Iron influence on dissolved color in lakes of the Upper Great Lakes States

Patrick L. Brezonik; Jacques C. Finlay; Claire G. Griffin; William A. Arnold; Evelyn H. Boardman; Noah Germolus; Raymond M. Hozalski; Leif G. Olmanson

doi:10.1371/journal.pone.0211979

Abstract

Colored dissolved organic matter (CDOM), a major component of the dissolved organic carbon (DOC) pool in many lakes, is an important controlling factor in lake ecosystem functioning. Absorption coefficients at 440 nm (a₄₄₀, m^-1), a common measure of CDOM, exhibited strong associations with dissolved iron (Fe_diss) and DOC in 280 lakes of the Upper Great Lakes States (UGLS: Minnesota, Wisconsin, and Michigan), as has been found in Scandinavia and elsewhere. Linear regressions between the three variables on UGLS lake data typically yielded R² values of 0.6–0.9, suggesting that some underlying common processes influence organic matter and Fe_diss. Statistical and experimental evidence, however, supports only a minor role for iron contributions to a₄₄₀ in UGLS lakes. Although both DOC and Fe_diss were significant variables in linear and log-log regressions on a₄₄₀, DOC was the stronger predictor; adding Fe_diss to the linear a₄₄₀-DOC model improved the R² only from 0.90 to 0.93. Furthermore, experimental additions of Fe^III to colored lake waters had only small effects on a₄₄₀ (average increase of 0.242 m^-1 per 100 μg/L of added Fe^III). For 136 visibly stained waters (with a₄₄₀ > 3.0 m^-1), where allochthonous DOM predominates, DOM accounted for 92.3 ± 5.0% of the measured a₄₄₀ values, and Fe_diss accounted for the remainder. In 75% of the lakes, Fe_diss accounted for < 10% of a₄₄₀, but contributions of 15–30% were observed for 7 river-influenced lakes. Contributions of Fe_diss in UGLS lakes to specific UV absorbance at 254 nm (SUVA₂₅₄) generally were also low. Although Fe_diss accounted for 5–10% of measured SUVA₂₅₄ in a few samples, on average, 98.1% of the SUVA₂₅₄ signal was attributable to DOM and only 1.9% to Fe_diss. DOC predictions from measured a₄₄₀ were nearly identical to those from a₄₄₀ corrected to remove Fe_diss contributions. Overall, variations in Fe_diss in most UGLS lakes have very small effects on CDOM optical properties, such as a₄₄₀ and SUVA₂₅₄, and negligible effects on the accuracy of DOC estimated from a₄₄₀, data for which can be obtained at broad regional scales by remote sensing methods.

Citation: Brezonik PL, Finlay JC, Griffin CG, Arnold WA, Boardman EH, Germolus N, et al. (2019) Iron influence on dissolved color in lakes of the Upper Great Lakes States. PLoS ONE 14(2): e0211979. https://doi.org/10.1371/journal.pone.0211979

Editor: Lee W. Cooper, University of Maryland Center for Environmental Science, UNITED STATES

Received: November 5, 2018; Accepted: January 24, 2019; Published: February 13, 2019

Copyright: © 2019 Brezonik et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data uploaded to the DRUM public repository are all the data that others would need to replicate all our results. The DOI is: https://doi.org/10.13020/kk4m-zx88.

Funding: PLB received a grant from the University of Minnesota Office of the Vice President for Research and the University of Minnesota's Faculty Retirees Association, no grant number provided (https://umra.umn.edu). RMH, PLB, and JCF received a grant from the National Science Foundation, CBET 1510332 (www.nsf.gov). JCF, PLB, LGO, and RMH received a grant from the Minnesota Environmental and Natural Resources Trust fund, as recommended by the Legislative-Citizen Commission on Minnesota Resources, no grant number provided (https://www.lccmr.leg.mn/). JCF received a grant from the Minnesota Sea Grant, no grant number provided (www.seagrant.umn.edu). LGO also received salary support from the University of Minnesota’s U-Spatial Program and Agricultural Experiment Station. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Research associating iron (Fe) concentrations and organic color (now called colored dissolved organic matter, or CDOM) in surface waters extends back to studies in Finland [1] and Sweden [2], but its nature and significance were poorly understood for many decades. CDOM plays a major role in the ecological functioning of lakes by affecting light penetration, temperature structure, metal bioavailability, and photochemical processes. Several recent studies, e.g., [3,4], have implicated Fe as a factor in the long-term increases observed in CDOM across Scandinavia [5, 6] and some other temperate regions–the so-called “browning” phenomenon [7]. Increasing total Fe (Fe_T: dissolved plus particulate Fe) in 27 of 30 Swedish rivers was estimated to account for an average of 25% of the variations in CDOM and up to 74% in northern Sweden [2]. Ekström et al. [8] proposed that long-term CDOM trends in Swedish rivers could be related to increasing Fe mobilization driven by increasing temperature and river discharge that increase the probability of anoxic conditions conducive to Fe solubilization.

Whether the Fe-CDOM relationship is actually causative or merely correlative may affect the use of CDOM, which can be retrieved on regional scales from satellite imagery, e.g., [9,10], to estimate concentrations of DOC, a major component in the aquatic carbon cycle. If Fe affects absorption coefficients (a_λ) at the wavelength (λ) used to quantify CDOM, variations in dissolved Fe or in the fraction of a_λ caused by Fe could affect the accuracy of DOC estimated from a_λ. Here we address this issue for lakes in the U.S. Upper Great Lakes States (UGLS).

Most recent studies on the influence of Fe on CDOM have focused on Swedish lakes. Based on observations from multi-basin Lake Mälaren, Köhler et al. [11] found decreasing dissolved Fe (Fe_diss) as water flowed through the basins, with concurrent declines in CDOM and a shift from colored allochthonous material to less colored autochthonous DOM. Weyhenmeyer et al. [4] found a linear relationship between dissolved organic carbon (DOC) and CDOM (measured as absorption coefficients at 420 nm, a₄₂₀) in a large dataset from Sweden and Canada, but the carbon-specific a₄₂₀ (a₄₂₀/DOC) increased nonlinearly, approaching an asymptotic value, with increasing Fe_T, which the authors considered to be all Fe_diss. Based on these findings, the authors inferred that Fe_diss affected apparent CDOM levels (i.e., absorption coefficients at 420 nm, a₄₂₀) and concluded that Fe_diss, pH, water residence time, and colored DOC all may be important factors for regional changes in lake browning. Alternative explanations for the browning phenomenon, including climate change [12] and recovery from acidification by atmospheric acid deposition, e.g., [5,13], are not necessarily inconsistent with a role for Fe.

