ABSTRACT
BACKGROUND AND AIM: Traffic-related ultrafine particle pollution near highways is associated with adverse health. Reducing exposure by use of portable air purifiers in homes is one approach to reducing this risk. However, the reaction of residents to having air purifiers in homes is not well studied. METHODS: Within the framework of our randomized crossover trial of air purifiers in homes near a major highway, we collected data about participants' use and reactions to air purifiers using questionnaires at their 30-day and 90-day home visits, recorded electricity consumption using HOBO monitors, and conducted structured interviews with participants. RESULTS: Nearly all 150 participants reported running the air purifiers virtually 24 h every day in both their living room and their bedroom in the prior month. The units' HOBO electricity use, from a subset of 45 participants, supported the participants' responses from the questionnaire. Approximately 80% of participants reported setting their air purifier on the medium setting. Tolerance to air purifier noise increased significantly between the 30-day and 90-day home visits, with approximately two thirds reporting not being bothered at all by the noise. The qualitative interviews in a subset of 26 participants yielded consistent responses to those from the questionnaires. Size of unit, airflow, and energy consumption were additional concerns that emerged during the interviews. CONCLUSIONS: Results from the questionnaires, HOBO data, and structured interviews all suggest participants had positive reactions towards the presence of in-home APs, and therefore may be receptive to using air purifiers in their homes on a regular basis. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04279249 . Registered 09 October 2019.
Subject(s)
Air Filters , Cross-Over Studies , Humans , Male , Female , Middle Aged , Adult , Aged , Air Pollution, Indoor/prevention & control , Air Pollution, Indoor/adverse effects , Vehicle Emissions/prevention & control , Vehicle Emissions/analysis , Traffic-Related Pollution/adverse effects , Traffic-Related Pollution/prevention & control , Surveys and Questionnaires , Housing , Particulate Matter/adverse effects , Particulate Matter/analysis , Time Factors , Environmental Exposure/adverse effects , Environmental Exposure/prevention & control , Air Pollutants/adverse effects , Air Pollutants/analysisABSTRACT
In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures.
Subject(s)
Air Pollutants , Environmental Monitoring , Particulate Matter , Vehicle Emissions , Air Pollutants/analysis , Particulate Matter/analysis , Vehicle Emissions/analysis , China , Air Pollution/statistics & numerical data , Traffic-Related Pollution/analysis , Soot/analysisABSTRACT
This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.
Subject(s)
Air Pollutants , Air Pollution , Gastrointestinal Microbiome , Humans , Male , London , Female , Middle Aged , Cross-Over Studies , Traffic-Related Pollution , Nitrogen DioxideABSTRACT
BACKGROUND: Mammographic density (MD) is the most important breast cancer biomarker. Ambient pollution is a carcinogen, and its relationship with MD is unclear. This study aims to explore the association between exposure to traffic pollution and MD in premenopausal women. METHODOLOGY: This Spanish cross-sectional study involved 769 women attending gynecological examinations in Madrid. Annual Average Daily Traffic (AADT), extracted from 1944 measurement road points provided by the City Council of Madrid, was weighted by distances (d) between road points and women's addresses to develop a Weighted Traffic Exposure Index (WTEI). Three methods were employed: method-1 (1dAADT), method-2 (1dAADT), and method-3 (e1dAADT). Multiple linear regression models, considering both log-transformed percentage of MD and untransformed MD, were used to estimate MD differences by WTEI quartiles, through two strategies: "exposed (exposure buffers between 50 and 200 m) vs. not exposed (>200 m)"; and "degree of traffic exposure". RESULTS: Results showed no association between MD and traffic pollution according to buffers of exposure to the WTEI (first strategy) for the three methods. The highest reductions in MD, although not statistically significant, were detected in the quartile with the highest traffic exposure. For instance, method-3 revealed a suggestive inverse trend (eßQ1 = 1.23, eßQ2 = 0.96, eßQ3 = 0.85, eßQ4 = 0.85, p-trend = 0.099) in the case of 75 m buffer. Similar non-statistically significant trends were observed with Methods-1 and -2. When we examined the effect of traffic exposure considering all the 1944 measurement road points in every participant (second strategy), results showed no association for any of the three methods. A slightly decreased MD, although not significant, was observed only in the quartile with the highest traffic exposure: eßQ4 = 0.98 (method-1), and eßQ4 = 0.95 (methods-2 and -3). CONCLUSIONS: Our results showed no association between exposure to traffic pollution and MD in premenopausal women. Further research is needed to validate these findings.
