Compound-Specific Radiocarbon Analysis of Low Molecular Weight Dicarboxylic Acids in Ambient Aerosols Using Preparative Gas Chromatography: Method Development
Compound-Specific Radiocarbon Analysis of Low Molecular Weight Dicarboxylic Acids in Ambient Aerosols Using Preparative Gas Chromatography: Method Development
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使用制备型气相色谱法对环境气溶胶中的低分子量二羧酸进行化合物特异性放射性碳分析:方法开发
DOI:
10.1021/acs.estlett.0c00887
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发表时间:
2021
影响因子:
10.9
通讯作者:
Zhang Gan
中科院分区:
文献类型:
--
作者:
Xu Buqing;Cheng Zhineng;Gustafsson Örjan;Kawamura Kimitaka;Jin Biao;Zhu Sanyuan;Tang Tiangang;Zhang Bolong;Li Jun;Zhang Gan
Low molecular weight dicarboxylic acids constitute a large fraction of atmospheric organic aerosols, which impact atmospheric radiative forcing and hence Earth’s climate. Radiocarbon (14C) is a unique approach to unambiguously distinguishing the relative contributions of biomass-derived and fossil sources. Here, we developed a compound-specific radiocarbon analysis (CSRA) method for individual dicarboxylic acids in atmospheric particulates. Specifically, the method starts with a dibutyl ester derivatization technique, followed by separation and harvesting of single compounds employing a preparative capillary gas chromatography in sufficient amounts for offline14C measurement with accelerator mass spectrometry. The optimized preparative steps yielded recoveries of >60% and purities of >99% for target molecules. The radiocarbon isotope compositions determined for reference standards taken through the entire method agree well with the original composition of each standard (R2= 0.9998). The applicability of the approach was demonstrated with ambient aerosol samples representing contrasting air mass regimes. This yielded two radically different yet system-consistent precursor sources. A minimum size of 50 μg of C of ambient dicarboxylic acids is needed for credible14C measurement. The established method for CSRA of dicarboxylic acids demonstrates a new analytical dimension for studies of the source and evolution of atmospheric secondary organic aerosols.