Advances in analytical chemistry have facilitated the characterization of dissolved organic matter (DOM), which has improved understanding of DOM sources and transformations in surface waters. For urban waters, however, where DOM diversity is likely to be high, the interpretation of DOM signatures is hampered by a lack of information on the influence of land cover and anthropogenic factors such as nutrient enrichment and release of organic contaminants. Here we explored the spatio-temporal variation in DOM composition in contrasting urban water bodies, based on spectrophotometry and fluorometry, size-exclusion chromatography, and ultrahigh-resolution mass spectrometry, to identify linkages between DOM signatures and potential drivers. The highly diverse DOM we observed distinguished lakes and ponds, which are characterized by a high proportion of autochthonous DOM, from rivers and streams where allochthonous DOM is more prevalent. Seasonal variation in DOM composition was apparent in all types of water bodies, apparently due to interactions between phenology and urban influences, such as nutrient supply, the percentage of green space surrounding the water bodies and point source pollution. Optical DOM properties also revealed the influence of effluents from wastewater treatment plants, suggesting that simple optical measurements can be useful in water quality assessment and monitoring, providing information about processes both within water bodies and in their catchments.
Urban freshwaters typically receive high loads of organic carbon; nutrients; and micropollutants, ranging from pharmaceuticals and personal care products to industrial chemicals and more (Schwarzenbach et al., 2006). Although routine wastewater treatment is increasingly effective, chemical stressors in urban freshwaters remain widespread. Prominent reasons are pollution legacies (Ladwig et al., 2017; Baume and Marcinek, 1993) and continued uncontrolled inputs, particularly by stormwater runoff (National Research Council, 2009). In addition, urban surface waters tend to suffer from severe hydromorphological modifications. This includes the lateral and vertical disconnection from floodplains and aquifers and results in large impacts on the extent and complexity of riverine habitat (White and Walsh, 2020). Furthermore, the disruption of connectivity limits the self-purification capacity of urban surface waters (D'Arcy et al., 2007), which can lead to turbid water and visually unpleasant and potentially harmful algal blooms (Carpenter et al., 1998). This and the limited recognition of urban freshwaters as providers of ecosystem services (Huser et al., 2016) call for improved water management strategies that consider ecological in addition to hygienic and chemical criteria (Gessner et al., 2014).
The concentration and chemical composition of dissolved organic matter (DOM), generally quantified as dissolved organic carbon (DOC), are key characteristics of aquatic ecosystems. Both concentration and composition are governed by allochthonous inputs and internal biological production and transformation processes (Williams et al., 2016). Typically, however, water quality monitoring only considers concentration and bulk quality properties (e.g., biological oxygen demand, BOD) as measures of DOM availability to, as well as degradation by, heterotrophic microbes (Jouanneau et al., 2014). This focus is at odds with the extreme diversity of DOM observed in freshwaters, where thousands of compounds can be chemically distinguished (Kellerman et al., 2014; Peter et al., 2020; Stanley et al., 2012). This high diversity and the strong spatio-temporal variation in DOM composition suggest much potential for DOM characteristics to provide insights into the state of freshwater ecosystems in water quality assessment and monitoring. In fact, additional insights into freshwater ecosystems may be gained if the very high diversity of DOM can be used to provide information about water quality for ecosystem assessment and monitoring purposes.
Recent progress in analytical methods has increasingly enabled the detailed characterization of DOM to elucidate the sources and fates in surface waters (Xenopoulos et al., 2021). Optical properties can inform us not only about the chemical characteristics of DOM but also, for example, about large-scale gradients in aquatic networks (Creed et al., 2015) or the degree of aquatic–terrestrial ecosystem coupling (Sankar et al., 2020; Lambert et al., 2015; Yamashita et al., 2010; Catalán et al., 2013). Fluorescence excitation–emission matrices (EEMs) can be processed by parallel factor analysis (PARAFAC) to identify independently fluorescing DOM components (Cory and Mcknight, 2005). Size-exclusion chromatography partitions bulk DOM into molecular size fractions, which also tend to differ in their origin and bioavailability (Huber et al., 2011). Finally, the advent of ultrahigh-resolution mass spectrometry (Fourier-transform ion cyclotron mass spectrometry – FT-ICR-MS or Orbitrap MS) has greatly refined the characterization of DOM, revealing associations between compositional turnover of DOM differing in molecular diversity and landscape-scale environmental gradients in lakes (Kellerman et al., 2014) and rivers (Peter et al., 2020).
