Stormwater Dissolved Organic Matter: Influence of Land Cover and

Nov 25, 2013 - molecular characteristics of DOM differ as a function of land cover, (2) DOM ... GLMs incorporating environmental factors and land cove...
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Stormwater Dissolved Organic Matter: Influence of Land Cover and Environmental Factors Shawn P. McElmurry,*,†,‡ David T. Long,‡,§ and Thomas C. Voice‡,§ †

Department of Civil and Environmental Engineering, Wayne State University, Detroit, Michigan 48202, United States Department of Civil and Environmental Engineering, Michigan State University, East Lansing, Michigan 48824, United States § Department of Geological Sciences, Michigan State University, East Lansing, Michigan 48824, United States ‡

S Supporting Information *

ABSTRACT: Dissolved organic matter (DOM) plays a major role in defining biological systems and it influences the fate and transport of many pollutants. Despite the importance of DOM, understanding of how environmental and anthropogenic factors influence its composition and characteristics is limited. This study focuses on DOM exported as stormwater from suburban and urban sources. Runoff was collected before entering surface waters and DOM was characterized using specific ultraviolet absorbance at 280 nm (a proxy for aromaticity), molecular weight, polydispersity and the fraction of DOM removed from solution via hydrophobic and H-bonding mechanisms. General linear models (GLMs) incorporating land cover, precipitation, solar radiation and selected aqueous chemical measurements explained variations in DOM properties. Results show (1) molecular characteristics of DOM differ as a function of land cover, (2) DOM produced by forested land is significantly different from other landscapes, particularly urban and suburban areas, and (3) DOM from land cover that contains paved surfaces and sewers is more hydrophobic than from other types of land cover. GLMs incorporating environmental factors and land cover accounted for up to 86% of the variability observed in DOM characteristics. Significant variables (p < 0.05) included solar radiation, water temperature and water conductivity.

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observed in urban streams.14 Therefore, there remains a need to evaluate Hedges’ hypothesis across a broad range of landscapes, particularly urban watersheds. The aim of this study was to evaluate how land cover and other factors (e.g., solution chemistry, climate) influence DOM characteristics with a particular emphasis on urban and suburban land cover/ use. Only a few studies have focused on structural differences in DOM and variations in DOM behavior resulting from landscape influences.8,9,15−17 In predominately rural watersheds, the structure of DOM has been found to relate to its origin, transport pathways and seasonal influences.9,18 While instructive, these studies evaluate DOM collected in surface waters, downstream from terrestrial sources with mixed land cover/use. There are two major difficulties in assessing how land cover/use impacts DOM with this approach: (1) DOM consists of a complex assemblage of chemical structures and characteristics, and analytical techniques provide only limited or proxy information on DOM quality; (2) most landscapes are heterogeneous with respect to land cover, and since DOM is rapidly processed in aquatic systems it is difficult to collect

INTRODUCTION Dissolved organic matter (DOM) constitutes the base of aquatic food chains in surface water systems, and it plays a critical role in the transport of many organic and inorganic molecules.1−4 Understanding the processes responsible for DOM production, behavior, and characteristics is critically important to assessing the health of ecological systems. DOM characteristics are determined by the original source of organic material and biogeochemical processing that occurs during transport through watersheds. Early investigations into the influence of land cover on DOM tended to focus on the amount of DOM produced, not its composition or quality.5,6 In 1980, Hedges7 proposed that different landscapes were likely to produce different types of DOM. Since this time, most studies exploring this concept have focused on agricultural and forested land cover/use.8−10 Similar to the River Continuum Concept,11 which describes the flow of energy and carbon in natural systems, a conceptual model of carbon cycling in urban watersheds has been articulated.12 Recent research exploring carbon cycling in urban systems suggests that extending observations from natural and agricultural watersheds is questionable.13 Kaushal and Belt (ref 12) noted “there are no concepts comparing ecological and biogeochemical functions in engineered headwaters vs. what we consider a traditional headwater stream ecosystem.” This is particularly important given the pervasive degradation of ecological health that is © 2013 American Chemical Society

