Traffic-related pollution and symptoms of depression and anxiety among Chinese adults: A population-based study

BACKGROUND

Limited understanding exists regarding the associations of traffic-related pollution with depression and anxiety symptoms in individuals residing within low- and middle-income countries.

METHODS

Data for this study were extracted from the Psychology and Behavior Investigation of Chinese Residents (PBICR) survey, implemented between June 20 and August 31, 2023. We determined residential proximity to major roadways through self-reports and evaluated depression symptoms using the Patient Health Questionnaire-9 (PHQ-9), along with anxiety symptoms assessed through the Generalized Anxiety Disorder-7 (GAD-7). We examined the associations between residential proximity to major roadways and depression and anxiety symptoms using logistic regressions and generalized linear models, while controlling for potential confounding variables.

RESULTS

This study comprised a total of 22,723 participants. The adjusted odds ratios (OR) for depression symptoms were 1.34 (95 % confidence interval (CI) 1.20, 1.51), 1.29 (95 % CI 1.17, 1.43), 1.34 (95 % CI 1.20, 1.49), and 1.32 (95 % CI 1.17, 1.49) among individuals residing within <50 m, 50-100 m, 101-200 m, and 201-300 m, respectively, in comparison to those residing >300 m from a major roadway. Individuals residing <50 m, 50-100 m, 101-200 m, and 201-300 m from a major roadway exhibited adjusted OR for anxiety symptoms of 1.49 (95 % CI 1.30, 1.69), 1.21 (95 % CI 1.07, 1.37), 1.38 (95 % CI 1.21, 1.56), and 1.38 (95 % CI 1.20, 1.59), respectively, in contrast to those residing >300 m.

CONCLUSIONS

This study provides valuable insights into the associations between environmental factors and mental health. The findings underscore the importance of integrating environmental considerations into comprehensive mental health frameworks, especially for individuals residing near high-traffic areas.

A trait-based investigation into evergreen woody plants for traffic-related air pollution mitigation over time

This study investigated influences of leaf traits on particulate matter (PM) wash-off and (re)capture (i.e., net removal) over time. Leaf samples were taken before and after three rainfall events from a range of 10 evergreen woody plants (including five different leaf types), which were positioned with an optical particle counter alongside a busy road. Scanning electron microscopy was used to quantify the density (no./mm2), mass (μg/cm2), and elemental composition of deposited particles. To enable leaf area comparison between scale-like leaves and other leaf types, a novel metric (FSA: foliage surface area per unit branch length) was developed, which may be utilised by future research. Vehicle-related particles constituted 15 % of total deposition, and there was a notable 50 % decrease in the proportion of tyre wear particles after rainfall. T. baccata presented the lowest proportion (11.1 %) of vehicle-related particle deposition but the most consistent performance in terms of net PM removal. Only four of the 10 plant specimens (C. japonica, C. lawsoniana, J. chinensis, and T. baccata) presented effective PM wash-off across all particle size fractions and rainfall intensities, with a generally positive relationship observed between rainfall intensity and wash-off. Mass deposition was more significantly determined by particle size than number density. Interestingly, larger particles were also less easily washed off than smaller particles. Some traits typically considered to be advantageous (e.g., greater hairiness) may in fact hinder net removal over time due to retention under rainfall. Small leaf area is one trait that may promote both accumulation and wash-off. However, FSA was found to be the most influential trait, with an inverse relationship between FSA and wash-off efficacy. This finding poses trade-offs and opportunities for green infrastructure design, which are discussed. Finally, numerous areas for future research are recommended, underlining the importance of systems approaches in developing vegetation management frameworks.

Traffic-related air pollution and Parkinson's disease in central California

BACKGROUND

Prior studies suggested that air pollution exposure may increase the risk of Parkinson's Disease (PD). We investigated the long-term impacts of traffic-related and multiple sources of particulate air pollution on PD in central California.

METHODS

Our case-control analysis included 761 PD patients and 910 population controls. We assessed exposure at residential and occupational locations from 1981 to 2016, estimating annual average carbon monoxide (CO) concentrations - a traffic pollution marker - based on the California Line Source Dispersion Model, version 4. Additionally, particulate matter (PM2.5) concentrations were based on a nationwide geospatial chemical transport model. Exposures were assessed as 10-year averages with a 5-year lag time prior to a PD diagnosis for cases and an interview date for controls, subsequently categorized into tertiles. Logistic regression models were used, adjusting for various factors.

RESULTS

Traffic-related CO was associated with an increased odds ratio for PD at residences (OR for T3 vs. T1: 1.58; 95% CI: 1.20, 2.10; p-trend = 0.02) and workplaces (OR for T3 vs. T1: 1.91; 95% CI: 1.22, 3.00; p-trend <0.01). PM2.5 was also positively associated with PD at residences (OR for T3 vs. T1: 1.62; 95% CI: 1.22, 2.15; p-trend <0.01) and workplaces (OR for T3 vs. T1: 1.85; 95% CI: 1.21, 2.85; p-trend <0.01). Associations remained robust after additional adjustments for smoking status and pesticide exposure and were consistent across different exposure periods.

CONCLUSION

We found that long-term modeled exposure to local traffic-related air pollution (CO) and fine particulates from multiple sources (PM2.5) at homes and workplaces in central California was associated with an increased risk of PD.

Improving traffic-related air pollution estimates by modelling minor road traffic volumes

Accurately estimating annual average daily traffic (AADT) on minor roads is essential for assessing traffic-related air pollution (TRAP) exposure, particularly in areas where most people live. Our study assessed the direct and indirect external validity of three methods used to estimate AADT on minor roads in Melbourne, Australia. We estimated the minor road AADT using a fixed-value approach (assuming 600 vehicles/day) and linear and negative binomial (NB) models. The models were generated using road type, road importance index, AADT and distance of the nearest major road, population density, workplace density, and weighted road density. External measurements of traffic counts, as well as black carbon (BC) and ultrafine particles (UFP), were conducted at 201 sites for direct and indirect validation, respectively. Statistical tests included Akaike information criterion (AIC) to compare models' performance, the concordance correlation coefficient (CCC) for direct validation, and Spearman's correlation coefficient for indirect validation. Results show that 88.5% of the roads in Melbourne are minor, yet only 18.9% have AADT. The performance assessment of minor road models indicated comparable performance for both models (AIC of 1,023,686 vs. 1,058,502). In the direct validation with external traffic measurements, there was no difference between the three methods for overall minor roads. However, for minor roads within residential areas, CCC (95% confidence interval [CI]) values were -0.001 (-0.17; 0.18), 0.47 (0.32; 0.60), and 0.29 (0.18; 0.39) for the fixed-value approach, the linear model, and the NB model, respectively. In the indirect validation, we found differences only on UFP where the Spearman's correlation (95% CI) for both models and fixed-value approach were 0.50 (0.37; 0.62) and 0.34 (0.19; 0.48), respectively. In conclusion, our linear model outperformed the fixed-value approach when compared against traffic and TRAP measurements. The methodology followed in this study is relevant to locations with incomplete minor road AADT data.

Chronic Occupational Exposure to Traffic Pollution Is Associated with Increased Carotid Intima-Media Thickness in Healthy Urban Traffic Control Police

Urban traffic officers in many low- and middle-income countries are exposed to high levels of traffic-related air pollutants (TRAP) while working vehicle control on heavily congested streets. The impact of chronic TRAP exposure on the cardiovascular health, including the carotid intima-media thickness (CIMT), of this outdoor occupational group remains unclear. This cross-sectional study compared the average mean and maximum CIMT measurements of two groups of relatively young, healthy traffic police (32 ± 7 years; 77% male) in Quito, Ecuador, who were without clinical evidence of serious cardiovascular or other disease. Previously published background data on PM10 (a TRAP surrogate) indicated that street levels of the pollutant were several orders of magnitude higher at the street intersections worked by traffic police compared to those working only in an office. Accordingly, officers permanently assigned to daily traffic control duties requiring them to stand 0-3 m from heavily trafficked street intersections were assigned to the high exposure group (n = 61). The control group (n = 54) consisted of officers from the same organization who were permanently assigned to office duties inside an administration building. Mean and maximum CIMT were measured with ultrasound. General linear models were used to compare the CIMT measurements of the high exposure and control groups, adjusting for covariates. The adjusted average mean and maximum CIMT measures of the high exposure group were increased by 11.5% and 10.3%, respectively, compared to the control group (p = 0.0001). These findings suggest that chronic occupational exposure to TRAP is associated with increased CIMT in traffic police. This is important since even small increases in arterial thickening over time may promote earlier progression to clinical disease and increased premature mortality risk.

Study of traffic-related pollution and its treatment with a particular focus on microplastics in tunnel wash and road runoff water

Treatment of tunnel wash runoff water and road runoff water before it reaches the environment is recommended to limit the negative consequences of traffic-related pollution. The efficiency of existing water treatment systems to remove traffic-related microplastic (MP) has not been sufficiently documented. Expanding the knowledge about traffic-related MP and documenting the treatment efficiency of MP in road tunnel wash water (TWW) and road runoff (RRW) treatment systems were the objectives of the presented project. TWW from the Tåsen tunnel, Norway, and RRW from the Fossbekken sedimentation pond were investigated in summer and winter situations. Six commonly available polymer types, tire rubber tread particles (TRP), and road marking paints (RMP) were analyzed in the selected samples. About 0.12 and 0.26 μg/L of polymers were identified in winter and summer TWWs. Significantly higher tire rubber and road marking paint concentrations were identified in the winter sample compared to summer sample. Suspended particle concentration in the Fossbekken RRW treatment pond effluent was lower in the summer than in the winter sample. About 0.002 and 0.0008 μg/L polymer masses were identified in winter and summer samples, respectively. TRP in the winter and summer samples were 0.7 and 0.2 μg7/L, and 13.4 μg/L RMP was found in the winter sample, while it was only 0.008 μg/L in the summer sample.

