Modeling residential fine particulate matter infiltration for exposure assessment

Exploring personal chemical exposures in China with wearable air pollutant monitors: A repeated-measure study in healthy older adults in Jinan, China

The health impact of airborne contaminants has been challenging to assess due to current limitations in measurement technologies. The emergence of wearable passive samplers coupled with high resolution mass spectrometry (HR-MS) chemical analysis has enabled comprehensive characterization of personal exposures. We conducted a repeated-measure study among 84 older adults in Jinan, China, as part of the Biomarkers for Air Pollutants Exposure (China BAPE) study. Study objectives were: 1) to characterize the occurrence, magnitude, and distribution of personal exposure to airborne contaminants; 2) to evaluate the temporal variation of chemical exposures across the study population; and 3) to identify behavioral and environmental factors that influence the observed variance in chemical exposures. The FreshAir wristband was worn by participants for three consecutive days each month from September 2018 to January 2019 and collected with paired time-activity logs. Passive air samplers were also deployed in parallel at a local outdoor air monitoring station. Spearman's Rho trend test and trajectory cluster analysis were used to identify exposure trends and variation patterns, respectively. Out of the 70 airborne compounds of potential concern screened, 26 compounds from 10 chemical classes were found to be above detection thresholds across >70% of the study population. Personal exposures were predominantly characterized by nine polycyclic aromatic hydrocarbons (PAHs), four phthalates, three nitroaromatics, and two volatile organic compounds (VOCs). Phthalate personal exposures were positively correlated with outdoor temperatures while the inverse relationship was observed for certain PAHs (p < 0.05). Specifically, dimethyl phthalate (r_s = 0.31) decreased as temperatures declined, while nitrobenzene (r_s = -0.35) and naphthalene (r_s = -0.40) increased as temperatures decreased. Compared to levels measured at the outdoor air monitoring site, personal exposure of phthalates was elevated (p < 0.05) and hexachlorobutadiene was lower across participants (p < 0.01). Personal exposure of these chemicals was further found to be weakly associated with daily duration participants spent outdoors. Individuals formed distinct clusters based on trajectories of chemical exposures across the sampling period (September to January), potentially suggestive of distinct emission sources. In conclusion, we demonstrate the feasibility of characterizing the occurrence and magnitude of personal exposure to airborne chemical contaminants using passive wristband samplers. The temporal variability of these personal exposure profiles was highlighted and with distinct trends identified across different groups of individuals. Future studies will integrate this data with other omics datasets collected from this population of Chinese older adults to investigate associations between exposure profiles and health relevant biomarkers, to provide evidence in feasibility of disease prevention through environmental improvements.

Overall health impacts of a potential increase in cycle commuting in Stockholm, Sweden

AIMS

To estimate the overall health impact of transferring commuting trips from car to bicycle.

METHODS

In this study registry information on the location of home and work for residents in Stockholm County was used to obtain the shortest travel route on a network of bicycle paths and roads. Current modes of travel to work were based on travel survey data. The relation between duration of cycling and distance cycled was established as a basis for selecting the number of individuals that normally would drive a car to work, but have a distance to work that they could bicycle within 30 minutes. The change in traffic flows was estimated by a transport model (LuTrans) and effects on road traffic injuries and fatalities were estimated by using national hospital injury data. Effects on air pollution concentrations were modelled using dispersion models.

RESULTS

Within the scenario, 111,000 commuters would shift from car to bicycle. On average the increased physical activity reduced the one-year mortality risk by 12% among the additional bicyclists. Including the number of years lost due to morbidity, the total number of disability adjusted life-years gained was 696. The amount of disability adjusted life-years gained in the general population due to reduced air pollution exposure was 471. The number of disability adjusted life-years lost by traffic injuries was 176. Also including air pollution effects among bicyclists, the net benefit was 939 disability adjusted life-years per year.

CONCLUSIONS

Large health benefits were estimated by transferring commuting by car to bicycle.

Estimating climate change-related impacts on outdoor air pollution infiltration

BACKGROUND

Rising temperatures due to climate change are expected to impact human adaptive response, including changes to home cooling and ventilation patterns. These changes may affect air pollution exposures via alteration in residential air exchange rates, affecting indoor infiltration of outdoor particles. We conducted a field study examining associations between particle infiltration and temperature to inform future studies of air pollution health effects.

METHODS

We measured indoor fine particulate matter (PM_2.5) in Atlanta in 60 homes (810 sampling-days). Indoor-outdoor sulfur ratios were used to estimate particle infiltration, using central site outdoor sulfur concentrations. Linear and mixed-effects models were used to examine particle infiltration ratio-temperature relationships, based on which we incorporated projected meteorological values (Representative Concentration Pathways intermediate scenario RCP 4.5) to estimate particle infiltration ratios in 20-year future (2046-2065) and past (1981-2000) scenarios.

