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Fine particulate matter (PM 2.5 ) air pollution varies spatially and temporally in concentration and composition and has been shown to cause or exacerbate adverse effects on human and ecological health. Biomonitoring using airborne tree leaf deposition as a proxy for particulate matter (PM) pollution has been explored using a variety of study designs, tree species, sampling strategies, and analytical methods. In the USA, relatively few have applied these methods using co-located fine particulate measurements for comparison and relying on one tree species with extensive spatial coverage, to capture spatial variation in ambient air pollution across an urban area. Here, we evaluate the utility of this approach, using a spatial saturation design and pairing tree leaf samples with filter-based PM 2.5 across Pittsburgh, Pennsylvania, with the goal of distinguishing mobile and stationary sources using PM 2.5 composition. Co-located filter and leaf-based measurements revealed some significant associations with traffic and roadway proximity indicators. We compared filter and leaf samples with differing protection from the elements (e.g., meteorology) and PM collection time, which may account for some variance in PM source and/or particle size capture between samples. To our knowledge, this study is among the first to use deciduous tree leaves from a single tree species as biomonitors for urban PM 2.5 pollution in the northeastern USA.

Spatial saturation studies using source-specific chemical tracers are commonly used to examine intra-urban variation in exposures and source impacts, for epidemiology and policy purposes. Most such studies, however, has been performed in North America and Europe, with substantial regional combustion-source contributions. In contrast, Auckland, New Zealand, a large western city, is relatively isolated in the south Pacific, with minimal impact from long-range combustion sources. However, fluctuating wind patterns, complex terrain, and an adjacent major port complicate pollution patterns within the central business district (CBD). We monitored multiple pollutants (fine particulate matter (PM 2.5 ), black carbon (BC), elemental composition, organic diesel tracers (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), and nitrogen dioxide (NO 2 )) at 12 sites across the ~5 km 2 CBD during autumn 2014, to capture spatial variation in traffic, diesel, and proximity to the port. PM 2.5 concentrations varied 2.5-fold and NO 2 concentrations 2.9-fold across the CBD, though constituents varied more dramatically. The highest-concentration constituent was sodium (Na), a distinct non-combustion-related tracer for sea salt (µ = 197.8 ng/m 3 (SD = 163.1 ng/m 3 )). BC, often used as a diesel-emissions tracer, varied more than five-fold across sites. Vanadium (V), higher near the ports, varied more than 40-fold across sites. Concentrations of most combustion-related constituents were higher near heavy traffic, truck, or bus activity, and near the port. Wind speed modified absolute concentrations, and wind direction modified spatial patterns in concentrations (i.e., ports impacts were more notable with winds from the northeast).

During winter nights, woodsmoke may be a predominant source of air pollution, even in cities with many sources. Since two major earthquakes resulted in major structural damage in 2010 and 2011, reliance on woodburning for home heating has increased substantially in Christchurch, New Zealand (NZ), along with intensive construction/demolition activities. Further, because NZ is a relatively isolated western country, it offers the unique opportunity to disentangle multiple source impacts in the absence of long-range transport pollution. Finally, although many spatial saturation studies have been published, and levoglucosan is an established tracer for woodburning emissions, few studies have monitored multiple sites simultaneously for this or other organic constituents, with the ability to distinguish spatial patterns for daytime vs. nighttime hours, in complex urban settings. We captured seven-day integrated samples of PM 2.5 , and elemental and organic tracers of woodsmoke and diesel emissions, during "daytime" (7 a.m. - 5:30 p.m.) and "nighttime" (7 p.m. - 5:30 a.m.) hours, at nine sites across commercial and residential areas, over three weeks in early winter (May 2014). At a subset of six sites, we also sampled during hypothesized "peak" woodburning hours (7 p.m. - 12 a.m.), to differentiate emissions during "active" residential woodburning, vs. overnight smouldering. Concentrations of PM 2.5 were, on average, were twice as high during nighttime than daytime [µ = 18.4 (SD = 6.13) vs. 9.21 (SD = 6.13) µg/m 3 ], with much greater differences in woodsmoke tracers (i.e., levoglucosan [µ = 1.83 (SD = 0.82) vs. 0.34 (SD = 0.17) µg/m 3 ], potassium) and indicators of treated- or painted-wood burning (e.g., arsenic, lead). Only nitrogen dioxide, calcium, iron, and manganese (tracers of vehicular emissions) were higher during daytime. Levoglucosan and most PAHs were higher during "active" woodburning, vs. overnight smouldering. Our time-stratified spatial saturation detected strong spatial variability throughout the study area, which distinctly differed during daytime vs. night time hours, and quantified the substantial contribution of woodsmoke to overnight spatial variation in PM 2.5 across Christchurch. Daytime vs. nighttime differences were greater than those observed across sites. Traffic, especially diesel, contributed substantially to daytime NO 2 and spatial gradients in non-woodsmoke constituents., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Health effects of fine particulate matter (PM 2.5 ) may vary by composition, and the characterization of constituents may help to identify key PM 2.5 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 PM 2.5 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.