Do objective and subjective traffic-related pollution, physical activity and nature exposure affect mental wellbeing? Evidence from Shenzhen, China

Urban environment (e.g. greenspaces, air pollution and traffic noise) and individuals' behaviours (e.g. physical activity) have all been associated with mental wellbeing. The large majority of studies on the influence of nature exposure on mental wellbeing assumed that multiple pathways act independently, ignoring the interactions among potential correlated pathways that engage simultaneously. The parallel mediation approach fails to explore the complex associations of combined exposure to air pollution, traffic noise and nature exposure with physical activity, which in turn affect mental wellbeing. Hence, the interest of understanding the sophisticated interactions among different pathways is warranted. We utilized structural equation modelling to simultaneously evaluate whether actual and perceived traffic-related pollution and physical activity mediate the associations between mental wellbeing and nature exposure, which was assessed by Normalized Difference Vegetation Index (NDVI), green view index (GVI), green space density and park accessibility. In summer 2022, we conducted questionnaires from 1772 adults residing in 117 neighbourhoods in Shenzhen, China. Nature exposure was positively and directly associated with mental wellbeing in the single mediator model that considered physical activity only. The indirect effects of nature exposure on mental wellbeing were observed through all pathways in all models, except through the perceived acoustic quality pathway in the serial mediation model. In addition, the percentage mediated by perceived air quality was higher than that of perceived acoustic quality. The influence of nature exposure on mental wellbeing was only for a small proportion mediated by the physical activity pathway. The associations between nature exposure and mental wellbeing were modified by individual characteristics, such as gender, age, income level and alcohol usage, but not employment status and smoking behaviour. These findings point out the importance of both objective and subjective environmental features and human behaviours on mental wellbeing, as well as the necessity of considering multiple pathways simultaneously.

Modeling and simulation analysis of vehicle pollution and carbon reduction management model based on system dynamics

The vehicle exhaust pollution has become an important source of air pollutant and CO2 emissions, with the continuous growth of the number of vehicles. Focusing on the increasingly serious problems of vehicle exhaust pollution and CO2 emissions, a management model of vehicle pollution reduction and carbon reduction was established by using system dynamics. Taking Beijing as the case study city, different emission reduction scenarios were designed. Different scenarios are analyzed, and the results reveal the following: (1) Although the carbon tax policy for motor vehicles can play a role in vehicle pollution reduction and carbon reduction to a certain extent, but as the simulation time goes on, the policy effect is gradually weakened. The emission reduction effect of new energy vehicle promotion policy is not significant, and there is a "lag effect" and a "seesaw effect." (2) The science and technology policy has multiple effects of environmental, economic, and health. It can significantly reduce vehicle pollution and carbon emissions, and achieve the peak carbon by 2030. (3) It is not that more policies are better for CO2 emission reduction, and there is a "crowding out effect" in the CS. (4) From the perspectives of long term, the science and technology policy is a more effective way to achieve the co-control of CO2 and PM2.5 and achieve the carbon peaking goal compared with other emission reduction scenarios. These results can provide reference for relevant departments to formulate emission reduction policies and realize the management of motor vehicle pollution reduction and carbon reduction.

Urban policy interventions to reduce traffic-related emissions and air pollution: A systematic evidence map

BACKGROUND

Urban areas are hot spots for human exposure to air pollution, which originates in large part from traffic. As the urban population continues to grow, a greater number of people risk exposure to traffic-related air pollution (TRAP) and its adverse, costly health effects. In many cities, there is a need and scope for air quality improvements through targeted policy interventions, which continue to grow including rapidly changing technologies.

OBJECTIVE

This systematic evidence map (SEM) examines and characterizes peer-reviewed evidence on urban-level policy interventions aimed at reducing traffic emissions and/or TRAP from on-road mobile sources, thus potentially reducing human exposures and adverse health effects and producing various co-benefits.

METHODS

This SEM follows a previously peer-reviewed and published protocol with minor deviations, explicitly outlined here. Articles indexed in Public Affairs Index, TRID, Medline and Embase were searched, limited to English, published between January 1, 2000, and June 1, 2020. Covidence was used to screen articles based on previously developed eligibility criteria. Data for included articles was extracted and manually documented into an Excel database. Data visualizations were created in Tableau.

RESULTS

We identified 7528 unique articles from database searches and included 376 unique articles in the final SEM. There were 58 unique policy interventions, and a total of 1,139 unique policy scenarios, comprising these interventions and different combinations thereof. The policy interventions fell under 6 overarching policy categories: 1) pricing, 2) land use, 3) infrastructure, 4) behavioral, 5) technology, and 6) management, standards, and services, with the latter being the most studied. For geographic location, 463 policy scenarios were studied in Europe, followed by 355 in Asia, 206 in North America, 57 in South America, 10 in Africa, and 7 in Australia. Alternative fuel technology was the most frequently studied intervention (271 times), followed by vehicle emission regulation (134 times). The least frequently studied interventions were vehicle ownership taxes, and studded tire regulations, studied once each. A mere 3 % of studies addressed all elements of the full-chain-traffic emissions, TRAP, exposures, and health. The evidence recorded for each unique policy scenario is hosted in an open-access, query-able Excel database, and a complementary interactive visualization tool. We showcase how users can find more about the effectiveness of the 1,139 included policy scenarios in reducing, increasing, having mixed or no effect on traffic emissions and/or TRAP.

CONCLUSION

This is the first peer-reviewed SEM to compile international evidence on urban-level policy interventions to reduce traffic emissions and/or TRAP in the context of human exposure and health effects. We also documented reported enablers, barriers, and co-benefits. The open-access Excel database and interactive visualization tool can be valuable resources for practitioners, policymakers, and researchers. Future updates to this work are recommended.

PROTOCOL REGISTRATION

Sanchez, K.A., Foster, M., Nieuwenhuijsen, M.J., May, A.D., Ramani, T., Zietsman, J. and Khreis, H., 2020. Urban policy interventions to reduce traffic emissions and traffic-related air pollution: Protocol for a systematic evidence map. Environment international, 142, p.105826.

Monitoring Vehicle Pollution and Fuel Consumption Based on AI Camera System and Gas Emission Estimator Model

Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.

The Impact of Community Shuttle Services on Traffic and Traffic-Related Air Pollution

Community shuttle services have the potential to alleviate traffic congestion and reduce traffic pollution caused by massive short-distance taxi-hailing trips. However, few studies have evaluated and quantified the impact of community shuttle services on urban traffic and traffic-related air pollution. In this paper, we propose a complete framework to quantitatively assess the positive impacts of community shuttle services, including route design, traffic congestion alleviation, and air pollution reduction. During the design of community shuttle services, we developed a novel method to adaptively generate shuttle stops with maximum service capacity based on residents' origin-destination (OD) data, and designed shuttle routes with minimum mileage by genetic algorithm. For traffic congestion alleviation, we identified trips that can be shifted to shuttle services and their potential changes in traffic flow. The decrease in traffic flow can alleviate traffic congestion and indirectly reduce unnecessary pollutant emissions. In terms of environmental protection, we utilized the COPERT III model and the spatial kernel density estimation method to finely analyze the reduction in traffic emissions by eco-friendly transportation modes to support detailed policymaking regarding transportation environmental issues. Taking Chengdu, China as the study area, the results indicate that: (1) the adaptively generated shuttle stops are more responsive to the travel demands of crowds compared with the existing bus stops; (2) shuttle services can replace 30.36% of private trips and provide convenience for 50.2% of commuters; (3) such eco-friendly transportation can reduce traffic emissions by 28.01% overall, and approximately 42% within residential areas.

Spatial distribution of health-risky road traffic noise pollution in Dessie City, North East Ethiopia

Objective

Dessie is the trade center for northeast Ethiopia. High traffic flow plus overacting of promotion made the city noisy. There is a shortage of relevant evidence that enforces policy makers to design intervention plans. Therefore, this study aimed to explore the health-risky road traffic noise pollution in Dessie City, Ethiopia.

Methods

The study was conducted by purposive selection of the study area and sampling sites of the city from May 31, 2021 –June 6, 2021. Noise level recordings were taken by a digital Sound Meter and location data was collected by Global Positioning System. Residential, health facility, commercial, and mixed sites were identified by field observation. A total of 20 noise sampling points were included. The sampling points were selected by considering World Health Organization guideline. The measurements were taken twice a day at peak hours, between 8:00–11:00am and 4:00–7:00pm on all days of the week. The sound level meter was placed at a height of 1.5m and 2m from the curb. A total of 280 sound level records were conducted over one week.

Results

Among twenty noise recording sites, more than 50% of them registered as excessive noisy sites for all types of site categories (health facility, residential, commercial, and mixed areas). For the seven days, average noise recordings were in the range of 66–72 dB at 83% of mixed areas; 33% of health facilities; 25% of residential areas, and 86% of commercial areas. The highest levels of noise pollution were seen at the Bus-station, Buanbuawuha Square, Tekuam, Arada, Ethio General Hospital, Ersha-seble, and Menafesha areas.

Conclusion

This study shows that the average noise level measurement within a week exceeded the permissible limits set by Ethiopia and the World Health Organization. It helps for policy development and timely actions against noise pollution and as baseline information for further investigation.

