Quantifying Disparities in Air Pollution Exposures across the United States Using Home and Work Addresses.

While human mobility plays a crucial role in determining ambient air pollution exposures and health risks, research to date has assessed risks on the basis of almost solely residential location. Here, we leveraged a database of ∼128-144 million workers in the United States and published ambient PM2.5 data between 2011 and 2018 to explore how incorporating information on both workplace and residential location changes our understanding of disparities in air pollution exposure. In general, we observed higher workplace exposures relative to home exposures, as well as increased exposures for nonwhite and less educated workers relative to the national average. Workplace exposure disparities were higher among racial and ethnic groups and job types than by income, education, age, and sex. Not considering workplace exposures can lead to systematic underestimations in disparities in exposure among these subpopulations. We also quantified the error in assigning workers home instead of a weighted home-and-work exposure. We observed that biases in associations between PM2.5 and health impacts by using home instead of home-and-work exposure were the highest among urban, younger populations.

Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings.

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with the corrected DustTrak data. In general, corrections developed by using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM2.5 are appropriate use-cases for mobile LCS data.

Long-term measurement study of urban environmental low frequency noise.

BACKGROUND: Environmental low frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked threat to population health. Environmental noise has historically been measured and regulated by A-weighted decibel (dBA) metrics, which more heavily weight frequencies between 2000 and 5000 Hz. Limited research has been conducted to measure and characterize the LFN components of urban environmental noise. OBJECTIVES: We characterized LFN noise at two urban sites in Greater Boston, Massachusetts (USA) using dBA and full spectrum noise measurements with aims to (1.) analyze spatio-temporal differences in the two datasets; (2.) compare and contrast LFN metrics with dBA noise metrics in the two sites; and (3.) assess meteorological covariate contributions to LFN in the dataset. METHODS: We measured A- and C-weighted, and flat, unweighted noise levels and 1/3-octave band continuously for 5 months using sound level meters sampling at f = 1 Hz and we recorded sound samples at 44.1 kHz. Our measurement sites were located in two urban, densely populated communities, burdened by close proximity to bus, rail, and aircraft routes. RESULTS: We found that (1.) LFN does not follow the same seasonal trends as A-weighted dBA loudness; there are spatial differences in LFN and its very low frequency noise components (VLFN) between two urban sites; (2.) VLFN and LFN are statistically significant drivers of LCeq (nearly independent of frequency) minus LAeq, (LCeq-LAeq) >10 dB, an accepted LFN metric; and (3.) LFN was minimally affected by high wind speeds at either Site. IMPACT STATEMENT: Environmental low-frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked risk to population health. We measured environmental noise across the full spectrum of frequencies continuously for five months at two urban sites located in Environmental Justice communities. We found that LFN did not follow the same seasonal trends as A-weighted (dBA) loudness, and we observed spatial differences in LFN and very low frequency noise (VLFN < 20 Hz) at the two sites. Not characterizing LFN and basing noise regulations only on A-weightings, a poor predictor of LFN, may expose populations to LFN levels of concern.

Relationship between traffic-related air pollution and inflammation biomarkers using structural equation modeling.

BACKGROUND: Evidence suggests that exposure to traffic-related air pollution (TRAP) and social stressors can increase inflammation. Given that there are many different markers of TRAP exposure, socio-economic status (SES), and inflammation, analytical approaches can leverage multiple markers to better elucidate associations. In this study, we applied structural equation modeling (SEM) to assess the association between a TRAP construct and a SES construct with an inflammation construct. METHODS: This analysis was conducted as part of the Community Assessment of Freeway Exposure and Health (CAFEH; N = 408) study. Air pollution was characterized using a spatiotemporal model of particle number concentration (PNC) combined with individual participant time-activity adjustment (TAA). TAA-PNC and proximity to highways were considered for a construct of TRAP exposure. Participant demographics on education and income for an SES construct were assessed via questionnaires. Blood samples were analyzed for high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and tumor necrosis factor-α receptor II (TNFRII), which were considered for the construct for inflammation. We conducted SEM and compared our findings with those obtained using generalized linear models (GLM). RESULTS: Using GLM, TAA-PNC was associated with multiple inflammation biomarkers. An IQR (10,000 particles/cm3) increase of TAA-PNC was associated with a 14 % increase in hsCRP in the GLM. Using SEM, the association between the TRAP construct and the inflammation construct was twice as large as the associations with any individual inflammation biomarker. SES had an inverse association with inflammation in all models. Using SEM to estimate the indirect effects of SES on inflammation through the TRAP construct strengthened confidence in the association of TRAP with inflammation. CONCLUSION: Our TRAP construct resulted in stronger associations with a combined construct for inflammation than with individual biomarkers, reinforcing the value of statistical approaches that combine multiple, related exposures or outcomes. Our findings are consistent with inflammatory risk from TRAP exposure.

