Prenatal air pollution exposure and ultrasound measures of fetal growth in Los Angeles, California.

BACKGROUND: Few previous studies examined the impact of prenatal air pollution exposures on fetal development based on ultrasound measures during pregnancy. METHODS: In a prospective birth cohort of more than 500 women followed during 1993-1996 in Los Angeles, California, we examined how air pollution impacts fetal growth during pregnancy. Exposure to traffic related air pollution was estimated using CALINE4 air dispersion modeling for nitrogen oxides (NOx) and a land use regression (LUR) model for nitrogen monoxide (NO), nitrogen dioxide (NO2) and NOx. Exposures to carbon monoxide (CO), NO2, ozone (O3) and particles <10µm in aerodynamic diameter (PM10) were estimated using government monitoring data. We employed a linear mixed effects model to estimate changes in fetal size at approximately 19, 29 and 37 weeks gestation based on ultrasound. RESULTS: Exposure to traffic-derived air pollution during 29 to 37 weeks was negatively associated with biparietal diameter at 37 weeks gestation. For each interquartile range (IQR) increase in LUR-based estimates of NO, NO2 and NOx, or freeway CALINE4 NOx we estimated a reduction in biparietal diameter of 0.2-0.3mm. For women residing within 5km of a monitoring station, we estimated biparietal diameter reductions of 0.9-1.0mm per IQR increase in CO and NO2. Effect estimates were robust to adjustment for a number of potential confounders. We did not observe consistent patterns for other growth endpoints we examined. CONCLUSIONS: Prenatal exposure to traffic-derived pollution was negatively associated with fetal head size measured as biparietal diameter in late pregnancy.

Determinants of the Spatial Distributions of Elemental Carbon and Particulate Matter in Eight Southern Californian Communities.

Emerging evidence indicates that near-roadway pollution (NRP) in ambient air has adverse health effects. However, specific components of the NRP mixture responsible for these effects have not been established. A major limitation for health studies is the lack of exposure models that estimate NRP components observed in epidemiological studies over fine spatial scale of tens to hundreds of meters. In this study, exposure models were developed for fine-scale variation in biologically relevant elemental carbon (EC). Measurements of particulate matter (PM) and EC less than 2.5 µm in aerodynamic diameter (EC2.5) and of PM and EC of nanoscale size less than 0.2 µm were made at up to 29 locations in each of eight Southern California Children's Health Study communities. Regression-based prediction models were developed using a guided forward selection process to identify traffic variables and other pollutant sources, community physical characteristics and land use as predictors of PM and EC variation in each community. A combined eight-community model including only CALINE4 near-roadway dispersion-estimated vehicular emissions accounting for distance, distance-weighted traffic volume, and meteorology, explained 51% of the EC0.2 variability. Community-specific models identified additional predictors in some communities; however, in most communities the correlation between predicted concentrations from the eight-community model and observed concentrations stratified by community were similar to those for the community-specific models. EC2.5 could be predicted as well as EC0.2. EC2.5 estimated from CALINE4 and population density explained 53% of the within-community variation. Exposure prediction was further improved after accounting for between-community heterogeneity of CALINE4 effects associated with average distance to Pacific Ocean shoreline (to 61% for EC0.2) and for regional NOx pollution (to 57% for EC2.5). PM fine spatial scale variation was poorly predicted in both size fractions. In conclusion, models of exposure that include traffic measures such as CALINE4 can provide useful estimates for EC0.2 and EC2.5 on a spatial scale appropriate for health studies of NRP in selected Southern California communities.

Conditions Leading to Elevated PM2.5 at Near-Road Monitoring Sites: Case Studies in Denver and Indianapolis.

We examined two near-road monitoring sites where the daily PM2.5 readings were among the highest of any near-road monitoring location in the U.S. during 2014-2016: Denver, Colorado, in February 2014 and Indianapolis, Indiana, in November 2016. At the Denver site, which had the highest measured U.S. near-road 24-hr PM2.5 concentrations in 2014, concentrations exceeded the daily National Ambient Air Quality Standards (NAAQS) on three days during one week in 2014; the Indianapolis site had the second-highest number of daily exceedances of any near-road site in 2016 and the highest 3-year average PM2.5 of any near-road site during 2014-2016. Both sites had hourly pollutant, meteorological, and traffic data available, making them ideal for case studies. For both locations, we compared air pollution observations at the near-road site to observations at other sites in the urban area to calculate the near-road PM2.5 "increment" and evaluated the effects of changes in meteorology and traffic. The Denver near-road site consistently had the highest PM2.5 values in the Denver area, and was typically highest when winds were near-downwind, rather than directly downwind, to the freeway. Complex Denver site conditions (near-road buildings and roadway alignment) likely contributed to higher PM2.5 concentrations. The increment at Indianapolis was also highest under near-downwind, rather than directly downwind, conditions. At both sites, while the near-road site often had higher PM2.5 concentrations than nearby sites, there was no clear correlation between traffic conditions (vehicle speed, fleet mix) and the high PM2.5 concentrations.

Costs of coronary heart disease and mortality associated with near-roadway air pollution.