Evaluation of the Use of Saliva Metabolome as a Surrogate of Blood Metabolome in Assessing Internal Exposures to Traffic-Related Air Pollution

In the omics era, saliva, a filtrate of blood, may serve as an alternative, noninvasive biospecimen to blood, although its use for specific metabolomic applications has not been fully evaluated. We demonstrated that the saliva metabolome may provide sensitive measures of traffic-related air pollution (TRAP) and associated biological responses via high-resolution, longitudinal metabolomics profiling. We collected 167 pairs of saliva and plasma samples from a cohort of 53 college student participants and measured corresponding indoor and outdoor concentrations of six air pollutants for the dormitories where the students lived. Grand correlation between common metabolic features in saliva and plasma was moderate to high, indicating a relatively consistent association between saliva and blood metabolites across subjects. Although saliva was less associated with TRAP compared to plasma, 25 biological pathways associated with TRAP were detected via saliva and accounted for 69% of those detected via plasma. Given the slightly higher feature reproducibility found in saliva, these findings provide some indication that the saliva metabolome offers a sensitive and practical alternative to blood for characterizing individual biological responses to environmental exposures.

Green space, air pollution, traffic noise and mental wellbeing throughout adolescence: Findings from the PIAMA study

BACKGROUND

Green space, air pollution and traffic noise exposure may be associated with mental health in adolescents. We assessed the associations of long-term exposure to residential green space, ambient air pollution and traffic noise with mental wellbeing from age 11 to 20 years.

METHODS

We included 3059 participants of the Dutch PIAMA birth cohort who completed the five-item Mental Health Inventory (MHI-5) at ages 11, 14, 17 and/or 20 years. We estimated exposure to green space (the average Normalized Difference Vegetation Index (NDVI) and percentages of green space in circular buffers of 300 m, 1000 m and 3000 m), ambient air pollution (particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide, PM_2.5 absorbance and the oxidative potential of PM_2.5) and road traffic and railway noise (L_den) at the adolescents' home addresses at the times of completing the MHI-5. Associations with poor mental wellbeing (MHI-5 score ≤ 60) were assessed by generalized linear mixed models with a logit link, adjusting for covariates.

RESULTS

The odds of poor mental wellbeing at age 11 to 20 years decreased with increasing exposure to green space in a 3000 m buffer (adjusted odds ratio (OR) 0.78 [95% CI 0.68-0.88] per IQR increase in the average NDVI; adjusted OR 0.77 [95% CI 0.67-0.88] per IQR increase in the total percentage of green space). These associations persisted after adjustment for air pollution and road traffic noise. Relationships between mental wellbeing and green space in buffers of 300 m and 1000 m were less consistent. Higher air pollution exposure was associated with higher odds of poor mental wellbeing, but these associations were strongly attenuated after adjustment for green space in a buffer of 3000 m, traffic noise and degree of urbanization. Traffic noise was not related to mental wellbeing throughout adolescence.

CONCLUSIONS

Residential exposure to green space may be associated with a better mental wellbeing in adolescents.

Residential proximity to major roadway and progression in physical disability in older adults in China

Evidence of a connection between living near major roadway and adverse health outcome has come to light. However, little is known about the effect of residential traffic exposure on aging-related physical disability and whether this effect can be modified. We used data derived from Chinese Longitudinal Healthy Longevity Surveys in 2018, with a total of 15,771 participants aged ≥ 65. Residential proximity to major roadway was used as a surrogate for traffic-related air pollution and physical function was measured using basic and instrumental activities of daily living. Logistic regression models were adopted for the analyses. Compared with those living > 300 m from major road, participants living within 200 m had higher risk of poorer physical functioning. The peak odds ratio was observed in severe disability caused by 101-200 m distance (OR = 1.34, 95% CI: 1.08, 1.66). And this association got stronger with longer duration of living close to major road. In dichotomized analyses using 200 m as a cutoff point, the effect associated with living close to major road was greater in male and current smoker. Specially, poor physical function appears earlier in female, but less severe than that in male. The result emphasizes the adverse effect of residential proximity to major roadways to aging-related declines in health-especially for basic living ability. This association was more pronounced in male and current smokers, which cannot be reversed by lowering the indoor air pollution.

An analysis of emission reduction strategy for light and heavy-duty vehicles pollutions in high spatial-temporal resolution and emission

Traffic as the main source of urban air pollution created severe problems for human health and sustainability. To propose a bottom-up method in emissions reduction, accurate emission inventories of vehicles in a medium-sized city are developed. Traffic emission factors were obtained using traffic flow data, traffic control cameras, and International Vehicle Emission (IVE) model to calculate CO, VOCs, NOx, SOx, and PM of passenger cars, taxis, and urban buses emission inventory. Annual overall emissions of CO, VOCs, NOx, SOx, and PM pollutants, respectively, are 346, 20.9, 25, 44.4, and 0.5 kt/year. VOC and CO emissions in the start-up phase are in the scale of the running phase, while NOx, SOx, and PM allocate much less than the running phase. The highest emission value of SOx and PM occurs in arterials while CO, VOC, and NOx in highways. Eight renovation scenarios have been designed to evaluate their environmental and economic efficiency. Two scenarios entitled "renovation of carbureted, Euro 1 and Euro 2 LDV" and "renovation of high mileage Euro 1 and Euro 2 standard urban buses" showed the highest decrease in pollution emission and pollution social costs. For these two important scenarios, implementation costs were calculated at 477 and 46 M$, while social costs decrease are calculated to be 172.6 and 77.5 M$, respectively. Renovation of vehicles could benefit both the government and society by reducing fuel consumption and pollutant emission. The emission mitigation scenarios considering mobile sources could be a guide for adopting policies in developing countries. Governmental and social cost-share and governmental and social repayment because of fuel-saving costs have also been calculated.

Association of traffic-related air pollution with Newborn's anthropometric indexes at birth

An emerging body of evidence has associated exposure to traffic-related air pollution (TRAP) during pregnancy with birth outcomes. However, the evidence on the association of TRAP exposure and neonatal anthropometric measurements (NAPM) in low and middle-income countries is very scarce yet. Therefore, we investigate the association of prenatal exposure to indicators of traffic and ambient particulate matter (PM) with NAPM. This cross-sectional study was based on hospital medical records of 4053 mother-neonate pairs between May 16, 2016, and December 5, 2018. PMs were estimated at residential addresses based on validated spatiotemporal models. Moreover, total street length in 100, 300 and 500m buffers around the home, residential distance to the ring road, major roads, heavy-traffic lights, gas station, motorway junction, bank, square, bus terminal, public parking and industrial land-use were calculated as indicators of traffic. The head circumference (HC), birth weight (BW) and birth length (BL) of neonates were collected as NAPM. Multivariate regression models were applied to evaluate the relationship between PMs and indicators of traffic with NAPM, controlled for relevant covariates. The median (IQR) of BW, BL, and HC of newborns were 3250 (592) gr, 51.0 (3.5) cm, 35 (2) cm, respectively. The adjusted models revealed that higher exposure to PM_2.5 and PM₁₀ was significantly related with lower BW and BL. Similar results were observed for total street length in a 100 m buffer around maternal home with BW and BL. Moreover, higher distance to heavy traffic lights was significantly associated with higher BW and BL. An IQR increase in PM₁₀ was significantly related to lower HC (95% CI: 0.11, -0.01, P-value = 0.03). An increase in distance from residential address to heavy traffic lights, ring roads, bus terminal, and transportation land-use was associated with higher HC. Overall, our findings suggested that higher prenatal exposure to TRAP was related with lower BW, BL and HC.

Air pollution and high-intensity interval exercise: Implications to anti-inflammatory balance, metabolome and cardiovascular responses

High-intensity interval exercise (HIIE) is an effective non-pharmacological tool for improving physiological responses related to health. When HIIE is performed in urban centers, however, the exerciser is exposed to traffic-related air pollution (TRAP), which is associated with metabolic, anti-inflammatory imbalance and cardiovascular diseases. This paradoxical combination has the potential for conflicting health effects. Thus, the aim of this study was to determine the effects of HIIE performed in TRAP exposure on serum cytokines, non-target metabolomics and cardiovascular parameters. Fifteen participants performed HIIE in a chamber capable to deliver filtered air (FA condition) or non-filtered air (TRAP condition) from a polluted site adjacent to the exposure chamber. Non-target blood serum metabolomics, blood serum cytokines and blood pressure analyses were collected in both FA and TRAP conditions at baseline, 10 min after exercise, and 1 h after exercise. The TRAP increased IL-6 concentration by 1.7 times 1 h after exercise (p < 0.01) and did not change the anti-inflammatory balance (IL-10/TNF-α ratio). In contrast, FA led to an increase in IL-10 and IL-10/TNF-α ratio (p < 0.01), by 2.1 and 2.3 times, respectively. The enrichment analysis showed incomplete fatty acid metabolism under the TRAP condition (p < 0.05) 10 min after exercise. There was also an overactivity of ketone body metabolism (p < 0.05) at 10 min and at 1 h after exercise with TRAP. Exercise-induced acute decrease in systolic blood pressure (SBP) was not observed at 10 min and impaired at 1 h after exercise (p < 0.05). These findings reveal that TRAP potentially attenuates health benefits often related to HIIE. For instance, the anti-inflammatory balance was impaired, accompanied by accumulation of metabolites related to energy supply and reduction to exercise-induced decrease in SBP.