Received: Revised: Accepted: Published: 45

June 17, 2013 November 19, 2013 November 25, 2013 November 25, 2013 dx.doi.org/10.1021/es402664t | Environ. Sci. Technol. 2014, 48, 45−53

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subwatersheds is assumed to follow the order urban ≪ agricultural < forested ≈ recreational golf course. Unlike samples collected from terrestrial runoff, aquatic processes were assumed to be a major influence on DOM for the 19 samples collected directly from the Grand and Red Cedar rivers, where mixed land cover influenced stream chemistry at sampling locations. Analytical Measures. Surface water samples were collected in acid washed 1 L HDPE bottles and immediately transported from the field, back to the laboratory in a dark cooler where they were prefiltered through acid washed type A/E glass-fiber filters (1 μm) and split for further analyses. DOM was quantified by measuring DOC via automated heat-persulfate oxidation following an additional filtration through a 0.45 μm PVDF filter.30 DOM quality was assessed by measuring specific ultraviolet (UV) absorbance (SUVA), MW, polydispersity, hydrophobicity and DOM retention by H-bonding mechanisms. SUVA is a surrogate for aromaticity and was calculated by dividing the amount of UV light absorbed at 280 nm by the concentration of dissolved organic carbon (DOC).31 The absorbance wavelength of 280 nm, as opposed to 254 nm, was used because: (1) the transfer of electrons between overlapping π-orbitals occurs at 280 nm wavelength for phenolic and other humic like organic substances, and (2) the presence of other dissolved species (e.g., nitrate, iron), which also absorb UV light and can be ubiquitous in natural waters, but do not absorb UV light at 280 nm.31−33 The number-averaged and weightaveraged MW of DOM, as well as polydispersity, was determined by size exclusion chromatography.31,34 The MW referenced throughout the remainder of the text is the weightaveraged MW. The amount of organic carbon retained by hydrophobic (i.e., hydrophobicity) and hydrogen bonding interactions was determined by solid phase extraction (SPE).35 Fractions isolated by this method relate to DOM structure. Conductivity, pH, and dissolved oxygen (DO) concentrations were measured using a Horiba U-10 multiprobe water quality analyzer. Alkalinity was determined by Gran titration.36 Statistical Analysis. SYSTAT (version 12.02.00) was used for all statistical analysis. Unless otherwise noted, an alpha-level of 0.05 was used to determine significance. Data were not included in the statistical analysis if they met the outlier criterion using Dixon’s Q-test at the 95% confidence level.37 The standard analysis of variance (ANOVA) was used to evaluate if differences between land cover existed and Tukey’s Honestly Significant Difference (HSD) test was used to evaluate if subclassifications of land cover could be grouped. For the purpose of statistical analysis, major classes of land cover identified were urban, agricultural, forested and mixed (see Supporting Information for more details). Samples collected from storm sewer outfalls on Michigan State University’s main campus were considered to be representative of suburban land cover since the central campus consists primarily of parking lots, roof tops, and landscaped areas dominated by grass lawns. In addition to these major classes of land cover, runoff from parking lots and recreational golf courses were also analyzed as separate groups. These subclasses of land cover were assumed to be representative of two dominate types of land cover present within suburban watersheds (paved surfaces and lawns, respectively). Samples from urban, forested and agricultural landscapes (group 1) were contrasted in an attempt to identify differences in DOM characteristics between these primary types of land

