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.

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.

Prenatal air pollution, maternal immune activation, and autism spectrum disorder.

BACKGROUND: Autism Spectrum Disorder (ASD) risk is highly heritable, with potential additional non-genetic factors, such as prenatal exposure to ambient particulate matter with aerodynamic diameter < 2.5 µm (PM2.5) and maternal immune activation (MIA) conditions. Because these exposures may share common biological effect pathways, we hypothesized that synergistic associations of prenatal air pollution and MIA-related conditions would increase ASD risk in children. OBJECTIVES: This study examined interactions between MIA-related conditions and prenatal PM2.5 or major PM2.5 components on ASD risk. METHODS: In a population-based pregnancy cohort of children born between 2001 and 2014 in Southern California, 318,751 mother-child pairs were followed through electronic medical records (EMR); 4,559 children were diagnosed with ASD before age 5. Four broad categories of MIA-related conditions were classified, including infection, hypertension, maternal asthma, and autoimmune conditions. Average exposures to PM2.5 and four PM2.5 components, black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-), were estimated at maternal residential addresses during pregnancy. We estimated the ASD risk associated with MIA-related conditions, air pollution, and their interactions, using Cox regression models to adjust for covariates. RESULTS: ASD risk was associated with MIA-related conditions [infection (hazard ratio 1.11; 95% confidence interval 1.05-1.18), hypertension (1.30; 1.19-1.42), maternal asthma (1.22; 1.08-1.38), autoimmune disease (1.19; 1.09-1.30)], with higher pregnancy PM2.5 [1.07; 1.03-1.12 per interquartile (3.73 µg/m3) increase] and with all four PM2.5 components. However, there were no interactions of each category of MIA-related conditions with PM2.5 or its components on either multiplicative or additive scales. CONCLUSIONS: MIA-related conditions and pregnancy PM2.5 were independently associations with ASD risk. There were no statistically significant interactions of MIA conditions and prenatal PM2.5 exposure with ASD risk.

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.

Maternal exposure to aircraft emitted ultrafine particles during pregnancy and likelihood of ASD in children.

BACKGROUND: There is increasing evidence for adverse health effects associated with aircraft-emitted particulate matter (PM) exposures, which are largely in the ultrafine (PM0.1) size fraction, but no previous study has examined neurodevelopmental outcomes. OBJECTIVE: To assess associations between maternal exposure to aircraft ultrafine particles (UFP) during pregnancy and offspring autism spectrum disorder (ASD) diagnosis. METHODS: This large, representative cohort study included 370,723 singletons born in a single healthcare system. Demographic data, maternal health information, and child's ASD diagnosis by age 5 were extracted from electronic medical records. Aircraft exposure estimates for PM0.1 were generated by the University of California Davis/California Institute of Technology Source Oriented Chemical Transport model. Cox proportional hazard models were used to assess associations between maternal exposure to aircraft PM0·1 in pregnancy and ASD diagnosis, controlling for covariates. RESULTS: Over the course of follow-up, 4,554 children (1.4 %) were diagnosed with ASD. Increased risk of ASD was associated with maternal exposure to aircraft PM0.1 [hazard ratio, HR: 1.02, (95 % confidence interval (CI): 1.01-1.03) per IQR = 0.02 µg/m3 increase during pregnancy. Associations were robust to adjustment for total PM0.1 and fine particulate matter (PM2.5), near-roadway air pollution, and other covariates. Noise adjustment modestly attenuated estimates of UFP effects, which remained statistically significant. DISCUSSION: The results strengthen the emerging evidence that maternal particulate matter exposure during pregnancy is associated with offspring ASD diagnosis and identify aircraft-derived PM0.1 as novel targets for further study and potential regulation.

Prenatal exposure to tailpipe and non-tailpipe tracers of particulate matter pollution and autism spectrum disorders.

BACKGROUND: Traffic-related air pollution exposure is associated with increased risk of autism spectrum disorder (ASD). It is unknown whether carbonaceous material from vehicular tailpipe emissions or redox-active non-tailpipe metals, eg. from tire and brake wear, are responsible. We assessed ASD associations with fine particulate matter (PM2.5) tracers of tailpipe (elemental carbon [EC] and organic carbon [OC]) and non-tailpipe (copper [Cu]; iron [Fe] and manganese [Mn]) sources during pregnancy in a large cohort. METHODS: This retrospective cohort study included 318,750 children born in Kaiser Permanente Southern California (KPSC) hospitals during 2001-2014, followed until age 5. ASD cases were identified by ICD codes. Monthly estimates of PM2.5 and PM2.5 constituents EC, OC, Cu, Fe, and Mn with 4 km spatial resolution were obtained from a source-oriented chemical transport model. These exposures and NO2 were assigned to each maternal address during pregnancy, and associations with ASD were assessed using Cox regression models adjusted for covariates. PM constituent effect estimates were adjusted for PM2.5 and NO2 to assess independent effects. To distinguish ASD risk associated with non-tailpipe from tailpipe sources, the associations with Cu, Fe, and Mn were adjusted for EC and OC, and vice versa. RESULTS: There were 4559 children diagnosed with ASD. In single-pollutant models, increased ASD risk was associated with gestational exposures to tracers of both tailpipe and non-tailpipe emissions. The ASD hazard ratios (HRs) per inter-quartile increment of exposure) for EC, OC, Cu, Fe, and Mn were 1.11 (95% CI: 1.06-1.16), 1.09 (95% CI: 1.04-1.15), 1.09 (95% CI: 1.04-1.13), 1.14 (95% CI: 1.09-1.20), and 1.17 (95% CI: 1.12-1.22), respectively. Estimated effects of Cu, Fe, and Mn (reflecting non-tailpipe sources) were largely unchanged in two-pollutant models adjusting for PM2.5, NO2, EC or OC. In contrast, ASD associations with EC and OC were markedly attenuated by adjustment for non-tailpipe sources. CONCLUSION: Results suggest that non-tailpipe emissions may contribute to ASD. Implications are that reducing tailpipe emissions, especially from vehicles with internal combustion engines, may not eliminate ASD associations with traffic-related air pollution.

