4.8 Article

Aromaticity Index with Improved Estimation of Carboxyl Group Contribution for Biogeochemical Studies

Journal

ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume 56, Issue 4, Pages 2729-2737

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.est.1c04575

Keywords

NOM; humic substances; FTICR MS; carboxylic groups; aromaticity index; deuteromethylation; isotopic labeling

Funding

  1. Russian Science Foundation [21-47-04405]
  2. German Research Foundation [445025664]
  3. Rudolf und Helene Glaser-Stiftung [T0083\30771\2017\kg]
  4. European Regional Development Funds (EFRE-Europe funds Saxony)
  5. Helmholtz Association
  6. Russian Science Foundation [21-47-04405] Funding Source: Russian Science Foundation

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This article proposes a new constrained aromaticity index (AI(con)), based on the measured distribution of carboxylic groups, to assess and explain the reactivity of natural organic matter. Compared to conventional indices, AI(con) performs better in describing the transformations and fate of aromatic compounds.
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(con) 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 n = 2 for carboxyl-accounted AI(con) calculation. The examination of the proposed AI(con) 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(con) performed better than AI(mod) for all compound classes in which aromatic moieties are expected: aromatics, condensed aromatics, and unsaturated compounds. Therefore, AI(con) referenced with two carboxyl groups is preferred over conventional AI and AI(mod) for biogeochemical studies where the aromaticity of compounds is important to understand the transformations and fate of NOM compounds.

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