Dissolved organic matter (DOM) connects aquatic and
terrestrial ecosystems, plays an important role in carbon (C) and nitrogen (N) cycles, and
supports aquatic food webs. Understanding DOM chemical composition and
reactivity is key for predicting its ecological role, but characterization is difficult as natural DOM is comprised of a large but unknown number of
distinct molecules. Photochemistry is one of the environmental processes
responsible for changing the molecular composition of DOM, and DOM
composition also defines its susceptibility to photochemical alteration.
Reliably differentiating the photosensitivity of DOM from different sources
can improve our knowledge of how DOM composition is shaped by photochemical
alteration and aid research into photochemistry's role in various DOM
transformation processes. Here we describe an approach for measuring and
comparing DOM photosensitivity consistently, based on the kinetics of changes in DOM fluorescence during 20 h photodegradation experiments. We identify several methodological choices that affect photosensitivity measurements and offer guidelines for adopting our methods, including the use of reference material, precise control of conditions affecting photon dose, leveraging actinometry to estimate photon dose instead of expressing results as a function of exposure time, and frequent (every 20 min) fluorescence and absorbance measurements during exposure to artificial sunlight. We then show that our approach can generate photosensitivity metrics across several sources of DOM, including freshwater wetlands, a stream, an estuary, and
The photochemical reactivity of dissolved organic matter (DOM) is inherently linked to its composition, and its photochemical behavior reflects compositional differences between samples. Several authors have discussed the fundamental processes involved in light absorption by DOM and the phenomena that may follow (Miller, 1998; Sharpless et al., 2014), including loss of absorbance (Del Vecchio and Blough, 2002), production of new substances (Gonsior et al., 2014; Blough and Zepp, 1995; Bushaw et al., 1996; Moran and Zepp, 1997), and loss of fluorescence (Blough and Del Vecchio, 2002). Absorption spectra and derived values, such as spectral slopes and their ratios, have long been used to characterize DOM (Blough and Del Vecchio, 2002; Helms et al., 2008; Twardowski et al., 2004). Fluorescence measurements arise from only a fraction of chromophoric DOM (CDOM) but are sensitive to small variations in DOM chemical composition (Blough and Del Vecchio, 2002). To the extent that photochemical reactivity is a property of DOM chemical composition (Boyle et al., 2009; Cory et al., 2014; Del Vecchio and Blough, 2004; Gonsior et al., 2013, 2009; Wünsch et al., 2017), comparing the potential for photochemical transformation of different DOM sources or treatments (hereafter called photosensitivity) may be a useful tool in the continuing effort to characterize DOM composition and to describe its susceptibility to sunlight-induced degradation. Such comparisons require robust methods that are sensitive enough to discern ecologically and chemically relevant differences between distinct DOM sources.
Research across ecosystem settings has measured changes in optical properties following sunlight or simulated-sunlight irradiation to infer changes in DOM composition. A general discussion of this approach and its bases has been previously published (Hansen et al., 2016; Kujawinski et al., 2004; Sulzberger and Durisch-Kaiser, 2009). Examples of recent research, using photochemical changes to make ecologically significant distinctions between DOM samples collected in specific ecosystems, have been described in detail elsewhere (Gonsior et al., 2013; Laurion and Mladenov, 2013; McEnroe et al., 2013; Minor et al., 2007). DOM photodegradation itself has ecological consequences, affecting overall carbon (C) cycling (Anesio and Granéli, 2003; Obernosterer and Benner, 2004), microbial heterotrophy of DOM (Amado et al., 2015; Cory et al., 2014; Lapierre and del Giorgio, 2014), and algal and submerged plant primary productivity (Arrigo and Brown, 1996; Thrane et al., 2014).