BACKGROUND: Emerging evidence indicates that the near-roadway air pollution (NRAP) mixture contributes to CHD, yet few studies have evaluated the associated costs. OBJECTIVE: We integrated an assessment of NRAP-attributable CHD in Southern California with new methods to value the associated mortality and hospitalizations. METHODS: Based on population-weighted residential exposure to NRAP (traffic density, proximity to a major roadway and elemental carbon), we estimated the inflation-adjusted value of NRAP-attributable mortality and costs of hospitalizations that occurred in 2008. We also estimated anticipated costs in 2035 based on projected changes in population and in NRAP exposure associated with California's plans to reduce greenhouse gas emissions. For comparison, we estimated the value of CHD mortality attributable to PM less than 2.5µm in diameter (PM2.5) in both 2008 and 2035. RESULTS: The value of CHD mortality attributable to NRAP in 2008 was between $3.8 and $11.5 billion, 23% (major roadway proximity) to 68% (traffic density) of the $16.8 billion attributable to regulated regional PM2.5. NRAP-attributable costs were projected to increase to $10.6 to $22 billion in 2035, depending on the NRAP metric. Cost of NRAP-attributable hospitalizations for CHD in 2008 was $48.6 million and was projected to increase to $51.4 million in 2035. CONCLUSIONS: We developed an economic framework that can be used to estimate the benefits of regulations to improve air quality. CHD attributable to NRAP has a large economic impact that is expected to increase by 2035, largely due to an aging population. PM2.5-attributable costs may underestimate total value of air pollution-attributable CHD.

Near-Roadway Air Pollution and Coronary Heart Disease: Burden of Disease and Potential Impact of a Greenhouse Gas Reduction Strategy in Southern California.

BACKGROUND: Several studies have estimated the burden of coronary heart disease (CHD) mortality from ambient regional particulate matter ≤ 2.5 µm (PM2.5). The burden of near-roadway air pollution (NRAP) generally has not been examined, despite evidence of a causal link with CHD. OBJECTIVE: We investigated the CHD burden from NRAP and compared it with the PM2.5 burden in the California South Coast Air Basin for 2008 and under a compact urban growth greenhouse gas reduction scenario for 2035. METHODS: We estimated the population attributable fraction and number of CHD events attributable to residential traffic density, proximity to a major road, elemental carbon (EC), and PM2.5 compared with the expected disease burden if the population were exposed to background levels of air pollution. RESULTS: In 2008, an estimated 1,300 CHD deaths (6.8% of the total) were attributable to traffic density, 430 deaths (2.4%) to residential proximity to a major road, and 690 (3.7%) to EC. There were 1,900 deaths (10.4%) attributable to PM2.5. Although reduced exposures in 2035 should result in smaller fractions of CHD attributable to traffic density, EC, and PM2.5, the numbers of estimated deaths attributable to each of these exposures are anticipated to increase to 2,500, 900, and 2,900, respectively, due to population aging. A similar pattern of increasing NRAP-attributable CHD hospitalizations was estimated to occur between 2008 and 2035. CONCLUSION: These results suggest that a large burden of preventable CHD mortality is attributable to NRAP and is likely to increase even with decreasing exposure by 2035 due to vulnerability of an aging population. Greenhouse gas reduction strategies developed to mitigate climate change offer unexploited opportunities for air pollution health co-benefits.

Traffic-related air pollution, particulate matter, and autism.

CONTEXT: Autism is a heterogeneous disorder with genetic and environmental factors likely contributing to its origins. Examination of hazardous pollutants has suggested the importance of air toxics in the etiology of autism, yet little research has examined its association with local levels of air pollution using residence-specific exposure assignments. OBJECTIVE: To examine the relationship between traffic-related air pollution, air quality, and autism. DESIGN: This population-based case-control study includes data obtained from children with autism and control children with typical development who were enrolled in the Childhood Autism Risks from Genetics and the Environment study in California. The mother's address from the birth certificate and addresses reported from a residential history questionnaire were used to estimate exposure for each trimester of pregnancy and first year of life. Traffic-related air pollution was assigned to each location using a line-source air-quality dispersion model. Regional air pollutant measures were based on the Environmental Protection Agency's Air Quality System data. Logistic regression models compared estimated and measured pollutant levels for children with autism and for control children with typical development. SETTING: Case-control study from California. PARTICIPANTS: A total of 279 children with autism and a total of 245 control children with typical development. MAIN OUTCOME MEASURES: Crude and multivariable adjusted odds ratios (AORs) for autism. RESULTS: Children with autism were more likely to live at residences that had the highest quartile of exposure to traffic-related air pollution, during gestation (AOR, 1.98 [95% CI, 1.20-3.31]) and during the first year of life (AOR, 3.10 [95% CI, 1.76-5.57]), compared with control children. Regional exposure measures of nitrogen dioxide and particulate matter less than 2.5 and 10 µm in diameter (PM2.5 and PM10) were also associated with autism during gestation (exposure to nitrogen dioxide: AOR, 1.81 [95% CI, 1.37-3.09]; exposure to PM2.5: AOR, 2.08 [95% CI, 1.93-2.25]; exposure to PM10: AOR, 2.17 [95% CI, 1.49-3.16) and during the first year of life (exposure to nitrogen dioxide: AOR, 2.06 [95% CI, 1.37-3.09]; exposure to PM2.5: AOR, 2.12 [95% CI, 1.45-3.10]; exposure to PM10: AOR, 2.14 [95% CI, 1.46-3.12]). All regional pollutant estimates were scaled to twice the standard deviation of the distribution for all pregnancy estimates. CONCLUSIONS: Exposure to traffic-related air pollution, nitrogen dioxide, PM2.5, and PM10 during pregnancy and during the first year of life was associated with autism. Further epidemiological and toxicological examinations of likely biological pathways will help determine whether these associations are causal.

