Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways.

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.

Roadway construction as a natural experiment to examine air pollution impacts on infant health.

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.

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.

Traffic-related air pollution in marginalized neighborhoods: a community perspective.

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.

COVID-19 pandemic reveals persistent disparities in nitrogen dioxide pollution.

The unequal spatial distribution of ambient nitrogen dioxide ([Formula: see text]), an air pollutant related to traffic, leads to higher exposure for minority and low socioeconomic status communities. We exploit the unprecedented drop in urban activity during the COVID-19 pandemic and use high-resolution, remotely sensed [Formula: see text] observations to investigate disparities in [Formula: see text] levels across different demographic subgroups in the United States. We show that, prior to the pandemic, satellite-observed [Formula: see text] levels in the least White census tracts of the United States were nearly triple the [Formula: see text] levels in the most White tracts. During the pandemic, the largest lockdown-related [Formula: see text] reductions occurred in urban neighborhoods that have 2.0 times more non-White residents and 2.1 times more Hispanic residents than neighborhoods with the smallest reductions. [Formula: see text] reductions were likely driven by the greater density of highways and interstates in these racially and ethnically diverse areas. Although the largest reductions occurred in marginalized areas, the effect of lockdowns on racial, ethnic, and socioeconomic [Formula: see text] disparities was mixed and, for many cities, nonsignificant. For example, the least White tracts still experienced ∼1.5 times higher [Formula: see text] levels during the lockdowns than the most White tracts experienced prior to the pandemic. Future policies aimed at eliminating pollution disparities will need to look beyond reducing emissions from only passenger traffic and also consider other collocated sources of emissions such as heavy-duty vehicles.

Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods.

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.

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.

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.

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 (PM2.5)) 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 PM2.5 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.

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 PM2.5 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.

Road traffic density and recurrent asthma emergency department visits among Medicaid enrollees in New York State 2005-2015.

BACKGROUND: Environmental exposures such as traffic may contribute to asthma morbidity including recurrent emergency department (ED) visits. However, these associations are often confounded by socioeconomic status and health care access. OBJECTIVE: This study aims to assess the association between traffic density and recurrence of asthma ED visits in the primarily low income Medicaid population in New York State (NYS) between 2005 and 2015. METHODS: The primary outcome of interest was a recurrent asthma ED visit within 1-year of index visit. Traffic densities (weighted for truck traffic) were spatially linked based on home addresses. Bivariate and multivariate logistic regression analyses were conducted to identify factors predicting recurrent asthma ED visits. RESULTS: In a multivariate model, Medicaid recipients living within 300-m of a high traffic density area were at a statistically significant risk of a recurrent asthma ED visit compared to those in a low traffic density area (OR = 1.31; 95% CI:1.24,1.38). Additionally, we evaluated effect measure modification for risk of recurrent asthma visits associated with traffic exposure by socio-demographic factors. The highest risk was found for those exposed to high traffic and being male (OR = 1.87; 95% CI:1.46,2.39), receiving cash assistance (OR = 2.11; 95% CI:1.65,2.72), receiving supplemental security income (OR = 2.21; 95% CI:1.66,2.96) and being in the 18.44 age group (OR = 1.59;95% CI 1.48,1.70) was associated with the highest risk of recurrent asthma ED visit. Black non-Hispanics (OR = 2.35; 95% CI:1.70,3.24), Hispanics (OR = 2.13; 95% CI:1.49,3.04) and those with race listed as "Other" (OR = 1.89 95% CI:1.13,3.16) in high traffic areas had higher risk of recurrent asthma ED visits as compared to White non-Hispanics in low traffic areas. CONCLUSION: We observed significant persistent disparities in asthma morbidity related to traffic exposure and race/ethnicity in a low-income population. Our findings suggest that even within a primarily low-income study population, socioeconomic differences persist. These differences in susceptibility in the extremely low-income group may not be apparent in health studies that use Medicaid enrollment as a proxy for low SES.