Subject(s)
Breast Density , Environmental Exposure , Premenopause , Humans , Female , Environmental Exposure/statistics & numerical data , Cross-Sectional Studies , Adult , Spain , Traffic-Related Pollution/adverse effects , Breast Neoplasms/epidemiology , Middle Aged , Vehicle Emissions/analysis , Mammography , Air Pollutants/analysisABSTRACT
OBJECTIVES: Marginalized communities are exposed to higher levels of traffic-related air pollution (TRAP) than the general population. TRAP exposure is linked to pulmonary toxicity, neurotoxicity, and cardiovascular toxicity often through mechanisms of inflammation and oxidative stress. Early life exposure to TRAP is also implicated in higher rates of asthma in these same communities. There is a critical need for additional epidemiological, in vivo, and in vitro studies to define the health risks of TRAP exposure affecting the most vulnerable groups to set strict, protective air pollution standards in these communities. MATERIALS AND METHODS: A literature review was conducted to summarize recent findings (2010-2024) concerning TRAP exposure and toxic mechanisms that are relevant to the most affected underserved communities. CONCLUSIONS: Guided by the perspectives of NYC community scientists, this contemporary review of toxicological and epidemiological studies considers how the exposome could lead to disproportionate exposures and health effects in underserved populations.
Subject(s)
Traffic-Related Pollution , Humans , Traffic-Related Pollution/adverse effects , Traffic-Related Pollution/analysis , Air Pollutants/analysis , Air Pollutants/adverse effects , Environmental Exposure/adverse effects , Residence Characteristics , Vehicle Emissions/toxicity , Vehicle Emissions/analysis , Air Pollution/adverse effects , Air Pollution/analysis , AnimalsABSTRACT
Traffic-related air pollution (TRAP) poses a significant public health risk that is associated with adverse birth outcomes. Large roadway infrastructure projects present a natural experiment to examine how resulting congestion change is associated with adverse birth outcomes for nearby populations. This study is designed to examine the influence of living close to a roadway before, during, and after a construction project using a difference-in-differences design. We integrated data on all large roadway construction projects (defined as widening of existing roads, building new roads, improving bridges, installing intelligent transportation systems, improving intersections, and installing or upgrading traffic signals) in Texas from 2007 to 2016 with Vital Statistic data for all births with residential addresses within 1 km of construction projects. Our outcomes included term low birth weight, term birth weight, preterm birth, and very preterm birth. Using a difference-in-differences design, we included births within 3 years of construction start and 2 years of construction end. In our main model, the exposed group is limited to pregnant individuals residing within 300 m of a construction project, and the control group includes those living within 300-1000 m from a project. We used regression models to estimate the influence of construction on infant health. We included 1,360 large roadway construction projects linked to 408,979 births. During construction, we found that the odds of term low birth weight increased by 19% (95% CI: 1.05, 1.36). However, we saw little evidence of an association for other birth outcomes. Contrary to our hypothesis of decreased TRAP after construction ends, we did not observe consistent improvements post-construction for pregnant individuals living within 300 m. Continued consideration of the influence of traffic congestion programs on birth outcomes is necessary to inform future policy decisions.
Subject(s)
Air Pollution , Infant Health , Humans , Texas , Air Pollution/analysis , Air Pollution/adverse effects , Female , Infant, Newborn , Pregnancy , Infant , Traffic-Related Pollution/adverse effects , Traffic-Related Pollution/analysis , Air Pollutants/analysis , Vehicle Emissions/analysis , Infant, Low Birth Weight , Birth Weight/drug effects , Premature Birth/epidemiology , AdultABSTRACT
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.
Subject(s)
Traffic-Related Pollution , Vehicle Emissions , Adult , Humans , Depression/epidemiology , Anxiety/epidemiology , Anxiety Disorders/epidemiology , China/epidemiologyABSTRACT
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.
Subject(s)
Air Pollutants , Air Pollution , Traffic-Related Pollution , Particulate Matter/analysis , Plants , Particle Size , Plant Leaves/chemistry , Air Pollutants/analysis , Environmental Monitoring , TreesABSTRACT
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.
