Reproducible determination of dissolved organic matter photosensitivity

Dissolved organic matter (DOM) connects aquatic and terrestrial ecosystems, plays an important role in C and N cycles, and supports aquatic food webs. Understanding DOM chemical composition and reactivity is key to predict 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 10 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 to measure and compare DOM photosensitivity consistently based on the kinetics of changes in DOM fluorescence during 20h photodegradation experiments. We assess the influence of experimental conditions that might affect reproducibility, discuss 15 our modelling approach, offer guidelines for adopting our methods, and illustrate possible applications for ecological inferences. Central to our approach is the use of a reference material, precise control of conditions, leveraging actinometry to estimate photon dose, and frequent (every 20 minutes) fluorescence and absorbance measurements during exposure to artificial sunlight. We compared DOM from freshwater wetlands, a stream, an estuary, and Sargassum sp. leachate and observed differences in sensitivity that could help identify or explain differences in their composition. Finally, we offer an example 20 applying our approach to compare DOM photosensitivity in two adjacent wetlands as seasonal hydrologic changes alter their DOM sources. Our approach may improve reproducibility when compared to other methods and captures time-resolved changes in optical properties that may have been missed previously.


Introduction
The photochemical reactivity of dissolved organic matter (DOM) is inherently linked to its composition and photochemical 25 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; , including loss of absorbance (Del , production of new substances (Gonsior et al., 2014;Blough and Zepp, 1995;Bushaw et al., 1996;Moran and Zepp, 1997), and loss of fluorescence . Absorption https://doi.org/10.5194/bg-2020-207 Preprint. Discussion started: 2 July 2020 c Author(s) 2020. CC BY 4.0 License. spectra and derived values such as spectral slopes and their ratios have long been used to characterize DOM (Blough and Del 30 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 . 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., 2009Gonsior et al., , 2013Wünsch Urban J. et al., 2017), comparing photosensitivity of different DOM sources or treatments may be a useful tool in the continuing effort to characterize DOM composition and to 35 describe its susceptibility to sunlight-induced degradation.
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 40 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 photo-reactivity 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., 45 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 50 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 55 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 reproducibility and temporal resolution.
Replicate vials are often sampled to ensure precision and improve reproducibility, but lamp space is finite, limiting temporal sampling resolution. 60 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 https://doi.org/10.5194/bg-2020-207 Preprint. Discussion started: 2 July 2020 c Author(s) 2020. CC BY 4.0 License.
Samples from the two freshwater wetland sites are used in the more detailed comparison presented in Section 3.3 and hence 95 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 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 100 the year (typically late winter-spring). These two sites, referred to as "smaller wetland" and "larger wetland", 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. One sampling site is within the smaller depression, which mostly lacks submerged and emergent vegetation and is hemmed closely by trees. The other site is within a 105 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 (2017-10-05, 2017-12-20, 2018-04-01).
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 110 reproducible method to compare 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).
Immediately prior to each experiment, 0.5-5 ml of the extract was evaporated under high-purity N2 gas, dissolved in 30 ml 115 ultrapure C-free Milli-Q water, and diluted to similar CDOM absorbance values to minimize any potential inner filter effects on fluorescence degradation kinetics. Absorbance (A) at 300 nm was used as a benchmark for dilution instead of adjustments based on measured [DOC] because it could be done quickly on the equipment used for the photochemical experiments and allowed consistent correction of inner filtering effects. We adjusted all samples to a raw absorbance of 0.12 (+ 0.01), which translates to a Napierian absorption coefficient (a) of 27.6 m -1 . Delaware Bay samples were too dilute to generate sufficient 120 volume to fill the photoirradiation system, so several sample extracts from throughout the depth profile of a single sample station were combined prior to evaporation.

