Hyper-local to regional exposure contrast of source-resolved PM2.5 components across the contiguous United States: implications for health assessment.

BACKGROUND: Improved understanding of sources and processes that drive exposure contrast of fine particulate matter (PM2.5) is essential for designing and interpreting epidemiological study outcomes. OBJECTIVE: We investigate the contribution of various sources and processes to PM2.5 exposure contrasts at different spatial scales across the continental United States. METHODS: We consider three cases: exposure contrast within a metro area, nationwide exposure contrast with high spatial resolution, and nationwide exposure contrast with low spatial resolution. Using national empirical model estimates of source- and chemically specific PM2.5 concentration predictions, we quantified the contribution of various sources and processes to PM2.5 exposure contrasts in these three cases. RESULTS: At the metro level (i.e., metropolitan statistical area; MSA), exposure contrasts of PM2.5 vary between -1.8 to 1.4 µg m-3 relative to the MSA-mean with about 50% of within-MSA exposure contrast of PM2.5 caused by cooking and mobile source primary PM2.5. For the national exposure contrast at low-resolution (i.e., using MSA-average mean concentrations), exposure contrasts (relative to the national mean: -3.9 to 3.2 µg m-3) are larger than within an MSA with ~80% of the variation due to secondary PM2.5. National exposure contrast at high resolution (census block) has the largest absolute range (relative to the national mean: -4.7 to 3.7 µg m-3) due to both regional and intra-urban contributions; on average, 65% of the national exposure contrast is due to secondary PM2.5 with the remaining from the primary PM2.5 (cooking and mobile source 26%, other 9%). IMPACT: Our study provides a comprehensive analysis of the sources and processes that contribute to exposure contrasts of PM2.5 across different geographic areas in the US. For the first time on a national scale, we used high spatial resolution source-specific exposure estimates to identify the primary contributors to PM2.5 exposure contrasts. The study also highlights the advantages of different study designs for investigating the health impacts of specific PM2.5 components. The findings provide novel insights that can inform public health policies aimed at reducing PM2.5 exposure and advance the understanding of the epidemiological study outcomes.

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.

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.

Mortality risk and long-term exposure to ultrafine particles and primary fine particle components in a national U.S. Cohort.

The objective of this study was to estimate all-cause, cardiopulmonary, and cancer mortality associations for long-term exposure to ultrafine particles (UFP) and primary PM2.5 components. We utilized high-resolution, national-scale exposure estimates for UFP (measured as particle number concentration; PNC) and three primary PM2.5 components, namely black carbon (BC), traffic-emitted organic PM2.5 (hereafter, hydrocarbon-like organic aerosols; HOA), and cooking-emitted organic PM2.5 (cooking organic aerosols; COA). Two analytic cohorts were constructed from a nationally representative U.S. health survey. The larger cohort consisted of 617,997 adults with information on a broad set of individual-level risk factors; the smaller cohort was further restricted to those with information on physical activity (n = 396,470). In single-pollutant models, PNC was significantly associated with all-cause (larger cohort HR = 1.03, 95% CI [1.02, 1.04]; smaller cohort HR = 1.02, 95% CI [1.00, 1.04]) and cancer mortality (larger cohort HR = 1.05, 95% CI [1.02, 1.08]; smaller cohort HR = 1.06, 95% CI [1.02, 1.10]). In two-pollutant models, mortality associations varied based on co-pollutant adjustment; PNC mortality associations were generally robust to controlling for PM10-2.5 and SO2, but not PM2.5. In contrast, we found some evidence that the HOA and COA mortality associations are independent of total PM2.5 mass exposure. Nevertheless, PM2.5 mass was the most robust predictor of air pollution related mortality, providing some support for current regulatory policies.

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 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.

Quantifying high-resolution spatial variations and local source impacts of urban ultrafine particle concentrations.

