Disentangling impacts of multiple pollutants on acute cardiovascular events in New York city: A case-crossover analysis.

BACKGROUND: Ambient air pollution contributes to an estimated 6.67 million deaths annually, and has been linked to cardiovascular disease (CVD), the leading cause of death. Short-term increases in air pollution have been associated with increased risk of CVD event, though relatively few studies have directly compared effects of multiple pollutants using fine-scale spatio-temporal data, thoroughly adjusting for co-pollutants and temperature, in an exhaustive citywide hospitals dataset, towards identifying key pollution sources within the urban environment to most reduce, and reduce disparities in, the leading cause of death worldwide. OBJECTIVES: We aimed to examine multiple pollutants against multiple CVD diagnoses, across lag days, in models adjusted for co-pollutants and meteorology, and inherently adjusted by design for non-time-varying individual and aggregate-level covariates, using fine-scale space-time exposure estimates, in an exhaustive dataset of emergency department visits and hospitalizations across an entire city, thereby capturing the full population-at-risk. METHODS: We used conditional logistic regression in a case-crossover design - inherently controlling for all confounders not varying within case month - to examine associations between spatio-temporal nitrogen dioxide (NO2), fine particulate matter (PM2.5), sulfur dioxide (SO2), and ozone (O3) in New York City, 2005-2011, on individual risk of acute CVD event (n = 837,523), by sub-diagnosis [ischemic heart disease (IHD), heart failure (HF), stroke, ischemic stroke, acute myocardial infarction]. RESULTS: We found significant same-day associations between NO2 and risk of overall CVD, IHD, and HF - and between PM2.5 and overall CVD or HF event risk - robust to all adjustments and multiple comparisons. Results were comparable by sex and race - though median age at CVD was 10 years younger for Black New Yorkers than White New Yorkers. Associations for NO2 were comparable for adults younger or older than 69 years, though PM2.5 associations were stronger among older adults. DISCUSSION: Our results indicate immediate, robust effects of combustion-related pollution on CVD risk, by sub-diagnosis. Though acute impacts differed minimally by age, sex, or race, the much younger age-at-event for Black New Yorkers calls attention to cumulative social susceptibility.

Neighborhood violence and socioeconomic deprivation influence associations between acute air pollution and temperature on childhood asthma in New York city.

Ambient air pollution, temperature, and social stressor exposures are linked with asthma risk, with potential synergistic effects. We examined associations for acute pollution and temperature exposures, with modification by neighborhood violent crime and socioeconomic deprivation, on asthma morbidity among children aged 5-17 years year-round in New York City. Using conditional logistic regression in a time-stratified, case-crossover design, we quantified percent excess risk of asthma event per 10-unit increase in daily, residence-specific exposures to PM2.5, NO2, SO2, O3, and minimum daily temperature (Tmin). Data on 145,834 asthma cases presenting to NYC emergency departments from 2005 to 2011 were obtained from the New York Statewide Planning and Research Cooperative System (SPARCS). Residence- and day-specific spatiotemporal exposures were assigned using the NYC Community Air Survey (NYCCAS) spatial data and daily EPA pollution and NOAA weather data. Point-level NYPD violent crime data for 2009 (study midpoint) was aggregated, and Socioeconomic Deprivation Index (SDI) scores assigned, by census tract. Separate models were fit for each pollutant or temperature exposure for lag days 0-6, controlling for co-exposures and humidity, and mutually-adjusted interactions (modification) by quintile of violent crime and SDI were assessed. We observed stronger main effects for PM2.5 and SO2 in the cold season on lag day 1 [4.90% (95% CI: 3.77-6.04) and 8.57% (5.99-11.21), respectively]; Tmin in the cold season on lag day 0 [2.26% (1.25-3.28)]; and NO2 and O3 in the warm season on lag days 1 [7.86% (6.66-9.07)] and 2 [4.75% (3.53-5.97)], respectively. Violence and SDI modified the main effects in a non-linear manner; contrary to hypotheses, we found stronger associations in lower-violence and -deprivation quintiles. At very high stressor exposures, although asthma exacerbations were highly prevalent, pollution effects were less apparent-suggesting potential saturation effects in socio-environmental synergism.

What Is "Socioeconomic Position (SEP)," and How Might It Modify Air Pollution-Health Associations? Cohering Findings, Identifying Challenges, and Disentangling Effects of SEP and Race in US City Settings.