Traffic-related air pollution associated pulmonary pathophysiologic changes and cardiac injury in elderly patients with COPD

Traffic-related air pollution (TRAP) has shown enormous environmental toxicity, but its cardiorespiratory health impact on chronic obstructive pulmonary disease (COPD) has been less studied. We followed a panel of 45 COPD patients with 4 repeated clinical visits across 14 months in a traffic-predominated urban area of Beijing, China, with concurrent measurements of TRAP metrics (fine particulate matter, black carbon, oxides of nitrogen and carbon monoxide). Linear mixed-effect models were performed to evaluate the associations and potential pathways linking traffic pollution to indicators of spirometry, cardiac injury, inflammation and oxidative stress. We observed that interquartile range increases in moving averages of TRAP exposures at prior up to 7 days were associated with significant reductions in large and small airway functions, namely decreases in forced vital capacity of 3.1-9.3% and forced expiratory flow 25-75% of 5.9-16.4%. Higher TRAP levels were also associated with worsening of biomarkers relevant to lung injury (hepatocyte growth factor and surfactant protein D) and cardiac injury (high-sensitivity cardiac troponin I, B-type natriuretic peptide and soluble ST2), as well as enhanced airway/systemic inflammation and oxidative stress. Mediation analyses showed that TRAP exposures may prompt cardiac injury, possibly via worsening pulmonary pathophysiology. These findings highlight the importance of traffic pollution control priority in urban areas.

The health effects of traffic-related air pollution: A review focused the health effects of going green

Traffic-related air pollution (TRAP) is global concern due to both the ecological damage of TRAP and the adverse health effects in Humans. Several strategies to reduce TRAP have been implemented, including the use of sustainable fuels, after-treatment technologies, and new energy vehicles. Such approaches can reduce the exhaust of particulate matter, adsorbed chemicals and a range of gases, but from a health perspective these approaches are not always successful. This review aims to discuss the approaches taken, and to then describe the likely health effects of these changes.

Child serum metabolome and traffic-related air pollution exposure in pregnancy

BACKGROUND

Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and childhood disorders. High-resolution metabolomics (HRM) has previously been employed to identify metabolic responses to traffic-related air pollution in adults, including pregnant women. Thus far, no studies have examined metabolic effects of air pollution exposure in utero on neonates.

METHODS

We retrieved stored neonatal blood spots for 241 children born in California between 1998 and 2007. These children were randomly selected from all California birth rolls to serve as birth-year matched controls for children with retinoblastoma identified from the California cancer registry for a case control study of childhood cancer. We estimated prenatal traffic-related air pollution exposure (particulate matter less than 2.5 μm (PM_2.5)) during the third-trimester using the California Line Source Dispersion Model, version 4 (CALINE4) based on residential addresses recorded at birth. We employed untargeted HRM to obtain metabolic profiles, and metabolites associated with air pollution exposure were identified using partial least squares (PLS) regression and linear regressions. Biological effects were characterized using pathway enrichment analyses adjusting for potential confounders including maternal age, race/ethnicity, and education.

RESULTS

In total we extracted 4038 and 4957 metabolite features from neonatal blood spots in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 reverse phase columns (negative ion mode), respectively. After controlling for confounding factors, partial least square regression (Variable Importance in Projection (VIP) ≥ 2) selected 402 HILIC positive and 182 C18 negative features as statistically significantly associated with increasing third trimester PM_2.5 exposure. Using pathway enrichment analysis, we identified metabolites in oxidative stress and inflammation pathways as being altered, primarily involving lipid metabolism.

CONCLUSION

The metabolite features and pathways associated with air pollution exposure in neonates suggest that maternal exposure during late pregnancy contributes to oxidative stress and inflammation in newborn children.

Integrated molecular response of exposure to traffic-related pollutants in the US trucking industry

Exposure to traffic-related pollutants, including diesel exhaust, is associated with increased risk of cardiopulmonary disease and mortality; however, the precise biochemical pathways underlying these effects are not known. To investigate biological response mechanisms underlying exposure to traffic related pollutants, we used an integrated molecular response approach that included high-resolution metabolomic profiling and peripheral blood gene expression to identify biological responses to diesel exhaust exposure. Plasma samples were collected from 73 non-smoking males employed in the US trucking industry between February 2009 and October 2010, and analyzed using untargeted high-resolution metabolomics to characterize metabolite associations with shift- and week-averaged levels of elemental carbon (EC), organic carbon (OC) and particulate matter with diameter ≤ 2.5 μm (PM2.5). Metabolic associations with EC, OC and PM2.5 were evaluated for biochemical processes known to be associated with disease risk. Annotated metabolites associated with exposure were then tested for relationships with the peripheral blood transcriptome using multivariate selection and network correlation. Week-averaged EC and OC levels, which were averaged across multiple shifts during the workweek, resulted in the greatest exposure-associated metabolic alterations compared to shift-averaged exposure levels. Metabolic changes associated with EC exposure suggest increased lipid peroxidation products, biomarkers of oxidative stress, thrombotic signaling lipids, and metabolites associated with endothelial dysfunction from altered nitric oxide metabolism, while OC exposures were associated with antioxidants, oxidative stress biomarkers and critical intermediates in nitric oxide production. Correlation with whole blood RNA gene expression provided additional evidence of changes in processes related to endothelial function, immune response, inflammation, and oxidative stress. We did not detect metabolic associations with PM2.5. This study provides an integrated molecular assessment of human exposure to traffic-related air pollutants that includes diesel exhaust. Metabolite and transcriptomic changes associated with exposure to EC and OC are consistent with increased risk of cardiovascular diseases and the adverse health effects of traffic-related air pollution.

Mitochondria and traffic-related air pollution linked coronary artery calcification: exploring the missing link

The continuing increase in the exposure to Traffic-related air pollution (TRAP) in the general population is predicted to result in a higher incidence of non-communicable diseases like cardiovascular disease. The chronic exposure of air particulate matter from TRAP upon the vascular system leads to the enhancement of deposition of calcium in the vasculature leading to coronary artery calcification (CAC), triggered by inflammatory reactions and endothelial dysfunction. This calcification forms within the intimal and medial layers of vasculature and the underlying mechanism that connects the trigger from TRAP is not well explored. Several local and systemic factors participate in this active process including inflammatory response, hyperlipidemia, presence of self-programmed death bodies and high calcium-phosphate concentrations. These factors along with the loss of molecules that inhibit calcification and circulating nucleation complexes influence the development of calcification in the vasculature. The loss of defense to prevent osteogenic transition linked to micro organelle dysfunction that includes deteriorated mitochondria, elevated mitochondrial oxidative stress, and defective mitophagy. In this review, we examine the contributory role of mitochondria involved in the mechanism of TRAP linked CAC development. Further we examine whether TRAP is an inducer or trigger for the enhanced progression of CAC.

Traffic-related air pollution and endurance exercise: Characterizing non-targeted serum metabolomics profiling

Although the exposure to traffic-related air pollution (TRAP) has emerged as one of main problem worldwide to inhabitants' health in urban centers, its impact on metabolic responses during exercise is poorly understood. The aim of study was to characterize the profile of non-target serum metabolomics during prolonged exercise performed under TRAP conditions. Ten healthy men completed two 90 min constant-load cycling trials under conditions of either TRAP or filtered air. Experimental trials were performed in a chamber located on an avenue with a high volume of vehicle traffic. Blood samples were taken at 30 min, 60 min, and 90 min of exercise. Based on Nuclear Magnetic Resonance metabolomics, the non-target analysis was used to assess the metabolic profile. Twelve, 16 and 18 metabolites were identified as discriminants. These were: at 30 min of exercise, the coefficient of determination (R²) 0.98, the predictive relevance, (Q²) 0.12, and the area under the curve (AUC) 0.91. After 60 min of exercise: (R²: 0.99, Q²: 0.09, AUC: 0.94); and at 90 min of exercise (R²: 0.91, Q²: <0.01, AUC: 0.89), respectively. The discriminant metabolites were then considered for the target analysis, which demonstrated that the metabolic pathways of glycine and serine metabolism (p = 0.03) had been altered under TRAP conditions at 30 min of exercise; arginine and proline metabolism (p = 0.04) at 60 min of exercise; and glycolysis (p = 0.05) at 90 min of exercise. The present results suggest that exposure to TRAP during prolonged exercise leads to a significant change in metabolomics, characterized by a transitional pattern and lastly, impairs the glucose metabolism.

Acute cardiovascular effects of traffic-related air pollution (TRAP) exposure in healthy adults: A randomized, blinded, crossover intervention study

Exposure to traffic-related air pollution (TRAP) may enhance the risk of cardiovascular disease. However, the short-term effects of TRAP components on the cardiovascular system are not well understood. We conducted a randomized, double-blinded, crossover intervention study in which 39 healthy university students spent 2 h next to a busy road. Participants wore a powered air-purifying respirator (PAPR) or an N95 mask. PAPRs were equipped with a filter for particulate matter (PM), a PM and volatile organic compounds (VOCs) filter or a sham filter. Participants were blinded to PAPR filter type and underwent randomized exposures four times, once for each intervention mode. Blood pressure (BP), heart rate (HR) and heart rate variability (HRV) were measured before, during and for 6 h after the roadside exposure. Linear mixed-effect models were used to evaluate the effects of the interventions relative to baseline controlling for other covariates. All HRV measures increased during and following exposure for all intervention modes. Some HRV measures (SDNN and rMSSD during exposure and SDNN after exposure) were marginally affected by PM filtration. Wearing the N95 mask affected VLF power and rMSSD responses to traffic exposure differently than the PAPR interventions. Both systolic and diastolic BP increased slightly during exposure, but then were generally lower than baseline after exposure for the sham and filter interventions. HR, which fell during exposure and mostly remained lower than baseline after exposure, was lower yet with all filter interventions compared to the sham mode following exposure. Therefore, short-term exposure to traffic acutely affects HRV, BP and HR, but N95 mask and PAPR interventions generally show little efficacy in reducing these effects. Removing the PM component of TRAP has some limited effects on HRV responses to exposure but exaggerates the traffic-related decrease in HR. HRV findings from N95 mask interventions need to be interpreted cautiously.

