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.

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.

Air Pollutant Patterns and Human Health Risk following the East Palestine, Ohio, Train Derailment.

On February 3, 2023, a train carrying numerous hazardous chemicals derailed in East Palestine, OH, spurring temporary evacuation of residents and a controlled burn of some of the hazardous cargo. Residents reported health symptoms, including headaches and respiratory, skin, and eye irritation. Initial data from U.S. Environmental Protection Agency (EPA) stationary air monitors indicated levels of potential concern for air toxics based on hazard quotient calculations. To provide complementary data, we conducted mobile air quality sampling on February 20 and 21 using proton transfer reaction-mass spectrometry. Measurements were taken at 1 s intervals along routes designed to sample both close to and farther from the derailment. Mobile air monitoring indicated that average concentrations of benzene, toluene, xylenes, and vinyl chloride were below minimal risk levels for intermediate and chronic exposures, similar to EPA stationary monitoring data. Levels of acrolein were high relative to those of other volatile organic compounds, with spatial analyses showing levels in East Palestine up to 6 times higher than the local rural background. Nontargeted analyses identified levels of additional unique compounds above background levels, some displaying spatiotemporal patterns similar to that of acrolein and others exhibiting distinct hot spots. These initial findings warrant follow-up mobile air quality monitoring to characterize longitudinal exposure and risk levels.

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.

Aerosol Generation During Nasal Airway Instrumentation.

OBJECTIVE: Airborne aerosol transmission, an established mechanism of SARS-CoV-2 spread, has been successfully mitigated in the health care setting through the adoption of universal masking. Upper airway endoscopy, however, requires direct access to the face, thereby potentially exposing the clinic environment to infectious particles. This study quantifies aerosol production during rigid nasal endoscopy (RNE) and RNE with debridement (RNED) as compared with intubation, a posited gold standard aerosol-generating procedure. STUDY DESIGN: Prospective cross-sectional study. SETTING: Subspecialty single-center clinic and surgical study. METHOD: Three aerosol detectors (NANOSCAN-3910, OPS-3330, and APS-3321) with a particle size sensitivity of 10 to 20,000 nm were utilized to detect particulate production during the clinical care of 209 patients undergoing RNE/RNED and 25 patients undergoing intubation. RESULTS: RNE and RNED produced statistically significant particles over baseline in 29.3% and 51.0% of subjects (P = .003-.049 and .002-.047, respectively). Intubation produced statistically significant particles in 31.2% (P = .001-.015). The mean ± SD particle diameter in all tests was 69.9 ± 10.5 nm with 99.7% <300 nm. There were no statistical differences in particle production among RNE, RNED, and intubation. The presence of concomitant cough, sneeze, or prolonged speech similarly did not significantly affect particle production during any procedure. CONCLUSIONS: Instrumentation of nasal airway produces airborne aerosols to a similar degree of those seen during intubation, independent of reactive patient behaviors such as cough or sneeze. These data suggest that an improved understanding is necessary of both the definition of an aerosol-generating procedure and the functional consequences of procedural aerosol generation in clinical settings.

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.

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.

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.

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.

Relationship between socioeconomic status, exposure to airborne pollutants, and chronic rhinosinusitis disease severity.

BACKGROUND: Air pollution directly interacts with airway mucosa, yet little is known about how pollutants affect upper airway inflammation. Studies have shown increased incidence of chronic rhinosinusitis (CRS), rhinitis, and asthma in areas with higher traffic pollution, and these neighborhoods are often associated with lower socioeconomic status (SES). The Area Deprivation Index (ADI) assesses neighborhood-level SES by zip code. The purpose of this study was to assess the relationship between SES and exposure to inhaled pollutants and CRS disease severity. METHODS: CRS patients with and without nasal polyps (CRSwNP and CRSsNP, respectively) were identified (total patients = 234; CRSwNP patients = 138; CRSsNP patients = 96). Pollutant concentrations, including particulate matter 2.5 (PM2.5 ), black carbon (BC), and nitrogen dioxide (NO2 ), were measured at 70 sites within the defined countywide sites and used to estimate patient exposures. SES was measured by ADI state deciles. Disease severity metrics included the modified Lund-Mackay score (LMS), the need for systemic steroids, and functional endoscopic sinus surgery (FESS). Associations were analyzed and identified using linear, logistic, and Poisson multivariable regression. RESULTS: The distribution of CRSsNP and CRSwNP patients across ADI state deciles was similar. ADI, however, was a predictor of exposure to airborne pollutants (PM2.5 , BC, and NO2 ) with a 1.39%, 2.39%, and 2.49% increase in PM2.5 , BC, and NO2 per increasing decile increment (p < 0.0001), respectively, which demonstrated a direct correlation between deprived neighborhoods and higher levels of exposure to PM2.5 , BC, and NO2 with an increase in pollutant levels per increase in ADI decile. Furthermore, ADI was a predictor for increased steroid treatment. CONCLUSION: Lower SES predicted higher exposure to air pollution and increased disease severity in patients with CRS as demonstrated by the increased need for steroid treatment.

Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments.