Objective: We aimed to assess racial differences in air pollution exposures to ambient fine particulate matter (particles with median aerodynamic diameter <2.5 µm [PM 2.5 ]) 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 PM 2.5 and BC using land use regression models. Correlates of PM 2.5 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 PM 2.5 (mean 16.1±0.75 versus 15.7±0.73µg/m 3 ; P =0.001) and BC (1.19±0.11 versus 1.16±0.13abs; P =0.001) compared with whites. Exposure to PM 2.5 , but not BC, was independently associated with higher blood glucose and worse arterial endothelial function. PM 2.5 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 PM 2.5 ., Conclusions: PM 2.5 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 PM 2.5 ., (© 2018 American Heart Association, Inc.)

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 [PM 2.5 ] 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. PM 2.5 and BC exposures were correlated with outcome measures., Results: Mean PM 2.5 and BC findings were not significantly different between CRSwNP and CRSsNP patients or patients with and without asthma. Among those with CRSsNP, PM 2.5 was significantly associated with undergoing FESS. For each unit increase in PM 2.5 , 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., (© 2017 ARS-AAOA, LLC.)

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 (2.8km 2 ), 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 (PM 2.5 ) 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 PM 2.5 , 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 PM 2.5 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., (Copyright © 2016. Published by Elsevier B.V.)

Background: Recent studies have suggested associations between air pollution and various birth outcomes, but the evidence for preterm birth is mixed., Objective: We aimed to assess the relationship between air pollution and preterm birth using 2008-2010 New York City (NYC) birth certificates linked to hospital records., Methods: We analyzed 258,294 singleton births with 22-42 completed weeks gestation to nonsmoking mothers. Exposures to ambient fine particles (PM2.5) and nitrogen dioxide (NO2) during the first, second, and cumulative third trimesters within 300 m of maternal address were estimated using data from the NYC Community Air Survey and regulatory monitors. We estimated the odds ratio (OR) of spontaneous preterm (gestation < 37 weeks) births for the first- and second-trimester exposures in a logistic mixed model, and the third-trimester cumulative exposures in a discrete time survival model, adjusting for maternal characteristics and delivery hospital. Spatial and temporal components of estimated exposures were also separately analyzed., Results: PM2.5 was not significantly associated with spontaneous preterm birth. NO2 in the second trimester was negatively associated with spontaneous preterm birth in the adjusted model (OR = 0.90; 95% CI: 0.83, 0.97 per 20 ppb). Neither pollutant was significantly associated with spontaneous preterm birth based on adjusted models of temporal exposures, whereas the spatial exposures showed significantly reduced odds ratios (OR = 0.80; 95% CI: 0.67, 0.96 per 10 μg/m3 PM2.5 and 0.88; 95% CI: 0.79, 0.98 per 20 ppb NO2). Without adjustment for hospital, these negative associations were stronger., Conclusion: Neither PM2.5 nor NO2 was positively associated with spontaneous preterm delivery in NYC. Delivery hospital was an important spatial confounder., Citation: Johnson S, Bobb JF, Ito K, Savitz DA, Elston B, Shmool JL, Dominici F, Ross Z, Clougherty JE, Matte T. 2016. Ambient fine particulate matter, nitrogen dioxide, and preterm birth in New York City. Environ Health Perspect 124:1283-1290; http://dx.doi.org/10.1289/ehp.1510266.

Fine particulate matter (PM2.5) air pollution, varying in concentration and composition, has been shown to cause or exacerbate adverse effects on both human and ecological health. The concept of biomonitoring using deciduous tree leaves as a proxy for intraurban PM air pollution in different areas has previously been explored using a variety of study designs (e.g., systematic coverage of an area, source-specific focus), deciduous tree species, sampling strategies (e.g., single day, multi-season), and analytical methods (e.g., chemical, magnetic) across multiple geographies and climates. Biomonitoring is a low-cost sampling method and may potentially fill an important gap in current air monitoring methods by providing low-cost, longer-term urban air pollution measures. As such, better understanding of the range of methods, and their corresponding strengths and limitations, is critical for employing the use of tree leaves as biomonitors for pollution to improve spatially resolved exposure assessments for epidemiological studies and urban planning strategies.