Experimental approaches connecting DOM chemical composition, its optical properties and their photochemical bases, and relevant ecological phenomena typically expose natural DOM samples to natural or simulated sunlight and measure the change in optical properties over time. In situ experiments have been used to explore the role of photodegradation relative to other transformations of DOM in aquatic ecosystems, but field studies are difficult, if not impossible, to reproduce (Cory et al., 2014; Groeneveld et al., 2016; Laurion and Mladenov, 2013). Laboratory-based irradiation experiments may allow greater reproducibility and logistical flexibility. Laboratory photodegradation experiments have tested the potential ecological significance of photodegradation and explored the fundamental photochemical mechanisms involved in photobleaching (Chen and Jaffé, 2016; Del Vecchio and Blough, 2002; Goldstone et al., 2004; Hefner et al., 2006). These experiments usually involve the simultaneous irradiation of DOM in several sample vials under polychromatic or monochromatic light. Vials are then destructively sampled for DOM measurements at intervals throughout the experiment or simply compared before and after light exposure. While powerful, these experiments require a trade-off in effort between the reproducibility and temporal resolution. Replicate vials are often sampled to ensure precision and improve reproducibility, but lamp space is finite, limiting the temporal sampling resolution.
Continuous measurement of a single sample undergoing controlled photoirradiation offers an alternative experimental approach. The kinetics of DOM fluorescence loss during photoirradiation experiments have been recently described (Murphy et al., 2018; Timko et al., 2015). These studies leveraged novel time series of frequent measurements (e.g., every 20 min) of fluorescence and ultraviolet–visible (UV–Vis) absorption, which allowed the modeling of distinct reactive components. Fluorescence losses were best described by the sum of two exponential decay terms, allowing straightforward and precise modeling of photosensitive fluorescence signals that degraded quickly, which may reflect chemically distinct processes contributing to fluorescence loss during photodegradation. This approach may offer the resolution required to compare photosensitivity between samples with small, but ecologically significant, differences in DOM composition.
The goals of this study are to (1) identify methodological barriers to reproducible determination of DOM photosensitivity and offer experimental guidelines to improve the studies of DOM photodegradation kinetics, (2) test our approach on samples from various environmental settings to see if our derived metrics of photosensitivity might respond to variability in DOM composition, and (3) analyze photosensitivity differences between different DOM sources in detail to better understand the links between DOM composition, environmental setting, and photochemical degradation processes. In a series of experiments, we explored potential sources of variability in photodegradation kinetics stemming from experimental conditions and methodology. We further develop a previously described experimental setup (Timko et al., 2015), showing that results are reproducible under controlled conditions using a common reference material, and suggest a set of best practices for collecting reproducible and high-resolution time series of fluorescence measurements during experimental irradiation of a single sample. Then we apply this approach to several natural DOM sources by building on and exploring new dimensions of an established modeling framework (Murphy et al., 2018) to identify photosensitivity differences that may be ecologically relevant. Finally, we thoroughly test DOM from two wetlands to show how these differences in photosensitivity metrics may help us link DOM composition to ecological phenomena.
We needed our system to irradiate samples without self-shading, even at
relatively high CDOM concentrations. The photoirradiation system circulates
an aqueous sample between a mixing reservoir (i.e., equilibration flask), a
solar simulator, and a spectrofluorometer, similar to a system described
previously (Timko et al., 2015). A photograph of the system can be found in Appendix A (Fig. A1). Samples were continuously circulated between a central mixing reservoir, and system components were connected by PEEK tubing (LEAP PAL Parts
Samples were irradiated as they were slowly pumped through a custom-built
flow cell (SCHOTT AG BOROFLOAT borosilicate glass; Hellma Analytics; 70 % to 85 % transmission between 300 and 350 nm; 85 % transmission at
wavelengths
Total sample exposure varied, depending on the total volume in the
photodegradation system. We controlled volume by completely filling the
tubing and flow cells (12.2 mL volume) and adjusting the volume added to the
equilibration flask. We used nitrite actinometry to calculate photon flux,
based on the response bandwidth between 330 and 380 nm of the nitrite
actinometer (Jankowski et al., 1999, 2000). Briefly, a solution of 1 mM sodium nitrite, 1 mM benzoic acid, and 2.5 mM sodium bicarbonate was circulated through the irradiation system, with regular measurements of fluorescence emission at 410 nm after excitation at 305 nm. Results are compared against a fluorescence calibration curve using 0–5