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 meteorology as risk factors for COVID-19 death in a cohort from Southern California.

BACKGROUND: Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models. METHODS: We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients. FINDINGS: We found significant associations between COVID-19 death and several air pollution exposures, including: PM2.5 mass, PM0.1 mass, PM2.5 nitrates, PM2.5 elemental carbon, PM2.5 on-road diesel, and PM2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM2.5 mass and PM2.5 nitrate to HR âˆ¼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects. INTERPRETATION: Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

Optimized environmental justice calculations for air pollution disparities in Southern California.

An Environmental Justice (EJ) analysis was carried out using full Chemical Transport Models (CTMs) over Los Angeles, California, to determine how the combination of domain size and spatial resolution affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. One set of simulations used the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem) with spatial resolution ranging from 250 m to 36 km, comparable to census tract sizes, over domains ranging in size from 320 km2 to 10,000 km2. A second set of simulations used the UCD/CIT CTM with spatial resolution ranging from 4 km to 24 km over domains ranging in size from 98,000 km2 to 1,000,000 km2. Overall WRF/Chem model accuracy improved approximately 9% as spatial resolution increased from 4 km to 250 m in present-day simulations, with similar results expected for future simulations. Exposure disparity results are consistent with previous findings: the average Non-Hispanic White person in the study domain experiences PM2.5 mass concentrations 6-14% lower than the average resident, while the average Black and African American person experiences PM2.5 mass concentrations that are 3-22% higher than the average resident. Predicted exposure disparities were a function of the model configuration. Increasing the spatial resolution finer than approximately 1 km produced diminishing returns because the increased spatial resolution came at the expense of reduced domain size in order to maintain reasonable computational burden. Increasing domain size to capture regional trends, such as wealthier populations living in coastal areas, identified larger exposure disparities but the benefits were limited. CTM configurations that use spatial resolution/domain size of 1 km/103 km2 and 4 km/104 km2 over Los Angeles can detect a 0.5 µg m-3 exposure difference with statistical power greater than 90%. These configurations represent a balanced approach between statistical power, sensitivity across socio-economic groups, and computational burden when predicting current and future air pollution exposure disparities in Los Angeles.

Comparison of size-resolved PM elements measured using aluminum foil and Teflon impaction substrates: Implications for ultrafine particle source apportionment and future sampling networks in California.

Measurement networks for ultrafine particulate matter (PM0.1) have been limited by the high costs for equipment, supplies, and labor associated with the need to collect PM0.1 samples on multiple substrates for full chemical analysis. Here we explore whether a single cascade impactor loaded with aluminum foil substrates is sufficient for PM0.1 source apportionment calculations in order to reduce those costs. An extraction method previously designed to measure elements on Teflon substrates was modified to accommodate features of aluminum foil substrates. Regression analysis between co-located aluminum foil and Teflon substrates in the particle diameter range 0.1-1.8 µm showed good agreement (R > 0.7) for 18 elements. Regression in the diameter range 0.1-0.18 µm (quasi-ultrafine particulate matter) was used to characterize the uncertainty introduced by the aluminum foil extraction method for the elements Li, K, V, Br, Rb, Mo, Cd, Sn, Sb, and Ba. This uncertainty was used to generate 30 simulated aluminum foil PM0.1 datasets at each of three sites, followed by source apportionment analysis using Positive Matrix Factorization (PMF). At two of the three sites, the PM0.1 source contributions calculated using aluminum foil substrates alone were almost identical to the PMF results from combined aluminum foil and Teflon substrates. The PM0.1 source contributions calculated using aluminum foil substrates at the third site were closer to the results from a previous Chemical Mass Balance (CMB) study than to the PMF results from the combined aluminum foil and Teflon substrates, possibly because the CMB study also relied exclusively on samples collected using aluminum foil substrates. The success of the PM0.1 source apportionment approach using aluminum foil substrates in a single cascade impactor provides a viable method for reducing costs in PM0.1 sampling networks by 40-47%. Similar results may be achievable at locations outside of California.

Adoption of low-carbon fuels reduces race/ethnicity disparities in air pollution exposure in California.

