Location-specific strategies for eliminating US national racial-ethnic [Formula: see text] exposure inequality.

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter ([Formula: see text]), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches.

Ambient Ultrafine Particulate Matter and Clinical Outcomes in Fibrotic Interstitial Lung Disease.

RATIONALE: Particulate matter ≤2.5µm (PM2.5) is associated with adverse outcomes in fibrotic interstitial lung disease (fILD), but the impact of ultrafine particulates (UFPs; aerodynamic diameter ≤100nm) remains unknown. OBJECTIVE: To evaluate UFP associations with clinical outcomes in fILD. METHODS: Multicenter, prospective cohort study enrolling patients with fILD from the University of Pittsburgh Simmons Center and Pulmonary Fibrosis Foundation Patient Registry (PFF-PR). Using a national-scale UFP model, we linked exposures using three approaches in Simmons (residential address geocoordinates, zip centroid geocoordinates, zip average) and two in PFF-PR where only 5-digit zip code was available (zip centroid, zip average). We tested UFP associations with transplant-free survival using multivariable Cox, baseline percent predicted forced vital capacity (FVC) and diffusion capacity of the lung (DLCO) using multivariable linear regressions, and decline in FVC and DLCO using linear mixed models, adjusting for age, sex, smoking, race, socioeconomic status, site, PM2.5, and nitrogen dioxide. RESULTS: Annual mean outdoor UFP levels for 2017 were estimated for 1416 Simmons and 1919 PFF-PR patients. Increased UFP level was associated with transplant-free survival in fully-adjusted Simmons residential address models (HR=1.08 per 1000 particles/cm3, 95%CI 1.01-1.15, p=0.02), but not PFF-PR models, which used less precise linkage approaches. Higher UFP was associated with lower baseline FVC and more rapid FVC decline in Simmons. CONCLUSIONS: Increased UFP exposure was associated with transplant-free survival and lung function in the cohort with precise residential location linkage. This work highlights the need for more robust regulatory networks to study the health effects of UFPs nationwide.

Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring.

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

Air quality-related health damages of food.

Agriculture is a major contributor to air pollution, the largest environmental risk factor for mortality in the United States and worldwide. It is largely unknown, however, how individual foods or entire diets affect human health via poor air quality. We show how food production negatively impacts human health by increasing atmospheric fine particulate matter (PM2.5), and we identify ways to reduce these negative impacts of agriculture. We quantify the air quality-related health damages attributable to 95 agricultural commodities and 67 final food products, which encompass >99% of agricultural production in the United States. Agricultural production in the United States results in 17,900 annual air quality-related deaths, 15,900 of which are from food production. Of those, 80% are attributable to animal-based foods, both directly from animal production and indirectly from growing animal feed. On-farm interventions can reduce PM2.5-related mortality by 50%, including improved livestock waste management and fertilizer application practices that reduce emissions of ammonia, a secondary PM2.5 precursor, and improved crop and animal production practices that reduce primary PM2.5 emissions from tillage, field burning, livestock dust, and machinery. Dietary shifts toward more plant-based foods that maintain protein intake and other nutritional needs could reduce agricultural air quality-related mortality by 68 to 83%. In sum, improved livestock and fertilization practices, and dietary shifts could greatly decrease the health impacts of agriculture caused by its contribution to reduced air quality.

EASIUR-HR: A Model To Evaluate Exposure Inequality Caused by Ground-Level Sources of Primary Fine Particulate Matter.

People of color disproportionately bear the health impacts of air pollution, making air quality a critical environmental justice issue. However, quantitative analysis of the disproportionate impacts of emissions is rarely done due to a lack of suitable models. Our work develops a high-resolution reduced-complexity model (EASIUR-HR) to evaluate the disproportionate impacts of ground-level primary PM2.5 emissions. Our approach combines a Gaussian plume model for near-source impacts of primary PM2.5 with a previously developed reduced-complexity model, EASIUR, to predict primary PM2.5 concentrations at a spatial resolution of 300 m across the contiguous United States. We find that low-resolution models underpredict important local spatial variation of air pollution exposure to primary PM2.5 emissions, potentially underestimating the contribution of these emissions to national inequality in PM2.5 exposure by more than a factor of 2. We apply EASIUR-HR to analyze the impacts of vehicle electrification on exposure disparities. While such a policy has small aggregate air quality impacts nationally, it reduces exposure disparity for race/ethnic minorities. Our high-resolution RCM for primary PM2.5 emissions (EASIUR-HR) is a new, publicly available tool to assess inequality in air pollution exposure across the United States.