Photoirradiation system
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). Samples were continuously 125 circulated between a central mixing reservoir and system components were connected by PEEK tubing (LEAP PAL Parts & Consumables, 0.0625" OD/0.030" ID). The central reservoir was a 25 mL borosilicate equilibrator flask with a magnetic stir https://doi.org/10.5194/bg-2020-207 Preprint. Discussion started: 2 July 2020 c Author(s) 2020. CC BY 4.0 License.
bar constantly rotating at its bottom. A micro gear pump (HNP Mikrosysteme, mzr-4665) was used to pump the sample with almost pulse-less flow through the system at a rate of 1.5+0.1 ml min -1 . The spectrophotometer flow cell and equilibrator flask were surrounded by a circulating water jacket set to 25 °C. 130 Samples were irradiated as they were slowly pumped through a custom-built flow cell (SCHOTT Borofloat borosilicate glass, Hellma Analytics, 70 to 85% transmission between 300 and 350 nm, 85% transmission at wavelengths >350 nm), with a total exposure path area of 101 cm 2 arranged in an Archimedean spiral and returned to the equilibrator flask. This 20x20 cm borosilicate spiral flow cell had a 1 mm deep x 2 mm wide long flow path covering the irradiation area and was located 135 underneath a solar simulator (Oriel Sol2A) with a 1,000 W Xe arc lamp equipped with an air mass (AM) 1.5 filter. Lamp output was checked periodically using an Oriel PV reference cell set to one sun which corresponds here to exactly 1,000 W m -2 and lamp power was held constant during irradiation experiments using a Newport 68951 Digital Exposure Controller.
Another tubing carried the sample from the equilibrator flask to a temperature-controlled square quartz fluorescence flow cell (1 cm x 1 cm) located within a Horiba Jobin Yvon Aqualog spectrofluorometer. Solar exposure varied depending on the total 140 volume in the photodegradation system. We controlled volume by completely filling the tubing and flow cells (12.2 mL volume) and adjusting volume added to the equilibration flask. With 10 mL volume added to the equilibrator (our typical experimental conditions), a 20 h irradiation experiment was equivalent to 1.0 day of exposure between 330 -380 nm at 45 °N latitude in mid-July where one day is ~15.75 h long. For the lowest total volume used here (0.5 mL in the equilibrator, total volume 12.7 mL), photon dose was 1.7 times higher than this estimate. We calculated a mean photon flux of 3.9x10 -5 mol 145 photons m -2 s -1 for experiments with 10 mL sample added once flow lines were filled (total sample volume 22.2 mL), based on a mean photon exposure of 0.23 µmol photons cm -2 min -1 (5 trials, standard deviation 0.0045). This calculated flux is based on nitrite actinometry and a response bandwidth between 330 and 380 nm (Jankowski et al., 1999(Jankowski et al., , 2000. Average July solar irradiance was modeled using the System for Transfer of Atmospheric Radiation model (Ruggaber et al., 1994) calculated just below the water surface as described previously (Fichot and Miller, 2010). 150 Past experiments revealed the importance of pH control on DOM fluorescence and photodegradation kinetics (Timko et al., 2015). We adjusted initial sample pH to 3.0 (+ 0.2) with HCl but did not control pH by autotitration. At pH 3 natural organic acids should generally be protonated regardless of compositional differences between DOM sources (Ritchie and Perdue, 2003). Starting at pH 3 and equilibrating the sample in an air-filled reaction vessel ensured minimal pH change during 155 irradiation, never changing by more than 0.2 pH units, in line with expectations from work on mechanisms explaining pH decreases during photooxidation (Xie et al., 2004).

Optical measurements
We used a Horiba Jobin Yvon Aqualog spectrofluorometer to collect time series of UV-Vis absorbance and excitation-emission matrix (EEM) fluorescence spectra throughout experiments. UV-Vis absorbance was measured at 3 nm intervals between 600 160 and 230 nm. Fluorescence excitation occurred at the same intervals, and emission spectra were recorded from 600 to 230 nm at 8 pixel CCD resolution, or approximately 3.24 nm intervals. EEMs integration times were 1 second. Milli-Q water (18.2 MW-cm) adjusted to pH 3 with concentrated HCl was circulated through the system and used as a measurement blank immediately prior to each experiment.

Experiments 165
Several sets of experiments explored method reproducibility, sensitivities to experimental conditions, and use in differentiating DOM sources. A series of experiments used SRNOM PPL extracts at varying concentrations and volumes added to the photodegradation system to test their influence on degradation kinetics. Different researchers in our group then repeated experiments with SRNOM PPL extracts to test reproducibility. We next compared SRNOM PPL extracts and SRNOM reference material reconstituted in ultrapure water (RO SRNOM) to test the effect of extraction on photodegradation kinetics. 170 After examining the methodological sensitivities with SRNOM, we compared the DOM sampled from several contrasting aquatic ecosystems. Samples were exposed to 20 hours 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 minute interval between each scan, generating a 175 time series of 60 EEM spectra for each experiment. Where applicable, time of EEM collection was converted to cumulative photon exposure (mol photon m -2 ) by multiplying time by calculated photon flux (mol photon m -2 s -1 ) using actinometry results generated with the same sample volume.