PURPOSE OF REVIEW: Environmental epidemiology has long considered socioeconomic position (SEP) to be an important confounder of pollution effects on health, given that, in the USA, lower-income and minority communities are often disproportionately exposed to pollution. In recent decades, a growing literature has revealed that lower-SEP communities may also be more susceptible to pollution. Given the vast number of material and psychosocial stressors that vary by SEP, however, it is unclear which specific aspects of SEP may underlie this susceptibility. As environmental epidemiology engages more rigorously with issues of differential susceptibility, it is pertinent to define SEP more clearly, to disentangle its many aspects, and to move towards identifying causal components. Myriad stressors and exposures vary with SEP, with effects accumulating and interacting over the lifecourse. Here, we ask: In the context of environmental epidemiology, how do we meaningfully characterize"SEP"? RECENT FINDINGS: In answering this question, it is critical to acknowledge that SEP, stressors, and pollution are differentially distributed by race in US cities. These distributions have been shaped by neighborhood sorting and race-based residential segregation rooted in historical policies and processes (e.g., redlining), which have served to concentrate wealth and opportunities for education and employment in predominantly-white communities. As a result, it is now profoundly challenging to separate SEP from race in the urban US setting. Here, we cohere evidence from our recent and on-going studies aimed at disentangling synergistic health effects among SEP-related stressors and pollutants. We consider an array of SEP-linked social stressors, and discuss persistent challenges in this epidemiology, many of which are related to spatial confounding among multiple pollutants and stressors. Combining quantitative results with insights from qualitative data on neighborhood perceptions and stress (including violence and police-community relations), we offer a lens towards unpacking the complex interplay among SEP, community stressors, race, and pollution in US cities.

Long-Term Ambient Air Pollution Exposures and Circulating and Stimulated Inflammatory Mediators in a Cohort of Midlife Adults.

BACKGROUND: Chronic exposure to air pollution may prime the immune system to be reactive, increasing inflammatory responses to immune stimulation and providing a pathway to increased risk for inflammatory diseases, including asthma and cardiovascular disease. Although long-term exposure to ambient air pollution has been associated with increased circulating markers of inflammation, it is unknown whether it also relates to the magnitude of inflammatory response. OBJECTIVES: The aim of this study was to examine associations between chronic ambient pollution exposures and circulating and stimulated levels of inflammatory mediators in a cohort of healthy adults. METHODS: Circulating interleukin (IL)-6, C-reactive protein (CRP) (n=392), and lipopolysaccharide stimulated production of IL-1ß, IL-6, and tumor necrosis factor (TNF)-α (n=379) were measured in the Adult Health and Behavior II cohort. Fine particulate matter [particulate matter with aerodynamic diameter less than or equal to 2.5 µm (PM2.5)] and constituents [black carbon (BC), and lead (Pb), manganese (Mn), zinc (Zn), and iron (Fe)] were estimated for each residential address using hybrid dispersion land use regression models. Associations between pollutant exposures and inflammatory measures were examined using linear regression; models were adjusted for age, sex, race, education, smoking, body mass index, and month of blood draw. RESULTS: There were no significant correlations between circulating and stimulated measures of inflammation. Significant positive associations were found between exposure to PM2.5 and BC with stimulated production of IL-6, IL-1ß, and TNF-α. Pb, Mn, Fe, and Zn exposures were positively associated with stimulated production of IL-1ß and TNF-α. No pollutants were associated with circulating IL-6 or CRP levels. DISCUSSION: Exposure to PM2.5, BC, Pb, Mn, Fe, and Zn was associated with increased production of inflammatory mediators by stimulated immune cells. In contrast, pollutant exposure was not related to circulating markers of inflammation. These results suggest that chronic exposure to some pollutants may prime immune cells to mount larger inflammatory responses, possibly contributing to increased risk for inflammatory disease. https://doi.org/10.1289/EHP7089.

Association of Fine Particulate Matter and Risk of Stroke in Patients With Atrial Fibrillation.

