DOI: 10.5061/dryad.x69p8czt8
title: Data from: Prediction of dissolved organic carbon concentrations in inland waters using optical proxies of aromaticity

description: The chemical structures of dissolved organic compounds in natural waters,
 including the degree of aromaticity, affect their physical, chemical, and
 biological properties and ultimately the fate of carbon in aquatic systems
 and during water treatment. Herein, a new fluorescence-based aromaticity
 index named ARIX is shown to link the composition of aquatic dissolved
 organic matter to its aromaticity across diverse aquatic systems in both
 bulk DOM and extracts. ARIX predicts SUVA, a widely used proxy of
 aromaticity, more accurately than prevailing optical indices. It also
 predicts the percentage of polycyclic aromatic and polyphenolic molecular
 formulas determined by FT-ICR MS and the ratio of “humic substances” to
 “building blocks” fractions determined by LC-OCD, indicating it is
 additionally a proxy of DOM molecular weight. In waterbodies exhibiting
 decoupling between DOC and absorbance linked to biogeochemical processing,
 DOC concentrations are accurately predicted using a multilinear model to
 account for interactions between light absorption and aromaticity. The
 results deliver new insights on widely discussed trends in DOM optical
 properties and on the molecular structures underlying optical measurements
 in the aquatic milieu. They further represent an important step toward
 improved real-time monitoring of DOC concentration, reactivity, and fate.
description: The data were compiled from published datasets cited in the
 manuscript and listed in the references. Fluorescence indices (ARIX, FIX,
 HIX, BIX) were calculated from fluorescence EEMs using the pickpeaks
 function in the drEEM toolbox available at https://dreem.openfluor.org/, which implements algorithms according to the cited references.  Absorption coefficients were calculated by multiplying the measured absorbance by 2.303 and dividing by the cuvette pathlength in m. Spectral slopes were calculated according to the cited references.
description: # Data from: Prediction of dissolved organic carbon concentrations in
 inland waters using optical proxies of aromaticity
 [https://doi.org/10.5061/dryad.x69p8czt8](https://doi.org/10.5061/dryad.x69p8czt8) ## Description of the data and file structure These data consist of measurements performed on dissolved organic matter samples collected in natural aquatic systems or at water treatment plants. They represent a subset of datasets established by researchers who are cited in the manuscript and listed in the references tab of the excel file. ### Files and variables #### File: Murphy_ARIX_archive.csv **Description:**  #### **Variables** | Abbreviation | Description | Unit of measurement | | :------------- | :----------------------------------------------------- | :------------------ | | Dataset | Name of dataset | - | | Geo\_Loc | Country /site code | - | | SiteType | Water source type | - | | FIX | Fluorescence Index, after Cory et al. 2010 | - | | FrI | Freshness Index, after Parlanti et al. 2000 | - | | BIX | Biological Index, after Huguet et al. 2009 | - | | HIX | Humification Index, after Ohno et al. 2002 | - | | HIX1999 | Humification Index, after Zsolnay et al. 1999 | - | | ARIX | Aromaticity Index, after Murphy 2025 (primary article) | - | | PARIX | PARAFAC Aromaticity Index, after Philibert et al. 2022 | - | | SUVA | Specific UV Absorbance (A254/DOC)   | m2gC-1 | | SUVA\_LC | Specific UV Absorbance determined by LC-OCD | m2gC-1 | | DOC | DOC concentration measured by carbon analyzer | mgL-1 | | DOC\_LC | DOC concentration measured by LC-OCD | mgL-1 | | A254\_LC | =DOC\_LC x SUVA\_LC / 100 | cm-1 | | A254 | Absorbance at 254 nm | cm-1 | | A275 | Absorbance at 275 nm | cm-1 | | A280 | Absorbance at 280 nm | cm-1 | | A350 | Absorbance at 350 nm | cm-1 | | S275\_295\_fit | Spectral slope, after Helms et al. 2008 | - | | S275\_295 | Spectral slope, after Yan et al. 2025 | - | | S380\_443 | Spectral slope, after Yan et al. 2025 | - | | ag\_275 | absorption coefficient at 275 nm | m-1 | | ag\_295 | absorption coefficient at 295 nm | m-1 | | ag\_380 | absorption coefficient at 380 nm | m-1 | | ag\_443 | absorption coefficient at 443 nm | m-1 | | BB\_LCOCD | LC-OCD "building blocks" fraction | mgL-1 | | HS\_LCOCD | LC-OCD "humic substances" fraction | mgL-1 | **Missing values are shown as blank cells.