In the present study we explored variation in the chemical composition of DOM over time and space in contrasting urban surface waters, hypothesizing that a detailed chemical characterization of DOM yields signatures of various human influences. To this end, we explored linkages between chemical composition of DOM and potential drivers determining DOM signatures, including land cover, eutrophication and chemical pollution, which we captured by using a suite of proxies. Our specific goals were to (i) describe spatio-temporal patterns of DOM composition across a range of urban freshwaters encompassing streams, rivers, ponds and lakes; (ii) identify environmental factors accounting for the observed patterns; and thereby (iii) explore how information on DOM composition could be included in urban freshwater assessment and monitoring, complementing approaches and metrics currently used.
The study was conducted in 32 freshwater sites located in the city of
Berlin, Germany. Nearly 6.5 % of the municipal area (889 km
Map of 32 sampling sites in the city of Berlin, including 7 lakes
(dark green), 7 ponds (light green), 9 streams (light blue) and 9 rivers
(dark blue), in turn including 2 heavily polluted stream sites and 2 heavily
polluted river sites. Wastewater treatment plants (WWTPs) are shown in
orange; arrows point to locations where the effluents are discharged
We repeatedly sampled all 32 sites in each of four campaigns conducted over
an annual cycle, first in spring (April–May 2016) and then in summer
(July–August 2016), autumn (September–October 2016) and winter
(February–March 2017). All field visits occurred during base flow conditions
(Fig. S5), We measured water temperature, pH, the dissolved oxygen (DO)
concentration and electrical conductivity using a handheld WTW Multi
3320 (pH 320, OxiCal-SL, Cond 340i, Weilheim, Germany) or a smarTROLL probe
(In-Situ, Fort Collins, CO, USA). We also collected integrative water
samples (2 L) from the upper 0.5 m water layer for chlorophyll
We determined total DOC concentrations by high-temperature catalytic
combustion and infrared spectrometry on a total carbon analyzer (TOC-V analyzer; Shimadzu, Kyoto,
Japan), with a 0.5 mg L
Iron can form stable complexes with DOC and interfere with optical DOC
measurements, so the two variables are not independent (Maranger and Pullin, 2003). We found that the quotient of light absorbance at 420 nm
(
We calculated several indices from the absorbance spectra (Table S3): the
specific UV absorption (SUVA
The molecular size distribution of DOM was analyzed by liquid size-exclusion
chromatography in combination with UV and IR detection of organic carbon and
UV detection of organic nitrogen (LC-OCD-OND) (Huber et al.,
2011). The instrument was calibrated with IHSS Suwannee River I humic acid
and fulvic acid standards (International Humic Substances Society, St Paul,
MN, USA). Carbon and nitrogen detectors were calibrated with potassium
hydrogen phthalate (C) and sodium nitrate (N). Limits of quantification were
0.1 mg C L
To examine the molecular composition of DOM, we used ultrahigh-resolution
Fourier-transform ion cyclotron mass spectrometry (FT-ICR-MS). We extracted
DOM on Agilent Bond Elut PPL solid-phase columns (Dittmar et al., 2008) from
1 L of filtered water acidified to pH 2. We then diluted extracts to 10
NO
We used repeated-measures ANOVA to test for differences among types of water
bodies and sampling periods (referred to as seasons hereafter) for a variety
of response variables; as the interaction between water body type and season
was not significant, we recomputed models including the main effects only.
Furthermore, we assessed the importance of seasonal variation in each water
body type by computing a respective variance component using a type-II ANOVA
(a.k.a. variance component analysis) for data from each water body type with
the season and site ID as random factors; this approach facilitates the
assessment of temporal variation as a fraction of total variation within
each water body type. Normal distribution was assessed graphically by
quantile plots and histograms. For ANOVA, data were
For constrained multivariate analyses we considered land cover adjacent to
the water bodies, trophic state and micropollutant load as drivers of
variation in DOM chemical composition. We used the percentages of urban
green space and paved areas as proxies for land cover; concentrations of TP,
NH
We followed a three-step approach to analyze the spatio-temporal patterns of DOM composition: first we identified major axes of variation in DOM composition by a principal component analysis (PCA) based on quantitative indicators of DOM, analytically accessible fractions thereof or quantitative proxies: DOC concentration, all absorbance and fluorescence indices, component-specific fluorescence intensities from PARAFAC normalized to DOC, and the size-exclusion chromatography data. Only the 27 sites sampled in all four seasons were included in this analysis. All variables were standardized to a mean of zero with a variance of 1 to ensure equal weighting and were projected onto the ordination space using Pearson correlations of the variables with PCA axes in a distance biplot (sensu Legendre and Legendre, 2012). To explore spatial patterns, we mapped PC1 and PC2 scores onto Berlin's landscape using QGIS (QGIS Development Team, 2017).