Importance: Air pollution is associated with cardiovascular outcomes. Specifically, fine particulate matter measuring 2.5 µm or less (PM2.5) is associated with thrombosis, stroke, and myocardial infarction. Few studies have examined particulate matter and stroke risk in individuals with atrial fibrillation (AF). Objective: To assess the association of residential-level pollution exposure in 1 year and ischemic stroke in individuals with AF. Design, Setting, and Participants: This cohort study included 31 414 individuals with AF from a large regional health care system in an area with historically high industrial pollution. All participants had valid residential addresses for geocoding and ascertainment of neighborhood-level income and educational level. Participants were studied from January 1, 2007, through September 30, 2015, with prospective follow-up through December 1, 2017. Data analysis was performed from March 14, 2018, to October 9, 2019. Exposures: Exposure to PM2.5 ascertained using geocoding of addresses and fine-scale air pollution exposure surfaces derived from a spatial saturation monitoring campaign and land-use regression modeling. Exposure to PM2.5 was estimated annually across the study period at the residence level. Main Outcomes and Measures: Multivariable-adjusted stroke risk by quartile of residence-level and annual PM2.5 exposure. Results: The cohort included 31 414 individuals (15 813 [50.3%] female; mean [SD] age, 74.4 [13.5] years), with a median follow-up of 3.5 years (interquartile range, 1.6-5.8 years). The mean (SD) annual PM2.5 exposure was 10.6 (0.7) µg/m3. A 1-SD increase in PM2.5 was associated with a greater risk of stroke after both adjustment for demographic and clinical variables (hazard ratio [HR], 1.08; 95% CI, 1.03-1.14) and multivariable adjustment that included neighborhood-level income and educational level (HR, 1.07; 95% CI, 1.00-1.14). The highest quartile of PM2.5 exposure had an increased risk of stroke relative to the first quartile (HR, 1.36; 95% CI, 1.18-1.58). After adjustment for clinical covariates, income, and educational level, risk of stroke remained greater for the highest quartile of exposure relative to the first quartile (HR, 1.21; 95% CI, 1.01-1.45). Conclusions and Relevance: This large cohort study of individuals with AF identified associations between PM2.5 and risk of ischemic stroke. The results suggest an association between fine particulate air pollution and cardiovascular disease and outcomes.

Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology.

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km2), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.

Putting Co-Exposures on Equal Footing: An Ecological Analysis of Same-Scale Measures of Air Pollution and Social Factors on Cardiovascular Disease in New York City.

Epidemiologic evidence consistently links urban air pollution exposures to health, even after adjustment for potential spatial confounding by socioeconomic position (SEP), given concerns that air pollution sources may be clustered in and around lower-SEP communities. SEP, however, is often measured with less spatial and temporal resolution than are air pollution exposures (i.e., census-tract socio-demographics vs. fine-scale spatio-temporal air pollution models). Although many questions remain regarding the most appropriate, meaningful scales for the measurement and evaluation of each type of exposure, we aimed to compare associations for multiple air pollutants and social factors against cardiovascular disease (CVD) event rates, with each exposure measured at equal spatial and temporal resolution. We found that, in multivariable census-tract-level models including both types of exposures, most pollutant-CVD associations were non-significant, while most social factors retained significance. Similarly, the magnitude of association was higher for an IQR-range difference in the social factors than in pollutant concentrations. We found that when offered equal spatial and temporal resolution, CVD was more strongly associated with social factors than with air pollutant exposures in census-tract-level analyses in New York City.

Caregiving and Place: Combining Geographic Information System (GIS) and Survey Methods to Examine Neighborhood Context and Caregiver Outcomes.