Effects of Fe on UV absorbance are well studied, but effects in the visible range are less well known. Weishaar et al. [14] found that absorbance at 254 nm (A₂₅₄) increased with Fe^III at the same rate in solutions with or without DOM. Poulin et al. [15] found A₂₅₄ increased linearly with Fe^III in DOM-containing solutions but found no effect for added Fe^II. They concluded that Fe^III should be accounted for in measurements of specific UV absorbance at 254 nm (SUVA₂₅₄; i.e., A₂₅₄ normalized by DOC) and provided an equation to make such corrections. Maloney et al. [16] found a nonlinear increase in carbon-specific absorptivity, a₃₂₀/DOC in the Fe_diss range of 1–4 mg/L in a humic-rich lake and reported that the spectral slope in the range 280–400 nm decreased as Fe_diss increased from 0.0 to 0.5 mg/L. They hypothesized that Fe_diss likely would affect light conditions in the visible range but made no measurements in this region. Kritzberg and Ekström [3] and Xiao et al. [17]) reported that adding Fe^III to CDOM-containing waters linearly increased absorptivity at 410–420 nm. Adding Fe^III (1600–3600 μg/L) to humic and fulvic acid reference materials also decreased spectral slopes in the UV range [17].

We have been studying characteristics of CDOM in UGLS lakes and mapping its distribution by field studies and satellite imagery [10,18,19]. The occurrence of major iron ore deposits in Minnesota led us to question whether Fe contributes to observed CDOM levels (measured as a₄₄₀) and/or affects its other optical properties, and whether that could affect DOC values inferred from a₄₄₀. This study had three primary objectives: (1) quantify the association between Fe and a₄₄₀ (our measure of CDOM) in surface waters of three UGLS ecoregions; (2) experimentally determine whether the association is causal, and if so evaluate the extent of Fe contributions to a₄₄₀ and other optical properties that characterize CDOM; and (3) quantify the influence of Fe variability on DOC estimated from a₄₄₀ in natural waters.

Methods

Sampling sites

We collected 450 samples from 280 water bodies (mostly lakes) in northern and central Minnesota, Wisconsin, and Michigan over the period 2014–2018 (Fig 1). Sampling occurred during summer (June-September). Nearly all 2014 and 2015 samples were from two lake-rich ecoregions of northeastern and east-central Minnesota: Northern Lakes and Forest (NLF) and North-Central Hardwood Forest (NCHF), which together contain ~ 9800 of the state’s ~12,000 lakes. The NLF is ~ 50% forested, and nearly a third of its area is wetlands or lakes. Agriculture and urban land cover constitute only small portions of this ecoregion (7 and 4%, respectively). In contrast, the NCHF is ~ 48% agricultural land and 9% urban. Forest cover constitutes 25% and wetlands ~ 10% of the NCHF. In 2016, sampling was extended to NLF and NCHF areas in Wisconsin and Michigan and the Northern Minnesota Wetlands (NMW) ecoregion, which has < 200 lakes, only a few of which are road-accessible.

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Fig 1. Map of study area showing ecoregions and sampling sites.

Red circles: sites with Fe_diss, a₄₄₀ and DOC data; black circles: additional sites with only a₄₄₀ and DOC data.

https://doi.org/10.1371/journal.pone.0211979.g001

Water usually was collected by small boat or kayak in the open water area, but collections were made from the ends of docks on small lakes where boat access was not feasible. Sites were selected to include a diversity of lake types, CDOM levels, and catchment land cover. The vast majority of sites were lakes, but six large rivers and six impoundments on large rivers were included. NLF lakes were in forested catchments (mixed conifers and hardwoods with substantial wetlands) with little to no human development; supplemental sampling in 2018 focused on the NLF ecoregion, where the vast majority of CDOM-rich lakes occur in the study area. Some NCHF lakes were in minimally developed catchments, but most were in urban to exurban areas and a few had catchments with row-crop agriculture.

Sampling/Field procedures

Water samples were collected from ~ 0.25 m depth using acid-washed, triple-rinsed polycarbonate or high-density polyethylene bottles and stored on ice until processed, usually the same evening. Secchi depth (SD) was measured by standard limnological procedures. Samples were collected at various depths on a few NLF lakes in 2018 to determine effects of stratification on CDOM and Fe_diss. Raw water was filtered through 0.45 μm Geotech trace-metal-certified capsule filters or pre-combusted (4 h at 450 ^oC) 0.7 μm Whatman glass fiber filters. Filtered water for DOC and Fe_diss analyses was acidified using 0.1 mL of 2 M HCl per 50 mL of sample and refrigerated (DOC) or frozen (Fe_diss) in pre-cleaned glass or plastic bottles, respectively. Unfiltered water for Fe_T analyses was acidified with 1 mL of concentrated HNO₃ per 50 mL sample and stored in the same manner as the Fe_diss samples. Un-acidified filtered water for CDOM analysis was refrigerated in 40 mL glass vials with no headspace. Filter blanks (DI water) showed no measurable DOC or CDOM. Chlorophyll-a (chl-a) was collected by vacuum filtration of water samples onto 0.22 μm cellulose nitrate filters that were then stored frozen until analysis.

Analytical methods

Absorbance was measured within a month of sample collection by scanning from 250 to 700 nm using a Shimadzu 1601UV-PC dual beam spectrophotometer with 1 cm or 5 cm quartz, depending on CDOM levels, and nanopure water in the reference cell. We tested whether length of storage affected a₄₄₀ measurements on filtered, refrigerated samples from three colored lakes with a₄₄₀ values of 5–30 m^-1 and found no detectable decreases in a₄₄₀ after one month of storage, which agrees with other studies [20]. Samples were allowed to warm to room temperature on the benchtop prior to measurements. Absorbance was converted to Napierian absorption coefficients using: (1) where: a_λ is the absorption coefficient (m^-1) and A_λ is absorbance, both at wavelength λ, and ℓ is cell path length (m). Absorbance was blank-corrected before conversion. CDOM is reported as absorption coefficient (m^-1) at 440 nm, a₄₄₀. SUVA₂₅₄ (L mg^-1 m^-1) was calculated by dividing absorbance at 254 nm by DOC concentration (mg/L), after correcting for cell path length. Contributions of Fe_diss to SUVA₂₅₄ were calculated using the equation of Poulin et al. [15]; subtraction of the Fe_diss contribution yielded DOM-based values, SUVA_254,DOM. Spectral slopes (S_λ2-λ1) were calculated from absorbance data for three wavelength regions (275–295, 350–400, and 400–460 nm) by taking the natural logarithm (ln) of A and computing slopes in Excel or by nonlinear fit of absorptivity data to Eq (2): (2) where λ_ref is a reference wavelength and S is the slope.

