A systematic review of cardiovascular emergency department visits, hospital admissions and mortality associated with ambient black carbon.

BACKGROUND: Black carbon (BC) is a ubiquitous component of particulate matter (PM) emitted from combustion-related sources and is associated with a number of health outcomes. OBJECTIVES: We conducted a systematic review to evaluate the potential for cardiovascular morbidity and mortality following exposure to ambient BC, or the related component elemental carbon (EC), in the context of what is already known about the associations between exposure to fine particulate matter (PM2.5) and cardiovascular health outcomes. DATA SOURCES: We conducted a stepwise systematic literature search of the PubMed database and employed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting our results. STUDY ELIGIBILITY CRITERIA: Studies meeting inclusion criteria (i.e., include a quantitative measurement of BC or EC used to characterize exposure and an effect estimate of the association of the exposure metric with ED visits, hospital admissions, or mortality due to cardiovascular disease) were evaluated for risk of bias in study design and results. STUDY APPRAISAL AND SYNTHESIS METHODS: Risk of bias evaluations assess some aspects of internal validity of study findings based on study design, conduct, and reporting and identify potential issues related to confounding or other biases. RESULTS: The results of our systematic review demonstrate similar results for BC or EC and PM2.5; that is, a generally modest, positive association of each pollutant measurement with cardiovascular emergency department visits, hospital admissions, and mortality. There is no clear evidence that health risks are greater for either BC or EC when compared to one another, or when either is compared to PM2.5. LIMITATIONS: We were unable to adequately evaluate the role of copollutant confounding or differential spatial heterogeneity for BC or EC compared to PM2.5. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Overall, the evidence at present indicates that BC or EC is consistently associated with cardiovascular morbidity and mortality but is not sufficient to conclude that BC or EC is independently associated with these effects rather than being an indicator for PM2.5 mass. SYSTEMATIC REVIEW REGISTRATION NUMBER: Not available.

Framework for assessing causality of air pollution-related health effects for reviews of the National Ambient Air Quality Standards.

To inform regulatory decisions on the risk due to exposure to ambient air pollution, consistent and transparent communication of the scientific evidence is essential. The United States Environmental Protection Agency (U.S. EPA) develops the Integrated Science Assessment (ISA), which contains evaluations of the policy-relevant science on the effects of criteria air pollutants and conveys critical science judgments to inform decisions on the National Ambient Air Quality Standards. This article discusses the approach and causal framework used in the ISAs to evaluate and integrate various lines of scientific evidence and draw conclusions about the causal nature of air pollution-induced health effects. The framework has been applied to diverse pollutants and cancer and noncancer effects. To demonstrate its flexibility, we provide examples of causality judgments on relationships between health effects and pollutant exposures, drawing from recent ISAs for ozone, lead, carbon monoxide, and oxides of nitrogen. U.S. EPA's causal framework has increased transparency by establishing a structured process for evaluating and integrating various lines of evidence and uniform approach for determining causality. The framework brings consistency and specificity to the conclusions in the ISA, and the flexibility of the framework makes it relevant for evaluations of evidence across media and health effects.

The short-term association of selected components of fine particulate matter and mortality in the Denver Aerosol Sources and Health (DASH) study.

BACKGROUND: Associations of short-term exposure to fine particulate matter (PM2.5) with daily mortality may be due to specific PM2.5 chemical components. Daily concentrations of PM2.5 components were measured over five years in Denver to investigate whether specific PM2.5 components are associated with daily mortality. METHODS: Daily counts of total and cause-specific deaths were obtained for the 5-county Denver metropolitan region from 2003 through 2007. Daily 24-hour concentrations of PM2.5, elemental carbon (EC), organic carbon (OC), sulfate and nitrate were measured at a central residential monitoring site. Using generalized additive models, we estimated relative risks (RRs) of daily death counts for daily PM2.5 and four PM2.5 component concentrations at single and distributed lags between the current and three previous days, while controlling for longer-term time trend and meteorology. RESULTS: RR of total non-accidental mortality for an inter-quartile increase of 4.55 µg/m(3) in PM2.5 distributed over 4 days was 1.012 (95 % confidence interval: 0.999, 1.025); RRs for EC and OC were larger (1.024 [1.005, 1.043] and 1.020 [1.000, 1.040] for 0.33 and 1.67 µg/m(3) increases, respectively) than those for sulfate and nitrate. We generally did not observe associations with cardiovascular and respiratory mortality except for associations with ischemic heart disease mortality at lags 3 and 0-3 depending on the component. In addition, there were associations with cancer mortality, particularly for EC and OC, possibly reflecting advanced deaths of a frail population. CONCLUSIONS: PM2.5 components possibly from combustion-related sources are more strongly associated with daily mortality than are secondary inorganic aerosols.