Mobile Sources Are Still an Important Source of Secondary Organic Aerosol and Fine Particulate Matter in the Los Angeles Region.

Secondary organic aerosol (SOA) is a significant component of atmospheric fine particulate matter. Mobile sources have historically been a major source of SOA precursors in urban environments, but decades of regulations have reduced their emissions. Less regulated sources, such as volatile chemical products (VCPs), are of growing importance. We analyzed ambient and emissions data to assess the contribution of mobile sources to SOA formation in Los Angeles during the period of 2009-2019. During this period, air quality in the Los Angeles region has improved, but organic aerosol (OA) concentrations did not decrease as much as primary pollutants. This appears to be largely due to SOA, whose mass fraction in OA increased over this period. In 2010, about half of the freshly formed SOA measured in Pasadena, CA appears to be formed from hydrocarbon (non-oxygenated) precursors. Chemical mass balance analysis indicates that these hydrocarbon SOA precursors (including intermediate volatility organic compounds) can largely be explained by emissions from mobile sources in 2010. Our analysis indicates that continued reduction in emissions from mobile sources should lead to additional significant decreases in atmospheric SOA and PM2.5 mass in the Los Angeles region.

Limited Secondary Organic Aerosol Production from Acyclic Oxygenated Volatile Chemical Products.

Volatile chemical products (VCPs) have recently been identified as potentially important unconventional sources of secondary organic aerosol (SOA), in part due to the mitigation of conventional emissions such as vehicle exhaust. Here, we report measurements of SOA production in an oxidation flow reactor from a series of common VCPs containing oxygenated functional groups and at least one oxygen within the molecular backbone. These include two oxygenated aromatic species (phenoxyethanol and 1-phenoxy-2-propanol), two esters (butyl butyrate and butyl acetate), and four glycol ethers (carbitol, methyl carbitol, butyl carbitol, and hexyl carbitol). We measured gas- and particle-phase products with a suite of mass spectrometers and particle-sizing instruments. Only the aromatic VCPs produce SOA with substantial yields. For the acyclic VCPs, ether and ester functionality promotes fragmentation and hinders autoxidation, whereas aromatic rings drive SOA formation in spite of the presence of ether groups. Therefore, our results suggest that a potential strategy to reduce urban SOA from VCPs would be to reformulate consumer products to include less oxygenated aromatic compounds.

National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data.

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R2 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R2: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.

Watching Paint Dry: Organic Vapor Emissions from Architectural Coatings and their Impact on Secondary Organic Aerosol Formation.

Emissions from volatile chemical products (VCPs) are emerging as a major source of anthropogenic secondary organic aerosol (SOA) precursors. Paints and coatings are an important class of VCPs that emit both volatile and intermediate volatility organic compounds (VOCs and IVOCs). In this study, we directly measured I/VOC emissions from representative water- (latex) and oil-based paints used in the U.S. Paint I/VOC emissions vary by several orders of magnitude by both the solvent and gloss level. Oil-based paints had the highest emissions (>105 µg/g-paint), whereas low-gloss interior paints (Flat, Satin, and Semigloss) all emitted ∼102 µg/g-paint. Emissions from interior paints are dominated by VOCs, whereas exterior-use paints emitted a larger fraction of IVOCs. Extended emission tests showed that most I/VOC emissions occur within 12-24 h after paint application, though some paints continue to emit IVOCs for 48 h or more. We used our data to estimate paint I/VOC emissions and the subsequent SOA production in the U.S. Total annual paint I/VOC emissions are 48-155 Gg (0.15-0.48 kg/person). These emissions contribute to the formation of 2.2-7.5 Gg of SOA annually. Oil-based paints contribute 70-98% of I/VOC emissions and 61-99% of SOA formation, even though they only account for a minority of paint usage.

Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort.

The purpose of this study was to estimate cardiopulmonary mortality associations for long-term exposure to PM2.5 species and sources (i.e., components) within the U.S. National Health Interview Survey cohort. Exposures were estimated through a chemical transport model for six species (i.e., elemental carbon (EC), primary organic aerosols (POA), secondary organic aerosols (SOA), sulfate (SO4), ammonium (NH4), nitrate (NO3)) and five sources of PM2.5 (i.e., vehicles, electricity-generating units (EGU), non-EGU industrial sources, biogenic sources (bio), "other" sources). In single-pollutant models, we found positive, significant (p < 0.05) mortality associations for all components, except POA. After adjusting for remaining PM2.5 (total PM2.5 minus component), we found significant mortality associations for EC (hazard ratio (HR) = 1.36; 95% CI [1.12, 1.64]), SOA (HR = 1.11; 95% CI [1.05, 1.17]), and vehicle sources (HR = 1.06; 95% CI [1.03, 1.10]). HRs for EC, SOA, and vehicle sources were significantly larger in comparison to those for remaining PM2.5 (per unit µg/m3). Our findings suggest that cardiopulmonary mortality associations vary by species and source, with evidence that EC, SOA, and vehicle sources are important contributors to the PM2.5 mortality relationship. With further validation, these findings could facilitate targeted pollution regulations that more efficiently reduce air pollution mortality.