RESULTS

The mean particle infiltration ratio in Atlanta was 0.70 ± 0.30, with a 0.21 lower ratio in summer compared to transition seasons (spring, fall). Particle infiltration ratios were 0.19 lower in houses using heating, ventilation, and air conditioning (HVAC) systems compared to those not using HVAC. We observed significant associations between particle infiltration ratios and both linear and quadratic models of ambient temperature for homes using natural ventilation and those using HVAC. Future temperature was projected to increase by 2.1 °C in Atlanta, which corresponds to an increase of 0.023 (3.9%) in particle infiltration ratios during cooler months and a decrease of 0.037 (6.2%) during warmer months.

DISCUSSION

We estimated notable changes in particle infiltration ratio in Atlanta for different 20-year periods, with differential seasonal patterns. Moreover, when stratified by HVAC usage, increases in future ambient temperature due to climate change were projected to enhance seasonal differences in PM_2.5 infiltration in Atlanta. These analyses can help minimize exposure misclassification in epidemiologic studies of PM_2.5, and provide a better understanding of the potential influence of climate change on PM_2.5 health effects.

Potential Effects on Travelers' Air Pollution Exposure and Associated Mortality Estimated for a Mode Shift from Car to Bicycle Commuting

This study aims to use dispersion-modeled concentrations of nitrogen oxides (NOx) and black carbon (BC) to estimate bicyclist exposures along a network of roads and bicycle paths. Such modeling was also performed in a scenario with increased bicycling. Accumulated concentrations between home and work were thereafter calculated for both bicyclists and drivers of cars. A transport model was used to estimate traffic volumes and current commuting preferences in Stockholm County. The study used individuals’ home and work addresses, their age, sex, and an empirical model estimate of their expected physical capacity in order to establish realistic bicycle travel distances. If car commuters with estimated physical capacity to bicycle to their workplace within 30 min changed their mode of transport to bicycle, >110,000 additional bicyclists would be achieved. Time-weighted mean concentrations along paths were, among current bicyclists, reduced from 25.8 to 24.2 μg/m³ for NOx and 1.14 to 1.08 μg/m³ for BC. Among the additional bicyclists, the yearly mean NOx dose from commuting increased from 0.08 to 1.03 μg/m³. This would be expected to yearly cause 0.10 fewer deaths for current bicycling levels and 1.7 more deaths for additional bicycling. This increased air pollution impact is much smaller than the decrease in the total population.

Is remaining indoors an effective way of reducing exposure to fine particulate matter during biomass burning events?

Bushfires, prescribed burns, and residential wood burning are significant sources of fine particles (aerodynamic diameter <2.5 μm; PM_2.5) affecting the health and well-being of many communities. Despite the lack of evidence, a common public health recommendation is to remain indoors, assuming that the home provides a protective barrier against ambient PM_2.5. The study aimed to assess to what extent houses provide protection against peak concentrations of outdoor PM_2.5 and whether remaining indoors is an effective way of reducing exposure to PM_2.5. The effectiveness of this strategy was evaluated by conducting simultaneous week-long indoor and outdoor measurements of PM_2.5 at 21 residences in regional areas of Victoria, Australia. During smoke plume events, remaining indoors protected residents from peak outdoor PM_2.5 concentrations, but the level of protection was highly variable, ranging from 12% to 76%. Housing stock (e.g., age of the house) and ventilation (e.g., having windows/doors open or closed) played a significant role in the infiltration of outdoor PM_2.5 indoors. The results also showed that leaving windows and doors closed once the smoke plume abates trapped PM_2.5 indoors and increased indoor exposure to PM_2.5. Furthermore, for approximately 50% of households, indoor sources such as cooking activities, smoking, and burning candles or incense contributed significantly to indoor PM_2.5. Implications: Smoke from biomass burning sources can significantly impact on communities. Remaining indoors with windows and doors closed is a common recommendation by health authorities to minimize exposures to peak concentrations of fine particles during smoke plume events. Findings from this study have shown that the protection from fine particles in biomass burning smoke is highly variable among houses, with information on housing age and ventilation status providing an approximate assessment on the protection of a house. Leaving windows closed once a smoke plume abates traps particles indoors and increases exposures.

