Effect of time-activity adjustment on exposure assessment for traffic-related ultrafine particles

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016-2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50-0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24-0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays'. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35-0.70; PM2.5 hourly R2 = 0.01-0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Long-term exposure to ambient air pollution and inflammatory response in the KORA study

Previous studies consistently showed an association between fine atmospheric particulate matter (PM2.5) and cardiovascular diseases. Concerns about adverse health effects of ultrafine particles (UFP) are growing but long-term studies are still scarce. In this study, we examined the association between long-term exposure to ambient air pollutants and blood biomarkers of inflammation and coagulation, including fibrinogen, high-sensitivity C-reactive protein (hs-CRP), serum amyloid A (SAA) adiponectin and interleukin-6 (IL-6), measured in the German KORA-S4 cohort study (1999-2001). IL-6 was available for older participants only, who were therefore considered as a subsample. Annual mean concentrations of UFP (as particle number concentration), particulate matter in different particles sizes (PM10, PMcoarse, PM2.5, PM2.5 absorbance), ozone (O3), and nitrogen oxides (NO2, NOX) were estimated by land-use regression models and assigned to participants' home addresses. We performed a multiple linear regression between each pollutant and each biomarker with adjustment for confounders. Per 1 interquartile range (IQR, 1945 particles/cm3) increase of UFP, fibrinogen increased by 0.70 % (0.04; 1.37) and hs-CRP increased by 3.16 % (-0.52; 6.98). Adiponectin decreased by -2.53 % (-4.78; -0.24) per 1 IQR (1.4 μg/m3) increase of PM2.5. Besides, PM2.5 was associated with increased IL-6 in the subsample. In conclusion, we observed that long-term exposure to air pollutants, including both fine and ultrafine particles, was associated with higher concentrations of pro-inflammatory and lower concentrations of an anti-inflammatory blood biomarkers, which is consistent with an increased risk for cardiovascular disease observed for long-term exposure to air pollutants.

The Exposure Peaks of Traffic-Related Ultrafine Particles Associated with Inflammatory Biomarkers and Blood Lipid Profiles

In this article, we explored the effects of ultrafine particle (UFP) peak exposure on inflammatory biomarkers and blood lipids using two novel metrics-the intensity of peaks and the frequency of peaks. We used data previously collected by the Community Assessment of Freeway Exposure and Health project from participants in the Greater Boston Area. The UFP exposure data were time-activity-adjusted hourly average concentration, estimated using land use regression models based on mobile-monitored ambient concentrations. The outcome data included C-reactive protein, interleukin-6 (IL-6), tumor necrosis factor-alpha receptor 2 (TNF-RII), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides and total cholesterol. For each health indicator, multivariate regression models were used to assess their associations with UFP peaks (N = 364-411). After adjusting for age, sex, body mass index, smoking status and education level, an increase in UFP peak exposure was significantly (p < 0.05) associated with an increase in TNF-RII and a decrease in HDL and triglycerides. Increases in UFP peaks were also significantly associated with increased IL-6 and decreased total cholesterol, while the same associations were not significant when annual average exposure was used. Our work suggests that analysis using peak exposure metrics could reveal more details about the effect of environmental exposures than the annual average metric.

Exposure to ultrafine particles and cognitive decline among older people in the United States

BACKGROUND

Some studies suggest that ambient particulate air pollution is associated with cognitive decline. However, the findings are mixed, and there is no relevant research examining the influences of ultrafine particles (UFP), which may have more toxicity than larger particles. We therefore conducted this study to investigate whether residential UFP exposure is associated with cognitive decline using data from the Alzheimer's Disease Research Centers in the United States.

METHODS

This is a longitudinal study of participants who were aged 65 years and older and had normal cognitive status at baseline. Residential UFP exposure, expressed as particle number concentrations (PNC), was assessed in 2016-2017 using a nationwide land use regression model, and was assigned to each participant using their 3-digit residential ZIP codes. Cognitive functions including memory, attention, language, executive function, and global function were assessed annually using 15 neuropsychological tests from March 2015 to February 2022. Linear mixed-effects models were used to examine the associations after adjustment for covariates including baseline age, sex, APOE ε4 status, race, education, smoking status, history of diabetes, quartiles of neighborhood median household income, and interaction terms of follow-up time with each covariate.

RESULTS

This study included 5646 participants (mean age 76 years, 65% female). On average, each participant had 4 annual visits. When PNC was treated as a continuous variable, there were no statistically or clinically significant changes in annual decline of each cognitive function in relation to an interquartile range elevation in PNC (4026 particles/cm3). Similarly, when PNC was treated as a categorical variable including five exposure groups, there were no linear exposure-response trends in annual decline of each cognitive function across the five exposure groups.

CONCLUSIONS

This study found no meaningful associations between residential UFP exposure and cognitive decline in global and domain-specific functions. There is a need for further research that assigns UFP exposure at a finer geographic scale.

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.

A new exposure metric for the cumulative effect of short-term exposure peaks of traffic-related ultrafine particles

INTRODUCTION

The adverse health outcomes of traffic-related ultrafine particles (UFPs) disproportionally impact near-highway neighborhoods. Current studies focus on either short-term health outcomes associated with short-term UFP exposures averaged over days or weeks, or long-term outcomes associated with long-term (yearly or longer) average UFP exposures. We hypothesized that frequent and repeated exposure to short-term UFP peaks that last for just hours could overwhelm or alter physiological defensive responses, resulting in long-term health issues. Herein, we propose a new exposure metric for measuring the cumulative effect of these peak exposures.

