Formaldehyde Exposure Racial Disparities in Southeast Texas.

Formaldehyde (HCHO) exposures during a full year were calculated for different race/ethnicity groups living in Southeast Texas using a chemical transport model tagged to track nine emission categories. Petroleum and industrial emissions were the largest anthropogenic sources of HCHO exposure in Southeast Texas, accounting for 44% of the total HCHO population exposure. Approximately 50% of the HCHO exposures associated with petroleum and industrial sources were directly emitted (primary), while the other 50% formed in the atmosphere (secondary) from precursor emissions of reactive compounds such as ethylene and propylene. Biogenic emissions also formed secondary HCHO that accounted for 11% of the total population-weighted exposure across the study domain. Off-road equipment contributed 3.7% to total population-weighted exposure in Houston, while natural gas combustion contributed 5% in Beaumont. Mobile sources accounted for 3.7% of the total HCHO population exposure, with less than 10% secondary contribution. Exposure disparity patterns changed with the location. Hispanic and Latino residents were exposed to HCHO concentrations +1.75% above average in Houston due to petroleum and industrial sources and natural gas sources. Black and African American residents in Beaumont were exposed to HCHO concentrations +7% above average due to petroleum and industrial sources, off-road equipment, and food cooking. Asian residents in Beaumont were exposed to HCHO concentrations that were +2.5% above average due to HCHO associated with petroleum and industrial sources, off-road vehicles, and food cooking. White residents were exposed to below average HCHO concentrations in all domains because their homes were located further from primary HCHO emission sources. Given the unique features of the exposure disparities in each region, tailored solutions should be developed by local stakeholders. Potential options to consider in the development of those solutions include modifying processes to reduce emissions, installing control equipment to capture emissions, or increasing the distance between industrial sources and residential neighborhoods.

Ultrafine particulate matter exposure during second year of life, but not before, associated with increased risk of autism spectrum disorder in BKMR mixtures model of multiple air pollutants.

Prenatal and early postnatal air pollution exposures have been shown to be associated with autism spectrum disorder (ASD) risk but results regarding specific air pollutants and exposure timing are mixed and no study has investigated the effects of combined exposure to multiple air pollutants using a mixtures approach. We aimed to evaluate prenatal and early life multipollutant mixtures for the drivers of associations of air pollution with ASD. This study examined 484 typically developing (TD) and 660 ASD children from the CHARGE case-control study. Daily air concentrations for NO2, O3, ultrafine (PM0.1), fine (PM0.1-2.5), and coarse (PM2.5-10) particles were predicted from chemical transport models with statistical bias adjustment based on ground-based monitors. Daily averages were calculated for each exposure period (pre-pregnancy, each trimester of pregnancy, first and second year of life) between 2000 and 2016. Air pollution variables were natural log-transformed and then standardized. Individual and joint effects of pollutant exposure with ASD, and potential interactions, were evaluated for each period using hierarchical Bayesian Kernel Machine Regression (BKMR) models, with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. In BKMR models, the PM group was associated with ASD in year 2 (group posterior inclusion probability (gPIP) = 0.75), and marginally associated in year 1 (gPIP = 0.497). PM2.5-10 appeared to drive the association (conditional PIP (cPIP) = 0.64) in year 1, while PM0.1 appeared to drive the association in year 2 (cPIP = 0.76), with both showing a moderately strong increased risk. Pre-pregnancy O3 showed a slight J-shaped risk of ASD (gPIP = 0.55). No associations were observed for exposures during pregnancy. Pre-pregnancy O3 and year 2 p.m.0.1 exposures appear to be associated with an increased risk of ASD. Future research should examine ultrafine particulate matter in relation to ASD.

Pre-pregnancy ozone and ultrafine particulate matter exposure during second year of life associated with decreased cognitive and adaptive functioning at aged 2-5 years.

