Denver Aerosol Sources And Health Research Articles

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

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

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

BackgroundAssociations 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.MethodsDaily 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.ResultsRR of total non-accidental mortality for an inter-quartile increase of 4.55 μg/m3 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/m3 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.ConclusionsPM2.5 components possibly from combustion-related sources are more strongly associated with daily mortality than are secondary inorganic aerosols.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-015-0037-4) contains supplementary material, which is available to authorized users.

Daily mortality and PM2.5 sources in Denver estimated using only particle-phase or total semi-volatile organic compounds (SVOCs)

Background: Source apportionment approaches have provided insights into chemical classes of fine particle matter (PM2.5) responsible for adverse health effects. The Denver Aerosol Sources and Health (DASH) study used the Positive Matrix Factorization (PMF) to identify chemical classes of PM2.5 in the Denver area. Aim: To compare estimated health effects of PM2.5 components with those of chemical classes identified by PMF based on particle-phase or total semi-volatile organic compounds (SVOCs). Methods: Daily measurements of four PM2.5 chemical components (elemental and organic carbon (EC and OC), sulfate, and nitrate), as well as a large number of organic compounds, were obtained over a 32 month period at a single monitoring station located in populated residential area in Denver. PMF factors were estimated based on particle-phase SVOCs only and total SVOCs adding estimated gas-phase SVOCs. The two approaches resulted in eight (Inorganic Ion, n alkane, summer odd n alkane, light n alkane polycyclic aromatic hydrocarbon (PAH), medium alkane alkanoic acid, PAH, winter methoxyphenol, and EC sterane) and seven (n alkane, light SVOC, PAH, summer ECOC, EC sterane, sulfate, and nitrate) factors, respectively. Daily death counts of cardiovascular and respiratory diseases were obtained from the Denver death certificate records. We used generalized additive models to estimate relative risks of mortality at lags 0 to 3 for each of PM2.5 components and PMF factors adjusting for daily humidity and temperature, long-term temporal trend, and day of week. Results: None of four PM2.5 components was associated with daily mortality. With the total SVOC source factors, the PAH factor was associated with cardiovascular mortality at lag 0; with both PMF approaches, n alkanes were associated with respiratory mortality at lag 3. Conclusions: Use of source apportionment allowed us to identify PM2.5 mortality associations that were not found using only single PM2.5 components.

Positive matrix factorization of PM2.5 - eliminating the effects of gas/particle partitioning of semivolatile organic compounds.

Gas-phase concentrations of semi-volatile organic compounds (SVOCs) were calculated from gas/particle (G/P) partitioning theory using their measured particle-phase concentrations. The particle-phase data were obtained from an existing filter measurement campaign (27 January 2003-2 October 2005) as a part of the Denver Aerosol Sources and Health (DASH) study, including 970 observations of 71 SVOCs (Xie et al., 2013). In each compound class of SVOCs, the lighter species (e.g. docosane in n alkanes, fluoranthene in PAHs) had higher total concentrations (gas + particle phase) and lower particle-phase fractions. The total SVOC concentrations were analyzed using positive matrix factorization (PMF). Then the results were compared with source apportionment results where only particle-phase SVOC concentrations were used (particle only-based study; Xie et al., 2013). For the particle only-based PMF analysis, the factors primarily associated with primary or secondary sources (n alkane, EC/sterane and inorganic ion factors) exhibit similar contribution time series (r = 0.92-0.98) with their corresponding factors (n alkane, sterane and nitrate+sulfate factors) in the current work. Three other factors (light n alkane/PAH, PAH and summer/odd n alkane factors) are linked with pollution sources influenced by atmospheric processes (e.g. G/P partitioning, photochemical reaction), and were less correlated (r = 0.69-0.84) with their corresponding factors (light SVOC, PAH and bulk carbon factors) in the current work, suggesting that the source apportionment results derived from particle-only SVOC data could be affected by atmospheric processes. PMF analysis was also performed on three temperature-stratified subsets of the total SVOC data, representing ambient sampling during cold (daily average temperature < 10 °C), warm (≥ 10 °C and ≤ 20 °C) and hot (> 20 °C) periods. Unlike the particle only-based study, in this work the factor characterized by the low molecular weight (MW) compounds (light SVOC factor) exhibited strong correlations (r = 0.82-0.98) between the full data set and each sub-data set solution, indicating that the impacts of G/P partitioning on receptor-based source apportionment could be eliminated by using total SVOC concentrations.