METHOD

We used UFP exposure data estimated by the Community Assessment of Freeway Exposure and Health (CAFEH) project, which recruited 704 participants from three pairs of near-highway/urban background neighborhoods in the Greater Boston Area between 2009 and 2012. CAFEH developed land use regression (LUR) models to estimate hourly averages of ambient UFP levels within the study areas based on mobile-monitored UFP data, and applied time-activity adjustment (TAA) to calculate adjusted final hourly estimates. Our alternative metric assigns cumulative peak exposure, which is determined as either the intensity (a high percentile of an individual's adjusted hourly UFP estimates) or the frequency (the number of hours with adjusted UFP estimates greater than a high percentile of all adjusted hourly UFP estimates of all participants in the study area) of UFP peaks.

RESULTS

After TAA was applied, for most of the time, our cumulative peak exposure metrics were not strongly correlated with the annual average. However, the level of correlation varied greatly from neighborhood to neighborhood (Spearman's R ranges from 0.39 to 0.97).

CONCLUSION

There was variation in UFP peak exposure that was not explained by the annual average, suggesting that our proposed peak metric distinct from annual average exposure metric.

Long-term commuting times and air quality relationship to COVID-19 in São Paulo

The Coronavirus Disease 2019 (COVID-19) epidemic is an unprecedented global health crisis and the effects may be related to environmental and socio-economic factors. In São Paulo, Brazil, the first death occurred in March 2020 and since then the numbers have grown to 175 new deaths per day in April 2021, positioning the city as the epicenter of the number of cases and deaths in Brazil. São Paulo is one of the largest cities in the world with more than 12 million inhabitants, a fleet of about 8 million vehicles and frequent pollutant concentrations above recommended values. Social inequalities are evident in the municipality, similarly to other cities in the world. This paper focuses on transportation activities related to air pollution and associated with cardiovascular and respiratory diseases especially on people who developed comorbidities during their whole life. This study relates travel trip data to air quality analysis and expanded to COVID-19 disease. This work studied the relationship of deaths in São Paulo due to COVID-19 with demographic density, with family income, with the use of public transport and with atmospheric pollution for the period between March 17th, 2020 and April 29th, 2021. The main results showed that generally passenger kilometers traveled, commuting times and air quality related diseases increase with residential distance from the city center, and thus, with decreasing residential density. PM_2.5 concentrations are positively correlated with COVID-19 deaths, regions with high urban densities have higher numbers of deaths and long-distance frequent trips can contribute to spread of the disease.

Distributions and determinants of time spent outdoors among school-age children in China

BACKGROUND

The outdoor time is a key factor that determines children's exposure to environmental contaminants. Meanwhile, children reap numerous physical and mental health benefits from playing outdoors.

OBJECTIVE

This study aimed to characterize the distributions and identify determinants of the time spent outdoors among school-age children in China.

METHODS

The study recruited 41,439 children aged 6-17 years in Mainland China using a multi-stage cluster random sampling method. This sample was nationally representative with sample weights created to account for the complex survey design. Information on time-activity patterns were collected with a standard questionnaire through face-to-face interviews. Frequency distributions of time outdoors were created by age group, gender, region, and other sociodemographic factors with sample weights. The key factors influencing time outdoors were identified using multivariable linear regression models.

RESULTS

The grand mean (±standard deviation) time outdoors was 101 ± 51 min/day for all the study participants, 22.3% having <60 min/day. Children spent longer time outdoors in warm seasons and on weekends. Children's outdoor time was significantly influenced by gender, age, urbanicity, region, annual total household expenditure, building environment, and meteorological conditions (annual mean temperature, sunshine time, and precipitation). Girls, 15-17 year old, and urban children had 6 min/day, 8 min/day, and 13 min/day less outdoor time than boys, 6-8 year old, and rural children, respectively. Most significantly, urban girls and high school students had the shortest outdoor time. The cement ground was the most important type of ground for children playing outside.

SIGNIFICANCE

This was the first national-scale study that characterized the time outdoors and where is it spent among school-age children in China. It revealed that one-fifth of Chinese children spent less than one hour outdoors every day, and urban girls had particular "indoor tendencies". The findings provide bases for future interventions and guidelines aimed at promoting children's physical activities.

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 air pollution exposure and neurodevelopment: A review and blueprint for a harmonized approach within ECHO

BACKGROUND

Air 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.

OBJECTIVES

We 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.

METHODS

We 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.

DISCUSSION

While 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.

CONCLUSIONS

In 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.

Health Lens Analysis: A Strategy to Engage Community in Environmental Health Research in Action

Health Lens Analysis is a tool to facilitate collaboration among diverse community stakeholders. We employed HLA as part of a community based participatory research (CBPR) and action study to mitigate the negative health effects of TRAP and ultrafine particles (UFPs) in Somerville, MA. HLA is a Health in All Policies tool with previously limited implementation in a North American context. As part of the HLA, community and academic partners engaged residents from across near-highway neighborhoods in a series of activities designed to identify health concerns and generate recommendations for policies and projects to improve health over an 18-month planning period. Noise barriers, which may reduce TRAP exposure among residents in addition to reducing traffic noise, were seen as an acceptable solution by community stakeholders. We found HLA to be an effective means to engage stakeholders from across sectors and diverse community residents in critical discourse about the health impacts of near-roadway exposures. The iterative process allowed the project team to fully explore the arguments for noise barriers and preferred health interventions, while building a stakeholder base interested in the mitigation of TRAP, thus, creating a shared language and understanding of the issue.

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.

Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence

Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM_2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM_2.5 personal exposure were 55.1(82.8) µg/m³ for men and 58.5(58.8) µg/m³ for women; corresponding figures for black carbon were 4.6(7.0) µg/m³ and 6.1(9.6) µg/m³. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R_spearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (R_spearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.

CBPR Partnerships and Near-Roadway Pollution: A Promising Strategy to Influence the Translation of Research into Practice

Community-based participatory research (CBPR) aims to engage those traditionally left out of the research process. Partnering with community stakeholders to design, plan, implement and disseminate research can facilitate translation into practice. Using qualitative research methods, we set out to explore the policy and practice implications of a CBPR partnership focused on reducing exposure to near-roadway pollution. Key Informant interviews (n = 13) were conducted with individuals from various entities (municipal, state and private) for whom partners to the Community Assessment of Freeway Exposure and Health (CAFEH) provided technical assistance between 2013 and 2017. The findings indicate community research partnerships may have the power to inform local planning efforts. Developers and planners who the partnership consulted indicated a greater awareness of the implications of near-roadway exposure. They also described making changes in their practice based on study findings. The CAFEH partnership has demonstrated active attention to translating knowledge can influence local planning and practice, albeit with some challenges.

Influence of travel behaviour and daily mobility on exposure to traffic-related air pollution

This study evaluates the daily exposure of urban residents across various commuting modes and destinations by intersecting data from a travel survey with exposure surfaces for ultrafine particles and black carbon, in Toronto, Canada. We demonstrate that exposure misclassification is bound to arise when we approximate daily exposure with the concentration at the home location. We also identify potential inequities in the distribution of exposure to traffic-related air pollution whereby those who are mostly responsible for the generation of traffic-related air pollution (drivers and passengers) are exposed the least while active commuters and transit riders, are exposed the most.

Residential green space, air pollution, socioeconomic deprivation and cardiovascular medication sales in Belgium: A nationwide ecological study

Green space may improve cardiovascular (CV) health, for example by promoting physical activity and by reducing air pollution, noise and heat. Socioeconomic and environmental factors may modify the health effects of green space. We examined the association between residential green space and reimbursed CV medication sales in Belgium between 2006 and 2014, adjusting for socioeconomic deprivation and air pollution. We analyzed data for 11,575 census tracts using structural equation models for the entire country and for the administrative regions. Latent variables for green space, air pollution and socioeconomic deprivation were used as predictors of CV medication sales and were estimated from the number of patches of forest, census tract relative forest cover and relative forest cover within a 600 m buffer around the census tract; annual mean concentrations of PM_2.5, BC and NO₂; and percentages of inhabitants that were foreign-born from lower- and mid-income countries, unemployed or had no higher education. A direct association between socioeconomic deprivation and CV medication sales [parameter estimate (95% CI): 0.26 (0.25; 0.28)] and inverse associations between CV medication sales and green space [-0.71 (-0.80; -0.61)] and air pollution [-1.62 (-1.69; -0.61)] were observed. In the regional models, the association between green space and CV medication sales was stronger in the region with relatively low green space cover (Flemish Region, standardized estimate -0.16) than in the region with high green space cover (Walloon Region, -0.10). In the highly urbanized Brussels Capital Region the association tended towards the null. In all regions, the associations between CV medication sales and socioeconomic deprivation were direct and more prominent. Our results suggest that there may be an inverse association between green space and CV medication sales, but socioeconomic deprivation was always the strongest predictor of CV medication sales.

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.

Viruses and non-allergen environmental triggers in asthma

Asthma is a complex inflammatory disease with many triggers. The best understood asthma inflammatory pathways involve signals characterized by peripheral eosinophilia and elevated immunoglobulin E levels (called T2-high or allergic asthma), though other asthma phenotypes exist (eg, T2-low or non-allergic asthma, eosinophilic or neutrophilic-predominant). Common triggers that lead to poor asthma control and exacerbations include respiratory viruses, aeroallergens, house dust, molds, and other organic and inorganic substances. Increasingly recognized non-allergen triggers include tobacco smoke, small particulate matter (eg, PM2.5), and volatile organic compounds. The interaction between respiratory viruses and non-allergen asthma triggers is not well understood, though it is likely a connection exists which may lead to asthma development and/or exacerbations. In this paper we describe common respiratory viruses and non-allergen triggers associated with asthma. In addition, we aim to show the possible interactions, and potential synergy, between viruses and non-allergen triggers. Finally, we introduce a new clinical approach that collects exhaled breath condensates to identify metabolomics associated with viruses and non-allergen triggers that may promote the early management of asthma symptoms.

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.

On the Need for Better Exposure Assessment for Air Pollution with High Spatial and Temporal Variation

The mainstay of air pollution health research has been fine particulate matter pollution (PM2 [...].