Data analyses
Fluorescence EEM spectra were inner-filter corrected and had 1st order Rayleigh scatter removed by the built-in Aqualog 180 software (based on Origin). Second order Rayleigh scatter was removed using an in-house Matlab toolbox following methods previously described  . EEM spectra were normalized by dividing fluorescence measurements by the area of the water 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: where a is the absorption coefficient at wavelength l, A is raw absorbance at wavelength l, and l is path length in m, here 0.01 (Hu et al., 2002).
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, 4-component models were fitted from each of the three SRNOM PPL extract experiments individually to confirm each experiment's data led to the same 195 PARAFAC model, then the model built from all three experiments was compared to each of these. All comparisons were confirmed using Tucker congruence (rex*rem > 0.99 for all components in all cases. Wavelengths below 270 nm were excluded due to high leverage on models that led to noisy loading spectra and for ready comparison to the PARAFAC models presented elsewhere (Murphy et al., 2018). The full data set of EEMs from all degradation experiments was then projected onto the 4- (2) where ft, total fluorescence normalized to the first EEM collected after the solar simulator lamp shutter opened at time t, is the sum of two fluorescence fractions (fL and fSL) undergoing decay at different rates (kL and kSL) (Murphy et al., 2018;Timko et al., 2015). 210 We modified Eq. 2 to replace time t with cumulative photon dose, assuming lamp photon output is constant throughout each experiment. If it can be properly measured, using cumulative photon exposure instead of time as the independent variable in models of fluorescence loss may allow better comparison of parameters between experiments, researchers, and experimental setups. The model is given in Eq. 3: 215 where fP is total normalized fluorescence after cumulative photon exposure P (in moles of photons). Other variables are the same as in Eq. 2. Photon dose estimations from nitrite actinometry can be applied to DOM irradiated under the same conditions if those conditions allow for optically thin solutions during exposure. The 1 mm pathlength spiral exposure cell we used should ensure optical thinness even in highly absorbent DOM solutions. 220 R software (v. 3.6.0) was used to fit bi-exponential models using the nlsLM function from the minpack.lm package, and R was also used for significance testing and plotting most results. https://doi.org/10.5194/bg-2020-207 Preprint. Discussion started: 2 July 2020 c Author(s) 2020. CC BY 4.0 License.

PARAFAC model
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 1 to 4 were 439, 412, 525, and 452 nm; however, only components 3 and 4 followed the bi-exponential decay pattern. Figure 2 shows an example of fluorescence change in each PARAFAC 230 component during photodegradation of SRNOM PPL. Component 3 in this study corresponds with F520 in Murphy et al., 2018, while Component 4 corresponds to the F450. Matching component spectra to models in the online OpenFluor database confirmed these matches, with Tucker congruence r values over 0.98 for emission spectra for both components. The weaker match between component 4 in this study and F450 in Murphy et al. is driven by differences in the excitation spectra (r = 0.949), but strong correlation between all 4 components in our PARAFAC model and higher information density in low wavelength 235 ranges of excitation spectra could interfere with excitation spectral signal discrimination. Components 1 and 2 in this study did not exhibit bi-exponential decay during photodegradation. In most experiments Component 1 decayed but did not follow a bi-exponential pattern, while Component 2 showed little net change. Differences in PARAFAC component matches and behavior between this study and Murphy et al. (2018) could arise from operating at a different pH (3 here vs. their minimum pH of 4). For example, despite spectral differences, Component 1 behaves similarly to F420 in Murphy et al. (2018), which 240 showed less rapid initial decay and a more linear overall pattern as pH decreased from 8 to 4 (see Fig. S4 in Murphy et al., 2018). Further results will focus on components 3 and 4 as they are most sensitive to photodegradation.

