Assessing the relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore, MD

Effects of in-utero personal exposure to PM2.5 sources and components on birthweight

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-h personal PM2.5 exposure monitoring sub-study. The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions evaluated the association between personal PM2.5 sources/components and birthweight, adjusting for gestational age, maternal age, race, infant sex, parity, diabetes status, temperature, maternal education, and smoking history. Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3295.8 g (484.1) and mean PM2.5 exposure was 21.3 (14.4) µg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2 g decrease in birthweight (95% CI - 197.7, - 0.6), and aged sea salt was associated with a 70.1 g decrease in birthweight (95% CI - 141.7, 1.4). Magnesium, sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass. This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

Alzheimer's Related Neurodegeneration Mediates Air Pollution Effects on Medial Temporal Lobe Atrophy

Exposure to ambient air pollution, especially particulate matter with aerodynamic diameter <2.5 μm (PM2.5) and nitrogen dioxide (NO2), are environmental risk factors for Alzheimer's disease and related dementia. The medial temporal lobe (MTL) is an important brain region subserving episodic memory that atrophies with age, during the Alzheimer's disease continuum, and is vulnerable to the effects of cerebrovascular disease. Despite the importance of air pollution it is unclear whether exposure leads to atrophy of the MTL and by what pathways. Here we conducted a longitudinal study examining associations between ambient air pollution exposure and MTL atrophy and whether putative air pollution exposure effects resembled Alzheimer's disease-related neurodegeneration or cerebrovascular disease-related neurodegeneration. Participants included older women (n = 627; aged 71-87) who underwent two structural brain MRI scans (MRI-1: 2005-6; MRI-2: 2009-10) as part of the Women's Health Initiative Memory Study of Magnetic Resonance Imaging. Regionalized universal kriging was used to estimate annual concentrations of PM2.5 and NO2 at residential locations aggregated to 3-year averages prior to MRI-1. The outcome was 5-year standardized change in MTL volumes. Mediators included voxel-based MRI measures of the spatial pattern of neurodegeneration of Alzheimer's disease (Alzheimer's disease pattern similarity scores [AD-PS]) and whole-brain white matter small-vessel ischemic disease (WM-SVID) volume as a proxy of global cerebrovascular damage. Structural equation models were constructed to examine whether the associations between exposures with MTL atrophy were mediated by the initial level or concurrent change in AD-PS score or WM-SVID while adjusting for sociodemographic, lifestyle, clinical characteristics, and intracranial volume. Living in locations with higher PM2.5 (per interquartile range [IQR]=3.17μg/m3) or NO2 (per IQR=6.63ppb) was associated with greater MTL atrophy (βPM2.5 = -0.29, 95% confidence interval [CI]=[-0.41,-0.18]; βNO2 =-0.12, 95%CI=[-0.23,-0.02]). Greater PM2.5 was associated with larger increases in AD-PS (βPM2.5 = 0.23, 95%CI=[0.12,0.33]) over time, which partially mediated associations with MTL atrophy (indirect effect= -0.10; 95%CI=[-0.15, -0.05]), explaining approximately 32% of the total effect. NO2 was positively associated with AD-PS at MRI-1 (βNO2=0.13, 95%CI=[0.03,0.24]), which partially mediated the association with MTL atrophy (indirect effect= -0.01, 95% CI=[-0.03,-0.001]). Global WM-SVID at MRI-1 or concurrent change were not significant mediators between exposures and MTL atrophy. Findings support the mediating role of Alzheimer's disease-related neurodegeneration contributing to MTL atrophy associated with late-life exposures to air pollutants. Alzheimer's disease-related neurodegeneration only partially explained associations between exposure and MTL atrophy suggesting the role of multiple neuropathological processes underlying air pollution neurotoxicity on brain aging.

Effects of In-Utero Personal Exposure to PM2.5 Sources and Components on Birthweight

Background

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-hour personal PM2.5 exposure monitoring sub-study.

Methods

The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions were used to evaluate the association between personal PM2.5 sources and birthweight. Additionally, high-loading components were evaluated with birthweight individually and in models further adjusted for PM2.5 mass.

Results

Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3,295.8g (484.1) and mean PM2.5 exposure was 21.3 (14.4) μg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2g decrease in birthweight (95% CI: -197.7, -0.6), while aged sea salt was associated with lower birthweight (β =-70.1; 95% CI: -141.7, 1.4). Magnesium sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass.

Conclusions

This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

Partitioning indoor-generated and outdoor-generated PM2.5 from real-time residential measurements in urban and peri-urban Beijing

Limited number of projects have attempted to partition and quantify indoor- and outdoor-generated PM_2.5 (PM_2.5^ig and PM_2.5^og) where strong indoor sources (e.g., solid fuel, tobacco smoke, or kerosene) exist. This study aimed to apply and refine a previous recursive model used to derive infiltration efficiency (F_inf) to additionally partition pollution concentrations into indoor and outdoor origins within residences challenged by elevated ambient and indoor combustion-related sources. During the winter of 2016 and summer of 2017 we collected residential measurements in 72 homes in urban and peri-urban Beijing, 12 of which had additional paired residential outdoor measurements during the summer season. Local ambient measurements were collected throughout. We then compared the calculated PM_2.5^ig and using (i) outdoor and (ii) ambient measurements as model inputs. The results from outdoor and ambient measurements were not significantly different, which suggests that ambient measurements can be used as a model input for pollution origin partitioning when paired outdoor measurements are not available. From the results calculated using ambient measurements, the mean percentage contribution of indoor-generated PM_2.5 was 19 % (σ = 22 %), and 7 % (11 %) of the total indoor PM_2.5 for peri-urban and urban homes respectively during the winter; and 18 % (18 %) and 6 % (10 %) of the total indoor PM_2.5 during the summer. Partitioning pollution into PM_2.5^ig and PM_2.5^og is important to allow investigation of distinct associations between health outcomes and particulate mixes, often with different physiochemical composition and toxicity. It will also inform targeted interventions that impact indoor and outdoor sources of pollution (e.g., domestic fuel switching vs. power generation), which are typically radically different in design and implementation.

In-utero personal exposure to PM2.5 impacted by indoor and outdoor sources and birthweight in the MADRES cohort

BACKGROUND

In-utero exposure to outdoor particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) is linked with low birthweight. However, previous results are mixed, likely due to measurement error introduced by estimating personal exposure from ambient data. This study investigated the effect of total personal PM_2.5 exposure on birthweight and whether it differed when it was more heavily impacted by sources of indoor vs outdoor origin in the MADRES cohort study.

METHODS

Personal PM_2.5 exposure was measured in 205 pregnant women in the 3rd trimester using 48 h integrated, filter-based sampling. Linear regression was used to test the association between personal PM_2.5 exposure and birthweight, adjusting for key covariates. Interactions of PM_2.5 with variables representing indoor sources of PM_2.5, home ventilation, or time spent indoors tested whether the effect of total PM_2.5 on birthweight varied when it was more impacted by sources of indoor vs outdoor origin.

RESULTS

In a sample of largely Hispanic (81%) pregnant women, total personal PM_2.5 was not significantly associated with birthweight (β = 38.6 per 1SD increase in PM_2.5; 95% CI:-21.1, 98.2). This association however, differed by home type (single family home: 156.9 (26.9, 287.0), 2-4 attached units:-16.6 (-111.9, 78.7), 5+ units:-62.6 (-184.9, 59.6), missing: 145.4 (-4.1, 294.9), interaction p = 0.028) and by household air conditioner use (none of the time: -27.6 (-101.5, 46.3) vs. some of the time: 139.9 (42.9, 237.0), interaction p = 0.008) Additionally, the effect of personal PM_2.5 on birthweight varied by time spent indoors (none or little of the time: - 45.1 (-208.3, 118.1) vs. most or all of the time: 57.1 (-7.3, 121.6), interaction p = 0.255).

CONCLUSIONS

While no significant association between total personal PM_2.5 exposure and birthweight was found, there was evidence that multi-unit housing (vs. single-family homes), candle and/or incense smoke, and greater outdoor source contributions to personal PM_2.5 were more strongly associated with lower birthweight.