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.

Background: Previous studies suggested a possible association between fine particulate matter air pollution (PM2.5) and nitrogen dioxide (NO2) and the development of hypertensive disorders of pregnancy, but effect sizes have been small and methodologic weaknesses preclude firm conclusions., Methods: We linked birth certificates in New York City in 2008-2010 to hospital discharge diagnoses and estimated air pollution exposure based on maternal address. The New York City Community Air Survey provided refined estimates of PM2.5 and NO2 at the maternal residence. We estimated the association between exposures to PM2.5 and NO2 in the first and second trimester and risk of gestational hypertension, mild preeclampsia, and severe preeclampsia among 268,601 births., Results: In unadjusted analyses, we found evidence of a positive association between both pollutants and gestational hypertension. However, after adjustment for individual covariates, socioeconomic deprivation, and delivery hospital, we did not find evidence of an association between PM2.5 or NO2 in the first or second trimester and any of the outcomes., Conclusions: Our data did not provide clear evidence of an effect of ambient air pollution on hypertensive disorders of pregnancy. Results need to be interpreted with caution considering the quality of the available exposure and health outcome measures and the uncertain impact of adjusting for hospital. Relative to previous studies, which have tended to identify positive associations with PM2.5 and NO2, our large study size, refined air pollution exposure estimates, hospital-based disease ascertainment, and little risk of confounding by socioeconomic deprivation, does not provide evidence for an association.

The causes of autism spectrum disorder (ASD) are not well known. Recent investigations have suggested that air pollution, including PM2.5, may play a role in the onset of this condition. The objective of the present work was to investigate the association between prenatal and early childhood exposure to fine particulate matter (PM2.5) and risk for childhood ASD. A population-based case-control study was conducted in children born between January 1, 2005 and December 31, 2009 in six counties in Southwestern Pennsylvania. ASD cases were recruited from specialty autism clinics, local pediatric practices, and school-based special needs services. ASD cases were children who scored 15 or above on the Social Communication Questionnaire (SCQ) and had written documentation of an ASD diagnosis. Controls were children without ASD recruited from a random sample of births from the Pennsylvania state birth registry and frequency matched to cases on birth year, gender, and race. A total of 217 cases and 226 controls were interviewed. A land use regression (LUR) model was used to create person- and time-specific PM2.5 estimates for individual (pre-pregnancy, trimesters one through three, pregnancy, years one and two of life) and cumulative (starting from pre-pregnancy) key developmental time periods. Logistic regression was used to investigate the association between estimated exposure to PM2.5 during key developmental time periods and risk of ASD, adjusting for mother's age, education, race, and smoking. Adjusted odds ratios (AOR) were elevated for specific pregnancy and postnatal intervals (pre-pregnancy, pregnancy, and year one), and postnatal year two was significant, (AOR=1.45, 95% CI=1.01-2.08). We also examined the effect of cumulative pregnancy periods; noting that starting with pre-pregnancy through pregnancy, the adjusted odds ratios are in the 1.46-1.51 range and significant for pre-pregnancy through year 2 (OR=1.51, 95% CI=1.01-2.26). Our data indicate that both prenatal and postnatal exposures to PM2.5 are associated with increased risk of ASD. Future research should include multiple pollutant models and the elucidation of the biological mechanism for PM2.5 and ASD., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Research on the health effects of fine particulate matter (PM2.5) frequently disregards the differences in particle composition between that measured on an ambient filter versus that measured in the corresponding extraction solution used for toxicological testing. This study presents a novel method for characterizing the differences, in metallic and organic species, between the ambient samples and the corresponding extracted solutions through characterization of extracted PM2.5 suspended on filters. Removal efficiency was found to be 98.0 ± 1.4% when measured using pre- and post-removal filter weights, however, this efficiency was significantly reduced to 80.2 ± 0.8% when measured based on particle mass in the extraction solution. Furthermore, only 47.2 ± 22.3% of metals and 24.8 ± 14.5% of organics measured on the ambient filter were found in the extraction solution. Individual metallic and organic components were extracted with varying efficiency, with many organics being lost entirely during extraction. Finally, extraction efficiencies of specific PM2.5 components were inversely correlated with total mass. This study details a method to assess compositional alterations resulting from extraction of PM2.5 from filters, emphasizing the need for standardized procedures that maintain compositional integrity of ambient samples for use in toxicology studies of PM2.5.

