| Abstract
| - Complexometric titrations are the primary source of metalspeciation data for aquatic systems, yet their interpretationin waters containing humic and fulvic acids remainsproblematic. In particular, the accuracy of inferred ambientfree metal ion concentrations and parameters quantifyingmetal complexation by natural ligands has been challengedbecause of the difficulties inherent in calibrating commonanalytical methods and in modeling the diverse array ofligands present. This work tests and applies a new methodof modeling titration data that combines calibration ofanalytical sensitivity (S) and estimation of concentrationsand stability constants for discrete natural ligand classes([Li]T and Ki) into a single step using nonlinear regressionand a new analytical solution to the one-metal/two-ligand equilibrium problem. When applied to jointly modeldata from multiple titrations conducted at differentanalytical windows, it yields accurate estimates of S, [Li]T,Ki, and [Cu2+] plus Monte Carlo-based estimates of theuncertainty in [Cu2+]. Jointly modeling titration data at low-and high-analytical windows leads to an efficientadaptation of the recently proposed “overload” approachto calibrating ACSV/CLE measurements. Application ofthe method to published data sets yields model results withgreater accuracy and precision than originally obtained.The discrete ligand-class model is also re-parametrized,using humic and fulvic acids, L1 class (K1 = 1013 M-1),and strong ligands (LS) with KS ≫ K1 as “natural components”.This approach suggests that Cu complexation in NWMediterranean Sea water can be well represented as0.8 ± 0.3/0.2 mg humic equiv/L, 13 ± 1 nM L1, and 2.5 ±0.1 nM LS with [Cu]T = 3 nM. In coastal seawater fromNarragansett Bay, RI, Cu speciation can be modeled as0.6 ± 0.1 mg humic equiv/L and 22 ± 1 nM L1 or ∼12 nML1 and ∼9 nM LS, with [Cu]T = 13 nM. In both waters,the large excess (∼10 nM) of high-affinity, Cu-binding ligandsover [Cu]T results in low equilibrium [Cu2+] of 10-14.5±0.2M and 10-13.3±0.4 M, respectively.
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