Joint association between ambient air pollutant mixture and pediatric asthma exacerbations

Exposure to air pollutants is known to exacerbate asthma, with prior studies focused on associations between single pollutant exposure and asthma exacerbations. As air pollutants often exist as a complex mixture, there is a gap in understanding the association between complex air pollutant mixtures and asthma exacerbations. We evaluated the association between the air pollutant mixture (52 pollutants) and pediatric asthma exacerbations.

Method

This study focused on children (age ≤ 19 years) who lived in Douglas County, Nebraska, during 2016-2019. A seasonal-scale joint association between the outdoor air pollutant mixture adjusting for potential confounders (temperature, precipitation, wind speed, and wind direction) in relation to pediatric asthma exacerbation-related emergency department (ED) visits was evaluated using the generalized weighted quantile sum (qWQS) regression with repeated holdout validation.

Results

We observed associations between air pollutant mixture and pediatric asthma exacerbations during spring (lagged by 5 days), summer (lag 0-5 days), and fall (lag 1-3 days) seasons. The estimate of the joint outdoor air pollutant mixture effect was higher during the summer season (adjusted-β_WQS = 1.11, 95% confidence interval [CI]: 0.66, 1.55), followed by spring (adjusted-β_WQS = 0.40, 95% CI: 0.16, 0.62) and fall (adjusted-β_WQS = 0.20, 95% CI: 0.06, 0.33) seasons. Among the air pollutants, PM_2.5, pollen, and mold contributed higher weight to the air pollutant mixture.

Conclusion

There were associations between outdoor air pollutant mixture and pediatric asthma exacerbations during the spring, summer, and fall seasons. Among the 52 outdoor air pollutant metrics investigated, PM_2.5, pollen (sycamore, grass, cedar), and mold (Helminthosporium, Peronospora, and Erysiphe) contributed the highest weight to the air pollutant mixture.

Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM2.5 exposure

BACKGROUND

Due to the adverse health effects of air pollution, researchers have advocated for personal exposure measurements whereby individuals carry portable monitors in order to better characterise and understand the sources of people's pollution exposure.

OBJECTIVES

The aim of this systematic review is to assess the differences in the magnitude and sources of personal PM2.5 exposures experienced between countries at contrasting levels of income.

METHODS

This review summarised studies that measured participants personal exposure by carrying a PM2.5 monitor throughout their typical day. Personal PM2.5 exposures were summarised to indicate the distribution of exposures measured within each country income category (based on low (LIC), lower-middle (LMIC), upper-middle (UMIC), and high (HIC) income countries) and between different groups (i.e. gender, age, urban or rural residents).

RESULTS

From the 2259 search results, there were 140 studies that met our criteria. Overall, personal PM2.5 exposures in HICs were lower compared to other countries, with UMICs exposures being slightly lower than exposures measured in LMICs or LICs. 34% of measured groups in HICs reported below the ambient World Health Organisation 24-h PM2.5 guideline of 15 μg/m3, compared to only 1% of UMICs and 0% of LMICs and LICs. There was no difference between rural and urban participant exposures in HICs, but there were noticeably higher exposures recorded in rural areas compared to urban areas in non-HICs, due to significant household sources of PM2.5 in rural locations. In HICs, studies reported that secondhand smoke, ambient pollution infiltrating indoors, and traffic emissions were the dominant contributors to personal exposures. While, in non-HICs, household cooking and heating with biomass and coal were reported as the most important sources.

CONCLUSION

This review revealed a growing literature of personal PM2.5 exposure studies, which highlighted a large variability in exposures recorded and severe inequalities in geographical and social population subgroups.

Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network

As part of our low-cost sensor network, we colocated multipollutant monitors containing sensors for particulate matter, carbon monoxide, ozone, nitrogen dioxide, and nitrogen monoxide at a reference field site in Baltimore, MD, for 1 year. The first 6 months were used for training multiple regression models, and the second 6 months were used to evaluate the models. The models produced accurate hourly concentrations for all sensors except ozone, which likely requires nonlinear methods to capture peak summer concentrations. The models for all five pollutants produced high Pearson correlation coefficients (r > 0.85), and the hourly averaged calibrated sensor and reference concentrations from the evaluation period were within 3-12%. Each sensor required a distinct set of predictors to achieve the lowest possible root-mean-square error (RMSE). All five sensors responded to environmental factors, and three sensors exhibited cross-sensitives to another air pollutant. We compared the RMSE from models (NO₂, O₃, and NO) that used colocated regulatory instruments and colocated sensors as predictors to address the cross-sensitivities to another gas, and the corresponding model RMSEs for the three gas models were all within 0.5 ppb. This indicates that low-cost sensor networks can yield useable data if the monitoring package is designed to comeasure key predictors. This is key for the utilization of low-cost sensors by diverse audiences since this does not require continual access to regulatory grade instruments.

MobiliSense cohort study protocol: do air pollution and noise exposure related to transport behaviour have short-term and longer-term health effects in Paris, France?

INTRODUCTION

MobiliSense explores effects of air pollution and noise related to personal transport habits on respiratory and cardiovascular health. Its objectives are to quantify the contribution of personal transport/mobility to air pollution and noise exposures of individuals; to compare exposures in different transport modes; and to investigate whether total and transport-related personal exposures are associated with short-term and longer-term changes in respiratory and cardiovascular health.

METHODS AND ANALYSIS

MobiliSense uses sensors of location, behaviour, environmental nuisances and health in 290 census-sampled participants followed-up after 1/2 years with an identical sensor-based strategy. It addresses knowledge gaps by: (1) assessing transport behaviour over 6 days with GPS receivers and GPS-based mobility surveys; (2) considering personal exposures to both air pollution and noise and improving their characterisation (inhaled doses, noise frequency components, etc); (3) measuring respiratory and cardiovascular outcomes (smartphone-assessed respiratory symptoms, lung function with spirometry, resting blood pressure, ambulatory brachial/central blood pressure, arterial stiffness and heart rate variability) and (4) investigating short-term and longer-term (over 1-2 years) effects of transport.

ETHICS AND DISSEMINATION

The sampling and data collection protocol was approved by the National Council for Statistical Information, the French Data Protection Authority and the Ethical Committee of Inserm. Our final aim is to determine, for communicating with policy-makers, how scenarios of changes in personal transport behaviour affect individual exposure and health.

A review and analysis of personal and ambient PM2.5 measurements: Implications for epidemiology studies

BACKGROUND

In epidemiology studies, ambient measurements of PM_2.5 are often used as surrogates for personal exposures. However, it is unclear the degree to which ambient PM_2.5 reflects personal exposures.

OBJECTIVE

In order to examine potential sources of bias in epidemiology studies, we conducted a review and meta-analysis of studies to determine the extent to which short-term measurements of ambient PM_2.5 levels are related to short-term measurements of personal PM_2.5 levels.

METHODS

We conducted a literature search of studies reporting both personal and ambient measurements of PM_2.5 published in the last 10 years (2009-2019) and incorporated studies published prior to 2009 from reviews.

RESULTS

Seventy-one studies were identified. Based on 17 studies reporting slopes, a meta-analysis revealed an overall slope of 0.56 μg/m³ (95% CI: [0.39, 0.73]) personal PM_2.5 per μg/m³ increase in ambient PM_2.5. Slopes for summer months were higher (slope = 0.73, 95% CI: [0.64, 0.81]) than for winter (slope = 0.46, 95% CI: [0.36, 0.57]). Based on 44 studies reporting correlations, we calculated an overall personal-ambient PM_2.5 correlation of 0.63 (95% CI: [0.55, 0.71]). Correlations were stronger in studies conducted in Canada (r = 0.86, 95% CI: [0.67, 0.94]) compared to the USA (r = 0.60, 95% CI: [0.49, 0.70]) and China (r = 0.60, 95% CI: [0.46, 0.71]). Correlations also were stronger in urban areas (r = 0.53, 95% CI: [0.43, 0.62]) vs. suburban areas (r = 0.36, 95% CI: [0.21, 0.49]).

SIGNIFICANCE

Our results suggest a large degree of variability in the personal-ambient PM_2.5 association and the potential for exposure misclassification and measurement error in PM_2.5 epidemiology studies.