Building on a unique exposure assessment project in New York, New York, we examined the relationship of particulate matter with aerodynamic diameter less than 2.5 μm and nitrogen dioxide with birth weight, restricting the population to term births to nonsmokers, along with other restrictions, to isolate the potential impact of air pollution on growth. We included 252,967 births in 2008-2010 identified in vital records, and we assigned exposure at the residential location by using validated models that accounted for spatial and temporal factors. Estimates of association were adjusted for individual and contextual sociodemographic characteristics and season, using linear mixed models to quantify the predicted change in birth weight in grams related to increasing pollution levels. Adjusted estimates for particulate matter with aerodynamic diameter less than 2.5 μm indicated that for each 10-µg/m(3) increase in exposure, birth weights declined by 18.4, 10.5, 29.7, and 48.4 g for exposures in the first, second, and third trimesters and for the total pregnancy, respectively. Adjusted estimates for nitrogen dioxide indicated that for each 10-ppb increase in exposure, birth weights declined by 14.2, 15.9, 18.0, and 18.0 g for exposures in the first, second, and third trimesters and for the total pregnancy, respectively. These results strongly support the association of urban air pollution exposure with reduced fetal growth.

Background: Recent epidemiological studies have examined the associations between air pollution and birth outcomes. Regulatory air quality monitors often used in these studies, however, were spatially sparse and unable to capture relevant within-city variation in exposure during pregnancy., Methods: This study developed two-week average exposure estimates for fine particles (PM2.5) and nitrogen dioxide (NO2) during pregnancy for 274,996 New York City births in 2008-2010. The two-week average exposures were constructed by first developing land use regression (LUR) models of spatial variation in annual average PM2.5 and NO2 data from 150 locations in the New York City Community Air Survey and emissions source data near monitors. The annual average concentrations from the spatial models were adjusted to account for city-wide temporal trends using time series derived from regulatory monitors. Models were developed using Year 1 data and validated using Year 2 data. Two-week average exposures were then estimated for three buffers of maternal address and were averaged into the last six weeks, the trimesters, and the entire period of gestation. We characterized temporal variation of exposure estimates, correlation between PM2.5 and NO2, and correlation of exposures across trimesters., Results: The LUR models of average annual concentrations explained a substantial amount of the spatial variation (R2 = 0.79 for PM2.5 and 0.80 for NO2). In the validation, predictions of Year 2 two-week average concentrations showed strong agreement with measured concentrations (R2 = 0.83 for PM2.5 and 0.79 for NO2). PM2.5 exhibited greater temporal variation than NO2. The relative contribution of temporal vs. spatial variation in the estimated exposures varied by time window. The differing seasonal cycle of these pollutants (bi-annual for PM2.5 and annual for NO2) resulted in different patterns of correlations in the estimated exposures across trimesters. The three levels of spatial buffer did not make a substantive difference in estimated exposures., Conclusions: The combination of spatially resolved monitoring data, LUR models and temporal adjustment using regulatory monitoring data yielded exposure estimates for PM2.5 and NO2 that performed well in validation tests. The interaction between seasonality of air pollution and exposure intervals during pregnancy needs to be considered in future studies.

Background: Braddock, Pennsylvania is home to the Edgar Thomson Steel Works (ETSW), one of the few remaining active steel mills in the Pittsburgh region. An economically distressed area, Braddock exceeds average annual (>15 μg/m3) and daily (>35 μg/m3) National Ambient Air Quality Standards (NAAQS) for particulate matter (PM2.5)., Methods: A mobile air monitoring study was designed and implemented in morning and afternoon hours in the summer and winter (2010-2011) to explore the within-neighborhood spatial and temporal (within-day and between-day) variability in PM2.5 and PM10., Results: Both pollutants displayed spatial variation between stops, and substantial temporal variation within and across study days. For summer morning sampling runs, site-specific mean PM2.5 ranged from 30.0 (SD = 3.3) to 55.1 (SD = 13.0) μg/m3. Mean PM10 ranged from 30.4 (SD = 2.5) to 69.7 (SD = 51.2) μg/m3, respectively. During summer months, afternoon concentrations were significantly lower than morning for both PM2.5 and PM10, potentially owing to morning subsidence inversions. Winter concentrations were lower than summer, on average, and showed lesser diurnal variation. Temperature, wind speed, and wind direction predicted significant variability in PM2.5 and PM10 in multiple linear regression models., Conclusions: Data reveals significant morning versus afternoon variability and spatial variability in both PM2.5 and PM10 concentrations within Braddock. Information obtained on peak concentration periods, and the combined effects of industry, traffic, and elevation in this region informed the design of a larger stationary monitoring network.