BACKGROUND: Little is known about the impact of neighborhood context on family caregivers, or how environmental factors combine with individual-level caregiver risk factors to affect caregiver outcomes. OBJECTIVES: To combine Geographic Information System (GIS) and survey methods to examine the effects of caregiver residence in disadvantaged/underserved neighborhoods on caregiver outcomes. RESEARCH DESIGN AND METHODS: Telephone surveys with 758 caregivers from the Pittsburgh Regional Caregiver Survey geocoded for classification into Environmental Justice Areas (EJAs) and Medically Underserved Areas (MUAs). We examine the impact of EJA/MUA caregiver residence on care recipient unmet needs for care, caregiver depression and burden, and positive aspects of caregiving, adjusting for sociodemographics, caregiving context, care recipient disability level, caregiving intensity, and additional risk factors. RESULTS: There was spatial clustering of caregiver depression and burden outside of the disadvantaged/underserved areas, while positive aspects of caregiving were clustered within EJAs/MUAs. Approximately 36% of caregivers lived in EJAs/MUAs, and they differed, sociodemographically, on caregiver risk factors and caregiver outcomes. Multivariable models showed that caregivers residing in EJAs/MUAs were less likely to be depressed and reported more positive aspects of caregiving after adjusting for known individual-level risk factors. Residence in disadvantaged/underserved areas also modified the effects of several risk factors on caregiver outcomes. DISCUSSION AND IMPLICATIONS: Caregiver outcomes show interesting spatial patterns. Unexpectedly, caregivers living in these potentially challenging environments were less depressed and reported more gains from caregiving after adjusting for known risk factors. Results suggest that socioeconomic disadvantage does not necessarily translate into poor caregiver outcomes. Understanding the mechanism for these effects is important to designing effective caregiver interventions. The paper also demonstrates the value of using GIS methods to study caregiving.

Violent crime and socioeconomic deprivation in shaping asthma-related pollution susceptibility: a case-crossover design.

BACKGROUND: The objective of this study was to quantify and compare the relative influence of community violent crime and socioeconomic deprivation in modifying associations between ozone and emergency department (ED) visits for asthma among children. METHODS: We used a spatiotemporal case-crossover analysis for all New York City EDs for the months May-September from 2005 to 2011 from a statewide administrative ED dataset. The data included 11 719 asthmatic children aged 5-18 years, and the main outcome measure was percentage of excess risk of asthma ED visit based on Cox regression analysis. RESULTS: Stronger ozone-asthma associations were observed for both elevated crime and deprivation (eg, on lag day 2, we found 20.0% (95% CI 10.2% to 30.6 %) and 21.0% (10.5% to 32.5%) increased risk per 10 ppb ozone, for communities in the highest vs lowest quartiles of violent crime and deprivation, respectively). However, in varied models accounting for both modifiers, only violence retained significance. CONCLUSIONS: The results suggest stronger spatiotemporal ozone-asthma associations in communities of higher violent crime or deprivation. Notably, violence was the more consistent and significant modifier, potentially mediating a substantial portion of socioeconomic position-related susceptibility.

Hybrid land use regression modeling for estimating spatio-temporal exposures to PM2.5, BC, and metal components across a metropolitan area of complex terrain and industrial sources.

Land use regression (LUR) modeling has become a common method for predicting pollutant concentrations and assigning exposure estimates in epidemiological studies. However, few LUR models have been developed for metal constituents of fine particulate matter (PM2.5) or have incorporated source-specific dispersion covariates in locations with major point sources. We developed hybrid AERMOD LUR models for PM2.5, black carbon (BC), and steel-related PM2.5 constituents lead, manganese, iron, and zinc, using fine-scale air pollution data from 37 sites across the Pittsburgh area. These models were designed with the aim of developing exposure estimates for time periods of interest in epidemiology studies. We found that the hybrid LUR models explained greater variability in PM2.5 (R2 = 0.79) compared to BC (R2 = 0.59) and metal constituents (R2 = 0.34-0.55). Approximately 70% of variation in PM2.5 was attributable to temporal variance, compared to 36% for BC, and 17-26% for metals. An AERMOD dispersion covariate developed using PM2.5 industrial emissions data for 207 sources was significant in PM2.5 and BC models; all metals models contained a steel mill-specific PM2.5 emissions AERMOD term. Other significant covariates included industrial land use, commercial and industrial land use, percent impervious surface, and summed railroad length.

Fine-Scale Source Apportionment Including Diesel-Related Elemental and Organic Constituents of PM2.5 across Downtown Pittsburgh.