ExpoApp: An integrated system to assess multiple personal environmental exposures

To assess environmental exposures at the individual level, new assessment methods and tools are required. We developed an exposure assessment system (ExpoApp) for smartphones. ExpoApp integrates: (i) geo-location and accelerometry measurements from a waist attached smartphone, (ii) data from portable monitors, (iii) geographic information systems, and (iv) individual's information. ExpoApp calculates time spent in microenvironments, physical activity level, inhalation rate, and environmental exposures and doses (e.g., green spaces, inhaled ultrafine particles- UFP). We deployed ExpoApp in a panel study of 158 adults from five cities (Amsterdam and Utrecht- the Netherlands, Basel- Switzerland, Norwich- UK, and Torino- Italy) with an UFP monitor. To evaluate ExpoApp, participants also carried a reference accelerometer (ActiGraph) and completed a travel-activity diary (TAD). System reliability and validity of measurements were evaluated by comparing the monitoring failure rate and the agreement on time spent in microenvironments and physical activity with the reference tools. There were only significant failure rate differences between ExpoApp and ActiGraph in Norwich. Agreement on time in microenvironments and physical activity level between ExpoApp and reference tools was 86.6% (86.5-86.7) and 75.7% (71.5-79.4), respectively. ExpoApp estimated that participants inhaled 16.5 × 10¹⁰ particles/day of UFP and had almost no contact with green spaces (24% of participants spent ≥30 min/day in green spaces). Participants with more contact with green spaces had higher inhaled dose of UFP, except for the Netherlands, where the relationship was the inverse. ExpoApp is a reliable system and provides accurate individual's measurements, which may help to understand the role of environmental exposures on the origin and course of diseases.

Assessing the transferability of landuse regression models for ultrafine particles across two Canadian cities

Land use regression (LUR) models have been increasingly used to predict intra-city variations in the concentrations of different air pollutants. However, limited research assessing the transferability of these models between cities has been published to date. In this study, LUR models were generated for Ultra-Fine Particles (UFP) (<0.1 um) using data collected from mobile monitoring campaigns in two Canadian cities, Montreal and Toronto. City-specific models were first generated for each city before the models were transferred to the second city with and without recalibration. The calibrated transferred models showed only a slight decrease in performance, with the coefficient of determination (R²), dropping from 0.49 to 0.36 for Toronto and from 0.41 to 0.38 for Montreal. Transferring models between cities with no calibration resulted in low R²; 0.11 in Toronto and 0.18 in Montreal. Moreover, two additional models were generated by combining data from the two cities. The first combined model (CM1) assumed a spatially invariant effect of the predictors, while the second (CM2) relaxed the assumption of spatial invariance for some of the model coefficients. The performance of both combined models (R² ranged between 0.41 for CM1 and 0.43 for CM2; root mean squared error (RMSE) ranged between 0.34 for CM1 and 0.33 for CM2) was found to be on par with the Toronto city-specific model and outperformed the Montreal model. The results of this study highlight that the UFP LUR models appear to support transferability of model structures between cities with similar geographical characteristics, with a minor drop in model fit and predictive skill.

Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community

Most empirical air quality models (e.g., land use regression) focus on urban areas. Mobile monitoring for model development offers the opportunity to explore smaller, rural communities - an understudied population. We use mobile monitoring to systematically sample all daylight hours (7 am to 7 pm) to develop empirical models capable of estimating hourly concentrations in Blacksburg, VA, a small town in rural Appalachia (population: 182 635). We collected ∼120 h of mobile monitoring data for particle number (PN) and black carbon (BC). We developed (1) daytime (12-h average) models that approximate long-term concentrations and (2) spatiotemporal models for estimating hourly concentrations. Model performance for the daytime models is consistent with previous fixed-site and short-term sampling studies; adjusted R² (10-fold CV R²) was 0.80 (0.69) for the PN model and 0.67 (0.58) for the BC model. The spatiotemporal models had comparable performance (10-fold CV R² for the PN [BC] models: 0.42 [0.25]) to previous mobile monitoring studies that isolate specific time periods. Temporal and spatial model coefficients had similar magnitudes in the spatiotemporal models suggesting both factors are important for exposure. We observed similar spatial patterns in Blacksburg (e.g., roadway gradients) as in other studies in urban areas suggesting similar exposure disparities exist in small, rural communities.

Chemical characterization and source apportionment of PM2.5 personal exposure of two cohorts living in urban and suburban Beijing

In the study, personal PM_2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM_2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM_2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM_2.5 exposures and produced poor correlations between personal and ambient PM_2.5. These results demonstrate that individual behavior strongly affects personal PM_2.5 exposure. Six primary sources of personal PM_2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM_2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.

The Exposome: Molecules to Populations

Derived from the term exposure, the exposome is an omic-scale characterization of the nongenetic drivers of health and disease. With the genome, it defines the phenome of an individual. The measurement of complex environmental factors that exert pressure on our health has not kept pace with genomics and historically has not provided a similar level of resolution. Emerging technologies make it possible to obtain detailed information on drugs, toxicants, pollutants, nutrients, and physical and psychological stressors on an omic scale. These forces can also be assessed at systems and network levels, providing a framework for advances in pharmacology and toxicology. The exposome paradigm can improve the analysis of drug interactions and detection of adverse effects of drugs and toxicants and provide data on biological responses to exposures. The comprehensive model can provide data at the individual level for precision medicine, group level for clinical trials, and population level for public health.

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 (β = 5.23, 95%CI: −0.68, 11.14 mmHg), PP (β = 4.27, 95%CI: −0.79, 9.32 mmHg), and hypertension (OR = 1.81, 95%CI: 0.94, 3.48), but not DBP (β = 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.

