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Background: Modern mobility can include mixtures of transportation options, which potentially impact pollution exposures and health.Objectives: To investigate variations in traffic-related air and ...

BACKGROUND There is considerable evidence that exposure to fine particulate matter (PM2.5) air pollution is associated with a variety of adverse health outcomes. However, true exposure-outcome associations are hampered by measurement issues, including compliance and exposure misclassification. OBJECTIVE This paper describes the use of the design-feedback iterative cycle to improve the design and usability of a new portable PM2.5 monitor for use in an epidemiologic study of personal air pollution measures. METHODS In total, 10 adults carried on their person a prefabricated PM2.5 monitor for 1 week over 3 waves of the iterative cycle. At the end of each wave, they participated in a 30-minute moderated focus group and completed 2 validated questionnaires on usability and views on research. The topics addressed included positives and negatives of the monitor, charging and battery life, desired features, and changes to the monitor from each previous wave. They also completed a log to record device wear time each day. The log also provided space to record any issues that may have arisen with the device or for general comments during the week of collection. RESULTS The major focus group topics included device size, noise, battery and charge time, and method for carrying the device. These topics formed the basis of iterative design changes; by the final cycle, the device was reasonably smaller, quieter, held a longer charge, and was more convenient to carry. System usability scores improved systematically across each wave (median scores of 50-66 on a 100-point scale), as did median daily wear time (approximately 749-789 minutes). CONCLUSIONS Both qualitative and quantitative measures showed an improvement in device usability over the 3 waves. This study demonstrates how the design-feedback iterative cycle can be used to improve the usability of devices manufactured for use in large epidemiologic studies on personal air pollution exposures.

BackgroundThere is considerable evidence that exposure to fine particulate matter (PM2.5) air pollution is associated with a variety of adverse health outcomes. However, true exposure-outcome associations are hampered by measurement issues, including compliance and exposure misclassification. ObjectiveThis paper describes the use of the design-feedback iterative cycle to improve the design and usability of a new portable PM2.5 monitor for use in an epidemiologic study of personal air pollution measures. MethodsIn total, 10 adults carried on their person a prefabricated PM2.5 monitor for 1 week over 3 waves of the iterative cycle. At the end of each wave, they participated in a 30-minute moderated focus group and completed 2 validated questionnaires on usability and views on research. The topics addressed included positives and negatives of the monitor, charging and battery life, desired features, and changes to the monitor from each previous wave. They also completed a log to record device wear time each day. The log also provided space to record any issues that may have arisen with the device or for general comments during the week of collection. ResultsThe major focus group topics included device size, noise, battery and charge time, and method for carrying the device. These topics formed the basis of iterative design changes; by the final cycle, the device was reasonably smaller, quieter, held a longer charge, and was more convenient to carry. System usability scores improved systematically across each wave (median scores of 50-66 on a 100-point scale), as did median daily wear time (approximately 749-789 minutes). ConclusionsBoth qualitative and quantitative measures showed an improvement in device usability over the 3 waves. This study demonstrates how the design-feedback iterative cycle can be used to improve the usability of devices manufactured for use in large epidemiologic studies on personal air pollution exposures.

Many studies have reported associations between daily particles less than 2.5 µm in aerodynamic diameter (PM2.5) and deaths, but they have been associational studies that did not use formal causal modeling approaches. On the basis of a potential outcome approach, we used 2 causal modeling methods with different assumptions and strengths to address whether there was a causal association between daily PM2.5 and deaths in Boston, Massachusetts (2004-2009). We used an instrumental variable approach, including back trajectories as instruments for variations in PM2.5 uncorrelated with other predictors of death. We also used propensity score as an alternative causal modeling analysis. The former protects against confounding by measured and unmeasured confounders and is based on the assumption of a valid instrument. The latter protects against confounding by all measured covariates, provides valid estimates in the case of effect modification, and is based on the assumption of no unmeasured confounders. We found a causal association of PM2.5 with mortality, with a 0.53% (95% confidence interval: 0.09, 0.97) and a 0.50% (95% confidence interval: 0.20, 0.80) increase in daily deaths using the instrumental variable and the propensity score, respectively. We failed to reject the null association with exposure after the deaths (P =0.93). Given these results, prior studies, and extensive toxicological support, the association between PM2.5 and deaths is almost certainly causal.