samples with clear land-cover signals. The first of these challenges is largely due to the ambiguous nature of DOM.19 Previous investigations have used chemical and physical fractionation, spectroscopic characterization, and the identification of specific biomolecules to quantify DOM properties that can distinguish DOM from different sources.8,15,16,20,21 Aromaticity, molecular weight (MW) and hydrophobicity are a few parameters that serve as indictors of bioavailability and transformation processes.22−24 The second challenge results from the complex transport pathways that export DOM from watersheds, and the transformation processes that occur along these pathways. It was initially suggested that aquatic microorganisms in the headwaters of riverine systems preferentially degrade low MW fractions of DOM, resulting in recalcitrant pools of high MW DOM in oceans, lakes and other receiving waters.11 However, the size-reactivity continuum model has challenged this assumption, suggesting that high MW DOM may be more reactive, such that this fraction provides a better indicator of microbial digenesis.25,26 In the present study, the influence of land cover, with a particular emphasis on urban and suburban landscapes, and other factors on DOM characteristics was investigated using a novel sampling scheme. The approach minimized the mixing of DOM from different sources and the influence of aquatic processes by sampling at the terrestrial-aquatic interface from catchments with a single type of land cover during runoff events. To assess the overarching hypothesis proposed by Hedges (ref 7) three specific hypotheses were tested: (1) watersheds with agricultural and forested land cover, which are dominated by vegetation, produce DOM with higher MW and with greater aromaticity than watersheds containing urban land cover; (2) watersheds characterized as having more natural flow paths will preferentially retain hydrophobic fractions of DOM, while urban watersheds with storm sewers will produce DOM that is relatively more hydrophobic; and (3) plant growth during the summer will decrease the hydrophobicity of DOM derived from terrestrial environments with extensive vegetative cover (e.g., forested).

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EXPERIMENTAL SECTION Sampling Scheme. A total of 146 samples were collected from 48 different locations within the Grand River watershed in central Michigan. Of the 48 locations, 29 sites isolated runoff from terrestrial sources with a single type of land cover (>95% of area) within the tributaries of the Red Cedar and Looking Glass Rivers. These terrestrial samples were collected directly from storm sewers and ephemeral streams and ponds immediately following (all within 24 h) the start of runoff events. Land cover was identified according to the Michigan Land Cover/Use Classification System using the ArcMAP (v. 9.1, ESRI) geographic information system.27−29 In urban areas, a combination of sewer maps and topography were used to describe modified watersheds, sometimes called sewersheds. The amount of vegetation associated with each type of land cover was qualitatively assumed to follow the order: forested > agricultural > golf courses > suburban > urban > parking lots. Although the sampling strategy was designed to focus on terrestrial processes, it is impossible to completely eliminate the effects of aquatic influences since some samples were collected in ephemeral streams and ponds. As a result of these limitations in sampling logistics, the influence of aquatic processes on DOM characteristics for samples collected within unique 46

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Figure 1. Characteristics of DOM in surface water runoff from six types of land cover arranged according to perceived amount of vegetative cover and from watersheds with a mixture of land cover. Box plots display the sample median, quartiles, and outliers (values plotted as stars are 1.5 times beyond the range where the central 50% of the observations fall).

cover. Samples from golf courses, parking lots and all urban landscapes (group 2) were contrasted to determine which of the major components of urban land cover influence DOM characteristics. In an attempt to differentiate aquatic processes from all other processes, samples collected from agricultural land, forested areas, golf courses and surface waters influenced by multiple land covers were contrasted (group 3). Because some DOM characteristics, such as the aromatic content, are thought to be seasonally dependent, the influence of climate was also investigated.21,38−41 Precipitation and solar radiation are examples of variables that are seasonally dependent and influence DOM spectroscopic properties.42−45 Environmental data were collected from a weather station located on MSU’s campus.46 General linear models (GLMs) describe a group of statistical techniques that are often used to investigate environmental problems or questions when it is necessary to account for multiple influential factors.47 For example, they have been used to demonstrate that cumulative ozone exposure contributes to seasonal variations in the production of organic acids by plants.48 GLMs were used in this study to evaluate the influence of climate and select water-chemistry parameters on DOM characteristics. The linear models generally described all data evenly and the errors had nearly uniform variance (see SI for more details). Two methods were used to determine the

effectiveness of statistical models in describing variations in DOM characteristics. The first method used was to evaluate the coefficient of determination (r2) of the resulting linear regression. GLMs unable to explain at least two-thirds of observed variation were considered suspect and other unidentified factors were likely important. Because it is possible to add an infinite number of model parameters to describe any trend, regardless of the true meaning behind model parameters, the Akaike Information Criterion (AIC), corrected for the limited sample size, was also used to evaluate statistical models. 49 Once an appropriate GLM was established, Tukey’s HSD test was used to evaluate differences between factors responsible for DOM characteristics.50