Changes in Ultrafine Particle Concentrations near a Major Airport Following Reduced Transportation Activity during the COVID-19 Pandemic.

Mobility reductions following the COVID-19 pandemic in the United States were higher, and sustained longer, for aviation than ground transportation activity. We evaluate changes in ultrafine particle (UFP, Dp < 100 nm, a marker of fuel-combustion emissions) concentrations at a site near Logan Airport (Boston, Massachusetts) in relation to mobility reductions. Several years of particle number concentration (PNC) data prepandemic [1/2017-9/2018] and during the state-of-emergency (SOE) phase of the pandemic [4/2020-6/2021] were analyzed to assess the emissions reduction impact on PNC, controlling for season and wind direction. Mean PNC was 48% lower during the first three months of the SOE than prepandemic, consistent with 74% lower flight activity and 39% (local)-51% (highway) lower traffic volume. Traffic volume and mean PNC for all wind directions returned to prepandemic levels by 6/2021; however, when the site was downwind from Logan Airport, PNC remained lower than prepandemic levels (by 23%), consistent with lower-than-normal flight activity (44% below prepandemic levels). Our study shows the effect of pandemic-related mobility changes on PNC in a near-airport community, and it distinguishes aviation-related and ground transportation source contributions.

Substantial Near-Field Air Quality Improvements at a General Aviation Airport Following a Runway Shortening.

Santa Monica Airport (SMO), a general aviation airport in Southern California, recently shortened its only runway by 225 m at both ends to limit jet aircraft operations. We evaluated the resulting changes in aviation activity and air quality by measuring particle number (PN), black carbon (BC), and lead (Pb) concentrations, before and after the runway was shortened at two near-airfield locations including a residential site. Postshortening, there was a 50% decrease in total operations, driven mostly by the greater than 80% decrease in jet operations; however, there was no significant change in piston engine aircraft operations (which use leaded fuel). We measured greater than 75%, 30%, and 75% reductions in the concentrations of PN, BC, and Pb, respectively, after the runway was shortened, largely due to enhanced dispersion resulting from the increased distance to the newly shortened runway. Overall, the runway shortening improved air quality in nearby areas such that airport impacts were comparable to or lower than impacts from other sources such as vehicular traffic. Until aviation fuel becomes completely unleaded, runway shortening or relocating operations away from the edge abutting residential areas may be the most effective environmental impact mitigation strategy for general aviation airports situated adjacent to residential areas.

A randomized crossover trial of HEPA air filtration to reduce cardiovascular risk for near highway residents: Methods and approach.