The absolute principal component scores (APCS) model was applied to on-road, background-adjusted measurements of NOx, CO, CO2, black carbon (BC), and particle number (PN) obtained from a continuously moving platform deployed during 16 afternoon sampling periods in Los Angeles, CA. High-emitter biasing observations were separated from the vehicle fleet population based on a sensitivity analysis of different a priori screening values of the ratio of CO to CO2. A BC/PN-rich feature consistent with heavy-duty vehicle exhaust, and a separate CO/CO2-rich feature consistent with light-duty vehicle exhaust, described 66% of the variance of the observations. We used bootstrapped APCS model predictions to estimate area-wide, average fuel-based emission factors and their respective 95% confidence limits. If no screening was used, we obtained incongruous average emission factors relative to recent field studies for NOx, CO, BC and PN (5.1, 2.0, 0.13 g/kg, and 1.0 × 10^15 particles/kg for heavy-duty vehicles, and 2.0, 111, 0.023 g/kg, and 0.09 × 10^15 particles/kg for light-duty vehicles, respectively). However, if reasonable a priori screening values were applied, which differentiate measurements reflecting high-emitter outliers, average emission factors for NOx, CO, BC and PN (12.8, 4.0, 0.37 g/kg, and 2.6 × 10^15 particles/kg for heavy-duty vehicles, and 1.5, 40.9, 0.004 g/kg, and 0.08 × 10^15 particles/kg for light-duty vehicles, respectively) were consistent with previous estimates based on remote sensing, vehicle chase studies, and recent dynamometer tests.

Background: The association between exposure to particle mass and mortality is well established; however, there are still uncertainties as to whether certain chemical components are more harmful than others. Moreover, understanding the health effects associated with exposure to pollutant mixtures may lead to new regulatory strategies. Objectives: Recently we have introduced a new approach that uses cluster analysis to identify distinct air pollutant mixtures by classifying days into groups based on their pollutant concentration profiles. In Boston during the years 1999–2009, we examined whether the effect of PM2.5 on total mortality differed by distinct pollution mixtures. Methods: We applied a time series analysis to examine the association of PM2.5 with daily deaths. Subsequently, we included an interaction term between PM2.5 and the pollution mixture clusters. Results: We found a 1.1% increase (95% CI: 0.0, 2.2) and 2.3% increase (95% CI: 0.9–3.7) in total mortality for a 10 μg/m3 increase in the same day and the two-day average of PM2.5 respectively. The association is larger in a cluster characterized by high concentrations of the elements related to primary traffic pollution and oil combustion emissions with a 3.7% increase (95% CI: 0.4, 7.1) in total mortality, per 10 μg/m3 increase in the same day average of PM2.5. Conclusions: Our study shows a higher association of PM2.5 on total mortality during days with a strong contribution of traffic emissions, and fuel oil combustion. Our proposed method to create multi-pollutant profiles is robust, and provides a promising tool to identify multi-pollutant mixtures which can be linked to the health effects. Keywords: Total mortality, Fine particulate air pollution, Pollutant mixtures