Past experiments revealed the importance of pH control on DOM fluorescence
and photodegradation kinetics (Timko et al., 2015). We adjusted the initial sample pH to 3.0 (
We used a HORIBA Jobin Yvon Aqualog spectrofluorometer to collect the time
series of UV–Vis absorbance and excitation emission matrix (EEM)
fluorescence spectra throughout the experiments. UV–Vis absorbance was measured at 3 nm intervals between 600 and 230 nm. Fluorescence excitation occurred at the same intervals, and emission spectra were recorded from 600 to 230 nm at 8 pixel charged coupled device (CCD) resolution or, approximately, 3.24 nm intervals. EEMs integration times were 1 s. Milli-Q water (18.2 M
Several sets of experiments explored method reproducibility, sensitivities to experimental conditions, and differences between DOM sources. For our first goal of identifying methodological barriers to the reproducible determination of DOM photosensitivity, we varied the concentrations and volumes of Suwannee River natural organic matter (SRNOM) PPL (Priority PolLutant) extracts added to the photodegradation system to test their influence on degradation kinetics. Different researchers in our group then repeated the experiments with SRNOM PPL extracts to test reproducibility. We explored the effects of storage time on filtered water sample photodegradation results. We then compared SRNOM PPL extracts and SRNOM reference material isolated by reverse osmosis reconstituted in ultrapure water (RO SRNOM) to test the effect of extraction on photodegradation kinetics. We approached our second goal – demonstrating the utility of our approach as a measure of DOM photosensitivity – by applying methodological guidelines developed in our tests of SRNOM to PPL extracts of DOM from a variety of aquatic ecosystem settings and sources (see Sect. 2.4). Finally, we ran experiments comparing the photosensitivity of DOM sampled from two adjacent freshwater sites in different seasons to better understand the links between DOM composition, environmental setting, and photochemical degradation processes.
In each experiment, a sample was exposed to 20 h of simulated sunlight, and EEM spectra were collected (using the “Sample Q” feature in Aqualog software), starting immediately before irradiation began, with a 17.5 min interval between each scan, generating a time series of 60 EEM spectra for each experiment. Where applicable, the time of EEM collection was converted to cumulative photon exposure (moles of photons per square meter) by multiplying time by calculated photon flux (moles of photons per square meter per second), using actinometry results generated with the same sample volume.
We used Suwannee River natural organic matter (SRNOM) obtained from the
International Humic Substances Society (IHSS) as a reference material (catalog no. 2R101N; isolated by reverse osmosis; Green et al., 2014). Freeze-dried SRNOM was dissolved in Milli-Q water and was prepared less than 1 week prior to use (hereafter called RO SRNOM). Dilutions approximately
corresponded to a dissolved organic carbon (DOC) concentration of 5 mg C L
Additional water samples were collected across a variety of aquatic
ecosystems to explore the range of our approach and to validate it. Sample
sources include two freshwater wetland sites (Caroline County, Maryland,
USA), one perennial stream (Parkers Creek, Calvert County, Maryland, USA;
collected September 2017), one estuary (Delaware Bay, USA; collected July
2016), and leachate from live
Samples from the two freshwater wetland sites are used in the more detailed comparison presented in Sect. 3.3, and hence, these sites merit additional description. Small topographic depressions are common throughout the interior of Delmarva Peninsula. These depressions persist in this low-elevation, low-relief landscape, and regular seasonal inundation has led to the development of wetland soils and biota in many of these depressions. Depressions on land not drained for agriculture are inundated for several months during most years. Some do not exchange water through surface flow with perennial stream networks, while others sustain downstream connections through temporary surface channels for several months in the wettest months of the year (typically late winter–spring). These two sites, referred to as the smaller wetland and larger wetland, respectively, are adjacent but lie within distinct topographic depressions. Their inundated areas expand and contract with water level fluctuations, and both may go entirely dry at the surface in the summer. If water levels are sufficiently high, their surface waters merge, and a temporary channel may fill and sustain export flow to the perennial stream network. Of the sampling sites, one is within the smaller depression, which mostly lacks submerged and emergent vegetation and is hemmed closely by trees. The other site is within a larger depression, where surface water is more exposed to light and features a variety of herbaceous submerged and aquatic plants. Experiments were run with DOM from both sites, sampled on three dates (5 October, 20 December 2017, and 1 April 2018).