Estimation of residential fine particulate matter infiltration in Shanghai, China

Ambient concentrations of fine particulate matter (PM_2.5) concentration is often used as an exposure surrogate to estimate PM_2.5 health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM_2.5 infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM_2.5 infiltration factor (F_inf) for each individual residence, we aimed to build models for residential PM_2.5F_inf prediction and to evaluate seasonal F_inf variations among residences. We repeated collected paired indoor and outdoor PM_2.5 filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM_2.5-bound sulfur on the filters was measured by X-ray fluorescence for PM_2.5F_inf calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R²) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PM_2.5F_inf across all observations was 0.83 (±0.18). F_inf was found higher and more varied in transitional season (12-25 °C) than hot (>25 °C) and cold (<12 °C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%-68.4% of the variance in 7-day averages of F_inf, The LOOCV analysis showed an R² of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PM_2.5F_inf between seasons and across residences within season indicated the important source of outdoor-generated PM_2.5 exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM_2.5 exposure.

Randomized Trial of Interventions to Improve Childhood Asthma in Homes with Wood-burning Stoves

BACKGROUND

Household air pollution due to biomass combustion for residential heating adversely affects vulnerable populations. Randomized controlled trials to improve indoor air quality in homes of children with asthma are limited, and no such studies have been conducted in homes using wood for heating.

OBJECTIVES

Our aims were to test the hypothesis that household-level interventions, specifically improved-technology wood-burning appliances or air-filtration devices, would improve health measures, in particular Pediatric Asthma Quality of Life Questionnaire (PAQLQ) scores, relative to placebo, among children living with asthma in homes with wood-burning stoves.

METHODS

A three-arm placebo-controlled randomized trial was conducted in homes with wood-burning stoves among children with asthma. Multiple preintervention and postintervention data included PAQLQ (primary outcome), peak expiratory flow (PEF) monitoring, diurnal peak flow variability (dPFV, an indicator of airway hyperreactivity) and indoor particulate matter (PM) PM2.5.

RESULTS

Relative to placebo, neither the air filter nor the woodstove intervention showed improvement in quality-of-life measures. Among the secondary outcomes, dPFV showed a 4.1 percentage point decrease in variability [95% confidence interval (CI)=-7.8 to -0.4] for air-filtration use in comparison with placebo. The air-filter intervention showed a 67% (95% CI: 50% to 77%) reduction in indoor PM2.5, but no change was observed with the improved-technology woodstove intervention.

CONCLUSIONS

Among children with asthma and chronic exposure to woodsmoke, an air-filter intervention that improved indoor air quality did not affect quality-of-life measures. Intent-to-treat analysis did show an improvement in the secondary measure of dPFV.

TRIAL REGISTRATION

ClincialTrials.gov NCT00807183. https://doi.org/10.1289/EHP849.

Investigation and modeling of the residential infiltration of fine particulate matter in Beijing, China

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM_2.5) and to develop models to predict PM_2.5 Finf in Beijing. Eighty-eight paired indoor-outdoor PM_2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM_2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM_2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM_2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM_2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM_2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification.

IMPLICATIONS

Failure to consider the differences between indoor and outdoor PM_2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM_2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM_2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM_2.5 of outdoor origin in epidemiological studies. Localized predictive models for PM_2.5 infiltration factor were also developed.

Modifications of exposure to ambient particulate matter: Tackling bias in using ambient concentration as surrogate with particle infiltration factor and ambient exposure factor

Numerous epidemiological studies explored health risks attributed to outdoor particle pollution. However, a number of these studies routinely utilized ambient concentration as a surrogate for personal exposure to ambient particles. This simplification ignored the difference between indoor and outdoor concentrations of outdoor originated particles and may bias the estimate of particle-health associations. Intending to avoid the bias, particle infiltration factor (F_inf), which describes the penetration of outdoor particles in indoor environment, and ambient exposure factor (α), which represents the fraction of outdoor particles people are truly exposed to, are utilized as modification factors to modify outdoor particle concentration. In this study, the probabilistic distributions of annually-averaged and seasonally-averaged F_inf and α were assessed for residences and residents in Beijing. F_inf of a single residence and α of an individual was estimated based on the mechanisms governing particle outdoor-to-indoor migration and human time-activity pattern. With this as the core deterministic model, probabilistic distributions of F_inf and α were estimated via Monte Carlo Simulation. Annually-averaged F_inf of PM_2.5 and PM₁₀ for residences in Beijing tended to be log-normally distributed as lnN(-0.74,0.14) and lnN(-0.94,0.15) with geometric mean value as 0.47 and 0.39, respectively. Annually-averaged α of PM_2.5 and PM₁₀ for Beijing residents also tended to be log-normally distributed as lnN(-0.59,0.12) and lnN(-0.73,0.13) with geometric mean value as 0.55 and 0.48, respectively. As for seasonally-averaged results, F_inf and α of PM_2.5 and PM₁₀ were largest in summer and smallest in winter. The obvious difference between these modification factors and unity suggested that modifications of ambient particle concentration need to be considered in epidemiological studies to avoid misclassifications of personal exposure to ambient particles. Moreover, considering the inter-individual difference of F_inf and α may lead to a brand new perspective of particle-health associations in further epidemiological study.