Fluorescence loss model fit and utility of model parameter estimates 250
Differences in biexponential model parameters between samples may allow reproducible comparisons of natural DOM photosensitivity. 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 3 and 4 in our data sets (see Fig. 10 below for an example of fit). The biexponential model represents the sum of two terms, often referred to as labile and semi-labile to reflect the large relative differences in exponential slopes (kL and kSL in Eq. 2). This model captures 255 loss of 2 pools of fluorescence intensity, possibly arising from 2 pools of DOM fluorophores decreasing in abundance at differing rates. Potential interpretation of these parameter values is discussed in section 3.3.

SRNOM experiments -experimental conditions and photon dose
Photodegradation kinetics in SRNOM trials were sensitive to many experimental conditions, but most importantly those that 260 affected cumulative photon exposure. 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 loss of absorbance at 254 nm and loss of fluorescence intensity of components 3 and 4 relative to starting values in experiments where total volume of sample varied. Sample volume predictably affects photon dose relative to the quantity of 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 265 rates from 1.5 to 8 mL per minute did not impact photon dose. 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.
However, the reader is reminded that actinometers do have limitations (e.g. broadband response measurement) and caveats 270 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 -1 (Timko et al. 2015). Concentration dependence in photochemistry is often assumed 275 to stem from self-shading alone, and past work has shown the importance of working with "optically thin" solutions or properly correcting for inner filter effects when measuring photochemical behavior. All solutions shown here were considered optically thin at 300 nm and greater wavelengths following the convention that for optically thin solutions, where AT is total (Napierian) absorption coefficient and L is path length in m (Hu et al., 2002). Although inner-filter corrections 280 can be applied to correct for self-shading in spectrophotometer cells with known geometry (Hu et al. 2002), these corrections cannot be easily applied in other irradiation designs (e.g. vials on their sides and spiral flow cells). The definition for optically thin solutions (Eq. 4) is somewhat vague, so we also tested the dependence of DOM concentration on photodegradation rates. Degradation patterns seemed to be sensitive to DOM concentration as well but the effects were less clear (Fig. 4). In general, 290 lower concentrations showed greater overall losses of absorbance and fluorescence. For the two most dilute solutions, PARAFAC C3 loss could not be modeled with a bi-exponential model, in contrast to all other samples throughout our study.
Our results suggest either that our solutions experienced self-shading despite meeting the conventional definition of optical thinness, or some other mechanism links CDOM concentration to absorbance or fluorescence degradation kinetics such as Exposure time, hours Absorbance at 254 nm, relative to start Exposure time, hours PARAFAC component 3 intensity, relative to start Exposure time, hours PARAFAC component 4 intensity,relative to start • 10 ml 5 ml 0.5 ml Exposure, mol photons m -2 Absorbance at 254 nm, relative to start Exposure, mol photons m -2 PARAFAC component 3 intensity, relative to start concentration-dependent charge transfer interactions (Sharpless and Blough, 2014). Further work is needed to explain these 295 findings.

300
Two researchers 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 repeated tests by the same researcher (Fig. 5). Two-tailed t-tests were not able to distinguish differences in means between trials run by each researcher for any biexponential model parameters (p-values all greater than 0.10). Exposure, mol photons m -2 Absorbance, 254 nm, relative to start Exposure, mol photons m -2 PARAFAC component 3 intensity,relative to start

Effects of solid-phase extraction
Fluorescence degradation from reconstituted RO SRNOM and SRNOM PPL extracts generated the same PARAFAC components. However, the overall loss of modeled components 3 and 4 differed between SRNOM PPL extracts and RO 310 SRNOM, as did kinetics of fluorescence loss (Fig. 6). The differences in fluorescence loss were small but systematic. Twotailed t-tests of relative fluorescence loss suggested differences between PPL and RO SRNOM in PARAFAC component 4 (pvalue < 0.01) with limited support for differences in component 3 (p-value = 0.06) and no support for differences in absorbance loss (p-value = 0.3 for 254 nm). Projecting the data onto a PARAFAC model built from RO SRNOM degradation data instead of SRNOM PPL data did not affect these results. Fitted model parameters from Eq. 3 suggest these differences stem from the 315 kinetics of the semi-labile fluorescence pool, with possible differences in the relative starting abundances of the labile vs. semilabile pools (Fig. 7 and Table 1). Rate constants of the labile pool did not vary for either PARAFAC component, suggesting extraction did not affect behavior of this pool, so studies focusing on this pool should not be affected by PPL extraction.
Capturing changes in this pool is one of the explicit advantages of our experimental system, and future work on environmental  photo-reactivity may focus on this time scale as photochemical reactions in the environment are often driven by initial rates 320 (Powers and Miller, 2015). However, slower degradation processes or longer irradiations may be affected by extraction.  Exposure, mol photons m -2 PARAFAC component 3 intensity, relative to start   Shared PARAFAC components suggest PPL extraction did not strongly alter the compositional bases of fluorescence 335 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 much 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 variation in photon dose, as volume and initial absorbance were equal across samples. If concentration of fluorophores affects degradation kinetics, differing 340 fluorophore concentrations between our PPL extracts and whole SRNOM could explain the discrepancy. Even though we adjusted all samples to 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 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 345 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).