DOC was measured on a Shimadzu TOC L_-CSN analyzer. Chl-a was measured by fluorometry after 90% acetone extraction of the chl-a filters. Fe_diss and Fe_T were analyzed in triplicate with 200 μg/L of yttrium added as an internal standard on a Thermo Scientific iCAP 6500 DUO ICP-OES or iCAP 7600 DUO ICP-OES instrument. Fe_diss was not analyzed on some low-CDOM waters sampled in 2016 that, based on 2014–2015 results, were expected to have low Fe_diss nor on some high-CDOM samples from lakes sampled multiple times in 2016. Based on analysis of the 2014–2016 data, we collected additional samples in 2018 from some rivers and lakes fed by rivers to measure Fe_T and Fe_diss and calculated particulate Fe (Fe_part) by difference.

Fe addition experiment.

The effect of adding Fe^III on a₄₄₀ was measured for surface water samples from six northern Minnesota lakes with a range of ambient a₄₄₀ and Fe_diss. The lake waters were circumneutral (pH 6.0–8.0). We used Fe^III because Poulin et al. [15] found no effect of Fe^II on UV absorbance of CDOM-containing solutions. Fe^III is the thermodynamically stable form in oxic water at circumneutral pH, which suggests that Fe^III-humic complexes predominate in surface waters. A 500 mL aliquot of filtered lake water (0.7 μm glass fiber filters) was placed in a 1.0 L beaker on a magnetic stirrer, and five 0.6 mL increments of a solution containing 77.1 mg/L of Fe^III were added sequentially. The additions were designed to yield measurable increases in Fe^III (total of 460 μg/L over the five increments) but not over-saturate the DOM. The ratio Fe^III/DOC was < 1 μmol/mg for the highest additions, lower than reported iron-binding capacities for humic materials, e.g., [21,22]. We also added similar amounts of Fe^III to deionized water to determine whether a₄₄₀ increased from uncomplexed Fe^III and to 0.01 M EDTA to determine whether a₄₄₀ increased when Fe^III was added to a colorless chelating agent. The Fe^III solution was prepared in 0.1 M HNO₃ from reagent-grade Fe₂(SO₄)₃·nH₂O, and the resulting Fe^III concentration was determined by triplicate ICP-OES analysis. After each Fe^III increment, sample pH was adjusted to within 0.1 of its ambient value by dropwise addition of 1 M NaOH. A preliminary experiment showed that the acidic Fe^III solution decreased the pH enough to affect the measured a₄₄₀. The effect of pH on CDOM absorbance is well known [23]. After pH stabilization, 5 mL aliquots were stored in the dark at 4 ^oC with no further filtration until absorbance was measured, ~ 24 h later.

Data analysis.

All observations (site-date combinations) were treated as separate data points; i.e., multiple samples from a lake across or within years were not averaged. Statistical analyses were done in JMP Pro 13.1 except for some simple regressions done in Excel 2016. Initial data inspection showed that distributions for a₄₄₀, DOC, and Fe_diss were skewed to low values but otherwise well distributed over the range of observed values (S1 Fig). Natural log transforms yielded more Gaussian-looking distributions but still did not satisfy the Shapiro-Wilks test for normality. Unless stated otherwise, statistical results are reported for untransformed data. In addition to simple and multiple regression analyses on subsets of the untransformed and log-transformed data, we analyzed relationships between a₄₄₀ and “de-trended” values of DOC and Fe_diss. The de-trended DOC analysis regressed the residuals from a regression of DOC vs. Fe_diss (i.e., the variance in DOC not explained by Fe_diss) against a₄₄₀. The de-trended Fe_diss analysis similarly used the residuals from a regression of Fe_diss vs. DOC (i.e., the variance in Fe_diss not explained by DOC) in a regression vs. a₄₄₀.

Results and discussion

Overview of water quality variables in study lakes

Broad ranges of a₄₄₀, DOC, and Fe_diss and two basic limnological variables, SD and chl-a, were measured in the study, and large differences were found between the two major ecoregions (NLF and NCHF; Table 1). Median, mean and maximum values of a₄₄₀, Fe_diss, and DOC were substantially higher for NLF lakes (dominated by forests) than NCHF lakes (dominated by agriculture). The median chl-a in NLF lakes was 4.1 μg/L (range 0–25 μg/L), and the median in NCHF lakes was 7.6 μg/L (range 1–98 μg/L). Lakes with obvious color (defined here as a₄₄₀ > 3.0 m^-1) also had low chl-a, nearly all < 20 μg/L [19]. The SD range was more limited (0.4–5.5 m) in NCHF lakes than NLF lakes (0.3–19.5 m), where high SD values were associated with deep, ultra-oligotrophic mine pit lakes. NLF lakes with a₄₄₀ > 3 m^-1 generally had SD < 3 m, and CDOM levels were the controlling factor for SD in highly colored lakes [24], some of which had SD values as low as 0.3 m. Higher mean than median values for the five variables (especially for a₄₄₀ and Fe_diss) in both ecoregions are indicative of non-normal (skewed) distributions.

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Table 1. Summary statistics for a₄₄₀, DOC, and Fe_diss and two basic limnological variables in the NLF and NCHF ecoregions.

https://doi.org/10.1371/journal.pone.0211979.t001

A principal components analysis to examine relationships among the above five variables showed that 90% of the variance was explained by the first two principal components (PCs) (Fig 2). DOC, a₄₄₀, and Fe_diss were clustered together with high positive loadings on PC1, which accounted for 67.2% of the variance. SD also had a high PC1 loading but in a negative direction. PC2, which accounted for 22.5% of the variance, was driven by a high negative loading of chl-a and smaller positive loadings of SD and Fe_diss. Overall, the results support the idea that a₄₄₀, Fe_diss, and DOC behave similarly as variables but behave differently from chl-a and SD.