Review and analysis of personal-ambient ozone measurements

Ambient ozone measurements are often used as surrogates for personal exposures. Due to the limited number of central ozone monitors and varying personal behavioral patterns, some level of variability between ambient and personal exposures is expected. Low-cost sensors and different ways to capture personal activity patterns are being developed as an effort to improve the accuracy of exposure assessment. However, it is still most common to use the traditional approach of using unadjusted ambient concentrations as surrogates for personal exposures. To our knowledge, there has not been a meta-analysis that summarizes the findings from studies that investigated the differences between personal and ambient ozone. We conducted a literature search in PubMed and Science Direct for peer-reviewed studies reporting at least one of the following in a numeric format: 1) personal-ambient measurements, 2) personal-ambient slopes, or 3) personal-ambient correlations to identify and summarize existing studies that investigated personal and ambient ozone concentrations. Twenty-two articles met inclusion criteria and were included in our review. Ambient concentrations almost always overestimated personal exposures. A meta-analysis of slopes showed an overall personal-ambient slope of 0.21 (95% CI: 0.15, 0.27) with high heterogeneity (97%) across studies. The correlations between personal and ambient ozone varied dramatically across subjects from a strong positive (0.77) to a moderate negative correlation (-0.43). Our study found that ambient measurements are not accurate representations of personal exposure, while the magnitude of exposure measurement error varied across studies. Different sources of ozone and how they contribute to true exposure levels for individuals in complementary ways need to be better addressed. The effort to better understand the impact of traditional exposure assessment on the risk estimates must be emphasized along with efforts to improve the current exposure assessment approaches to provide context for interpreting the results from ozone epidemiological studies. Implications: The traditional approach of using ambient ozone measurements as surrogates for personal exposures is likely to result in exposure misclassification, which is a well-recognized source of bias in epidemiological studies. There are efforts to characterize the differences between ambient and personal ozone measurements, though, to our knowledge, there has not been a meta-analysis that summarizes the findings of different studies. Better understanding of the pattern and magnitude of exposure error for ambient and personal ozone can provide directions for future studies and context for interpreting the results from ozone epidemiological studies.

Insights into the source-specific health risk of ambient particle-bound metals in the Pearl River Delta region, China

Quantification of source-specific health risks of PM2.5 plays an essential role in health-oriented air pollution control. However, there is limited evidence supporting the source-based risk apportionment of particle-bound metals. In this study, source-specific cancer and non-cancer risk characterization of 12 particle-bound metals was performed in the Pearl River Delta (PRD) region, China. A combination of health risk assessment model and receptor-based source apportionment modeling with positive matrix factorization (PMF) was applied for characterizing the spatial-temporal patterns for inhalation health risks of particle-bound metals in three main city clusters, inland area and coastal area in the region from December 2014 through July 2016. Results showed that the carcinogenic risk of particle-bound metals for adults (4.13 × 10-5) was higher than that for children (9.53 × 10-6) in the PRD region. The highest and significant non-carcinogenic risk was found in the northwest city cluster. Industrial emission (63.3%) were the dominant contributors to the cancer risk, while the main contributors to the non-cancer risk were the vehicle emission source (33.2%) in the dry season and industrial emission (30.8%) in the wet season. Our results provide important evidence for spatial source-specific health risks with temporal characteristics of particle-bound metals in most densely populated areas in the southern China, and suggest that reduction of industrial and vehicle emissions could facilitate more cost-effective PM2.5 control measures to improve human health.

Association of ambient particle pollution with gestational diabetes mellitus and fasting blood glucose levels in pregnant women from two Chinese birth cohorts

BACKGROUND

Fasting blood glucose may capture the adverse effects of air pollution on pregnant women better than the incidence of gestational diabetes mellitus (GDM), but evidence on the association between air pollution and maternal glucose concentrations is limited.

OBJECTIVE

To investigate the associations between air pollutants, GDM and fasting blood glucose during pregnancy.

METHODS

We recruited 2326 pregnant women from two birth cohorts located in Guangzhou and Heshan, the Pearl River Delta region (PRD), China. PM₁₀, PM_2.5 and black carbon (BC) exposure concentrations in the first and second trimesters of pregnancy were collected at fixed-site monitoring stations for each cohort. Multiple logistic regressions were employed to estimate the associations between particle pollution and GDM. Mixed-effects models were used to evaluate the associations of air pollutants with blood glucose levels. Restricted cubic spline functions were fitted to visualize the concentration-response relationships. Distributed lag non-linear models were used to estimate week-specific lag effects of particle pollution exposure on GDM and blood glucose. Unconstrained distributed lag models with lags of 0-3 weeks were used to examine potential cumulative effects.

RESULTS

We observed positive and significant associations of PM₁₀, PM_2.5 and BC exposure with fasting glucose, particularly in the second trimester. PM₁₀, PM_2.5 and BC were strongly correlated and displayed similar cumulative (lag 0-3 weeks) associations with fasting blood glucose. Exposure to particle pollution was not associated with 1-h or 2-h blood glucose. Models estimating the association between air pollutants and GDM were consistent with statistical insignificance.

CONCLUSIONS

Based on the results of the present study, exposure to air pollution during pregnancy exerts cumulative, adverse effects on fasting glucose levels. This study provides preliminary support for the use of blood glucose levels to explore the potential health impact of air pollution on pregnant women.

Interactive Effect of Diurnal Temperature Range and Temperature on Mortality, Northeast Asia

BACKGROUND

The diurnal temperature range (DTR) represents temperature variability within a day and has been reported as a potential risk factor for mortality. Previous studies attempted to identify the role of temperature in the DTR-mortality association, but results are inconclusive. The aim of this study was to investigate the interactive effect of temperature and DTR on mortality using a multicountry time series analysis.

METHODS

We collected time series data for mortality and weather variables for 57 communities of three countries (Taiwan, Korea, and Japan) in Northeast Asia (1972-2012). Two-stage time series regression with a distributed lag nonlinear model and meta-analysis was used to estimate the DTR-mortality association changing over temperature strata (six strata were defined based on community-specific temperature percentiles). We first investigated the whole population and then, the subpopulations defined by temperature distribution (cold and warm regions), sex, and age group (people <65 and ≥65 years of age), separately.

RESULTS

The DTR-mortality association changed over temperature strata. The relative risk (RR) of mortality for 10°C increase in DTR was larger for high-temperature strata compared with cold-temperature strata (e.g., = 1.050; 95% confidence interval [CI] = 1.040, 1.060 at extreme-hot stratum and RR = 1.040; 95% CI = 1.031, 1.050 at extreme-cold stratum); extreme-hot and -cold strata were defined as the days with daily mean temperature above 90th and below 10th percentiles each community's temperature distribution. Such increasing pattern was more pronounced in cold region and in people who were 65 years or older.

CONCLUSIONS

We found evidence that the DTR-related mortality may increase as temperature increases.

Linkage between Particulate Matter Properties and Lung Function in Schoolchildren: A Panel Study in Southern China

While several scientific studies have linked PM_2.5 to decreased lung function, there is still some degree of uncertainty regarding which particulate physicochemical properties are most harmful. We followed a panel of 57 healthy schoolchildren (857 person-days) to investigate the associations between a wide variety of PM_2.5 and lung function in Heshan, China in 2016 for three periods. We monitored the daily concentrations of mass, chemical composition, size, number, surface area, and volume of particulate mixture. Associations of lung function with various particle metrics were estimated using generalized estimating equations and unconstrained distributed lag models. Random forest model was used to compare the relative importance of exposure metrics. Immediate (lag 0) associations of PM_2.5 and carbonaceous aerosols with reduced FEV₁ and MMEF, and accumulation-mode particles with FEV₁ were found. Slightly delayed (lag 1, 2) effects on PEF were particularly prominent for Aitken-mode particles. Possible cumulative (lags 0-2) effects of PM_2.5 and carbonaceous aerosols on PEF and Aitken-mode particles on FEV₁, MMEF, and PEF were observed. This study provides comprehensive evidence that the physicochemical properties of particulate mixtures are associated with reduced lung function in children. Organic carbon (OC) may be an important risk factor for the decreased lung function related to PM exposure.

Associations of daily weather and ambient air pollution with objectively assessed sleep duration and fragmentation: a prospective cohort study

OBJECTIVE

Given the lack of studies examining the associations between daily weather and air pollution with nightly objective sleep over multiple weeks, we quantified these associations in a prospective cohort of healthy participants with episodic migraine.