Spatio-temporal changes of road traffic noise pollution at ecoregional scale

Noise pollution is a pervasive factor that increasingly threatens natural resources and human health worldwide. In particular, large-scale changes in road networks have driven shifts in the acoustic environment of rural landscapes during the past few decades. Using sampling plots from the Spanish Landscape Monitoring System (SISPARES), 16 km² each, we modelled the spatio-temporal changes in road traffic noise pollution in Ecoregion 1 of Spain (approximately 66,000 km²). We selected a study period that was characterised by significant changes in the size of the road network and the vehicle fleet (i.e. between 1995 and 2014) and used standard and validated acoustic computation methods for environmental noise modelling (i.e. European Directive, 2002/49/EC) within sampling plots. We then applied a multiple linear regression to expand noise modelling throughout the whole of Ecoregion 1. Our results showed that the noise level increased by 1.7 dB(A) in average per decade in approximately 65% of the territory, decreased by 1.3 dB(A) per decade in about 33%, and remained unchanged in 2%. This suggests that road traffic noise pollution levels may not grow homogeneously in large geographical areas, maybe due to the concentration of large fast traffic flows on modern motorways connecting towns. Our research exemplifies how landscape monitoring systems such as cost-effective approaches may play an important role when assessing spatio-temporal patterns and the impact of anthropogenic noise pollution at large geographical scales, and even more so in a global context of constricted resources and limited availability of historical data on traffic and environmental noise monitoring.

The impact of Traffic-Related air pollution on child and adolescent academic Performance: A systematic review

BACKGROUND

The negative health impacts of traffic-related air pollution (TRAP) have been investigated for many decades, however, less attention has been paid to the effect of TRAP on children's academic performance. Understanding the TRAP-academic performance relationship will assist in identifying mechanisms for improving students' learning and aid policy makers in developing guidance for protecting children in school environments.

METHODS

This systematic review assessed the relationship between TRAP and academic performance. Web of Science, PubMed, CINAHL, Medline, PsycINFO, SPORTDiscus, Scopus and ERIC databases were searched for relevant, peer reviewed, articles published in English. Articles assessing exposure to TRAP pollutants (through direct measurement, local air quality monitoring, modelling, or road proximity/density proxy measures) and academic performance (using standardised tests) in children and adolescents were included. Risk of bias was assessed within and between studies.

RESULTS

Of 3519 search results, 10 relevant articles were included. Nine studies reported that increased exposure to some TRAP was associated with poorer student academic performance. Study methodologies were highly heterogeneous and no consistent patterns of association between specific pollutants, age groups, learning domains, exposure windows, and exposure locations were established. There was a serious risk of bias within individual studies and confidence in the body of evidence was low.

CONCLUSIONS

This review found evidence suggestive of a negative association between TRAP and academic performance. However, the quality of this evidence was low. The existing body of literature is small, lacks the inclusion of high-quality exposure measures, and presents limitations in reporting. Future research should focus on using valid and reliable exposure measures, individual-level data, consistent controlling for confounders and longitudinal study designs.

Exposure Profile to Traffic Related Pollution in Pediatric Age: A Biomonitoring Study

The aim of this study was to trace an exposure profile to traffic-derived pollution during pediatric age. For this purpose, two biomonitoring campaigns for the determination of urinary (u-) methyl tert-butyl ether (MTBE), ethyl tert-butyl ether (ETBE), tert-amyl methyl ether (TAME), and diisopropyl ether (DIPE) were carried out in two different periods of the year (summer 2017 and winter 2018), among a large sample of healthy children (n = 736; 5–11 years old) living in rural and urban areas in central Italy. The quantification of u-MTBE, u-ETBE, u-TAME, and u-DIPE was performed by HS-SPME-GC/MS technique and information on participants was collected by a questionnaire. u-DIPE concentrations resulted always under the LOQ. u-TAME mean levels were similar in both seasons (18.7 ng L⁻¹ in summer vs. 18.9 ng L⁻¹ in winter), while u-MTBE and u-ETBE levels were, respectively, 69.9 and 423.5 ng L⁻¹ (summer) and 53.3 and 66.2 ng L⁻¹ (winter). Main predictors of urinary excretion resulted the time spent in motor vehicles, being male and younger.

Traffic-related pollution history (1994-2014) determined using urban lake sediments from Nanjing, China

With the development of urbanisation and the increasing number of modern vehicles, traffic contamination has become an important source of environmental pollution. Most previous studies have focused on using roadside soil or plants to determine the spatial pattern of traffic pollutants along roads and the factors that influence this pattern, whereas few studies have reconstructed pollution histories caused by traffic using suitable methods. In this study, two gravity cores were obtained from Qianhu Lake, which is in the Zhongshan tourist area of Nanjing City and is distant from industrial areas. An accurate chronological framework covering the period from 1994 to 2014 was established using the correlation between the variation in grain size of the sediment cores and the variation in annual rainfall in Nanjing City. Moreover, magnetic and chemical parameters were also measured, and the results demonstrated that concentration-related magnetic parameters exhibited different correlations with different heavy metal concentrations. These correlations were significantly positive for Zn, Pb, and Co; weakly positive for Ni; absent for Cr; and negative for V. Combined with statistical data on industrial emissions and private cars in Nanjing City since 1994, the observed variations in magnetic susceptibility, anhysteretic remanent magnetisation, saturation isothermal remanent magnetisation, Zn, Pb, and Co, were controlled by traffic activities in the tourist area but not by industry. Therefore, the variations in these parameters record the traffic pollution history of the study area. Combined with the obtained chronological framework, the traffic-related pollution history could be divided into two stages: 1) from 1994 to 2003, when traffic-related pollution became increasingly serious because of the exponential increase in the number of private cars and the prosperity of tourism; 2) from 2003 to 2014, when traffic-related pollution continuously increased but at a much slower rate than in stage 1. This slower rate of increase was probably related to the maximum carrying capacity of the tourist area and technological innovations in automobile manufacturing, as well as improvements in fuels.

Variability of traffic noise pollution levels as a function of city size variables

Noise levels measured in 27 cities with different areas (from 0.6 km² to 59.27 km²) and populations (from approximately 2000 to 70,000 inhabitants) were compared with respect to five different urban characteristics (population, area, total street length, density, and linear density). Comparisons were conducted for both overall city noise levels and noise registered on five types of roads with different functionality using the Categorisation Method. The results showed that four of the five cities' characteristics presented a significant correlation with the noise levels (all except for density). The calculated correlations were better for noise levels in the different categories than the overall noise values, with higher explained variability on the streets with more traffic. Therefore, the road categorisation method can be used not only to assess the noise variations within cities, but also to better explain the effect of noise on the analysed city characteristics. The results of the calculated relationships enable the estimation of noise levels both currently and in future urban developments of noise values on different types of streets.

Associations of traffic-related air pollution and greenery with academic outcomes among primary schoolchildren

BACKGROUND

There is preliminary evidence that greenery/greenspace around schools may be positively associated with children's cognitive development and academic outcomes, whereas traffic-related air pollution (TRAP) may have a detrimental effect. Few studies have examined pathways between both exposures and academic outcomes. This study aimed to assess associations between greenery, road traffic density (a proxy for TRAP) surrounding primary (elementary) schools, and academic achievement of primary schoolchildren in Melbourne, Australia.

METHODS

This cross-sectional study examined mean academic scores in Years 3 and 5 for primary schools (n = 851) in Greater Melbourne. Scores were from the 2018 'National Assessment Program - Literacy and Numeracy' (NAPLAN) in five domains: 'Reading'; 'Writing'; 'Spelling'; 'Grammar & Punctuation' and 'Numeracy'. Greenery was measured within school boundaries and surrounding Euclidean buffers (100, 300, 1000 and 2000 m) using the Normalized Difference Vegetation Index (NDVI). Measured TRAP proxies were weighted road density (WRD) within the buffers and distance to a major road. Generalised Linear Models were used to examine associations of greenery and TRAP with academic scores (adjusted for school socio-educational status), and to identify mediating pathways.

RESULTS

Greenery was positively associated with Reading scores in Year 3 (all buffers except 2000 m) and in Year 5 (all buffers), with Numeracy in Years 3 and 5 (all buffers) and with Grammar & Punctuation in Year 5 (all buffers). WRD was inversely associated with Reading scores in Year 5 (all buffers), with Numeracy in Year 3 (all buffers) and Year 5 (300 and 1000 m buffers), and with Grammar & Punctuation in Year 3 (100 and 300 m buffers) and Year 5 (all buffers). Distance to a major road was not associated with any score. TRAP partially mediated associations of greenery within 300 m with Numeracy in Year 3 and Grammar & Punctuation in Year 5, and within 2000 m for Reading in Year 5.

CONCLUSIONS

Preliminary evidence indicated that greenery around primary schools was positively associated with Reading, Numeracy and Grammar & Punctuation scores, with TRAP mediating some associations. Further research is required to improve TRAP exposure assessment around schools to verify these findings and inform town/school planners and educators regarding optimal school locations and environments for promoting learning.