BACKGROUND: Near highway residents are exposed to elevated levels of traffic-related air pollution (TRAP), including ultrafine particles, which are associated with adverse health effects. The efficacy of using in-home air filtration units that reduce exposure and potentially yield health benefits has not been tested in a randomized controlled trial. METHODS: We will conduct a randomized double-blind crossover trial of portable air filtration units for 200 adults 30 years and older who live in near-highway homes in Somerville, MA, USA. We will recruit participants from 172 households. The intervention periods will be one month of true or sham filtration, followed by a one-month wash out period and then a month of the alternate intervention. The primary health outcome will be systolic blood pressure (BP); secondary outcome measures will include diastolic and central BP, C-Reactive Protein (CRP) and D-dimer. Reasons for success or failure of the intervention will be evaluated in a subset of homes using indoor/outdoor monitoring for particulate pollution, personal monitoring, size and composition of particulate pollution, tracking of time spent in the room with the filter, and interviews for qualitative feedback. RESULTS: This trial has begun recruitment and is expected to take 2-3 years to be completed. Recruitment has been particularly challenging because of additional precautions required by the COVID-19 pandemic. DISCUSSION: This study has the potential to shed light on the value of using portable air filtration in homes close to highways to reduce exposure to TRAP and whether doing so has benefits for cardiovascular health.

Prenatal Ambient Ultrafine Particle Exposure and Childhood Asthma in the Northeastern United States.

Rationale: Ambient ultrafine particles (UFPs; with an aerodynamic diameter < 0.1 µm) may exert greater toxicity than other pollution components because of their enhanced oxidative capacity and ability to translocate systemically. Studies examining associations between prenatal UFP exposure and childhood asthma remain sparse. Objectives: We used daily UFP exposure estimates to identify windows of susceptibility of prenatal UFP exposure related to asthma in children, accounting for sex-specific effects. Methods: Analyses included 376 mother-child dyads followed since pregnancy. Daily UFP exposure during pregnancy was estimated by using a spatiotemporally resolved particle number concentration prediction model. Bayesian distributed lag interaction models were used to identify windows of susceptibility for UFP exposure and examine whether effect estimates varied by sex. Incident asthma was determined at the first report of asthma (3.6 ± 3.2 yr). Covariates included maternal age, education, race, and obesity; child sex; nitrogen dioxide (NO2) and temperature averaged over gestation; and postnatal UFP exposure. Measurements and Main Results: Women were 37.8% Black and 43.9% Hispanic, with 52.9% reporting having an education at the high school level or lower; 18.4% of children developed asthma. The cumulative odds ratio (95% confidence interval) for incident asthma per doubling of the UFP exposure concentration across pregnancy was 4.28 (1.41-15.7), impacting males and females similarly. Bayesian distributed lag interaction models indicated sex differences in the windows of susceptibility, with the highest risk of asthma seen in females exposed to higher UFP concentrations during late pregnancy. Conclusions: Prenatal UFP exposure was associated with asthma development in children, independent of correlated ambient NO2 and temperature. Findings will benefit future research and policy-makers who are considering appropriate regulations to reduce the adverse effects of UFPs on child respiratory health.

Effect of Reducing Ambient Traffic-Related Air Pollution on Blood Pressure: A Randomized Crossover Trial.

Exposure to traffic-related air pollution (TRAP) may contribute to increased prevalence of hypertension and elevated blood pressure (BP) for residents of near-highway neighborhoods. Relatively few studies have investigated the effects of reducing TRAP exposure on short-term changes in BP. We assessed whether reducing indoor TRAP concentrations by using stand-alone high-efficiency particulate arrestance (HEPA) filters and limiting infiltration through doors and windows effectively prevented acute (ie, over a span of hours) increases in BP. Using a 3-period crossover design, 77 participants were randomized to attend three 2-hour-long exposure sessions separated by 1-week washout periods. Each participant was exposed to high, medium, and low TRAP concentrations in a room near an interstate highway. Particle number concentrations, black carbon concentrations, and temperature were monitored continuously. Systolic BP (SBP), diastolic BP, and heart rate were measured every 10 minutes. Outcomes were analyzed with a linear mixed model. The primary outcome was the change in SBP from 20 minutes from the start of exposure. SBP increased with exposure duration, and the amount of increase was related to the magnitude of exposure. The mean change in SBP was 0.6 mm Hg for low exposure (mean particle number and black carbon concentrations, 2500 particles/cm3 and 149 ng/m3), 1.3 mm Hg for medium exposure (mean particle number and black carbon concentrations, 11 000 particles/cm3 and 409 ng/m3), and 2.8 mm Hg for high exposure (mean particle number and black carbon concentrations, 30 000 particles/cm3 and 826 ng/m3; linear trend P=0.019). There were no statistically significant differences in the secondary outcomes, diastolic BP, or heart rate. In conclusion, reducing indoor concentrations of TRAP was effective in preventing acute increases in SBP.