METHODS

Ninety-eight participants completed daily electronic diaries and wore an actigraph for an average of 45 ds, and a total 4406 nights of data were collected. Nightly sleep characteristics including duration, wake after sleep onset (WASO), and efficiency were assessed using wrist actigraphy. Daily weather parameters and air pollution levels were collected from local weather station and ground-level air quality monitors. We used linear fixed effects models adjusting for participant, day of the week, and day of the year (for weather analysis), and additionally adjusted for temperature and relative humidity (for air pollution analysis).

RESULTS

The participants were 35 ± 12 yrs old and 86 were women. A 10 °F higher daily average temperature was associated with 0.88 (95% CI: 0.06, 1.70) minutes longer WASO and 0.14% (95% CI: -0.01%, 0.30%) lower sleep efficiency on that night. A 14 parts per billion (ppb) (interquartile range) higher daily maximum eight-h ozone was associated with 7.51 (95% CI: 3.23, 11.79) minutes longer sleep duration on that night. Associations did not differ between cold (October-March) and warm (April-September) seasons.

CONCLUSIONS

Higher daily ozone was associated with longer sleep duration and modest associations were observed between higher temperature and lower WASO and lower efficiency.

Ambient air pollution is associated with pediatric pneumonia: a time-stratified case-crossover study in an urban area

BACKGROUND

Pneumonia, the leading reason underlying childhood deaths, may be triggered or exacerbated by air pollution. To date, only a few studies have examined the association of air pollution with emergency department (ED) visits for pediatric pneumonia, with inconsistent results. Therefore, we aimed to elucidate the impact of short-term exposure to particulate matter (PM) and other air pollutants on the incidence of ED visits for pediatric pneumonia.

METHODS

PM_2.5, PM₁₀, and other air pollutant levels were measured at 11 air quality-monitoring stations in Kaohsiung City, Taiwan, between 2008 and 2014. Further, we extracted the medical records of non-trauma patients aged ≤17 years and who had visited an ED with the principal diagnosis of pneumonia. A time-stratified case-crossover study design was employed to determine the hazard effect of air pollution in a total of 4024 patients.

RESULTS

The single-pollutant model suggested that per interquartile range increment in PM_2.5, PM₁₀, nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) on 3 days before the event increased the odds of pediatric pneumonia by 14.0% [95% confidence interval (CI), 5.1-23.8%], 10.9% (95% CI, 2.4-20.0%), 14.1% (95% CI, 5.0-24.1%), and 4.5% (95% CI, 0.8-8.4%), respectively. In two-pollutant models, PM_2.5 and NO₂ were significant after adjusting for PM₁₀ and SO₂. Subgroup analyses showed that older children (aged ≥4 years) were more susceptible to PM_2.5 (interaction p = 0.024) and children were more susceptible to NO₂ during warm days (≥26.5 °C, interaction p = 0.011).

CONCLUSIONS

Short-term exposure to PM_2.5 and NO₂ possibly plays an important role in pediatric pneumonia in Kaohsiung, Taiwan. Older children are more susceptible to PM_2.5, and all children are more susceptible to NO₂ during warm days.

Estimation of personal exposure to fine particles (PM2.5) of ambient origin for healthy adults in Hong Kong

Personal exposure and ambient fine particles (PM_2.5) measurements for 13 adult subjects (ages 19-57) were conducted in Hong Kong between April 2014 and June 2015. Six to 21 personal samples (mean = 19) per subject were obtained throughout the study period. Samples were analyzed for mass by gravimetric analysis, and 19 elements (from Na to Pb) were analyzed using X-Ray Fluorescence. Higher subject-specific correlations between personal and ambient sulfur (r_s = 0.92; p < 0.001) were found as compared to PM_2.5 mass (r_s = 0.79; p < 0.001) and other elements (0.06 < r_s < 0.86). Personal vs. ambient sulfur regression yielded an average exposure factor (F_pex) of 0.73 ± 0.02, supporting the use of sulfur as a surrogate to estimate personal exposure to PM_2.5 of ambient origin (E_a). E_a accounted for 41-82% and 57-73% of total personal PM_2.5 exposures (P) by season and by subject, respectively. The importance of both E_a and non-ambient exposures (E_na, 11.2 ± 5.6 μg/m³; 32.5 ± 10.9%) are noted. Mixed-effects models were applied to estimate the relationships between ambient PM_2.5 concentrations and their corresponding exposure variables (E_a, P). Higher correlations for E_a (0.90; p < 0.001) than for P (0.58; p < 0.01) were found. A calibration coefficient < 1 suggests an attenuation of 22% (ranging 16-28%) of the true effect estimates when using average ambient concentrations at central monitoring stations as surrogates for E_a. Stationary ambient data can be used to assess population exposure only if PM exposure is dominated by E_a.

Usability of a Personal Air Pollution Monitor: Design-Feedback Iterative Cycle Study

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.

Estimation of personal ozone exposure using ambient concentrations and influencing factors

Evidence is limited regarding whether ambient monitoring can properly represent personal ozone exposure. We conducted a longitudinal panel study to measure personal exposure to ozone using real-time personal ozone monitors. Corresponding ambient ozone concentrations and possible influencing factors (meteorological conditions and activity patterns) were also collected. We used linear mixed-effect models to analyze personal-ambient ozone concentration associations and possible influencing factors. Ambient ozone concentrations were around two to three times higher than personal ozone (43.1 μg/m³ on average) and their correlations were weak with small slopes (0.35) and marginal R square (R_M²) values (0.24). Larger R_M² values were found under high temperature (>29.5 °C), low humidity (<62.1%), good ventilation conditions (>4 h) and for individuals spent longer time outdoors (>0.6 h). In final model, personal ozone exposure was positively associated with ambient concentrations and ventilation conditions, but inversely correlated with ambient temperature and humidity. The models explained >50% of personal ozone concentration variabilities. Our results highlight that ambient ozone concentration alone is not a suitable surrogate for individual exposure assessment. Meteorological conditions (temperature and humidity) and activity patterns (windows opening and outdoor activities) that affecting personal ozone exposure should be taken into account.

Sources of household air pollution: The association with lung function and respiratory symptoms in middle-aged adult

INTRODUCTION

The objective of the present study was to investigate the relationship between sources of household air pollution, respiratory symptoms and lung function.

METHODS

3039 adults aged from 40 to 65 participated in the 2011-2013 ELISABET cross-sectional survey in northern France. Lung function was measured using spirometry. During a structured interview, respiratory symptoms, household fuels, exposure to moulds, and use of ventilation were recorded on a questionnaire.

RESULTS

The self-reported presence of mould in at least two rooms (not including the bathroom and the kitchen) was associated with a 2.5% lower predicted forced expiratory volume in 1 s (95% confidence interval, -4.7 to -0.29; p-trend <0.05) and a higher risk of wheezing (p-trend < 0.001). Visible condensation was associated with wheezing (p < .05) and chronic cough (p < .05). There were no significant associations with the type of household fuel or inadequate ventilation/aeration. Similar results were found when the analyses were restricted to participants without known respiratory disease.

CONCLUSION

Our results suggest that the presence of mould (known to be associated with more severe asthma symptoms) could also have an impact on respiratory symptoms and lung function in the general population and in populations without known respiratory disease.

A National County-Level Assessment of U.S. Nursing Facility Characteristics Associated with Long-Term Exposure to Traffic Pollution in Older Adults

Long-term exposure to ambient air pollution increases disease risk in older adults. Nursing facilities located near major roadways potentially expose older adults to traffic pollution. No studies, however, have described the association between nursing facilities and traffic pollution. We obtained data on facility- and census-tract-level characteristics of 15,706 U.S. facilities from the Medicare Nursing Home Compare datasets. We calculated distance to major roadways and traffic density for each facility. In the contiguous U.S. (as of 2014), 345,792 older adults, about 27% of residents in non-hospital facilities, lived within 150 m major roadways (A1 or A2) in 3876 (28% of sampled) facilities. Nationally, for-profit facilities, high-occupancy facilities, and facilities in census tracts with higher percentages of minorities were more likely to have higher exposure to traffic. Counties in Virginia, New York City, and Rhode Island have the highest percent of residents and facilities near major roads. Nationally, over one-quarter of sampled facilities are located near major roadways. Attributes potentially associated with higher exposure to traffic included “for-profit” and “higher minority census tract”. Proximity to major roadways may be an important factor to consider in siting nursing facilities. Our results inform potential intervention strategy at both county and facility level.