Individual and Neighborhood Socioeconomic Status and the Association between Air Pollution and Cardiovascular Disease

BACKGROUND

Long-term fine particulate matter (PM2.5) exposure is linked with cardiovascular disease, and disadvantaged status may increase susceptibility to air pollution-related health effects. In addition, there are concerns that this association may be partially explained by confounding by socioeconomic status (SES).

OBJECTIVES

We examined the roles that individual- and neighborhood-level SES (NSES) play in the association between PM2.5 exposure and cardiovascular disease.

METHODS

The study population comprised 51,754 postmenopausal women from the Women's Health Initiative Observational Study. PM2.5 concentrations were predicted at participant residences using fine-scale regionalized universal kriging models. We assessed individual-level SES and NSES (Census-tract level) across several SES domains including education, occupation, and income/wealth, as well as through an NSES score, which captures several important dimensions of SES. Cox proportional-hazards regression adjusted for SES factors and other covariates to determine the risk of a first cardiovascular event.

RESULTS

A 5 μg/m3 higher exposure to PM2.5 was associated with a 13% increased risk of cardiovascular event [hazard ratio (HR) 1.13; 95% confidence interval (CI): 1.02, 1.26]. Adjustment for SES factors did not meaningfully affect the risk estimate. Higher risk estimates were observed among participants living in low-SES neighborhoods. The most and least disadvantaged quartiles of the NSES score had HRs of 1.39 (95% CI: 1.21, 1.61) and 0.90 (95% CI: 0.72, 1.07), respectively.

CONCLUSIONS

Women with lower NSES may be more susceptible to air pollution-related health effects. The association between air pollution and cardiovascular disease was not explained by confounding from individual-level SES or NSES. Citation: Chi GC, Hajat A, Bird CE, Cullen MR, Griffin BA, Miller KA, Shih RA, Stefanick ML, Vedal S, Whitsel EA, Kaufman JD. 2016. Individual and neighborhood socioeconomic status and the association between air pollution and cardiovascular disease. Environ Health Perspect 124:1840-1847; http://dx.doi.org/10.1289/EHP199.

Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5 in Central North Carolina

Air pollution health studies of fine particulate matter (diameter ≤2.5 μm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 μg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 μg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies.

A CFD modeling study in an urban street canyon for ultrafine particles and population exposure: The intake fraction approach

Air quality in street canyons is of major importance, since the highest pollution levels are often encountered in these microenvironments. The canyon effect (reduced natural ventilation) makes them "hot spots" for particulate pollution contributing to adverse health effects for the exposed population. In this study we tried to characterize the influence of UFP (ultrafine particle) emissions from traffic on population exposure in an urban street canyon, by applying the intake fraction (iF) approach. One month long measurements of UFP levels have been monitored and used for the need of this study. We applied a three dimensional computational fluid dynamic (CFD) model based on real measurements for the simulation of UFP levels. We used infiltration factors, evaluated on a daily basis for the under study area, to estimate the indoor UFP levels. As a result the intake fraction for the pedestrians, residents and office workers is in the range of (1E-5)-(1E-4). The street canyon is mostly residential justifying partially the higher value of intake fraction for residents (1E-4). The above iF value is on the same order of magnitude with the corresponding one evaluated in a relative street canyon study. The total iF value in this microenvironment is one order of magnitude higher than ours, explained partially by the different use and activities. Two specific applications of iF to assess prioritization among emission sources and environmental justice issues are also examined. We ran a scenario with diesel and gasoline cars and diesel fueled vehicle seems to be a target source to improve overall iF. Our application focus on a small residential area, typical of urban central Athens, in order to evaluate high resolution iF. The significance of source-exposure relationship study in a micro scale is emphasized by recent research.

Development of a Canadian socioeconomic status index for the study of health outcomes related to environmental pollution

Background

Socioeconomic status (SES) is an important determinant of health and potential modifier of the effects of environmental contaminants. There has been a lack of comprehensive indices for measuring overall SES in Canada. Here, a more comprehensive SES index is developed aiming to support future studies exploring health outcomes related to environmental pollution in Canada.