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Fig 2. Plot of eigenvalues for the first two principal components of a principal components analysis of five variables (a₄₄₀, DOC, Fe_diss, chl-a, and SD) for the whole data set.

https://doi.org/10.1371/journal.pone.0211979.g002

Our 2014–2016 measurements were on near-surface samples because most CDOM effects of interest are near-surface phenomena. Other recent studies on Fe-CDOM interactions, e.g., [3,4], also focused on surface water samples. Data from summer 2018 for lakes with a range of near-surface a₄₄₀ indicate that a₄₄₀ and Fe_diss may vary with depth, with a₄₄₀ decreases and Fe_diss increases in near-bottom waters of highly colored lakes (S1 Table). These trends could be caused by seasonal variations in CDOM and in-lake cycling processes for Fe and CDOM, a topic beyond the scope of this paper.

All 2014–2016 samples analyzed for Fe were filtered (0.7 μm filters) prior to analysis, and the results are defined operationally as Fe_diss, which comprises Fe in true solution, including that complexed by DOM, and colloidal Fe associated with macromolecular DOM and hydrous Fe oxide particles too small to be retained on filters. We excluded particulate Fe (Fe_part) associated with filterable particles from analysis because we considered it inappropriate to include Fe in plankton or mineral particles. Three lines of evidence support this decision.

First, analyses of Fe_T and Fe_diss on samples collected in 2018 from moderate- to high-CDOM lakes showed that Fe_part was only a small fraction of Fe_T (average of 10.3%, range 0–23%; S2 Table), and most of Fe_T was Fe_diss. These samples were from lakes with moderate-to-high Fe_diss concentrations, and most of the lakes had river inflows. Samples from the associated rivers had a higher fraction of Fe_part−average of ~ 27%, or ~ 20% when one sample from a high runoff event was excluded (S2 Table). These results agree with the findings of Weyhenmeyer et al. [4], who reported that Fe_part was not an important component of Fe_T in Swedish lakes. Kritzberg and Ekström [3] found that Fe_part was an important fraction of Fe_T in Swedish rivers. Our more limited sampling found that Fe_part was more important in rivers than lakes, but Fe_diss was still dominant in rivers.

Second, concentrations of total suspended matter (TSM) were generally < 10 mg/L in lakes with a₄₄₀ > 3.0 m^-1; the average TSM for 53 lakes sampled in 2016 with a₄₄₀ > 3.0 m^-1 was only 3.8 mg/L. Third, CDOM-rich UGLS lakes occur in highly vegetated, forested catchments, where soil erosion is low, similar in terrain and ecological conditions to the Swedish and Canadian lakes where Fe_part was found not to be important [4].

Fe_diss is linearly correlated a₄₄₀ and DOC

Strong correlations were found between Fe_diss and a₄₄₀, our measure of CDOM, as well as for Fe_diss and DOC, for each year and for the complete data set (Fig 3, Table 2). Similar correlations were obtained for log-transformed data (S3 Table). Values of Fe_diss and a₄₄₀ generally were higher and more scattered in 2016 than in the two previous years, probably for two reasons. First, unusually high precipitation across Minnesota in 2016 broke many daily and monthly records at individual locations, likely resulted in higher export of Fe and DOM from catchments to lakes, and thus led to higher concentrations. Second, we sampled three times as many sites in 2016 than in 2014 or 2015. These sites covered a larger geographic range and had a greater proportion of catchments in agricultural, urban, or mixed-use landscapes, resulting in a greater diversity of geochemical conditions among sites than for previous years, thus accounting for the greater scatter. Because of the inter-annual differences, R² for the total data set for Fe_diss vs. a₄₄₀ (0.67) was lower than for the individual years. Overall, however, the results are consistent with century-old [1] and more recent studies [4] associating CDOM and Fe concentrations in lakes.

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Fig 3.

(a) a₄₄₀ and (b) DOC vs. Fe_diss for sampling years 2104–2016; triangles = 2014, squares = 2015, circles = 2016; dashed lines are regression fits for all years (see Table 1 for regression statistics).

https://doi.org/10.1371/journal.pone.0211979.g003

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Table 2. Dissolved Fe-CDOM and Fe-DOC relationships.

https://doi.org/10.1371/journal.pone.0211979.t002

DOC is a stronger a₄₄₀ predictor than Fe_diss

Although many catchment and water quality conditions affect lake CDOM levels, we are most interested here in the relative effects of DOC and Fe_diss on a₄₄₀ because a₄₄₀ is used to quantify CDOM and often used to predict DOC, e.g., [19]. As shown below, both DOC and Fe_diss are strong predictors of a₄₄₀, but DOC is stronger. We performed simple and multiple regression analyses with a₄₄₀ as predicted variable and DOC and Fe_diss as predictor variables (Table 3). Regressions were performed using the entire a₄₄₀ range and just for sites with a₄₄₀ > 3.0 m^-1 because related work [19] showed a break in the DOC-a₄₄₀ relationship around a₄₄₀ = 3.0 m^-1. A tight fit between the two variables was found above this value, but much more scatter and a higher slope were found below. Griffin et al. [19] interpreted this finding to indicate that low-color DOM from autochthonous and anthropogenic sources was an important, but variable DOC contributor in waters with a₄₄₀ < 3.0 m^-1, and these sources were less important in high-CDOM waters dominated by allochthonous (humic-like) DOM.

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Table 3. Simple and multiple regression relationships for a₄₄₀ vs. DOC and Fe_diss.

https://doi.org/10.1371/journal.pone.0211979.t003

DOC exhibited stronger relationships with a₄₄₀ than did Fe_diss, but both variables were significant in multiple regressions (Table 3). Addition of Fe_diss as a second variable increased R² by only 0.03 for the entire a₄₄₀ data range and 0.01 for a₄₄₀ > 3.0 m^-1. Similar results were found using log-transformed data (S4 Table), except that R² increased more when adding Fe_diss as a second variable (0.09 for all data, 0.03 for a₄₄₀ > 3.0 m^-1). De-trending to remove the influence of Fe_diss on the a₄₄₀–DOC relationship yielded an R² of 0.32. Removal of the influence of DOC on the a₄₄₀-Fe_diss relationship yielded an even lower R² of 0.12.