To quantify the fine-scale spatial variations and local source impacts of urban ultrafine particle (UFP) concentrations, we conducted 3-6 weeks of continuous measurements of particle number (a proxy for UFP) and other air pollutant (CO, NO2, and PM2.5) concentrations at 32 sites in Pittsburgh, Pennsylvania during the winters of 2017 and 2018. Sites were selected to span a range of urban land use attributes, including urban background, near local and arterial roads, traffic intersections, urban street canyon, near-highway, near large industrial source, and restaurant density. The spatial variations in urban particle number concentrations varied by about a factor of three. Particle number concentrations are 2-3 times more spatially heterogeneous than PM2.5 mass. The observed order of spatial heterogeneity is UFP > NO2 > CO > PM2.5. On average, particle number concentrations near local roads with a cluster of restaurants and near arterial roads are roughly two times higher than the urban background. Particle number concentrations in the urban street canyon, downwind of a major highway, and near large industrial sources are 2-4 times higher than background concentrations. While traffic is known as an important contributor to particle number concentrations, restaurants and industrial emissions also contribute significantly to spatial variations in Pittsburgh. Particle size distribution measurements using a mobile laboratory show that the local spatial variations in particle number concentrations are dictated by concentrations of particles smaller than 50 nm. A large fraction of urban residents (e.g., ~50%) in Pittsburgh live near local sources and are therefore exposed to 50%-300% higher particle number concentrations than urban background location. These locally emitted particles may have greater health effects than background particles.

Seasonally Varying Secondary Organic Aerosol Formation From In-Situ Oxidation of Near-Highway Air.

The extent to which motor vehicles contribute to ambient secondary organic aerosol (SOA) remains uncertain. Here, we present in situ measurements of SOA formation at a near-highway site with substantial tree-cover 10 m from Interstate 40 near Durham, North Carolina. In July 2015 (summer) and February 2016 (winter), we exposed ambient air to a range of oxidant (O3 and OH) concentrations in an oxidation flow reactor (OFR), resulting in hours to weeks of equivalent atmospheric aging. We observed substantial seasonal variation in SOA formation upon OFR aging; diurnally varying OA enhancements of ∼3-8 µg m-3 were observed in summer and significantly lower enhancements (∼0.5-1 µg m-3) in winter. Measurements in both seasons showed consistent changes in bulk OA properties (chemical composition; volatility) with OFR aging. Mild increases in traffic-related SOA precursors during summer partly explains the seasonal variation. However, biogenic emissions, with sharp temperature dependence, appear to dominate summer OFR-SOA. Our analysis indicates that SOA observed in the OFR is similar (within a factor of 2) to that predicted to form from traffic and biogenic precursors using literature yields, especially in winter. This study highlights the utility of the OFR for studying the prevalence of SOA precursors in complex real-world settings.

Reduced Ultrafine Particle Concentration in Urban Air: Changes in Nucleation and Anthropogenic Emissions.

Nucleation is an important source of ambient ultrafine particles (UFP). We present observational evidence of the changes in the frequency and intensity of nucleation events in urban air by analyzing long-term particle size distribution measurements at an urban background site in Pittsburgh, Pennsylvania during 2001-2002 and 2016-2017. We find that both frequency and intensity of nucleation events have been reduced by 40-50% over the past 15 years, resulting in a 70% reduction in UFP concentrations from nucleation. On average, the particle growth rates are 30% slower than 15 years ago. We attribute these changes to dramatic reductions in SO2 (more than 90%) and other pollutant concentrations. Overall, UFP concentrations in Pittsburgh have been reduced by ∼48% in the past 15 years, with a ∼70% reduction in nucleation, ∼27% in weekday local sources (e.g., weekday traffic), and 49% in the regional background. Our results highlight that a reduction in anthropogenic emissions can considerably reduce nucleation events and UFP concentrations in a polluted urban environment.

Exploring Divergent Volatility Properties from Yield and Thermodenuder Measurements of Secondary Organic Aerosol from α-Pinene Ozonolysis.

There are large uncertainties in the parameters dictating the gas-particle partitioning of secondary organic aerosols (SOA), although this process has major influences on their atmospheric lifecycle. Here, we extract parameters that describe the partitioning of SOA from α-pinene ozonolysis using measurements from a dual-thermodenuder (TD) system that constrains both the equilibrium and the kinetic properties that dictate SOA phase partitioning. Parallel TDs that vary in temperature and residence time were used with an evaporation-kinetics model to extract parameter values. An evaporation coefficient of an order of 0.1 best describes the observed evaporation, suggesting equilibration time scales of atmospheric SOA on the order of minutes to hours. A total of 20-40% of SOA mass consists of low-volatility material (saturation concentration of <0.3 µg m(-3)) in the TD-derived SOA volatility distribution. While distinct from existing parametrizations from aerosol growth experiments, derived values are consistent with recent observations of slow room-temperature evaporation of SOA and contributions from extremely low volatility organic compounds formed during α-pinene ozonolysis. The volatility parameters thus determined suggest that SOA yields and enthalpies of evaporation are substantially higher, and products less volatile, than is currently assumed in atmospheric models. These results will help improve the representation of SOA in air-quality and climate models.