Systematic review of the effects of black carbon on cardiovascular disease among individuals with pre-existing disease.

OBJECTIVES: Recent interest has developed in understanding the health effects attributable to different components of particulate matter. This review evaluates the effects of black carbon (BC) on cardiovascular disease in individuals with pre-existing disease using evidence from epidemiologic and experimental studies. METHODS: A systematic literature search was conducted to identify epidemiologic and experimental studies examining the relationship between BC and cardiovascular health effects in humans with pre-existing diseases. Nineteen epidemiologic and six experimental studies were included. Risk of bias was evaluated for each study. RESULTS: Evidence across studies suggested ambient BC is associated with changes in subclinical cardiovascular health effects in individuals with diabetes and coronary artery disease (CAD). Limited evidence demonstrated that chronic respiratory disease does not modify the effect of BC on cardiovascular health. CONCLUSIONS: Results in these studies consistently demonstrated that diabetes is a risk factor for BC-related cardiovascular effects, including increased interleukin-6 and ECG parameters. Cardiovascular effects were associated with BC in individuals with CAD, but few comparisons to individuals without CAD were provided in the literature.

The sensitivity of health effect estimates from time-series studies to fine particulate matter component sampling schedule.

The US Environmental Protection Agency air pollution monitoring data have been a valuable resource commonly used for investigating the associations between short-term exposures to PM2.5 chemical components and human health. However, the temporally sparse sampling on every third or sixth day may affect health effect estimation. We examined the impact of non-daily monitoring data on health effect estimates using daily data from the Denver Aerosol Sources and Health (DASH) study. Daily concentrations of four PM2.5 chemical components (elemental and organic carbon, sulfate, and nitrate) and hospital admission counts from 2003 through 2007 were used. Three every-third-day time series were created from the daily DASH monitoring data, imitating the US Speciation Trend Network (STN) monitoring schedule. A fourth, partly irregular, every-third-day time series was created by matching existing sampling days at a nearby STN monitor. Relative risks (RRs) of hospital admissions for PM2.5 components at lags 0-3 were estimated for each data set, adjusting for temperature, relative humidity, longer term temporal trends, and day of week using generalized additive models, and compared across different sampling schedules. The estimated RRs varied somewhat between the non-daily and daily sampling schedules and between the four non-daily schedules, and in some instances could lead to different conclusions. It was not evident which features of the data or analysis were responsible for the variation in effect estimates, although seeing similar variability in resampled data sets with relaxation of the every-third-day constraint suggests that limited power may have had a role. The use of non-daily monitoring data can influence interpretation of estimated effects of PM2.5 components on hospital admissions in time-series studies.

Positive matrix factorization of a 32-month series of daily PM2.5 speciation data with incorporation of temperature stratification.