Except for RO SRNOM samples used to test the effect of solid-phase
extraction and wetland samples used for the storage time experiment
described below, all samples were solid-phase extracted, using a proprietary
styrene divinyl benzene polymer resin (Agilent PPL Bond Elut), following a
procedure described previously (Dittmar et al., 2008). PPL
extracts were used because our goal is to develop a reproducible method to
compare the photochemical behavior of natural organic matter without the
influence of the sample matrix. Extracts allow longer storage, isolate
organic matter from potentially photosensitive matrices, and capture
representative photosensitive organic matter fractions
(Murphy et al., 2018). While filtration to 0.2
Immediately prior to each experiment, 0.5–5 mL of the extract was evaporated under high-purity N
Fluorescence EEM spectra were inner filter corrected and had first-order
Rayleigh scatter removed by the built-in Aqualog software (based on origin).
Second-order Rayleigh scatter was removed using an in-house MATLAB toolbox,
following methods previously described (Zepp et al., 2004). EEM spectra were normalized by dividing fluorescence measurements by the area of the Raman scatter peak of the water blanks. Data were processed in MATLAB R2018a, using an in-house toolbox and the drEEM toolbox (Murphy et al., 2013). Absorbance data were converted to absorption coefficients using Eq. (1) as follows:
We fitted a four-component parallel factor analysis (PARAFAC) model to data
from three SRNOM PPL extract experiments (60 EEMs each; 180 EEMs in total). PARAFAC models with three, four, and five components were fitted to the three SRNOM PPL extract experiment EEMs. The four-component model was chosen as it exhibited better component spectral characteristics than the others. Emission spectra from the components matched the four components identified in similar experiments (Murphy et al., 2018). Split-half validation is often used to validate PARAFAC models fitted to data sets where each EEM represents a different DOM source but may not be appropriate for data sets where EEMs are not independent. Instead, four-component models were fitted from each of the three SRNOM PPL extract experiments individually to confirm that each experiment's data led to the same PARAFAC model, and then the model built from all three experiments was compared to each of these. All comparisons were confirmed using Tucker congruence (
Previous studies (Murphy et al., 2018; Del Vecchio and Blough, 2002) used a
biexponential model to describe fluorescence loss during photo-exposure, as
described in Eq. (2), as follows:
We modified Eq. (2) to replace time
Results from fitting Eq. (3) are reported as four separate parameters, i.e.,
R software (v. 3.6.0) was used to fit biexponential models using the nlsLM function from the minpack.lm package, and R was also used for significance testing and plotting most results.
We stated three goals of this study, claiming we would (1) identify methodological barriers to the reproducible determination of DOM photosensitivity and offer experimental guidelines to improve studies of DOM photodegradation kinetics, (2) test our approach on samples from various environmental settings to see if our derived metrics of photosensitivity might respond to variability in DOM composition, and (3) analyze photosensitivity differences between different DOM sources in detail to better understand the links between DOM composition, environmental setting, and photochemical degradation processes. Our results are presented and discussed in the same order. Section 3.1 discusses experiments using SRNOM that identify several sources of experimental variability that influence photodegradation results which are crucial for applying our approach with confidence but also relevant to other methods of experimental DOM photodegradation. Section 3.2 shows that we were able to successfully apply our method to experiments using several different DOM sources. Finally, Sect. 3.3 presents a detailed comparison of experiments using samples from two freshwater wetlands to discuss the ecological relevance of photosensitivity differences measured with our approach. Sections 3.1 and 3.3 are further divided into topically distinct subsections for convenience.
Our results confirm many of the findings reported by Murphy et al. (2018) in
that the fitted PARAFAC model of SRNOM PPL photodegradations produced
similar components despite the independent data collection and analysis by
different researchers (Fig. 1). Emission maxima for components one to four were 439, 412, 525, and 452 nm; however, only components three and four followed the biexponential decay pattern. Figure 2 shows an example of the fluorescence change in each PARAFAC component during photodegradation of SRNOM PPL. Component three in this study corresponds to
Example of fluorescence change in PARAFAC components during photodegradation. Data show the degradation of SRNOM PPL.