High-Spatial-Resolution Estimates of Ultrafine Particle Concentrations across the Continental United States.

There is growing evidence that ultrafine particles (UFP; particles smaller than 100 nm) are likely more toxic than larger particles. However, the health effects of UFP remain uncertain due in part to the lack of large-scale population-based exposure assessment. We develop a national-scale empirical model of particle number concentration (PNC; a measure of UFP) using data from mobile monitoring and fixed sites across the United States and a land-use regression (LUR) modeling framework. Traffic, commercial land use, and urbanicity-related variables explain much of the spatial variability of PNC (base model R2 = 0.77, RMSE = 2400 cm-3). Model predictions are robust across a diverse set of evaluations [random 10-fold holdout cross-validation (HCV): R2 = 0.72, RMSE = 2700 cm-3; spatially defined HCV: R2 = 0.66, RMSE = 3000 cm-3; evaluation against an independent data set: R2 = 0.54, RMSE = 2600 cm-3]. We apply our model to predict PNC at ∼6 million residential census blocks in the contiguous United States. Our estimates are annual average concentrations for 2016-2017. The predicted national census-block-level mean PNC ranges between 1800 and 26 600 cm-3 (population-weighted average: 6500 cm-3), with hotspots in cities and near highways. Our national PNC model predicts large urban-rural, intra-, and inter-city contrasts. PNC and PM2.5 are moderately correlated at the city scale, but uncorrelated at the regional/national scale. Our high-spatial-resolution national PNC estimates are useful for analyzing population exposure (socioeconomic disparity, epidemiological health impact) and environmental policy and regulation.

Cancer mortality risk, fine particulate air pollution, and smoking in a large, representative cohort of US adults.

PURPOSE: Air pollution and smoking are associated with various types of mortality, including cancer. The current study utilizes a publicly accessible, nationally representative cohort to explore relationships between fine particulate matter (PM2.5) exposure, smoking, and cancer mortality. METHODS: National Health Interview Survey and mortality follow-up data were combined to create a study population of 635,539 individuals surveyed from 1987 to 2014. A sub-cohort of 341,665 never-smokers from the full cohort was also created. Individuals were assigned modeled PM2.5 exposure based on average exposure from 1999 to 2015 at residential census tract. Cox Proportional Hazard models were utilized to estimate hazard ratios for cancer-specific mortality controlling for age, sex, race, smoking status, body mass, income, education, marital status, rural versus urban, region, and survey year. RESULTS: The risk of all cancer mortality was adversely associated with PM2.5 (per 10 µg/m3 increase) in the full cohort (hazard ratio [HR] 1.15, 95% confidence interval [CI] 1.08-1.22) and the never-smokers' cohort (HR 1.19, 95% CI 1.06-1.33). PM2.5-morality associations were observed specifically for lung, stomach, colorectal, liver, breast, cervix, and bladder, as well as Hodgkin lymphoma, non-Hodgkin lymphoma, and leukemia. The PM2.5-morality association with lung cancer in never-smokers was statistically significant adjusting for multiple comparisons. Cigarette smoking was statistically associated with mortality for many cancer types. CONCLUSIONS: Exposure to PM2.5 air pollution contributes to lung cancer mortality and may be a risk factor for other cancer types. Cigarette smoking has a larger impact on cancer mortality than PM2.5 , but is associated with similar cancer types.

Spatial Correlation of Ultrafine Particle Number and Fine Particle Mass at Urban Scales: Implications for Health Assessment.