Weyhenmeyer et al. [14] similarly found that a least squares model using ln DOC and ln Fe_diss explained 86% of the variance in ln a₄₂₀. Linear de-trending of their data showed that DOC explained 38% of the variance when the Fe signal had been removed, and Fe explained 25% of the variance when the DOC signal was removed. Comparable de-trended values for ln-ln relationships of our data are 25% for DOC with the Fe_diss signal removed and 12% for Fe_diss when the DOC signal was removed. Although numerical values of Weyhenmeyer et al.’s original and de-trended R² results differ from ours, the overall outcomes of the analyses are similar: de-trending caused a large decrease in fit for a_λ-DOC relationships and even a larger decrease for a_λ-Fe_diss relationships. Together, these findings indicate that DOC is the more important explanatory variable statistically, but Fe_diss does explain some variance in a₄₄₀ beyond that produced by the correlation between DOC and Fe_diss.

As noted above, DOC and Fe_diss, the two main chemical determinants of a₄₄₀, are themselves moderately correlated for the complete data set (Fig 3B) and within each year. In each case, R² for the Fe_diss-DOC relationship was lower than that for the corresponding Fe_diss-a₄₄₀ relationship (Table 2), and 2016 values were more scattered than those for the previous years; R² for the total data set was only 0.58. Regression equations between Fe_diss and a₄₄₀ (Table 2, Fig 3A) had x-intercepts of a₄₄₀ < 1 m^-1. In contrast, best-fit lines for linear regressions of Fe_diss vs. DOC had x-intercepts of 5–7 mg/L DOC (Table 2, Fig 3B). Together, these findings suggest that (i) Fe_diss is associated with the colored component of DOM and (ii) on average across all sites ~ 6 mg/L of DOC is not associated with Fe_diss. This likely represents low-color DOM with a low abundance of Fe-binding ligand groups, probably of autochthonous or anthropogenic origin. Photo-degradation of CDOM also could contribute to the low-color DOM pool, but it is uncertain whether CDOM photo-degradation reduces Fe binding capacity.

Weyhenmeyer et al. [14] found a curvilinear relationship (R² = 0.49) between the ratio a_λ/DOC and Fe_diss that might be interpreted as a measure of the effect of Fe_diss on the fraction of DOC that is colored. We found a similar relationship (Fig 4A) for our data; R² = 0.64 for a₄₄₀/DOC vs. ln Fe_diss. As discussed above, however, the nature of DOM in low-CDOM waters (a₄₄₀ < 3.0 m^-1) likely differs from that in high-CDOM waters. The latter consists primarily of allochthonous, humic-like DOM; the former derives from various sources with generally lower color intensity and probably fewer binding sites for Fe_diss. Consequently, trends in a₄₄₀/DOC vs. Fe_diss may simply reflect changes in the nature of DOM as a₄₄₀/DOC increases. A plot of the relationship for sites dominated by allochthonous DOM (those with a₄₄₀ > 3.0 m^-1; Fig 4B) yielded an R² of only 0.46, and there was little trend in the ratio for Fe_diss > 300 μg/L. Overall, the close fit between a₄₄₀ and DOC for waters with a₄₄₀ > 3.0 m^-1 (Table 3) suggests that the DOM for these sites was dominated by humic-colored DOM.

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Fig 4.

(a) a₄₄₀/DOC vs. Fe_diss for all data, R² = 0.64; (b) a₄₄₀/DOC vs. Fe_diss for sites with a₄₄₀ > 3.0 m^-1, R² = 0.46. (R² are for fit of a₄₄₀/DOC to ln(Fe_diss)).

https://doi.org/10.1371/journal.pone.0211979.g004

Fe_diss had minor effects on other CDOM optical properties

The above results show that Fe_diss should be considered when evaluating a₄₄₀. Thus, it is worthwhile to assess whether other common optical measurements also are affected by Fe_diss. Results similar to those for a₄₄₀/DOC were obtained for SUVA₂₅₄, a more common DOC-normalized optical measure. For the whole data set, a moderate fit was found for both uncorrected SUVA₂₅₄ vs. ln Fe_diss (R² = 0.67) and for SUVA_254,DOM vs. ln Fe_diss (R² = 0.64). For samples dominated by allochthonous DOM (a₄₄₀ > 3.0 m^-1), both relationships had lower R² (0.50 and 0.45, respectively, for uncorrected and Fe-corrected SUVA₂₅₄), with little trend above Fe_diss = 300 μg/L (Fig 5A).

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Fig 5.

(a) SUVA_254,DOM vs. Fe_diss for sites with a₄₄₀ > 3.0 m^-1, R² = 0.45 (R² is for fit to ln(Fe_diss)); (b) fraction of SUVA₂₅₄ for all sites caused by DOM (i.e., corrected for Fe^III contribution) vs. Fe_diss.

https://doi.org/10.1371/journal.pone.0211979.g005

Fe_diss contributions to SUVA₂₅₄, calculated according to [15], were small (mean = 1.9%, std. dev. = 1.9%, n = 271); on average, across all UGLS samples with Fe_diss and SUVA₂₅₄ data, more than 98% of the SUVA₂₅₄ signal thus could be attributed to DOM. A small number of samples, however, had larger Fe_diss contributions to SUVA₂₅₄ (Fig 5B); 18 had Fe_diss contributions > 5%, and two had contributions > 10%. The lake with the largest contribution (11.9%), Crystal Lake (WI), is an ultra-clear oligotrophic seepage lake with low DOC (2.5 mg/L) and a SUVA₂₅₄ of only 0.64 L mg^-1 m^-1; it is an outlier relative to most lakes in the region. More relevant here are samples with higher Fe_diss and DOC. Seven samples with Fe_diss of 1000–1500 μg/L, had Fe_diss contributions to SUVA₂₅₄ of 4.4–6.7%, and Fe_diss contributions for three samples with Fe_diss > 1500 μg/L were 6.9–10.3%.

SUVA₂₅₄ values corrected for Fe_diss were slightly lower than uncorrected values, but of the 15 samples with original SUVA₂₅₄ > 5.0 L mg^-1 m^-1, 10 still had values > 5.0 after correction. The common upper limit for DOM-caused SUVA in natural waters is 5.0 L mg^-1 m^-1 [15]. Average SUVA₂₅₄ values for the 15 samples before and after correction (S5 Table) were 5.33 and 5.15 L mg^-1 m^-1, respectively. A third of these samples were from Johnson Lake, Minnesota (Itasca County), a small bog lake that generally had the highest CDOM and SUVA₂₅₄ levels in our studies. The average SUVA₂₅₄ before Fe-correction for Johnson Lake of 5.41 L mg^-1 m^-1 decreased to 5.23 L mg^-1 m^-1 after correction. Although high nitrate/nitrite concentrations (tens of mg/L range) may affect levels of SUVA₂₅₄ [25], concentrations of these ions were very low (few μg/L) in Johnson Lake and the other lakes we studied.