Photodegradation kinetics in SRNOM trials were sensitive to many experimental conditions but most importantly to those that affected cumulative photon exposure. Key influences included the volume of the sample added to the irradiation system and DOM concentration, and we also tested for differences in results due to unknown discrepancies between individual researchers. Measurements made as a function of exposure time could obscure these differences if photon exposure was not instead directly estimated. In this subsection, we describe these methodological influences on the results and demonstrate the utility of directly expressing results as a function of estimated photon exposure instead of exposure time.
Total volume of sample in the system affected degradation kinetics by altering the cumulative photon exposure relative to the abundance of optically active molecules. Figure 3 shows the loss of absorbance at 254 nm and loss of fluorescence intensity of components three and four, relative to starting values, in experiments where the total volume of the sample varied. Sample volume predictably affects photon dose relative to the quantity of the starting material because, in all trials, a fixed volume of the total volume is exposed to light at any time before returning to the mixing vessel. We found that flow rates from 1.5 to 8 mL per minute did not impact the photon dose (data not shown). Removing the magnetic stir bar in the equilibration vessel seemed to have a slight effect on absorbance and, to a lesser degree, fluorescence loss, so it was used throughout subsequent experiments. Expressing loss of absorbance and fluorescence as a function of estimated photon exposure rather than a function of time seems necessary to ensure comparability with other experimental systems, and we will follow this convention where possible.
Photodegradation time series of absorbance at 254 nm and
fluorescence intensities of PARAFAC components three and four relative to starting values. Data are shown from experiments with SRNOM PPL that varied the volume of the sample added to the mixing reactor (after filling flow cell lines). Panels
However, the reader is reminded that actinometers do have limitations (e.g.,
broadband response measurement) and caveats exist for their successful
interpretation. Because CDOM absorption spectra generally increase
exponentially with decreasing wavelengths, many experimental designs may
violate the requirement that samples are optically thin when irradiated (Hu
et al., 2002). The irradiation cell used here has a depth of 1 mm, which
should prevent self-shading during photo-exposure at all concentrations
tested. Previous work using this system showed that fluorescence loss was
independent of SRNOM concentrations between 25 and 100 mg L
Degradation patterns seemed to be sensitive to DOM concentration as well, but
the effects were less clear (Fig. 4). In general, lower concentrations
showed greater overall losses of absorbance and fluorescence. For the two
most dilute solutions, PARAFAC C
Of the researchers, two followed the same protocols with the same material (SRNOM PPL) as a test of reproducibility due to sample handling. Agreement between researchers was good, and results varied to a similar degree as the tests were repeated by the same researcher (Fig. 5). The two-tailed
Photodegradation time series of
Photodegradation time series of PARAFAC components three
Use of extracts vs. whole water samples is another major methodological
choice that can affect results. Fluorescence degradation from reconstituted
RO SRNOM and SRNOM PPL extracts generated the same PARAFAC components.
However, the overall loss of modeled components three and four differed between SRNOM PPL extracts and RO SRNOM, as did kinetics of fluorescence loss (Fig. 6). The differences in fluorescence loss were small but systematic.
The two-tailed
Photodegradation time series of PARAFAC components three
Fitted biexponential model parameters (Eq. 3) from the time series
of the loss of PARAFAC components three and four in irradiation experiments that compare RO SRNOM to PPL SRNOM (see Fig. 6 for data).