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RESULTS DOM Characteristics. Ephemeral forest ponds and drainage entering golf course ponds were found to contain the greatest concentration of DOM of all landscapes investigated (Figure 1a). Despite the high variability (standard deviation (SD) = 9.0 mg/L), the average DOM concentration (25.5 mg/ L) from forested areas was found to be greater (p < 0.001) than from areas classified as agricultural (10.5 mg/L), urban (9.9 mg/L), and suburban (7.9 mg/L). The average concentration of DOM from golf courses (21.6 mg/L) was greater than that observed in stormwater runoff from parking lots (21.6 mg/L; p 47

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= 0.002) and urban landscapes (p < 0.001). Even in samples where aquatic influences were suspected (forest and golf course ponds), DOM concentrations were found to be significantly (p < 0.001) different from agricultural areas and surface waters with mixed land cover (μ = 10.3 mg/L). Overall, the concentration of DOM in runoff was found to vary depending on land cover and concentrations generally followed the assumed order of vegetative cover: forested ≈ golf courses ≫ agricultural ≈ urban. On the basis of the MW, forested land cover produced DOM that was distinct. The MW of DOM collected from forests was typically ∼2 kDa in size. All other samples were found to have MWs less than 1.5 kDa, significantly different from all other types of primary land covers (group 1) (Figure 1b). Similar to DOM concentrations, the average MW of DOM followed the presumed order of vegetative cover, forested > agricultural > urban ≈ suburban, with only urban and suburban samples not being different from one another. When considering subclassifications of land cover, the average MW of DOM produced from golf courses (1.43 kDa) was found to be larger than from parking lots (0.98 kDa; p < 0.001), urban land cover (1.22 kDa; p = 0.001), and suburban land cover (1.18 kDa; p = 0.001). The average MW of DOM in runoff from agricultural land (1.44 kDa), golf courses or surface waters with mixed land cover watersheds (1.45 kDa) was found to be similar. DOM produced from forested land and parking lots was found to have a much greater mean polydispersity, 2.00 and 1.65, respectively, with greater variability (SD = 0.61 and 0.64), than all other sources of DOM (Figure 1c). Samples from terrestrial sources assumed to have the greatest amount of vegetation were found to produce DOM with higher SUVA (2.21, 2.84, and 2.29 L mg C−1 m−1 for agricultural, forested, and golf courses, respectively), suggesting a greater amount of aromatic structure (Figure 1d). On the basis of SUVA values, the aromaticity of DOM followed the order forested > agricultural ≈ urban > suburban. A strong correlation (r2 = 0.91) was observed between the average MW and SUVA for DOM from forested land, but not from other types of land cover (Supporting Information Figure SI1). For the primary types of land cover (group 1), DOM hydrophobicity (measured by SPE retention) followed the general order suburban > urban ≫ agricultural > forested (Figure 2), with DOM from urban (44.3%) and suburban (46.7%) samples being greater than agricultural (31.1%) and forested (29.9%) samples (p < 0.02). The amount of organic carbon retained through H-bonding tended to follow the same trend as hydrophobicity, with the exception of DOM from forested areas. On the basis of H-bonding, forest samples were found to produce DOM that was retained significantly (p < 0.015) more by H-bonding mechanisms (22.9%) than DOM from the other primary types of land cover (agricultural, 6.8%, and urban, 12.5%). Additionally, DOM derived from forests exhibited different H-bonding characteristics than DOM derived from surface waters impacted by a mixture of land cover (p < 0.001). Differences between DOM from urban areas and parking lots were indistinguishable based on hydrophobicity and the extent of DOM retained by H-bonding. A strong correlation (r2 = 0.89) was observed between SUVA and hydrophobicity of DOM from catchments with urban land cover (highlighted by oval 1 in Figure 3). The overall trend can be explained by the linear relationship hydrophobic DOM = 22.7(SUVA) − 2.3, where hydrophobic DOM is measured by percent DOC retained by hydrophobic mechanisms via SPE

Figure 2. Percentage of DOM retained on cartridges with different forms of retention mechanisms for samples from subwatersheds with specific types of land cover. The standard error is plotted as error bars from the mean.