Two communities, one highway and the fight for clean air: the role of political history in shaping community engagement and environmental health research translation.

BACKGROUND: This paper explores strategies to engage community stakeholders in efforts to address the effects of traffic-related air pollution (TRAP). Communities of color and low-income communities are disproportionately impacted by environmental threats including emissions generated by major roadways. METHODS: Qualitative instrumental case study design was employed to examine how community-level factors in two Massachusetts communities, the City of Somerville and Boston's Chinatown neighborhood, influence the translation of research into practice to address TRAP exposure. Guided by the Interactive Systems Framework (ISF), we drew on three data sources: key informant interviews, observations and document reviews. Thematic analysis was used. RESULTS: Findings indicate political history plays a significant role in shaping community action. In Somerville, community organizers worked with city and state officials, and embraced community development strategies to engage residents. In contrast, Chinatown community activists focused on immediate resident concerns including housing and resident displacement resulting in more opposition to local municipal leadership. CONCLUSIONS: The ISF was helpful in informing the team's thinking related to systems and structures needed to translate research to practice. However, although municipal stakeholders are increasingly sympathetic to and aware of the health impacts of TRAP, there was not a local legislative or regulatory precedent on how to move some of the proposed TRAP-related policies into practice. As such, we found that pairing the ISF with a community organizing framework may serve as a useful approach for examining the dynamic relationship between science, community engagement and environmental research translation. Social workers and public health professionals can advance TRAP exposure mitigation by exploring the political and social context of communities and working to bridge research and community action.

Reductions in traffic-related black carbon and ultrafine particle number concentrations in an urban neighborhood during the COVID-19 pandemic.

We investigated changes in traffic-related air pollutant concentrations in an urban area during the COVID-19 pandemic. The study was conducted in a mixed commercial-residential neighborhood in Somerville (MA, USA), where traffic is the dominant source of air pollution. Measurements were made between March 27 and May 14, 2020, coinciding with a dramatic reduction in traffic (71% drop in car and 46% drop in truck traffic) due to business shutdowns and a statewide stay-at-home advisory. Indicators of fresh vehicular emissions (ultrafine particle number concentration [PNC] and black carbon [BC]) were measured with a mobile monitoring platform on an interstate highway and major and minor roadways. Our results show that depending on road class, median PNC and BC contributions from traffic were 60-68% and 22-46% lower, respectively, during the lockdown compared to pre-pandemic conditions, and corresponding reductions in total on-road concentrations were 45-69% and 22-56%, respectively. A higher BC: PNC concentration ratio was observed during the lockdown period likely indicative of the higher fraction of diesel vehicles in the fleet during the lockdown. Overall, the scale of reductions in ultrafine particle and BC concentrations was commensurate with the reductions in traffic. This natural experiment allowed us to quantify the direct impacts of reductions in traffic emissions on neighborhood-scale air quality, which are not captured by the regional regulatory-monitoring network. These results underscore the importance of measurements of appropriate proxies for traffic emissions at relevant spatial scales. Our results are useful for exposure analysis as well as city and regional planners evaluating mitigation strategies for traffic-related air pollution.

Impacts of Aviation Emissions on Near-Airport Residential Air Quality.

