California Line Source Dispersion Model Research Articles

Joint effects of traffic-related air pollution and hypertensive disorders of pregnancy on maternal postpartum depressive and anxiety symptoms.

Ambient air pollution has been linked to postpartum depression. However, few studies have investigated the effects of traffic-related NOx on postpartum depression and whether any pregnancy-related factors might increase susceptibility. To evaluate the association between traffic-related NOx and postpartum depressive and anxiety symptoms, and effect modification by pregnancy-related hypertension. This study included 453 predominantly low-income Hispanic/Latina women in the MADRES cohort. Daily traffic-related NOx concentrations by road class were estimated using the California LINE-source dispersion model (CALINE4) at participants' residential locations and averaged across pregnancy. Postpartum depressive and anxiety symptoms were evaluated by a validated questionnaire (Postpartum Distress Measure, PDM) at 1, 3, 6 and 12 months postpartum. Multivariate linear regressions were performed to estimate the associations at each timepoint. Interaction terms were added to the linear models to assess effect modification by hypertensive disorders of pregnancy (HDPs). Repeated measurement analyses were conducted by using mixed effect models. We found prenatal traffic-related NOx was associated with increased PDM scores. Specifically, mothers exposed to an IQR (0.22 ppb) increase in NOx from major roads had 3.78% (95% CI: 0.53-7.14%) and 5.27% (95% CI: 0.33-10.45%) significantly higher 3-month and 12-month PDM scores, respectively. Similarly, in repeated measurement analyses, higher NOx from major roads was associated with 3.06% (95% CI: 0.43-5.76%) significantly higher PDM scores across the first year postpartum. Effect modification by HDPs was observed: higher freeway/highway and total NOx among mothers with HDPs were associated with significantly higher PDM scores at 12 months postpartum compared to those without HDPs. This study shows that prenatal traffic-related air pollution was associated with postpartum depressive and anxiety symptoms. The study also found novel evidence of greater susceptibility among women with HDPs, which advances the understanding of the relationships between air pollution, maternal cardiometabolic health during pregnancy and postpartum mental health. Our study has potential implications for clinical intervention to mitigate the effects of traffic-related pollution on postpartum mental health disorders. The findings can also offer valuable insights into urban planning strategies concerning the implementation of emission control measures and the creation of green spaces.

Prenatal air pollution exposure is associated with inflammatory, cardiovascular, and metabolic biomarkers in mothers and newborns

BackgroundPrenatal air pollution exposure has been associated with individual inflammatory, cardiovascular, and metabolic biomarkers in mothers and neonates. However, studies of air pollution and a comprehensive panel of biomarkers across maternal and cord blood samples remain limited. Few studies used data-driven methods to identify biomarker groupings that converge biomarkers from multiple biological pathways. This study aims to investigate the impacts of prenatal air pollution on groups of biomarkers in maternal and cord blood samples. MethodsIn the Maternal And Developmental Risks from Environmental and Social Stressors (MADRES) cohort, 87 biomarkers were quantified from 45 trimester 1 maternal blood and 55 cord blood samples. Pregnancy and trimester 1-averaged concentrations of particulate matter ≤2.5 μm and ≤10 μm in diameter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3) were estimated, using inverse distance squared weighted spatial interpolation from regulatory air monitoring stations. Traffic-related NOx was assessed using California Line Source Dispersion Model: freeway/highway roads, non-freeway major roads, non-freeway minor roads, and their sum as total NOx. Elastic Net (EN) regression within the rexposome R package was used to group biomarkers and assess their associations with air pollution. ResultsIn maternal samples, trimester 1-averaged PM10 was associated with elevated inflammation biomarkers and lowered cardiovascular biomarkers. NO2 exhibited positive associations with cardiovascular and inflammation markers. O3 was inversely associated with inflammation, metabolic, and cardiovascular biomarkers. In cord blood, pregnancy-averaged PM2.5 was associated with higher cardiovascular biomarkers and lower metabolic biomarkers. PM10 was associated with lower inflammation and higher cardiovascular biomarkers. Total and major road NOx was associated with lower cardiovascular biomarkers. ConclusionPrenatal air pollution exposure was associated with changes in biomarkers related to inflammation, cardiovascular, metabolic, cancer, and neurological function in both mothers and neonates. This study shed light on mechanisms by which air pollution can influence biological function during pregnancy.

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

BackgroundPrior 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. MethodsOur 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. ResultsTraffic-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. ConclusionWe 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.