The relationship between traffic-related air pollution exposures and allostatic load score among youth with type 1 diabetes in the SEARCH cohort

OBJECTIVE

We investigated the effects of chronic exposures to particulate and traffic-related air pollution on allostatic load (AL) score, a marker of cumulative biological risk, among youth with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Participants were drawn from five clinical sites of the SEARCH for Diabetes in Youth (SEARCH) study (n = 2338). Baseline questionnaires, anthropometric measures, and a fasting blood test were taken at a clinic visit between 2001 and 2005. AL was operationalized using 10 biomarkers reflecting cardiovascular, metabolic, and inflammatory risk. Annual residential exposures to PM2.5 and proximity to heavily-trafficked major roadways were estimated for each participant. Poisson regression models adjusted for sociodemographic and lifestyle factors were conducted for each exposure.

RESULTS

No significant associations were observed between exposures to PM2.5 or proximity to traffic and AL score, however analyses were suggestive of effect modification by race for residential distance to heavily-trafficked major roadways (p = 0.02). In stratified analyses, residing <100, 100-<200 and 200-<400 m compared to 400 m or more from heavily-trafficked major roadways was associated with 11%, 26% and 14% increases in AL score, respectively (95% CIs: -4, 29; 9, 45; -1, 30) for non-white participants compared to 6%, -2%, and -2% changes (95% CIs: -2, 15; -10, 7; -8, 6) for white participants.

CONCLUSIONS

Among this population of youth with type 1 diabetes, we did not observe consistent relationships between chronic exposures to particulate and traffic-related air pollution and changes in AL score, however associations for traffic-related pollution exposures may differ by race/ethnicity and warrant further examination.

Metabolomics analysis of maternal serum exposed to high air pollution during pregnancy and risk of autism spectrum disorder in offspring

BACKGROUND

Previously, numerous epidemiologic studies reported an association between autism spectrum disorder (ASD) and exposure to air pollution during pregnancy. However, there have been no metabolomics studies investigating the impact of pregnancy pollution exposure to ASD risk in offspring.

OBJECTIVES

To identify differences in maternal metabolism that may reflect a biological response to exposure to high air pollution in pregnancies of offspring who later did or did not develop ASD.

METHODS

We obtained stored mid-pregnancy serum from 214 mothers who lived in California's Central Valley and experienced the highest levels of air pollution during early pregnancy. We estimated each woman's average traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 μm) during the first trimester using the California Line Source Dispersion Model, version 4 (CALINE4). By utilizing liquid chromatography-high resolution mass spectrometry, we identified the metabolic profiles of maternal serum for 116 mothers with offspring who later developed ASD and 98 control mothers. Partial least squares discriminant analysis (PLS-DA) was employed to select metabolic features associated with air pollution exposure or autism risk in offspring. We also conducted extensive pathway enrichment analysis to elucidate potential ASD-related changes in the metabolome of pregnant women.

RESULTS

We extracted 4022 and 4945 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for potential confounders, we identified 167 and 222 discriminative features (HILIC and C18, respectively). Pathway enrichment analysis to discriminate metabolic features associated with ASD risk indicated various metabolic pathway perturbations linked to the tricarboxylic acid (TCA) cycle and mitochondrial function, including carnitine shuttle, amino acid metabolism, bile acid metabolism, and vitamin A metabolism.

CONCLUSION

Using high resolution metabolomics, we identified several metabolic pathways disturbed in mothers with ASD offspring among women experiencing high exposure to traffic-related air pollution during pregnancy that were associated with mitochondrial dysfunction. These findings provide us with a better understanding of metabolic disturbances involved in the development of ASD under adverse environmental conditions.

Trace element concentrations in ambient air as a function of distance from road

Traffic-related air pollution is associated with various adverse health effects. In the absence of more complicated exposure assessment techniques, many environmental health studies have used the natural logarithm of distance to road as a proxy for traffic-related exposures. However, research validating this proxy and further explaining the spatial patterns and elemental composition of traffic-related particulate matter air pollution remains limited. In this study, we collected air samples using a mobile particle concentrator that allowed for high sample loading from major roadways in the Greater Boston Area. We found that concentrations of Cl, Ti, V, Cr, Mn, Fe, Co, Cu, Zn, Sr, Zr, Sn, Ba, and Pb were significantly associated with the natural logarithm of distance to road in coarse particulate matter, and total fine particulate mass concentrations of Al, Ca, Ti, Cr, Mn, Fe, Cu, and Zn were significantly associated with natural logarithm of distance to road in fine particulate matter. Road type (A1 or A2 [primary roads or highways] versus A3 [secondary and connecting roads]) was not a significant predictor of any traffic-related elements in particulate matter air pollution. Our results help identify traffic-related elements in particulate matter air pollution and support the use of logarithm of distance to road as a proxy for traffic-related particulate matter air pollution exposure assessment in epidemiological studies.

Application of high-resolution metabolomics to identify biological pathways perturbed by traffic-related air pollution

BACKGROUND

Substantial research has investigated the adverse effects of traffic-related air pollutants (TRAP) on human health. Convincing associations between TRAP and respiratory and cardiovascular diseases are known, but the underlying biological mechanisms are not well established. High-resolution metabolomics (HRM) is a promising platform for untargeted characterization of molecular mechanisms between TRAP and health indexes.

OBJECTIVES

We examined metabolic perturbations associated with short-term exposures to TRAP, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), organic carbon (OC), and elemental carbon (EC) among 180 participants of the Center for Health Discovery and Well-Being (CHDWB), a cohort of Emory University-affiliated employees.

METHODS

A cross-sectional study was conducted on baseline visits of 180 CHDWB participants enrolled during 2008-2012, in whom HRM profiling was determined in plasma samples using liquid chromatography-high-resolution mass spectrometry with positive and negative electrospray ionization (ESI) modes. Ambient pollution concentrations were measured at an ambient monitor near downtown Atlanta. Metabolic perturbations associated with TRAP exposures were assessed following an untargeted metabolome-wide association study (MWAS) framework using feature-specific Tobit regression models, followed by enriched pathway analysis and chemical annotation.

RESULTS

Subjects were predominantly white (76.1%) and non-smokers (95.6%), and all had at least a high school education. In total, 7821 and 4123 metabolic features were extracted from the plasma samples by the negative and positive ESI runs, respectively. There are 3421 features significantly associated with at least one air pollutant by negative ion mode, and 1691 features by positive ion mode. Biological pathways enriched by features associated with the pollutants are primarily involved in nucleic acids damage/repair (e.g., pyrimidine metabolism), nutrient metabolism (e.g., fatty acid metabolism), and acute inflammation (e.g., histidine metabolism and tyrosine metabolism). NO2 and EC were associated most consistently with these pathways. We confirmed the chemical identity of 8 metabolic features in negative ESI and 2 features in positive ESI, including metabolites closely linked to oxidative stress and inflammation, such as histamine, tyrosine, tryptophan, and proline.

CONCLUSIONS

We identified a range of ambient pollutants, including components of TRAP, associated with differences in the metabolic phenotype among the cohort of 180 subjects. We found Tobit models to be a robust approach to handle missing data among the metabolic features. The results were encouraging of further use of HRM and MWAS approaches for characterizing molecular mechanisms underlying exposure to TRAP.

Reported Neighborhood Traffic and the Odds of Asthma/Asthma-Like Symptoms: A Cross-Sectional Analysis of a Multi-Racial Cohort of Children

Asthma in children poses a significant clinical and public health burden. We examined the association between reported neighborhood traffic (a proxy for traffic-related air pollution) and asthma among 855 multi-racial children aged 4-8 years old who participated in the Environmental Influences on Child Health Outcomes (ECHO) cohort. We hypothesized that high neighborhood traffic density would be associated with the prevalence of asthma. Asthma/asthma-like symptoms (defined as current and/or past physician diagnosed asthma, past wheezing, or nighttime cough or wheezing in the past 12 months) was assessed by parental report. The relationship between neighborhood traffic and asthma/asthma-like symptoms was assessed using logistic regression. The prevalence of asthma/asthma-like symptoms among study participants was 23%, and 15% had high neighborhood traffic. Children with significant neighborhood traffic had a higher odds of having asthma/asthma-like symptoms than children without neighborhood traffic [adjusted OR = 2.01 (95% CI: 1.12, 3.62)] after controlling for child's race-ethnicity, age, sex, maternal education, family history of asthma, play equipment in the home environment, public parks, obesity and prescribed asthma medication. Further characterization of neighborhood traffic is needed since many children live near high traffic zones and significant racial/ethnic disparities exist.

Urban policy interventions to reduce traffic emissions and traffic-related air pollution: Protocol for a systematic evidence map

INTRODUCTION

Cities are the world's engines of economic growth, innovation, and social change, but they are also hot spots for human exposure to air pollution, mainly originating from road traffic. As the urban population continues to grow, a greater quantity of people risk exposure to traffic-related air pollution (TRAP), and therefore also risk adverse health effects. In many cities, there is scope for further improvement in air quality through targeted urban policy interventions. The objective of this protocol is to detail the methods that will be used for a systematic evidence map (SEM) which will identify and characterize the evidence on policy interventions that can be implemented at the urban-level to reduce traffic emissions and/or TRAP from on-road mobile sources, thus reducing human exposures and adverse health impacts.