Figure 3. Amount of DOM retained on the hydrophobic cartridge compared to the amount of aromaticity, measured as SUVA, for DOM collected from subwatershed with specific and mixed land cover. A strong correlation (r2 = 0.89) exists for watersheds with urban land cover (oval 1), while no trend was observed for areas dominated by vegetative cover (oval 2).

and SUVA is in units of L mgC−1 m−1. However, DOM collected from golf courses and parking lots, which are assumed to be indicative of lawns and paved surfaces in suburban areas, plot in different regions. Unlike the strong trend observed for DOM from urban land cover, DOM derived from areas dominated by vegetative cover did not appear to show a simultaneous increase in hydrophobicity and SUVA (i.e., aromaticity) (highlighted by oval 2 in Figure 3). The fraction of hydrophobic DOM from forests was seasonally dependent (Figure 4). The hydrophobicity of DOM reached a seasonal low in late summer, early autumn and peaked during the winter. In an attempt to elucidate factors responsible for variations in DOM characteristics (SUVA, MW, polydispersity, and hydrophobicity), the influence of climate related variables (e.g., solar flux) and solution properties (e.g., alkalinity) were investigated further using GLMs. 48

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function of land cover and the sample alkalinity (mg L−1 as CaCO3). These factors were only able to account for 64% of the variability in hydrophobicity; however, land cover (p < 0.001) and alkalinity (p = 0.032) were found to be significant sources of variation.

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DISCUSSION Influence of Land Cover. Direct measurement of high concentrations of DOM leaving forested areas (Figure 1a) describes carbon exports that were previously observed in surface waters and soil leachates.51,52 The MW of DOM generally tended to follow the assumed amount of vegetative cover, with forested land cover producing DOM with the highest MW and paved surfaces producing DOM with the lowest MW (Figure 1). The MW of DOM was within the range observed by others in surface waters.53−56 SUVA values in this study (λ = 280 nm) are comparable to the specific UV absorbance (λ = 254 nm) reported for surface waters influenced by analogous land cover.8 Lower MW and SUVA values from agricultural land relative those from forested areas observed in this study may have been due to the incorporation of animal waste on farm land.57 Unlike previous reports that described a strong correlation between MW and SUVA for DOM across multiple landscapes,20 this trend was only observed for forested land cover. Overall, differences in the MW and SUVA of DOM between the primary types of land cover support the hypothesis proposed by Hedges (ref 7) that land cover directly impacts DOM properties. The distinctly larger MW of DOM from forested land is consistent with large inputs of plant exudates. 58 This interpretation is also supported by a relatively high retention of DOM via H-bonding from forests (Figure 2). Plant exudates, such as terpenoids and flavonoids, can be effectively isolated by the H-bonding cartridge and are considered significant precursors to aquatic fulvic acids.58−60 The large mean polydispersity for DOM from forested land (Figure 1c) is indicative of a diverse MW assemblage, consistent with the high ecological diversity present in these landscapes. The large variation in polydispersity points to differences between samples in the MW distribution of DOM. Forested land and parking lots were found to have the greatest range of polydispersity values. While one might expect smaller variations in polydispersity of DOM derived from row-crop agriculture or golf courses given the low diversity of land cover, the large range in polydispersity for DOM from forested land and parking lots was unexpected. Because most of the parking lots were proximate to deciduous trees, the variability in forested and parking lot samples may be due to seasonal inputs of deciduous leaves. Unfortunately, limited data collection in fall and winter prevented a robust analysis of this possibility. The increase in DOM hydrophobicity in urban and suburban samples, relative to more natural landscapes (Figure 2), may reasonably be due to the presence of petroleum hydrocarbons in runoff (not measured). The level of hydrophobicity in DOM collected from parking lots (μ = 44.7%) was similar to samples collected from suburban (μ = 46.7%) and urban (μ = 44.3%) catchments. While urban and suburban landscapes include varying amounts of pavement, these surfaces appear to be primarily responsible for high levels of DOM hydrophobicity. Hydrologic Flow Paths. The MW and hydrophobicity of DOM appeared to be influenced by a continuum of hydrologic flow paths, which were not directly measured. Hydrologic flow paths are known to influence the distribution and quality of