This study presents source apportionment results for PM2.5 from applying positive matrix factorization (PMF) to a 32-month series of daily PM2.5 compositional data from Denver, CO, including concentrations of sulfate, nitrate, bulk elemental carbon (EC) and organic carbon (OC), and 51 organic molecular markers (OMMs). An optimum 8-factor solution was determined primarily based on the interpretability of the PMF results and rate of matching factors from bootstrapped PMF solutions with those from the base case solution. These eight factors were identified as inorganic ion, n-alkane, EC/sterane, light n-alkane/polycyclic aromatic hydrocarbon (PAH), medium alkane/alkanoic acid, PAH, winter/methoxyphenol and summer/odd n-alkane. The inorganic ion factor dominated the reconstructed PM2.5 mass (sulfate + nitrate + EC + OC) in cold periods (daily average temperature < 10 °C; 43.7% of reconstructed PM2.5 mass) whereas the summer/odd n-alkane factor dominated in hot periods (> 20 °C; 53.1%). The two factors had comparable relative contributions of 26.5% and 27.1% in warm periods with temperatures between 10 °C and 20 °C. Each of the seven factors resolved in a previous study (Dutton et al., 2010b) using a 1-year data set from the same location matches one factor from the current work based on comparing factor profiles. Six out of the seven matched pairs of factors are linked to similar source classes as suggested by the strong correlations between factor contributions (r = 0.89 - 0.98). Temperature-stratified source apportionment was conducted for three subsets of the data in the current study, corresponding to the cold, warm and hot periods mentioned above. The cold period (7-factor) solution exhibited a similar distribution of reconstructed PM2.5 mass as the full data set solution. The factor contributions of the warm period (7-factor) solution were well correlated with those from the full data set solution (r = 0.76 - 0.99). However, the reconstructed PM2.5 mass was distributed more to inorganic ion, n-alkane and medium alkane/alkanoic acid factors in the warm period solution than in the full data set solution. For the hot period (6-factor) solution, PM2.5 mass distribution was quite different from that of the full data set solution, as illustrated by regression slopes as low as 0.2 and as high as 4.8 of each matched pair of factors across the two solutions.

Positive matrix factorization of PM(2.5): comparison and implications of using different speciation data sets.

To evaluate the utility and consistency of different speciation data sets in source apportionment of PM(2.5), positive matrix factorization (PMF) coupled with a bootstrap technique for uncertainty assessment was applied to four different 1-year data sets composed of bulk species, bulk species and water-soluble elements (WSE), bulk species and organic molecular markers (OMM), and all species. The five factors resolved by using only the bulk species best reproduced the observed concentrations of PM(2.5) components. Combining WSE with bulk species as PMF inputs also produced five factors. Three of them were linked to soil, road dust, and processed dust, and together contributed 26.0% of reconstructed PM(2.5) mass. A 7-factor PMF solution was identified using speciated OMM and bulk species. The EC/sterane and summertime/selective aliphatic factors had the highest contributions to EC (39.0%) and OC (53.8%), respectively. The nine factors resolved by including all species as input data are consistent with those from the previous two solutions (WSE and bulk species, OMM and bulk species) in both factor profiles and contributions (r = 0.88-1.00). The comparisons across different solutions indicate that the selection of input data set may depend on the PM components or sources of interest for specific source-oriented health study.

The temporal lag structure of short-term associations of fine particulate matter chemical constituents and cardiovascular and respiratory hospitalizations.

BACKGROUND: In air pollution time-series studies, the temporal pattern of the association of fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in aerodynamic diameter) and health end points has been observed to vary by disease category. The lag pattern of PM2.5 chemical constituents has not been well investigated, largely because daily data have not been available. OBJECTIVES: We explored the lag structure for hospital admissions using daily PM2.5 chemical constituent data for 5 years in the Denver Aerosol Sources and Health (DASH) study. METHODS: We measured PM2.5 constituents, including elemental carbon, organic carbon, sulfate, and nitrate, at a central residential site from 2003 through 2007 and linked these daily pollution data to daily hospital admission counts in the five-county Denver metropolitan area. Total hospital admissions and subcategories of respiratory and cardiovascular admissions were examined. We assessed the lag structure of relative risks (RRs) of hospital admissions for PM2.5 and four constituents on the same day and from 1 to 14 previous days from a constrained distributed lag model; we adjusted for temperature, humidity, longer-term temporal trends, and day of week using a generalized additive model. RESULTS: RRs were generally larger at shorter lags for total cardiovascular admissions but at longer lags for total respiratory admissions. The delayed lag pattern was particularly prominent for asthma. Elemental and organic carbon generally showed more immediate patterns, whereas sulfate and nitrate showed delayed patterns. CONCLUSION: In general, PM2.5 chemical constituents were found to have more immediate estimated effects on cardiovascular diseases and more delayed estimated effects on respiratory diseases, depending somewhat on the constituent.