Exposure Assessment Using Secondary Data Sources in Unconventional Natural Gas Development and Health Studies

Studies of unconventional natural gas development (UNGD) and health have ranked participants along a gradient of geographic information system (GIS)-based activity that incorporated the distance between participants' home addresses and unconventional natural gas wells. However, studies have used different activity metrics, making result comparisons across the studies difficult. The existing studies have only incorporated wells, without accounting for other components of development (e.g., compressors, impoundments, and flaring events), for which it is often difficult to obtain reliable data but may have relevance to health. Our aims were to (1) describe, in space and time, UNGD-related compressors, impoundments, and flaring events; (2) evaluate whether and how to incorporate these into UNGD activity assessment; and (3) evaluate associations of these different approaches with mild asthma exacerbations. We identified 361 compressor stations, 1218 impoundments, and 216 locations with flaring events. A principal component analysis identified a single component that was approximately an equal mix of the metrics for compressors, impoundments, and four phases of well development (pad preparation, drilling, stimulation, and production). However, temporal coverage for impoundments and flaring data was sparse. Ultimately, we evaluated three UNGD activity metrics, including two based on the existing studies and a novel metric that included well pad development, drilling, stimulation, production, and compressor engine aspects of UNGD. The three metrics had varying magnitudes of association with mild asthma exacerbations, although the highest category of each metric (vs the lowest) was associated with the outcome.

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment

Analyzing individual exposure in urban areas offers several challenges where both the individual's activities and air pollution levels demonstrate a large degree of spatial and temporal dynamics. This review article discusses the concepts, key elements, current developments in assessing personal exposure to urban air pollution (seventy-two studies reviewed) and respective advantages and disadvantages. A new conceptual structure to organize personal exposure assessment methods is proposed according to two classification criteria: (i) spatial-temporal variations of individuals' activities (point-fixed or trajectory based) and (ii) characterization of air quality (variable or uniform). This review suggests that the spatial and temporal variability of urban air pollution levels in combination with indoor exposures and individual's time-activity patterns are key elements of personal exposure assessment. In the literature review, the majority of revised studies (44 studies) indicate that the trajectory based with variable air quality approach provides a promising framework for tackling the important question of inter- and intra-variability of individual exposure. However, future quantitative comparison between the different approaches should be performed, and the selection of the most appropriate approach for exposure quantification should take into account the purpose of the health study. This review provides a structured basis for the intercomparing of different methodologies and to make their advantages and limitations more transparent in addressing specific research objectives.

The Role of Environmental Health Literacy When Developing Traffic Pollution Fact Sheets for Puerto Rican Adults

Environmental health literacy is particularly relevant to racial/ethnic and linguistic minority populations who are likely to live near major roadways and highways. We conducted exploratory research to develop ways to communicate the risks of traffic-related air pollution to Puerto Rican adults living in and near Boston, Massachusetts. We held two initial focus groups with Spanish-speaking Puerto Rican adults (N = 16) enrolled in the Boston Puerto Rican Health Study (BPRHS). Most had only a high school education or less and earned a low income. We used thematic analysis of transcripts to suggest ways to improve three fact sheets designed to communicate BPRHS findings to the community. Based on these results, we made substantial revisions. We then conducted a second set of two focus groups with the same participants to assess revisions. Participants viewed the revised fact sheets more favorably and indicated that they found them easier to read. Lessons learned about improving readability and relevance included increasing text size, adding more graphics, chunking text, and providing more technical details. Designing successful environmental health communication tools that retain scientific accuracy is not a simple task. There is need for systematic attempts to evaluate and report on health literacy and community engagement processes for developing materials that are easy to read, culturally relevant, and that communicate complex environmental health information and concepts in ways people can understand and act on.

A Dynamic Three-Dimensional Air Pollution Exposure Model for Hong Kong

INTRODUCTION

High-density high-rise cities have become a more prominent feature globally. Air quality is a significant public health risk in many of these cities. There is a need to better understand the extent to which vertical variation in air pollution and population mobility in such cities affect exposure and exposure-response relationships in epidemiological studies.

METHODS

We used a novel strategy to execute a staged model development that incorporated horizontal and vertical pollutant dispersion, building infiltration, and population mobility patterns in estimating traffic-related air pollution (TRAP) exposures in the Hong Kong Special Administrative Region (HK SAR).

Two street-level spatial monitoring campaigns were undertaken to facilitate the creation of a two-dimensional land-use regression (LUR) model. A network of approximately 100 passive nitric oxide-nitrogen dioxide (NO-NO2) monitors was deployed for two-week periods during the cool and warm seasons. Sampling locations were selected based on population and road network density with a range of physical and geographical characteristics represented. Eight sets of portable monitors for black carbon (BC) and particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5) were rotated so as to be deployed at 80 locations for a 24-hour period. Land-use, geographical, and emissions layers were combined with the spatial monitoring campaign results to create spatiotemporal exposure models.

Vertical air pollution monitoring was carried out at six strategic locations for two weeks in the warm season and two weeks in the cool season. Continuous measurements were carried out at four different heights of a residential building and on both sides of a street canyon. The heights ranged from as close to street level as practically possible up to a maximum of 50 meters (i.e., below the 20th floor). Paired indoor monitoring was included to allow the calculation of infiltration coefficients to feed into the dynamic component of the exposure model.

The final phase of model development addressed population mobility. A population-representative travel behavior survey (n = 89,358) was used to produce the dynamic component of the model, with time-weighted exposure estimates split between home and work or school. Transport microenvironment exposures were taken from published literature. Time-activity exposure estimates were split by age, sex, and employment status.

Development of the exposure model in distinct packages allowed the application of a staged approach to an existing cohort data set. Mortality risk estimates for an elderly cohort of 66,000 Hong Kong residents were calculated using increasing exposure model complexity.

RESULTS

The street-level (2-dimensional [2D]) LUR modeling captured important spatial parameters and represented spatial patterns of air quality in Hong Kong that were consistent with the literature. Higher concentrations of gaseous pollutants were centered in Kowloon and the northern region of Hong Kong Island. PM2.5 and BC predictions exhibited a north-south/west-east gradient, with higher concentrations in the northwest due to regional transport of particulate pollutants from Mainland China. While the degree of explained variance of the models was in line with other LUR modeling efforts in Asia, R2 values ranged from 0.46 (NO2) to 0.59 (PM2.5).

