Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Dutton SJ

PROVIDER: S-EPMC2678721 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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

Dutton Steven J SJ Williams Daniel E DE Garcia Jessica K JK Vedal Sverre S Hannigan Michael P MP

Atmospheric environment (Oxford, England : 1994) 20090401 12

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 i ...[more]

PMID: 20161318

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Project description: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.

Project description:Ambient particulate matter of aerodynamic diameter less than 2.5 microns PM2.5) levels in Delhi routinely exceed World Health Organization (WHO) guidelines and Indian National Ambient Air Quality Standards (NAAQS) for acceptable levels of daily exposure. Only a handful of studies have examined the short-term mortality effects of PM in India, with none from Delhi examining the contribution of PM2.5.ObjectivesWe aimed to analyze the association between short-term PM2.5 exposures and daily nonaccidental mortality in Delhi, India.MethodsUsing generalized additive Poisson regression models, we examined the association between daily PM2.5 exposures and nonaccidental mortality between June 2010 and December 2016. Daily exposures to PM2.5 were estimated using an ensemble averaging technique developed by our research group, and mortality data were obtained from the Municipal Corporations of Delhi and the New Delhi Municipal Council.ResultsMedian exposures to PM2.5 were 91.1 µg/m3 (interquartile range = 68.9, 126.2), with minimum and maximum exposures of 21.4 µg/m3 and 276.7 µg/m3, respectively. Total nonaccidental deaths recorded in Delhi during the study period were 700,512. Each 25 µg/m3 increment in exposure was associated with a 0.8% (95% confidence intervals [CI] = 0.3, 1.3%) increase in daily nonaccidental mortality in the study population and a 1.5% (95% CI = 0.8, 2.2%) increase in mortality among those with 60 years of age or over. The exposure-response relationship was nonlinear in nature, with relative risk rising rapidly before tapering off above 125 µg/m3. Meeting WHO guidelines for acceptable levels of exposure over the study period would have likely averted 17,526 (95% CI = 6,837, 25,589) premature deaths, with older and male populations disproportionately affected.DiscussionThis study provides robust evidence of the impact of short-term exposure to PM2.5 on nonaccidental mortality with important considerations for various stakeholders including policymakers and physicians. Most importantly, we find that reducing exposures significantly below current levels would substantially decrease the mortality burden associated with PM2.5.

Project description:In recent years, PM2.5 and O3 have been the two main pollutants affecting public health in China, but the interaction of the two pollutants on human health remains unclear. A two-stage analytical approach was used to investigate the relationships of PM2.5-O3 co-pollution with nonaccidental, cardiovascular, and respiratory mortality levels across 14 cities in China. We first utilized a generalized additive model (GAM) to determine the city-specific associations of PM2.5 and O3 with daily mortality. The associations were then combined at the national and regional levels using meta-analysis. To investigate the potential interactions between the two pollutants and cause-specific mortality, we performed stratified analyses by co-pollutant exposure levels and the synergy index (SI) (SI > 1 indicates a synergistic interaction). The effect of changes in the two pollutants' concentrations (in 10 μg/m3 increases) on mortality was assessed. The stratification analysis results suggested that each 10 μg/m3 increase in PM2.5 at lag0-1 (lag01) in the low, moderate, and high strata of the O3 concentrations increased nonaccidental mortality by 0.07% (95% confidence interval: -0.03%, 0.17%), 0.33% (0.13%, 0.53%), and 0.68% (0.30%, 1.06%), respectively, with significant between-group differences (p < 0.001). Moreover, each 10 μg/m3 increase in O3 (lag01) in the low, moderate, and high strata of the PM2.5 concentrations increased nonaccidental mortality by 0.15% (-0.06%, 0.36%), 0.53% (0.19%, 0.87%), and 0.75% (0.14%, 1.36%), respectively, with significant between-group differences (p < 0.001). We also found substantial synergistic interactions between the two pollutants and nonaccidental, cardiovascular, and respiratory mortality levels, with SI values of 1.48, 1.51, and 1.33, respectively. Additionally, a subgroup analysis revealed that the interaction of these two pollutants on nonaccidental mortality were greater in South China compared to elsewhere, and during the warm season compared to during the cold season. Our findings suggested that the simultaneous control of PM2.5 and O3 within the context of combined air pollution could significantly decrease the disease risk, especially in southern China and during the warm season.

Project description:Research efforts to better characterize the differential toxicity of PM2.5 (particles with aerodynamic diameters less than or equal to 2.5 μm) speciation are often hindered by the sparse or non-existent coverage of ground monitors. The Multi-angle Imaging SpectroRadiometer (MISR) aboard NASA's Terra satellite is one of few satellite aerosol sensors providing information of aerosol shape, size and extinction globally for a long and continuous period that can be used to estimate PM2.5 speciation concentrations since year 2000. Currently, MISR only provides a 17.6 km product for its entire mission with global coverage every 9 days, a bit too coarse for air pollution health effects research and to capture local spatial variability of PM2.5 speciation. In this study, generalized additive models (GAMs) were developed using MISR prototype 4.4 km-resolution aerosol data with meteorological variables and geographical indicators, to predict ground-level concentrations of PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) in Southern California between 2001 and 2015 at the daily level. The GAMs are able to explain 66%, 62%, 55% and 58% of the daily variability in PM2.5 sulfate, nitrate, OC and EC concentrations during the whole study period, respectively. Predicted concentrations capture large regional patterns as well as fine gradients of the four PM2.5 species in urban areas of Los Angeles and other counties, as well as in the Central Valley. This study is the first attempt to use MISR prototype 4.4 km-resolution AOD (aerosol optical depth) components data to predict PM2.5 sulfate, nitrate, OC and EC concentrations at the sub-regional scale. In spite of its low temporal sampling frequency, our analysis suggests that the MISR 4.4 km fractional AODs provide a promising way to capture the spatial hotspots and long-term temporal trends of PM2.5 speciation, understand the effectiveness of air quality controls, and allow our estimated PM2.5 speciation data to be linked with common spatial units such as census tract or zip code in epidemiological studies. This modeling strategy needs to be validated in other regions when more MISR 4.4 km data becoming available in the future.

Dataset Information

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

Publications

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

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