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.

What a Difference a Day Makes: Examining the Lag Patterns of PM 2.5 Constituents

Vol. 120, No. 8 News | Science SelectionsOpen AccessWhat a Difference a Day Makes: Examining the Lag Patterns of PM2.5 Constituentsis accompanied byThe Temporal Lag Structure of Short-term Associations of Fine Particulate Matter Chemical Constituents and Cardiovascular and Respiratory Hospitalizations Tanya Tillett Tanya Tillett Search for more papers by this author Published:1 August 2012https://doi.org/10.1289/ehp.120-a321bView Article in:中文版AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit Exposure to fine particulate matter (PM2.5) is associated with a number of adverse health conditions, including cardiovascular and respiratory ailments, but less is known about associations with its specific chemical constituents. Past studies on the short-term effects of PM2.5 constituents have relied on data from monitoring networks that sample every third or sixth day. Now researchers report their findings on the daily lag patterns between exposure to individual PM2.5 constituents and hospital admissions for various conditions [EHP 120(8):1094–1099; Kim et al.].PM2.5 is a complex mixture of water droplets and solid particles of carbon, nitrate, sulfate, metals, trace elements, and other constituents. In this study, researchers used information provided by the Denver Aerosol Sources and Health (DASH) study, which measured PM2.5 constituents daily for five years (2003–2007) in the Colorado capital. They focused on four of the main constituents of PM2.5—elemental carbon (EC), organic carbon (OC), sulfate, and nitrate.The researchers obtained data on discharge diagnoses following nonelective hospital admissions (e.g., emergency, urgent, or trauma care) in the five-county Denver metropolitan area. They categorized cardiovascular diagnoses as ischemic heart disease, congestive heart failure, or cerebrovascular disease, and respiratory diagnoses were categorized as chronic obstructive pulmonary disease, asthma, or pneumonia. The investigators analyzed lag patterns for exposure to each PM2.5 constituent and hospital admissions for the six cardiovascular and respiratory illnesses on the day of admission and each of 14 days prior.On average there were 236 nonelective hospital admissions per day; 19% of these patients had a discharge diagnosis of cardiovascular disease, and 16% had a discharge diagnosis of respiratory disease. Cardiovascular hospital admissions were most strongly associated with a lag of 0–1 day, with the pattern most dominant for EC and OC, and for ischemic heart disease. Respiratory hospital admissions were most strongly associated with a longer lag of 2–5 days, with the pattern again most dominant for EC and OC, and with asthma showing the strongest associations. Sulfate and nitrate showed less association with any diagnosis at any lag. Lag patterns were consistent after adjusting for exposure to the gaseous copollutants carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone.The study is limited by its reliance on only one residential monitoring site for ambient PM2.5 and its chemical constituents, but the authors write that the strong correlations between the data obtained from that site and from a nearby federal monitoring site support the validity of the study design. The authors plan future studies to explore whether specific sources of PM2.5 exhibit similar time lag effects.FiguresReferencesRelatedDetailsRelated articlesThe Temporal Lag Structure of Short-term Associations of Fine Particulate Matter Chemical Constituents and Cardiovascular and Respiratory Hospitalizations18 May 2012Environmental Health Perspectives Vol. 120, No. 8 August 2012Metrics About Article Metrics Publication History Originally published1 August 2012Published in print1 August 2012 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Note to readers with disabilities EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days.

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. Bi-weekly 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.

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.