Methods

SES variables (n = 22, Census Canada 2006) were selected based on: cultural identities, housing characteristics, variables identified in Canadian environmental injustice studies and a previous deprivation index (Pampalon index). Principal component analysis with a single varimax rotation (factor loadings ≥ │60│) was performed on SES variables for 52974 census dissemination areas (DA). The final index was created by averaging the factor scores per DA according to the three components retained. The index was validated by examining its association with preterm birth (gestational age < 37 weeks), term low birth weight (LBW, <2500 g), small for gestational age (SGA, <10 percentile of birth weight for gestational age) and PM_2.5 (particulate matter ≤ 2.5 μm) exposures in Edmonton, Alberta (1999–2008).

Results

Index values exhibited a relatively normal distribution (median = 0.11, mean = 0.0, SD = 0.58) across Canada. Values in Alberta tended to be higher than in Newfoundland and Labrador, Northwest Territories and Nunavut (Pearson chi-square p < 0.001 across provinces). Lower quintiles of our index and the Pampalon’s index confirmed know associations with a higher prevalence of LBW, SGA, preterm birth and PM_2.5 exposure. Results with our index exhibited greater statistical significance and a more consistent gradient of PM_2.5 levels and prevalence of pregnancy outcomes.

Conclusions

Our index reflects more dimensions of SES than an earlier index and it performed superiorly in capturing gradients in prevalence of pregnancy outcomes. It can be used for future research involving environmental pollution and health in Canada.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-1992-y) contains supplementary material, which is available to authorized users.

Indoor particulate matter in rural, wood stove heated homes

Ambient particulate matter (PM) exposures have adverse impacts on public health, but research evaluating indoor PM concentrations in rural homes in the United States using wood as fuel for heating is limited. Our objectives were to characterize indoor PM mass and particle number concentrations (PNCs), quantify infiltration of outdoor PM into the indoor environment, and investigate potential predictors of concentrations and infiltration in 96 homes in the northwestern US and Alaska using wood stoves as the primary source of heating. During two forty-eight hour sampling periods during the pre-intervention winter of a randomized trial, we assessed PM mass (<2.5μm) and PNCs (particles/cm(3)) in six size fractions (0.30-0.49, 0.50-0.99, 1.00-2.49, 2.5-5.0, 5.0-10.0, 10.0+μm). Daily mean (sd) PM2.5 concentrations were 28.8 (28.5)μg/m(3) during the first sampling period and 29.1 (30.1)μg/m(3) during the second period. In repeated measures analyses, household income was inversely associated with PM2.5 and smaller size fraction PNCs, in particular. Time of day was a significant predictor of indoor and outdoor PM2.5 concentrations, and infiltration efficiency was relatively low (Finf (sd)=0.27 (0.20)). Our findings demonstrate relatively high mean PM concentrations in these wood burning homes and suggest potential targets for interventions for improving indoor air quality and health in rural settings.

Quantifying the contribution of ambient and indoor-generated fine particles to indoor air in residential environments

Indoor fine particles (FPs) are a combination of ambient particles that have infiltrated indoors, and particles that have been generated indoors from activities such as cooking. The objective of this paper was to estimate the infiltration factor (Finf ) and the ambient/non-ambient components of indoor FPs. To do this, continuous measurements were collected indoors and outdoors for seven consecutive days in 50 non-smoking homes in Halifax, Nova Scotia in both summer and winter using DustTrak (TSI Inc) photometers. Additionally, indoor and outdoor gravimetric measurements were made for each 24-h period in each home, using Harvard impactors (HI). A computerized algorithm was developed to remove (censor) peaks due to indoor sources. The censored indoor/outdoor ratio was then used to estimate daily Finfs and to determine the ambient and non-ambient components of total indoor concentrations. Finf estimates in Halifax (daily summer median = 0.80; daily winter median = 0.55) were higher than have been reported in other parts of Canada. In both winter and summer, the majority of FP was of ambient origin (daily winter median = 59%; daily summer median = 84%). Predictors of the non-ambient component included various cooking variables, combustion sources, relative humidity, and factors influencing ventilation. This work highlights the fact that regional factors can influence the contribution of ambient particles to indoor residential concentrations.

PRACTICAL IMPLICATIONS

Ambient and non-ambient particles have different risk management approaches, composition, and likely toxicity. Therefore, a better understanding of their contribution to the indoor environment is important to manage the health risks associated with fine particles (FPs) effectively. As well, a better understanding of the factors Finf can help improve exposure assessment and contribute to reduced exposure misclassification in epidemiologic studies.

Long-term exposure to traffic-related air pollution and progression of carotid artery atherosclerosis: a prospective cohort study

OBJECTIVES

Epidemiological studies have demonstrated associations between long-term exposure to traffic-related air pollution and coronary heart disease (CHD). Atherosclerosis is the principal pathological process responsible for CHD events, but effects of traffic-related air pollution on progression of atherosclerosis are not clear. This study aimed to investigate associations between long-term exposure to traffic-related air pollution and progression of carotid artery atherosclerosis.