BACKGROUND: This study sought to investigate the association of prenatal and early life exposure to a mixture of air pollutants on cognitive and adaptive outcomes separately in children with or without autism spectrum disorder (ASD). METHODS: Utilizing data from the CHARGE case-control study (birth years: 2000-2016), we predicted daily air concentrations of NO2, O3, and particulate matter <0.1 µm (PM0.1), between 0.1 and 2.5 µm (PM0.1-2.5), and between 2.5 and 10 µm (PM2.5-10) using chemical transport models with ground-based monitor adjustments. Exposures were evaluated for pre-pregnancy, each trimester, and the first two years of life. Individual and combined effects of pollutants were assessed with Vineland Adaptive Behavior Scales (VABS) and Mullen Scales of Early Learning (MSEL), separately for children with ASD (n = 660) and children without ASD (typically developing (TD) and developmentally delayed (DD) combined; n = 753) using hierarchical Bayesian Kernel Machine Regression (BKMR) models with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. RESULTS: Pre-pregnancy Ozone was strongly negatively associated with all scores in the non-ASD group (group posterior inclusion probability (gPIP) = 0.83-1.00). The PM group during year 2 was also strongly negatively associated with all scores in the non-ASD group (gPIP = 0.59-0.93), with PM0.1 driving the group association (conditional PIP (cPIP) = 0.73-0.96). Weaker and less consistent associations were observed between PM0.1-2.5 during pre-pregnancy and ozone during year 1 and VABS scores in the ASD group. CONCLUSIONS: These findings prompt further investigation into ozone and ultrafine PM as potential environmental risk factors for neurodevelopment.

Associations of Autism Spectrum Disorder with PM2.5 Components: A Comparative Study Using Two Different Exposure Models.

This retrospective cohort study examined associations of autism spectrum disorder (ASD) with prenatal exposure to major fine particulate matter (PM2.5) components estimated using two independent exposure models. The cohort included 318 750 mother-child pairs with singleton deliveries in Kaiser Permanente Southern California hospitals from 2001 to 2014 and followed until age five. ASD cases during follow-up (N = 4559) were identified by ICD codes. Prenatal exposures to PM2.5, elemental (EC) and black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-) were constructed using (i) a source-oriented chemical transport model and (ii) a hybrid model. Exposures were assigned to each maternal address during the entire pregnancy, first, second, and third trimester. In single-pollutant models, ASD was associated with pregnancy-average PM2.5, EC/BC, OM, and SO42- exposures from both exposure models, after adjustment for covariates. The direction of effect estimates was consistent for EC/BC and OM and least consistent for NO3-. EC/BC, OM, and SO42- were generally robust to adjustment for other components and for PM2.5. EC/BC and OM effect estimates were generally larger and more consistent in the first and second trimester and SO42- in the third trimester. Future PM2.5 composition health effect studies might consider using multiple exposure models and a weight of evidence approach when interpreting effect estimates.

Air pollution and the sequelae of COVID-19 patients: A multistate analysis.

IMPORTANCE: Recent evidence links air pollution to the severity COVID-19 symptoms and to death from the disease. To date, however, few studies have assessed whether air pollution affects the sequelae to more severe states or recovery from COVID-19 in a cohort with individual data. OBJECTIVE: To assess how air pollution affects the transition to more severe COVID-19 states or to recovery from COVID-19 infection in a cohort with detailed patient information. DESIGN AND OUTCOMES: We used a cohort design that followed patients admitted to hospital in the Kaiser Permanente Southern California (KPSC) Health System, which has 4.7 million members with characteristics similar to the general population. Enrollment began on 06/01/2020 and ran until 01/30/2021 for all patients admitted to hospital while ill with COVID-19. All possible states of sequelae were considered, including deterioration to intensive care, to death, discharge to recovery, or discharge to death. Transition risks were estimated with a multistate model. We assessed exposure using chemical transport model that predicted ambient concentrations of nitrogen dioxide, ozone, and fine particulate matter (PM2.5) at a 1 km scale. RESULTS: Each increase in PM2.5 concentration equivalent to the interquartile range was associated with increased risk of deterioration to intensive care (HR of 1.16; 95% CI: 1.12-1.20) and deterioration to death (HR of 1.11; 95% CI: 1.04-1.17). Results for ozone were consistent with PM2.5 effects, but ozone also affected the transition from recovery to death: HR of 1.24 (95% CI: 1.01-1.51). NO2 had weaker effects but displayed some elevated risks. CONCLUSIONS: PM2.5 and ozone were significantly associated with transitions to more severe states while in hospital and to death after discharge from hospital. Reducing air pollution could therefore lead to improved prognosis for COVID-19 patients and a sustainable means of reducing the health impacts of coronaviruses now and in the future.

