Skolkovo Institute of Science and Technology, Moscow 121205, Russia.
Department of Analytical Chemistry, Research Group BioGeoOmics, Helmholtz Centre for Environmental Research─UFZ, Leipzig DE-04318, Germany.
Environ Sci Technol. 2022 Feb 15;56(4):2729-2737. doi: 10.1021/acs.est.1c04575. Epub 2022 Jan 27.
Natural organic matter (NOM) components measured with ultrahigh-resolution mass spectrometry (UHRMS) are often assessed by molecular formula-based indices, particularly related to their aromaticity, which are further used as proxies to explain biogeochemical reactivity. An aromaticity index (AI) is calculated mostly with respect to carboxylic groups abundant in NOM. Here, we propose a new constrained AI based on the measured distribution of carboxylic groups among individual NOM components obtained by deuteromethylation and UHRMS. Applied to samples from diverse sources (coal, marine, peat, permafrost, blackwater river, and soil), the method revealed that the most probable number of carboxylic groups was two, which enabled to set a reference point = 2 for carboxyl-accounted AI calculation. The examination of the proposed AI showed the smallest deviation to the experimentally determined index for all NOM samples under study as well as for individual natural compounds obtained from the Coconut database. In particular, AI performed better than AI for all compound classes in which aromatic moieties are expected: aromatics, condensed aromatics, and unsaturated compounds. Therefore, AI referenced with two carboxyl groups is preferred over conventional AI and AI for biogeochemical studies where the aromaticity of compounds is important to understand the transformations and fate of NOM compounds.
天然有机物(NOM)成分采用超高分辨率质谱(UHRMS)进行测量,通常采用基于分子公式的指数进行评估,尤其是与芳香性相关的指数,这些指数进一步被用作解释生物地球化学反应性的替代指标。芳香度指数(AI)主要是根据 NOM 中丰富的羧酸基团计算得出的。在这里,我们提出了一种新的基于测量的约束 AI,该 AI 基于通过氘甲基化和 UHRMS 获得的单个 NOM 成分中羧酸基团的分布。应用于来自不同来源的样品(煤、海洋、泥炭、永久冻土、黑水河流和土壤),该方法表明羧酸基团的最可能数量为两个,这使得可以为羧基 AI 计算设定参考点 = 2。对所提出的 AI 的检验表明,与所有研究中的 NOM 样品以及从椰子数据库获得的单个天然化合物相比,所有 AI 的偏差最小。特别是,对于所有预期含有芳香部分的化合物类别,AI 的性能均优于 AI:芳烃、稠环芳烃和不饱和化合物。因此,在需要了解化合物芳香性以理解 NOM 化合物的转化和归宿的生物地球化学研究中,参考两个羧基的 AI 优于常规 AI 和 AI。