Intra-urban spatial variability of PM2.5-bound carbonaceous components.

The Denver Aerosol Sources and Health (DASH) study was designed to evaluate associations between PM2.5 species and sources and adverse human health effects. The DASH study generated a five-year (2003-2007) time series of daily speciated PM2.5 concentration measurements from a single, special-purpose monitoring site in Denver, CO. To evaluate the ability of this site to adequately represent the short term temporal variability of PM2.5 concentrations in the five county Denver metropolitan area, a one year supplemental set of PM2.5 samples was collected every sixth day at the original DASH monitoring site and concurrently at three additional sites. Two of the four sites, including the original DASH site, were located in residential areas at least 1.9 km from interstate highways. The other two sites were located within 0.3 km of interstate highways. Concentrations of elemental carbon (EC), organic carbon (OC), and 58 organic molecular markers were measured at each site. To assess spatial variability, site pairs were compared using the Pearson correlation coefficient (r) and coefficient of divergence (COD), a statistic that provides information on the degree of uniformity between monitoring sites. Biweekly co-located samples collected from July 2004 to September 2005 were also analyzed and used to estimate the uncertainty associated with sampling and analytical measurement for each species. In general, the two near-highway sites exhibited higher concentrations of EC, OC, polycyclic aromatic hydrocarbons (PAHs), and steranes than did the more residential sites. Lower spatial heterogeneity based on r and COD was inferred for all carbonaceous species after considering their divergence and lack of perfect correlations in co-located samples. Ratio-ratio plots combined with available gasoline- and diesel-powered motor vehicle emissions profiles for the region suggested a greater impact to high molecular weight (HMW) PAHs from diesel-powered vehicles at the near-highway sites and a more uniformly distributed impact to ambient hopanes from gasoline-powered motor vehicles at all four sites.

Intra-urban spatial variability and uncertainty assessment of PM2.5 sources based on carbonaceous species.

To identify the sources of PM2.5 - bound carbonaceous species and examine the spatial variability of source contributions in the Denver metropolitan area, positive matrix factorization (PMF) was applied to one year of every sixth day ambient PM2.5 compositional data, including elemental carbon (EC), organic carbon (OC), and 32 organic molecular markers, from four sites (two residential and two near-traffic). Statistics (median, inner quantiles and 5th - 95th percentiles range) of factor contributions, expressed as reconstructed carbonaceous mass (EC + OC), were estimated from PMF solutions of replicate datasets generated by using a stationary block bootstrap technique. A seven-factor solution was resolved for a set of data pooled across the four sites, as it gave the most interpretable results and had the highest rate of neural network factor matching (76.9%). Identified factors were primarily associated with high plant wax, summertime emission, diesel vehicle emission, fossil fuel combustion, motor vehicle emission, lubricating oil combustion and wood burning. Pearson correlation coefficients (r) and coefficients of divergence (COD) were used to assess spatial variability of factor contributions. The summertime emission factor exhibited the highest spatial correlation (r = 0.74 - 0.88) and lowest CODs (0.32 - 0.38) among all resolved factors; while the three traffic dominated factors (diesel vehicle emission, motor vehicle emission and lubricating oil combustion) showed lower correlations (r = 0.47 - 0.55) and higher CODs (0.41 - 0.53) on average. Average total EC and OC mass were apportioned to each factor and showed a similar distribution across the four sites. Modeling uncertainties were defined as the 5th - 95th percentile range of the factor contributions derived from valid bootstrap PMF solutions, and were highly correlated with the median factor contribution in each factor (r = 0.77 - 0.98). Source apportionment was also performed on site specific datasets; the results exhibited similar factor profiles and temporal variation in factor contribution as those obtained for the pooled dataset, indicating that the four sites are primarily influenced by similar types of sources. On the other hand, differences were observed in absolute factor contributions between PMF solutions for the pooled versus site-specific datasets, likely due to the large uncertainties in EC and OC factor profiles derived from the site specific datasets with limited numbers of observations.

Regional variations in particulate matter composition and the ability of monitoring data to represent population exposures.