METHODS

Articles will be searched for and selected based on a predetermined search strategy and eligibility criteria. A variety of databases will be searched for relevant articles published in English between January 1, 2000 and June 1, 2020 to encompass the interdisciplinary nature of this SEM, and articles will be stored and screened using Rayyan QCRI. Predetermined study characteristics will be extracted and coded from included studies in a Microsoft Excel sheet, which will serve as an open access, interactive database, and two authors will review the coded data for consistency. The database will be queryable, and various interactive charts, graphs, and maps will be created using Tableau Public for data visualization. The results of the evidence mapping will be detailed via narrative summary.

CONCLUSION

This protocol serves to increase transparency of the SEM methods and provides an example for researchers pursuing future SEMs.

Ambient air pollution, traffic proximity and coronary atherosclerotic phenotype in China

BACKGROUND

Exposure to ambient air pollution is associated with cardiovascular risk, potentially via atherosclerosis promotion. The disease mechanisms underlying these associations remain uncertain.

OBJECTIVES

We aim to investigate the relationship of air pollution and traffic proximity with subclinical atherosclerosis, using coronary plaque phenotypes to gain insight into potential mechanisms.

METHODS

Coronary plaque total and component volumes, high-risk plaque (HRP) appearance, and luminal stenosis were characterized by coronary computed tomography angiography in 2279 patients with atherosclerosis at baseline between 2014 and 2017. Annual average exposure to air pollutants including fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) was estimated by air pollution models for individual participants. Multiple linear regression models were used to assess the association of each exposure with plaque phenotypes and coronary stenosis, controlling for potential confounders. Multiple logistic regression models were used to estimate associations with plaque vulnerability.

RESULTS

The studied population was 60.2±9.2 years old. PM_2.5 and NO₂ concentrations were significantly associated with a 5.0% (95%CI: 0.3, 9.9%, per 15 μg/m³ increase for PM_2.5), 12.0% (95%CI: 2.5, 22.5% per 20 μg/m³ for NO₂) larger volume of non-calcified plaque, respectively. Increase in O₃ concentration was associated with a 12.2% (95%CI: 2.2, 23.2%, per 5 μg/m³ O₃) larger volume of calcified plaque and a 12.8% (95%CI: 0.9, 26.2%) greater lumen narrowing. Increased PM_2.5 and NO₂, was also associated with increase in HRP, determined by the napkin ring sign (odds ratio: 1.41 [95%CI: 1.10, 1.80] for PM_2.5 and 1.78 [95%CI: 1.20, 2.63] for NO₂) and positive remodeling index (OR: 1.11 [95%CI: 1.01, 1.21] for PM_2.5 and 1.20 [95%CI: 1.02, 1.42] for NO₂), respectively, indicating increased plaque vulnerability.

CONCLUSION

Long-term exposures to air pollution were associated with greater plaque volume and luminal stenosis, and increased plaque vulnerability with attendant risk of plaque rupture and erosion.

Estimating the frequency of hormesis and other non-monotonic responses in plants experiencing road traffic pollution in urban areas and experimental pollutant exposure

Various plant traits are widely utilised to assess environment health. However, non-monotonic responses in plants (hormesis and non-hormetic ones) can induce an incorrect assessment of contamination level because they have maximums and/or minimums. Hence, an increase in the pollution level will not always be accompanied by plant index deteriorations. The frequencies of non-monotonic responses, especially non-hormetic responses, have been insufficiently studied for plant traits. This study analysed the frequencies of non-monotonic changes in plants experiencing urban chemical pollution (B. pendula, T. cordata and T. officinale) and with different pollutant exposures (heavy metals, herbicide glyphosate, formaldehyde and sodium chloride) in experiments (T. aestivum and P. sativum). In the city, we evaluated the traits in plants with the same ontogenetic stages on plots near roads with various traffic and similar abiotic conditions. In urban areas, non-monotonic responses were found in both woody (B. pendula and T. cordata) and herbaceous (T. officinale) species for most traits. Their frequencies corresponded to the proportion of monotonic responses (B. pendula) or were even higher (T. cordata and T. officinale). In studied trees, non-monotonic responses were more common in biochemical traits compared with non-biochemical ones. With experimental pollutant exposure, non-monotonic responses were obtained for most traits of both dicotyledonous (P. sativum) and monocotyledonous (T. aestivum) plants, and their frequency was significantly higher than for monotonic ones. Non-hormetic responses significantly prevailed among non-monotonic changes of plant indexes in the city and experiments. Thus, it is necessary to consider both hormesis and non-hormetic responses to assess correctly environmental quality using plant indexes.

Evaluating a multipollutant metric for use in characterizing traffic-related air pollution exposures within near-road environments

Accurately characterizing human exposures to traffic-related air pollutants (TRAPs) is critical to public health protection. However, quantifying exposure to this single source is challenging, given its extremely heterogeneous chemical composition. Efforts using single-species tracers of TRAP are, thus, lacking in their ability to accurately reflect exposures to this complex mixture. There have been recent discussions centered on adopting a multipollutant perspective for sources with many emitted pollutants to maximize the benefits of control expenditures as well as to minimize population and ecosystem exposure. As part of a larger study aimed to assess a complete emission-to-exposure pathway of primary traffic pollution and understand exposure of individuals in the near-road environment, an intensive field campaign measured TRAPs and related data (e.g., meteorology, traffic counts, and regional air pollutant levels) in Atlanta along one of the busiest highway corridors in the US. Given the dynamic nature of the near-road environment, a multipollutant exposure metric, the Integrated Mobile Source Indicator (IMSI), which was generated based on emissions-based ratios, was calculated and compared to traditional single-species methods for assessing exposure to mobile source emissions. The current analysis examined how both traditional and non-traditional metrics vary spatially and temporally in the near-road environment, how they compare with each other, and whether they have the potential to offer more accurate means of assigning exposures to primary traffic emissions. The results indicate that compared to the traditional single pollutant specie, the multipollutant IMSI metric provided a more spatially stable method for assessing exposure, though variations occurred based on location with varying results among the six sites within a kilometer of the highway.

Maternal serum metabolome and traffic-related air pollution exposure in pregnancy

BACKGROUND

Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and neurodevelopmental disorders. By utilizing high-resolution metabolomics (HRM), we investigated perturbations of the maternal serum metabolome in response to traffic-related air pollution to identify biological mechanisms.

METHODS

We retrieved stored mid-pregnancy serum samples from 160 mothers who lived in the Central Valley of California known for high air particulate levels. We estimated prenatal traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 μm) during first-trimester using the California Line Source Dispersion Model, version 4 (CALINE4) based on residential addresses recorded at birth. We used liquid chromatography-high resolution mass spectrometry to obtain untargeted metabolic profiles and partial least squares discriminant analysis (PLS-DA) to select metabolic features associated with air pollution exposure. Pathway analyses were employed to identify biologic pathways related to air pollution exposure. As potential confounders we included maternal age, maternal race/ethnicity, and maternal education.

RESULTS

In total we extracted 4038 and 4957 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for confounding factors, PLS-DA (Variable Importance in Projection (VIP) ≥2) yielded 181 and 251 metabolic features (HILIC and C18, respectively) that discriminated between the high (n = 98) and low exposed (n = 62). Pathway enrichment analysis for discriminatory features associated with air pollution indicated that in maternal serum oxidative stress and inflammation related pathways were altered, including linoleate, leukotriene, and prostaglandin pathways.

CONCLUSION

The metabolomic features and pathways we found to be associated with air pollution exposure suggest that maternal exposure during pregnancy induces oxidative stress and inflammation pathways previously implicated in pregnancy complications and adverse outcomes.

A new approach for inferring traffic-related air pollution: Use of radar-calibrated crowd-sourced traffic data

BACKGROUND

Crowd-sourced traffic data potentially allow prediction of traffic-related air pollution at high temporal and spatial resolution.

OBJECTIVES

To examine associations (1) of radar-based traffic measurements with congestion colors displayed on crowd-sourced traffic data maps and (2) of black carbon (BC) levels with radar and crowd-sourced traffic data.

METHODS

At an off-ramp of an interstate and a small one-way street in a mixed-use area in New York City, we used radar devices to obtain vehicle speeds and flows (hourly counts) for cars and trucks. At these radar sites and at an additional non-radar equipped site at a 2-way street, we monitored BC levels using aethalometers in the summer and early fall of 2017. At all three sites, free-flow traffic conditions typically did not occur due to the nearby presence of traffic lights and forced turns. We also downloaded real-time traffic maps from a crowd-sourced traffic data provider and assigned an ordinal integer congestion color code CCC to the congestion colors, ranging from 1 (dark red) to 5 (gray).

RESULTS

CCC increased with vehicle speed. Traffic flow was highest for intermediate speeds and intermediate CCC. Regression analyses showed that BC levels increased with either segregated or total vehicle flows. At the off-ramp, time-dependent BC levels can be inferred from time-dependent CCC and radar-derived mean vehicle flow data. A unit decrease in CCC for a mean traffic flow of 100 vehicles/h was associated with a mean (95% CI) increase in BC levels of 0.023 (0.028, 0.018) μg/m³. At the small 1-way and the 2-way street, BC levels were also negatively associated with CCC, though at a >0.05 significance level.

CONCLUSIONS

Use of inexpensive crowd-sourced traffic data holds great promise in air pollution modeling and health studies. Time-dependent traffic-related primary air pollution levels may be inferred from radar-calibrated crowd-sourced traffic data, in our case radar-derived mean traffic flow and widely available CCC data. However, at some locations mean traffic flow data may already be available.

Perturbations of the arginine metabolome following exposures to traffic-related air pollution in a panel of commuters with and without asthma

BACKGROUND

Mechanisms underlying the effects of traffic-related air pollution on people with asthma remain largely unknown, despite the abundance of observational and controlled studies reporting associations between traffic sources and asthma exacerbation and hospitalizations.