Impacts of aviation emissions on air quality in and around residences near airports remain underexamined. We measured gases (CO, CO2, NO, and NO2) and particles (black carbon, particle-bound aromatic hydrocarbons, fine particulate matter (PM2.5), and ultrafine particles (reported using particle number concentrations (PNC) as a proxy)) continuously for 1 month at a residence near the Logan International Airport, Boston. The residence was located under a flight trajectory of the most utilized runway configuration. We found that when the residence was downwind of the airport, the concentrations of all gaseous and particulate pollutants (except PM2.5) were 1.1- to 4.8-fold higher than when the residence was not downwind of the airport. Controlling for runway usage and meteorology, the impacts were highest during overhead landing operations: average PNC was 7.5-fold higher from overhead landings versus takeoffs on the closest runway. Infiltration of aviation-origin emissions resulted in indoor PNC that were comparable to ambient concentrations measured locally on roadways and near highways. In addition, ambient NO2 concentrations at the residence exceeded those measured at regulatory monitoring sites in the area including near-road monitors. Our results highlight the need for further characterization of outdoor and indoor impacts of aviation emissions at the neighborhood scale to more accurately estimate residential exposures.

Traffic-related particulate matter affects behavior, inflammation, and neural integrity in a developmental rodent model.

Recent studies indicate that exposure to airborne particulate matter (PM) is associated with cognitive delay, depression, anxiety, autism, and neurodegenerative diseases; however, the role of PM in the etiology of these outcomes is not well-understood. Therefore, there is a need for controlled animal studies to better elucidate the causes and mechanisms by which PM impacts these health outcomes. We assessed the effects of gestational and early life exposure to traffic-related PM on social- and anxiety-related behaviors, cognition, inflammatory markers, and neural integrity in juvenile male rats. Gestating and lactating rats were exposed to PM from a Boston (MA, USA) traffic tunnel for 5 h/day, 5 days/week for 6 weeks (3 weeks gestation, 3 weeks lactation). The target exposure concentration for the fine fraction of nebulized PM, measured as PM2.5, was 200 µg/m3. To assess anxiety and cognitive function, F1 male juveniles underwent elevated platform, cricket predation, nest building, social behavior and marble burying tests at 32-60 days of age. Upon completion of behavioral testing, multiple cytokines and growth factors were measured in these animals and their brains were analyzed with diffusion tensor MRI to assess neural integrity. PM exposure had no effect on litter size or weight, or offspring growth; however, F1 litters developmentally exposed to PM exhibited significantly increased anxiety (p = 0.04), decreased cognition reflected in poorer nest-organization (p = 0.04), and decreased social play and allogrooming (p = 0.003). MRI analysis of ex vivo brains revealed decreased structural integrity of neural tissues in the anterior cingulate and hippocampus in F1 juveniles exposed to PM (p < 0.01, p = 0.03, respectively). F1 juvenile males exposed to PM also exhibited significantly decreased plasma levels of both IL-18 (p = 0.03) and VEGF (p = 0.04), and these changes were inversely correlated with anxiety-related behavior. Chronic exposure of rat dams and their offspring to traffic-related PM during gestation and lactation decreases social behavior, increases anxiety, impairs cognition, decreases levels of inflammatory and growth factors (which are correlated with behavioral changes), and disrupts neural integrity in the juvenile male offspring. Our findings add evidence that exposure to traffic-related air pollution during gestation and lactation is involved in the etiology of autism spectrum disorder and other disorders which include social and cognitive deficits and/or increased anxiety.

Ultrafine Particle Number Concentration Model for Estimating Retrospective and Prospective Long-Term Ambient Exposures in Urban Neighborhoods.

Short-term exposure to ultrafine particles (UFP; <100 nm in diameter), which are present at high concentrations near busy roadways, is associated with markers of cardiovascular and respiratory disease risk. To date, few long-term studies (months to years) have been conducted due to the challenges of long-term exposure assignment. To address this, we modified hybrid land-use regression models of particle number concentrations (PNCs; a proxy for UFP) for two study areas in Boston (MA) by replacing the measured PNC term with an hourly model and adjusting for overprediction. The hourly PNC models used covariates for meteorology, traffic, and sulfur dioxide concentrations (a marker of secondary particle formation). We compared model performance against long-term PNC data collected continuously from 9 years before and up to 3 years after the model-development period. Model predictions captured the major temporal variations in the data and model performance remained relatively stable retrospectively and prospectively. The Pearson correlation of modeled versus measured hourly log-transformed PNC at a long-term monitoring site for 9 years prior was 0.74. Our results demonstrate that highly resolved spatial-temporal PNC models are capable of estimating ambient concentrations retrospectively and prospectively with generally good accuracy, giving us confidence in using these models in epidemiological studies.