Exposure to fine particulate matter causes oxidative and methylated DNA damage in young adults: A longitudinal study

An increased understanding is needed of the physiological effects and plausible biological mechanisms that link PM_2.5 (particulate matter with an aerodynamic diameter below 2.5μm) exposure to mortality and morbidities such as atherosclerosis and respiratory disease. PM_2.5 causes carcinogenic health effects. Biomonitoring in humans has suggested that 8-oxo-7, 8-dihydro-2-deoxyguanosine (8-oxodG) and N7-methylguanine (N7-MeG) are correlated with oxidative and methylated DNA damage. Thus, it is meaningful to explore the mechanisms of mutagenesis and carcinogenesis associated with oxidative and methylated DNA damage by simultaneously measuring these two markers. We recruited 72 participants from 2 areas (residential and commercial as well as residential and industrial) in the greater Taipei metropolitan area at baseline. Personal samplers were used to collect 24-hour PM_2.5-integrated samples. All participants completed an interview, and blood and urine samples were collected the next morning. All collection procedures were repeated twice after a two-month follow-up period. Urinary 8-oxodG and N7-MeG were assayed as biomarkers of oxidative and methylated DNA damage, respectively. Plasma superoxide dismutase (SOD) and glutathione peroxidase-1 (GPX-1) were measured as biomarkers of antioxidants. Urinary 1-hydroxypyrene (1-OHP) was used as a biomarker of exposure to polycyclic aromatic hydrocarbons (PAHs). The mean PM_2.5 level was 37.3μg/m³ at baseline. PM_2.5 concentrations were higher during winter than during spring and summer. After adjusting for confounds through a generalized estimating equation (GEE) analysis, N7-MeG was significantly increased by 8.1% (β=0.034, 95% CIs=0.001-0.068) per 10μg/m³ increment in PM_2.5. 8-oxodG levels were positively correlated with N7-MeG according to both cross-sectional and longitudinal analyses, and 1-OHP was significantly associated with increasing 8-oxodG and N7-MeG concentrations. Exposure to PM_2.5 increases methylated DNA damage. The mean level of urinary N7-MeG was 1000-fold higher than that of 8-oxodG.

Investigation of an Indoor Air Quality Sensor for Asthma Management in Children

Monitoring indoor air quality is critical because Americans spend 93% of their life indoors, and around 6.3 million children suffer from asthma. We want to passively and unobtrusively monitor the asthma patient's environment to detect the presence of two asthma-exacerbating activities: smoking and cooking using the Foobot sensor. We propose a data-driven approach to develop a continuous monitoring-activity detection system aimed at understanding and improving indoor air quality in asthma management. In this study, we were successfully able to detect a high concentration of particulate matter, volatile organic compounds, and carbon dioxide during cooking and smoking activities. We detected 1) smoking with an error rate of 1%; 2) cooking with an error rate of 11%; and 3) obtained an overall 95.7% percent accuracy classification across all events (control, cooking and smoking). Such a system will allow doctors and clinicians to correlate potential asthma symptoms and exacerbation reports from patients with environmental factors without having to personally be present.

Ozone exposure assessment for children in Greece - Results from the RESPOZE study

Ozone exposure of 179 children in Athens and Thessaloniki, Greece was assessed during 2013-2014, by repeated weekly personal measurements, using passive samplers. O₃ was also monitored at school locations of participants to characterize community-level ambient exposure. Average personal concentrations in the two cities (5.0 and 2.8ppb in Athens and Thessaloniki, respectively) were considerably lower than ambient concentrations (with mean personal/ambient ratios of 0.13-0.15). The temporal variation of personal concentrations followed the -typical for low-latitude areas- pattern of cold-warm seasons. However, differences were detected between temporal distributions of personal and ambient concentrations, since personal exposures were affected by additional factors which present seasonal variability, such as outdoor activity and house ventilation. Significant spatial contrasts were observed between urban and suburban areas, for personal concentrations in Athens, with higher exposure for children residing in the N-NE part of the area. In Thessaloniki, spatial variations in personal concentrations were less pronounced, echoing the spatial pattern of ambient concentrations, a result of complex local meteorology and the smaller geographical expansion of the study area. Ambient concentration was identified as the most important factor influencing personal exposures (correlation coefficients between 0.36 and 0.67). Associations appeared to be stronger with ambient concentrations measured at school locations of children, than to those reported by the nearest site of the air quality monitoring network, indicating the importance of community-representative outdoor monitoring for characterization of personal-ambient relationships. Time spent outdoors by children was limited (>90% of the time they remained indoors), but -due to the lack of indoor sources- it was found to exert significant influence on personal concentrations, affecting inter-subject and spatiotemporal variability. Additional parameters that were identified as relevant for the determination of personal concentrations were indoor ventilation conditions (specifically indoor times with windows open) and the use of wood-burning in open fireplaces for heating as an ozone sink.

Characterization of ambient-generated exposure to fine particles using sulfate as a tracer in the Chinese megacity of Guangzhou

Total personal exposures can differ from the concentrations measured at stationary ambient monitoring sites. To provide further insight into factors affecting exposure to particles, chemical tracers were used to separate total personal exposure into its ambient and non-ambient components. Simultaneous measurements of ambient and personal exposure to fine particles (PM_2.5) were conducted in eight districts of Guangzhou, a megacity in South China, during the winter of 2011. Considerable significant correlations (Spearman's Rho, r_s) between personal exposures and ambient concentrations of sulfate (SO₄^2-; r_s>0.68) were found in contrast to elemental carbon (EC; r_s>0.37). The average fraction of personal SO₄^2- to ambient SO₄^2- resulting in an adjusted ambient exposure factor of α=0.72 and a slope of 0.73 was determined from linear regression analysis when there were minimal indoor sources of SO₄^2-. From all data pooled across the districts, the estimated average ambient-generated and non-ambient-generated exposure to PM_2.5 were 55.3μg/m³ (SD=23.4μg/m³) and 18.1μg/m³ (SD=29.1μg/m³), respectively. A significant association was found between ambient-generated exposure and ambient PM_2.5 concentrations (Pearson's r=0.51, p<0.001). As expected, the non-ambient generated exposure was not related to the ambient concentrations. This study highlights the importance of both ambient and non-ambient components of total personal exposure in the megacity of Guangzhou. Our results support the use of SO₄^2- as a tracer of personal exposure to PM_2.5 of ambient origin in environmental and epidemiological studies.

Influence of exposure differences on city-to-city heterogeneity in PM2.5-mortality associations in US cities

BACKGROUND

Multi-city population-based epidemiological studies have observed heterogeneity between city-specific fine particulate matter (PM_2.5)-mortality effect estimates. These studies typically use ambient monitoring data as a surrogate for exposure leading to potential exposure misclassification. The level of exposure misclassification can differ by city affecting the observed health effect estimate.

METHODS

The objective of this analysis is to evaluate whether previously developed residential infiltration-based city clusters can explain city-to-city heterogeneity in PM_2.5 mortality risk estimates. In a prior paper 94 cities were clustered based on residential infiltration factors (e.g. home age/size, prevalence of air conditioning (AC)), resulting in 5 clusters. For this analysis, the association between PM_2.5 and all-cause mortality was first determined in 77 cities across the United States for 2001-2005. Next, a second stage analysis was conducted evaluating the influence of cluster assignment on heterogeneity in the risk estimates.

RESULTS

Associations between a 2-day (lag 0-1 days) moving average of PM_2.5 concentrations and non-accidental mortality were determined for each city. Estimated effects ranged from -3.2 to 5.1% with a pooled estimate of 0.33% (95% CI: 0.13, 0.53) increase in mortality per 10 μg/m³ increase in PM_2.5. The second stage analysis determined that cluster assignment was marginally significant in explaining the city-to-city heterogeneity. The health effects estimates in cities with older, smaller homes with less AC (Cluster 1) and cities with newer, smaller homes with a large prevalence of AC (Cluster 3) were significantly lower than the cluster consisting of cities with older, larger homes with a small percentage of AC.