Atmospheric Age Distribution of Primary and Secondary Inorganic Aerosols in a Polluted Atmosphere.

The community multiscale air quality (CMAQ) model was modified to track the evolution of the atmospheric age (τ) distribution of primary particulate matter (PPM) and secondary inorganic aerosol components (nitrate, sulfate, and ammonium ion, NSA). The modified CMAQ gas and aerosol mechanisms represent the same species emitted at different times as an age-resolved mixture, using multiple age-tagged variables and a dynamic age-bin advancing scheme. The model was applied to study the spatial and temporal evolution of τ for PPM and NSA in January 2013 to understand the formation and regional transport of PM and the precursor gases during severe winter pollution episodes in China. The results showed that increases in PPM and NSA concentrations during high pollution periods in polluted urban areas were typically associated with increases in the mean atmospheric age (τ̅) due to the accumulation of local emissions and regional transport of aged pollutants. Some of the rapid sulfate growth events at the beginning of multiday air pollution episodes were driven by regional transport of aged particles. In heavily polluted cities, while most of the monthly average PPM had τ less than 10 h, more than half of the sulfate had τ greater than 20-30 h. Regional distributions showed that very aged sulfate particles with τ > 96 h accounted for a significant portion of the total sulfate and had a very broad spatial distribution. However, aged ammonium ions had very low concentrations. Aged nitrate also had lower concentrations and more limited spatial distributions than sulfate due to differences in the atmospheric lifetime between SO2 and NOx. The estimated NOx lifetime of approximately ∼24 h in China agrees with a satellite-based estimation of 21 h. Potential applications of the age distribution analysis include evaluating the impacts of meteorology on air quality and developing short-term emission control strategies.

Diversity of Carbonyl Compounds in Biogas and Natural Gas Revealed Using High-Resolution Mass Spectrometry and Nontarget Analysis.

Airborne carbonyl compounds such as formaldehyde, acrolein, and methyl ethyl ketone have long been chemicals-of-concern in the environment due to their reactivity and their potential for negative health effects. Standard methods for determining carbonyls in air, which focus on a set of 15 or fewer compounds, involve derivatization to form nonvolatile hydrazones, which can readily be analyzed via liquid chromatography (LC) with ultraviolet detectors. Here, we apply a new LC-high-resolution mass spectrometry (HRMS) method to natural gas and a variety of upgraded biofuels to better assess their total carbonyl profile using the inherent selectivity of the standard sampling methodology and the selectivity and sensitivity of HRMS. The standard method accounted for only 64% of the total carbonyl content in natural gas and between 26 and 45% of the total carbonyl content in biogas sources, with the balance detected by the new LC/HRMS method. An additional 540 compounds with molecular formulas consistent with carbonyl compounds were detected compared to only 14 target compounds using the standard method. These results demonstrate that the established method dramatically under-reports both the total carbonyl load and the diversity of carbonyl species in natural gas and biogas samples.

Chemical and Toxicological Properties of Emissions from a Light-Duty Compressed Natural Gas Vehicle Fueled with Renewable Natural Gas.

Biogas consisting primarily of methane (CH4) and carbon dioxide (CO2) can be upgraded to a transportation fuel referred to as renewable natural gas (RNG) by removing CO2 and other impurities. RNG has energy content comparable to fossil compressed natural gas (CNG) but with lower life-cycle greenhouse gas (GHG) emissions. In this study, a light-duty cargo van was tested with CNG and two RNG blends on a chassis dynamometer in order to compare the toxicity of the resulting exhaust. Tests for reactive oxygen species (ROS), biomarker expressions (CYP1A1, IL8, COX-2), and mutagenicity (Ames) show that RNG exhaust has toxicity that is comparable or lower than CNG exhaust. Statistical analysis reveals associations between toxicity and tailpipe emissions of benzene, dibenzofuran, and dihydroperoxide dimethyl hexane (the last identification is considered tentative/uncertain). Further gas-phase toxicity may be associated with tailpipe emissions of formaldehyde, dimethyl sulfide, propene, and methyl ketene. CNG exhaust contained higher concentrations of these potentially toxic chemical constituents than RNG exhaust in all of the current tests. Photochemical aging of the vehicle exhaust did not alter these trends. These preliminary results suggest that RNG adoption may be a useful strategy to reduce the carbon intensity of transportation fuels without increasing the toxicity of the vehicle exhaust.

