Low-Cost Hourly Ambient Black Carbon Measurements at Multiple Cities in Africa.

There is a notable lack of continuous monitoring of air pollutants in the Global South, especially for measuring chemical composition, due to the high cost of regulatory monitors. Using our previously developed low-cost method to quantify black carbon (BC) in fine particulate matter (PM2.5) by analyzing reflected red light from ambient particle deposits on glass fiber filters, we estimated hourly ambient BC concentrations with filter tapes from beta attenuation monitors (BAMs). BC measurements obtained through this method were validated against a reference aethalometer between August 2 and 23, 2023 in Addis Ababa, Ethiopia, demonstrating a very strong agreement (R2 = 0.95 and slope = 0.97). We present hourly BC for three cities in sub-Saharan Africa (SSA) and one in North America: Abidjan (Côte d'Ivoire), Accra (Ghana), Addis Ababa (Ethiopia), and Pittsburgh (USA). The average BC concentrations for the measurement period at the Abidjan, Accra, Addis Ababa Central summer, Addis Ababa Central winter, Addis Ababa Jacros winter, and Pittsburgh sites were 3.85 µg/m3, 5.33 µg/m3, 5.63 µg/m3, 3.89 µg/m3, 9.14 µg/m3, and 0.52 µg/m3, respectively. BC made up 14-20% of PM2.5 mass in the SSA cities compared to only 5.6% in Pittsburgh. The hourly BC data at all sites (SSA and North America) show a pronounced diurnal pattern with prominent peaks during the morning and evening rush hours on workdays. A comparison between our measurements and the Goddard Earth Observing System Composition Forecast (GEOS-CF) estimates shows that the model performs well in predicting PM2.5 for most sites but struggles to predict BC at an hourly resolution. Adding more ground measurements could help evaluate and improve the performance of chemical transport models. Our method can potentially use existing BAM networks, such as BAMs at U.S. Embassies around the globe, to measure hourly BC concentrations. The PM2.5 composition data, thus acquired, can be crucial in identifying emission sources and help in effective policymaking in SSA.

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.

Living in environmental justice areas worsens asthma severity and control: Differential interactions with disease duration, age at onset, and pollution.

BACKGROUND: Impoverished and historically marginalized communities often reside in areas with increased air pollution. OBJECTIVE: We evaluated the association between environmental justice (EJ) track and asthma severity and control as modified by traffic-related air pollution (TRAP). METHODS: We performed a retrospective study of 1526 adult asthma patients in Allegheny County, Pa, enrolled in an asthma registry during 2007-20. Asthma severity and control were determined using global guidelines. EJ tract designation was based on residency in census tracts with ≥30% non-White and/or ≥20% impoverished populations. TRAP exposures (NO2 and black carbon) for each census tract were normalized into pollution quartiles. Generalized linear model analyses determined the effect of EJ tract and TRAP on asthma. RESULTS: TRAP exposure in the highest quartile range was more frequent among patients living in an EJ tract (66.4% vs 20.8%, P < .05). Living in an EJ tract increased the odds of severe asthma in later onset asthma. The odds of uncontrolled asthma increased with disease duration in all patients living in EJ tracts (P < .05). Living in the highest quartile of NO2 also increased the odds of uncontrolled asthma in patients with severe disease (P < .05), while there was no effect of TRAP on uncontrolled asthma in patients with less severe disease (P > .05). CONCLUSIONS: Living in an EJ tract increased the odds of severe and uncontrolled asthma and was influenced by age at onset, disease duration, and potentially by TRAP exposure. This study underscores the need to better understand the complex environmental interactions that affect lung health in groups that have been economically and/or socially marginalized.

Primary and Secondary Organic Aerosol Formation from Asphalt Pavements.

Asphalt is ubiquitous across cities and a source of organic compounds spanning a wide range of volatility and may be an overlooked source of urban organic aerosols. The emission rate and composition depend strongly on temperature, but emissions have been observed at both application temperatures and surface temperatures during warm sunny days. Here we report primary organic aerosol (POA) emissions and secondary organic aerosol (SOA) production from asphalt. We reheated real-world asphalt samples to application-relevant temperatures (∼130 °C) and typical summertime road-surface temperatures (∼55 °C) and then flushed the emitted vapors into an environmental oxidation chamber containing ammonium sulfate seed particles. SOA was then formed following the photo-oxidation of emissions under high-NOx conditions typical of urban atmospheres. We find that POA only forms at application temperature as it does not require further oxidation, whereas SOA forms under both conditions; with the resulting POA and SOA both being semi-volatile. While total OA formation rates were substantially greater under the limited time spent under application conditions, SOA formation from passive asphalt heating presents a potential long-term source, as heating continues for the lifetime of the road surface. This suggests that persistent asphalt solar heating is likely a considerable and continued source of summertime SOA in urban environments.

Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data.

Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM2.5 concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM2.5 measurements from a pre-existing low-cost sensors network, and then conducted a two-step backward selection to gradually eliminate variables with potential emission heterogeneity from the base models. We then introduced the regression-enhanced random forest method into the model development. Finally, contemporaneous urinary 1-hydroxypyrene was used to evaluate the PM2.5 predictions generated from the two approaches. The two-step approach increased the average external validation R2 from 0.49 to 0.65, and decreased the RMSE from 3.56 µg/m3 to 2.96 µg/m3. For the regression-enhanced random forest models, the average R2 of the external validation was 0.54, and the RMSE was 3.40 µg/m3. We also observed significant and comparable relationships between urinary 1-hydroxypyrene levels and PM2.5 predictions from both improved models. This PM2.5 model estimation strategy could improve the extrapolating ability of random forest models in areas with sparse monitoring data.

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.

Asthma prevalence and control among schoolchildren residing near outdoor air pollution sites.

INTRODUCTION: Outdoor air pollution (OAP) contributes to poor asthma outcomes and remains a public health concern in Pittsburgh. The purpose of this study was to determine the prevalence of childhood asthma and its rate of control among Pittsburgh schoolchildren residing near OAP sites. METHODS: Participants were recruited from schools near OAP sites. Asthma prevalence and control were assessed using a validated survey. Demographics and socioeconomic status were collected by survey, BMI was calculated, secondhand smoke (SHS) exposure was assessed by salivary cotinine levels, and OAP was assessed by mobile platform monitoring. Multivariate analysis adjusted for confounders. RESULTS: In 1202 Pittsburgh elementary school students surveyed, 50.9% were female, average age was 8.5 years (SD = 1.9), 52.2% were African American and 60.6% had public health insurance. SHS exposure was relatively high at 33.9%, 17.1% of students were obese, and 70% had exposure to particulate matter (PM2.5) greater than the World Health Organization standard of 10 µg/m3. Overall prevalence of asthma was 22.5% with PM2.5, nitric oxide (NOx), sulfur (S), and zinc (Zn) significantly related to odds of asthma. Among the 270 children previously diagnosed with asthma, 59.3% were not well controlled with PM2.5, black carbon, and silicon (Si) significantly related to odds of uncontrolled asthma. CONCLUSIONS: These results demonstrate that asthma prevalence and poor disease control are significantly elevated in Pittsburgh schoolchildren exposed to high levels of OAP. Future efforts need to focus on primary prevention of asthma by reducing exposure to OAP in at risk populations.

Impact of a pollution breach at a coke oven factory on asthma control in nearby vulnerable adults.

BACKGROUND: Previous studies have related sulfur dioxide (SO2) exposure to asthma exacerbations. We utilized the University of Pittsburgh Asthma Institute registry to study associations of asthma exacerbations between 2 geographically distinct populations of adults with asthma. OBJECTIVE: Our objective was to examine whether asthma symptoms worsened following a significant fire event that destroyed pollution control equipment at the largest coke works in the United States. METHODS: Two groups of patients with asthma, namely, those residing within 10 miles of the coke works fire (the proximal group [n = 39]) and those residing beyond that range (the control group [n = 44]), were geocoded by residential address. Concentrations of ambient air SO2 were generated by using local University of Pittsburgh Asthma Institute registry air monitoring data. Factory emissions were also evaluated. Data from a patient historical acute exposure survey and in-person follow-up data were evaluated. Inferential statistics were used to compare the groups. RESULTS: In the immediate postfire period (6-8 weeks), the level of emissions of SO2 from the factory emissions increased to 25 times more than the typical level. Following the pollution control breach, the proximal cohort self-reported an increase in medication use (risk ratio = 1.76; 95% CI = 1.1-2.8; P < .01) and more exacerbations. In a small subset of the follow-up cohort of those who completed the acute exposure survey only, asthma control metrics improved. CONCLUSIONS: Real-world exposure to a marked increase in ambient levels of SO2 from a pollution control breach was associated with worsened asthma control in patients proximal to the event, with the worsened control improving following repair of the controls. Improved spatial resolution of air pollutant measurements would enable better examination of exposures and subsequent health impacts.

Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models.

Previous studies have characterized spatial patterns of pollution with land use regression (LUR) models from distributed passive or filter samplers at low temporal resolution. Large-scale deployment of low-cost sensors (LCS), which typically sample in real time, may enable time-resolved or real-time modeling of concentration surfaces. The aim of this study was to develop spatiotemporal models of PM2.5, NO2, and CO using an LCS network in Pittsburgh, Pennsylvania. We modeled daily average concentrations in August 2016-December 2017 across 50 sites. Land use variables included 13 time-independent (e.g., elevation) and time-dependent (e.g., temperature) predictors. We examined two models: LUR and a machine-learning-enabled land use model (land use random forest, LURF). The LURF models outperformed LUR models, with increase in the average externally cross-validated R2 of 0.10-0.19. Using wavelet decomposition to separate short-lived events from the regional background, we also created time-decomposed LUR and LURF models. Compared to the standard model, this resulted in improvement in R2 of up to 0.14. The time-decomposed models were more influenced by spatial parameters. Mapping our models across Allegheny County, we observed that time-decomposed LURF models created robust PM2.5 predictions, suggesting that this approach may improve our ability to map air pollutants at high spatiotemporal resolution.