** The file is a compilation of nine 9 datasets listed below. Data owners are listed under Data source, with full references below: | Dataset | Bioregions | Data source | | :------------ | :-------------------------------------------------- | :----------------------------------- | | Alaska Rivers | Boreal North America  | (Johnston, Carey et al. 2021) | | Yukon Lakes | Boreal North America  | (Johnston, Striegl et al. 2020) | | Everglades  | Subtropical North American wetland  | (Timko, Romera-Castillo et al. 2014) | | SUEZ | Europe: Continental, Mediterranean, China, Cameroon | (Philibert, Luo et al. 2022) | | Horsens | Continental Europe (Denmark) | (Stedmon and Markager 2005) | | Australia | Southeastern Australia | (Acharya, Holland et al. 2023) | | Congo | Congo River, Africa | (Lambert, Bouillon et al. 2016) | | S. America | Brazil, Chile, Uruguay | (Graeber, Gelbrecht et al. 2012) | | Isolates | Freshwater, coastal, Antarctica, deep sea | (Kellerman, Guillemette et al. 2018) | References:   * Acharya, S., A. Holland, G. Rees, A. Brooks, D. Coleman, C. Hepplewhite, S. Mika, N. Bond, and E. Silvester (2023). Relevance of tributary inflows for driving molecular composition of dissolved organic matter (DOM) in a regulated river system. Water Research, 237: 119975. * Cory, R. M., M. P. Miller, D. M. McKnight, J. J. Guerard, and P. L. Miller (2010). Effect of instrument-specific response on the analysis of fulvic acid fluorescence spectra. Limnology and Oceanography: Methods, 8: 67–78. * Huguet, A., L. Vacher, S. Relexans, S. Saubusse, J. M. Froidefond, and E. Parlanti (2009). Properties of fluorescent dissolved organic matter in the Gironde Estuary. Organic Geochemistry, 40(6): 706–719. * Graeber, D., J. Gelbrecht, M. T. Pusch, C. Anlanger, and D. von Schiller (2012). Agriculture has changed the amount and composition of dissolved organic matter in Central European headwater streams. Science of The Total Environment, 438: 435–446. * Helms, J. R., A. Stubbins, J. D. Ritchie, E. C. Minor, D. J. Kieber, and K. Mopper (2008). Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. Limnology and Oceanography, 53(3): 955–969. * Johnston, S. E., J. C. Carey, A. Kellerman, D. C. Podgorski, J. Gewirtzman, and R. G. M. Spencer (2021). Controls on Riverine Dissolved Organic Matter Composition Across an Arctic-Boreal Latitudinal Gradient. Journal of Geophysical Research: Biogeosciences, 126(9): e2020JG005988. * Johnston, S. E., R. G. Striegl, M. J. Bogard, M. M. Dornblaser, D. E. Butman, A. M. Kellerman, K. P. Wickland, D. C. Podgorski, and R. G. M. Spencer (2020). Hydrologic connectivity determines dissolved organic matter biogeochemistry in northern high-latitude lakes. Limnology and Oceanography, 65(8): 1764–1780. * Kellerman, A. M., F. Guillemette, D. C. Podgorski, G. R. Aiken, K. D. Butler, and R. G. M. Spencer (2018). Unifying Concepts Linking Dissolved Organic Matter Composition to Persistence in Aquatic Ecosystems. Environmental Science & Technology, 52(5): 2538–2548. * Lambert, T., S. Bouillon, F. Darchambeau, P. Massicotte, and A. V. Borges (2016). Shift in the chemical composition of dissolved organic matter in the Congo River network. Biogeosciences, 13(18): 5405–5420. * Murphy, K. R. (2025). Prediction of dissolved organic carbon concentrations in inland waters using optical proxies of aromaticity. * Ohno, T. (2002). Fluorescence inner-filtering correction for determining the humification index of dissolved organic matter. Environmental Science and Technology, 36: 742–746. * Parlanti, E., K. Wörz, L. Geoffroy, and M. Lamotte (2000). Dissolved organic matter fluorescence spectroscopy as a tool to estimate biological activity in a coastal zone submitted to anthropogenic inputs. Organic Geochemistry, 31(12): 1765–1781. * Philibert, M., S. Luo, L. Moussanas, Q. Yuan, E. Filloux, F. Zraick, and K. R. Murphy (2022). Drinking water aromaticity and treatability is predicted by dissolved organic matter fluorescence. Water Research, 220: 118592. * Stedmon, C. A., and S. Markager (2005). Resolving the variability of dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis. Limnology and Oceanography, 50: 686–697. * Timko, S. A., C. Romera-Castillo, R. Jaffé, and W. J. Cooper (2014). Photo-reactivity of natural dissolved organic matter from fresh to marine waters in the Florida Everglades, USA. Environmental Science: Processes & Impacts, 16(4): 866–878. * Yan, M., S. Mo, Z. Liu, and G. Korshin (2025). Absorptivity Inversely Proportional to Spectral Slope in CDOM. Environmental Science & Technology, 59(14): 7156–7164. * Zsolnay, A., E. Baigar, M. Jimenez, B. Steinweg, and F. Saccomandi (1999). Differentiating with fluorescence spectroscopy the sources of dissolved organic matter in soils subjected to drying. Chemosphere, 38(1): 45–50. ## Code/software The data are in text format (*.csv).