CONCLUSIONS

This is the first study that attempted to examine whether multiple exposure factors could explain the heterogeneity in PM_2.5-mortality associations. The results of this study were found to explain a small portion (6%) of this heterogeneity.

An evaluation tool kit of air quality micro-sensing units

Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website.

Assessment of Exposure of Elementary Schools to Traffic Pollution by GIS Methods

AIM

The susceptibility of children to polluted air has been pointed out several times in the past. Generally, children suffer from higher exposure to air pollutants than adults because of their higher physical activity, higher metabolic rate and the resultant increase in minute ventilation. The aim of this study was to examine the exposure characteristics of public elementary schools in Prague (the capital of the Czech Republic).

METHODS

The exposure was examined by two different methods: by the proximity of selected schools to major urban roads and their location within the modeled urban PM10 concentration fields. We determined average daily traffic counts for all roads within 300 m of 251 elementary schools using the national road network database and geographic information system and calculated by means of GIS tools the proximity of the schools to the roads. In the second method we overlapped the GIS layer of predicted annual urban PM10 concentration field with that of geocoded school addresses.

RESULTS

The results showed that 208 Prague schools (almost 80%) are situated in a close proximity (<300 m) of roads exhibiting high traffic loads. Both methods showed good agreement in the proportion of highly exposed schools at risk; however, we found significant differences in the locations of schools at risk determined by the two methods.

CONCLUSION

We argue that results of similar proximity studies should be treated with caution before they are used in risk based decision-making process, since different methods may provide different outcomes.

Assessment of indoor and outdoor PM species at schools and residences in a high-altitude Ecuadorian urban center

An air monitoring campaign to assess children's environmental exposures in schools and residences, both indoors and outdoors, was conducted in 2010 in three low-income neighborhoods in Z1 (north), Z2 (central), and Z3 (southeast) zones of Quito, Ecuador - a major urban center of 2.2 million inhabitants situated 2850 m above sea level in a narrow mountainous basin. Z1 zone, located in northern Quito, historically experienced emissions from quarries and moderate traffic. Z2 zone was influenced by heavy traffic in contrast to Z3 zone which experienced low traffic densities. Weekly averages of PM samples were collected at schools (one in each zone) and residences (Z1 = 47, Z2 = 45, and Z3 = 41) every month, over a twelve-month period at the three zones. Indoor PM2.5 concentrations ranged from 10.6 ± 4.9 μg/m(3) (Z1 school) to 29.0 ± 30.5 μg/m(3) (Z1 residences) and outdoor PM2.5 concentrations varied from 10.9 ± 3.2 μg/m(3) (Z1 school) to 14.3 ± 10.1 μg/m(3) (Z2 residences), across the three zones. The lowest values for PM10-2.5 for indoor and outdoor microenvironments were recorded at Z2 school, 5.7 ± 2.8 μg/m(3) and 7.9 ± 2.2 μg/m(3), respectively. Outdoor school PM concentrations exhibited stronger associations with corresponding indoor values making them robust proxies for indoor exposures in naturally ventilated Quito public schools. Correlation analysis between the school and residential PM size fractions and the various pollutant and meteorological parameters from central ambient monitoring (CAM) sites suggested varying degrees of temporal relationship. Strong positive correlation was observed for outdoor PM2.5 at Z2 school and its corresponding CAM site (r = 0.77) suggesting common traffic related emissions. Spatial heterogeneity in PM2.5 concentrations between CAM network and sampled sites was assessed using Coefficient of Divergence (COD) analysis. COD values were lower when CAM sites were paired with outdoor measurements (<0.2) and higher when CAM and indoor values were compared (>0.2), suggesting that CAM network in Quito may not represent actual indoor exposures.

Framingham risk score modifies the effect of PM10 on heart rate variability

Health conditions may greatly modify the association between particulate matter (PM) and heart rate variability (HRV), but whether the modification of PM effect by coronary artery disease (CAD) risk status depends on the PM levels remains unknown. We investigated the associations between personal exposures to PM with aerodynamic diameter of ≤10μm (PM10) and ≤2.5μm (PM2.5) and concurrent HRV, and whether the effect of PM on HRV was modified by Framingham risk score (FRS) in healthy subjects with different PM exposure levels. Personal exposures to PM10 and PM2.5 were measured for 24h in 152 volunteers of community residents who were free of cardiovascular disease in two cities (Zhuhai and Wuhan) that differ in air quality. Simultaneously, 24h HRV indices were obtained from 3-channel Holter monitor. FRS was calculated based on age, sex, lipid profiles, blood pressure, diabetes, and smoking status. Linear regression models were constructed after adjusting for potential confounders. We found significant decrease in total power (TP) and low power (LF) with increased PM10 concentrations (P for trend<0.05) in the high PM levels city (Wuhan) and total population, but not in the low PM levels city (Zhuhai). We also observed significant modification of FRS on PM10 effect in Wuhan. Interestingly, elevated PM10 was associated in a greater decreased HRV in the low FRS subgroup, but not in the high FRS subgroup. However, we did not find any significant main effects of PM2.5 or PM2.5-FRS interactions on HRV in city-specified or city-combined analyses. Overall, the findings indicate that individual coronary risk profiles may modulate the association between particulate air pollution and HRV in high PM exposure levels.

The association of long-term exposure to PM2.5 on all-cause mortality in the Nurses' Health Study and the impact of measurement-error correction

BACKGROUND

Long-term exposure to particulate matter less than 2.5 μm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality. The methods used to assess exposure, such as area averages, nearest monitor values, land use regressions, and spatio-temporal models in these studies are subject to measurement error. However, to date, no study has attempted to incorporate adjustment for measurement error into a long-term study of the effects of air pollution on mortality.

METHODS

We followed 108,767 members of the Nurses' Health Study (NHS) 2000-2006 and identified all deaths. Biennial mailed questionnaires provided a detailed residential address history and updated information on potential confounders. Time-varying average PM2.5 in the previous 12-months was assigned based on residential address and was predicted from either spatio-temporal prediction models or as concentrations measured at the nearest USEPA monitor. Information on the relationships of personal exposure to PM2.5 of ambient origin with spatio-temporal predicted and nearest monitor PM2.5 was available from five previous validation studies. Time-varying Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95 percent confidence intervals (95%CI) for each 10 μg/m(3) increase in PM2.5. Risk-set regression calibration was used to adjust estimates for measurement error.

RESULTS

Increasing exposure to PM2.5 was associated with an increased risk of mortality, and results were similar regardless of the method chosen for exposure assessment. Specifically, the multivariable adjusted HRs for each 10 μg/m(3) increase in 12-month average PM2.5 from spatio-temporal prediction models were 1.13 (95%CI:1.05, 1.22) and 1.12 (95%CI:1.05, 1.21) for concentrations at the nearest EPA monitoring location. Adjustment for measurement error increased the magnitude of the HRs 4-10% and led to wider CIs (HR = 1.18; 95%CI: 1.02, 1.36 for each 10 μg/m(3) increase in PM2.5 from the spatio-temporal models and HR = 1.22; 95%CI: 1.02, 1.45 from the nearest monitor estimates).

CONCLUSIONS

These findings support the large body of literature on the adverse effects of PM2.5, and suggest that adjustment for measurement error be considered in future studies where possible.

Estimating risk of emergency room visits for asthma from personal versus fixed site measurements of NO2

BACKGROUND

We examined the impact of data source and exposure measurement error for ambient NO2 on risk estimates derived from a case-crossover study of emergency room visits for asthma in Windsor, Canada between 2002 and 2009.

METHODS

Paired personal and fixed-site NO2 data were available from an independent population (47 children and 48 adults) in Windsor between 2005 and 2006. We used linear regression to estimate the relationship and measurement error variance induced between fixed site and personal measurements of NO2, and through a series of simulations, evaluated the potential for a Bayesian model to adjust for this change in scale and measurement error. Finally, we re-analyzed data from the previous case-crossover study adjusting for the estimated change in slope and measurement error.

RESULTS

Correlations between paired NO2 measurements were weak (R(2)≤0.08) and slopes were far from unity (0.0029≤β≤0.30). Adjusting the previous case-crossover analysis suggested a much stronger association between personal NO2 (per 1ppb) (Odds Ratio (OR)=1.276, 95% Credible Interval (CrI): 1.034, 1.569) and emergency room visits for asthma among children relative to the fixed-site estimate (OR=1.024, 95% CrI 1.004-1.045).