Figure 4. Seasonal influence on the hydrophobicity of DOM from catchments with the different types of land cover.

General Linear Models. After screening multiple environmental parameters (e.g., pH, precipitation), a combination of land cover descriptors, water temperature (°C), and the mean maximum solar flux density (W m−2) for the week prior to sampling were found to account for 67% of the variation in SUVA observed. While other parameters are likely to influence SUVA, evidenced by one-third of the variability not being accounted for (r2 = 0.67), all variables were found to be significant (p < 0.001). On the basis of the resulting GLM, the SUVA was found to be different for contrasts between catchments containing storm sewer infrastructure and those where aquatic processes may play a role: suburban to forested (p = 0.003), parking lots to forested (p = 0.023), urban to forested (p = 0.001), urban to golf courses (p = 0.025). There was also a significant difference in SUVA observed between samples originated from forested land cover and surface waters with mixed land cover watersheds (p = 0.003). A GLM that incorporated land cover, the total precipitation (mm) for the week prior to sampling, the mean maximum solar flux density (W m−2) for the month prior to sampling and the conductivity (mS cm−1) of the sample solution was able to account for 86% of the observed variation in DOM MW. With the exception of the interaction between land cover and solution conductivity (p = 0.08), all variable were found to be significant (p < 0.009). The variation in polydispersity was similarly explained by the type of land cover, total precipitation (mm) and mean maximum solar flux density (W m−2) for the month prior to sampling, and the conductivity (mS cm−1) of the sample solution. Approximately 79% of polydispersity variation was explained by the GLM model which had an AIC of 16.0. Using the same model for polydispersity as that used to explain MW resulted in a similar coefficient of determination but a higher likelihood of bias and uncertainty (AIC = 45.8) so the model unique to polydispersity was used. Variations observed in polydispersity were significant for all the factors included in the GLM model: land cover (p = 0.011), the interaction between land cover and the mean monthly solar flux density (p = 0.002), and the interaction between land cover and conductivity (p < 0.001). Unlike aromaticity, MW, and polydispersity, variations in hydrophobicity did not appear to be the result of climaterelated variables (e.g., solar radiation). The percent DOM retained by hydrophobic mechanisms was found to be a 49