Low-cost NO2 sensors have been widely deployed for atmospheric sampling. While their initial performance has been characterized, few studies have examined their long-term degradation. This study focused on the performance of Alphasense low-cost NO2 sensors (NO2-B42F and NO2-B43F) over 4 years (2016-2020). A total of 29 NO2 sensors from 10 batches were collocated 78 times at two sites with reference instruments. Raw signals from "functional" NO2 sensors correlated linearly with reference NO2 concentrations. After long-term deployment, sensor raw signals started to deviate from reference NO2 concentrations due to sensor aging, an accumulated effect after sensor unpacking. Several sensors eventually became "non-functional" as sensor raw signals showed no correlation with reference NO2 concentrations. Sensor aging and non-functionality may be primarily caused by expiration of the ozone (O3) scrubber built into these sensors so that sensors responded to both ambient NO2 and O3. The influence of O3 on sensor response is quantified through the permutation importance method. Most of the sensors are non-functional after approximately 200-400 days of deployment, and no sensor was functional after 400 days of deployment. This result agrees well with the estimated lifetime of the built-in ozone scrubbers considering the ambient ozone concentration in the Pittsburgh area where these sensors were deployed. To ensure reliable data quality in long-term field deployments, we recommend collocating NO2 sensors with reference instruments regularly after 200-400 days of deployment to identify and replace non-functional sensors in a timely manner.

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 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.

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.

Changes in criteria air pollution levels in the US before, during, and after Covid-19 stay-at-home orders: Evidence from regulatory monitors.

The widespread and rapid social and economic changes from Covid-19 response might be expected to dramatically improve air quality. However, national monitoring data from the US Environmental Protection Agency for criteria pollutants (PM2.5, ozone, NO2, CO, PM10) provide inconsistent support for that expectation. Specifically, during stay-at-home orders, average PM2.5 levels were slightly higher (~10% of its multi-year interquartile range [IQR]) than expected; average ozone, NO2, CO, and PM10 levels were slightly lower (~30%, ~20%, ~27%, and ~1% of their IQR, respectively) than expected. The timing of peak anomaly, relative to the stay-at-home orders, varied by pollutant (ozone: 2 weeks before; NO2, CO: 3 weeks after; PM10: 2 weeks after); but, by 5-6 weeks after stay-at-home orders, the concentration anomalies appear to have ended. For PM2.5, ozone, CO, and PM10, no US state had lower-than-expected pollution levels for all weeks during stay-at-home-orders; for NO2, only Arizona had lower-than-expected levels for all weeks during stay-at-home orders. Our findings show that the enormous changes from the Covid-19 response have not lowered PM2.5 levels across the US beyond their normal range of variability; for ozone, NO2, CO, and PM10 concentrations were lowered but the reduction was modest and transient.

Lagged Association of Ambient Outdoor Air Pollutants with Asthma-Related Emergency Department Visits within the Pittsburgh Region.

Asthma affects millions of people globally and is especially concerning in populations living with poor air quality. This study examines the association of ambient outdoor air pollutants on asthma-related emergency department (ED) visits in children and adults throughout the Pittsburgh region. A time-stratified case-crossover design is used to analyze the lagged effects of fine particulate matter (PM2.5) and gaseous pollutants, e.g., ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) on asthma-related ED visits (n = 6682). Single-, double-, and multi-pollutant models are adjusted for temperature and analyzed using conditional logistic regression. In children, all models show an association between O3 and increased ED visits at lag day 1 (OR: 1.12, 95% CI, 1.03-1.22, p < 0.05) for the double-pollutant model (OR: 1.10, 95% CI: 1.01-1.20, p < 0.01). In adults, the single-pollutant model shows associations between CO and increased ED visits at lag day 5 (OR: 1.13, 95% CI, 1.00-1.28, p < 0.05) and average lag days 0-5 (OR: 1.22, 95% CI: 1.00-1.49, p < 0.05), and for NO2 at lag day 5 (OR: 1.04, 95% CI: 1.00-1.07, p < 0.05). These results show an association between air pollution and asthma morbidity in the Pittsburgh region and underscore the need for mitigation efforts to improve public health outcomes.

Asphalt-related emissions are a major missing nontraditional source of secondary organic aerosol precursors.

Asphalt-based materials are abundant and a major nontraditional source of reactive organic compounds in urban areas, but their emissions are essentially absent from inventories. At typical temperature and solar conditions simulating different life cycle stages (i.e., storage, paving, and use), common road and roofing asphalts produced complex mixtures of organic compounds, including hazardous pollutants. Chemically speciated emission factors using high-resolution mass spectrometry reveal considerable oxygen and reduced sulfur content and the predominance of aromatic (~30%) and intermediate/semivolatile organic compounds (~85%), which together produce high overall secondary organic aerosol (SOA) yields. Emissions rose markedly with moderate solar exposure (e.g., 300% for road asphalt) with greater SOA yields and sustained SOA production. On urban scales, annual estimates of asphalt-related SOA precursor emissions exceed those from motor vehicles and substantially increase existing estimates from noncombustion sources. Yet, their emissions and impacts will be concentrated during the hottest, sunniest periods with greater photochemical activity and SOA production.