Determination of PM Mass Emissions from an Aircraft Turbine Engine Using Particle Effective Density


Inventories of particulate matter (PM) emissions from civil aviation and air quality models need to be validated using up-to-date measurement data corrected for sampling artifacts. We compared the measured black carbon (BC) mass and the total PM mass determined from particle size distributions (PSD) and effective density for a commercial turbofan engine CFM56-7B26/3. The effective density was then used to calculate the PM mass losses in the sampling system. The effective density was determined using a differential mobility analyzer and a centrifugal particle mass analyzer, and increased from engine idle to take-off by up to 60%. The determined mass-mobility exponents ranged from 2.37 to 2.64. The mean effective density determined by weighting the effective density distributions by PM volume was within 10% of the unit density (1000kg/m3) that is widely assumed in aircraft PM studies. We found ratios close to unity between the PM mass determined by the integrated PSD method and the real-time BC mass measurements. The integrated PSD method achieved higher precision at ultra-low PM concentrations at which current mass instruments reach their detection limit. The line loss model predicted ~60% PM mass loss at engine idle, decreasing to ~27% at high thrust. Replacing the effective density distributions with unit density lead to comparable estimates that were within 20% and 5% at engine idle and high thrust, respectively. These results could be used for the development of a robust method for sampling loss correction of the future PM emissions database from commercial aircraft engines.


Mechanical and Aerospace Engineering

Second Department


Keywords and Phrases

Aviation; Particulate Emissions; Aircraft Turbine Engines; Black Carbon; Effective Density; Mass Emissions; Particulate Matter; Black Carbon; Accuracy; Air Quality; Limit Of Detection

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


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© 2014 Elsevier, All rights reserved.

Publication Date

01 Dec 2014