SETTING

Healthy volunteers in metropolitan Vancouver, Canada.

PARTICIPANTS AND OUTCOME MEASURES

509 participants aged 30-65 years were recruited and followed for approximately 5 years. At baseline and end of follow-up, participants underwent carotid artery ultrasound examinations to assess atherosclerosis severity, including carotid intima-media thickness, plaque area, plaque number and total area. Annual change of each atherosclerosis marker during the follow-up period was calculated as the difference between these two measurements divided by years of follow-up. Living close to major roads was defined as ≤150 m from a highway or ≤50 m from a major road. Residential exposures to traffic-related air pollutants including black carbon, fine particles, nitrogen dioxide and nitric oxide were estimated using high-resolution land-use regression models. The data were analysed using general linear models adjusting for various covariates.

RESULTS

At baseline, there were no significant differences in any atherosclerosis markers between participants living close to and those living away from major roads. After follow-up, the differences in annual changes of these markers between these two groups were small and not statistically significant. Also, no significant associations were observed with concentrations of traffic-related air pollutants including black carbon, fine particles, nitrogen dioxide and nitric oxide.

CONCLUSIONS

This study did not find significant associations between traffic-related air pollution and progression of carotid artery atherosclerosis in a region with lower levels and smaller contrasts of ambient air pollution.

A new exposure metric for traffic-related air pollution? An analysis of determinants of hopanes in settled indoor house dust

BACKGROUND

Exposure to traffic-related air pollution (TRAP) can adversely impact health but epidemiologic studies are limited in their abilities to assess long-term exposures and incorporate variability in indoor pollutant infiltration.

METHODS

In order to examine settled house dust levels of hopanes, engine lubricating oil byproducts found in vehicle exhaust, as a novel TRAP exposure measure, dust samples were collected from 171 homes in five Canadian cities and analyzed by gas chromatography-mass spectrometry. To evaluate source contributions, the relative abundance of the highest concentration hopane monomer in house dust was compared to that in outdoor air. Geographic variables related to TRAP emissions and outdoor NO2 concentrations from city-specific TRAP land use regression (LUR) models were calculated at each georeferenced residence location and assessed as predictors of variability in dust hopanes.

RESULTS

Hopanes relative abundance in house dust and ambient air were significantly correlated (Pearson's r=0.48, p<0.05), suggesting that dust hopanes likely result from traffic emissions. The proportion of variance in dust hopanes concentrations explained by LUR NO2 was less than 10% in Vancouver, Winnipeg and Toronto while the correlations in Edmonton and Windsor explained 20 to 40% of the variance. Modeling with household factors such as air conditioning and shoe removal along with geographic predictors related to TRAP generally increased the proportion of explained variability (10-80%) in measured indoor hopanes dust levels.

CONCLUSIONS

Hopanes can consistently be detected in house dust and may be a useful tracer of TRAP exposure if determinants of their spatiotemporal variability are well-characterized, and when home-specific factors are considered.

Risk-based indicators of Canadians' exposures to environmental carcinogens

BACKGROUND

Tools for estimating population exposures to environmental carcinogens are required to support evidence-based policies to reduce chronic exposures and associated cancers. Our objective was to develop indicators of population exposure to selected environmental carcinogens that can be easily updated over time, and allow comparisons and prioritization between different carcinogens and exposure pathways.

METHODS

We employed a risk assessment-based approach to produce screening-level estimates of lifetime excess cancer risk for selected substances listed as known carcinogens by the International Agency for Research on Cancer. Estimates of lifetime average daily intake were calculated using population characteristics combined with concentrations (circa 2006) in outdoor air, indoor air, dust, drinking water, and food and beverages from existing monitoring databases or comprehensive literature reviews. Intake estimates were then multiplied by cancer potency factors from Health Canada, the United States Environmental Protection Agency, and the California Office of Environmental Health Hazard Assessment to estimate lifetime excess cancer risks associated with each substance and exposure pathway. Lifetime excess cancer risks in excess of 1 per million people are identified as potential priorities for further attention.

RESULTS

Based on data representing average conditions circa 2006, a total of 18 carcinogen-exposure pathways had potential lifetime excess cancer risks greater than 1 per million, based on varying data quality. Carcinogens with moderate to high data quality and lifetime excess cancer risk greater than 1 per million included benzene, 1,3-butadiene and radon in outdoor air; benzene and radon in indoor air; and arsenic and hexavalent chromium in drinking water. Important data gaps were identified for asbestos, hexavalent chromium and diesel exhaust in outdoor and indoor air, while little data were available to assess risk for substances in dust, food and beverages.