Local trends in ozone concentration may differ by meteorological conditions. Furthermore, the trends occurring at the extremes of the Ozone distribution are often not reported even though these may be very different than the trend observed at the mean or median and they may be more relevant to health outcomes. Classify days of observation over a 16-year period into broad categories that capture salient daily local weather characteristics. Determine the rate of change in mean and median O3 concentrations within these different categories to assess how concentration trends are impacted by daily weather. Further examine if trends vary for observations in the extremes of the O3 distribution. We used k-means clustering to categorize days of observation based on the maximum daily temperature, standard deviation of daily temperature, mean daily ground level wind speed, mean daily water vapor pressure and mean daily sea-level barometric pressure. The five cluster solution was determined to be the appropriate one based on cluster diagnostics and cluster interpretability. Trends in cluster frequency and pollution trends within clusters were modeled using Poisson regression with penalized splines as well as quantile regression. There were five characteristic groupings identified. The frequency of days with large standard deviations in hourly temperature decreased over the observation period, whereas the frequency of warmer days with smaller deviations in temperature increased. O3 trends were significantly different within the different weather groupings. Furthermore, the rate of O3 change for the 95th percentile and 5th percentile was significantly different than the rate of change of the median for several of the weather categories.We found that O3 trends vary between different characteristic local weather patterns. O3 trends were significantly different between the different weather groupings suggesting an important interaction between changes in prevailing weather conditions and O3 concentration.

Background: Modern urban travel includes mixtures of transit options, which potentially impact individual pollution exposures and health. This study aims to investigate variations in traffic-related air pollution and noise levels experienced in traffic in Chengdu, China. Methods: Real-time PM2.5, black carbon (BC), and noise levels were measured for four transportation modes (car, bus, subway, and shared bike) on scripted routes in three types of neighborhoods (urban core, developing neighborhood, and suburb). Each mode of transportation in each neighborhood was sampled five times in summer and winter, respectively. After quality control, mixed effect models were built for the three pollutants separately. Results: Air pollutants had much higher concentrations in winter. Urban Core had the highest PM2.5 and BC concentrations across seasons compared to the other neighborhoods. The mixed effect model indicated that car commutes were associated with lower PM2.5 (&minus, 34.4 &mu, g/m3, 95% CI: &minus, 47.5, &minus, 21.3), BC (&minus, 2016.4 ng/m3, 3383.8, &minus, 648.6), and noise (&minus, 9.3 dBA, 10.5, &minus, 8.0) levels compared with other modes, subway commutes had lower PM2.5 (&minus, 11.9 &mu, 95% CI: 47.5, &minus, 21.3), but higher BC (2349.6 ng/m3, 95% CI: 978.1, 3722.1) and noise (3.0 dBA, 95% CI: 1.7, 4.3) levels than the other three modes of transportation. Conclusion: Personal exposure to air pollution and noise vary by season, neighborhood, and transportation modes. Exposure models accounting for environmental, meteorological, and behavioral factors, and duration of mixed mode commuting may be useful for health studies of urban traffic microenvironments.

Background: The importance of describing, understanding and regulating multi-pollutant mixtures has been highlighted by the US National Academy of Science and the Environmental Protection Agency. Furthering our understanding of the health effects associated with exposure to mixtures of pollutants will lead to the development of new multi-pollutant National Air Quality Standards. Objectives: Introduce a framework within which diagnostic methods that are based on our understanding of air pollution mixtures are used to validate the distinct air pollutant mixtures identified using cluster analysis. Methods: Six years of daily gaseous and particulate air pollution data collected in Boston, MA were classified solely on their concentration profiles. Classification was performed using k-means partitioning and hierarchical clustering. Diagnostic strategies were developed to identify the most optimal clustering. Results: The optimal solution used k-means analysis and contained five distinct groups of days. Pollutant concentrations and elemental ratios were computed in order to characterize the differences between clusters. Time-series regression confirmed that the groups differed in their chemical compositions. The mean values of meteorological parameters were estimated for each group and air mass origin between clusters was examined using back-trajectory analysis. This allowed us to link the distinct physico-chemical characteristics of each cluster to characteristic weather patterns and show that different clusters were associated with distinct air mass origins. Conclusions: This analysis yielded a solution that was robust to outlier points and interpretable based on chemical, physical and meteorological characteristics. This novel method provides an exciting tool with which to identify and further investigate multi-pollutant mixtures and link them directly to health effects studies. Keywords: Multipollutant mixtures, Cluster analysis, Effect modification, Air pollution profiles, k-Means, Hierarchical clustering