Background: Epidemiologic evidence suggests that chronic stress may alter susceptibility to air pollution. However, persistent spatial confounding between these exposures may limit the utility of epidemiologic methods to disentangle these effects and cannot identify physiologic mechanisms for potential differential susceptibilities., Objectives: Using a rat model of social stress, we compared respiratory responses to fine concentrated ambient particles (CAPs) and examined biological markers of inflammation., Methods: Twenty-four 12-week-old male Sprague-Dawley rats were randomly assigned to four groups [stress/CAPs, stress/filtered air (FA), nonstress/CAPs, nonstress/FA]. Stress-group animals were individually introduced into the home cage of a dominant male twice weekly. Blood drawn at sacrifice was analyzed for immune and inflammatory markers. CAPs were generated using the Harvard ambient particle concentrator, which draws real-time urban ambient fine particles, enriching concentrations approximately 30 times. CAPs/FA exposures were delivered in single-animal plethysmographs, 5 hr/day for 10 days, and respiratory function was continuously monitored using a Buxco system., Results: Stressed animals displayed higher average C-reactive protein, tumor necrosis factor-alpha, and white blood cell counts than did nonstressed animals. Only among stressed animals were CAPs exposures associated with increased respiratory frequency, lower flows, and lower volumes, suggesting a rapid, shallow breathing pattern. Conversely, in animals with elevated CAPs exposures alone, we observed increased inspiratory flows and greater minute volumes (volume of air inhaled or exhaled per minute)., Conclusions: CAPs effects on respiratory measures differed significantly, and substantively, by stress group. Higher CAPs exposures were associated with a rapid, shallow breathing pattern only under chronic stress. Blood measures provided evidence of inflammatory responses. Results support epidemiologic findings that chronic stress may alter respiratory response to air pollution and may help elucidate pathways for differential susceptibility.

Background: There is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques., Methods: We measured fine particulate matter (PM2.5), nitrogen dioxide (NO2), and elemental carbon (EC) outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations., Results: PM2.5 was strongly associated with the central site monitor (R2 = 0.68). Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76). EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52). NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction), and with higher concentrations during summer (R2 = 0.56)., Conclusion: Each pollutant examined displayed somewhat different spatial patterns within urban neighborhoods, and were differently related to local traffic and meteorology. Our results indicate a need for multi-pollutant exposure modeling to disentangle causal agents in epidemiological studies, and further investigation of site-specific and meteorological modification of the traffic-concentration relationship in urban neighborhoods.

Air pollution exposure patterns may contribute to known spatial patterning of asthma morbidity within urban areas. While studies have evaluated the relationship between traffic and outdoor concentrations, few have considered indoor exposure patterns within low socioeconomic status (SES) urban communities. In this study, part of a prospective birth cohort study assessing asthma etiology in urban Boston, we collected indoor and outdoor 3-4 day samples of nitrogen dioxide (NO2) and fine particulate matter (PM2.5) in 43 residences across multiple seasons from 2003 to 2005. Homes were chosen to represent low SES households, including both cohort and non-cohort residences in similar neighborhoods, and consisted almost entirely of multiunit residences. Reflectance analysis and X-ray fluorescence spectroscopy were performed on the particle filters to determine elemental carbon (EC) and trace element concentrations, respectively. Additionally, information on home characteristics (e.g. type, age, stove fuel) and occupant behaviors (e.g. smoking, cooking, cleaning) were collected via a standardized questionnaire. The contributions of outdoor and indoor sources to indoor concentrations were quantified with regression analyses using mass balance principles. For NO2 and most particle constituents (except outdoor-dominated constituents like sulfur and vanadium), the addition of selected indoor source terms improved the model's predictive power. Cooking time, gas stove usage, occupant density, and humidifiers were identified as important contributors to indoor levels of various pollutants. A comparison between cohort and non-cohort participants provided another means to determine the influence of occupant activity patterns on indoor-outdoor ratios. Although the groups had similar housing characteristics and were located in similar neighborhoods, cohort members had significantly higher indoor concentrations of PM2.5 and NO2, associated with indoor activities. We conclude that the effect of indoor sources may be more pronounced in high-density multiunit dwellings, and that future epidemiological studies in these populations should explicitly consider these sources in assigning exposures.