Bedding-generated particulate matter: implications for rodent studies.

OBJECTIVES: Rodents used in scientific research are typically housed in cages containing natural bedding materials. Despite extensive evidence of biological harm from inhaled particulate matter (PM), relatively little work has been performed to measure bedding-generated PM exposure in caged animals used in basic science research. Our objectives were to determine whether bedding-generated PM was present in significant concentrations in rodent cages and to identify the main factors affecting the accumulation and attenuation of bedding-generated PM inside cages. MATERIALS AND METHODS: We measured PM2.5 concentrations in cages containing common bedding materials (pine, aspen, paper, and corncob) with filter top isolator absent or present on the cages. PM2.5 concentrations were monitored with rats inside cages as well as during artificial manipulation of the bedding (designed to simulate rodent activity). RESULTS AND DISCUSSION: Upon rodent digging or mechanical/manual stirring, all four bedding materials produced significant increases in PM2.5 concentrations (as much as 100-200 µg/m3 PM2.5, 50- to 100-fold higher than during periods of no rodent activity), and concentrations in cages fitted with filter tops were an order of magnitude higher than in cages without filter tops. Elevated concentrations were sustained for longer durations in cages with filter tops (5-10 minutes) compared to cages with only bar lids (0-2 minutes). CONCLUSIONS: These results indicate that standard laboratory housing conditions can expose rodents to substantial levels of PM2.5. Bedding-generated PM has potential implications as an environmental agent in rodent studies.

A field study to estimate inhalation rates for use in a particle inhalation rate exposure metric.

Particle inhalation rate (PIR) is an air pollution exposure metric that relies on age-, sex-, and physical activity-specific estimates of minute respiratory volume (MRV; L/min-kg) to account for personal inhalation patterns. United States Environmental Protection Agency (USEPA)-generated MRV estimates derive primarily from relatively homogenous populations without substantial cardiorespiratory challenges. To determine if these MRV estimates are relevant to populations in generally poor cardiorespiratory health (e.g., the Boston Puerto Rican Health Study (BPRHS) population) or whether population-specific estimates are needed, we 1) estimated population-specific MRVs and compared them to USEPA MRV estimates, and 2) compared exposure distributions and health effect estimates using PIR with population-specific MRVs, PIR with USEPA MRVs, and ambient particle number concentration (PNC). We recruited 40 adults (80% Puerto Rican, mean age = 60.2 years) in Boston with health characteristics similar to the BPRHS population. We measured pulse, oxygen saturation, respiration rate, and inspiratory volume while participants walked, stood, sat, and lay down. Pulse, respiration rate, inspiratory volume, and MRV were greater when participants were walking/standing compared to sitting or lying down. We then calculated MRVs adjusted for age, sex, measured body weight, and physical activity using data from 19 Puerto Rican participants who wore a nose clip or held their nostrils closed. We applied the population-specific and USEPA MRVs to estimate ultrafine particle exposure for participants in the BPRHS (n = 781). We compared exposure distributions and health effect estimates using the PIR with population-specific MRV estimates, PIR with USEPA MRV estimates, and ambient concentrations. We found that while population-specific MRVs differed from USEPA MRVs, particularly for unhealthy participants, PIR exposure distributions and health effect estimates were similar using each exposure metric. Confidence intervals were narrower using the PIR metrics than ambient PNC, suggesting increased statistical efficiency. Even in our understudied population, using USEPA MRVs did not meaningfully change PIR estimates.

Metabolomic assessment of exposure to near-highway ultrafine particles.