Health effects of fine particulate matter (PM2.5) may vary by composition, and the characterization of constituents may help to identify key PM2.5 sources, such as diesel, distributed across an urban area. The composition of diesel particulate matter (DPM) is complicated, and elemental and organic carbon are often used as surrogates. Examining multiple elemental and organic constituents across urban sites, however, may better capture variation in diesel-related impacts, and help to more clearly separate diesel from other sources. We designed a "super-saturation" monitoring campaign of 36 sites to capture spatial variance in PM2.5 and elemental and organic constituents across the downtown Pittsburgh core (~2.8 km²). Elemental composition was assessed via inductively-coupled plasma mass spectrometry (ICP-MS), organic and elemental carbon via thermal-optical reflectance, and organic compounds via thermal desorption gas-chromatography mass-spectrometry (TD-GCMS). Factor analysis was performed including all constituents-both stratified by, and merged across, seasons. Spatial patterning in the resultant factors was examined using land use regression (LUR) modelling to corroborate factor interpretations. We identified diesel-related factors in both seasons; for winter, we identified a five-factor solution, describing a bus and truck-related factor [black carbon (BC), fluoranthene, nitrogen dioxide (NO2), pyrene, total carbon] and a fuel oil combustion factor (nickel, vanadium). For summer, we identified a nine-factor solution, which included a bus-related factor (benzo[ghi]fluoranthene, chromium, chrysene, fluoranthene, manganese, pyrene, total carbon, total elemental carbon, zinc) and a truck-related factor (benz[a]anthracene, BC, hopanes, NO2, total PAHs, total steranes). Geographic information system (GIS)-based emissions source covariates identified via LUR modelling roughly corroborated factor interpretations.

Spatial Patterns in Rush-Hour vs. Work-Week Diesel-Related Pollution across a Downtown Core.

Despite advances in monitoring and modelling of intra-urban variation in multiple pollutants, few studies have attempted to separate spatial patterns by time of day, or incorporated organic tracers into spatial monitoring studies. Due to varying emissions sources from diesel and gasoline vehicular traffic, as well as within-day temporal variation in source mix and intensity (e.g., rush-hours vs. full-day measures), accurately assessing diesel-related air pollution within an urban core can be challenging. We allocated 24 sampling sites across downtown Pittsburgh, Pennsylvania (2.8 km²) to capture fine-scale variation in diesel-related pollutants, and to compare these patterns by sampling interval (i.e., "rush-hours" vs. "work-week" concentrations), and by season. Using geographic information system (GIS)-based methods, we allocated sampling sites to capture spatial variation in key traffic-related pollution sources (i.e., truck, bus, overall traffic densities). Programmable monitors were used to collect integrated work-week and rush-hour samples of fine particulate matter (PM2.5), black carbon (BC), trace elements, and diesel-related organics (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), in summer and winter 2014. Land use regression (LUR) models were created for PM2.5, BC, total elemental carbon (EC), total organic carbon (OC), elemental (Al, Ca, Fe), and organic constituents (total PAHs, total hopanes), and compared by sampling interval and season. We hypothesized higher pollution concentrations and greater spatial contrast in rush-hour, compared to full work-week samples, with variation by season and pollutant. Rush-hour sampling produced slightly higher total PM2.5 and BC concentrations in both seasons, compared to work-week sampling, but no evident difference in spatial patterns. We also found substantial spatial variability in most trace elements and organic compounds, with comparable spatial patterns using both sampling paradigms. Overall, we found higher concentrations of traffic-related trace elements and organic compounds in rush-hour samples, and higher concentrations of coal-related elements (e.g., As, Se) in work-week samples. Mean bus density was the strongest LUR predictor in most models, in both seasons, under each sampling paradigm. Within each season and constituent, the bus-related terms explained similar proportions of variance in the rush-hour and work-week samples. Rush-hour and work-week LUR models explained similar proportions of spatial variation in pollutants, suggesting that the majority of emissions may be produced during rush-hour traffic across downtown. Results suggest that rush-hour emissions may predominantly shape overall spatial variance in diesel-related pollutants.

Not so little differences: variation in hot weather risk to young children in New York City.