Epidemiologic studies have demonstrated that relative risks for mortality associated with ambient particulate matter (PM) concentrations vary with location in the U.S. with larger associations in both magnitude and strength observed in the East compared to the West. Two factors potentially contributing to the regional heterogeneity in PM-mortality associations observed are regional variations in PM composition and the ability of a single PM concentration estimate to represent the community-average exposure for an entire study area, which may lead to regional differences in exposure error. Variations in PM composition and the proportion of the population living in proximity to ambient monitors, an indicator of potential exposure error, are examined for the 20 most populated and 10 mid-size study areas included in the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Clear differences in PM and in the proportion of the population living in proximity to ambient monitors are found for some of these cities. Differences in these exposure parameters may be interpreted more reasonably in terms of north-south differences compared to east-west differences, and may need to be considered when conducting future epidemiologic studies that aim to examine the factors that influence the regional variability in PM-mortality associations.

Temporal patterns in daily measurements of inorganic and organic speciated PM2.5 in Denver.

Airborne particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) has been linked to a wide range of adverse health effects and as a result is currently regulated by the U.S. Environmental Protection Agency. PM2.5 originates from a multitude of sources and has heterogeneous physical and chemical characteristics. These features complicate the link between PM2.5 emission sources, ambient concentrations and health effects. The goal of the Denver Aerosol Sources and Health (DASH) study is to investigate associations between sources and health using daily measurements of speciated PM2.5 in Denver. The datxa set being collected for the DASH study will be the longest daily speciated PM2.5 data set of its kind covering 5.5 years of daily inorganic and organic speciated measurements. As of 2008, 4.5 years of bulk measurements (mass, inorganic ions and total carbon) and 1.5 years of organic molecular marker measurements have been completed. Several techniques were used to reveal long-term and short-term temporal patterns in the bulk species and the organic molecular marker species. All species showed a strong annual periodicity, but their monthly and seasonal behavior varied substantially. Weekly periodicities appear in many compound classes with the most significant weekday/weekend effect observed for elemental carbon, cholestanes, hopanes, select polycyclic aromatic hydrocarbons (PAHs), heavy n-alkanoic acids and methoxyphenols. Many of the observed patterns can be explained by meteorology or anthropogenic activity patterns while others do not appear to have such obvious explanations. Similarities and differences in these findings compared to those reported from other cities are highlighted.

Source Apportionment Using Positive Matrix Factorization on Daily Measurements of Inorganic and Organic Speciated PM(2.5).

Particulate matter less than 2.5 microns in diameter (PM(2.5)) has been linked with a wide range of adverse health effects. Determination of the sources of PM(2.5) most responsible for these health effects could lead to improved understanding of the mechanisms of such effects and more targeted regulation. This has provided the impetus for the Denver Aerosol Sources and Health (DASH) study, a multi-year source apportionment and health effects study relying on detailed inorganic and organic PM(2.5) speciation measurements.In this study, PM(2.5) source apportionment is performed by coupling positive matrix factorization (PMF) with daily speciated PM(2.5) measurements including inorganic ions, elemental carbon (EC) and organic carbon (OC), and organic molecular markers. A qualitative comparison is made between two models, PMF2 and ME2, commonly used for solving the PMF problem. Many previous studies have incorporated chemical mass balance (CMB) for organic molecular marker source apportionment on limited data sets, but the DASH data set is large enough to use multivariate factor analysis techniques such as PMF.Sensitivity of the PMF2 and ME2 models to the selection of speciated PM(2.5) components and model input parameters was investigated in depth. A combination of diagnostics was used to select an optimum, 7-factor model using one complete year of daily data with pointwise measurement uncertainties. The factors included 1) a wintertime/methoxyphenol factor, 2) an EC/sterane factor, 3) a nitrate/polycyclic aromatic hydrocarbon (PAH) factor, 4) a summertime/selective aliphatic factor, 5) an n-alkane factor, 6) a middle oxygenated PAH/alkanoic acid factor and 7) an inorganic ion factor. These seven factors were qualitatively linked with known PM(2.5) emission sources with varying degrees of confidence. Mass apportionment using the 7-factor model revealed the contribution of each factor to the mass of OC, EC, nitrate and sulfate. On an annual basis, the majority of OC and EC mass was associated with the summertime/selective aliphatic factor and the EC/sterane factor, respectively, while nitrate and sulfate mass were both dominated by the inorganic ion factor. This apportionment was found to vary substantially by season. Several of the factors identified in this study agree well with similar assessments conducted in St. Louis, MO and Pittsburgh, PA using PMF and organic molecular markers.