Exposure to traffic-related air pollutants has been associated with increased risk of adverse cardiopulmonary outcomes and mortality; however, the biochemical pathways linking exposure to disease are not known. To delineate biological response mechanisms associated with exposure to near-highway ultrafine particles (UFP), we used untargeted high-resolution metabolomics to profile plasma from 59 participants enrolled in the Community Assessment of Freeway Exposure and Health (CAFEH) study. Metabolic variations associated with UFP exposure were assessed using a cross-sectional study design based upon low (mean 16,000 particles/cm3) and high (mean 24,000 particles/cm3) annual average UFP exposures. In comparing quantified metabolites, we identified five metabolites that were differentially expressed between low and high exposures, including arginine, aspartic acid, glutamine, cystine and methionine sulfoxide. Analysis of the metabolome identified 316 m/z features associated with UFP, which were consistent with increased lipid peroxidation, endogenous inhibitors of nitric oxide and vehicle exhaust exposure biomarkers. Network correlation analysis and metabolic pathway enrichment identified 38 pathways and included variations related to inflammation, endothelial function and mitochondrial bioenergetics. Taken together, these results suggest UFP exposure is associated with a complex series of metabolic variations related to antioxidant pathways, in vivo generation of reactive oxygen species and processes critical to endothelial function.

Relationship of Time-Activity-Adjusted Particle Number Concentration with Blood Pressure.

Emerging evidence suggests long-term exposure to ultrafine particulate matter (UFP, aerodynamic diameter < 0.1 µm) is associated with adverse cardiovascular outcomes. We investigated whether annual average UFP exposure was associated with measured systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and hypertension prevalence among 409 adults participating in the cross-sectional Community Assessment of Freeway Exposure and Health (CAFEH) study. We used measurements of particle number concentration (PNC, a proxy for UFP) obtained from mobile monitoring campaigns in three near-highway and three urban background areas in and near Boston, Massachusetts to develop PNC regression models (20-m spatial and hourly temporal resolution). Individual modeled estimates were adjusted for time spent in different micro-environments (time-activity-adjusted PNC, TAA-PNC). Mean TAA-PNC was 22,000 particles/cm³ (sd = 6500). In linear models (logistic for hypertension) adjusted for the minimally sufficient set of covariates indicated by a directed acyclic graph (DAG), we found positive, non-significant associations between natural log-transformed TAA-PNC and SBP (ß = 5.23, 95%CI: -0.68, 11.14 mmHg), PP (ß = 4.27, 95%CI: -0.79, 9.32 mmHg), and hypertension (OR = 1.81, 95%CI: 0.94, 3.48), but not DBP (ß = 0.96, 95%CI: -2.08, 4.00 mmHg). Associations were stronger among non-Hispanic white participants and among diabetics in analyses stratified by race/ethnicity and, separately, by health status.

Combining Measurements from Mobile Monitoring and a Reference Site To Develop Models of Ambient Ultrafine Particle Number Concentration at Residences.

Significant spatial and temporal variation in ultrafine particle (UFP; <100 nm in diameter) concentrations creates challenges in developing predictive models for epidemiological investigations. We compared the performance of land-use regression models built by combining mobile and stationary measurements (hybrid model) with a regression model built using mobile measurements only (mobile model) in Chelsea and Boston, MA (USA). In each study area, particle number concentration (PNC; a proxy for UFP) was measured at a stationary reference site and with a mobile laboratory driven along a fixed route during an ∼1-year monitoring period. In comparing PNC measured at 20 residences and PNC estimates from hybrid and mobile models, the hybrid model showed higher Pearson correlations of natural log-transformed PNC ( r = 0.73 vs 0.51 in Chelsea; r = 0.74 vs 0.47 in Boston) and lower root-mean-square error in Chelsea (0.61 vs 0.72) but no benefit in Boston (0.72 vs 0.71). All models overpredicted log-transformed PNC by 3-6% at residences, yet the hybrid model reduced the standard deviation of the residuals by 15% in Chelsea and 31% in Boston with better tracking of overnight decreases in PNC. Overall, the hybrid model considerably outperformed the mobile model and could offer reduced exposure error for UFP epidemiology.