Oxygenated Aromatic Compounds are Important Precursors of Secondary Organic Aerosol in Biomass-Burning Emissions.

Biomass burning is the largest combustion-related source of volatile organic compounds (VOCs) to the atmosphere. We describe the development of a state-of-the-science model to simulate the photochemical formation of secondary organic aerosol (SOA) from biomass-burning emissions observed in dry (RH <20%) environmental chamber experiments. The modeling is supported by (i) new oxidation chamber measurements, (ii) detailed concurrent measurements of SOA precursors in biomass-burning emissions, and (iii) development of SOA parameters for heterocyclic and oxygenated aromatic compounds based on historical chamber experiments. We find that oxygenated aromatic compounds, including phenols and methoxyphenols, account for slightly less than 60% of the SOA formed and help our model explain the variability in the organic aerosol mass (R2 = 0.68) and O/C (R2 = 0.69) enhancement ratios observed across 11 chamber experiments. Despite abundant emissions, heterocyclic compounds that included furans contribute to ∼20% of the total SOA. The use of pyrolysis-temperature-based or averaged emission profiles to represent SOA precursors, rather than those specific to each fire, provide similar results to within 20%. Our findings demonstrate the necessity of accounting for oxygenated aromatics from biomass-burning emissions and their SOA formation in chemical mechanisms.

Modeling Atmospheric Age Distribution of Elemental Carbon Using a Regional Age-Resolved Particle Representation Framework.

The aging process of soot particles has significant implications when estimating their impacts on air quality and climate. In this study, the source-oriented University of California at Davis/California Institute of Technology model with externally mixed aerosol representation is expanded to track the age distribution of elemental carbon (EC) in Southeast Texas. EC with the age of 0-3 h (i.e., emitted less than 3 h ago) accounted for ∼70-90% of the total in urban Houston and 20-40% in rural areas of southeast Texas in August 2000. Significant diurnal variations in the mean age of EC are predicted, with higher contributions from fresh particles during the morning and early evening due to increased traffic emission and reduced atmospheric mixing. Spatially, the mean age of EC decreases with proximity to major sources. Ground-level EC with the age >6 h is less than 20% of the first age group over land, and background EC accounts for the majority over the Gulf of Mexico. Differences in EC spatial distribution indicate that age distribution could have regional impact on aerosol optical and hygroscopic properties, and thus potentially affect cloud formation and radiation balance. Appropriately accounting for the differential properties due to age distribution is needed to better evaluate aerosol direct and indirect effects.

Seasonal and Annual Source Appointment of Carbonaceous Ultrafine Particulate Matter (PM0.1) in Polluted California Cities.

Samples of ultrafine particle matter mass (PM0.1) were collected over 12 months at three cities in California: Los Angeles, East Oakland, San Pablo, and over six months at Fresno. Molecular markers adjusted for volatility and reactivity were used to calculate PM0.1 source contributions. Wood burning was a significant source of PM0.1 organic carbon (OC) during the winter months in northern California (17-47%) but made smaller contributions in other months (0-8%) and was minor in all seasons in Los Angeles (0-5%), except December (17%) during holiday celebrations. Meat cooking was the largest source of PM0.1 OC across all sites (13-29%), followed by gasoline combustion (7-21%). Motor oil and diesel fuel combustion made smaller contributions to PM0.1 OC (3-10% and 3-7%, respectively). Unresolved sources accounted for 22-56% of the PM0.1 OC. The lack of a clear seasonal profile for this unresolved OC suggests that it may be a primary source rather than secondary organic aerosol (SOA). PM0.1 elemental carbon (EC) was dominated by diesel fuel combustion with less than 15% contribution from other sources. All sources besides wood smoke exhibited relatively constant seasonal source contributions to PM0.1 OC reflecting approximately constant emissions over the annual cycle. Annual-average source contributions to PM0.1 OC calculated with traditional molecular markers were similar to the source contributions calculated with the modified molecular markers that account for volatility and reactivity.