Controlled human exposure to diesel exhaust: a method for understanding health effects of traffic-related air pollution.

Diesel exhaust (DE) is a major component of air pollution in urban centers. Controlled human exposure (CHE) experiments are commonly used to investigate the acute effects of DE inhalation specifically and also as a paradigm for investigating responses to traffic-related air pollution (TRAP) more generally. Given the critical role this model plays in our understanding of TRAP's health effects mechanistically and in support of associated policy and regulation, we review the methodology of CHE to DE (CHE-DE) in detail to distill critical elements so that the results of these studies can be understood in context. From 104 eligible publications, we identified 79 CHE-DE studies and extracted information on DE generation, exposure session characteristics, pollutant and particulate composition of exposures, and participant demographics. Virtually all studies had a crossover design, and most studies involved a single DE exposure per participant. Exposure sessions were typically 1 or 2 h in duration, with participants alternating between exercise and rest. Most CHE-DE targeted a PM concentration of 300 µg/m3. There was a wide range in commonly measured co-pollutants including nitrogen oxides, carbon monoxide, and total organic compounds. Reporting of detailed parameters of aerosol composition, including particle diameter, was inconsistent between studies, and older studies from a given lab were often cited in lieu of repeating measurements for new experiments. There was a male predominance in participants, and over half of studies involved healthy participants only. Other populations studied include those with asthma, atopy, or metabolic syndrome. Standardization in reporting exposure conditions, potentially using current versions of engines with modern emissions control technology, will allow for more valid comparisons between studies of CHE-DE, while recognizing that diesel engines in much of the world remain old and heterogeneous. Inclusion of female participants as well as populations more susceptible to TRAP will broaden the applicability of results from CHE-DE studies.

Controlled human exposure to diesel exhaust: results illuminate health effects of traffic-related air pollution and inform future directions.

Air pollution is an issue of increasing interest due to its globally relevant impacts on morbidity and mortality. Controlled human exposure (CHE) studies are often employed to investigate the impacts of pollution on human health, with diesel exhaust (DE) commonly used as a surrogate of traffic related air pollution (TRAP). This paper will review the results derived from 104 publications of CHE to DE (CHE-DE) with respect to health outcomes. CHE-DE studies have provided mechanistic evidence supporting TRAP's detrimental effects on related to the cardiovascular system (e.g., vasomotor dysfunction, inhibition of fibrinolysis, and impaired cardiac function) and respiratory system (e.g., airway inflammation, increased airway responsiveness, and clinical symptoms of asthma). Oxidative stress is thought to be the primary mechanism of TRAP-induced effects and has been supported by several CHE-DE studies. A historical limitation of some air pollution research is consideration of TRAP (or its components) in isolation, limiting insight into the interactions between TRAP and other environmental factors often encountered in tandem. CHE-DE studies can help to shed light on complex conditions, and several have included co-exposure to common elements such as allergens, ozone, and activity level. The ability of filters to mitigate the adverse effects of DE, by limiting exposure to the particulate fraction of polluted aerosols, has also been examined. While various biomarkers of DE exposure have been evaluated in CHE-DE studies, a definitive such endpoint has yet to be identified. In spite of the above advantages, this paradigm for TRAP is constrained to acute exposures and can only be indirectly applied to chronic exposures, despite the critical real-world impact of living long-term with TRAP. Those with significant medical conditions are often excluded from CHE-DE studies and so results derived from healthy individuals may not apply to more susceptible populations whose further study is needed to avoid potentially misleading conclusions. In spite of limitations, the contributions of CHE-DE studies have greatly advanced current understanding of the health impacts associated with TRAP exposure, especially regarding mechanisms therein, with important implications for regulation and policy.

Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19.

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing-Tianjin-Hebei (BTH), Chengdu-Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.