Figure 7. Fitted biexponential model parameters (Eq. 3) from the time series of PARAFAC components 3 and 4 (see Fig. 5 for data). f is unitless and k is mol photons m -2 . C3 and C4 denote PARAFAC components 3 and 4. Error bars represent mean + standard deviation from three experiments. Two-tailed t-tests suggest differences in kSL for both components
Preliminary experiments showed photochemical behavior of filtered whole water (before extraction) was affected by cold storage duration, precluding reproducible experiments on samples collected at different times. Unstable behavior was observed 350 over time in whole water wetland samples with high DOC concentrations (15-40 mg/L) in experiments run without dilution using a 3x3 mm flow cell in the spectrofluorometer (Fig. 8). While DOM absorbance seems stable in seawater samples after storage at 4° C up to 1 year (Swan et al., 2009), concentrated DOM in inland waters may be unstable in cold storage conditions, affecting its optical properties or responses to photoirradiation. Further work is required to understand the cause of this behavior, but losses of DOC and changes to optical properties during cold storage of samples have been reported elsewhere 355 (Peacock et al., 2015). High DOC concentrations may also promote flocculation (von Wachenfeldt and Tranvik, 2008), which is known to specifically involve CDOM (Wachenfeldt et al., 2009). DOM or matrix composition may also affect storage stability, Differing degrees of instability between sources (e.g. in Fig. 8 large wetland sample becomes noisier than small wetland) suggest differences in DOM chemical composition may affect sensitivity to storage. Reproducible use of whole water samples instead of extracts may be possible if irradiation experiments are conducted within a week of sample collection, or if 360 samples are diluted prior to storage, but this will require further investigation and would assume no major differences in the sample matrix. wetland, and results seemed to change with storage time. First experiments with each wetland water source were run 5-8 days after sample collection, second experiments were run 9-13 days after sample collection, and third experiments were run 14-16 days after collection. The trend in most cases toward lower relative photosensitivity in measured variables and in some cases increasing data noise as samples aged informed the decision to use solid phase extracts to improve reproducibility.

Guidelines for photodegradation fluorescence kinetics experiments 370
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 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 research goals do not explicitly include understanding effects of pH during photodegradation, we recommend bringing all samples to the same starting pH and controlling pH during the course of photodegradation 375 experiments, or starting experiments at pH 3 and ensuring 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 more meaningful comparison between lab groups. Repeated 380 experiments with the same standard can identify sources of error and quantify variability due to experimental procedures.
Checking this variability against 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. 385 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 dilute (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 390 on photochemical rates.
Photon dose obviously affects degradation kinetics. Our experimental system offered several procedural choices that could affect photon dose, including volume of sample in the system and lamp intensity. Researchers should carefully control these parameters and ensure their procedures are generating reproducible results by running several replicated experiments with a 395 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. Ideally, samples should be irradiated under optically thin conditions when actinometry measurements can be used to estimate photon doses for kinetic modelling (e.g. 400 using Eq. 3 instead of 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 total fluorescence loss and its kinetics. Storage of water samples for greater than two weeks led to changes 405 in fluorescence loss patterns, even when filtered to 0.2 µm (Fig. 8). We expect this may be due to the high DOC concentrations used in those experiments, as these may be more susceptible to flocculation or other aggregation processes than dilute samples, but further work would be required to test this. We recommend using extracts with greater storage stability to allow comparison over time, unless all experiments can be conducted shortly after sample collection or previous experience shows that the optical properties of the DOM in question are stable for the duration of storage. Comparisons of kinetics between extracts and whole 410 water samples should be made with care, but experiments using such comparisons may help disentangle the role of DOM chemical composition from other matrix effects in determining photodegradation behavior and sensitivity. Matrix effects may be especially important for extrapolating lab photodegradation findings to inferences at ecosystem scales. For example, if the approach described here is used to investigate longitudinal changes in DOM photosensitivity along a river network, tying these findings to residence times and photon doses in the field would be difficult without considering light attenuation by inorganic 415 chromophores and particles. Matrix constituents may also fundamentally alter the photosensitivity of DOM by participating in charge-transfer processes. We recommend using DOM isolated from its matrix by extraction here not because it is a sufficient approach to understand these phenomena, but as a foundation to explore this complexity.