BackgroundAir pollution exposure is ubiquitous with demonstrated effects on morbidity and mortality. A growing literature suggests that prenatal air pollution exposure impacts neurodevelopment. We posit that the Environmental influences on Child Health Outcomes (ECHO) program will provide unique opportunities to fill critical knowledge gaps given the wide spatial and temporal variability of ECHO participants.ObjectivesWe briefly describe current methods for air pollution exposure assessment, summarize existing studies of air pollution and neurodevelopment, and synthesize this information as a basis for recommendations, or a blueprint, for evaluating air pollution effects on neurodevelopmental outcomes in ECHO.MethodsWe review peer-reviewed literature on prenatal air pollution exposure and neurodevelopmental outcomes, including autism spectrum disorder, attention deficit hyperactivity disorder, intelligence, general cognition, mood, and imaging measures. ECHO meta-data were compiled and evaluated to assess frequency of neurodevelopmental assessments and prenatal and infancy residential address locations. Cohort recruitment locations and enrollment years were summarized to examine potential spatial and temporal variation present in ECHO.DiscussionWhile the literature provides compelling evidence that prenatal air pollution affects neurodevelopment, limitations in spatial and temporal exposure variation exist for current published studies. As >90% of the ECHO cohorts have collected a prenatal or infancy address, application of advanced geographic information systems-based models for common air pollutant exposures may be ideal to address limitations of published research.ConclusionsIn ECHO we have the opportunity to pioneer unifying exposure assessment and evaluate effects across multiple periods of development and neurodevelopmental outcomes, setting the standard for evaluation of prenatal air pollution exposures with the goal of improving children's health.

Research on the health effects of fine particulate matter (PM2.5) frequently disregards the differences in particle composition between that measured on an ambient filter versus that measured in the corresponding extraction solution used for toxicological testing. This study presents a novel method for characterizing the differences, in metallic and organic species, between the ambient samples and the corresponding extracted solutions through characterization of extracted PM2.5 suspended on filters. Removal efficiency was found to be 98.0 ± 1.4% when measured using pre- and post-removal filter weights, however, this efficiency was significantly reduced to 80.2 ± 0.8% when measured based on particle mass in the extraction solution. Furthermore, only 47.2 ± 22.3% of metals and 24.8 ± 14.5% of organics measured on the ambient filter were found in the extraction solution. Individual metallic and organic components were extracted with varying efficiency, with many organics being lost entirely during extraction. Finally, extraction efficiencies of specific PM2.5 components were inversely correlated with total mass. This study details a method to assess compositional alterations resulting from extraction of PM2.5 from filters, emphasizing the need for standardized procedures that maintain compositional integrity of ambient samples for use in toxicology studies of PM2.5. [ABSTRACT FROM AUTHOR]

Background: Recent epidemiological studies have examined the associations between air pollution and birth outcomes. Regulatory air quality monitors often used in these studies, however, were spatially sparse and unable to capture relevant within-city variation in exposure during pregnancy. Methods: This study developed two-week average exposure estimates for fine particles (PM2.5) and nitrogen dioxide (NO2) during pregnancy for 274,996 New York City births in 2008-2010. The two-week average exposures were constructed by first developing land use regression (LUR) models of spatial variation in annual average PM2.5 and NO2 data from 150 locations in the New York City Community Air Survey and emissions source data near monitors. The annual average concentrations from the spatial models were adjusted to account for city-wide temporal trends using time series derived from regulatory monitors. Models were developed using Year 1 data and validated using Year 2 data. Two-week average exposures were then estimated for three buffers of maternal address and were averaged into the last six weeks, the trimesters, and the entire period of gestation. We characterized temporal variation of exposure estimates, correlation between PM2.5 and NO2, and correlation of exposures across trimesters. Results: The LUR models of average annual concentrations explained a substantial amount of the spatial variation (R2 = 0.79 for PM2.5 and 0.80 for NO2). In the validation, predictions of Year 2 two-week average concentrations showed strong agreement with measured concentrations (R2 = 0.83 for PM2.5 and 0.79 for NO2). PM2.5 exhibited greater temporal variation than NO2. The relative contribution of temporal vs. spatial variation in the estimated exposures varied by time window. The differing seasonal cycle of these pollutants (bi-annual for PM2.5 and annual for NO2) resulted in different patterns of correlations in the estimated exposures across trimesters. The three levels of spatial buffer did not make a substantive difference in estimated exposures. Conclusions: The combination of spatially resolved monitoring data, LUR models and temporal adjustment using regulatory monitoring data yielded exposure estimates for PM2.5 and NO2 that performed well in validation tests. The interaction between seasonality of air pollution and exposure intervals during pregnancy needs to be considered in future studies. [ABSTRACT FROM AUTHOR]

