Grantee Research Project Results
Final Report: Development and Assessment of Environmental Indicators: Application to Mobile Source Impacts on Emissions, Air Quality and Health Outcomes
EPA Grant Number: R833626Title: Development and Assessment of Environmental Indicators: Application to Mobile Source Impacts on Emissions, Air Quality and Health Outcomes
Investigators: Russell, Armistead G. , Sarnat, Stefanie Ebelt , Mulholland, James , Sarnat, Jeremy , Klein, Mitchel , Tolbert, Paige
Institution: Georgia Institute of Technology , Emory University
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 2007 through September 30, 2010 (Extended to September 30, 2011)
Project Amount: $499,512
RFA: Development of Environmental Health Outcome Indicators (2006) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air Toxics , Human Health , Air
Objective:
• Develop approaches to identify outcome-based indicators and apply those approaches to mobile
sources in Atlanta for the period 1998-2004 using data and methods of varying detail and complexity.
• Test a range of integrated indicators for the impact of mobile source emission changes on air quality
and cardiovascular health.
• Develop and apply approaches for assessing these indicators for their ability to represent a range of
outcomes associated with mobile source emissions and policies.
• Evaluate select indicators using an independent, new data set of emissions, air quality and
cardiovascular health endpoints for 2005-2009 to assess the approaches developed for identifying,
testing, assessing and refining outcome indicators.
Summary/Accomplishments (Outputs/Outcomes):
Activities
A multidisciplinary project was conducted where air quality data analyses were conducted to develop a set of "Integrated Mobile Source Indicators" [IMSI]). These indicators were then used in epidemiologic analyses to assess if the integraterd indicators have potential benefits over single species, or other source apportionment procedures, when used in health association studies. In addition to the IMSI, the project also developed a different approach to using potassium (K) as an indicator for biomass burning (called Kb). Results strongly suggest that Kb is a better indicator for biomass burning. Further, a method to better estimate the secondary aerosol (SOA) fraction of the particulate matter was developed. The IMSI, SOA fraction and Kb, can readily be found from routine monitoring data. Having developed new indicators for the impacts of biomass burning, mobile sources (including diesel and gasoline vehicle impacts, separately), allow for providing greater differentiation of the sources on PM using routine monitoring data.
Methods
The approach used to develop and assess indicators and indicator sets and the use of independent data for evaluation is sketched in Figure 1. We discuss indicator development strategies for single and multipollutant species, conducted health association modeling and sensitivity analysis, and explored the propagation of uncertainties from emissions and ambient concentrations in the indicators, using data for Atlanta during 1999-2004 (Pachon et al., 2011).
Results
The above framework was used to develop a series of indicators for use in epidemiologic analyses. The indicators were developed, primarily, to address particitioning the organic aerosol fraction of the fine particulate matter because that is a major fraction of the current PM, that fraction is expected to grow, and the sources of the other major compoenents are generally viewed as being better known (e.g., sulfate). The specific source impact indicators studied are for biomass burning, secondary organic aerosol and mobile sources. As part of each indicator development, potential uncertainties were assessed.
Developing an improved indicator for biomass burning impacts
Elemental potassium has been extensively used as an indicator of biomass burning in the source apportionment of PM2.5. However, soil dust and sea-salt are also significant sources of atmospheric potassium. We present a method to estimate the fraction of potassium associated with biomass burning (Kb) based on a linear regression with iron. The estimated fraction has a significantly greater correlation with levoglucosan (R2=0.63), an organic tracer of biomass burning, than total potassium (R2=0.39). We explore temporal and spatial variability of Kb over a period of six years in the Atlanta area. Kb is larger in spring when biomass burning activity is more prevalent and during weekends due to the use of fireplaces in winter and outdoor charcoal cooking in summer. Kb is the predominate form of potassium in rural areas. The use of Kb in a receptor model results in a lower fraction of PM2.5 apportioned to biomass burning and a greater fraction to mobile sources. Results suggest that Kb is a good indicator of biomass burning as opposed to total K in source apportionment studies when source profiles are not available. The use of Kb in health studies can help to distinguish the potential impacts of biomass burning and mobile sources on cardiovascular diseases.
