Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-17-12097-2017 |
Classifying aerosol type using in situ surface spectral aerosol optical properties | |
Schmeisser, Lauren1,2,18; Andrews, Elisabeth1,2; Ogren, John A.1; Sheridan, Patrick1; Jefferson, Anne1,2; Sharma, Sangeeta3; Kim, Jeong Eun4; Sherman, James P.5; Sorribas, Mar6; Kalapov, Ivo7; Arsov, Todor7; Angelov, Christo7; Mayol-Bracero, Olga L.8; Labuschagne, Casper9,10; Kim, Sang-Woo11; Hoffer, Andras12; Lin, Neng-Huei13; Chia, Hao-Ping13; Bergin, Michael14; Sun, Junying15,16; Liu, Peng17; Wu, Hao17 | |
2017-10-12 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS |
ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2017 |
卷号 | 17期号:19 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Canada; South Korea; Spain; Bulgaria; South Africa; Hungary; Taiwan; Peoples R China |
英文摘要 | Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering (A) over circle ngstrom exponent (SAE), absorption (A) over circle ngstrom exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes. Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station. The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000412824300003 |
WOS关键词 | FILTER-BASED MEASUREMENTS ; VISIBLE-LIGHT ABSORPTION ; BROWN CARBON ; BLACK CARBON ; ANGSTROM EXPONENT ; SIZE DISTRIBUTION ; ORGANIC-CARBON ; DUST AEROSOLS ; PUERTO-RICO ; SOUTH-POLE |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/26209 |
专题 | 地球科学 |
作者单位 | 1.NOAA, Earth Syst Res Lab, Boulder, CO USA; 2.Univ Colorado Boulder, CIRES, Boulder, CO USA; 3.Environm & Climate Change Canada, Sci & Technol Branch, Toronto, ON, Canada; 4.Natl Inst Meteorol Sci, Environm Meteorol Res Div, Seoul, South Korea; 5.Appalachian State Univ, Dept Phys & Astron, Boone, NC 28608 USA; 6.INTA, Atmospher Res & Instrumentat Branch, Atmospher Sounding Stn, El Arenosillo, Mazagon 21130, Huelva, Spain; 7.Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Sofia, Bulgaria; 8.Univ Puerto Rico, Dept Environm Sci, San Juan, PR 00936 USA; 9.South African Weather Serv, Stellenbosch, South Africa; 10.North West Univ, Unit Environm Sci & Management, Potchefstroom Campus, Potchefstroom, South Africa; 11.Seoul Natl Univ, Sch Earth & Environm Sci, Seoul 08826, South Korea; 12.MTA PE Air Chem Res Grp, POB 158, H-8201 Veszprem, Hungary; 13.Natl Cent Univ, Dept Atmospher Sci, Taoyuan, Taiwan; 14.Duke Univ, Dept Civil & Environm Engn, Durham, NC 27706 USA; 15.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China; 16.Chinese Acad Meteorol Sci, Key Lab Atmospher Chem, CMA, Beijing 100081, Peoples R China; 17.Qinghai Meteorol Bur, China GAW Baseline Observ, Xining 810001, Peoples R China; 18.Univ Washington, Dept Atmospher Sci, Seattle, WA 98195 USA |
推荐引用方式 GB/T 7714 | Schmeisser, Lauren,Andrews, Elisabeth,Ogren, John A.,et al. Classifying aerosol type using in situ surface spectral aerosol optical properties[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2017,17(19). |
APA | Schmeisser, Lauren.,Andrews, Elisabeth.,Ogren, John A..,Sheridan, Patrick.,Jefferson, Anne.,...&Wu, Hao.(2017).Classifying aerosol type using in situ surface spectral aerosol optical properties.ATMOSPHERIC CHEMISTRY AND PHYSICS,17(19). |
MLA | Schmeisser, Lauren,et al."Classifying aerosol type using in situ surface spectral aerosol optical properties".ATMOSPHERIC CHEMISTRY AND PHYSICS 17.19(2017). |
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