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DOM.61 Hydrologic effects become particularly acute for samples collected from urban locations were runoff is predominantly transported over impervious surfaces and through sewer networks. In addition to short-circuiting natural physicochemical process, biological processing of organic carbon in storm drains also appears to be limited.62 The flow path for DOM from landscapes without sewers is likely to (1) be less direct, (2) include extensive contact with soil, vegetation and detritus, and (3) undergo biological transformations. Variations in transport pathways are likely to result in differences in other chemical constituents, not just DOM. Conductivity and alkalinity were two chemical measurements found to be integrally linked to DOM MW and hydrophobicity, and it is conceivable that both of these parameters are reflective of variations in hydrologic flow paths. The importance of solution conductivity in explaining DOM characteristics was unexpected. Four possible explanations for the influence of land cover dependent conductivity on DOM MW and polydispersity are considered. First, conductivity could simply be a proxy for the seasonal impact of road salt in urban landscapes where it is applied during the winter months. Second, conductivity may be indicative of where in the hydrograph samples were collected.63 As an illustrative example, consider an urban watershed where the detection of ions present in solution, measured as conductivity, function as a conservative tracer. If samples are collected at the initial onset of overland flow from impervious surfaces containing road salt, water samples will contain high amounts of this tracer, as well as organic material. Later in the hydrograph, more indirect pathways, such as infiltration of groundwater into the sewer lines, are likely to constitute a larger proportion of the flow. Because soils are found to preferentially retain high MW DOM,64 samples derived from later times would be marked by lower conductivity and possibly lower MW fractions of DOM. Third, increased ionic strength, related to conductivity, may increase thermodynamic driving forces for intermolecular assembly and hence higher MW DOM. Fourth, conductivity may be related to microbial processes, with some microbial populations transforming or releasing organic material under different ionic strength conditions. Unfortunately, no clear explanation of why conductivity and MW are related comes forth, therefore we cannot eliminate any of these possibilities and this is a topic for further study. The hypothesis that landscapes with indirect flow paths preferentially retain hydrophobic fractions of DOM is consistent with Figure 2, which shows the DOM from areas drained by storm sewers is more hydrophobic than from other locations, and Figure 3, where the hydrophobicity of DOM from urban sources increases with SUVA. The same trend in SUVA is not present for land cover expected to have indirect flow paths (e.g., forested). Observed differences in hydrophobicity were not explained by the influence of pH on DOM protonation (SI Table SI2). On the basis of these results, the selective removal of hydrophobic fractions of DOM during transport through soils that was originally reported in forests,65,66 appears to occur in other types of landscapes. Similar to conductivity, alkalinity may also be indicative of the flow path experienced by DOM. Rainwater has extremely low alkalinity. Water which passes through soils and leaf litter (i.e., interflow), where organic acids are likely to dissolve, are prone to have a low alkalinity. Alternatively, groundwater that has slowly seeped into storm sewers will contain a high amount of carbonates, silicates and other constituents which will

increase alkalinity. As these different water masses elute during the hydrograph, it is reasonable to infer that the alkalinity will vary. Likewise, the production of DOM during runoff events has been found to vary based on catchment hydrology.67,68 As discussed earlier, DOM with indirect flow paths through leaf litter and soil may be stripped of hydrophobic DOM.69 Furthermore, soils also demonstrate chromatographic tendencies to release predominantly hydrophilic DOM when flushed.70 As a result, alkalinity may correlate to DOM hydrophobicity because of when in the hydrograph the samples were collected. Trends in DOM MW and hydrophobicity are consistent with a framework proposed by others and suggest a more rigorous assessment of flow paths is warranted. Future studies are likely to benefit from isotopic analysis that could be used to identify the age and source of DOM pools62,71 as well as characterize hydrologic flow paths.72 Impact of Climate. As shown in Figure 4, the hydrophobicity of DOM reached a seasonal low in late summer, early autumn and peaked during the winter, supporting our third hypothesis. This observation is consistent with heteroaliphatic constituents (e.g., polysaccharides) being produced during the growing season when plants exude an array of organic biomolecules. While specific biomarkers were not investigated, three seasonally dependent variables were related to DOM characteristics: water temperature, precipitation and solar radiation. For catchments dominated by short-stature vegetation (e.g., agricultural, golf courses) a possible negative correlation between SUVA and the mean weekly solar flux density was observed (Supporting Information Figure SI2). Some of the sites dominated by vegetation that did not show a decrease in SUVA with increased solar radiation were forested catchments. Decreases in SUVA with increases in solar radiation suggests the breakdown of aromatic moieties. Solar induced breakdown of DOM chromophores, sometimes call photobleaching, has been reported in wetlands and marine waters.73−75 The lack of photodegradation being observed in DOM from forested catchments could reasonably be explained by shielding from the forest canopy.76 However, because the sampling scheme attempted to isolate organic material immediately following dissolution in runoff, the short residence time makes aqueous photobleaching, as normally considered, unlikely. Alternatively, the inverse relationship between SUVA and the mean weekly solar flux density could be coincidental. In agricultural watersheds, periods of soil dryness are implicated in controlling the structural composition of DOM with aromatic rich DOM being exported when soils are saturated during high flow events.9 It is plausible to assume that the change in weekly solar radiation serves as a proxy for soil moisture content. Regardless, these results add to evidence highlighting the important role climate, specifically the amount of sunlight, plays in controlling DOM composition. Another climate related variable, water temperature, was also found to be significant. Heterotrophic microorganisms preferentially consume carbohydrates, proteins and other nutrient rich sources leaving behind aromatic structures.23 As water temperature increases, microbial growth can be expected to increase. Therefore, as water temperature increased, the influence of microbial processes may explain the associated increases in aromaticity (i.e., SUVA). Distinguishing between microbial and other processes associated with increased solar radiation within this set of samples is difficult because microbial 50

