Quantifying aerosol size distributions and their temporal variability in the Southern Great Plains, USA
Abstract
A quality-controlled, 5-year dataset of aerosol number size distributions (particles with diameters (Dp) from 7 nm through 14 µm) was developed using observations from a scanning mobility particle sizer, aerodynamic particle sizer, and a condensation particle counter at the Department of Energy's Southern Great Plains (SGP) site. This data set was used for two purposes. First, typical characteristics of the aerosol size distribution (number, surface area, and volume) were calculated for the SGP site, both for the entire dataset and on a seasonal basis, and size distribution lognormal fit parameters are provided. While the median size distributions generally had similar shapes (four lognormal modes) in all the seasons, there were some significant differences between seasons. These differences were most significant in the smallest particles(Dp<30 nm) and largest particles (Dp>800 nm).Second, power spectral analysis was conducted on this long-term dataset to determine key temporal cycles of total aerosol concentrations, as well as aerosol concentrations in specified size ranges. The strongest cyclic signal was associated with a diurnal cycle in total aerosol number concentrations that was driven by the number concentrations of the smallest particles(Dp<30 nm). This diurnal cycle in the smallest particles occurred in all seasons in ~50% of the observations,suggestingmore »
- Authors:
-
[1];
[1];
- Colorado State Univ., Fort Collins, CO (United States)
- Colorado State Univ., Fort Collins, CO (United States). Physics Dept.
- Univ. of California, Riverside, CA (United States)
- Publication Date:
- Research Org.:
- Colorado State Univ., Fort Collins, CO (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF); National Aeronautics and Space Administration (NASA)
- OSTI Identifier:
- 1594088
- Grant/Contract Number:
- SC0016051; DGE-1321845; 80NSSC18K0149
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Atmospheric Chemistry and Physics (Online)
- Additional Journal Information:
- Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 19; Journal Issue: 18; Related Information: Marinescu, P. J., Levin, E. J. T., Collins, D., and Kreidenweis, S. M. (2019), SGP Merged Aerosol Size Distribution (CPC + SMPS + APS), U.S. Department of Energy ARM Data Center, Oak Ridge, Tennessee, USA, https://doi.org/10.5439/1511037.; Journal ID: ISSN 1680-7324
- Publisher:
- European Geosciences Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; aerosol particle size distribution observations; Southern Great Plains Site; aerosol-cloud interactions
Citation Formats
Marinescu, Peter J., Levin, Ezra J. T., Collins, Don, Kreidenweis, Sonia M., and van den Heever, Susan C. Quantifying aerosol size distributions and their temporal variability in the Southern Great Plains, USA. United States: N. p., 2019.
Web. doi:10.5194/acp-19-11985-2019.
Marinescu, Peter J., Levin, Ezra J. T., Collins, Don, Kreidenweis, Sonia M., & van den Heever, Susan C. Quantifying aerosol size distributions and their temporal variability in the Southern Great Plains, USA. United States. https://doi.org/10.5194/acp-19-11985-2019
Marinescu, Peter J., Levin, Ezra J. T., Collins, Don, Kreidenweis, Sonia M., and van den Heever, Susan C. Thu .
"Quantifying aerosol size distributions and their temporal variability in the Southern Great Plains, USA". United States. https://doi.org/10.5194/acp-19-11985-2019. https://www.osti.gov/servlets/purl/1594088.
@article{osti_1594088,
title = {Quantifying aerosol size distributions and their temporal variability in the Southern Great Plains, USA},
author = {Marinescu, Peter J. and Levin, Ezra J. T. and Collins, Don and Kreidenweis, Sonia M. and van den Heever, Susan C.},
abstractNote = {A quality-controlled, 5-year dataset of aerosol number size distributions (particles with diameters (Dp) from 7 nm through 14 µm) was developed using observations from a scanning mobility particle sizer, aerodynamic particle sizer, and a condensation particle counter at the Department of Energy's Southern Great Plains (SGP) site. This data set was used for two purposes. First, typical characteristics of the aerosol size distribution (number, surface area, and volume) were calculated for the SGP site, both for the entire dataset and on a seasonal basis, and size distribution lognormal fit parameters are provided. While the median size distributions generally had similar shapes (four lognormal modes) in all the seasons, there were some significant differences between seasons. These differences were most significant in the smallest particles(Dp<30 nm) and largest particles (Dp>800 nm).Second, power spectral analysis was conducted on this long-term dataset to determine key temporal cycles of total aerosol concentrations, as well as aerosol concentrations in specified size ranges. The strongest cyclic signal was associated with a diurnal cycle in total aerosol number concentrations that was driven by the number concentrations of the smallest particles(Dp<30 nm). This diurnal cycle in the smallest particles occurred in all seasons in ~50% of the observations,suggesting a persistent influence of new particle formation events on the number concentrations observed at the SGP site. This finding is in contrast with earlier studies that suggest new particle formation is observed primarily in the springtime at this site. The timing of peak concentrations associated with this diurnal cycle was shifted by several hours depending on the season,which was consistent with seasonal differences in insolation and boundary layer processes. Significant diurnal cycles in number concentrations were also found for particles with Dp between 140 and 800 nm, with peak concentrations occurring in the overnight hours, which were primarily associated with both nitrate and organic aerosol cycles. Weaker cyclic signals were observed for longer timescales (days to weeks) and are hypothesized to be related to the timescales of synoptic weather variability. The strongest periodic signals (3.5–5 and 7 d cycles) for these longer timescales varied depending on the season, with no cyclic signals and the lowest variability in the summer.},
doi = {10.5194/acp-19-11985-2019},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 18,
volume = 19,
place = {United States},
year = {2019},
month = {9}
}
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