OBJECTIVES: High ambient temperatures are associated with significant health risk in the United States. The risk to children has been minimally explored, and often young children are considered as a single age group despite marked physiologic and social variation among this population from infancy through preschool. This study explored the heterogeneity of risk of heat among young children. STUDY DESIGN: Using a time-stratified, case-crossover design, we evaluated associations between maximum daily temperature (Tmax) and ED visits (n = 1,002,951) to New York City (NYC) metropolitan area hospitals for children aged 0-4 years in May-September, 2005-2011. METHODS: Conditional logistic regression analysis estimated risks for an interquartile range of Tmax for 0-6 lag days. Stratified analyses explored age strata by year, race/ethnic groups, and diagnostic codes. Sensitivity analyses controlled for same day ambient ozone, particulate matter <2.5 microns, and relative humidity and, separately, explored race groups without ethnicity and different diagnostic code groupings. RESULTS: Children ages 0-4 years had increased risk of emergency department visits with increased Tmax on lag days 0, 1, and 3. The association was strongest on lag day 0, when an increase in Tmax of 13 °F conferred an excess risk of 2.6% (95% confidence interval [CI]: 2.2-3.0). Stratifying by age, we observed significant positive associations for same-day exposures, for 1-4 year olds. Children less than 1 year of age showed a significant positive association with Tmax only on lag day 3. The race/ethnicity stratified analysis revealed a similar lag pattern for all subgroups. The diagnostic group analysis showed percent excess risk for heat-specific diagnoses (16.6% [95% CI: 3.0-31.9]); general symptoms (10.1% [95% CI: 8.2-11.9]); infectious (4.9% [95% CI: 3.9-5.9]); and injury (5.1% [95% CI: 3.8-6.4]) diagnoses. CONCLUSION: We found a significant risk of ED visits in young children with elevated Tmax. Risk patterns vary based on age with infants showing delayed risk and toddlers and preschoolers with same day risk. In addition, the finding of increased risk of injury associated with higher temperatures is novel. Altogether, these findings suggest a need for a tailored public health response, such as different messages to caregivers of different age children, to protect children from the effects of heat. Next steps include examining specific subcategories of diagnoses to develop protective strategies and better anticipate the needs of population health in future scenarios of climate change.

Particulate Matter Air Pollution and Racial Differences in Cardiovascular Disease Risk.

OBJECTIVE: We aimed to assess racial differences in air pollution exposures to ambient fine particulate matter (particles with median aerodynamic diameter <2.5 µm [PM2.5]) and black carbon (BC) and their association with cardiovascular disease (CVD) risk factors, arterial endothelial function, incident CVD events, and all-cause mortality. APPROACH AND RESULTS: Data from the HeartSCORE study (Heart Strategies Concentrating on Risk Evaluation) were used to estimate 1-year average air pollution exposure to PM2.5 and BC using land use regression models. Correlates of PM2.5 and BC were assessed using linear regression models. Associations with clinical outcomes were determined using Cox proportional hazards models, adjusting for traditional CVD risk factors. Data were available on 1717 participants (66% women; 45% blacks; 59±8 years). Blacks had significantly higher exposure to PM2.5 (mean 16.1±0.75 versus 15.7±0.73µg/m3; P=0.001) and BC (1.19±0.11 versus 1.16±0.13abs; P=0.001) compared with whites. Exposure to PM2.5, but not BC, was independently associated with higher blood glucose and worse arterial endothelial function. PM2.5 was associated with a higher risk of incident CVD events and all-cause mortality combined for median follow-up of 8.3 years. Blacks had 1.45 (95% CI, 1.00-2.09) higher risk of combined CVD events and all-cause mortality than whites in models adjusted for relevant covariates. This association was modestly attenuated with adjustment for PM2.5. CONCLUSIONS: PM2.5 exposure was associated with elevated blood glucose, worse endothelial function, and incident CVD events and all-cause mortality. Blacks had a higher rate of incident CVD events and all-cause mortality than whites that was only partly explained by higher exposure to PM2.5.

Air pollutants may be environmental risk factors in chronic rhinosinusitis disease progression.

BACKGROUND: Little is known about the role of environmental exposures in the pathophysiology of chronic rhinosinusitis (CRS). In this study, we measured the impact of air pollutants (particulate matter 2.5 [PM2.5 ] and black carbon [BC]) on CRS with nasal polyposis (CRSwNP) and CRS without nasal polyposis (CRSsNP). METHODS: Spatial modeling from pollutant monitoring sites was used to estimate exposures surrounding residences for patients meeting inclusion criteria (total patients, n = 234; CRSsNP, n = 96; CRSwNP, n = 138). Disease severity outcome measures included modified Lund-Mackay score (LMS), systemic steroids, number of functional endoscopic sinus surgeries (FESS), and 22-item Sino-Nasal Outcome Test (SNOT-22) score. PM2.5 and BC exposures were correlated with outcome measures. RESULTS: Mean PM2.5 and BC findings were not significantly different between CRSwNP and CRSsNP patients or patients with and without asthma. Among those with CRSsNP, PM2.5 was significantly associated with undergoing FESS. For each unit increase in PM2.5 , there was a 1.89-fold increased risk in the proportion of CRSsNP patients who required further surgery (p = 0.015). This association was not identified in CRSwNP patients (p = 0.445). BC was also significantly associated with SNOT-22 score in the CRSsNP group. For each 0.1-unit increase in BC, there was a 7.97-unit increase in SNOT-22 (p = 0.008). A similar, although not significant, increase in SNOT-22 was found with increasing BC in the CRSwNP group (p = 0.728). CONCLUSION: Air pollutants correlate with CRS symptom severity that may be influenced by exposure levels, with a more pronounced impact on CRSsNP patients. This study is the first to demonstrate the possible role of inhalant pollutants in CRS phenotypes, addressing a critical knowledge gap in environmental risk factors for disease progression.