Subject(s)
Air Pollutants , Air Pollution , Parkinson Disease , Traffic-Related Pollution , Humans , Air Pollutants/toxicity , Air Pollutants/analysis , Environmental Exposure/analysis , Parkinson Disease/epidemiology , Parkinson Disease/etiology , Air Pollution/adverse effects , Air Pollution/analysis , Particulate Matter/toxicity , Particulate Matter/analysis , Dust/analysis , California/epidemiologyABSTRACT
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.
Subject(s)
Air Pollutants , Air Pollution , Traffic-Related Pollution , Humans , Particulate Matter/analysis , Models, Statistical , Linear Models , Air Pollution/analysis , Air Pollutants/analysis , Vehicle Emissions/analysisABSTRACT
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.
Subject(s)
Occupational Exposure , Traffic-Related Pollution , Male , Humans , Female , Carotid Intima-Media Thickness , Police , Cross-Sectional Studies , Vehicle Emissions , Occupational Exposure/adverse effectsABSTRACT
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.
Subject(s)
Traffic-Related Pollution , Microplastics , Plastics , Rubber , PolymersABSTRACT
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.
Subject(s)
Air Pollutants , Air Pollution , Traffic-Related Pollution , Humans , Air Pollutants/analysis , Air Pollution/prevention & control , Air Pollution/analysis , Vehicle Emissions/prevention & control , Vehicle Emissions/analysis , PolicyABSTRACT
BACKGROUND: Although characterizing the inequality in pollution exposure burden across ethnic groups and the ethnic-specific exposure associations is of great social and public health importance, it has not been systematically investigated in large population studies. METHODS: The UK Biobank data (N = 485, 806) of individual-level air ambient and traffic-related pollution exposure, biomarkers routinely used in clinical practice, genotype, life-style factors, and socioeconomic status were analyzed. Air pollution exposure estimates were compared among six genetically inferred ethnic groups. We also quantified the association between exposure and biomarkers within and across ethnicities. RESULTS: Non-European participants (defined by genetics) disproportionately bear a higher burden of exposure than their European counterparts even after adjusting for covariables including socioeconomic status. For example, exposure to NO2 in people with African ancestry was 30.7 % higher (p = 1.5E-786) than European subjects. Within the genetically defined African group, larger African genetic ancestry proportion (AGAP) was linked to higher ambient air pollutant exposure. Trans-Ethnic analysis identified 32 clinical biomarkers associated with environmental exposure. For 13 biomarkers, the association with exposure was significantly different or even in opposing directions across ethnic groups. CONCLUSIONS: Substantial disparities in air pollution exposure was observed among genetically-defined ethnic groups. Most importantly, we show that the impact of exposure on biomarkers varies by ethnicity. Reducing the disproportionally high exposure burden on non-European populations and alleviating the adverse consequences in an ethnic-specific manner are of great urgency and significance.
Subject(s)
Air Pollutants , Air Pollution , Traffic-Related Pollution , Humans , Traffic-Related Pollution/analysis , Air Pollutants/analysis , Air Pollution/analysis , Environmental Exposure/analysis , Social Class , Particulate Matter/analysisABSTRACT
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.
Subject(s)
Air Pollution , Traffic-Related Pollution , Adult , Humans , Noise , China , Exercise , Environmental ExposureABSTRACT
As the result of diesels extensive production and use as fuel for transportation, pollution with such complex mixtures of hydrocarbons is a major concern worldwide. The present studys focus was to investigate the presence of diesel-degrading bacteria in different Danube Delta freshwater sediments. Ten bacterial strains capable to grow in a minimal medium with diesel as the sole carbon source were isolated and characterized in this study. Based on the phenotypic and molecular characteristics, the ten strains belong to four genera and seven species, such as Pseudomonas (P. aeruginosa, P. nitroreducens, P. resinovorans, P. multiresinivorans), Acinetobacter (A. tandoii), Bacillus (B. marisflavi), and Stenotrophomonas (S. maltophilia). All these bacteria were excellent biosurfactant producers, and they were able to tolerate saturated hydrocarbons, like n-heptane, n-decane, n-pentadecane, and n-hexadecane. The ten strains possess at least one alkane hydroxylase gene in their genome, and they were also able to tolerate and degrade diesel. Higher biodegradation rates of diesel were acquired for the strains from the genera Pseudomonas, Acinetobacter, and Stenotrophomonas, compared with that obtained for the Bacillus strain. Due to their remarkable potential to degrade diesel and produce biosurfactants, the ten isolated bacteria are attractive candidates for bioremediation of diesel-polluted environments.(AU)
