Development of Tools for the Identification of Secondary Organic Aerosol in Australian Cities
CSIRO, October 2008
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- Development of Tools for the Identification of Secondary Organic Aerosol in Australian Cities (PDF - 1,401 KB) | (RTF - 7,847 KB)
- Development of Tools for the Identification of Secondary Organic Aerosol in Australian Cities - Appendix A (PDF - 1,154 KB) | (RTF - 5,200 KB)
- Development of Tools for the Identification of Secondary Organic Aerosol in Australian Cities - Appendix B (PDF - 1,287 KB) | (RTF - 12,412 KB)
- Development of Tools for the Identification of Secondary Organic Aerosol in Australian Cities - Appendix C (PDF - 1,593 KB) | (RTF - 30,507 KB)
Executive summary
This is the final report for the Clean Air Research Program Project 15: Development of tools for the identification of secondary organic aerosol (SOA) in Australian cities.
Currently the contribution of SOA to particulate mass in Australian cities is poorly quantified. SOA is produced in the atmosphere by the oxidation of volatile organic compounds (VOCs) of biogenic or anthropogenic origin, to produce semi-volatile compounds that partition to existing particles. SOA consists entirely of fine particles (PM2.5) and the contribution of SOA to the ambient PM load in Australian cities may lead to exceedences of the National Environment Protection Measure (NEPM) advisory reporting standard for PM2.5. Recent epidemiological research indicates that exposure to PM2.5 is associated with a range of health impacts. The mechanism for these impacts, however, including the role of SOA, is not well-understood.
This project involves the exploration of four techniques for the quantification of SOA, one a direct tracer and three that measured properties of the atmospheric aerosol that may be related to the SOA composition, including aerosol volatility, ratio of organic carbon to elemental carbon (OC/EC) and aerosol microphysics. In addition, the project seeks to incorporate SOA formation into a three-dimensional urban airshed model and evaluate prediction of SOA against observations.
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