OBJECTIVES

To identify molecular pathways perturbed following traffic pollution exposures, we analyzed data as part of the Atlanta Commuters Exposure (ACE-2) study, a crossover panel of commuters with and without asthma.

METHODS

We measured 27 air pollutants and conducted high-resolution metabolomics profiling on blood samples from 45 commuters before and after each exposure session. We evaluated metabolite and metabolic pathway perturbations using an untargeted metabolome-wide association study framework with pathway analyses and chemical annotation.

RESULTS

Most of the measured pollutants were elevated in highway commutes (p < 0.05). From both negative and positive ionization modes, 17,586 and 9087 metabolic features were extracted from plasma, respectively. 494 and 220 unique features were associated with at least 3 of the 27 exposures, respectively (p < 0.05), after controlling confounders and false discovery rates. Pathway analysis indicated alteration of several inflammatory and oxidative stress related metabolic pathways, including leukotriene, vitamin E, cytochrome P450, and tryptophan metabolism. We identified and annotated 45 unique metabolites enriched in these pathways, including arginine, histidine, and methionine. Most of these metabolites were not only associated with multiple pollutants, but also differentially expressed between participants with and without asthma. The analysis indicated that these metabolites collectively participated in an interrelated molecular network centering on arginine metabolism, underlying the impact of traffic-related pollutants on individuals with asthma.

CONCLUSIONS

We detected numerous significant metabolic perturbations associated with in-vehicle exposures during commuting and validated metabolites that were closely linked to several inflammatory and redox pathways, elucidating the potential molecular mechanisms of traffic-related air pollution toxicity. These results support future studies of metabolic markers of traffic exposures and the corresponding molecular mechanisms.

Traffic noise exposure alters nestling physiology and telomere attrition through direct, but not maternal, effects in a free-living bird

Anthropogenic impacts, such as noise pollution from transportation networks, can serve as stressors to some wildlife species. For example, increased exposure to traffic noise has been found to alter baseline and stress-induced corticosterone levels, reduce body condition and reproductive success, and increase telomere attrition in free-living birds. However, it remains unknown if alterations in nestling phenotype are due to direct or indirect effects of noise exposure. For example, indirect (maternal) effects of noise may occur if altered baseline and stress-induced corticosterone in mothers results in differential deposition of yolk steroids or other components in eggs. Noise exposure may also alter nestling corticosterone levels directly, given that nestlings cannot escape the nest during development. Here, we examined maternal versus direct effects of traffic noise exposure on baseline and stress-induced corticosterone levels, and body condition (as measured by size-corrected mass) in nestling tree swallows (Tachycineta bicolor). We used a two-way factorial design and partially cross-fostered eggs between nests exposed to differing levels (i.e. amplitudes) of traffic noise. For nestlings that were not cross-fostered, we also investigated the effects of traffic noise on telomere dynamics. Our results show a positive relationship between nestling baseline and stress-induced corticosterone and nestling noise exposure, but not maternal noise exposure. While we did not find a relationship between noise and body condition in nestlings, nestling baseline corticosterone was negatively associated with body condition. We also found greater telomere attrition for nestlings from nests with greater traffic noise amplitudes. These results suggest that direct, rather than maternal, effects result in potentially long-lasting consequences of noise exposure. Reduced nestling body condition and increased telomere attrition have been shown to reduce post-fledging survival in this species. Given that human transportation networks continue to expand, strategies to mitigate noise exposure on wildlife during critical periods (i.e. breeding) may be needed to maintain local population health in free-living passerines, such as tree swallows.

Carbon load in airway macrophages, DNA damage and lung function in taxi drivers exposed to traffic-related air pollution

To evaluate the potential applicability of carbon load in airway macrophages as a marker of exposure to traffic-related air pollution (TRAP) and its association with parameters of comet assay as a marker of DNA damage, and pulmonary function tests (PFTs) in the group of taxi drivers in Iran. One hundred four male taxi drivers with at least 1-year job history were randomly selected from registered drivers in the taxi union. Airway macrophages were obtained via sputum induction, and then the area of airway macrophages occupied by carbon was measured. DNA damage was determined by comet assay. PFTs were measured by the spirometer. Most of the participants (89.4%) were non-smoker. In this study, 52.7% of non-smoker participants were able to give a sample of sputum with macrophage. Carbon content of airway macrophages was 0.2 μm². There was no significant difference in pulmonary function and comet assay parameters in terms of smoking status. There was an inverse correlation between carbon load with each of comet assay and PFTs parameters, although not statistically significant. This study identified that long-term exposure to TRAP can be a risk factor for pulmonary disorders.

Polycyclic aromatic hydrocarbons in urban road dust, Afghanistan: Implications for human health

Polycyclic aromatic hydrocarbons (PAHs) were analyzed in road and aerial dust to assess their concentration, composition profile, distribution, emission sources, and potential human health risks. Sixteen priority PAHs and Benzo [e]pyrene (BeP) were analyzed in 13 aerial dust samples from Jalalabad, and 78 road dust samples from Kabul and Jalalabad cities, Afghanistan. The mean concentration of ∑17PAHs in road dust from Kabul and Jalalabad were 427  μg kg^-1 and 288  μg kg^-1, respectively whereas ∑17PAHs in aerial dust from Jalalabad averaged 200  μg kg^-1. Fluoranthene (Flu), Chrysene (Chr), Benzo [b]fluoranthene (BbF), Benzo [k]fluoranthene (BkF) and BeP were major individual PAH species. The composition patterns of the PAHs were dominated by 5-6-ring PAHs (51% in road dust from Kabul; 44% in road dust from Jalalabad; and 44% in aerial dust) followed by 4-ring and 2-3-ring PAHs. Source apportionment of the road dust PAHs by the molecular diagnostic ratios (MDR) and principal component analysis (PCA), indicated signatures of PAHs sources (including vehicular exhaust, coal/wood combustion and oil spill). The Benzo [a]pyrene (BaP) toxicity equivalent values (BaP_eq17PAHs) for road dust were 75  μg kg^-1 (Kabul) and 36 μg kg^-1 (Jalalabad); and 35 μg kg^-1 for aerial dust (Jalalabad). BaP and Dibenz [a,h]anthracene (DahA) together contributed > 50% of the BaP_eq associated cancer risk. All incremental lifetime cancer risk (ILCR) due to human exposure to road and aerial dust PAHs were in the order of 10^-7, which is one-fold lower than the threshold (10^-6). The noncancerous risk (Hazard Index < 1) on exposure to dust was also negligible for both subpopulations.

Impact of short-term traffic-related air pollution on the metabolome - Results from two metabolome-wide experimental studies

Exposure to traffic-related air pollution (TRAP) has been associated with adverse health outcomes but underlying biological mechanisms remain poorly understood. Two randomized crossover trials were used here, the Oxford Street II (London) and the TAPAS II (Barcelona) studies, where volunteers were allocated to high or low air pollution exposures. The two locations represent different exposure scenarios, with Oxford Street characterized by diesel vehicles and Barcelona by normal mixed urban traffic. Levels of five and four pollutants were measured, respectively, using personal exposure monitoring devices. Serum samples were used for metabolomic profiling. The association between TRAP and levels of each metabolic feature was assessed. All pollutant levels were significantly higher at the high pollution sites. 29 and 77 metabolic features were associated with at least one pollutant in the Oxford Street II and TAPAS II studies, respectively, which related to 17 and 30 metabolic compounds. Little overlap was observed across pollutants for metabolic features, suggesting that different pollutants may affect levels of different metabolic features. After observing the annotated compounds, the main pathway suggested in Oxford Street II in association with NO2 was the acyl-carnitine pathway, previously found to be associated with cardio-respiratory disease. No overlap was found between the metabolic features identified in the two studies.

The associations of air pollution, traffic noise and green space with overweight throughout childhood: The PIAMA birth cohort study

BACKGROUND

Air pollution, traffic noise and absence of green space may contribute to the development of overweight in children.

OBJECTIVES

To investigate the combined associations of air pollution, traffic noise and green space with overweight throughout childhood.

METHODS

We used data for 3680 participants of the Dutch PIAMA birth cohort. We estimated exposure to air pollution, traffic noise and green space (i.e. the average Normalized Difference Vegetation Index (NDVI) and percentages of green space in circular buffers of 300 m and 3000 m) at the children's home addresses at the time of parental reported weight and height measurements. Associations of these exposures with overweight from age 3 to 17 years were analyzed by generalized linear mixed models, adjusting for potential confounders. Odds ratios (OR's) are presented for an interquartile range increase in exposure.

RESULTS

odds of being overweight increased with increasing exposure to NO₂ (adjusted OR 1.40 [95% confidence interval (CI) 1.12-1.74] per 8.90 µg/m³) and tended to decrease with increasing exposure to green space in a 3000 m buffer (adjusted OR 0.86 [95% CI 0.71-1.04] per 0.13 increase in the NDVI; adjusted OR 0.86 [95% CI 0.71-1.03] per 29.5% increase in the total percentage of green space). After adjustment for NO₂, the associations with green space in a 3000 m buffer weakened. No associations of traffic noise with overweight throughout childhood were found. In children living in an urban area, living further away from a park was associated with a lower odds of being overweight (adjusted OR 0.67 [95% CI 0.52-0.85] per 359.6 m).

CONCLUSIONS

Exposure to traffic-related air pollution, but not traffic noise or green space, may contribute to childhood overweight. Future studies examining the associations of green space with childhood overweight should account for air pollution exposure.