Modal Bin Hybrid Model: A Surface Area Consistent, Triple Moment Sectional Method for Use in Process-oriented Modeling of Atmospheric Aerosols

Journal Article · Tue Sep 10 00:00:00 EDT 2013 · Journal of Geophysical Research. D. (Atmospheres), 118(17):10,011-10,040

DOI:https://doi.org/10.1002/jgrd.50685· OSTI ID:1130691

Kajino, Mizuo; Easter, Richard C.; Ghan, Steven J.

A triple moment sectional method, Modal Bin Hybrid Model (MBHM), has been developed. In addition to number and mass (volume), surface area is predicted (and preserved), which is important for gas-to-particle mass transfer and light extinction cross section. The performance of MBHM was evaluated against double moment sectional (DMS) methods with various size resolutions up to BIN256 (BINx: x is number of sections over three orders of magnitude in size, ΔlogD = 3/x) for simulating evolution of particles under simultaneously occurring nucleation, condensation and coagulation processes. Because MBHM gives a physically consistent form of the intra-sectional distributions, errors and biases of MBHM at BIN4-8 resolution were almost equivalent to those of DMS at BIN16-32 resolution for various important variables such as the moments Mk (k: 0, 2, 3), dMk/dt, and the number and volume of particles larger than a certain diameter. Another important feature of MBHM is that only a single bin is adequate to simulate full aerosol dynamics for particles whose size distribution can be approximated by a single lognormal mode. This flexibility is useful for process-oriented (multi category and/or mixing state) modeling: primary aerosols whose size parameters would not differ substantially in time and space can be expressed by a single or a small number of modes, whereas secondary aerosols whose size changes drastically from one to several hundred nanometers can be expressed by a number of modes. Added dimensions can be applied to MBHM to represent mixing state or photo-chemical age for aerosol mixing state studies.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1130691
• Report Number(s): PNNL-SA-93195; KP1701000
• Journal Information: Journal of Geophysical Research. D. (Atmospheres), 118(17):10,011-10,040, Journal Name: Journal of Geophysical Research. D. (Atmospheres), 118(17):10,011-10,040
• Country of Publication: United States
• Language: English