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

BackgroundMaternal 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. MethodsWe 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. ResultsIn 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. ConclusionThe 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.

Simulation and measurement of air quality in the traffic congestion area

The traffic congestion in the Hsuehshan tunnel and at the Toucheng interchange has led to traffic-related air pollution with increasing concern. To ensure the authenticity of our simulation, the concentration of the last 150 m in Hsuehshan tunnel was simulated using the computational fluid dynamics fluid model. The air quality at the Toucheng interchange along a 2 km length highway was simulated using the California Line Source Dispersion Model. The differences in air quality between rush hours and normal traffic conditions were also investigated. An unmanned aerial vehicle (UAV) with installed PM2.5 sensors was developed to obtain the three-dimensional distribution of pollutants. On different roads, during the weekend, the concentrations of pollutants such as SOx, CO, NO, and PM2.5 were observed to be in the range of 0.003–0.008, 7.5–15, 1.5–2.5 ppm, and 40–80 μg m− 3, respectively. On weekdays, the vehicle speed and the natural wind were 60 km h− 1 and 2.0 m s− 1, respectively. On weekdays, the SOx, CO, NO, and PM2.5 concentrations were found to be in the range of 0.002–0.003, 3–9, 0.7–1.8 ppm, and 35–50 μg m− 3, respectively. The UAV was used to verify that the PM2.5 concentrations of vertical changes at heights of 9.0, 7.0, 5.0, and 3.0 m were 45–48, 30–35, 25–30, and 50–52 μg m− 3, respectively. In addition, the predicted PM2.5 concentrations were 40–45, 25–30, 45–48, and 45–50 μg m− 3 on weekdays. These results provide a reference model for environmental impact assessments of long tunnels and traffic jam-prone areas. These models and data are useful for transportation planners in the context of creating traffic management plans.

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

BackgroundPreviously, 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. ObjectivesTo 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. MethodsWe 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. ResultsWe 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. ConclusionUsing 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.

Metabolic dysfunction modifies the influence of traffic-related air pollution and noise exposure on late-life dementia and cognitive impairment: A cohort study of older Mexican-Americans.

For 1,612 elderly Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) followed for up to 10 years, we estimated residential-based local traffic-related exposures relying on the California Line Source Dispersion Model version 4 (CALINE4) for nitrogen oxides (NOx) and the SoundPLAN software package (Version 8.0; NAVCON, Fullerton, CA) that implements the Federal Highway Administration Traffic Noise Model (TNM) for noise, respectively. We used Cox proportional hazard models to estimate the joint effects of NOx or noise exposures and obesity, hyperglycemia, or low high-density lipoprotein (HDL) cholesterol. The risk of developing dementia/CIND among participants with hyperglycemia who also were exposed to high levels of NOx (≥3.44 parts per billion [ppb] [75th percentile]) or noise (≥65 dB) was 2.4 (1.4, 4.0) and 2.2 (1.7, 3.9), respectively. For participants with low HDL-cholesterol, the estimated hazard ratios for dementia/CIND were 2.5 (1.4, 4.3) and 1.8 (1.0, 3.0) for those also exposed to high levels of NOx (≥3.44 ppb) or noise (≥65 dB), respectively, compared with those without metabolic dysfunction exposed to low traffic-related air pollution or noise levels. Exposure to traffic-related air pollution or noise most strongly increases the risk of dementia/CIND among older Mexican-Americans living in California who also exhibit hyperglycemia or low HDL-cholesterol.

Traffic-related Air Pollution and Incident Obesity during Ages 10-18 years in the Southern California Children’s Health Study

Childhood obesity is a global public health crisis, often continuing into adulthood and contributing to increased morbidity and mortality. A growing literature suggests traffic pollution may contribute to increased BMI in childhood, but few studies have examined whether this translates to an increased risk of obesity. In this study we evaluated how traffic-related air pollution relates to incident obesity in a prospective study of schoolchildren from ages 10 to 18 years.The study population comprised 3430 children from eight Southern California communities who were not obese at study entry, recruited over three waves in 1992-93, 1995-96, and 2002-03. Children were followed until age 18 or high school graduation, and height and weight measurements were collected longitudinally. Residential traffic pollution exposure at baseline was estimated as nitrogen oxides (NOx) using the California line-source dispersion model. Multilevel Poisson regression models were fitted to estimate incidence rate ratios (IRR) for obesity associated with traffic-related NOx exposure, adjusting for a set of confounding variables selected a priori using a directed acyclic graph.Participants were primarily non-Hispanic White (44%) or Hispanic (40%). At study entry the median age was 9.7 (IQR: 9.3-10.1) and most children were of normal weight (78%). Traffic-related NOx was associated with increased risk of childhood obesity: IRR=1.10 (1.01-1.19) per average standard deviation within community. Further adjustment for baseline body mass index category increased the risk: IRR=1.14 (1.05-1.24).Exposure to traffic was associated with higher obesity risk in childhood. These findings strengthen the scientific evidence linking childhood obesity and air pollution from near-roadway sources, a modifiable risk factor which could be targeted through vehicular emissions reductions and exposure policies.