CONCLUSIONS

Our findings suggest that risk estimates based on fixed-site NO2 concentrations may differ substantially from estimates based on personal exposures if the change in scale and/or measurement error is large. In practice, one must always keep the scale being used in mind when interpreting risk estimates and not assume that coefficients for ambient concentrations reflect risks at the personal level.

Source-specific fine particulate air pollution and systemic inflammation in ischaemic heart disease patients

OBJECTIVE

To compare short-term effects of fine particles (PM2.5; aerodynamic diameter <2.5 µm) from different sources on the blood levels of markers of systemic inflammation.

METHODS

We followed a panel of 52 ischaemic heart disease patients from 15 November 2005 to 21 April 2006 with clinic visits in every second week in the city of Kotka, Finland, and determined nine inflammatory markers from blood samples. In addition, we monitored outdoor air pollution at a fixed site during the study period and conducted a source apportionment of PM2.5 using the Environmental Protection Agency's model EPA PMF 3.0. We then analysed associations between levels of source-specific PM2.5 and markers of systemic inflammation using linear mixed models.

RESULTS

We identified five source categories: regional and long-range transport (LRT), traffic, biomass combustion, sea salt, and pulp industry. We found most evidence for the relation of air pollution and inflammation in LRT, traffic and biomass combustion; the most relevant inflammation markers were C-reactive protein, interleukin-12 and myeloperoxidase. Sea salt was not positively associated with any of the inflammatory markers.

CONCLUSIONS

Results suggest that PM2.5 from several sources, such as biomass combustion and traffic, are promoters of systemic inflammation, a risk factor for cardiovascular diseases.

The effects of PM2.5 and its components from indoor and outdoor sources on cough and wheeze symptoms in asthmatic children

Particulate matter with aerodynamic diameter <2.5 μm (PM2.5) is associated with asthma exacerbation. In the Children's Air Pollution Asthma Study, we investigated the longitudinal association of PM2.5 and its components from indoor and outdoor sources with cough and wheeze symptoms in 36 asthmatic children. The sulfur tracer method was used to estimate infiltration factors. Mixed proportional odds models for an ordinal response were used to relate daily cough and wheeze scores to PM2.5 exposures. The odds ratio associated with being above a given symptom score for a SD increase in PM2.5 from indoor sources (PMIS) was 1.24 (95% confidence interval: 0.92-1.68) for cough and 1.63 (1.11-2.39) for wheeze. Ozone was associated with wheeze (1.82, 1.19-2.80), and cough was associated with indoor PM2.5 components from outdoor sources (denoted with subscript "OS") bromine (BrOS: 1.32, 1.05-1.67), chlorine (ClOS: 1.27, 1.02-1.59) and pyrolyzed organic carbon (OPOS: 1.49, 1.12-1.99). The highest effects were seen in the winter for cough with sulfur (SOS: 2.28, 1.01-5.16) and wheeze with organic carbon fraction 2 (OC2OS: 7.46, 1.19-46.60). Our results indicate that exposure to components originating from outdoor sources of photochemistry, diesel and fuel oil combustion is associated with symptom's exacerbation, especially in the winter. PM2.5 mass of indoor origin was more strongly associated with wheeze than with cough.

Sources of indoor air pollution in New York City residences of asthmatic children

Individuals spend ∼90% of their time indoors in proximity to sources of particulate and gaseous air pollutants. The sulfur tracer method was used to separate indoor concentrations of particulate matter (PM) PM2.5 mass, elements and thermally resolved carbon fractions by origin in New York City residences of asthmatic children. Enrichment factors relative to sulfur concentrations were used to rank species according to the importance of their indoor sources. Mixed effects models were used to identify building characteristics and resident activities that contributed to observed concentrations. Significant indoor sources were detected for OC1, Cl, K and most remaining OC fractions. We attributed 46% of indoor PM2.5 mass to indoor sources related to OC generation indoors. These sources include cooking (NO2, Si, Cl, K, OC4 and OP), cleaning (most OC fractions), candle/incense burning (black carbon, BC) and smoking (K, OC1, OC3 and EC1). Outdoor sources accounted for 28% of indoor PM2.5 mass, mainly photochemical reaction products, metals and combustion products (EC, EC2, Br, Mn, Pb, Ni, Ti, V and S). Other indoor sources accounted for 26% and included re-suspension of crustal elements (Al, Zn, Fe, Si and Ca). Indoor sources accounted for ∼72% of PM2.5 mass and likely contributed to differences in the composition of indoor and outdoor PM2.5 exposures.

Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies

BACKGROUND

Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures.

METHODS

Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects' homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models.

RESULTS

When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5.

CONCLUSIONS

Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.

Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies

BACKGROUND

Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures.

METHODS

Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects' homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models.

RESULTS

When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5.

CONCLUSIONS

Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.

Estimating personal exposures from ambient air pollution measures: using meta-analysis to assess measurement error

BACKGROUND

Although ambient concentrations of particulate matter ≤10 μm (PM10) are often used as proxies for total personal exposure, correlation (r) between ambient and personal PM10 concentrations varies. Factors underlying this variation and its effect on health outcome-PM exposure relationships remain poorly understood.

METHODS

We conducted a random-effects meta-analysis to estimate effects of study, participant, and environmental factors on r; used the estimates to impute personal exposure from ambient PM10 concentrations among 4,012 nonsmoking, participants with diabetes in the Women's Health Initiative clinical trial; and then estimated the associations of ambient and imputed personal PM10 concentrations with electrocardiographic measures, such as heart rate variability.

RESULTS

We identified 15 studies (in years 1990-2009) of 342 participants in five countries. The median r was 0.46 (range = 0.13 to 0.72). There was little evidence of funnel plot asymmetry but substantial heterogeneity of r, which increased 0.05 (95% confidence interval = 0.01 to 0.09) per 10 µg/m increase in mean ambient PM10 concentration. Substituting imputed personal exposure for ambient PM10 concentrations shifted mean percent changes in electrocardiographic measures per 10 µg/m increase in exposure away from the null and decreased their precision, for example, -2.0% (-4.6% to 0.7%) versus -7.9% (-15.9% to 0.9%), for the standard deviation of normal-to-normal RR interval duration.

CONCLUSIONS

Analogous distributions and heterogeneity of r in extant meta-analyses of ambient and personal PM2.5 concentrations suggest that observed shifts in mean percent change and decreases in precision may be generalizable across particle size.

Validity of using annual mean particulate matter concentrations as measured at fixed site in assessing personal exposure: an exposure assessment study in Japan

From 2003 through 2005, we compared annual mean particulate matter (PM) and nitrogen dioxide (NO₂) concentrations as measured at fixed-site monitoring stations in 6 Japanese cities with those measured inside and outside subject residences and during personal monitoring. A total of 65 households participated in indoor and outdoor residential exposure monitoring. In summer and autumn, we also performed personal monitoring of one resident of each household. On each day, personal samplers were used to collect 24-h samples of PM and NO₂ simultaneously from the fixed sites, indoor and outdoor, and from those undergoing personal monitoring. We found good correlations between the fixed-site and outdoor measurements for annual mean (average of 7-day × 4-season) concentrations of PM₂.₅, PM₁₀₋₂.₅, PM10 and NO₂ (Spearman's rank correlation coefficients (ρ) ≥ 0.75). However, the correlations between the fixed-site and indoor measurements were moderate to low. In summer and autumn, the correlations between the fixed-site and personal mean concentrations of PM₂.₅ (ρ = 0.62), PM10 (ρ = 0.58), and NO₂ (ρ = 0.70) were acceptable. However, because people spend most of their time indoors, these correlations for annual mean concentrations were not estimated to be high. Our results are important in allowing researchers to estimate the effects of resulting measurement errors of PM and NO₂.

Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.

Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia

Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM(2.5) and its components (elemental carbon (EC), SO(4)), O(3), carbon monoxide (CO), and nitrogen oxides (NOx). Metrics were applied in a study investigating the respiratory health effects of these pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related pollutants (CO, NO(x), and EC), but little spatial variability among ZIP code centroids for regional pollutants (PM(2.5), SO(4), and O(3)); (ii) for all pollutants except NO(x), temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong (r>0.82) for all pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.