Spectral slopes, a measure of DOM composition, are also influenced by Fe_diss [16,17]. Plots of the spectral slopes S_350-400 and S_400-460 versus the ratio Fe_diss/a₄₄₀ showed no trends, but S_275-295 had a trend of smaller slopes with increasing Fe_diss/a₄₄₀, albeit with considerable scatter. S_275-295 values > 0.020 generally were from sites with a₄₄₀ < 3.0 m^-1, where low-colored autochthonous and anthropogenic DOM was dominant. Sites dominated by allochthonous DOM (a₄₄₀ > 3.0 m^-1) had lower scatter, but the trend explained little variance in S_275-295 (R² = 0.13). The trend in S_275-295 generally agrees with findings of others [16,17], who reported that Fe_diss decreased spectral slopes. The lack of trends in S_350-400 and S_400-460, however, reinforces the conclusion that Fe_diss at levels found in UGLS lakes does not strongly influence absorbance in the UV-A and visible regions.

Addition of Fe_diss had minor effects on a₄₄₀

To measure effects of Fe_diss on a₄₄₀ directly, we added known amounts of an acidified Fe^III solution to six lake waters with a range of a₄₄₀, DOC, and Fe_diss (Table 4). Although a₄₄₀ increased linearly with added Fe^III after readjusting the pH to the original value (Fig 6), the rate was small. The average rate of increase, 0.242 m^-1 per 100 μg/L of added Fe^III, was within the range observed by others: 0.19 and 0.29 m^-1 per 100 μg/L of added Fe^III ([3,17], respectively). The weak response to Fe^III additions indicates that changes in Fe_diss have only small effects on a₄₄₀, and inspection of absorbance spectra over the range 250–500 nm showed no changes in shapes of the spectra. Addition of 3.0 mL of acidified stock Fe^III solution to 500 mL of deionized water or to 500 mL of 0.01 M EDTA at pH 6.5 yielded no measurable increases in a₄₄₀.

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Fig 6. a₄₄₀ vs. Fe_diss for six waters in iron addition experiment; slopes and statistical information on best-fit lines in Table 4.

https://doi.org/10.1371/journal.pone.0211979.g006

Download:

Table 4. Chemical characteristics of lakes and results for iron addition experiment.

https://doi.org/10.1371/journal.pone.0211979.t004

If the relationships in Fig 6 apply across the entire Fe^III range, a substantial fraction of the ambient a₄₄₀ remains at Fe_diss = 0; extrapolating the best-fit lines in Fig 6 to the ordinate yielded a₄₄₀ values in the range 90.3–99.7% (mean of 95.5%) of the ambient a₄₄₀. On average for the six lakes, ~ 95% of measured a₄₄₀ thus can be attributed to DOM and only ~ 5% to enhanced absorptivity from Fe_diss or Fe_diss-DOM complexes. Xiao et al. [17] reported that Fe_diss (or Fe-DOM complexes) was responsible for up to 56% of a₄₁₀ in 13 natural waters. Their highest value, however, was from a Finnish groundwater spring with very low a₄₁₀ (0.2 m^-1) and Fe_diss = 42 μg/L. DOC was not reported but likely very low. It was an outlier among the waters, and Fe contributions to a₄₁₀ for the 12 other samples were 0.6–8.7% (mean = 2.8%).

The fact that a₄₄₀ did not increase when Fe^III was added to DI water or to an EDTA solution but increased by small amounts when added to CDOM-rich waters indicates that the increase is caused by interaction of Fe^III with DOM molecules and not Fe^III absorbance itself. The chemical nature of Fe interactions with DOM is complicated [26], and how they may affect absorption of visible light (e.g., at 440 nm) still is not well understood. Fe^III-complexes with carboxylate groups in humic substances can undergo photochemical reduction to Fe^II [27,28], and the occurrence of Fe^II-humic complexes in oxic waters thus cannot be ruled out. There also is evidence that some Fe associated with aquatic humic substances is bound irreversibly, apparently not as conventional metal-ligand complexes [29–31]. The literature has conflicting information on Fe^II stability in the presence of humic substances. Complexation by humic substances inhibited Fe^II autoxidation rates (oxidation by O₂) [32], but fulvic acid accelerated Fe^II oxidation by hydrogen peroxide (an intermediate in O₂ reduction to H₂O) [33]. Nonetheless, several studies [34,35] reported that Fe^III forms stronger complexes with DOM than Fe^II and probably is the predominant Fe-DOM form in oxic waters. Stability constants (K_f) for Fe^III and Fe^II with 12 DOM sources [35] were 10²−10⁴ higher for Fe^III than for Fe^II although stability constants varied widely among the DOM sources. Overall, our results indicate that Fe^III complexation by DOM has very small effects on CDOM chromophoric groups.

Application of experimental results to field data

We applied the experimental results to our field data to further evaluate Fe_diss effects on a₄₄₀, which is critical to know before attempting to use a₄₄₀ to predict DOC. For example, the Fe_diss-a₄₄₀ regression of the 2015 data (Fig 7) showed that some data points were far from the regression line. For the largest outliers (six high and eight low), we estimated the change in a₄₄₀ that would occur if Fe_diss were adjusted to the “best fit” values of the regression relationship. The difference between measured and best-fit Fe_diss, multiplied by 0.242 m^-1 per 100 μg/L of Fe_diss (average slope of the a₄₄₀-Fe^III relationship, Fig 6), provided estimates of the a₄₄₀ change caused by the Fe_diss change. The results showed small a₄₄₀ changes even for waters with large differences between measured and best-fit Fe_diss. For example, Blueberry Lake had the highest measured Fe_diss (1224 μg/L), and the best-fit Fe_diss for its measured a₄₄₀ (19.6 m^-1) is 690 μg/L. If the latter value represented the Fe_diss in this lake, a₄₄₀ would be 18.3 m^-1, a decrease of 1.3 m^-1 (a 6.6% change). Similar changes were found for the other waters with large differences between measured and best-fit Fe_diss (S6 Table); the average a₄₄₀ change for the six high outliers was– 3.7% (range −1.8 to − 6.6%), and the average for the eight low outliers was +2.2% (range 1.2 to 2.9%).

Download:

Fig 7. Fe_diss vs. a₄₄₀ for 2015 data; best-fit regression line and statistics given in Table 2.

