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Atmospheric humic-like substances (HULIS) in Chongqing, Southwest China: Abundance, light absorption properties, and potential sources
Tang, Tian1,2; Wang, Huanbo1; Tao, Hongli1; Yang, Fumo3; Chen, Yang4; Huo, Tingting1; Yang, Hao1; An, Qi1; Li, Xiran1
2023-11-01
摘要Atmospheric humic-like substances (HULIS) have a great impact on radiative forcing and atmospheric oxidative capacity due to their efficient light absorption properties. Knowledge on atmospheric HULIS abundance and light absorption properties in southwest China is still very limited. In this study, a total of 81 daily samples of fine particulate matter (PM2.5) were collected at two sites in Chongqing during four one-month periods in 2015, each of which representing a typical season. Mass concentrations of HULIS in term of carbon (HULIS-C) were measured by a total organic carbon analyzer, while mass absorption efficiency at 365 nm (MAE(365)) and ab-sorption & Aring;ngstrom exponent (AAE) were estimated from light absorption of HULIS that was recorded by UV-visible spectrophotometry. The annual mean concentration of HULIS-C was around 3.4 +/- 1.6 mu g m(- 3), while the annual mean values of MAE(365) and AAE were approximately 2.7 +/- 0.7 m(2) g(-1)C and 4.0 +/- 0.2, respectively. Seasonal variations were significant for MAE(365) but negligible for AAE at both sites. A direct comparison of MAE(365) of HULIS in this study with the median MAE365 values from different sources in laboratory studies indicated that crop residues burning and/or wood burning were the primary sources of light-absorbing HULIS, as is also supported by the strong correlation between the light absorption coefficient at 365 nm (Abs(365)) and K+ (r > 0.85, p < 0.01) throughout the year. Furthermore, the good correlation between Abs365 and secondary organic carbon implied secondary sources also contributed to light-absorbing HULIS (e.g., nitrated aromatic compounds production), as evidenced by the good correlation between Abs365 and NO(3)(-)and NH4+. The contribution of pri-mary and secondary sources to light-absorbing HULIS was quantified using a machine learning algorithm, which showed >65% contribution from primary sources during the cold season, but only 35% during the warm seasons.
关键词HULIS Light absorption Source identification Random forest analysis
DOI10.1016/j.atmosres.2023.107016
发表期刊ATMOSPHERIC RESEARCH
ISSN0169-8095
卷号295页码:11
通讯作者Wang, Huanbo(hbwang@swust.edu.cn)
收录类别SCI
WOS记录号WOS:001082023600001
语种英语