Spatiotemporally resolved air exchange rate as a modifier of acute air pollution-related morbidity in Atlanta

Epidemiological studies frequently use central site concentrations as surrogates of exposure to air pollutants. Variability in air pollutant infiltration due to differential air exchange rates (AERs) is potentially a major factor affecting the relationship between central site concentrations and actual exposure, and may thus influence observed health risk estimates. In this analysis, we examined AER as an effect modifier of associations between several urban air pollutants and corresponding emergency department (ED) visits for asthma and wheeze during a 4-year study period (January 1999-December 2002) for a 186 ZIP code area in metro Atlanta. We found positive associations for the interaction between AER and pollution on asthma ED visits for both carbon monoxide (CO) and nitrogen oxides (NO(x)), indicating significant or near-significant effect modification by AER on the pollutant risk-ratio estimates. In contrast, the interaction term between particulate matter (PM)(2.5) and AER on asthma ED visits was negative and significant. However, alternative distributional tertile analyses showed PM(2.5) and AER epidemiological model results to be similar to those found for NOx and CO (namely, increasing risk ratios (RRs) with increasing AERs when ambient PM(2.5) concentrations were below the highest tertile of their distribution). Despite the fact that ozone (O(3)) was a strong independent predictor of asthma ED visits in our main analysis, we found no O(3)-AER effect modification. To our knowledge, our findings for CO, NOx, and PM(2.5) are the first to provide an indication of short-term (i.e., daily) effect modification of multiple air pollution-related risk associations with daily changes in AER. Although limited to one outcome category in a single large urban locale, the findings suggest that the use of relatively simple and easy-to-derive AER surrogates may reflect intraurban differences in short-term exposures to pollutants of ambient origin.

Refined ambient PM2.5 exposure surrogates and the risk of myocardial infarction

Using a case-crossover study design and conditional logistic regression, we compared the relative odds of transmural (full-wall) myocardial infarction (MI) calculated using exposure surrogates that account for human activity patterns and the indoor transport of ambient PM(2.5) with those calculated using central-site PM(2.5) concentrations to estimate exposure to PM(2.5) of outdoor origin (exposure to ambient PM(2.5)). Because variability in human activity and indoor PM(2.5) transport contributes exposure error in epidemiologic analyses when central-site concentrations are used as exposure surrogates, we refer to surrogates that account for this variability as "refined" surrogates. As an alternative analysis, we evaluated whether the relative odds of transmural MI associated with increases in ambient PM(2.5) is modified by residential air exchange rate (AER), a variable that influences the fraction of ambient PM(2.5) that penetrates and persists indoors. Use of refined exposure surrogates did not result in larger health effect estimates (ORs=1.10-1.11 with each interquartile range (IQR) increase), narrower confidence intervals, or better model fits compared with the analysis that used central-site PM(2.5). We did observe evidence for heterogeneity in the relative odds of transmural MI with residential AER (effect-modification), with residents of homes with higher AERs having larger ORs than homes in lower AER tertiles. For the level of exposure-estimate refinement considered here, our findings add support to the use of central-site PM(2.5) concentrations for epidemiological studies that use similar case-crossover study designs. In such designs, each subject serves as his or her own matched control. Thus, exposure error related to factors that vary spatially or across subjects should only minimally impact effect estimates. These findings also illustrate that variability in factors that influence the fraction of ambient PM(2.5) in indoor air (e.g., AER) could possibly bias health effect estimates in study designs for which a spatiotemporal comparison of exposure effects across subjects is conducted.

Associations between summertime ambient pollutants and respiratory morbidity in New York City: comparison of results using ambient concentrations versus predicted exposures

Epidemiological analyses of air quality often estimate human exposure from ambient monitoring data, potentially leading to exposure misclassification and subsequent bias in estimated health risks. To investigate this, we conducted a case-crossover study of summertime ambient ozone and fine particulate matter (PM(2.5)) levels and daily respiratory hospitalizations in New York City during 2001-2005. Comparisons were made between associations estimated using two pollutant exposure metrics: observed concentrations and predicted exposures from the EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) model. Small, positive associations between interquartile range mean ozone concentrations and hospitalizations were observed and were strongest for 0-day lags (hazard ratio (HR)=1.013, 95% confidence interval (CI): 0.998, 1.029) and 3-day lags (HR=1.006, 95% CI: 0.991, 1.021); applying mean predicted ozone exposures yielded similar results. PM(2.5) was also associated with admissions, strongest at 2- and 4-day lags, with few differences between exposure metrics. Subgroup analyses support recognized sociodemographic differences in concentration-related hospitalization risk, whereas few inter-stratum variations were observed in relation to SHEDS exposures. Predicted exposures for these spatially homogenous pollutants were similar across sociodemographic strata, therefore SHEDS predictions coupled with the case-crossover design may have masked observable heterogeneity in risks. However, significant effect modification was found for subjects in the top exposure-to-concentration ratio tertiles, suggesting risks may increase as a consequence of infiltration or greater exposure to outdoor air.

Air pollution exposure prediction approaches used in air pollution epidemiology studies

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

Estimating the concentration of indoor particles of outdoor origin: a review

Recent toxicological results highlight the importance of separating exposure to indoor- and outdoor-generated particles, due to their different physicochemical and toxicological properties. In this framework, a number of studies have attempted to estimate the relative contribution of particles of indoor and outdoor origins to indoor concentrations, using either statistical analysis of indoor and outdoor concentration time-series or mass balance equations. The aim of this work is to review and compare the methodologies developed in order to determine the ambient particle infiltration factor (F(INF)) (i.e., the fraction of ambient particles that enter indoors and remains suspended). The different approaches are grouped into four categories according to their methodological principles: (1) steady-state assumption using the steady-state form of the mass balance equation; (2) dynamic solution of the mass balance equation using complex statistical techniques; (3) experimental studies using conditions that simplify model calculations (e.g., decreasing the number of unknowns); and (4) infiltration surrogates using a particulate matter (PM) constituent with no indoor sources to act as surrogate of indoor PM of outdoor origin. Examination of the various methodologies and results reveals that estimating infiltration parameters is still challenging. The main difficulty lies in the separate calculation of penetration efficiency (P) and deposition rate (k). The values for these two parameters that are reported in the literature vary significantly. Deposition rate presents the widest range of values, both between studies and size fractions. Penetration efficiency seems to be more accurately calculated through the application of dynamic models. Overall, estimates of the infiltration factor generated using dynamic models and infiltration surrogates show good agreement. This is a strong argument in favor of the latter methodology, which is simple and easy to apply when chemical speciation data are available.

Ambient fine particulate matter alters cerebral hemodynamics in the elderly

BACKGROUND AND PURPOSE

Short-term elevations in fine particulate matter air pollution (PM2.5) are associated with increased risk of acute cerebrovascular events. Evidence from the peripheral circulation suggests that vascular dysfunction may be a central mechanism. However, the effects of PM2.5 on cerebrovascular function and hemodynamics are unknown.

METHODS

We used transcranial Doppler ultrasound to measure beat-to-beat blood flow velocity in the middle cerebral artery at rest and in response to changes in end-tidal CO2 (cerebral vasoreactivity) and arterial blood pressure (cerebral autoregulation) in 482 participants from the Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) of Boston study. We used linear mixed effects models with random subject intercepts to evaluate the association between cerebrovascular hemodynamic parameters and mean PM2.5 levels 1 to 28 days earlier adjusting for age, race, medical history, meteorologic covariates, day of week, temporal trends, and season.

RESULTS

An interquartile range increase (3.0 µg/m(3)) in mean PM2.5 levels during the previous 28 days was associated with an 8.6% (95% confidence interval, 3.7%-13.8%; P<0.001) higher cerebral vascular resistance and a 7.5% (95% confidence interval, 4.2%-10.6%; P<0.001) lower blood flow velocity at rest. Measures of cerebral vasoreactivity and autoregulation were not associated with PM2.5 levels.

CONCLUSIONS

In this cohort of community-dwelling seniors, exposure to PM2.5 was associated with higher resting cerebrovascular resistance and lower cerebral blood flow velocity. If replicated, these findings suggest that alterations in cerebrovascular hemodynamics may underlie the increased risk of particle-related acute cerebrovascular events.