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Funding

populations were not assessed and water temperature generally increased with mean weekly solar flux. However, a decrease in the amount of photodegradation in forested subwatersheds due to the forest canopy and increased microbial growth from rising water temperatures may both have contributed to the highest average SUVA values observed for DOM from forested areas. The influence of land cover and solar flux density on MW and polydispersity may be attributed to plant growth, particularly in golf courses and forested landscapes.77 The production of leaf soluble proteins by plants increases with solar radiation.78 These proteins and other biological compounds likely increase the MW of organic constituents present in these systems.58 One additional point worth highlighting: MW and polydispersity characteristics were best described by mean solar flux density based on a monthly rather than a weekly basis, as was the case for SUVA. This suggests the influence of solar radiation on MW and polydispersity depend on longer temporal trends, such as growing seasons or droughts. Implications. The sampling strategy employed is unlike other previous studies as it is more likely to capture terrestrial “signals” often muted by aquatic processes. Because of this unique approach the following critical observations are made: (1) urban landscapes produce DOM lower in MW and aromaticity than more natural landscapes, (2) urban and suburban land cover have considerably different hydrologic flow paths which likely impact DOM characteristics, and (3) both land cover and environmental factors combine to determine DOM characteristics. Because the majority of DOM studied thus far is from natural environments, it is important to stress these landscapes are not representative of DOM from urban areas, where paved surfaces and sewers strongly impact DOM characteristics. Ultimately, shifts in DOM characteristics, including those attributed to anthropogenic influences, will have ecological consequences. To ensure the sustainability of urban and suburban ecosystems, DOM derived from these landscapes should resemble DOM originating from natural watersheds. Obtaining DOM with natural characteristics will likely require significant changes to urban infrastructure and current land use practices.

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Provost and Vice President for Finance and Operations, Michigan State University, MSU-WATER (Michigan State University-Watershed Action Through Education and Research) Project. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Thank you to Michigan State University, the City of East Lansing, and Ingham County for sharing critical information regarding sewer infrastructure and land cover data. We would also like to acknowledge the insightful comments provided by four anonymous reviewers which greatly improved this manuscript.

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ABBREVIATIONS AIC Akaike information criterion ANOVA analysis of variance DI deionized water (>18MΩ cm) DO dissolved oxygen DOC dissolved organic carbon DOM dissolved organic matter GLMs general linear models HSD Tukey’s honestly significant difference test MW molecular weight SUVA specific ultraviolet absorbance at 280 nm SPE solid phase extraction UV ultraviolet light

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ASSOCIATED CONTENT

S Supporting Information *

Description of land cover/use classifications (Table SI1) and average water chemistry for land cover groups (Table SI2); relationship between SUVA and molecular weight (Figure SI1); correlation between SUVA and weekly mean solar flux (Figure SI2) observed for the six types of land cover investigated; details describing experimental methods, GLM development, and model parameters (Table SI3); and plots of GLM model prediction versus observed DOM characteristics (Figure SI3). This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Phone: 313-577-3876. Fax: 313-577-3881. E-mail: s. [email protected]. Author Contributions

The manuscript was written through equal contributions of all authors. All authors have given approval to the final version of the manuscript. 51

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