Exponential decay rates (k) were calculated at each monitoring location. While it was clear that k values were higher during the warm season than the cool season, no robust patterns were identified relating to the canyon physical parameters. Therefore, a single decay rate was used for each pollutant across the whole region for derivation of the 3-dimensional (3D) exposure layer (k = 0.004 and 0.012 for PM2.5 and BC, respectively). An alternative decay profile that capped decay at 20 meters above street level was proposed and evaluated. The electrochemical sensors deployed during the canyon campaigns did not exhibit the degree of interunit precision necessary to detect vertical variations in gaseous pollutants, and these results were excluded from the study.

We found that values of the median infiltration efficiencies (Finf) for both BC and PM2.5 were especially high during the cool season (91%). Finf values were somewhat lower during the warm season (81% and 88% for PM2.5 and BC, respectively), and we found a significant negative correlation between air conditioning use and Finf. The Finf for a mechanically ventilated office building was 45% and 40% during the cool and warm seasons, respectively.

Dynamic exposure estimates were compared against home outdoor estimates. As expected, the addition of an indoor component decreased time-weighted exposure estimates, which were balanced out to some extent by the inclusion of transport microenvironments. Overall, mean time-weighted exposures for the full dynamic model were around 20% lower than home outdoor estimates.

Higher levels of exposures were found with working adults and students than for those neither in work nor study. This was due to the increased mobility of people going to work or school. The exposures to PM2.5, BC, and NO2 were, respectively, 13%, 39%, and 14% higher for people who were under age 18, compared with people who were 65 or older. Exposure estimates for the female population were approximately 4% lower.

The availability of an existing cohort data set of elderly Hong Kong residents (n = 66,820) facilitated the calculation and comparison of mortality risk estimates for the different exposure models.

Overall, results indicated that the application of exposure estimates that incorporated infiltration, vertical, and to a lesser extent, dynamic components resulted in higher hazard ratios (HRs) than the standard street-level model and increased the number of significant associations with all-natural-cause, cardiovascular, and respiratory mortality outcomes.

CONCLUSIONS

The results from the study provided the first evidence that considering air pollution exposure in a dynamic 3D landscape would benefit epidemiological studies. Higher HRs and a greater number of significant associations were found between mortality and pollutant exposures that would not have been found had standard 2D exposure models been used. Dynamic models can also identify differential exposures between population subtypes (e.g., students and working adults; those neither in work nor study).

Improved urban building design appears to be stimulating the dispersion of local TRAP in street canyons. Conversely, Finf values found in naturally ventilated buildings were high, and residences provided little protection from ambient air pollution.

We have demonstrated that the creation of effective advanced exposure models is possible in Asian cities without an undue burden on resources. We recommend that vertical exposure patterns be incorporated in future epidemiological studies in high-rise cities where the floor of residence is recorded in health record data.

Evaluation of daily time spent in transportation and traffic-influenced microenvironments by urban Canadians

Exposure to traffic and traffic-related air pollution is associated with a wide array of health effects. Time spent in a vehicle, in active transportation, along roadsides, and in close proximity to traffic can substantially contribute to daily exposure to air pollutants. For this study, we evaluated daily time spent in transportation and traffic-influenced microenvironments by urban Canadians using the Canadian Human Activity Pattern Survey (CHAPS) 2 results. Approximately 4-7% of daily time was spent in on- or near-road locations, mainly associated with being in a vehicle and smaller contributions from active transportation. Indoor microenvironments can be impacted by traffic emissions, especially when located near major roadways. Over 60% of the target population reported living within one block of a roadway with moderate to heavy traffic, which was variable with income level and city, and confirmed based on elevated NO₂ exposure estimated using land use regression. Furthermore, over 55% of the target population ≤ 18 years reported attending a school or daycare in close proximity to moderate to heavy traffic, and little variation was observed based on income or city. The results underline the importance of traffic emissions as a major source of exposure in Canadian urban centers, given the time spent in traffic-influenced microenvironments.

Lessons from in-home air filtration intervention trials to reduce urban ultrafine particle number concentrations

BACKGROUND

Exposure to airborne ultrafine particle (UFP; <100 nm in aerodynamic diameter) is an emerging public health problem. Nevertheless, the benefit of using high efficiency particulate arrestance (HEPA) filtration to reduce UFP concentrations in homes is not yet clear.

METHODS

We conducted a randomized crossover study of HEPA filtration without a washout period in 23 homes of low-income Puerto Ricans in Boston and Chelsea, MA (USA). Most participants were female, older adults who were overweight or obese. Particle number concentrations (PNC, a proxy for UFP) were measured indoors and outdoors at each home continuously for six weeks. Homes received both HEPA filtration and sham filtration for three weeks each in random order.

RESULTS

Median PNC under HEPA filtration was 50-85% lower compared to sham filtration in most homes, but we found no benefit in terms of reduced inflammation; associations between hsCRP, IL-6, or TNFRII in blood samples and indoor PNC were inverse and not statistically significant.

CONCLUSIONS

Limitations to our study design likely contributed to our findings. Limitations included carry-over effects, a population that may have been relatively unresponsive to UFP, reduction in PNC even during sham filtration that limited differences between HEPA and sham filtration, window opening by participants, and lack of fine-grained (room-specific) participant time-activity information. Our approach was similar to other recent HEPA intervention studies of particulate matter exposure and cardiovascular risk, suggesting that there is a need for better study designs.