Evaluation of the Relationship between Momentum Wakes behind Moving Vehicles and Dispersion of Vehicle Emissions Using Near-Roadway Measurements.

A parameterization of initial vertical dispersion coefficient (σz,init) was developed for incorporation into California line source dispersion model, version 4 (CALINE4) and AMS/EPA regulatory model (AERMOD) to better predict pollutant concentrations near roadways. The momentum wake theory of moving vehicles indicates that both vehicle-induced turbulence (VIT) and dispersion occur in the vehicle wake. Based on a literature review, it is postulated that σz,init near roadways can be estimated using a "wake area model" concept of effective wake area defined as the vehicle height times the wake length, vehicle density, and vehicle type. A total of 523 5-min near-roadway simultaneous measurements (2016-2018) of pollutant concentrations and meteorological and traffic information were used to evaluate the model. Two roadways with distinct fleet composition and simple road configurations were selected for monitoring. The near-roadway σz,init ranged from 1 to 4 m for light-duty vehicles (LDVs) and from 3 to 7 m for fleet-mix (LDVs and heavy-duty vehicles (HDVs)). The results demonstrate that the dispersion contribution from one HDV was 31 times larger than that from one LDV. Calculated pollutant dispersion using the wake area model compared favorably with measurements (R2 = 0.91, slope = 1.07). These results indicate that σz,init varies with vehicle density and HDVs. Pollutant dispersion related to the vehicle wakes can be used to correctly parameterize dispersion models and improve prediction of pollutant concentrations near roadways.

Air pollution, noise exposure, and metabolic syndrome – A cohort study in elderly Mexican-Americans in Sacramento area

BackgroundPrevious studies suggested that air pollutants may increase the incidence of metabolic syndrome, but the potential impact from traffic sources is not well-understood. This study aimed to investigate associations between traffic-related nitrogen oxides (NOx) or noise pollution and risk of incident metabolic syndrome and its components in an elderly Mexican-American population. MethodsA total of 1,554 Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) cohort were followed from 1998 to 2007. We used anthropometric measures and biomarkers to define metabolic syndrome according to the recommendations of the Third Adult Treatment Panel of the National Cholesterol Education Program (NCEP ATP III). Based on participants’ residential addresses at baseline, estimates of local traffic-related NOx were generated using the California Line Source Dispersion Model version 4 (CALINE4), and of noise employing the SoundPLAN software package. We used Cox regression models with calendar time as the underlying time scale to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations of air pollution or noise with metabolic syndrome or its components. ResultsEach per unit increase of traffic-related NOx (2.29 parts per billion (ppb)) was associated with a 15% (HR = 1.15, 95% CI: 1.04–1.28) lower level of high-density lipoprotein cholesterol (HDL-cholesterol), and each 11.6 decibels (dB) increase in noise increased the risk of developing metabolic syndrome by 17% (HR = 1.17, 95% CI: 1.01–1.35). ConclusionPolicies aiming to reduce traffic-related air pollution and noise might mitigate the risk of metabolic syndrome and its components in vulnerable populations.

Maternal Serum Metabolome and Ambient Air Pollution Exposure in Pregnancy

PDS 71: Exposome, Johan Friso Foyer, Floor 1, August 27, 2019, 4:30 PM - 5:30 PM Background: Maternal exposure to ambient 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 ambient 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 ambient air pollution exposure 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. Results: In total we extracted 4,038 and 4,957 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. Controlling for confounding factors, 181 and 251 discriminatory metabolic features (HILIC and C18, respectively) were selected (Variable Importance in Projection (VIP) >= 2). 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.