An environmental justice (EJ) analysis shows that adoption of low-carbon energy sources in the year 2050 reduces the race/ethnicity disparity in air pollution exposure in California by as much as 20% for PM2.5 mass and by as much as 40% for PM0.1 mass. An ensemble of six different energy scenarios constructed using the energy-economic optimization model CA-TIMES were evaluated in future years. Criteria pollutant emissions were developed for each energy scenario using the CA-REMARQUE model using 4 km spatial resolution over four major geographic areas in California: the greater San Francisco Bay Area including Sacramento (SFBA&SAC), the San Joaquin Valley (SJV), Los Angeles (LA), and San Diego (SD). The Weather Research & Forecasting (WRF) model was used to predict future meteorology fields by downscaling two different climate scenario (RCP4.5 and RCP8.5) generated by two different GCMs (the Community Climate System Model and the Canadian Earth Systems Model). Simulations were performed over 32 weeks randomly selected during the 10 year window from the year 2046 to 2055 to build up a long-term average in the presence of ENSO variability. The trends associated with low-carbon energy adoption were relatively stable across the ensemble of locations and scenarios. Deeper reductions in the carbon intensity of energy sources progressively reduced exposure to PM2.5 mass and PM0.1 mass for all California residents. The greater adoption of low-carbon fuels also reduced the racial disparity in the PM exposure. The three energy scenarios that achieved an ~80% reduction in GHG emissions relative to 1990 levels simultaneously produced the greatest reduction in PM exposure for all California residents and the greatest reduction in the racial disparity of that exposure. These findings suggest that the adoption of low-carbon energy can improve public health and reduce racial disparities through an improvement in air quality.

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.

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.

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.

Statistical analysis of trace contaminants measured in biogas.

Biogas is a renewable energy fuel that can be treated to increase purity so that the resulting "biomethane" can be injected into the natural gas pipeline grid. The trace contaminants in biogas and biomethane make up a small fraction of the total gas but they still have the potential to cause adverse health effects and pipeline corrosion. This study investigates the statistical distributions of 17 trace metals, six mercaptans, hydrogen sulfide, ammonia, and six additional trace organic compounds. Twelve of these 31 trace contaminants have been previously identified as constituents of concern based on their toxicity profiles and through health risk assessment studies. Untreated and treated samples of biogas were collected from 12 different biogas production facilities using diverse feedstocks throughout California. Results show that most biogas trace contaminants follow a single log-normal distribution or a bi-modal lognormal distribution depending on the type of production facility. Treatment of biogas demonstrates some removal for all trace contaminants, but four constituents of concern (copper, lead, hydrogen sulfide, and methyl mercaptan) are predicted to have a >1% probability of exceeding trigger levels even after common treatments. This finding suggests that enhanced monitoring may be warranted for these contaminants. Several trace metals and volatile organic compounds (VOCs) were found to have seasonal trends with greater concentrations in the summer and lower concentrations in the winter suggesting that seasonal variation should be considered in future monitoring plans.

Positive matrix factorization of ultrafine particle mass (PM0.1) at three sites in California.

Ultrafine particles (UFPs) are an emerging air quality concern because of their enhanced toxicity compared to larger airborne particles. This study aims to better understand source contributions to UFP mass (PM0.1) at multiples sites across California. Three-day average samples of PM0.1 collected over a full year at San Pablo, East Oakland, and Los Angeles were analyzed using Positive Matrix Factorization (PMF). Seven PM0.1 source-factors were identified at all locations: Factor1- Gasoline+Motor Oil+Meat Cooking+Natural Gas+SOA (31-53% PM0.1 mass), Factor 2- Diesel+Motor Oil (25-45% PM0.1 mass), Factor 3-Wood Burning (6-12% PM0.1 mass), Factor 4-Shipping and other heavy fuel oil combustion (2-3% PM0.1 mass), Factor 5-Sea Spray (4-8% PM0.1 mass), Factor 6-Sb Brake Wear (1-3% PM0.1 mass) and Factor 7-Sn - Unknown (1-7% PM0.1 mass). PM0.1 wood burning contributions were highest in the winter season when residential wood combustion was active. The monthly-averaged PM0.1 source apportionment results calculated by PMF are consistent with the PM0.1 source apportionment results calculated using Chemical Mass Balance (CMB) from the same sampling campaign. PMF distinguished Diesel+Motor Oil from Gasoline+Motor Oil+Meat Cooking+Natural Gas+SOA based on the species EC3 (a sub-fraction of elemental carbon that is volatilized and oxidized at temperatures between 700 and 775 °C), but PMF failed to further resolve the major sources of PM0.1 OC because unique tracers were not measured. PMF resolved "Shipping and other heavy fuel oil combustion" and Sea Spray sources based on inorganic tracers V and Br. The PMF factor rich in Sb very likely comes from brake wear associated with on-road vehicles and railway operations. The undefined Sn factor may be indicative of local industrial sources and traffic emission, but further research will be required to confirm this hypothesis. The PM0.1 source apportionment results contained in the current study further characterize the seasonal and spatial patterns of UFP concentrations in California.

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.