Developing an Indicator for Secondary Organic Aerosol
In the Southeastern US, organic carbon (OC) comprises about 30% of the PM2.5 mass. A large fraction of OC is estimated to be from secondary origin. Long-term estimates of SOC and uncertainties are necessary in the evaluation of air quality policy effectiveness and epidemiologic studies. Four methods to estimate secondary organic carbon (SOC) and respective uncertainties are compared utilizing PM2.5 chemical composition and gas phase data available in Atlanta from 1999 to 2007. The elemental carbon (EC) tracer and the regression methods, which rely on the use of tracer species of primary and secondary OC formation, provided intermediate estimates of SOC. The other two methods, chemical mass balance (CMB) and positive matrix factorization (PMF) solve mass balance equations to estimate primary and secondary fractions based on source profiles and statistically-derived common factors, respectively. The CMB method had the highest estimate of SOC while the PMF led to the lowest. The comparison of SOC uncertainties, estimated based on the propagation of errors approach, led to the regression method having the lowest uncertainty among the four methods. We compared the estimates with the water soluble fraction of the OC (WSOC), which has been suggested as a surrogate of SOC when biomass burning is negligible, and found a similar trend with SOC estimates from the regression method. The regression method also showed the best correlation with daily SOC estimates from CMB using molecular markers. The regression method shows advantages over the other methods in the calculation of a long-term series of SOC estimates.
Developing Integrated Mobile Source Indicators for use in Epidemiologic Analyses
The analysis of long-term emission trends and pollutant concentrations is used to develop relationships between traffic emissions and single and multipollutant indicators of mobile sources. Using concentration-response functions, a direct link between emissions and health outcomes is developed and then is converted into benefits using estimates of illness costs. Together, this information is grouped in to indicator sets for use by policy-makers. Relationships are explored and compared for Atlanta, GA and Dallas, TX. The use of multipollutant indicators resulted in a more consistent savings estimates from health benefits due to mobile source controls, being able to better differentiate between vehicle categories. A vehicular ozone indicator was developed from sensitivities of ozone to mobile NOx emissions, finding an inverse response of ozone concentrations to NOx emissions, which is expected for NOx-rich areas. The indicator sets developed that particularly useful in determining how to apply the base indicators in different locations, evaluation and to assess uncertainties.
Conclusions:
Environmental indicators were developed and evaluated to assess the impact of mobile sources on emissions, air quality and health outcomes. Single species and multipollutant indicators are discussed. CO, NOx and EC were used in the construction of integrated mobile source indicators (IMSI). IMSI have stronger spatial representativeness, suggesting they are better indicators of the regional impact of mobile sources. They agree well with observed trends of traffic and they have stronger associations with emergency department visits for cardiovascular diseases (CVD), possibly due to their better spatial representativeness. The changes in the incidence of adverse CVD impacts as result of the change in indicators of mobile source activity were examined. Single and multipollutant indicators were compared, finding that a multipollutant framework is more consistent to understanding health risk from mobiles source emissions than using single species. The concept of indicator sets, which include a group of indicators and their relationships, along with associated attributes, facilitates a comprehensive analysis of the air quality chain, from emissions to ambient concentrations and to health outcomes. This proposed framework is of great utility for policy makers in the setting of cost-benefit analysis of air pollution reduction
Journal Articles on this Report : 13 Displayed | Download in RIS Format
| Other project views: | All 19 publications | 14 publications in selected types | All 14 journal articles |
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Darrow LA, Klein M, Sarnat JA, Mulholland JA, Strickland MJ, Sarnat SE, Russell AG, Tolbert PE. The use of alternative pollutant metrics in time-series studies of ambient air pollution and respiratory emergency department visits. Journal of Exposure Science & Environmental Epidemiology 2011;21(1):10-19. |