BACKGROUND: Differences in traffic-related air pollution (TRAP) composition may cause heterogeneity in associations between air pollution exposure and cardiovascular health outcomes. Clustering multi-pollutant measurements allows investigation of effect modification by TRAP profiles. METHODS: We measured TRAP components with fixed-site and on-road instruments for two two-week periods in Baltimore, Maryland. We created representative TRAP profiles for cold and warm seasons using predictive k-means clustering. We predicted cluster membership for 1005 participants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution with follow-up between 2000 and 2012. We estimated cluster-specific relationships between coronary artery calcification (CAC) progression and long-term exposure to fine particulate matter (PM(2.5)) and oxides of nitrogen (NO(X)). RESULTS: We identified two clusters in the cold season, notable for higher ratios of gases and ultrafine particles, respectively. A 5 μg/m(3) difference in PM(2.5) was associated with 17.0 (95% Confidence Interval [CI]: 7.2, 26.7) and 42.6 (95% CI: 25.7, 59.4) Agatston units/year CAC progression among participants in clusters 1 and 2, respectively (effect modification p=0.006). A 40ppb difference in NO(X) was associated with 22.2 (95% CI: 7.7, 36.7) and 41.9 (95% CI: 23.7, 60.2) Agatston units/year CAC progression in clusters 1 and 2, respectively (p=0.08). Similar trends occurred using clusters identified from warm season measurements. Clusters correlated highly with baseline pollution level. CONCLUSIONS: Clustering TRAP measurements identified spatial differences in composition. We found evidence of greater CAC progression rates per unit PM(2.5) exposures among people living in areas characterized by high ratios of ultrafine particle counts relative to NO(X) concentrations.

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NO(X)) and ozone (O(3)) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NO(X) and O(3), with LOOCV R(2)s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NO(X) had LOOCV R(2)s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O(3). Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NO(X) and O(3) and are a better source of data for these models than 2-week passive badge data.

Background: Many time series studies have reported associations between daily PM2.5 and daily deaths, but they have been associational studies that did not use formal causal modeling. Methods: Base...

Fine particulate (PM2.5) air pollution has been consistently linked to survival, but reported effect estimates are geographically heterogeneous. Exposure to different types of particle mixtures may explain some of this variation.We used k-means cluster analyses to identify cities with similar pollution profiles, (ie, PM2.5 composition) across the United States. We examined the impact of PM2.5 on survival, and its variation across clusters of cities with similar PM2.5 composition, among Medicare enrollees in 81 US cities (2000-2010). We used time-varying annual PM2.5 averages, measured at ambient central monitoring sites, as the exposure of interest. We ran by-city Cox models, adjusting for individual data on previous cardiopulmonary-related hospitalizations and stratifying by follow-up time, age, gender, and race. This eliminates confounding by factors varying across cities and long-term trends, focusing on year-to-year variations of air pollution around its city-specific mean and trend. We then pooled the city-specific effects using a random effects meta-regression. In this second stage, we also assessed effect modification by cluster membership and estimated cluster-specific PM2.5 effects.We followed more than 19 million subjects and observed more than 6 million deaths. We found a harmful impact of annual PM2.5 concentrations on survival (hazard ratio = 1.11 [95% confidence interval = 1.01, 1.23] per 10 μg/m). This effect was modified by particulate composition, with higher effects observed in clusters containing high concentrations of nickel, vanadium, and sulfate. For instance, our highest effect estimate was observed in cities with harbors in the Northwest, characterized by high nickel, vanadium, and elemental carbon concentrations (1.9 [1.1, 3.3]). We observed null or negative associations in clusters with high oceanic and crustal particles.To the best of our knowledge, this is the first study to examine the association between PM2.5 composition and survival. Our findings indicate that long-term exposure to fuel oil combustion and power plant emissions have the highest impact on survival.