Photosensitivity differences between DOM sources 420
We compared several DOM sources in order to see whether high resolution fluorescence time series could reveal differences in photosensitivity between sources. Fig. 9 shows the degradation of PARAFAC components 3 and 4 relative to starting intensities in samples from different DOM sources. Both components showed potentially divergent decay patterns among DOM sources, with Sargassum leachate starkly diverging from bulk DOM sources. Fitted biexponential model parameters of decay in PARAFAC components 3 and 4 are shown in Table 2, with parameters from component 4 plotted in Fig. 10 (similar 425 plot for component 3 can be found in Appendix A, Fig. A1). We did not conduct repeated trials with every DOM source shown here due to logistical constraints, but t-tests on three trials each with SRNOM and one of the wetland samples supported potential differences in fL and fSL in both PARAFAC components, and possible differences in kSL in component 3. Notably, these two DOM sources had biexponential parameter values that were among the most similar compared to other sources (see "Small wetland" and "SRNOM" in Fig. 10), which suggests that our approach is sensitive enough to detect small differences. 430  Exposure, mol photons m -2 PARAFAC component 3 intensity, relative to start  Fig. 6 for data). f is unitless and k is mol photons m -2 . source, small wetland trials include three trials with the same sample source (to test non-SRNOM system stability) and two trials with samples from other dates, and large wetland trials include one trial each from three sampling dates. p-values of biexponential model fits all below 1x10 -6 except for kSL for Sargassum, p = 0.016. Model parameters for every individual trial can be found in associated data set (Armstrong, 2020  The outlier in our comparison of DOM sources was Sargassum leachate extract, which was expected given the unique composition and the presence of phlorotannins (Powers et al., 2019). The natural DOM used in a previous study (Murphy et al., 2018) that yielded PARAFAC components appearing in all photodegradation experiments did not include leachates, only natural bulk DOM. Interestingly, this sample alone showed little or very slow semi-labile fluorescence loss with total fluorescence loss of projected PARAFAC components 3 and 4 dominated by rapid initial loss. Future studies using leaf or 450 soil/sediment leachates, or lysed algal cells, or other putative sources of natural DOM instead of bulk natural DOM itself need to test this modelling approach more thoroughly to ensure it is appropriate, but using other leachate sources may highlight the compositional basis of the semi-labile fluorescence decay that seems ubiquitous in bulk natural DOM but absent in Sargassum leachate here.

Interpreting biexponential model parameters
In other studies (e.g. Murphy et al., 2018;Timko et al., 2015) the rate parameters kL and kSL have received the most attention, as different average rates of change in these two pools indicate differences in DOM chemical composition, matrix composition, environmental conditions (if experiments are performed in situ), or experimental conditions, making these values potentially useful metrics of compositional differences between DOM sources. However, differences in loss of fluorescence between 460 samples may also arise from differing relative abundances of these two pools at the beginning of the time series. Figure 11 shows degradation time series from two experiments, along with fitted model parameters. These experiments compare DOM sampled in October 2017 from the two freshwater wetlands in Maryland. Figure 11 shows loss of PARAFAC component 3 (see Fig. A2 in Appendix A for a similar plot showing loss of component 4). The model fits are shown against the data in upper panels, while the modelled fits for each of the two terms from Eq. 3 ( / 12 3 7 and 5/ 12 63 7 ) are plotted separately 465 against the data in lower panels. This visualization is useful to weigh the contribution of differing rate parameters (kL and kSL) against the relative abundance of their respective fractions (fL and fSL) at the onset of the experiment in determining overall differences in photodegradation behavior between samples. Component 3 loss models show similar kL values but different relative fractions of the "fast" pool of fluorescence loss at the start of the experiment. Differences in these starting fractions between samples may play a role in overall differences in degradation kinetics in component 4 as well. This highlights one of 470 the strengths of our approach -the ability to capture optical properties of DOM that change very quickly during photodegradation. The modelled labile portion of fluorescence contributes negligibly to total fluorescence after receiving between 0.5 and 1.2 moles of photons per square meter, (3-10 hours of irradiation with our experimental setup). Future work relating the photon dose required to reach this point and the environmental conditions affecting this dose in natural DOM could improve knowledge of DOM origins, residence times, and interactions with other degradation processes. 475