Subject(s)
Humans , Bacteria , Fresh Water , Hydrocarbons , Biodegradation, Environmental , Vehicle Emissions , Traffic-Related Pollution , Microbiology , Microbiological TechniquesABSTRACT
BACKGROUND: Stroke remains the second cause of death worldwide. The mechanisms underlying the adverse association of exposure to traffic-related air pollution (TRAP) with overall cardiovascular disease may also apply to stroke. Our objective was to systematically evaluate the epidemiological evidence regarding the associations of long-term exposure to TRAP with stroke. METHODS: PubMed and LUDOK electronic databases were searched systematically for observational epidemiological studies from 1980 through 2019 on long-term exposure to TRAP and stroke with an update in January 2022. TRAP was defined according to a comprehensive protocol based on pollutant and exposure assessment methods or proximity metrics. Study selection, data extraction, risk of bias (RoB) and confidence assessments were conducted according to standardized protocols. We performed meta-analyses using random effects models; sensitivity analyses were assessed by geographic area, RoB, fatality, traffic specificity and new studies. RESULTS: Nineteen studies were included. The meta-analytic relative risks (and 95% confidence intervals) were: 1.03 (0.98-1.09) per 1 µg/m3 EC, 1.09 (0.96-1.23) per 10 µg/m3 PM10, 1.08 (0.89-1.32) per 5 µg/m3 PM2.5, 0.98 (0.92; 1.05) per 10 µg/m3 NO2 and 0.99 (0.94; 1.04) per 20 µg/m3 NOx with little to moderate heterogeneity based on 6, 5, 4, 7 and 8 studies, respectively. The confidence assessments regarding the quality of the body of evidence and separately regarding the presence of an association of TRAP with stroke considering all available evidence were rated low and moderate, respectively. CONCLUSION: The available literature provides low to moderate evidence for an association of TRAP with stroke.
Subject(s)
Air Pollution , Cardiovascular Diseases , Stroke , Traffic-Related Pollution , Humans , Stroke/epidemiology , Databases, Factual , Air Pollution/adverse effectsABSTRACT
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.
Subject(s)
Air Pollutants , Air Pollution , Traffic-Related Pollution , Air Pollutants/analysis , Air Pollution/prevention & control , Air Pollution/analysis , Carbon , Carbon Dioxide/analysis , Environmental Monitoring/methods , Vehicle Emissions/analysisABSTRACT
BACKGROUND: Traffic-related air pollutants lead to increased risks of many diseases. Understanding travel patterns and influencing factors are important for mitigating traffic exposures. However, there is a lack of national large-scale research. OBJECTIVE: This study aimed to evaluate the daily travel patterns of Chinese adults and provide basic data for traffic exposure and health risk research. METHODS: We conducted the first nation-wide survey of travel patterns of adults (aged 18 and above) in China during 2011-2012. We conducted a cross-sectional study based on a nationally representative sample of 91, 121 adults from 31 provinces in China. We characterized typical travel patterns by cluster analysis and identified the associated factors of each pattern using multiple logistic regression and generalized linear regression models. RESULTS: We found 115 typical daily travel patterns of Chinese adults and the top 11 accounted for 94% of the population. The interaction of age, urban and rural areas, income levels, gender, educational levels, city population and temperature affect people's choice of travel patterns. The average travel time of Chinese adults is 45 ± 40 min/day, with the longest travel time by the combination of walking and car (70 min/day). Gender has the largest effect on travel time (B = -8.94, 95% CI: -8.95, -8.93), followed by city GDP (B = -4.23, 95% CI: -4.23, -4.22), urban and rural areas (B = -3.62, 95% CI: -3.63, -3.61), age (B = -2.21, 95% CI: -2.21, -2.2), educational levels (B = -1.53, 95% CI: -1.53, -1.52), city area (B = -1.4, 95% CI: -1.4, -1.39) and temperature (B = 1.21, 95% CI: 1.2, 1.21). SIGNIFICANCE: This study was the first nation-wide study on traffic activity patterns in China, which provides basic data for traffic exposure and health risk research and provides the basis for the state to formulate transportation-related policies.
Subject(s)
Air Pollutants , Air Pollution , Traffic-Related Pollution , Vehicle Emissions , Adult , Humans , Air Pollutants/analysis , Air Pollution/analysis , Cities , Cross-Sectional Studies , East Asian People , Vehicle Emissions/analysis , Traffic-Related Pollution/analysis , Traffic-Related Pollution/statistics & numerical dataABSTRACT
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.