Lessons from in-home air filtration intervention trials to reduce urban ultrafine particle number concentrations

BACKGROUND

Exposure to airborne ultrafine particle (UFP; <100 nm in aerodynamic diameter) is an emerging public health problem. Nevertheless, the benefit of using high efficiency particulate arrestance (HEPA) filtration to reduce UFP concentrations in homes is not yet clear.

METHODS

We conducted a randomized crossover study of HEPA filtration without a washout period in 23 homes of low-income Puerto Ricans in Boston and Chelsea, MA (USA). Most participants were female, older adults who were overweight or obese. Particle number concentrations (PNC, a proxy for UFP) were measured indoors and outdoors at each home continuously for six weeks. Homes received both HEPA filtration and sham filtration for three weeks each in random order.

RESULTS

Median PNC under HEPA filtration was 50-85% lower compared to sham filtration in most homes, but we found no benefit in terms of reduced inflammation; associations between hsCRP, IL-6, or TNFRII in blood samples and indoor PNC were inverse and not statistically significant.

CONCLUSIONS

Limitations to our study design likely contributed to our findings. Limitations included carry-over effects, a population that may have been relatively unresponsive to UFP, reduction in PNC even during sham filtration that limited differences between HEPA and sham filtration, window opening by participants, and lack of fine-grained (room-specific) participant time-activity information. Our approach was similar to other recent HEPA intervention studies of particulate matter exposure and cardiovascular risk, suggesting that there is a need for better study designs.

Association of Long-Term Near-Highway Exposure to Ultrafine Particles with Cardiovascular Diseases, Diabetes and Hypertension

Ultrafine particle (UFP) concentrations are elevated near busy roadways, however, their effects on prevalence of cardiovascular diseases, diabetes, and hypertension are not well understood. To investigate these associations, data on demographics, diseases, medication use, and time of activities were collected by in-home surveys for 704 participants in three pairs of near-highway and urban background neighborhoods in and near Boston (MA, USA). Body mass index (BMI) was measured for a subset of 435 participants. Particle number concentration (PNC, a measure of UFP) was collected by mobile monitoring in each area. Intra-neighborhood spatial-temporal regression models (approximately 20 m resolution) were used to estimate hourly ambient PNC at the residences of participants. We used participant time activity information to adjust annual average residential PNC values and assign individualized time activity adjusted annual average PNC exposures (TAA-PNC). Using multivariate logistic regression models, we found an odds ratio (OR) of 1.35 (95% CI: 0.83, 2.22) of TAA-PNC with stroke and ischemic heart diseases (S/IHD), an OR of 1.14 (95% CI: 0.81, 1.62) with hypertension, and an OR of 0.71 (95% CI: 0.46, 1.10) for diabetes. A subset analysis controlling for BMI produced slightly stronger associations for S/IHD (OR = 1.61, 95% CI: 0.88, 2.92) and hypertension (OR = 1.28, 95% CI: 0.81, 2.02), and no association with diabetes (OR = 1.09, 95% CI = 0.61, 1.96). Further research is needed with larger sample sizes and longitudinal follow-up.

Occupational exposures and determinants of ultrafine particle concentrations during laser hair removal procedures

BACKGROUND

Occupational exposures to ultrafine particles in the plume generated during laser hair removal procedures, the most commonly performed light based cosmetic procedure, have not been thoroughly characterized. Acute and chronic exposures to ambient ultrafine particles have been associated with a number of negative respiratory and cardiovascular health effects. Thus, the aim of this study was to measure airborne concentrations of particles in a diameter size range of 10 nm to 1 μm in procedure rooms during laser hair removal procedures.

METHODS

TSI Model 3007 Condensation Particle Counters were used to quantify the particle count concentrations in the waiting and procedure rooms of a dermatology office. Particle concentrations were sampled before, during, and after laser hair removal procedures, and characteristics of each procedure were noted by the performing dermatologist.

RESULTS

Twelve procedures were sampled over 4 days. Mean ultrafine particle concentrations in the waiting and procedure rooms were 14,957.4 particles/cm³ and 22,916.8 particles/cm³ (p < 0.0001), respectively. Compared to background ultrafine particle concentrations before the procedure, the mean concentration in the procedure room was 2.89 times greater during the procedure (p = 0.009) and 2.09 times greater after the procedure (p = 0.007). Duration of procedure (p = 0.006), body part (p = 0.013), and the use of pre-laser lotion/type of laser (p = 0.039), were the most important predictors of ultrafine particle concentrations. Use of a smoke evacuator (a recommended form of local exhaust ventilation) positioned at 30.5 cm from the source, as opposed to the recommended 1-2 in., lowered particle concentrations, but was not a statistically significant predictor (p = 0.49).

CONCLUSIONS

Laser hair removal procedures can generate high exposures to ultrafine particles for dermatologists and other individuals performing laser hair removal, with exposure varying based on multiple determinants.

Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles

Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (<10 °C) hours with wind directions parallel to and perpendicular downwind from highways. In Somerville, correlation and error statistics were typically acceptable, and all models predicted concentration gradients extending ∼100 m from the highway. In contrast, in Chinatown, PNC trends differed among models, and predictions were poorly correlated with measurements likely due to effects of street canyons and nonhighway particle sources. Our results demonstrate the importance of selecting PNC models that align with study area characteristics (e.g., dominant sources and building geometry). We applied widely available models to typical urban study areas; therefore, our results should be generalizable to models of hourly averaged PNC in similar urban areas.