Although intra-urban air pollution differs by season, few monitoring networks provide adequate geographic density and year-round coverage to fully characterize seasonal patterns. Here, we report winter intra-urban monitoring and land-use regression (LUR) results from the New York City Community Air Survey (NYCCAS). Two-week integrated samples of fine particles (PM2.5), black carbon (BC), nitrogen oxides (NOx) and sulfur dioxide (S02) were collected at 155 city-wide street-level locations during winter 2008-2009. Sites were selected using stratified random sampling, randomized across sampling sessions to minimize spatio-temporal confounding. LUR was used to identify GIS-based source indicators associated with higher concentrations. Prediction surfaces were produced using kriging with external drift. Each pollutant varied twofold or more across sites, with higher concentrations near midtown Manhattan. All pollutants were positively correlated, particularly PM2.5 and BC (Spearman's r= 0.84). Density of oil-burning boilers, total and truck traffic density, and temporality explained 84% of PM2.5 variation. Densities of total traffic, truck traffic, oil-burning boilers and industrial space, with temporality, explained 65% of BC variation. Temporality, built space, bus route location, and traffic density described 67% of nitrogen dioxide variation. Residual oil-burning units, nighttime population and temporality explained 77% of S02 variation. Spatial variation in combustion-related pollutants in New York City was strongly associated with oil-burning and traffic density. Chronic exposure disparities and unique local sources can be identified through year-round saturation monitoring. [ABSTRACT FROM AUTHOR]

Routine air monitoring provides data to assess urban scale temporal variation in pollution concentrations in relation to regulatory standards, but is not well suited to characterizing intraurban spatial variation in pollutant concentrations from local sources. To address these limitations and inform local control strategies, New York City developed a program to track spatial patterns of multiple air pollutants in each season of the year. Monitor locations include 150 distributed street-level sites chosen to represent a range of traffic, land-use and other characteristics. Integrated samples are collected at each distributed site for one 2-week session each season and in every 2-week period at five reference locations to track city-wide temporal variation. Pollutants sampled include PM2.5 and constituents, nitrogen oxides, black carbon, ozone (summer only) and sulfur dioxide (winter only). During the first full year of monitoring more than 95% of designed samples were completed. Agreement between colocated samples was good (absolute mean % difference 3.2-8.9%). Street-level pollutant concentrations spanned a much greater range than did concentrations at regulatory monitors, especially for oxides of nitrogen and sulfur dioxide. Monitoring to characterize intraurban spatial gradients in ambient pollution usefully complements regulatory monitoring data to inform local air quality management. [ABSTRACT FROM AUTHOR]

Objective: Epidemiologic studies of air pollution effects on respiratory health report significant modification by sex, although results are not uniform. Importantly, it remains unclear whether modifications are attributable to socially derived gendered exposures, to sex-linked physiological differences, or to some interplay thereof. Gender analysis, which aims to disaggregate social from biological differences between males and females, may help to elucidate these possible sources of effect modification. Data sources and data extraction: A PubMed literature search was performed in July 2009, using the terms "respiratory" and any of "sex" or "gender" or "men and women" or "boys and girls" and either "PM2.5" (particulate matter ≤ 2.5 μm in aerodynamic diameter) or "NO2" (nitrogen dioxide). I reviewed the identified studies, and others cited therein, to summarize current evidence of effect modification, with attention to authors' interpretation of observed differences. Owing to broad differences in exposure mixes, outcomes, and analytic techniques, with few studies examining any given combination thereof, meta-analysis was not deemed appropriate at this time. Data synthesis: More studies of adults report stronger effects among women, particularly for older persons or where using residential exposure assessment. Studies of children suggest stronger effects among boys in early life and among girls in later childhood. Conclusions: The qualitative review describes possible sources of difference in air pollution response between women and men, which may vary by life stage, coexposures, hormonal status, or other factors. The sources of observed effect modifications remain unclear, although gender analytic approaches may help to disentangle gender and sex differences in pollution response. A framework for incorporating gender analysis into environmental epidemiology is offered, along with several potentially useful methods from gender analysis. [ABSTRACT FROM AUTHOR]

Abstract: Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO2) and PM2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies. [Copyright &y& Elsevier]

Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system, and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households. As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3–4 day samples of nitrogen dioxide (NO2) and fine particulate matter (PM2.5) in 43 low SES residences across multiple seasons from 2003 to 2005. Elemental carbon (EC) concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally located ambient monitor. The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO2 with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50m buffer of a home and distance from a truck route as important contributors to indoor levels of NO2 and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population. [Copyright &y& Elsevier]