R833626 (Final) R829213 (Final) |
Exit Exit |
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Demokritou P, Kavouras IG, Harrison D, Koutrakis P. Development and evaluation of an impactor for a PM2.5 speciation sampler. Journal of the Air & Waste Management Association 2001;51(4):514-523. |
R833626 (Final) R827353 (Final) R827353C017 (Final) |
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Flanders WD, Klein M, Darrow LA, Strickland MJ, Sarnat SE, Sarnat JA, Waller LA, Winquist A, Tolbert PE. A method for detection of residual confounding in time-series and other observational studies. Epidemiology 2011;22(1):59-67. |
R833626 (Final) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
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Flanders WD, Klein M, Darrow LA, Strickland MJ, Sarnat SE, Sarnat JA, Waller LA, Winquist A, Tolbert PE. A method to detect residual confounding in spatial and other observational studies. Epidemiology 2011;22(6):823-826. |
R833626 (Final) R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
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Goldman GT, Mulholland JA, Russell AG, Gass K, Strickland MJ, Tolbert PE. Characterization of ambient air pollution measurement error in a time-series health study using a geostatistical simulation approach. Atmospheric Environment 2012;57:101-108. |
R833626 (Final) R829213 (Final) R833866 (Final) R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2012) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
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Ivey C, Holmes H, Shi G, Balachandran S, Hu Y, Russell AG. Development of PM2.5 source profiles using a hybrid chemical transport-receptor modeling approach. Environmental Science & Technology 2017;51(23):13788-13796. |
R833626 (Final) R833866 (Final) R834799 (Final) |
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Lamy F, Bolte J, Santelmann M, Smith C. Development and evaluation of multiple-objective decision-making methods for watershed management planning. Journal of the American Water Resources Association 2002;38(2):517-529. |
R833626 (Final) R827146 (Final) |
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Maier ML, Balachandran S, Sarnat SE, Turner JR, Mulholland JA, Russell AG. Application of an ensemble-trained source apportionment approach at a site impacted by multiple point sources. Environmental Science & Technology 2013;47(8):3743-3751. |
R833626 (Final) R833866 (Final) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
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Pachon JE, Balachandran S, Hu Y, Weber RJ, Mulholland JA, Russell AG. Comparison of SOC estimates and uncertainties from aerosol chemical composition and gas phase data in Atlanta. Atmospheric Environment 2010;44 (32):3907-3914. |
R833626 (2010) R833626 (Final) R833866 (Final) |
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Pachon JE, Balachandran S, Hu Y, Mulholland JA, Darrow LA, Sarnat JA, Tolbert PE, Russell AG. Development of outcome-based, multipollutant mobile source indicators. Journal of the Air & Waste Management Association 2012;62(4):431-442. |
R833626 (Final) R833866 (Final) R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
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Pachon JE, Weber RJ, Zhang X, Mulholland JA, Russell AG. Revising the use of potassium (K) in the source apportionment of PM2.5. Atmospheric Pollution Research 2013;4(1):14-21. |
R833626 (Final) R833866 (Final) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) |
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Redman JD, Holmes HA, Balachandran S, Maier ML, Zhai X, Ivey C, Digby K, Mulholland JA, Russell AG. Development and evaluation of a daily temporal interpolation model for fine particulate matter species concentrations and source apportionment. Atmospheric Environment 2016;140:529-538. |
R833626 (Final) R833866 (Final) R834799 (2016) R834799 (Final) |
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Sarnat JA, Moise T, Shpund J, Liu Y, Pachon JE, Qasrawi R, Abdeen Z, Brenner, S, Nassar K, Saleh R, Schauer JJ. Assessing the spatial and temporal variability of fine particulate matter components in Israeli, Jordanian, and Palestinian cities. Atmospheric Environment 2010;44(20):2383-2392. |
R833626 (2010) R833626 (Final) |
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Supplemental Keywords:
Indicators, IMSI, Secondary Organic Carbon (SOC), Mobile sources, Health residualsProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.