Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach

Tetsuro Kikuchi; Toshihiko Anzai; Takao Ouchi

doi:10.1016/j.scitotenv.2023.166892

Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach

Sci Total Environ. 2023 Dec 15:904:166892. doi: 10.1016/j.scitotenv.2023.166892. Epub 2023 Sep 6.

Authors

Tetsuro Kikuchi¹, Toshihiko Anzai², Takao Ouchi³

Affiliations

¹ Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan. Electronic address: tekikuchi@affrc.go.jp.
² Tropical Agriculture Research Front, JIRCAS, 1091-1 Maezato-Kawarabaru, Ishigaki, Okinawa 907-0002, Japan. Electronic address: anzai0128@affrc.go.jp.
³ Ibaraki Kasumigaura Environmental Science Center, 1853 Okijuku-machi, Tsuchiura, Ibaraki 300-0023, Japan. Electronic address: ta_oouchi@pref.ibaraki.lg.jp.

PMID: 37683858
DOI: 10.1016/j.scitotenv.2023.166892

Abstract

Dissolved organic matter (DOM) plays important roles not only in maintaining the productivity and functioning of aquatic ecosystems but also in the global carbon cycle, although the sources and biogeochemical functions of terrestrially derived DOM have not been fully elucidated, particularly in the tropics and subtropics. This study aimed to evaluate the factors influencing spatiotemporal variability in (i) the concentration and composition of DOM, including dissolved organic carbon (DOC), ultraviolet absorption coefficient at 254-nm wavelength (a₂₅₄), and components identified by fluorescence excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC), and (ii) the concentration of dissolved iron (DFe) across freshwater systems (rivers, forested streams, and dam reservoirs) on a tropical island (Ishigaki Island, Japan) based on the results of water quality monitoring at 2-month intervals over a 2-year period. Random forests (RF) machine learning algorithm was employed, with the catchment characteristics (land use, soil type) and water temperature as the predictor variables for DOM and the composition of DOM (EEM-PARAFAC components) and hydrochemistry (water temperature, pH, and concentrations of divalent cations) as the predictor variables for DFe. The RF models for DOC, a₂₅₄, and three humic-like components exhibited excellent predictive performance, indicating that these DOM properties are not only seasonally variable but also strongly influenced by the compositions of land uses and soil types in the upstream watershed. Poorly drained riparian lowland soil (Gleyic Fluvisols) was identified as the most important catchment parameter that positively influences these DOM variables. The RF model also explained a large portion of the variation in DFe, while terrestrial humic-like components were the most important parameters, emphasizing their significance as organic ligands for iron. These results improve our understanding of the impacts of terrestrial DOM and iron loadings on tropical and subtropical coastal ecosystems as well as on regional and global carbon budgets.

Keywords: DOM; EEM-PARAFAC; Freshwater; Iron; Random forests; Tropics.