PM(2.5) Characterization for Time Series Studies: Pointwise Uncertainty Estimation and Bulk Speciation Methods Applied in Denver.

Many studies have identified associations between adverse health effects and short-term exposure to particulate matter less than 2.5 microns in diameter (PM(2.5)). These effects, however, are not consistent across geographical regions. This may be due in part to variations in the chemical make-up of PM(2.5) resulting from unique combinations of sources, both primary and secondary, in different regions. The Denver Aerosol Sources and Health (DASH) study is a multi-year time series study designed to characterize the daily chemical composition of PM(2.5) in Denver, identify the major contributing sources, and investigate associations between sources and a broad array of adverse health outcomes.Measurement methodology, field blank correction, pointwise uncertainty estimation and detection limit consideration are discussed in the context of bulk speciation for the DASH study. Results are presented for the first 4.5 years of mass, inorganic ion and bulk carbon speciation. The derived measurement uncertainties were propagated using the root sum of squares method and show good agreement with precision estimates derived from bi-weekly duplicate samples collected on collocated samplers. Gravimetric mass has the most uncertainty of any measurement and reconstructed mass generated from the sum of the individual species shows less uncertainty than measured mass on average. The methods discussed provide a good framework for PM(2.5) speciation measurements and are generalizable to analysis of other environmental measures.

PM(2.5) Characterization for Time Series Studies: Organic Molecular Marker Speciation Methods and Observations from Daily Measurements in Denver.

Particulate matter less than 2.5 microns in diameter (PM(2.5)) has been shown to have a wide range of adverse health effects and consequently is regulated in accordance with the US-EPA's National Ambient Air Quality Standards. PM(2.5) originates from multiple primary sources and is also formed through secondary processes in the atmosphere. It is plausible that some sources form PM(2.5) that is more toxic than PM(2.5) from other sources. Identifying the responsible sources could provide insight into the biological mechanisms causing the observed health effects and provide a more efficient approach to regulation. This is the goal of the Denver Aerosol Sources and Health (DASH) study, a multi-year PM(2.5) source apportionment and health study.The first step in apportioning the PM(2.5) to different sources is to determine the chemical make-up of the PM(2.5). This paper presents the methodology used during the DASH study for organic speciation of PM(2.5). Specifically, methods are covered for solvent extraction of non-polar and semi-polar organic molecular markers using gas chromatography-mass spectrometry (GC-MS). Vast reductions in detection limits were obtained through the use of a programmable temperature vaporization (PTV) inlet along with other method improvements. Results are presented for the first 1.5 years of the DASH study revealing seasonal and source-related patterns in the molecular markers and their long-term correlation structure. Preliminary analysis suggests that point sources are not a significant contributor to the organic molecular markers measured at our receptor site. Several motor vehicle emission markers help identify a gasoline/diesel split in the ambient data. Findings show both similarities and differences when compared with other cities where similar measurements and assessments have been made.

Source apportionment of in vitro reactive oxygen species bioassay activity from atmospheric particulate matter.

Recent atmospheric particulate matter health studies have suggested that the redox activity is an important factor in particulate matter toxicology, and that reactive oxygen species (ROS) activity may be an important characteristic of particulate matter that is associated with adverse health effects. In this study, associations between atmospheric particulate matter sources and in vitro ROS activities are investigated. Ambient concentrations of fine particle water-soluble elements and total organic and elemental carbon were measured daily in Denver for the 2003 calendar year. The data were used in a multivariate factor analysis source apportionment model, positive matrix factorization (PMF), to determine the contributions of nine sources or factors: a mobile source factor, a water soluble carbon factor, a sulfate factor, a soil dust source, an iron source, two point sources characterized by water soluble toxic metals, a pyrotechnique factor, and a platinum group metal factor. Aqueous leachates, including water soluble and colloidal components, as well as insoluble particles that pass through a 0.2 microm pore size filter, of 45 randomly selected PM samples, were assayed to quantify ROS activity using an in vitro rat alveolar macrophage assay. Results show that PM-stimulated in vitro ROS production was significantly positively correlated with the contributions from three sources: the iron source, the soil dust source and the water soluble carbon factor. The iron source accounted for the greatest fraction of the measured variability in redox activity, followed by the soil dust and the water-soluble carbon factor. Seventy-seven percent of the in vitro ROS activity was explained by a linear combination of these three source contributions.