Spatial variation in diesel-related elemental and organic PM2.5 components during workweek hours across a downtown core.

Capturing intra-urban variation in diesel-related pollution exposures remains a challenge, given its complex chemical mix, and relatively few well-characterized ambient-air tracers for the multiple diesel sources in densely-populated urban areas. To capture fine-scale spatial resolution (50×50m grid cells) in diesel-related pollution, we used geographic information systems (GIS) to systematically allocate 36 sampling sites across downtown Pittsburgh, PA, USA (2.8km2), cross-stratifying to disentangle source impacts (i.e., truck density, bus route frequency, total traffic density). For buses, outbound and inbound trips per week were summed by route and a kernel density was calculated across sites. Programmable monitors collected fine particulate matter (PM2.5) samples specific to workweek hours (Monday-Friday, 7 am-7 pm), summer and winter 2013. Integrated filters were analyzed for black carbon (BC), elemental carbon (EC), organic carbon (OC), elemental constituents, and diesel-related organic compounds [i.e., polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes]. To our knowledge, no studies have collected this suite of pollutants with such high sampling density, with the ability to capture spatial patterns during specific hours of interest. We hypothesized that we would find substantial spatial variation for each pollutant and significant associations with key sources (e.g. diesel and gasoline vehicles), with higher concentrations near the center of this small downtown core. Using a forward stepwise approach, we developed seasonal land use regression (LUR) models for PM2.5, BC, total EC, OC, PAHs, hopanes, steranes, aluminum (Al), calcium (Ca), and iron (Fe). Within this small domain, greater concentration differences were observed in most pollutants across sites, on average, than between seasons. Higher PM2.5 and BC concentrations were found in the downtown core compared to the boundaries. PAHs, hopanes, and steranes displayed different spatial patterning across the study area by constituent. Most LUR models suggested a strong influence of bus-related emissions on pollution gradients. Buses were more dominant predictors compared to truck and vehicular traffic for several pollutants. Overall, we found substantial variation in diesel-related concentrations in a very small downtown area, which varied across elemental and organic components.

Spatio-temporal ozone variation in a case-crossover analysis of childhood asthma hospital visits in New York City.

BACKGROUND: Childhood asthma morbidity has been associated with short-term air pollution exposure. To date, most investigations have used time-series models, and it is not well understood how exposure misclassification arising from unmeasured spatial variation may impact epidemiological effect estimates. Here, we develop case-crossover models integrating temporal and spatial individual-level exposure information, toward reducing exposure misclassification in estimating associations between air pollution and child asthma exacerbations in New York City (NYC). METHODS: Air pollution data included: (a) highly spatially-resolved intra-urban concentration surfaces for ozone and co-pollutants (nitrogen dioxide and fine particulate matter) from the New York City Community Air Survey (NYCCAS), and (b) daily regulatory monitoring data. Case data included citywide hospital records for years 2005-2011 warm-season (June-August) asthma hospitalizations (n=2353) and Emergency Department (ED) visits (n=11,719) among children aged 5-17 years. Case residential locations were geocoded using a multi-step process to maximize positional accuracy and precision in near-residence exposure estimates. We used conditional logistic regression to model associations between ozone and child asthma exacerbations for lag days 0-6, adjusting for co-pollutant and temperature exposures. To evaluate the effect of increased exposure specificity through spatial air pollution information, we sequentially incorporated spatial variation into daily exposure estimates for ozone, temperature, and co-pollutants. RESULTS: Percent excess risk per 10ppb ozone exposure in spatio-temporal models were significant on lag days 1 through 5, ranging from 6.5 (95% CI: 0.2-13.1) to 13.0 (6.0-20.6) for inpatient hospitalizations, and from 2.9 (95% CI: 0.1-5.7) to 9.4 (6.3-12.7) for ED visits, with strongest associations consistently observed on lag day 2. Spatio-temporal excess risk estimates were consistently but not statistically significantly higher than temporal-only estimates on lag days 0-3. CONCLUSION: Incorporating case-level spatial exposure variation produced small, non-significant increases in excess risk estimates. Our modeling approach enables a refined understanding of potential measurement error in temporal-only versus spatio-temporal air pollution exposure assessments. As ozone generally varies over much larger spatial scales than that observed within NYC, further work is necessary to evaluate potential reductions in exposure misclassification for populations spanning wider geographic areas, and for other pollutants.