Because fine particulate matter (PM 2.5 ) differs in chemical composition, source apportionment is frequently used for identification of relative contributions of multiple sources to outdoor concentrations. Indoor air pollution and source apportionment is often overlooked, though people in northern climates may spend up to 90% of their time inside. We selected 21 homes for a 1-week indoor sampling session during summer (July to September 2011), repeated in winter (January to March 2012). Elemental analysis was performed using inductively-coupled plasma mass spectrometry (ICP-MS), and factor analysis was used to determine constituent grouping. Multivariate modeling was run on factor scores to corroborate interpretations of source factors based on a literature review. For each season, a 5-factor solution explained 86–88% of variability in constituent concentrations. Indoor sources (i.e. cooking, smoking) explained greater variability than did outdoor sources in these industrial communities. A smoking factor was identified in each season, predicted by number of cigarettes smoked. Cooking factors were also identified in each season, explained by frequency of stove cooking and stovetop frying. Significant contributions from outdoor sources including coal and motor vehicles were also identified. Higher coal and secondary-related elemental concentrations were detected during summer than winter. Our findings suggest that source contributions to indoor concentrations can be identified and should be examined in relation to health effects. [ABSTRACT FROM AUTHOR]

Characterizing near-source spatio-temporal variation is a long -standing challenge in air pollution epidemiology, and common intra-urban modeling approaches [e.g., land use regression (LUR)], do not account for short-term meteorological variation. Atmospheric dispersion modeling approaches, such as AERMOD, can account for near-source pollutant behavior by capturing source-meteorological interactions, but requires external validation and resolved background concentrations. In this study, we integrate AERMOD-based predictions for source-specific fine particle (PM 2.5 ) concentrations into LUR models derived from total ambient PM 2.5 measured at 36 unique sites selected to represent different source and elevation profiles, during summer and winter, 2012–2013 in Pittsburgh, Pennsylvania (PA). We modeled PM 2.5 emissions from 207 local stationary sources in AERMOD, utilizing the monitoring locations as receptors, and hourly meteorological information matching each sampling period. Finally, we compare results of the integrated LUR/AERMOD hybrid model to those of the AERMOD + background and standard LUR models, at the full domain scale and within a 5 km 2 sub-domain surrounding a large industrial facility. The hybrid model improved out-of-sample prediction accuracy by 2–10% over LUR alone, though performance differed by season, in part due to within-season temporal variability. We found differences up to 10 μg/m 3 in predicted concentrations, and observed the largest differences within the industrial sub-domain. LUR underestimated concentrations from 500 to 2500 m downwind of major sources. The hybrid modeling approach we developed may help to improve intra-urban exposure estimates, particularly in regions of large industrial sources, sharp elevation gradients, or complex meteorology (e.g., frequent inversion events), such as Pittsburgh, PA. More broadly, the approach may inform the development of spatio-temporal modeling frameworks for air pollution exposure assessment for epidemiology. [ABSTRACT FROM AUTHOR]

Impacts of industrial emissions on outdoor air pollution in nearby communities are well-documented. Fewer studies, however, have explored impacts on indoor air quality in these communities. Because persons in northern climates spend a majority of their time indoors, understanding indoor exposures, and the role of outdoor air pollution in shaping such exposures, is a priority issue. Braddock and Clairton, Pennsylvania, industrial communities near Pittsburgh, are home to an active steel mill and coke works, respectively, and the population experiences elevated rates of childhood asthma. Twenty-one homes were selected for 1-week indoor sampling for fine particulate matter (PM 2.5 ) and black carbon (BC) during summer 2011 and winter 2012. Multivariate linear regression models were used to examine contributions from both outdoor concentrations and indoor sources. In the models, an outdoor infiltration component explained 10 to 39% of variability in indoor air pollution for PM 2.5 , and 33 to 42% for BC. For both PM 2.5 models and the summer BC model, smoking was a stronger predictor than outdoor pollution, as greater pollutant concentration increases were identified. For winter BC, the model was explained by outdoor pollution and an open windows modifier. In both seasons, indoor concentrations for both PM 2.5 and BC were consistently higher than residence-specific outdoor concentration estimates. Mean indoor PM 2.5 was higher, on average, during summer (25.8 ± 22.7 μg/m 3 ) than winter (18.9 ± 13.2 μg/m 3 ). Contrary to the study's hypothesis, outdoor concentrations accounted for only little to moderate variability (10 to 42%) in indoor concentrations; a much greater proportion of PM 2.5 was explained by cigarette smoking. Outdoor infiltration was a stronger predictor for BC compared to PM 2.5 , especially in winter. Our results suggest that, even in industrial communities of high outdoor pollution concentrations, indoor activities – particularly cigarette smoking – may play a larger role in shaping indoor exposures. [ABSTRACT FROM AUTHOR]