Aviation Emissions Impact Ambient Ultrafine Particle Concentrations in the Greater Boston Area

Ultrafine particles are emitted at high rates by jet aircraft. To determine the possible impacts of aviation activities on ambient ultrafine particle number concentrations (PNCs), we analyzed PNCs measured from 3 months to 3.67 years at three sites within 7.3 km of Logan International Airport (Boston, MA). At sites 4.0 and 7.3 km from the airport, average PNCs were 2- and 1.33-fold higher, respectively, when winds were from the direction of the airport compared to other directions, indicating that aviation impacts on PNC extend many kilometers downwind of Logan airport. Furthermore, PNCs were positively correlated with flight activity after taking meteorology, time of day and week, and traffic volume into account. Also, when winds were from the direction of the airport, PNCs increased with increasing wind speed, suggesting that buoyant aircraft exhaust plumes were the likely source. Concentrations of other pollutants [CO, black carbon (BC), NO, NO2, NOx, SO2, and fine particulate matter (PM2.5)] decreased with increasing wind speed when winds were from the direction of the airport, indicating a different dominant source (likely roadway traffic emissions). Except for oxides of nitrogen, other pollutants were not correlated with flight activity. Our findings point to the need for PNC exposure assessment studies to take aircraft emissions into consideration, particularly in populated areas near airports.

Deployment-Associated Exposure Surveillance With High-Resolution Metabolomics

OBJECTIVE

The aim of this study was to assess the suitability of high-resolution metabolomics (HRM) for measure of internal exposure and effect biomarkers from deployment-related environmental hazards.

METHODS

HRM provides extensive coverage of metabolism and data relevant to a broad spectrum of environmental exposures. This review briefly describes the analytic platform, workflow, and recent applications of HRM as a prototype environmental exposure surveillance system.

RESULTS

Building upon techniques available for contemporary occupational medicine and exposure sciences, HRM methods are able to integrate external exposures, internal body burden of environmental agents, and relevant biological responses with health outcomes.

CONCLUSIONS

Systematic analysis of existing Department of Defense Serum Repository samples will provide a high-quality, cross-sectional reference dataset for deployment-associated exposures while at the same time establishing a foundation for precision medicine.

Association of modeled long-term personal exposure to ultrafine particles with inflammatory and coagulation biomarkers

BACKGROUND

Long-term exposure to fine particulate matter has been linked to cardiovascular disease and systemic inflammatory responses; however, evidence is limited regarding the effects of long-term exposure to ultrafine particulate matter (UFP, <100nm). We used a cross-sectional study design to examine the association of long-term exposure to near-highway UFP with measures of systemic inflammation and coagulation.

METHODS

We analyzed blood samples from 408 individuals aged 40-91years living in three near-highway and three urban background areas in and near Boston, Massachusetts. We conducted mobile monitoring of particle number concentration (PNC) in each area, and used the data to develop and validate highly resolved spatiotemporal (hourly, 20m) PNC regression models. These models were linked with participant time-activity data to determine individual time-activity adjusted (TAA) annual average PNC exposures. Multivariable regression modeling and stratification were used to assess the association between TAA-PNC and single peripheral blood measures of high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), tumor-necrosis factor alpha receptor II (TNFRII) and fibrinogen.

RESULTS

After adjusting for age, sex, education, body mass index, smoking and race/ethnicity, an interquartile-range (10,000particles/cm(3)) increase in TAA-PNC had a positive non-significant association with a 14.0% (95% CI: -4.6%, 36.2%) positive difference in hsCRP, an 8.9% (95% CI: -0.4%, 10.9%) positive difference in IL-6, and a 5.1% (95% CI: -0.4%, 10.9%) positive difference in TNFRII. Stratification by race/ethnicity revealed that TAA-PNC had larger effect estimates for all three inflammatory markers and was significantly associated with hsCRP and TNFRII in white non-Hispanic, but not East Asian participants. Fibrinogen had a negative non-significant association with TAA-PNC.

CONCLUSIONS

Our findings suggest an association between annual average near-highway TAA-PNC and subclinical inflammatory markers of CVD risk.

Transferability and generalizability of regression models of ultrafine particles in urban neighborhoods in the Boston area

Land use regression (LUR) models have been used to assess air pollutant exposure, but limited evidence exists on whether location-specific LUR models are applicable to other locations (transferability) or general models are applicable to smaller areas (generalizability). We tested transferability and generalizability of spatial-temporal LUR models of hourly particle number concentration (PNC) for Boston-area (MA, U.S.A.) urban neighborhoods near Interstate 93. Four neighborhood-specific regression models and one Boston-area model were developed from mobile monitoring measurements (34-46 days/neighborhood over one year each). Transferability was tested by applying each neighborhood-specific model to the other neighborhoods; generalizability was tested by applying the Boston-area model to each neighborhood. Both the transferability and generalizability of models were tested with and without neighborhood-specific calibration. Important PNC predictors (adjusted-R(2) = 0.24-0.43) included wind speed and direction, temperature, highway traffic volume, and distance from the highway edge. Direct model transferability was poor (R(2) < 0.17). Locally-calibrated transferred models (R(2) = 0.19-0.40) and the Boston-area model (adjusted-R(2) = 0.26, range: 0.13-0.30) performed similarly to neighborhood-specific models; however, some coefficients of locally calibrated transferred models were uninterpretable. Our results show that transferability of neighborhood-specific LUR models of hourly PNC was limited, but that a general model performed acceptably in multiple areas when calibrated with local data.