Exploring the Effects of Organic Matter Characteristics on Fe(II) Oxidation Kinetics in Coastal Seawater

Abstract

The iron­(II) oxidation kinetic process was studied at 25 stations in coastal seawater of the Macaronesia region (9 around Cape Verde, 11 around the Canary Islands, and 5 around Madeira). In a physicochemical context, experiments were carried out to study the pseudo-first-order oxidation rate constant (k′, min–1) over a range of pH (7.8, 7.9, 8.0, and 8.1) and temperature (10, 15, 20, and 25 °C). Deviations from the calculated k cal ′ at the same T, pH, and S were observed for most of the stations. The measured t 1/2 (ln 2/k′, min) values at the 25 stations ranged from 1.82 to 3.47 min (mean 1.93 ± 0.76 min) and for all but two stations were lower than the calculated t 1/2 of 3.21 ± 0.2 min. In a biogeochemical context, nutrients and variables associated with the organic matter spectral properties (CDOM and FDOM) were analyzed to explain the observed deviations. The application of a multilinear regression model indicated that k′ can be described (R = 0.921 and SEE = 0.064 for pH = 8 and T = 25 °C) from a linear combination of three organic variables, k′OM = k cal ′ −0.11* TDN + 29.9*b DOM + 33.4*C1humic, where TDN is the total dissolved nitrogen, b DOM is the spectral peak obtained from colored dissolved organic matter (DOM) analysis when protein-like or tyrosine-like components are present, and C1humic is the component associated with humic-like compounds obtained from the parallel factor analysis of the fluorescent DOM. Results show that compounds with N in their structures mainly explain the observed k′ increase for most of the samples, although other components could also play a relevant role. Experimentally, k′ provides the net result between the compounds that accelerate the process and those that slow it down.

Dissolved Organic Matter Characteristics and Composition of Saline Lakes in the Badain Jaran Desert, China

“…The preliminary results showed that the effects of salinity on most spectroscopic characteristics were lower than 10% (Figure S1). As such effects were relatively small, we conducted direct measurements on the filtered and diluted water samples (DOC < 5 mg L –1 and salinity <10) according to the previous studies of saline lake water and seawater ,,− without using a further desalination approach.…”

Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation

“…The dissolved ligands could affect the solubility, reactivity, bioavailability of Fe in natural water, and the primary productivity, i.e., iron biogeochemical cycle, which is crucial to many environmental processes . Via natural or anthropogenic inputs, 3 sources (biological productivity, , siderophores, and synthetic chelators), 6 kinds (amino acid, mono- and poly saccharide, phenols, nitrogen metabolites, porphyrin, and amino-polycarboxylate), and 12 species coexist in DOM as the main ligands. − …”

“…These ligands are often divided into two types in typical detection, i.e., strong and weak ligands; strong ligands are generally specific inductive ligands with a low concentration level (∼10 –10 mol/L) in the ocean, , and weak ligands are the main type. , The current dFe speciation is determined by competitive ligand exchange/adsorption (according to complexation equilibrium constant) and molecular-level characterizations. , In the aqueous phase, the complexation equilibrium constant can reflect the order and degree of metal binding to different ligands at a certain pH 1 . All of them require instruments (electrochemistry and liquid chromatography mass-spectrometry) and complicated pretreatment.…”

“…The gap between Fe speciation and bioavailability should be bridged. The free ion activity, gill surface interaction, biotic ligand, multiple linear regression, nonideal competitive adsorption, biodynamics, and Windermere humic aqueous models have been developed to explore existing metal speciation in the hydrosphere, each with different approximations, structures, assumptions, and strengths/weaknesses. ,, The understanding of metal speciation has been hampered by the required resources and a variety of water chemistry parameters as input variables . These dFe speciation prediction models should be assessed with actual phytoplankton-based dFe uptake kinetics method, but it is limited by the long experimental times and complicated steps.…”

“…14 The free ion activity model has been proposed to predict metal bioavailability, but it does not address the complexing effect of organic ligands 20 and the photogenerated active Fe(III) ion. The photodegradation is a key factor in the control of dFe speciation, 13 particularly in the euphotic zone with high irradiation and organic matter content. In addition, Fe bioavailability is immensely correlated with Fe-photodegradation in the euphotic zone.…”