Composition and Toxicity of Biogas Produced from Different Feedstocks in California.

Biogas is a renewable energy source composed of methane, carbon dioxide, and other trace compounds produced from anaerobic digestion of organic matter. A variety of feedstocks can be combined with different digestion techniques that each yields biogas with different trace compositions. California is expanding biogas production systems to help meet greenhouse gas reduction goals. Here, we report the composition of six California biogas streams from three different feedstocks (dairy manure, food waste, and municipal solid waste). The chemical and biological composition of raw biogas is reported, and the toxicity of combusted biogas is tested under fresh and photochemically aged conditions. Results show that municipal waste biogas contained elevated levels of chemicals associated with volatile chemical products such as aromatic hydrocarbons, siloxanes, and certain halogenated hydrocarbons. Food waste biogas contained elevated levels of sulfur-containing compounds including hydrogen sulfide, mercaptans, and sulfur dioxide. Biogas produced from dairy manure generally had lower concentrations of trace chemicals, but the combustion products had slightly higher toxicity response compared to the other feedstocks. Atmospheric aging performed in a photochemical smog chamber did not strongly change the toxicity (oxidative capacity or mutagenicity) of biogas combustion exhaust.

Relationships between greenness and low birth weight: Investigating the interaction and mediation effects of air pollution.

BACKGROUND: Associations between residential greenness and improved birth weight have been increasingly reported, but underlying mechanisms and interactions with other environmental exposures are still unclear. OBJECTIVES: To study the relationships between low birth weight (LBW, <2500 g), residential greenness, and the potential influence of air pollution in these relationships (interaction and mediation) in California, over the period 2001-2008. METHODS: Residential greenness around maternal homes was characterized using the Normalized Difference Vegetation Index (NDVI). Complementary indicators of air pollution exposure reflected its main components. Birth weight and maternal characteristics were obtained from birth certificate records. In this case-cohort study, associations between greenness and LBW were investigated using multi-level Poisson regression with random effect at the hospital level. We investigated potential interaction of greenness and air pollutants on both additive and multiplicative scales. Mediation analyses were conducted to estimate the potential contribution of local variations in air pollutant concentrations associated with greenness on LBW risk. RESULTS: In total 72,632 LBW cases were included. A reduction of LBW risk was associated with an increase in NDVI (adjusted risk ratio per inter-quartile range in NDVI: 0.963; 95% confidence interval: 0.947; 0.978). We observed no interaction between NDVI and air pollution on LBW risk. The estimated mediating effect of fine particulate matter in the impact of greenness on LBW was 12%. CONCLUSION: This large study confirms that residential greenness is associated with a reduced risk of LBW and suggests that greenness might benefit to LBW partly through a local reduction in air pollution.

Ultrafine Particle Emissions from Natural Gas, Biogas, and Biomethane Combustion.

Biogas and biomethane (=purified biogas) are major renewable fuels that play a pivotal role in the evolving global energy economy. Here, we measure ultrafine particle (UFP; Dp (particle diameter) < 100 nm) emissions from the combustion of biomethane and biogas produced from five different representative sources: two food waste digesters, two dairy waste digesters, and one landfill. Combustion exhaust for each of these sources is measured from one or more representative sectors including electricity generation, motor vehicles, and household use. Results show that UFP emissions are similar when using biomethane and natural gas with similar sulfur and siloxane content. Approximately 70% of UFPs emitted from water heaters and cooking stoves were semivolatile, but 30% of the UFPs were nonvolatile and did not evaporate even under extremely high dilution conditions. Photochemical aging of biomethane combustion exhaust and natural gas combustion exhaust produced similar amounts of secondary organic aerosol (SOA) formation. The results of the current study suggest that widespread adoption of biogas and biomethane as a substitute for natural gas will not significantly increase ambient concentrations of primary and secondary UFPs if advanced combustion technology is used and the sulfur and siloxane content is similar for biogas/biomethane and natural gas.