Shared PARAFAC components suggest PPL extraction did not strongly alter the compositional bases of fluorescence photosensitivity in the RO SRNOM, but the differences in losses suggest researchers should take care when comparing extracts to original samples in future photodegradation kinetics studies. We are not sure what gave rise to these differences, but the RO SRNOM likely contains more highly polar compounds, such as (poly)saccharides and related compounds (e.g., glycosates). Differences between PPL and RO samples here are probably not due to variations in the photon dose, as volume and initial absorbance were equal across samples. If the concentration of fluorophores affects degradation kinetics, differing fluorophore concentrations between our PPL extracts and whole SRNOM could explain the discrepancy. Even though we adjusted all samples to a similar starting absorbance, selective enrichment or dilution of absorbing or fluorescing compounds in extracts could affect the mechanism responsible for any concentration dependence. Differences in electronic coupling and charge transfer abilities (Del Vecchio and Blough, 2004; Sharpless and Blough, 2014) could arise in extracts and affect fluorescence degradation kinetics. RO SRNOM may present matrix effects relative to extracted SRNOM PPL, as metals and other possible interferences are still present (albeit at much lower concentrations relative to DOC than in source water), despite the cation exchange and desalting treatments that accompanied the original reverse osmosis isolation (Kuhn et al., 2014).
It has been established that initial pH and pH change during photodegradation affects fluorescence photodegradation kinetics (Timko et al., 2015). We chose to conduct experiments at pH 3.0 because control by autotitration was not possible during these experiments due to contamination from the pH probe, and starting at pH 3 ensured minimal pH change during photodegradation. If the research goals do not explicitly include understanding the effects of pH during photodegradation, we recommend bringing all samples to the same starting pH, and controlling pH during the course of photodegradation experiments, or starting experiments at pH 3 and ensuring that change during the experiment is minimal.
Using a reference material allows consistency within and between research labs. We recommend using SRNOM as it has been widely studied and characterized (Green et al., 2014). Comparing total absorbance and fluorescence loss and degradation kinetics of SRNOM to DOM sources of interest will allow for a more meaningful comparison between lab groups. Repeated experiments with the same standard can identify sources of error and quantify variability due to experimental procedures. Checking this variability against the variability among repeated measurements of a sample may allow common variability to be estimated and, thus, reduce the need for replication in future runs with similar DOM sources. We also used SRNOM (after solid-phase extraction) as the basis for our PARAFAC model of fluorescence change during photodegradation and projected this model onto the rest of our data set, standardizing fluorescence losses between DOM sources to the same signal.
For research into compositional changes in DOM during photodegradation, test materials should be brought to similar starting absorbance. We adjusted all samples to a raw absorbance of 0.12 at 300 nm (with a 1 cm path length), but this may be difficult or less ecologically meaningful with naturally diluted (e.g., ocean) or concentrated (e.g., leachates) DOM sources. If possible, testing different DOM concentrations for the same sample is recommended in order to establish any concentration dependence on photochemical rates. In our system, photon dose obviously affects degradation kinetics. Our experimental system offered several procedural choices that could affect photon dose, including the volume of the sample in the system and lamp intensity. Researchers should carefully control these parameters and ensure that their procedures are generating reproducible results by running several replicated experiments with reference material. We encourage repeating this process with multiple individuals within a lab to understand the impact of individual methodological choices on results (e.g., gravimetric measurement of volume added vs. pipetting; preparation of samples). We strongly encourage at least reporting actinometry results or assumed actinometry for the experimental conditions used in order to better compare photon doses across studies and in the environment. While the additional work of actinometry is not trivial, we believe this represents one way of improving the reproducibility of degradation kinetics that avoids the limitations of using time alone. Even this approach could be improved – our actinometer did not directly measure radiation across the UV spectrum, which could allow a more accurate quantification of the cumulative photon dose. Striking a balance between the effort required and reproducibility is difficult, but we believe our work illustrates some of the limitations of conventional approaches where photon exposure cannot be reliably calculated, and we hope our efforts inspire alternative approaches to overcoming these limitations. Ideally, samples should be irradiated under optically thin conditions when actinometry measurements or other approaches can be used to estimate photon doses for kinetic modeling (e.g., using Eq. 3 instead of Eq. 2).
Photodegradation is affected by both DOM composition and matrix conditions.