Linking photosensitivity to DOM sources in dynamic ecosystems
High resolution photodegradation experiments of natural DOM can reveal fundamental photophysical behavior of ecological importance. We believe the approach described here can help unravel sources or light histories of DOM in natural settings.
One of our overall goals 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 480 Fig. 11, 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 fL) than the smaller wetland (Table 3). These wetlands are depressions located less than 100 m from each other, but with isolated surface water during the October 2017 sampling. They differ in basin size, canopy cover, and vegetation communities. Our data and fitted model parameters suggest that DOM in the larger wetland has either previously been exposed to sunlight that has depleted the 485 potential for "fast" decaying fluorescence, or that differences in source material or other processing of DOM pools in each wetland have given rise to relatively less photosensitive material in the larger wetland. In winter, water levels rose in each depression, and eventually both depressions were connected by surface flow from the larger to smaller wetland.
Photosensitivity differences show DOM composition and reactivity are affected by these phenomena. Figure  2018. This is an especially dynamic period in the seasonal cycles that affect DOM in this area -the October sampling is just before deciduous leaves senesce and fall, and the December sampling occurred less than a month before rising surface water levels connected the two depressions. Figure 12 shows that we may be able to capture the effects of ecosystem phenomena on DOM sensitivity. kL values for both PARAFAC components do not show any obvious pattern, while kSL values are very similar at each sampling site for all three dates but may be changing between dates due to some shift in DOM composition over time 495 affecting both sites. The most obvious pattern is in fL and fSL. These differ between sites in October and December, suggesting that despite their proximity, conditions at these sites differ enough to affect DOM photosensitivity in their surface water. The larger depression has less of the faster-decaying fluorescence, either due to differences in the source of the material on the landscape or depletion relative to the smaller depression reflecting greater light exposure and natural degradation. These differences are homogenized in April, when surface water mixing (and shorter residence times in surface storage due to export) 500 means site-specific processes are less influential in shaping DOM composition.
These photosensitivity differences may have consequences for other ecosystem processes. For example, if low fL at the time of sampling reflects high rates of photodegradation in wetland surface water, photopriming may contribute to microbial heterotrophy. Or wetland DOM with high fL may influence downstream ecosystems, if DOM exported to stream networks is 505 then susceptible to photodegradation which alters its lability to heterotrophs (Judd et al., 2007) or promotes flocculation (Helms et al., 2013). The sensitivity of our approach may also allow revisiting questions of longitudinal dynamics of light exposure in stream systems (Larson et al., 2007). This example is not conclusive for these sites but is presented to illustrate the possible uses of our method. Clearly much more 510 research is needed to explain the observed differences in photodegradation kinetics between these two wetlands and 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, ultrahigh resolution mass spectrometry, or nuclear magnetic resonance, and could be combined with other experimental approaches probing natural DOM sources and transformations. 515

Conclusion 525
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 time scales relevant for investigating degradation processes in the environment. This approach can be used to ensure 530 experiments conducted at different times or by different researchers can be compared. 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

Data and code availability
Data and code used in this analysis are available from the Dryad repository at https://doi.org/10.5061/dryad.hmgqnk9d9 (Armstrong, 2020). 550

Author contribution
AA developed the method's applications for ecological inference, collected the data, analyzed the data, and drafted the manuscript. LP assisted in the method's conception, collected the data, assisted with data analysis, and edited the manuscript.