CONCLUSIONS

The ability to track changes in potential population exposures to environmental carcinogens over time, as well as to compare between different substances and exposure pathways, is necessary to support comprehensive, evidence-based prevention policy. We used estimates of lifetime excess cancer risk as indicators that, although based on a number of simplifying assumptions, help to identify important data gaps and prioritize more detailed data collection and exposure assessment needs.

Modeling the residential infiltration of outdoor PM(2.5) in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

BACKGROUND

Epidemiologic studies of fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM(2.5))] typically use outdoor concentrations as exposure surrogates. Failure to account for variation in residential infiltration efficiencies (F(inf)) will affect epidemiologic study results.

OBJECTIVE

We aimed to develop models to predict F(inf) for > 6,000 homes in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study of PM(2.5) exposure, subclinical cardiovascular disease, and clinical outcomes.

METHODS

We collected 526 two-week, paired indoor-outdoor PM(2.5) filter samples from a subset of study homes. PM(2.5) elemental composition was measured by X-ray fluorescence, and F(inf) was estimated as the indoor/outdoor sulfur ratio. We regressed F(inf) on meteorologic variables and questionnaire-based predictors in season-specific models. Models were evaluated using the R² and root mean square error (RMSE) from a 10-fold cross-validation.

RESULTS

The mean ± SD F(inf) across all communities and seasons was 0.62 ± 0.21, and community-specific means ranged from 0.47 ± 0.15 in Winston-Salem, North Carolina, to 0.82 ± 0.14 in New York, New York. F(inf) was generally greater during the warm (> 18°C) season. Central air conditioning (AC) use, frequency of AC use, and window opening frequency were the most important predictors during the warm season; outdoor temperature and forced-air heat were the best cold-season predictors. The models predicted 60% of the variance in 2-week F(inf), with an RMSE of 0.13.

CONCLUSIONS

We developed intuitive models that can predict F(inf) using easily obtained variables. Using these models, MESA Air will be the first large epidemiologic study to incorporate variation in residential F(inf) into an exposure assessment.

Association of long-term exposure to community noise and traffic-related air pollution with coronary heart disease mortality

In metropolitan areas, road traffic is a major contributor to ambient air pollution and the dominant source of community noise. The authors investigated the independent and joint influences of community noise and traffic-related air pollution on risk of coronary heart disease (CHD) mortality in a population-based cohort study with a 5-year exposure period (January 1994-December 1998) and a 4-year follow-up period (January 1999-December 2002). Individuals who were 45-85 years of age and resided in metropolitan Vancouver, Canada, during the exposure period and did not have known CHD at baseline were included (n = 445,868). Individual exposures to community noise and traffic-related air pollutants, including black carbon, particulate matter less than or equal to 2.5 μm in aerodynamic diameter, nitrogen dioxide, and nitric oxide, were estimated at each person's residence using a noise prediction model and land-use regression models, respectively. CHD deaths were identified from the provincial death registration database. After adjustment for potential confounders, including traffic-related air pollutants or noise, elevations in noise and black carbon equal to the interquartile ranges were associated with 6% (95% confidence interval: 1, 11) and 4% (95% confidence interval: 1, 8) increases, respectively, in CHD mortality. Subjects in the highest noise decile had a 22% (95% confidence interval: 4, 43) increase in CHD mortality compared with persons in the lowest decile. These findings suggest that there are independent effects of traffic-related noise and air pollution on CHD mortality.

Changes in residential proximity to road traffic and the risk of death from coronary heart disease

BACKGROUND

Residential proximity to road traffic is associated with increased coronary heart disease (CHD) morbidity and mortality. It is unknown, however, whether changes in residential proximity to traffic could alter the risk of CHD mortality.

METHODS

We used a population-based cohort study with a 5-year exposure period and a 4-year follow-up period to explore the association between changes in residential proximity to road traffic and the risk of CHD mortality. The cohort comprised all residents aged 45-85 years who resided in metropolitan Vancouver during the exposure period and without known CHD at baseline (n = 450,283). Residential proximity to traffic was estimated using a geographic information system. CHD deaths during the follow-up period were identified using provincial death registration database. The data were analyzed using logistic regression.

RESULTS

Compared with the subjects consistently living away from road traffic (>150 m from a highway or >50 m from a major road) during the 9-year study period, those consistently living close to traffic (<or=150 m from a highway or <or=50 m from a major road) had the greatest risk of CHD mortality (relative risk [RR] = 1.29 [95% confidence interval = 1.18-1.41]). By comparison, those who moved closer to traffic during the exposure period had less increased risk than those who were consistently exposed (1.20 [1.00-1.43]), and those who moved away from traffic had even less increase in the risk (1.14 [0.95-1.37]). All analyses were adjusted for baseline age, sex, pre-existing comorbidities (diabetes, chronic obstructive pulmonary disease, hypertensive heart disease), and neighborhood socioeconomic status.