THE SENSITIVITY OF TIME-SERIES HEALTH EFFECT ESTIMATES TO FINE PARTICULATE MATTER (PM2.5) CHEMICAL COMPONENTS SAMPLING SCHEDULES

Background and Aims: The U.S. EPA Speciation Trends Network (STN) is a resource used to investigate the association between PM2.5 chemical components and health in time-series studies. However, the temporally-sparse sampling on every third or sixth day reduces the number of days available for analysis and may influence resulting associations and interpretations. The Denver Aerosol Sources and Health (DASH) study obtained daily speciated PM2.5 data over five years allowing us to examine the impact of less than daily monitoring data on health effect estimates. Methods: We performed a time-series analysis using daily PM2.5 chemical component concentrations and daily hospitalization counts from 2003 through 2007. Three every third-day sets of time series data for component concentrations were created from daily data, imitating the STN monitoring schedule. A fourth scenario was created using DASH data limited to the actual valid sampling days reported at the closest STN monitor. Four PM2.5 chemical components, elemental and organic carbon, sulfate, and nitrate, measured in the DASH study were examined. Relative risks (RRs per interquartile range increases) of hospitalizations for PM2.5 components at day lags 0 to 3 were estimated for each of the four systematically-missing and for the complete time-series using the Generalized Additive Model. Estimated RRs were compared across sampling schedules. Results: The estimated RRs often varied between limited and complete sampling schedules and between the four limited sampling schedules, depending on PM2.5 components and hospitalization diagnoses. For example, the association of asthma hospitalization and of nitrate at lag0 using complete time-series data (RR = 0.999; 95 % confidence interval (CI) = 0.998, 1.010; 0.86 µg/m3 increase) was elevated and statistically significant in one of the every-third-day scenarios (RR = 1.024; 95 % CI=1.005, 1.043). Conclusions: Estimated effects of PM2.5 components on hospitalization in time-series studies were sensitive to the completeness of daily sampling and the selection of sampling days.

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.

Temporal patterns in daily measurements of inorganic and organic speciated PM 2.5 in Denver

Airborne particulate matter less than 2.5 μm in aerodynamic diameter (PM 2.5) 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. PM 2.5 originates from a multitude of sources and has heterogeneous physical and chemical characteristics. These features complicate the link between PM 2.5 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 PM 2.5 in Denver. The datxa set being collected for the DASH study will be the longest daily speciated PM 2.5 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.

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.

The Denver Aerosol Sources and Health (DASH) study: Overview and early findings

Improved understanding of the sources of air pollution that are most harmful could aid in developing more effective measures for protecting human health. The Denver Aerosol Sources and Health (DASH) study was designed to identify the sources of ambient fine particulate matter (PM 2.5) that are most responsible for the adverse health effects of short-term exposure to PM 2.5. Daily 24-h PM 2.5 sampling began in July 2002 at a residential monitoring site in Denver, Colorado, using both Teflon and quartz filter samplers. Sampling is planned to continue through 2008. Chemical speciation is being carried out for mass, inorganic ionic compounds (sulfate, nitrate and ammonium), and carbonaceous components, including elemental carbon, organic carbon, temperature-resolved organic carbon fractions and a large array of organic compounds. In addition, water-soluble metals were measured daily for 12 months in 2003. A receptor-based source apportionment approach utilizing positive matrix factorization (PMF) will be used to identify PM 2.5 source contributions for each 24-h period. Based on a preliminary assessment using synthetic data, the proposed source apportionment should be able to identify many important sources on a daily basis, including secondary ammonium nitrate and ammonium sulfate, diesel vehicle exhaust, road dust, wood combustion and vegetative debris. Meat cooking, gasoline vehicle exhaust and natural gas combustion were more challenging for PMF to accurately identify due to high detection limits for certain organic molecular marker compounds. Measurements of these compounds are being improved and supplemented with additional organic molecular marker compounds. The health study will investigate associations between daily source contributions and an array of health endpoints, including daily mortality and hospitalizations and measures of asthma control in asthmatic children. Findings from the DASH study, in addition to being of interest to policymakers, by identifying harmful PM 2.5 sources may provide insights into mechanisms of PM effect.

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 μm 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.