Second, we used the same dataset as the dependent matrix in a redundancy analysis (RDA) with the set of potential drivers described above used as predictor variables. The goal of the RDA was to identify potential drivers of DOM composition and thereby assess, reciprocally, whether various DOM descriptors are ecologically informative. We started with the full RDA model and forward-selected drivers (Legendre and Legendre, 2012). For hypothesis tests in the RDA, permutations were restricted to account for repeated measurements at the same sites across seasons by first permuting sets of four seasonal measurements across sites and then permuting across seasons within each site. To check our ability to identify drivers behind major variation observed in DOM composition, we used Procrustes analysis to assess the similarity of PCA and RDA ordinations, including a permutation-based test of the non-randomness of the superimposition achieved (Mardia et al., 1979; Peres-Neto and Jackson, 2001).
Third, we exploited results of the FT-ICR-MS to facilitate interpretation of
the two major axes of variation in DOM chemical composition resulting from
the PCA. The FT-ICR-MS data were only available for three seasons and were
purely compositional (relative intensities) as the many thousands of
compounds contained in the spectra cannot be calibrated to yield
concentrations. To link the quantitative and compositional datasets, we
correlated PCA scores with compound-specific relative intensities of the
mass spectra. The compound-specific correlation coefficients were then used
as color codes in van Krevelen plots. FT-ICR-MS-derived information such as
the richness or average weight of specific molecular groups was also
projected onto the PCA ordination space as arrows, provided correlation
coefficients were
Among all physico-chemical variables, only DOC concentration and temperature
differed significantly among types of water bodies (
Separate ANOVAs for each water body type showed that seasonal variation in
TP and NH
The analysis of TrOCs identified acesulfame, a widely used artificial
sweetener (Buerge et al., 2009), in 72 out of a total of 120 samples taken
at 32 sites across all seasons (Table S7). Similarly, two corrosion
inhibitors, benzotriazole and methylbenzotriazole (Cotton and Scholes,
1967; Tamil Selvi et al., 2003), occurred in 68 and 63 samples,
respectively. Fifteen other TrOCs were detected in at least 2 and up to 62
samples (Table S7). Rivers showed the highest concentrations throughout the year. The first principal component of the PCA considering all TrOCs
explained 61 % of the total variance (Fig. S3) and separated streams and
rivers with higher concentrations from ponds and lakes where concentrations
of TrOCs were lower and often undetectable, particularly in ponds (Table S8). The strong positive correlations between most of the TrOCs suggested
the applicability of a simple average TrOC concentration as a proxy for
micropollutant load in further analysis; this mean was computed across all
TrOCs after
PARAFAC modeling resulted in seven components referred to as C1–C7 (Table S4, Fig. S1). Components C6 and C7 were previously found to be protein-like, whereas all other components have been reported as humic-like (Table S4). In contrast to the standard physico-chemical variables and results from size-exclusion chromatography (Table S7), the PARAFAC components and absorbance and fluorescence indices generally showed significant differences among water body types (Tables S5 and S6).
The first axis of the PCA analyzing spatio-temporal patterns of DOM chemical
composition explained 34 % of the total variance (Fig. 2). PC1 was largely
defined by the negative loadings for C1 and C2 (representing humic
substances originating from wastewater treatment), SUVA
Ordination of sites
PC2 explained an additional 21 % of the total variance and correlated
positively with HMWS-N (mg N L
High-resolution mass spectrometric analyses of samples from three seasons provided additional insights into the chemical composition of DOM. Overall, we detected 6446 molecular formulas, most of them representing molecular groups typical of humic material derived from soils. This includes highly unsaturated O-rich compounds, polyphenols and other aromatic structures, followed by unsaturated aliphatic polyphenols and polycyclic aromatic compounds with aliphatic chains. The van Krevelen plots revealed a positive correlation of lignin-like molecules and carbohydrates with PC1 of DOM and identified these molecules as abundant in lakes (Fig. S2). In contrast, the negative association of proteins with PC1 was typical of streams. Information on the molecular groups identified by FT-ICR-MS and projected onto the PCA space (Fig. 2d) showed carbohydrates and sugars containing N, S or P to be positively related to PC1. Furthermore, PC1 was negatively related to black carbon, polyphenols and polycyclic aromatic compounds with aliphatic chains, which are all typical of soil-derived humic material, as well as with unsaturated aliphatics, saturated fatty acids and peptides, indicating that all of these molecular groups were more important in streams. Lastly, the computed molecular lability boundary (MLB), carbohydrates, sugars without heteroatoms (N, S or P) and unsaturated aliphatics were positively related to PC2, while AI, DBE, black carbon and polyphenols were negatively related to PC2.