Objective Finely resolved PM2.5 exposure measurements at the level of individual participants or over a targeted geographic area can be challenging due to the cost, size and weight of the monitoring equipment. We propose re-purposing the low-cost, portable and lightweight Shinyei PPD42NS particle counter as a particle counting device. Previous field deployment of this sensor suggests that it captures trends in ambient PM2.5 concentrations, but important characteristics of the sensor response have yet to be determined. Laboratory testing was undertaken in order to characterize performance. Methods The Shinyei sensors, in-line with a TSI Aerosol Particle Sizer (APS) model 3321, tracked particle decay within an aerosol exposure chamber. Test atmospheres were composed of monodisperse polystyrene spheres with diameters of 0.75, 1, 2 3 and 6 um as well as a polydisperse atmosphere of ASHRAE test dust #1. Results Two-minute block averages of the sensor response provide a measurement with low random error, within sensor, for particles in the 0.75–6μm range with a limit of detection of 1 μg/m3. The response slope of the sensors is idiomatic, and each sensor requires a unique response curve. A linear model captures the sensor response for concentrations below 50 μg/m3 and for concentrations above 50 μg/m3 a non-linear function captures the response and saturates at 800 μg/m3. The Limit of Detection (LOD) is 1 μg/m3. The response time is on the order of minutes, making it appropriate for tracking short-term changes in concentration. Conclusions When paired with prior evaluation, these sensors are appropriate for use as ambient particle counters for low and medium concentrations of respirable particles (< 100 ug/m3). Multiple sensors deployed over a spatial grid would provide valuable spatio-temporal variability in PM2.5 and could be used to validate exposure models. When paired with GPS tracking, these devices have the potential to provide time and space resolved exposure measurements for a large number of participants, thus increasing the power of a study.

OBJECTIVE: Finely resolved PM2.5 exposure measurements at the level of individual participants can be challenging due to the cost, size and weight of the monitoring equipment. We propose re-purposi...

Background: Studies of ambient air pollution and microvascular function have shown conflicting results. Aim: We investigated whether the association between fine particle mass with diameter ≤2.5μm (PM 2.5 ) and microvascular function varies according to air pollution characteristics. Methods: We assessed baseline pulse amplitude and the ratio of fingertip pulse wave amplitude pre- and post- brachial artery occlusion (PAT ratio) in 1,365 participants of the Framingham Offspring and Third Generation Cohorts. We used K-means clustering to categorize mixtures of air pollutants into 5 distinct clusters of days with similar multi-pollutant profiles using elemental data and gases. We assessed the interaction between preceding day PM 2.5 and cluster adjusting for season, meteorology and covariates. Results: We observed differences in associations between PM 2.5 and baseline pulse amplitude by cluster (P=0.02 for interaction). On days with either low overall PM 2.5 levels but dominated by road and traffic dust and a high proportion of ultrafine particles (cluster 1) or high contributions of oil and wood combustion (cluster 5), higher PM 2.5 was associated with higher baseline pulse amplitude (see Figure). In contrast, on days with either a strong contribution of crustal materials, a mixture of fine and ultrafine particles, or agglomerated particles from regional sources (cluster 2, 3, and 4 respectively), PM 2.5 was not significantly associated with baseline pulse amplitude. We observed similar, non-significant associations between PM 2.5 and PAT ratio across the air pollution mixture clusters (P=0.14 for interaction). Conclusions: Air pollution mixtures with contributions from heating oil and wood combustion or traffic and road dust, both having high proportions of ultra-fine particles, were associated with altered microvascular tone. Our findings suggest that specific mixtures of particulate pollution may have distinct vascular consequences and support further studies of air pollution clusters to inform public policy. .