Exposure to traffic-related fine particulate matter 2.5 causes respiratory damage via peroxisome proliferator-activated receptor gamma-regulated inflammation.

Exposure to particulate matter 2.5 (PM2.5) potentially triggers airway inflammation. Peroxisome proliferator-activated receptor gamma (PPARγ) has been reported to regulate inflammatory responses in diverse cell types. Therefore, this work investigated the mechanisms of PPARγ in regulating traffic-related PM2.5-induced airway inflammation. Using the diffusion flame burner soot generation, traffic-related PM2.5 was generated and adsorbed. BALB/c male mice and human bronchial epithelial cells (16-HBE) were exposed to PM2.5 alone or co-treatment with rosiglitazone (RSG), an agonist of PPARγ. To the end of exposure, bronchoalveolar lavage fluid (BALF), venous blood and arterial blood, trachea, bronchus and lung tissues were collected. The levels of IL-1ß, IL-6, and IL-17 were detected by ELISA, and the cell types in BALF were counted. Hematoxylin-eosin (H&E) assay were used to analyze the pathological conditions of lung, bronchus, and pulmonary artery. Apoptosis was detected by TUNEL, and PPARγ expression in lung and bronchus was detected by immunohistochemical (IHC) staining. Western Blot was used to detect PPARγ, NF-kB, AP-1 and STAT3 expression in lung and bronchus. The viability was detected by MTT method. PM2.5 exposure caused pathological damage to the lung, bronchus and pulmonary artery tissue, which induced apoptosis of bronchial epithelial cells. PM2.5 exposure caused local inflammation of the whole body and airway. PPARγ expression increased after PM2.5 exposure. PM2.5 exposure regulated the downstream signaling pathways to affect the inflammatory response through PPARγ. Exposure to traffic-related PM2.5 caused respiratory damage via PPARγ-regulated inflammation.

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.

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.

Early postnatal exposure to traffic-related air pollution and asthma in adolescents: vulnerability factors in the PARIS birth cohort.

BACKGROUND: Associations between early traffic-related air pollution (TRAP) exposure and respiratory and allergic morbidity in adolescents are inconsistent. However, sub-groups might be more vulnerable to the health effects of this exposure. OBJECTIVES: We investigated associations between early exposure to TRAP and respiratory and allergic morbidity at age 13 years in the PARIS birth cohort, and potential modifying effects of sex, parental allergy, stressful family event and lower respiratory tract infections (LRTI). METHODS: This study deals with data from 732 children of the PARIS birth cohort followed up using repeated questionnaires until 13 years of age. Prenatal TRAP exposure was assessed by measuring daily concentrations of nitrogen dioxide at the nearest station to mother's home. Early postnatal TRAP exposure was calculated for each child during the first year of life by a nitrogen oxides (NOx) air dispersion model taking into account both residence and daycare. Associations between TRAP exposures and asthma, rhinitis and related symptoms were assessed using multivariable logistic regression models adjusted for potential confounding factors. Effect modification was explored by testing multiplicative interactions. RESULTS: An increase in interquartile range (17.0 µg/m3) of early postnatal NOx exposure was positively related to current asthma (adjusted odds ratio aOR = 1.21; 95% confidence interval CI: 1.02, 1.43), severe wheeze (aOR = 1.23; 95% CI: 1.02, 1.47) and persistent asthma at 13 years old (aOR = 1.26; 95% CI: 1.03, 1.55) and tended to be associated with asthma ever. Parental history of allergy, asthma, early stressful family event and LRTI modified these associations with TRAP exposure. No relationship with rhinitis was found. Prenatal TRAP exposure did not show any association with respiratory and allergic morbidity. DISCUSSION: This study is one of the first to show several modifiers of the association between early postnatal TRAP exposure and asthma at adolescence. Not all adolescents seem equally affected by early postnatal TRAP exposure: those presenting parental history of allergy, especially asthma, those with early stressful family event or LRTI appear to be more vulnerable.

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.