Circled data: outliers examined for effects of Fe_diss on a₄₄₀ (S6 Table). Dotted line: best-fit linear relationship: Fe_diss = 36.8a₄₄₀−30.7; R² = 0.79. Dashed lines illustrate change in a₄₄₀ for some outliers when Fe_diss was changed to “best-fit” value.

https://doi.org/10.1371/journal.pone.0211979.g007

“Iron-corrected” a₄₄₀ values for samples with Fe_diss data are estimates of the a₄₄₀ attributable to DOM alone (a_440,OM). These were obtained by multiplying measured Fe_diss values by 0.242 (average slope of the a₄₄₀-Fe^III relationships in Fig 6) and subtracting the result from measured a₄₄₀. For 136 waters with a₄₄₀ > 3.0 m^-1, a_440,OM was 92.3 ± 5.0% (range of 71.3–99.7%) of measured a₄₄₀. Fe_diss accounted for < 10% of a₄₄₀ in most lakes (102, or 75%), but in seven lakes it accounted for 15–30% of measured a₄₄₀ (S7 Table). These lakes generally were river-influenced systems with relatively high Fe_diss concentrations and/or high Fe_diss/DOC ratios, and all were samples collected in the high rainfall year 2016. Of the six lakes that also had data for 2014 or 2015, only Big Sandy Lake and Big Sandy River Lake had Fe_diss contributions to a₄₄₀ > 10% in those years. We conclude that high rainfall promotes Fe export to lakes, resulting in higher Fe_diss contributions to a₄₄₀, and that lakes influenced by rivers with high CDOM and Fe are more likely to have relatively high Fe_diss contributions to a₄₄₀.

DOC can be predicted from a₄₄₀ without correcting for Fe_diss

It is now possible to assess whether correction for the presence of Fe_diss is needed to allow accurate prediction of DOC from a₄₄₀. Regressions of measured DOC versus a_440,OM and measured DOC versus measured a₄₄₀ yielded very similar relationships for the same data set (a₄₄₀ > 3.0 m^-1; N = 134): (3) (4) The DOC predicted by Eq 3 from the estimated a_440,OM for each site was compared to the DOC predicted from the measured a₄₄₀-DOC relationship (Eq 4); the relationship was almost exactly 1:1 (slope = 0.997; R² = 0.99) (S2 Fig). Consequently, we conclude that DOC predictions from measured a₄₄₀ are just as accurate for our study sites as DOC predictions from a₄₄₀ corrected to remove the influence of Fe_diss. Moreover, for waters where a₄₄₀ > 3.0 m^-1, a₄₄₀ is a good predictor of DOC (Table 3).

Long-term trends in CDOM and the role of Fe_diss in UGLS lakes

Long-term data across UGLS surface waters for CDOM [18] and Fe_diss are scarce. The extent of regional increases in CDOM, or whether such increases could be attributed to increases in Fe_diss, thus is unknown. Smaller-scale analyses suggest, however, that regional CDOM trends are more complicated than observed in Scandinavia and likely driven by climatic and hydrologic variations. For example, substantial intra- and inter-annual variations but no monotonic trends were found in a₄₄₀ and DOC for 20 small lakes in Upper Michigan over six years [36]. Climatic conditions that affected carbon loadings from upland forests and wetlands were considered the drivers of these variations. Similarly, Brezonik et al. [18] found large a₄₄₀ variations in seven lakes of the northern Wisconsin LTER program (all within a radius of 10 km, [37]) for the period 1990–2012, but only colored Crystal Bog had increasing a₄₄₀ over the whole period. In a study on optical properties of the LTER lakes, Jane et al. [37] found inconsistent trends in DOC since 1990, with increases in two (including Crystal Bog), decreases in four, and no trend in one. Optical properties related to DOM chemical characteristics varied more with climatic conditions than DOC concentrations.

Björnerås et al. [38] recently reported temporal increases in Fe on broad scales in European and North American freshwaters. Their data overlap our region at only one site (in north-central Wisconsin). Additional data from the Wisconsin LTER lakes, which were not included in the Björnerås et al. study, showed that Fe increased in Crystal Bog, decreased in Trout Bog, and had no trends in the other five lakes since the 1980s (Mann-Kendall test; N. Lottig, Univ. Wisconsin, pers. comm., 2017). As noted above, Crystal Bog is the only LTER lake with increasing CDOM during the same period. The intra- and inter-annual variability in Fe and CDOM was high in all the lakes. In Crystal Bog, Fe_diss averaged 200 μg/L for six pre-1990 measurements and 330 μg/L for eight post-2010 measurements; the average increase of 130 μg/L could account for an a₄₄₀ increase of only ~ 0.3 m^-1 (based on the average slope in Fig 6), but a₄₄₀ in Crystal Bog actually increased by ~ 5.5 m^-1 over this time [18]. Moreover, pH data for Crystal Bog showed no trends over the period of record (1981–2016) (S3 Fig). The increases in DOC [37] and a₄₄₀ [18] in Crystal Bog thus cannot be explained by declining acidity, and the lack of similar trends in the other LTER lakes suggests that long-term climatic changes also are not responsible for the trends.

Conclusions

Our examination of the role of dissolved iron in optical properties of DOM in lakes supports three main conclusions. First, a₄₄₀ and Fe_diss are well correlated in surface waters of the UGLS, with R² values of 0.73–0.77 for individual years, as has been shown in studies elsewhere. Second, experimental data show that iron has small effects on CDOM measured as a₄₄₀, but it is not the dominant factor for a₄₄₀ or SUVA₂₅₄ variations in UGLS lakes. The average increase in a₄₄₀ with added Fe_diss (0.242 m^-1 per 100 μg/L) means that increasing Fe_diss by 400 μg/L would increase a₄₄₀ by only ~ 1.0 m^-1. Even this level of Fe_diss variation leads to an a₄₄₀ change less than the expected error in using a₄₄₀ as a proxy for DOC (RMSE of 1.3–1.8 m^-1, Table 3). Third, estimates of DOC based on measured a₄₄₀ and a_440,DOM (i.e., a₄₄₀ corrected for Fe_diss) were essentially the same. Consequently, our data indicate that ambient levels of Fe_diss have only a minor influence on CDOM optical properties (a₄₄₀ and SUVA₂₅₄) and do not affect DOC estimates based on a₄₄₀ in lakes of our study area.