Our results show that the chemical composition of DOM in contrasting surface
waters of the metropolitan area of Berlin, Germany, is highly diverse. This
reflects both aquatic–terrestrial linkages and DOM transformations within
the aquatic systems (Fonvielle et al., 2021). Clear
differences among the four types of water bodies we investigated were due to
distinct signatures of streams and rivers vs. ponds and lakes. This was
revealed especially by the first principal component (PC1) of a PCA (Fig. 2), which reflects the dominant gradient defined by variation in DOM
composition across the 32 urban sites included in the study. Since optical
measurements play an important role in our analysis of DOM, it is important
to consider potential interference by iron. Elevated iron concentrations
lead to brownification, similar to effects of allochthonous DOM, and Fe and
DOC also form stable complexes, so the two variables are not
independent (Maranger et al., 2003). However, for Fe data available from the
Senate of Berlin for two of our lakes (L5 and L7 – 0.06
Stream DOM exhibited higher aromaticity (as indicated by SUVA
In contrast to natural landscapes, however, the linkage of urban waters with their terrestrial surroundings is mediated by paved surfaces and engineered flow paths, including roof runoff into rain gutters, extensive (partially leaky) sanitation networks, and sewage overflows in WWTPs that are activated following heavy rainfall or snowmelt. The urban gradient from allochthonous to autochthonous DOM sources we document could thus be driven by surface runoff rather than soil seepage and subsequent delivery of DOM to surface waters via groundwater Although we did not sample after major storms (Fig. S5), we would expect legacy effects of past runoff events to differ among sites, depending on the extent of green space and impervious surface area in the surroundings of the sites. This interpretation is supported by higher levels of proteins (Fig. 2) characterizing the urban streams and rivers, as opposed to soil-derived humic DOM signatures typical of unimpacted streams and rivers (Hutchins et al., 2017). The proteins could originate from surface runoff integrating various sources of urban pollution, but they might also derive from WWTPs, as implied by the nature of some of the PARAFAC components (Table S4). For instance, the humic fluorophore C2 has been reported in WWTP effluents that may be discharged into urban surface waters (Murphy et al., 2011). Point source inputs were also identified as drivers of DOM composition by the influence of TrOCs in our RDA and their correlations with C2 and C7, all of which are components of WWTP effluents.
Lakes differ from streams by a typically greater importance of autochthonous
production. Since this production is fostered by abundant nutrient supply
(given sufficient light), elevated nutrient concentrations should coincide
with DOM signatures indicative of autochthonous carbon sources. This pattern
has been found in agricultural streams, where the freshness index
Similarly, the TP concentration was significantly related to DOM composition in our RDA, where phosphorus-rich water bodies also proved to have more allochthonous than autochthonous DOM. This points to inputs from urban surface runoff rather than groundwater inflow where long flow paths and residence times provide ample opportunities for phosphorus immobilization. As with N, additional phosphorus may derive from WWTP effluents, as suggested by the positive relationship between TP concentration and the fluorophore C2 as a putative tracer of WWTP effluents (Murphy et al., 2011). Overall, the negative relationships between nutrient availability and the importance of autochthonous components in the DOM pool suggest that while streams and rivers may efficiently collect N and P from the urban environment, lakes are more efficient at channeling nutrients into autochthonous production. Thus, the autochthonous DOM signature in urban lakes appears to be largely independent of nutrient supply and rather be facilitated by longer water residence times, higher water temperature and favorable light conditions.
Our results on urban surfaces driving urban allochthonous DOM composition meet our expectation that land cover notably influences the composition of DOM in urban surface waters (Williams et al., 2016; Sankar et al., 2020). This conclusion is supported by results of our RDA, which identified the presence of green spaces in the perimeter of the water bodies as a significant influence. However, the relationship between land cover and DOM composition must be interpreted with caution because all lakes were situated in areas with green spaces in their surroundings, whereas streams ran through areas dominated by buildings and paved surfaces. The urban running waters, more than lakes and ponds, thus received high surface runoff during rain events, including high inputs of pollutants and allochthonous DOM.