Spatial patterning in PM2.5 constituents under an inversion-focused sampling design across an urban area of complex terrain.

Health effects of fine particulate matter (PM2.5) vary by chemical composition, and composition can help to identify key PM2.5 sources across urban areas. Further, this intra-urban spatial variation in concentrations and composition may vary with meteorological conditions (e.g., mixing height). Accordingly, we hypothesized that spatial sampling during atmospheric inversions would help to better identify localized source effects, and reveal more distinct spatial patterns in key constituents. We designed a 2-year monitoring campaign to capture fine-scale intra-urban variability in PM2.5 composition across Pittsburgh, PA, and compared both spatial patterns and source effects during "frequent inversion" hours vs 24-h weeklong averages. Using spatially distributed programmable monitors, and a geographic information systems (GIS)-based design, we collected PM2.5 samples across 37 sampling locations per year to capture variation in local pollution sources (e.g., proximity to industry, traffic density) and terrain (e.g., elevation). We used inductively coupled plasma mass spectrometry (ICP-MS) to determine elemental composition, and unconstrained factor analysis to identify source suites by sampling scheme and season. We examined spatial patterning in source factors using land use regression (LUR), wherein GIS-based source indicators served to corroborate factor interpretations. Under both summer sampling regimes, and for winter inversion-focused sampling, we identified six source factors, characterized by tracers associated with brake and tire wear, steel-making, soil and road dust, coal, diesel exhaust, and vehicular emissions. For winter 24-h samples, four factors suggested traffic/fuel oil, traffic emissions, coal/industry, and steel-making sources. In LURs, as hypothesized, GIS-based source terms better explained spatial variability in inversion-focused samples, including a greater contribution from roadway, steel, and coal-related sources. Factor analysis produced source-related constituent suites under both sampling designs, though factors were more distinct under inversion-focused sampling.

Spatial variation in inversion-focused vs 24-h integrated samples of PM2.5 and black carbon across Pittsburgh, PA.

A growing literature explores intra-urban variation in pollution concentrations. Few studies, however, have examined spatial variation during "peak" hours of the day (e.g., rush hours, inversion conditions), which may have strong bearing for source identification and epidemiological analyses. We aimed to capture "peak" spatial variation across a region of complex terrain, legacy industry, and frequent atmospheric inversions. We hypothesized stronger spatial contrast in concentrations during hours prone to atmospheric inversions and heavy traffic, and designed a 2-year monitoring campaign to capture spatial variation in fine particles (PM2.5) and black carbon (BC). Inversion-focused integrated monitoring (0600-1100 hours) was performed during year 1 (2011-2012) and compared with 1-week 24-h integrated results from year 2 (2012-2013). To allocate sampling sites, we explored spatial distributions in key sources (i.e., traffic, industry) and potential modifiers (i.e., elevation) in geographic information systems (GIS), and allocated 37 sites for spatial and source variability across the metropolitan domain (~388 km(2)). Land use regression (LUR) models were developed and compared by pollutant, season, and sampling method. As expected, we found stronger spatial contrasts in PM2.5 and BC using inversion-focused sampling, suggesting greater differences in peak exposures across urban areas than is captured by most integrated saturation campaigns. Temporal variability, commercial and industrial land use, PM2.5 emissions, and elevation were significant predictors, but did not more strongly predict concentrations during peak hours.