Wildfire air pollution and daily mortality in a large urban area.

Unusual air pollution episodes, such as when smoke from wildfires covers a large urban area, can be used to attempt to detect associations between short-term increases in particulate matter (PM) concentrations and subsequent mortality without relying on the sophisticated statistical models that are typically required in the absence of such episodes. The objective of this study was to explore whether acute increases in PM concentrations from wildfire smoke cause acute increases in daily mortality. The temporal patterns of daily nonaccidental deaths and daily cardiorespiratory deaths for June of 2002 in the Denver metropolitan area were examined and compared to those in two nearby counties in Colorado that were not affected by the wildfire smoke and to daily deaths in Denver in June of 2001. Abrupt increases in PM concentrations in Denver occurred on 2 days in June of 2002 as a result of wildfire smoke drifting over the Denver area. Small peaks in mortality corresponded to both of the PM peaks, but the first mortality peak also corresponded to a peak of mortality in the control counties, and cardiorespiratory deaths began to increase on the day before the second peak. Further, there was no detectable increase in cardiorespiratory deaths in the hours immediately following the PM peaks. Although the findings from this study do not rule out the possibility of small increases in mortality due to abrupt and dramatic increases in PM concentrations from wildfire smoke, in a population of over 2 million people no perceptible increases in daily mortality could be attributed to such events.

Estimating effects of ambient PM(2.5) exposure on health using PM(2.5) component measurements and regression calibration.

Most air pollution and health studies conducted in recent years have examined how a health outcome is related to pollution concentrations from a fixed outdoor monitor. The pollutant effect estimate in the health model used indicates how ambient pollution concentrations are associated with the health outcome, but not how actual exposure to ambient pollution is related to health. In this article, we propose a method of estimating personal exposures to ambient PM(2.5) (particulate matter less than 2.5 microm in diameter) using sulfate, a component of PM(2.5) that is derived primarily from ambient sources. We demonstrate how to use regression calibration in conjunction with these derived values to estimate the effects of personal ambient PM(2.5) exposure on a continuous health outcome, forced expiratory volume in 1 s (FEV(1)), using repeated measures data. Through simulation, we show that a confidence interval (CI) for the calibrated estimator based on large sample theory methods has an appropriate coverage rate. In an application using data from our health study involving children with moderate to severe asthma, we found that a 10 microg/m3 increase in PM(2.5) was associated with a 2.2% decrease in FEV(1) at a 1-day lag of the pollutant (95% CI: 0.0-4.3% decrease). Regressing FEV(1) directly on ambient PM(2.5) concentrations from a fixed monitor yielded a much weaker estimate of 1.0% (95% CI: 0.0-2.0% decrease). Relatively small amounts of personal monitor data were needed to calibrate the estimate based on fixed outdoor concentrations.

Importance of the personal endotoxin cloud in school-age children with asthma.

BACKGROUND: A number of studies have observed associations between the amount of endotoxin in urban dust and chronic asthma severity, but a direct relationship between personal exposure to household endotoxin and acute asthma worsening has not yet been defined. OBJECTIVE: We sought to investigate the relationship between day-to-day changes in personal endotoxin exposure and asthma severity. METHODS: In the winter and spring of 1999 through 2000, endotoxin exposures were monitored in asthmatic schoolchildren by using portable, as opposed to stationary, monitors designed to measure inhalable and respirable particulate matter less than or equal to 2.5 and 10 microm in diameter. Children were followed with daily measurements of FEV(1) and asthma symptoms. RESULTS: Over a 24-hour period, median daily personal endotoxin exposures ranged from 0.08 EU/m(3) (measured at a particulate matter size range