Exposure to traffic related air pollution in pregnancy and infancy in relation to autism spectrum disorder in California

PDS 73: Neurological effects, Johan Friso Foyer, Floor 1, August 26, 2019, 10:30 AM - 12:00 PM Background/Aim: Studies are accumulating that suggest associations between air pollutants and autism spectrum disorder (ASD) in children. However, most studies include rather small numbers of cases. Our objective was to conduct a statewide study in California focusing on road traffic related exposure during pregnancy and the first year of life to assess related risks for childhood ASD. Methods: We used a population based registry linkage design including state-wide California birth data retrieved from Office of Vital Statistics birth data 1998-2010. ASD (during the study period assessed as autistic disorder (DSM IV-R)) cases (n= 30,514) were identified through records maintained at the California Department of Developmental Services and linked to birth records. Birth record controls were matched on sex and year 1:10. Exposure to road traffic related CO, NOx and PM2.5 concentrations during pregnancy and first year of life were estimated using the California Line Source Dispersion Model 4, a modified Gaussian line-source dispersion model. We estimated unadjusted and adjusted odds ratios (OR) and 95% confidence intervals (CI) using logistic regression. Results: Exposure to interquartile-range increases of the correlated pollutants CO, NOx and PM2.5 related to increased ORs for ASD of 10-13% (e.g. NOx: OR=1.11; 95% CI:1.10,1.12) for prenatal and similarly for infant exposure, adjusting for maternal age, education, race/ethnicity, maternal birth place and other potential confounders. Including prenatal and infant exposure in the same model suggested potentially stronger associations for infant exposure. Conclusions: Our state-wide population based study including a large number of ASD cases and avoiding self-selection bias, indicates small to moderately increased risks for ASD related to pregnancy and early life exposure to traffic generated air pollution. These findings confirm and extend earlier smaller studies suggesting the need for public health policies to regulate air pollution exposure to protect populations from the adverse impacts on the developing fetus.

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

BackgroundMaternal 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. MethodsWe 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. ResultsIn 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. ConclusionThe 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.

Association between ambient air pollution and breast cancer risk: The multiethnic cohort study.

Previous studies using different exposure methods to assess air pollution and breast cancer risk among primarily whites have been inconclusive. Air pollutant exposures of particulate matter and oxides of nitrogen were estimated by kriging (NOx , NO2 , PM10 , PM2.5 ), land use regression (LUR, NOx , NO2 ) and California Line Source Dispersion model (CALINE4, NOx , PM2.5 ) for 57,589 females from the Multiethnic Cohort, residing largely in Los Angeles County from recruitment (1993-1996) through 2010. Cox proportional hazards models were used to examine the associations between time-varying air pollution and breast cancer incidence adjusting for confounding factors. Stratified analyses were conducted by race/ethnicity and distance to major roads. Among all women, breast cancer risk was positively but not significantly associated with NOx (per 50 parts per billion [ppb]) and NO2 (per 20 ppb) determined by kriging and LUR and with PM2.5 and PM10 (per 10 μg/m3 ) determined by kriging. However, among women who lived within 500 m of major roads, significantly increased risks were observed with NOx (hazard ratio [HR] = 1.35, 95% confidence interval [95% CI]: 1.02-1.79), NO2 (HR = 1.44, 95% CI: 1.04-1.99), PM10 (HR = 1.29, 95% CI: 1.07-1.55) and PM2.5 (HR = 1.85, 95% CI: 1.15-2.99) determined by kriging and NOx (HR = 1.21, 95% CI:1.01-1.45) and NO2 (HR = 1.26, 95% CI: 1.00-1.59) determined by LUR. No overall associations were observed with exposures assessed by CALINE4. Subgroup analyses suggested stronger associations of NOx and NO2 among African Americans and Japanese Americans. Further studies of multiethnic populations to confirm the effects of air pollution, particularly near-roadway exposures, on the risk of breast cancer is warranted.

Prenatal and Infant Traffic Related Air Pollution and Autism Spectrum Disorder: A California State-Wide Study