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.

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.

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.

A risk-based model to assess environmental justice and coronary heart disease burden from traffic-related air pollutants.

BACKGROUND: Communities need to efficiently estimate the burden from specific pollutants and identify those most at risk to make timely informed policy decisions. We developed a risk-based model to estimate the burden of black carbon (BC) and nitrogen dioxide (NO2) on coronary heart disease (CHD) across environmental justice (EJ) and non-EJ populations in Allegheny County, PA. METHODS: Exposure estimates in census tracts were modeled via land use regression and analyzed in relation to US Census data. Tracts were ranked into quartiles of exposure (Q1-Q4). A risk-based model for estimating the CHD burden attributed to BC and NO2 was developed using county health statistics, census tract level exposure estimates, and quantitative effect estimates available in the literature. RESULTS: For both pollutants, the relative occurrence of EJ tracts (> 20% poverty and/or > 30% non-white minority) in Q2 - Q4 compared to Q1 progressively increased and reached a maximum in Q4. EJ tracts were 4 to 25 times more likely to be in the highest quartile of exposure compared to the lowest quartile for BC and NO2, respectively. Pollutant-specific risk values (mean [95% CI]) for CHD mortality were higher in EJ tracts (5.49 × 10- 5 [5.05 × 10- 5 - 5.92 × 10- 5]; 5.72 × 10- 5 [5.44 × 10- 5 - 6.01 × 10- 5] for BC and NO2, respectively) compared to non-EJ tracts (3.94 × 10- 5 [3.66 × 10- 5 - 4.23 × 10- 5]; 3.49 × 10- 5 [3.27 × 10- 5 - 3.70 × 10- 5] for BC and NO2, respectively). While EJ tracts represented 28% of the county population, they accounted for about 40% of the CHD mortality attributed to each pollutant. EJ tracts are disproportionately skewed toward areas of high exposure and EJ residents bear a greater risk for air pollution-related disease compared to other county residents. CONCLUSIONS: We have combined a risk-based model with spatially resolved long-term exposure estimates to predict CHD burden from air pollution at the census tract level. It provides quantitative estimates of effects that can be used to assess possible health disparities, track temporal changes, and inform timely local community policy decisions. Such an approach can be further expanded to include other pollutants and adverse health endpoints.

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.

Urban Organic Aerosol Exposure: Spatial Variations in Composition and Source Impacts.

We conducted a mobile sampling campaign in a historically industrialized terrain (Pittsburgh, PA) targeting spatial heterogeneity of organic aerosol. Thirty-six sampling sites were chosen based on stratification of traffic, industrial source density, and elevation. We collected organic carbon (OC) on quartz filters, quantified different OC components with thermal-optical analysis, and grouped them based on volatility in decreasing order (OC1, OC2, OC3, OC4, and pyrolyzed carbon (PC)). We compared our ambient OC concentrations (both gas and particle phase) to similar measurements from vehicle dynamometer tests, cooking emissions, biomass burning emissions, and a highway traffic tunnel. OC2 and OC3 loading on ambient filters showed a strong correlation with primary emissions while OC4 and PC were more spatially homogeneous. While we tested our hypothesis of OC2 and OC3 as markers of fresh source exposure for Pittsburgh, the relationship seemed to hold at a national level. Land use regression (LUR) models were developed for the OC fractions, and models had an average R2 of 0.64 (SD = 0.09). The paper demonstrates that OC2 and OC3 can be useful markers for fresh emissions, OC4 is a secondary OC indicator, and PC represents both biomass burning and secondary aerosol. People with higher OC exposure are likely inhaling more fresh OC2 and OC3, since secondary OC4 and PC varies much less drastically in space or with local primary sources.

Intracity Variability of Particulate Matter Exposure Is Driven by Carbonaceous Sources and Correlated with Land-Use Variables.

Localized primary emissions of carbonaceous aerosol are the major drivers of intracity variability of submicron particulate matter (PM1) concentrations. We investigated spatial variations in PM1 composition with mobile sampling in Pittsburgh, Pennsylvania, United States and performed source-apportionment analysis to attribute primary organic aerosol (OA) to traffic (HOA) and cooking OA (COA). In high-source-impact locations, the PM1 concentration is, on average, 2 µg m-3 (40%) higher than urban background locations. Traffic emissions are the largest source contributing to population-weighted exposures to primary PM. Vehicle-miles traveled (VMT) can be used to reliably predict the concentration of HOA and localized black carbon (BC) in air pollutant spatial models. Restaurant count is a useful but imperfect predictor for COA concentration, likely due to highly variable emissions from individual restaurants. Near-road cooking emissions can be falsely attributed to traffic sources in the absence of PM source apportionment. In Pittsburgh, 28% and 9% of the total population are exposed to >1 µg m-3 of traffic- and cooking-related primary emissions, with some populations impacted by both sources. The source mix in many U.S. cities is similar; thus, we expect similar PM spatial patterns and increased exposure in high-source areas in other cities.