While we found that the same PARAFAC model captured fluorescence decay in
both SRNOM and solid-phase extracts of SRNOM (as in Murphy et al., 2018), extraction did affect the total fluorescence loss and its kinetics. However, we chose to use extracts for further experiments, in accordance with our research priorities, and because our samples were not stable when stored as whole water samples. Preliminary experiments showed that the storage of water samples for greater than 2 weeks led to changes in fluorescence loss patterns, even when filtered to 0.2
After establishing procedures to understand and control the experimental
influences on DOM photosensitivity, our comparison of the photodegradation of
several DOM sources sought to reveal differences in photosensitivity arising
from DOM. Figure 8 shows the degradation of PARAFAC components three and four
relative to the starting intensities in samples from different DOM sources. Both
components showed potentially divergent decay patterns among DOM sources,
with
Photodegradation time series of PARAFAC components three
Fitted biexponential model parameters (Eq. 3) from the time series
of PARAFAC component four (see Fig. 9 for data).
The outlier in our comparison of DOM sources was
Differences in biexponential model parameters between samples may allow
reproducible comparisons of natural DOM photosensitivity. While the
differences in the parameter values described in Sect. 3.2 were encouraging,
we wanted to know more about the potential ecological relevance of these
differences. This approach has been used before, given the excellent fit of
this type of model to photodegradation data sets, and biexponential models
indeed provided excellent fits to fluorescence losses in PARAFAC components three and four in our data sets. The biexponential model represents the sum of two terms, often referred to as labile and semilabile, to reflect the large relative differences in exponential slopes (
In other studies (e.g., Murphy et al., 2018; Timko et al., 2015) the rate parameters
Data and model fit of PARAFAC component three loss in experiments
with two wetland samples collected October 2017. Panels
High-resolution photodegradation experiments of natural DOM can reveal the
fundamental photophysical behavior of ecological importance. We believe the
approach described here can help unravel sources or light exposure histories
of DOM in natural settings. Of our overall goals, one is to determine
relative photosensitivity among samples. The biexponential models that fit
experimental photodegradation data may help with these comparisons. For
example, in the two wetland samples compared in Fig. 10, distinct patterns
of photodegradation suggest distinct DOM composition. DOM fluorescence in
the larger wetland had relatively less fast-decaying fluorescence in
photosensitive PARAFAC components (parameter
These photosensitivity differences may have consequences for other ecosystem
processes. For example, if low
Fitted biexponential model parameters (Eq. 3) from the time
series of PARAFAC component three, comparing DOM from large and small wetland
sampling sites collected on different dates.
Changes to absorbance spectra after photodegradation show
advantages of high-resolution fluorescence data.
We can use this example to justify the effort involved in modeling
fluorescence decay kinetics by comparing these inferences to those possible
with simpler approaches. Modeling kinetics of fluorescence loss allows us to
resolve processes apparently occurring at different rates, which is obvious in the
large differences between
Photodegradation of DOM extracts in the lab does not replicate in situ photodegradation of DOM in surface waters. However, in situ photodegradation of DOM in surface water is extremely convoluted. The complexity of DOM chemical composition, surface water matrix composition, simultaneous ecological processes that also alter DOM composition, and the natural dynamism of surface water systems are intertwined and make it difficult to understand the role of photodegradation of DOM in surface water ecosystems. Our approach represents one step in the direction of disentangling this story but leaves many questions unanswered. We demonstrated several sources of potential variability in degradation kinetics that require more attention, any of which may affect our understanding of different influences on in situ photodegradation and its ecological consequences. Further research is required to understand how differences in DOM composition alone (as isolated in our work with extracts) interact with matrix composition (Grebel et al., 2009; Poulin et al., 2014; Timko et al., 2015; Stirchak et al., 2019) and how these reactivity differences affect other DOM transformation processes (Amado et al., 2015; Chen and Jaffé, 2016; Lønborg et al., 2016) and ecosystem scale or macrosystem scale or biogeochemistry (Anderson et al., 2019; Pickard et al., 2017; Rutledge et al., 2010).
This example is not conclusive for these sites. Though we demonstrate differences in photosensitivity between samples that have plausible hypothetical causes and consequences for ecosystem processes, these remain speculative. Our example does illustrate the possible uses of our method. Clearly, much more research is needed to explain the observed differences in photodegradation kinetics between these two wetlands and to test these hypotheses, ideally with more detailed data on DOM composition associated with differing photosensitivity. Regardless, our approach can complement established techniques for describing DOM, such as bulk optical properties, ultra-high resolution mass spectrometry, or nuclear magnetic resonance, and could be combined with other experimental approaches probing natural DOM sources and transformations.