Supporting information

S1 Fig. Histograms of data distributions for a₄₄₀ (CDOM), DOC, Fe_diss, and SUVA₂₅₄.

Upper plots: untransformed data; lower plots: log-transformed (ln) values.

https://doi.org/10.1371/journal.pone.0211979.s001

(DOCX)

S2 Fig. DOC predicted from Fe-corrected a₄₄₀ (a_440,OM) (Eq 3) vs. DOC predicted from measured a₄₄₀.

(Eq 4). Best-fit line: a_440,OM = 0.997a₄₄₀ + 0.032; R² = 0.99; RMSE = 0.685, slope SE = 0.0086, p < 0.0001.

https://doi.org/10.1371/journal.pone.0211979.s002

(DOCX)

S3 Fig. Time trend for pH in Crystal Bog, Vilas County, Wisconsin, 1981–2016; data from the North Temperate Lakes Long Term Ecological Research (LTER) program (http://lter.limnology.wisc.edu).

https://doi.org/10.1371/journal.pone.0211979.s003

(DOCX)

S1 Table. Vertical profile data for three NLF lakes with a wide range of surface CDOM (a₄₄₀) values.

https://doi.org/10.1371/journal.pone.0211979.s004

(DOCX)

S2 Table. Fe_T, Fe_diss, and % Fe_diss for 2018 lake and associated river samples from the NLF ecoregion.

https://doi.org/10.1371/journal.pone.0211979.s005

(DOCX)

S3 Table. Fe_diss-a₄₄₀ and Fe_diss-DOC relationships for log-transformed data.

https://doi.org/10.1371/journal.pone.0211979.s006

(DOCX)

S4 Table. Log-transformed regression relationships for a₄₄₀ vs. DOC and Fe_diss.

https://doi.org/10.1371/journal.pone.0211979.s007

(DOCX)

S5 Table. SUVA₂₅₄ values for samples with measured SUVA₂₅₄ > 5.0 before and after Fe_diss correction.

https://doi.org/10.1371/journal.pone.0211979.s008

(DOCX)

S6 Table. Changes in a₄₄₀ for 2015 waters that had large differences between measured and best-fit Fe_diss after Fe_diss was changed to the best-fit value.

https://doi.org/10.1371/journal.pone.0211979.s009

(DOCX)

S7 Table. Samples with measured a₄₄₀ > 3.0 m^-1 having 15–30% of a₄₄₀ caused by Fe_diss and 70–85% caused by colored DOM.

https://doi.org/10.1371/journal.pone.0211979.s010

(DOCX)

Acknowledgments

We gratefully acknowledge support from the National Science Foundation, the Minnesota Environmental and Natural Resources Trust fund, as recommended by the Legislative-Citizen Commission on Minnesota Resources, and Univ. of Minnesota’s Office of the VP for Research and Retirees Association, U-Spatial Program, Sea Grant Program, and Agricultural Experiment Station. We thank numerous collaborators and student workers for assistance in sample collection and analysis. The senior author gratefully acknowledges mentoring early in his career by G.F. Lee, himself an iron researcher.

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Figures

Abstract

Introduction

Methods

Sampling sites

Sampling/Field procedures

Analytical methods

Fe addition experiment.

Data analysis.

Results and discussion

Overview of water quality variables in study lakes

Fediss is linearly correlated a440 and DOC

DOC is a stronger a440 predictor than Fediss

Fediss had minor effects on other CDOM optical properties

Addition of Fediss had minor effects on a440

Application of experimental results to field data

DOC can be predicted from a440 without correcting for Fediss

Long-term trends in CDOM and the role of Fediss in UGLS lakes

Conclusions

Supporting information

S1 Fig. Histograms of data distributions for a440 (CDOM), DOC, Fediss, and SUVA254.

S2 Fig. DOC predicted from Fe-corrected a440 (a440,OM) (Eq 3) vs. DOC predicted from measured a440.

S3 Fig. Time trend for pH in Crystal Bog, Vilas County, Wisconsin, 1981–2016; data from the North Temperate Lakes Long Term Ecological Research (LTER) program (http://lter.limnology.wisc.edu).

S1 Table. Vertical profile data for three NLF lakes with a wide range of surface CDOM (a440) values.

S2 Table. FeT, Fediss, and % Fediss for 2018 lake and associated river samples from the NLF ecoregion.

S3 Table. Fediss-a440 and Fediss-DOC relationships for log-transformed data.

S4 Table. Log-transformed regression relationships for a440 vs. DOC and Fediss.

S5 Table. SUVA254 values for samples with measured SUVA254 > 5.0 before and after Fediss correction.

S6 Table. Changes in a440 for 2015 waters that had large differences between measured and best-fit Fediss after Fediss was changed to the best-fit value.

S7 Table. Samples with measured a440 > 3.0 m-1 having 15–30% of a440 caused by Fediss and 70–85% caused by colored DOM.

Acknowledgments

References

Fe_diss is linearly correlated a₄₄₀ and DOC

DOC is a stronger a₄₄₀ predictor than Fe_diss

Fe_diss had minor effects on other CDOM optical properties

Addition of Fe_diss had minor effects on a₄₄₀

DOC can be predicted from a₄₄₀ without correcting for Fe_diss

Long-term trends in CDOM and the role of Fe_diss in UGLS lakes

S1 Fig. Histograms of data distributions for a₄₄₀ (CDOM), DOC, Fe_diss, and SUVA₂₅₄.

S2 Fig. DOC predicted from Fe-corrected a₄₄₀ (a_440,OM) (Eq 3) vs. DOC predicted from measured a₄₄₀.

S1 Table. Vertical profile data for three NLF lakes with a wide range of surface CDOM (a₄₄₀) values.

S2 Table. Fe_T, Fe_diss, and % Fe_diss for 2018 lake and associated river samples from the NLF ecoregion.

S3 Table. Fe_diss-a₄₄₀ and Fe_diss-DOC relationships for log-transformed data.

S4 Table. Log-transformed regression relationships for a₄₄₀ vs. DOC and Fe_diss.

S5 Table. SUVA₂₅₄ values for samples with measured SUVA₂₅₄ > 5.0 before and after Fe_diss correction.

S6 Table. Changes in a₄₄₀ for 2015 waters that had large differences between measured and best-fit Fe_diss after Fe_diss was changed to the best-fit value.

S7 Table. Samples with measured a₄₄₀ > 3.0 m^-1 having 15–30% of a₄₄₀ caused by Fe_diss and 70–85% caused by colored DOM.