Background: Studies are accumulating that suggest associations between air pollutants and autism spectrum disorder (ASD) in children. However, most studies include rather small numbers of cases. Our objective was to use data for the state of California focussing on road traffic related exposure during pregnancy and the first year of life to assess related risks for childhood ASD.Methods: We used a registry linkage design based on state-wide California birth data retrieved from Office of Vital Statistics birth rolls 1998-2010. ASD (at the time of our study assessed as autistic disorder (DSM IV-R)) cases (n~35,000) were identified through records maintained at the California Department of Developmental Services and linked to birth records. Birth record controls were matched on sex and year 1:10. Prenatal / first year road traffic related CO, NOx and PM2.5 concentrations from both gasoline and diesel vehicles were estimated using CAlifornia LINE Source Dispersion Model 4, a modified Gaussian line-source dispersion model. We estimated risks using logistic regression.Results: Exposure during pregnancy to interquartile-range increases of the correlated pollutants CO, NOx and PM2.5 related to increased odds ratios (OR) for ASD, adjusting for maternal age, education, race/ethnicity, maternal birth place and other potential confounders: CO OR&amp;#61;1.14; 95% CI 1.13, 1.15; NOx OR&amp;#61;1.12; 1.10, 1.13; PM2.5 OR&amp;#61;1.13; 1.11, 1.14. Results were similar for boys and girls.Conclusions: Our state-wide population based study including a large number of ASD cases and avoiding participation bias, indicates increased risks for ASD of around 10% related to prenatal exposure to traffic generated air pollution. These findings confirm and extend earlier smaller studies suggesting the need for public health policies to regulate air pollution exposure to protect the developing fetus.

Longitudinal associations of in utero and early life near-roadway air pollution with trajectories of childhood body mass index

BackgroundEvidence suggests that childhood near-roadway air pollution (NRAP) exposures contribute to increased body mass index (BMI); however, effects of NRAP exposure during the vulnerable periods including in utero and first year of life have yet to be established. In this study, we examined whether exposure to elevated concentrations of NRAP during in utero and/or first year of life increase childhood BMI growth.MethodsParticipants in the Children’s Health Study enrolled from 2002 to 2003 with annual visits over a four-year period and who changed residences before study entry were included (n = 2318). Annual height and weight were measured and lifetime residential NRAP exposures including in utero and first year of life periods were estimated by nitrogen oxides (NOx) using the California line-source dispersion model. Linear mixed effects models assessed in utero or first year near-road freeway and non-freeway NOx exposures and BMI growth after adjusting for age, sex, race/ethnicity, parental education, Spanish questionnaire, and later childhood near-road NOx exposure.ResultsA two-standard deviation difference in first year of life near-road freeway NOx exposure was associated with a 0.1 kg/m2 (95% confidence interval (CI): 0.03, 0.2) faster increase in BMI growth per year and a 0.5 kg/m2 (95% CI: 0.02, 0.9) higher attained BMI at age 10 years.ConclusionsHigher exposure to early life NRAP increased the rate of change of childhood BMI and resulted in a higher attained BMI at age 10 years that were independent of later childhood exposures. These findings suggest that elevated early life NRAP exposures contribute to increased obesity risk in children.

Near-roadway monitoring of vehicle emissions as a function of mode of operation for light-duty vehicles

ABSTRACTDetermination of the effect of vehicle emissions on air quality near roadways is important because vehicles are a major source of air pollution. A near-roadway monitoring program was undertaken in Chicago between August 4 and October 30, 2014, to measure ultrafine particles, carbon dioxide, carbon monoxide, traffic volume and speed, and wind direction and speed. The objective of this study was to develop a method to relate short-term changes in traffic mode of operation to air quality near roadways using data averaged over 5-min intervals to provide a better understanding of the processes controlling air pollution concentrations near roadways. Three different types of data analysis are provided to demonstrate the type of results that can be obtained from a near-roadway sampling program based on 5-min measurements: (1) development of vehicle emission factors (EFs) for ultrafine particles as a function of vehicle mode of operation, (2) comparison of measured and modeled CO2 concentrations, and (3) application of dispersion models to determine concentrations near roadways. EFs for ultrafine particles are developed that are a function of traffic volume and mode of operation (free flow and congestion) for light-duty vehicles (LDVs) under real-world conditions. Two air quality models—CALINE4 (California Line Source Dispersion Model, version 4) and AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model)—are used to predict the ultrafine particulate concentrations near roadways for comparison with measured concentrations. When using CALINE4 to predict air quality levels in the mixing cell, changes in surface roughness and stability class have no effect on the predicted concentrations. However, when using AERMOD to predict air quality in the mixing cell, changes in surface roughness have a significant impact on the predicted concentrations.Implications: The paper provides emission factors (EFs) that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real-world conditions. The good agreement between monitoring and modeling results indicates that high-resolution, simultaneous measurements of air quality and meteorological and traffic conditions can be used to determine real-world, fleet-wide vehicle EFs as a function of vehicle mode of operation under actual driving conditions.