Our research identified several methodological issues that can improve photodegradation experiments and leveraged this knowledge to show how photosensitivity differences may relate to DOM composition and environmental setting using a case study. Photodegradation experiments have improved our understanding of the role of DOM light sensitivity in ecological processes. As researchers continue to explore related questions and experiments proliferate, it is important to use approaches that constrain the influence of experimental conditions and promise reproducible or at least comparable results. Our method allows reproducible and relatively short experiments that capture photosensitivity differences between varying sources of natural DOM on timescales relevant for investigating degradation processes in the environment. This approach can be used to ensure experiments conducted at different times or by different researchers can be compared. Our work illustrates several obstacles to reproducing and comparing studies of photodegradation kinetics, highlights underappreciated sources of uncertainty, and offers an approach that improves upon past methodological limitations. It also captures distinct fast dynamics that differ between samples that would be lost in experiments measuring only total changes in optical properties or using far fewer time points. We explored the possibility of using this approach for inferences about ecosystem processes by comparing photosensitivity metrics between samples from two adjacent wetland areas, showing that photosensitivity differed in space and time in patterns that generated plausible hypotheses. Closer parsing of the biexponential decay parameters from modeled fluorescence loss may also allow for the differentiation of DOM sources, past exposure to photodegradation, or future photodegradation potential in other ecosystem settings.
Appendix A includes figures and tables that complement the information in the main text.
Fitted biexponential model parameters (Eq. 3) for comparison
between RO and PPL SRNOM.
Fitted biexponential model parameters (Eq. 3) for different DOM
sources.
Fitted biexponential model parameters (Eq. 3) for wetland DOM
samples.
Photograph of the photoirradiation system, with key components
labeled. A micro-gear pump circulates the sample between the equilibration
chamber, a flow cell cuvette inside the spectrofluorometer, and the spiral
exposure flow cell underneath the solar simulator lamp. A water bath set to
25
Fitted biexponential model parameters (Eq. 3) from the time
series of PARAFAC component three (see Fig. 9 for data).
Data and model fit of PARAFAC component four loss in experiments
with two wetland samples. Panels
Fitted biexponential model parameters (Eq. 3) from the time
series of PARAFAC component four, comparing DOM from the large and small wetland sampling sites collected on different dates.
EEMs of filtered source water samples compared to reconstituted solid-phase extracts.
We ran a series of experiments testing the effects of storage time on
photodegradation kinetics, which are relevant to our overall results but were
performed under different conditions. These preceded the other experiments,
and the experimental setup was modified based on their results. These used
filtered water samples (not extracts) taken from the small and large wetland
sites described above but were collected in November 2017. They were
filtered, stored in the dark at 4
Time series of photodegradation experiments on whole water
wetland samples.
Data and code used in this analysis are available from the Dryad repository at
AWA developed the method's applications for ecological inference, collected the data, analyzed the data, and drafted and edited the paper. LP assisted in the method's conception, collected the data, assisted with data analysis, and edited the paper. MG conceived the method, designed and optimized the instrument system, assisted with data analysis, and edited the paper.
The authors declare that they have no conflict of interest.
Margaret Palmer provided valuable feedback during the planning and preparation of the paper. Katherine Martin provided the sample from Parkers Creek that is included in the comparison of DOM source material. Jessalyn Davis assisted with actinometry measurements. The authors thank the reviewers for their careful and insightful comments, which improved this paper.
This is contribution 6002 (CBL 2021-072) of the University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory.
Alec Armstrong was supported, in part by the U.S. National Science Foundation (grant no. DBI-1052875) and a cooperative agreement (grant no. 58-1245-3-278) between the United States Department of Agriculture, the University of Maryland, and the National Socio-Environmental Synthesis Center.
This paper was edited by Clare Woulds and reviewed by Patrick Neale, Andrew Wozniak, and two anonymous referees.