Influence of vapor wall loss in laboratory chambers on yields of secondary organic aerosol.

Secondary organic aerosol (SOA) constitutes a major fraction of submicrometer atmospheric particulate matter. Quantitative simulation of SOA within air-quality and climate models--and its resulting impacts--depends on the translation of SOA formation observed in laboratory chambers into robust parameterizations. Worldwide data have been accumulating indicating that model predictions of SOA are substantially lower than ambient observations. Although possible explanations for this mismatch have been advanced, none has addressed the laboratory chamber data themselves. Losses of particles to the walls of chambers are routinely accounted for, but there has been little evaluation of the effects on SOA formation of losses of semivolatile vapors to chamber walls. Here, we experimentally demonstrate that such vapor losses can lead to substantially underestimated SOA formation, by factors as much as 4. Accounting for such losses has the clear potential to bring model predictions and observations of organic aerosol levels into much closer agreement.

Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5.

Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 µm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [µg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.

"What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health".

Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chemical species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chemical and physical properties of complex air pollutant mixtures, and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiological and toxicological research, and to better quantify the effects of specific source sectors and mitigation strategies.

Adverse Reproductive Health Outcomes and Exposure to Gaseous and Particulate-Matter Air Pollution in Pregnant Women.

Introduction: There is growing epidemiologic evidence of associations between maternal exposure to ambient air pollution and adverse birth outcomes, such as preterm birth (PTB). Recently, a few studies have also reported that exposure to ambient air pollution may also increase the risk of some common pregnancy complications, such as preeclampsia and gestational diabetes mellitus (GDM). Research findings, however, have been mixed. These inconsistent results could reflect genuine differences in the study populations, the study locations, the specific pollutants considered, the designs of the study, its methods of analysis, or random variation. Dr. Jun Wu of the University of California­ Irvine, a recipient of HEI's Walter A. Rosenblith New Investigator Award, and colleagues have examined the association between air pollution and adverse birth and pregnancy outcomes in California women. In addition, they examined the effect modification by socioeconomic status (SES) and other factors. Approach: A retrospective nested case­control study was conducted using birth certificate data from about 4.4 million birth records in California from 2001 to 2008. Wu and colleagues analyzed data on low birth weight (LBW) at term (infants born between 37 and 43 weeks of gestation and weighing less than 2500 g), PTB (infants born before 37 weeks of gestation), and preeclampsia (including eclampsia) of the mother during the pregnancy. In addition, they obtained data on GDM for the years 2006­ 2008. In the analyses, all outcomes were included as binary variables. Maternal residential addresses at the time of delivery were geocoded, and a large suite of air pollution exposure metrics was considered, such as (1) regulatory monitoring data on concentrations of criteria pollutants NO2, PM2.5 (particulate matter ≤ 2.5 µm in aerodynamic diameter), and ozone (O3) estimated by empirical Bayesian kriging; (2) concentrations of primary and secondary PM2.5 and PM0.1 components and sources estimated by the University of California­Davis Chemical Transport Model; (3) traffic-related ultrafine particles and concentrations of carbon monoxide (CO) and nitrogen oxides (NOx) estimated by a modified CALINE4 air pollution dispersion model; and (4) proximity to busy roads, road length, and traffic density calculated for different buffer sizes using geographic information system tools. In total, 50 different exposure metrics were available for the analyses. The exposure of primary interest was the mean of the entire pregnancy period for each mother. For the health analyses, controls were randomly selected from the source population. PTB controls were matched on conception year. Term LBW, preeclampsia, and GDM were analyzed using generalized additive mixed models with inclusion of a random effect per hospital. PTB analyses were conducted using conditional logistic regression, with no adjustment for hospital. The main results­ adjusted for race and education as categorical variables and adjusted for maternal age and median household income at the census-block level­were derived from single-pollutant models. Main results and interpretation: In its independent review of the study, the HEI Health Review Committee concluded that Wu and colleagues had conducted a comprehensive nested case­control study of air pollution and adverse birth and pregnancy outcomes. The very large data set and the extensive exposure assessment were strengths of the study. The study documented associations between increases in various air pollution metrics and increased risks of PTB, whereas the evidence was weaker overall for term LBW; in addition, decreases in many air pollution metrics were associated with an increased risk of preeclampsia and GDM, an unexpected result. The investigators suggested that underreporting in the registry data, especially in lower-SES groups, might have caused the many negative associations found for preeclampsia and GDM. In addition, poor geocoding was listed as a potential explanation, affecting in particular the results that were based on measures of proximity to busy roads and traffic density in the smallest buffer size (50 m). However, those issues were not fully explored. In general, the Committee thought that the analysis of road traffic indicators in the 50 m buffer was hampered by the lack of contrast and that the results are therefore difficult to interpret. Some other issues with the analytical approaches should be considered when interpreting the results. Only a subset of controls was used, to reduce computational demands. Hence, some models did not converge, especially in the subgroup analyses. Most of the results in the report were based on analyses using single-pollutant models, which is a reasonable approach but ignores that people are exposed to complex mixtures of pollutants. The Committee believed that the few two-pollutant models that were run provided important insights: these models showed the strongest association for PM2.5 mass, whereas components and source-specific positive associations largely disappeared after adjusting for PM2.5 mass. This study adds to the ongoing debate about whether some particle components and sources are of greater public health concern than others.