CONCLUSIONS

Living close to major roadways was associated with increased risk of coronary mortality, whereas moving away from major roadways was associated with decreased risk.

Exploring variation and predictors of residential fine particulate matter infiltration

Although individuals spend the majority of their time indoors, most epidemiological studies estimate personal air pollution exposures based on outdoor levels. This almost certainly results in exposure misclassification as pollutant infiltration varies between homes. However, it is often not possible to collect detailed measures of infiltration for individual homes in large-scale epidemiological studies and thus there is currently a need to develop models that can be used to predict these values. To address this need, we examined infiltration of fine particulate matter (PM(2.5)) and identified determinants of infiltration for 46 residential homes in Toronto, Canada. Infiltration was estimated using the indoor/outdoor sulphur ratio and information on hypothesized predictors of infiltration were collected using questionnaires and publicly available databases. Multiple linear regression was used to develop the models. Mean infiltration was 0.52 ± 0.21 with no significant difference across heating and non-heating seasons. Predictors of infiltration were air exchange, presence of central air conditioning, and forced air heating. These variables accounted for 38% of the variability in infiltration. Without air exchange, the model accounted for 26% of the variability. Effective modelling of infiltration in individual homes remains difficult, although key variables such as use of central air conditioning show potential as an easily attainable indicator of infiltration.

The relationship between averaged sulfate exposures and concentrations: results from exposure assessment panel studies in four U.S. cities

This analysis examines differences between measured ambient indoor, and personal sulfate concentrations across cities, seasons, and individuals to elucidate how these differences may impact PM2.5 exposure measurement error. Data were analyzed from four panel studies conducted in Atlanta, Baltimore, Boston, and Steubenville (OH). Among the study locations, 1912 person-days of personal sulfate data were collected over 396 days involving 245 individual sampling sessions. Long-term differences in ambient and personal levels averaged over time are examined. Differences between averaged ambient and personal sulfate among and within cities were observed, driven by between subject and city differences in sulfate infiltration, F(inf), from outdoors to indoors. Neglecting this source of variability in associations may introduce bias in studies examining long-term exposures and chronic health. Indoor sulfate was highly correlated with and similar in magnitude to personal sulfate, suggesting indoor PM monitoring may be another means of characterizing true exposure variability.

Personal PM10 exposure in asthmatic adults in Padova, Italy: seasonal variability and factors affecting individual concentrations of particulate matter

Personal exposure to PM(10) measured in different seasons in a sample of asthmatic subjects living in Padova (Northern Italy) was compared with simultaneously measured outdoor PM(10) concentrations. The specific contribution of ambient PM(10) and other factors to individual exposure was evaluated in one of the areas of Europe with the worst air pollution. Thirty-one asthmatic subjects (21 non-smokers and 10 smokers) carried personal PM(10) monitors for six 24-hr sessions, in different seasons of the year. Concomitant daily 24-hr ambient PM(10) concentrations were measured by air quality monitoring networks. A multivariate analysis was performed to identify factors explaining personal exposure to PM(10), using a random effect model. The analysis on the 31 subjects referred to a total of 155 observations. The mean personal PM(10) exposure was higher (range 79.3-126.1microg/m(3)) than the outdoor concentrations (range 37.3-85.4microg/m(3)) in all seasons; and personal exposures varied less than outdoor PM(10) levels from one season to another. Smokers had significantly higher personal PM(10) concentrations than non-smokers (127.99 vs 78.8microg/m(3); T=-5.70; p<0.001). Moderate correlations emerged between outdoor and personal PM(10) concentrations. The correlation improved after excluding subjects exposed to active or passive smoking (median Pearson's R 0.41 vs 0.26). Considering all the subjects, smoking was the main factor affecting personal exposure, contributing to 41% of the variability. Outdoor PM(10) concentrations (25%), temperature (12%) and season (15%) also contributed to personal PM(10) exposure. Outdoor PM(10) (46%), temperature (20%), season (19%) and time spent indoors (6%) were significantly associated with personal exposure in non-smokers. We concluded that it is crucial to perform personal monitoring and to evaluate the complexity of factors that contribute to individual PM exposure. While tobacco smoke was the primary source of PM(10) in all subjects, the contribution of ambient components was particularly relevant for the personal exposure levels of our non-smokers living in a highly-polluted environment.