2009 Progress Report: Development and Assessment of Environmental Indicators: Application to Mobile Source Impacts on Emissions, Air Quality and Health Outcomes

EPA Grant Number: R833626
Title: Development and Assessment of Environmental Indicators: Application to Mobile Source Impacts on Emissions, Air Quality and Health Outcomes
Investigators: Russell, Armistead G. , Klein, Mitchel , Mulholland, James , Sarnat, Stefanie Ebelt , Sarnat, Jeremy , Tolbert, Paige
Institution: Georgia Institute of Technology , Emory University
EPA Project Officer: Nolt-Helms, Cynthia
Project Period: October 1, 2007 through September 30, 2010 (Extended to September 30, 2011)
Project Period Covered by this Report: October 1, 2008 through September 30,2009
Project Amount: $499,512
RFA: Development of Environmental Health Outcome Indicators (2006) RFA Text |  Recipients Lists
Research Category: Health Effects , Health

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.

Progress Summary:

During the period covered by this report we have focused on the following activities: analysis of health outcome residuals, design of air quality indicators from mobile sources, and comparison of estimates of secondary organic carbon (SOC).

Our analysis of health outcome residuals from epidemiologic models in which air pollution has not been included as a variable, has been useful to identify air quality indicators more associated with health endpoints. We have tested relationships between different air quality species and respiratory and cardiovascular diseases. We found weak correlations between species and outcomes (R2 <0.1), but yet statistically significant (p-value <0.05).

We have confirmed that summer-related species, such as sulfate (SO4) and ozone (O3), have significant association with respiratory diseases and species that are more prominent in winter, such as  NOx, EC, OC, Br, K and Zn, are better correlated with cardiovascular diseases. These results support our approach to use health outcome residuals in the design of more complex air quality indicators.

In fact, using dimension-reduction techniques, we identified groups of pollutants that show association with health. Combustion-related factors (mobile and wood burning), more prominent in colder months, showed association with cardiovascular diseases, and summer-related factors, such as secondary sulfate, were more correlated with respiratory diseases.

In the design of air quality indicators we have integrated information from singles species associated with mobile sources (EC, CO, NO). The Integrated Mobile Source Indicator (IMSI), as called in this study, has a typical daily-pattern of automobile activity and a smoother performance on an annual-basis, compensating for the difference in summer/winter concentrations of singles species.

We have also advanced in the estimation of the vehicular fraction of the ozone. We have used sensitivity fields of ozone to NOx and to VOC from Air Quality Models (AQM) and ratios of mobile/total NOx and VOC emissions, to assess the contribution of mobile sources to ambient ozone.

Finally, we complemented our estimation of the SOC fraction in Atlanta using receptor models in addition to the tracer and the regression methods and compared our estimates with the water soluble fraction of the OC (WSOC), which has been suggested as a surrogate of SOC in conditions where biomass burning in negligible.
 
While CMB typically gives the higher estimate of the SOC (47% of OC), PMF is on the low end (25% of OC). The regression and tracer methods estimate an average SOC of 28-33% of OC. The tracer and the PMF methods gave the lowest correlation with the WSOC fraction, as well as the highest bias and error. The regression and CMB models resolved intermediate values of SOC and reproduced more closely the WSOC measurements.
 
Among the four methods, the regression estimate was typically in the center of the range of the other estimates, showed less daily variability and reproduced the WSOC measurements most closely.   Long-term SOC estimates using the regression method can be used for epidemiologic analysis and improving air quality modeling, without the need of additional information than the air quality data, and requires less data manipulation than other approaches.

Future Activities:

Our focus for the subsequent period will be the design of a greater number of air quality indicators that integrate information on mobile source impacts and show association with health endpoints. We will use a variety of grouping techniques and assess the correlation between the different metrics. These sets of indicators will be implemented in the epidemiologic models to confirm and quantify associations with health endpoints. Finally, the chosen indicators will be evaluated and refined using an independent set of emissions, air quality and health endpoints from the period 2005-2009.

References:

 


Journal Articles on this Report : 1 Displayed | Download in RIS Format

Other project views: All 17 publications 12 publications in selected types All 12 journal articles
Type Citation Project Document Sources
Journal Article Lee D, Balachandran S, Pachon J, Shankaran R, Lee S, Mulholland JA, Russell AG. Ensemble-trained PM2.5 source apportionment approach for health studies. Environmental Science & Technology 2009;43(18):7023-7031. R833626 (2009)
R831076 (Final)
R832159 (Final)
R833866 (2009)
R833866 (Final)
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  • Supplemental Keywords:

    Indicators, Secondary Organic Carbon, SOC, Epidemiologic models, Health residuals

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    Progress and Final Reports:

    Original Abstract
  • 2008 Progress Report
  • 2010 Progress Report
  • Final Report