The epidemiological evidence for ultrafine particles (UFP; particles with diameter <100 nm) causing chronic health effects independent of fine particulate matter (PM2.5) mass is inconclusive. A prevailing view is that urban UFP and PM2.5 mass have different spatial patterns, which should allow epidemiological studies to distinguish their independent, chronic health effects. We investigate intraurban spatial correlation of PM2.5 and UFP exposures in Pittsburgh, Pennsylvania. Measurements and predictions of a land-use regression model indicate moderate spatial correlation between particle number concentrations (PNC; a proxy for UFP) and PM2.5 (R2 of 0.38 and 0.41, respectively). High-resolution (1-km) chemical transport model simulations predict stronger spatial correlation (R2 ≈ 0.8). The finding of moderate to strong spatial correlation was initially surprising because secondary aerosol contributes the vast majority of PM2.5 mass. However, intraurban spatial patterns of both PNC and PM2.5 are driven by local emissions and both pollutants largely behave as passive tracers at time scales of 1 day or less required for transport across most urban environments. Although previous research has shown little temporal correlation between PNC and PM2.5, our finding of moderate to strong spatial correlation may complicate epidemiological analyses to separate the chronic health effects of PNC from PM2.5 mass.

Optimizing Emissions Reductions from the U.S. Power Sector for Climate and Health Benefits.

Improved air quality and human health are often discussed as "co-benefits" of mitigating climate change, yet they are rarely considered when designing or implementing climate policies. We analyze the implications of integrating health and climate when determining the best locations for replacing power plants with new wind, solar, or natural gas to meet a CO2 reduction target in the United States. We employ a capacity expansion model with integrated assessment of climate and health damages, comparing portfolios optimized for benefits to climate alone or both health and climate. The model estimates county-level health damages and accounts for uncertainty by using a range of air quality models (AP3, EASIUR, and InMAP) and concentration-response functions (American Cancer Society and Harvard Six Cities). We find that reducing CO2 by 30% yields $21-68 billion in annual health benefits, with an additional $9-36 billion possible when co-optimizing for climate and health benefits. Additional benefits accrue from prioritizing emissions reductions in counties with high population exposure. Total health benefits equal or exceed climate benefits across a wide range of modeling assumptions. Our results demonstrate the value of considering health in climate policy design and the need for interstate cooperation to achieve additional health benefits equitably.

Urban Oxidation Flow Reactor Measurements Reveal Significant Secondary Organic Aerosol Contributions from Volatile Emissions of Emerging Importance.

Mobile sampling studies have revealed enhanced levels of secondary organic aerosol (SOA) in source-rich urban environments. While these enhancements can be from rapidly reacting vehicular emissions, it was recently hypothesized that nontraditional emissions (volatile chemical products and upstream emissions) are emerging as important sources of urban SOA. We tested this hypothesis by using gas and aerosol mass spectrometry coupled with an oxidation flow reactor (OFR) to characterize pollution levels and SOA potentials in environments influenced by traditional emissions (vehicular, biogenic), and nontraditional emissions (e.g., paint fumes). We used two SOA models to assess contributions of vehicular and biogenic emissions to our observed SOA. The largest gap between observed and modeled SOA potential occurs in the morning-time urban street canyon environment, for which our model can only explain half of our observation. Contributions from VCP emissions (e.g., personal care products) are highest in this environment, suggesting that VCPs are an important missing source of precursors that would close the gap between modeled and observed SOA potential. Targeted OFR oxidation of nontraditional emissions shows that these emissions have SOA potentials that are similar, if not larger, compared to vehicular emissions. Laboratory experiments reveal large differences in SOA potentials of VCPs, implying the need for further characterization of these nontraditional emissions.

Comparing regional stove-usage patterns and using those patterns to model indoor air quality impacts.

Monitoring improved cookstove adoption and usage in developing countries can help anticipate potential health and environmental benefits that may result from household energy interventions. This study explores stove-usage monitor (SUM)-derived usage data from field studies in China (52 stoves, 1422 monitoring days), Honduras (270 stoves, 630 monitoring days), India (19 stoves, 565 monitoring days), and Uganda (38 stoves, 1007 monitoring days). Traditional stove usage was found to be generally similar among four seemingly disparate countries in terms of cooking habits, with average usage of between 171 and 257 minutes per day for the most-used stoves. In Honduras, where survey-based usage data were also collected, there was only modest agreement between sensor data and self-reported user data. For Indian homes, we combined stove-usage data with a single-zone Monte Carlo box model to estimate kitchen-level PM2.5 and CO concentrations under various scenarios of cleaner cookstove adoption. We defined clean cookstove performance based on the International Standards Organization (ISO) voluntary guidelines. Model results showed that even with 75% displacement of traditional stoves with the cleanest available stove (ISO tier-5), World Health Organization 24 hours PM2.5 standards were exceeded in 96.4% of model runs, underscoring the importance of full displacement.

Reducing secondary organic aerosol formation from gasoline vehicle exhaust.