Predictors of intra-community variation in air quality.

Air quality has emerged as a key determinant of important health outcomes in children and adults. This study aims to identify factors that influence local, within-community air quality, and to build a model for traffic-related air pollution (TRP).We utilized concentrations of NO(2), NO, and total oxides of nitrogen (NO(x)), which were measured at 942 locations in 12 southern California communities. For each location, population density, elevation, land-use, and several indicators of traffic were calculated. A spatial random effects model was used to study the relationship of these predictors to each TRP.Variation in TRP was strongly correlated with traffic on nearby freeways and other major roads, and also with population density and elevation. After accounting for traffic, categories of land-use were not associated with the pollutants. Traffic had a larger relative impact in small urban (low regional pollution) communities than in large urban (high regional pollution) communities. For example, our best fitting model explained 70% of the variation in NO(x) in large urban areas and 76% in small urban areas. Compared with living at least 1,500 m from a freeway, living within 250 m of a freeway was associated with up to a 41% increase in TRP in a large urban area, and up to a 75% increase in small urban areas.Thus, traffic strongly affects local air quality in large and small urban areas, which has implications for exposure assessment and estimation of health risks.

Temporal and spatial patterns of ambient endotoxin concentrations in Fresno, California.

BACKGROUND: Endotoxins are found in indoor dust generated by human activity and pets, in soil, and adsorbed onto the surfaces of ambient combustion particles. Endotoxin concentrations have been associated with respiratory symptoms and the risk of atopy and asthma in children. OBJECTIVE: We characterized the temporal and spatial variability of ambient endotoxin in Fresno/Clovis, California, located in California's Central Valley, to identify correlates and potential predictors of ambient endotoxin concentrations in a cohort of children with asthma [Fresno Asthmatic Children's Environment Study (FACES)]. METHODS: Between May 2001 and October 2004, daily ambient endotoxin and air pollutants were collected at the central ambient monitoring site of the California Air Resources Board in Fresno and, for shorter time periods, at 10 schools and indoors and outdoors at 84 residences in the community. Analyses were restricted to May-October, the dry months during which endotoxin concentrations are highest. RESULTS: Daily endotoxin concentration patterns were determined mainly by meteorologic factors, particularly the degree of air stagnation. Overall concentrations were lowest in areas distant from agricultural activities. Highest concentrations were found in areas immediately downwind from agricultural/pasture land. Among three other measured air pollutants [fine particulate matter, elemental carbon (a marker of traffic in Fresno), and coarse particulate matter (PMc)], PMc was the only pollutant correlated with endotoxin. Endotoxin, however, was the most spatially variable. CONCLUSIONS: Our data support the need to evaluate the spatial/temporal variability of endotoxin concentrations, rather than relying on a few measurements made at one location, in studies of exposure and and respiratory health effects, particularly in children with asthma and other chronic respiratory diseases.

An attributable risk model for exposures assumed to cause both chronic disease and its exacerbations.

BACKGROUND: Many chronic diseases are the product of an underlying pathologic condition and superimposed acute exacerbations. This model may apply to several conditions such as asthma, other obstructive lung diseases, or atherosclerosis. For exposures affecting both the development of chronic disease and its exacerbation, the usual methods to derive attributable risks (AR) are inappropriate. METHODS: We expand traditional risk assessment methods to estimate the AR for exacerbations under a "chronic disease model." We use asthma in children as the chronic disease and air pollution as the exposure of interest. We estimate bronchitis symptom exacerbations attributable to air pollution, using data from the Children's Health Study to estimate asthma prevalence and symptom occurrence, and we examine the distribution of exposure and its acute and chronic effects. RESULTS: In the combined AR model, 39.8% of exacerbations were attributable to air pollution, compared with 33.5% in the traditional model, which ignores a chronic effect of pollution on asthma development. Thus, there is a 1.19-fold higher estimated burden with the combined model. The difference is due to exacerbations caused by other factors (ie, not by air pollution) but nonetheless occurring among those assumed to have asthma that developed due to traffic-related pollution. The proposed model is applicable to other risk factors that play a role both in both the development of a chronic disease and its exacerbation. CONCLUSIONS: Traditional approaches to the calculation of attributable risk may underestimate the health impact of long-term environmental or other exposures that produce both chronic and acute disease.