Volatility of primary organic aerosol emitted from light duty gasoline vehicles.

Primary organic aerosol (POA) emitted from light duty gasoline vehicles (LDGVs) exhibits a semivolatile behavior in which heating the aerosol and/or diluting the aerosol leads to partial evaporation of the POA. A single volatility distribution can explain the median evaporation behavior of POA emitted from LDGVs but this approach is unable to capture the full range of measured POA volatility during thermodenuder (TD) experiments conducted at atmospherically relevant concentrations (2-5 µg m(-3)). Reanalysis of published TD data combined with analysis of new measurements suggest that POA emitted from gasoline vehicles is composed of two types of POA that have distinctly different volatility distributions: one low-volatility distribution and one medium-volatility distribution. These correspond to fuel combustion-derived POA and motor oil POA, respectively. Models that simultaneously incorporate both of these distributions are able to reproduce experimental results much better (R(2) = 0.94) than models that use a single average or median distribution (R(2) = 0.52). These results indicate that some fraction of POA emitted from LDGVs is essentially nonvolatile under typical atmospheric dilution levels. Roughly 50% of the vehicles tested in the current study had POA emissions dominated by fuel combustion products (essentially nonvolatile). Further testing is required to determine appropriate fleet-average emissions rates of the two POA types from LDGVs.

Identifying PM2.5 and PM0.1 sources for epidemiological studies in California.

The University of California-Davis_Primary (UCD_P) model was applied to simultaneously track ∼ 900 source contributions to primary particulate matter (PM) in California for seven continuous years (January 1st, 2000 to December 31st, 2006). Predicted source contributions to primary PM2.5 mass, PM1.8 elemental carbon (EC), PM1.8 organic carbon (OC), PM0.1 EC, and PM0.1 OC were in general agreement with the results from previous source apportionment studies using receptor-based techniques. All sources were further subjected to a constraint check based on model performance for PM trace elemental composition. A total of 151 PM2.5 sources and 71 PM0.1 sources contained PM elements that were predicted at concentrations in general agreement with measured values at nearby monitoring sites. Significant spatial heterogeneity was predicted among the 151 PM2.5 and 71 PM0.1 source concentrations, and significantly different seasonal profiles were predicted for PM2.5 and PM0.1 in central California vs southern California. Population-weighted concentrations of PM emitted from various sources calculated using the UCD_P model spatial information differed from the central monitor estimates by up to 77% for primary PM2.5 mass and 148% for PM2.5 EC because the central monitor concentration is not representative of exposure for nearby population. The results from the UCD_P model provide enhanced source apportionment information for epidemiological studies to examine the relationship between health effects and concentrations of primary PM from individual sources.