On-road gasoline vehicles are a major source of secondary organic aerosol (SOA) in urban areas. We investigated SOA formation by oxidizing dilute, ambient-level exhaust concentrations from a fleet of on-road gasoline vehicles in a smog chamber. We measured less SOA formation from newer vehicles meeting more stringent emissions standards. This suggests that the natural replacement of older vehicles with newer ones that meet more stringent emissions standards should reduce SOA levels in urban environments. However, SOA production depends on both precursor concentrations (emissions) and atmospheric chemistry (SOA yields). We found a strongly nonlinear relationship between SOA formation and the ratio of nonmethane organic gas to oxides of nitrogen (NOx) (NMOG:NOx), which affects the fate of peroxy radicals. For example, changing the NMOG:NOx from 4 to 10 ppbC/ppbNOx increased the SOA yield from dilute gasoline vehicle exhaust by a factor of 8. We investigated the implications of this relationship for the Los Angeles area. Although organic gas emissions from gasoline vehicles in Los Angeles are expected to fall by almost 80% over the next two decades, we predict no reduction in SOA production from these emissions due to the effects of rising NMOG:NOx on SOA yields. This highlights the importance of integrated emission control policies for NOx and organic gases.

Urban Ultrafine Particle Exposure Assessment with Land-Use Regression: Influence of Sampling Strategy.

Sampling strategies in the collection of ultrafine particle (UFP) data to develop land-use regression (LUR) models can strongly influence the resulting exposure estimates. Here, we systematically examine how much sampling is needed to develop robust and stable UFP LUR models. To address this question, we collected 3-6 weeks of continuous measurements of UFP concentrations at 32 sites in Pittsburgh, Pennsylvania covering a wide range of urban land-use attributes. Through systematic subsampling of this data set, we evaluate the performance of hundreds of LUR models with varying numbers of sampling days and daily sampling durations. Our base LUR model derived from wintertime average concentrations explained about 80% of the spatial variability in the data (adjusted R2 ∼ 0.8). The performance of the LUR models degrades with decreasing number of sampling days and sampling duration per day. For our data set, 1-3 h of sampling per day for 10-15 days provided UFP concentration estimates comparable to models derived from the entire data set. Small numbers of repeated sampling per site (1-3 days) at short duration (∼15-60 min per day) result in poor performance ( R2 < 0.5), similar to previous UFP LUR models. This study provides guidelines for the design of future measurement campaigns and monitoring networks to generate robust UFP LUR models for exposure assessments. Further study in other locations with more sites is needed to evaluate these guidelines over a broader range of conditions.

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling.

This study presents land-use regression (LUR) models for submicron particulate matter (PM1) components from an urban area. Models are presented for mass concentrations of inorganic species (SO4, NO3, NH4), organic aerosol (OA) factors, and total PM1. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km2 area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R2 = 0.80, HOA R2 = 0.67) and lowest for secondary inorganic species (SO4 R2 = 0.47, NH4 R2 = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM1 components through a subsampling analysis.

Detailed Speciation of Intermediate Volatility and Semivolatile Organic Compound Emissions from Gasoline Vehicles: Effects of Cold-Starts and Implications for Secondary Organic Aerosol Formation.

Over the past two decades vehicle emission standards in the United States have been dramatically tightened with the goal of reducing urban air pollution. Secondary organic aerosol (SOA) is the dominant contributor to urban organic aerosol. Experiments were conducted at the California Air Resources Board Haagen-Smit Laboratory to characterize exhaust organics from 20 gasoline vehicles recruited from the California in-use fleet. The vehicles spanned a wide range of emission certification standards. We comprehensively characterized intermediate volatility and semivolatile organic compound emissions using thermal desorption two-dimensional gas-chromatography-mass-spectrometry with electron impact (GC × GC-EI-MS) and vacuum-ultraviolet (GC × GC-VUV-MS) ionization. Single-ring aromatic compounds with unsaturated C4 and C5 substituents contribute a large fraction of the intermediate volatility organic compound (IVOC) emissions in gasoline vehicle exhaust. The analyses of quartz filters used in GC × GC-VUV-MS show that primary organic aerosol emissions were dominated by motor oil. We combined our new emissions data with published SOA yield parametrizations to estimate SOA formation potential. After 24 h of oxidation, IVOC emissions contributed 45% of  SOA formation;  BTEX compounds (benzene, toluene, xylenes, and ethylbenzene), 40%;  other VOC aromatics, 15%. The composition of IVOC emissions was consistent across the test fleet, suggesting that future reductions in vehicular emissions will continue to reduce SOA formation and ambient particulate mass levels.