Vehicular emissions and concentrations in school zones: A case study

Recent research has revealed that human exposure to air pollutants such as CO, NOX, and particulates can lead to respiratory diseases, especially among school-age children. Towards understanding such health impacts, this work estimates local-scale vehicular emissions and concentrations near a highway traffic network, where a school zone is located in. In the case study, VISSIM traffic micro-simulation is used to estimate the source of vehicular emissions at each roadway segment. The local-scale emission sources are then used as inputs to the California line source dispersion model (CALINE4) to estimate concentrations across the study area. To justify the local-scale emissions modeling approach, the simulation experiment is conducted under various traffic conditions. Different meteorological conditions are considered for emission dispersion. The work reveals that emission concentrations are usually higher at locations closer to the congested segments, freeway ramps and major arterial intersections. Compared to the macroscopic estimation (i.e. using network-average emission factors), the results show significantly different emission patterns when the local-scale emission modeling approach is used. In particular, it is found that the macroscopic approach over-estimates emission concentrations at freeways and under-estimations are observed at arterials and local streets. The results of the study can be used to compare to the US environmental protection agency (EPA) standards or any other air quality standard to further identify health risk in a fine-grained manner.

Measurement and modelling of nitrogen dioxide (NO2) emissions: a marker for traffic-related air pollution in Doha, Qatar

In the State of Qatar, recent statistics show a continued increase in the motor-vehicle fleet commensurate with population growth and economic development. This trend, together with the rapid expansion of urban areas and the increased dependence on automobiles, has resulted in an increase in pollution near traffic sources, indicating that the risk of exposure to vehicles’ emissions is higher and that these emissions must be considered in terms of their spatial and temporal occurrence. So far, there are no studies conducted in Qatar to evaluate the traffic-related air pollution. This study is carried out to monitor the levels of nitrogen dioxide (NO2) as a marker of pollution related to traffic. The aim of this study is to build a baseline for traffic-related pollution in Qatar by monitoring and modelling NO2 emissions. The levels of traffic-related air pollution by NO2 were investigated at six major intersections along the C-ring road. The relationship of NO2 was established with traffic volume in each intersection during December 2012 and March–April 2013. Significant differences were established between the pollutant concentrations in each intersection. The CALifornia LINE Source Dispersion Model, version 4 air dispersion model employed had estimated the influence of the measured NO2 concentration on the predicted NO2 values by 31.12%. The low percentage may have accounted for the uncertainties brought by the vehicle emission factor and non-availability of temporal dynamics during the time of sampling. Non-parametric Spearman’s correlation test resulted in a significant correlation between measured and predicted values of NO2 concentrations, rs = 0.525 and p = 0.012.

Roadside levels and traffic emission rates of PM2.5 and BTEX in Ho Chi Minh City, Vietnam

A monitoring program was designed and implemented to characterize roadside levels of PM2.5 and BTEX in Ho Chi Minh City, Vietnam, and to generate input data for CALINE (California LINE Source Dispersion Model) inverse modeling. Monitoring was done during Dec 2007–Jan 2008, on both weekdays and weekends, and yielded 284 hourly BTEX samples (adsorption tubes), 24 samples of 8 h-PM2.5 and 42 samples of 24 h-PM2.5 (by MiniVol samplers). The air sampling was done at 8 points on both sides of one street that had an average traffic flow, simultaneously meteorology data and vehicle flows were recorded. Roadside 24 h-PM2.5 levels were 97 ± 31 (53–151) μg m−3, higher on weekdays than weekends. Diurnal BTEX variation patterns were consistent with the diurnal flows of 6 vehicle categories moving on the street. BTEX levels were reduced with the increase in downwind distance from traffic lanes (approximately by 15% for each 5 m increment). Principal component analysis also confirmed the association between roadside pollution levels and traffic. A calculation algorithm was developed to remove the urban background, contributed by other sources than traffic in the selected street, from the roadside measured pollution levels. Urban background contributed a majority of PM2.5 (90–98%) and hourly BTEX (67–97%) with higher contributions at upwind side of the street and at late evening hours when less traffic was observed. CALINE inverse modeling produced explainable fleet hourly emission rates (g km−1 h−1) and vehicle emission factors (EF, mg veh−1 km−1). The obtained EF for gasoline and diesel vehicles were comparable with recent measurements made in Asian cities, as well as with calculated EFs for European and US urban fleets about 10–15 years ago. To minimize the collinearity problem encountered in inverse traffic modeling it is essential that the monitoring is done at different times to capture significant variations in the street traffic compositions.