 6.0 μg/m3 increase in NO2 was associated with a -1.9% [95% confidence interval: -3.7%; -0.1%] decrease in mean global LV wall thickness. Furthermore, we found associations between TRAP and increased cardiac AT. A 2,242 n/cm3 increase in PNC was associated with a 4.3% [-1.7%; 10.4%] increase in mean total cardiac AT. Associations were more pronounced in women and in participants with diabetes. Our exploratory study indicates that long-term exposure to TRAP is associated with subclinical cardiometabolic disease states, particularly in metabolically vulnerable subgroups.

Forecasting Daily Air Quality Index and Early Warning System for Estimating Ambient Air Pollution on Road Networks Using Gaussian Dispersion Model with Deep Learning Algorithm

The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN.

An Integrated Field Study of Turbulence and Dispersion Variations in Road Microenvironments.

Traffic-related air pollutants (TRAPs) emitted from vehicle tailpipes disperse into nearby microenvironments, posing potential exposure risks. Thus, accurately identifying the emission hotspots of TRAPs is essential for assessing potential exposure risks. We investigated the relationship between turbulent kinetic energy (TKE) and pollutant dispersion (D) through an integrated field measurement. A five-year near-road sampling campaign (5 min based) near a light-duty vehicle-restricted roadway and an on-road sampling campaign (5 s based) on isolated proving grounds were conducted. The D was first calculated based on vehicle emission and pollutant concentrations and then paired with TKE measurements. Here, 198 near-road and 377 on-road measurement pairs were collected. In the near-road measurements, TKE and D showed a positive relationship (R2 ≥ 0.69) with the vehicle flow rate, while they showed similar decay patterns and sensitivity to vehicle types in the on-road measurements. A relationship between TKE and D (TKE-D) was developed through these measurements, demonstrating a robust correlation (R2 ≥ 0.61) and consistent slope values (1.1-1.3). These findings provide field evidence for the positive association between TKE and D, irrespective of the measurement techniques or locations. The TKE-D relationship enables vehicle emission estimation with TKE as the sole input, facilitating the identification of emission hotspots with high spatiotemporal resolution.

12513 Prenatal Traffic Exposures Are Associated With Earlier Age At Menarche

Abstract Disclosure: E.L. Silva: None. Z. Wang: None. S. Rifas-Shiman: None. A. Fleisch: None. H. Gibson: None. D. Gold: None. J. Chavarro: None. M. Hivert: None. E. Oken: None. B. Coull: None. J.E. Hart: None. T. James-Todd: None. S. Mahalingaiah: None. Background: The trend towards younger age at menarche over several decades has raised concern as earlier menarche is associated with various adverse health outcomes throughout adulthood including higher risk of early menopause, reproductive cancers, and metabolic disease. Some have suggested environmental exposures early in life may contribute to the temporal trend. Here we aimed to determine the extent to which prenatal residential measures of fine particulate matter (PM2.5), black carbon (BC), and other markers for traffic-related air pollution are associated with earlier age at menarche. Study Methods: Our study population included all female offspring in Project Viva, a pre-birth prospective cohort, with available age at menarche data. We used daily spatiotemporal models to estimate prenatal residential PM2.5 and BC exposures overall and by trimester. Residential proximity to major roadway and traffic density within a 100m buffer were measured at birth. Age at menarche was assessed via questionnaires during pre-teen and teen years. We used Cox proportional hazards modeling, adjusted for sociodemographic variables and census tract median household income, to estimate hazard ratios (HRs) for the association between air pollutants and age at menarche (months), where HR&amp;gt;1 indicates earlier onset of age at menarche associated with increased exposure to air pollutants. We weighted observations by the inverse probability of having available outcome data among all female offspring to account for cohort attrition. Results: The study included 638 females born between 2000 and 2003. Mean (standard deviation (SD)) age at menarche was 12.7 (1.4) years. Mean (SD) prenatal PM2.5 and BC exposures were 11.3 (1.5) g/m3 and 0.72 (0.21) g/m3, respectively. Only 7% of the cohort lived within 100m of a major roadway at birth. Prenatal PM2.5 exposure was not associated with age at menarche and the exposure distribution was mostly below US standards at the time (range: 6.8-15.8 g/m3, interquartile range (IQR): 2.0-3.0 g/m3). Higher overall prenatal and 3rd trimester BC were associated with earlier age at menarche [e.g., HR: 1.26 (95% CI, 1.04, 1.54) per IQR increment in 3rd trimester BC]. Living within 100m of a major roadway (versus 200m) [HR = 1.42 (95% CI, 1.08, 1.86)] and living in areas in the 2nd or 3rd (versus 1st) tertile of traffic density [2nd tertile: HR = 1.37 (95% CI, 1.07, 1.76); 3rd tertile: HR = 1.30 (95% CI, 1.02, 1.66)] at birth were also associated with earlier age at menarche. Conclusions: Higher prenatal exposure to BC, closer residential proximity to road major roadways, and higher traffic density were all associated with earlier age at menarche. The 3rd prenatal trimester and the window around birth may be especially important for traffic-related endocrine-disrupting and pro-inflammatory exposures with potential to accelerate puberty timing among females. Presentation: 6/1/2024

Mitigating child exposure to traffic-related air pollution on the school commute: Views of parents and teachers in England

Children are particularly vulnerable to the adverse health effects of traffic-related air pollution (TRAP) due to their developing physiologies. A survey was distributed to schools in England for completion by parents and teachers. The survey examined perceptions of TRAP on the school commute and assessed views on potential interventions for reducing exposure. 76.7% of parents and 75.8% of teachers were concerned about TRAP at their school. The most common cause for concern was school proximity to a busy road (44.2% parents and 42.5% teachers).Active travel was the most common measure already undertaken. The biggest obstacles to reducing school TRAP were driving convenience, school proximity to busy roads, and lack of parental support. Parents and teachers considered local authorities most responsible for improving school TRAP. Active travel was a popular and desirable intervention for reducing potential child exposure, and parental education on this and related topics were also desirable.

Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study

BackgroundEpidemiological evidence regarding the association between air pollution and resting heart rate (RHR), a predictor of cardiovascular disease and mortality, is limited and inconsistent. ObjectivesWe used wearable devices and time-series analysis to assess the exposure-response relationship over an extended lag period. MethodsNinety-seven elderly individuals (>65 years) from the Taipei Basin participated from May to November 2020 and wore Garmin® smartwatches continuously until the end of 2021 for heart rate monitoring. RHR was defined as the daily average of the lowest 30-min heart rate. Air pollution exposure data, covering lag periods from 0 to 60 days, were obtained from nearby monitoring stations. We used distributed lag non-linear models and linear mixed-effect models to assess cumulative effects of air pollution. Principal component analysis was utilized to explore underlying patterns in air pollution exposure, and subgroup analyses with interaction terms were conducted to explore the modification effects of individual factors. ResultsAfter adjusting for co-pollutants in the models, an interquartile range increase of 0.18 ppm in carbon monoxide (CO) was consistently associated with increased RHR across lag periods of 0–1 day (0.31, 95 % confidence interval [CI]: 0.24–0.38), 0–7 days (0.68, 95 % CI: 0.57–0.79), and 0–50 days (1.02, 95 % CI: 0.82–1.21). Principal component analysis identified two factors, one primarily influenced by CO and nitrogen dioxide (NO2), indicative of traffic sources. Increases in the varimax-rotated traffic-related score were correlated with higher RHR over 0–1 day (0.36, 95 % CI: 0.25–0.47), 0–7 days (0.62, 95 % CI: 0.46–0.77), and 0–50 days (1.27, 95 % CI: 0.87–1.67) lag periods. Over a 0–7 day lag, RHR responses to traffic pollution were intensified by higher temperatures (β = 0.80 vs. 0.29; interaction p-value [P_int] = 0.011). Males (β = 0.66 vs. 0.60; P_int < 0.0001), hypertensive individuals (β = 0.85 vs. 0.45; P_int = 0.028), diabetics (β = 0.96 vs. 0.52; P_int = 0.042), and those with lower physical activity (β = 0.70 vs. 0.54; P_int < 0.0001) also exhibited stronger responses. Over a 0–50 day lag, males (β = 0.99 vs. 0.96; P_int < 0.0001), diabetics (β = 1.66 vs. 0.69; P_int < 0.0001), individuals with lower physical activity (β = 1.49 vs. 0.47; P_int = 0.0006), and those with fewer steps on lag day 1 (β = 1.17 vs. 0.71; P_int = 0.029) showed amplified responses. ConclusionsProlonged exposure to traffic-related air pollution results in cumulative cardiovascular risks, persisting for up to 50 days. These effects are more pronounced on warmer days and in individuals with chronic conditions or inactive lifestyles.

Commuter exposure to ultrafine particles (UFPs), lung deposited surface area (LDSA), and noise in Ho Chi Minh City, Vietnam

IntroductionTransport is a main contributor to air and noise pollution, particularly in urban areas with the high number of motorized vehicles. Promoting active transport (i.e., cycling and walking) and public transport is an instrumental factor in reducing traffic-related air and noise pollution. However, commuters of these transport modes are often exposed to high air and noise pollution than other commuter groups. MethodsThe levels of exposure to UFP, LDSA, and noise during commuting of different transport modes in Ho Chi Minh City (HCMC) were measured and compared. Then, the dose of inhaled pollutants during commuting was calculated for each commuter group by considering the travel time and minute ventilation. ResultsThe level of exposure to air and noise pollution varies across transport modes. Cyclists and motorcyclists were exposed to the highest level of air and noise pollution. Motorcyclists and cyclists were exposed to the highest UFP number concentrations during their trips, 3.6*105 and 3.1*105 particles/cm3, respectively. Car commuters were exposed to the lowest air and noise pollution level, followed by bus commuters. When considering the minute ventilation and travel time, cyclists inhaled significantly higher levels of air pollution than other commuters, followed by pedestrians, motorcyclists, bus commuters, and car users. As a result, the health impacts of exposure to traffic-related air and noise pollution for active commuters could be greater than other commuter groups. ConclusionsPromoting active and public transport is a promising approach to improving air quality and reducing congestion in HCMC. Using these transport modes also has potential positive health effects on commuters through physical activities. However, active commuters are faced with the highest risk of exposure to high air and noise pollution levels. To promote active transport and public transport in HCMC, measures to reduce the level of exposure to air and noise pollution for these commuter groups are needed.