Except for ponds and some lakes, all investigated water bodies had direct surface water connections, which could result in spatial autocorrelation. In addition, spatial patterns may arise from the prominent land cover gradients in Berlin, ranging from forested areas to densely populated urban centers. Since the sampling design of our study does not lend itself to a formal analysis of spatial autocorrelation, we explored spatial patterns with DOM proxies in maps (Fig. 1b, c) but found no obvious relationships. Instead, type-specific characteristics of the water bodies were pronounced, largely independently of hydrological connections. Factors potentially contributing to the resulting heterogeneity across the surface waters in the city include specific local stressors such as point source inputs of pollutants, spatially variable urban surface runoff delivering allochthonous DOM and hydraulic-engineering structures such as sluices. Thus, our map of DOM composition (Fig. 1b, c) could be interpreted as visualizing heterogeneity in the conditions of urban surface freshwaters.
Seasonal variation in DOM signatures occurred in all types of water bodies
mostly independently from variation among the four water body types. With a
few exceptions, H3 being the most prominent example, seasonal variation in
DOM composition was consistent across all water body types (Fig. 3a, b).
Assessed separately at each site (Fig. 3b), DOM was generally fresher in
summer and autumn than in winter and spring, as indicated by higher ratios
of
At least four potential processes could account for the observed seasonal turnover in DOM composition: exudates of aquatic primary producers, microbial and sunlight-induced transformation of DOM, and terrestrial inputs from riparian vegetation (Spencer et al., 2009; Cory et al., 2015), all of which could be influenced by the urban environment. Seasonal variation in light conditions could be important in influencing DOM composition by primary producers, independently of nutrient supply (see above), and temperature changes might also play a role, especially in determining rates of microbial DOM transformations. Pulses of leaf litter falling, swept or blown into urban water bodies could be an additional source of DOM varying with season (Gessner et al., 1999). This holds particularly for urban green spaces and water courses lined by woody riparian vegetation. However, quantification of the relative importance of different drivers of seasonal patterns remains difficult based on the data currently available for urban settings.
The ponds and streams included in our study showed higher and less predictable seasonal changes in DOM composition than the lakes and rivers, as revealed by the pattern along PC2 (Fig. 3). This indicates that the nature and degree of aquatic–terrestrial coupling in urban settings leaves an imprint on seasonal changes in DOM composition. Therefore, the more extensive the time series data from surveys of DOM dynamics, the better they can give information about ecosystem conditions, complementing established procedures in water quality assessment and monitoring.
The fact that our analysis of DOM composition revealed specific
characteristics of individual water bodies underlines the potential value of
DOM descriptors as indicators that could be included in water quality
assessment and monitoring. Some sites deviated from the general pattern
observed for water bodies of the same type. P4, for example, was formerly
connected to an old waste water treatment plant and appeared to be
influenced by previously unrecognized stormwater runoff. This legacy matches
the particularly high levels of nutrients characterizing this site,
especially NH
The composition of DOM analyzed in a suite of contrasting water bodies of a large metropolitan area, the city of Berlin in Germany, is diverse, varying widely in molecular size and other features related to the degree of allochthonous inputs and conveying a distinct urban character. DOM features clearly differentiated water body types, from lakes with highly abundant autochthonous DOM to streams with more allochthonous DOM. Seasonal variation in DOM was prevalent in all water body types but likely to be driven not only by phenology but also by urban influences such as nutrient supply, WWTP effluents, reduced leaf litter input or flashy runoff resulting from sealed surfaces. Nutrient supply, the percentage of green space and concentrations of trace organic pollutants (as proxies for point source influences) were identified as drivers of DOM composition. In particular, simple optical measurements of DOM characteristics were sufficient to detect WWTP effluents, a result that was corroborated by our data on TrOCs. This suggests that optical analysis of DOM could be a useful approach to complement current water quality assessments and monitoring. Such analyses are fast, inexpensive and easily implemented and could be further supported by more sophisticated, potentially automated analyses such as the mass spectrometric quantification of TrOCs. DOM composition can provide information about processes both within water bodies and in the terrestrial surroundings; therefore, water quality assessments could benefit from integrating information on DOM composition. Robustness of the approach would increase if the DOM assessments were based on time series or even continuous monitoring, for which knowledge and technology are already available; this could indeed strengthen assessments as implemented in legal frameworks such as the EU Water Framework Directive.
The data are available at
The supplement related to this article is available online at:
All authors contributed to designing the study. CRGQ and SHO collected the data. CRGQ carried out the optical analysis and the PARAFAC modeling; GAS carried out the FT-ICR-MS analysis. CRGQ and GAS conducted the statistical analysis. CRGQ led the manuscript writing, jointly with GAS. All authors discussed results and edited the manuscript.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank Antje Köhler at the Senate Berlin (SenUVK) for water quality data, authorities and private land owners for providing access to the study sites, Cleo Ninja Stratmann for obtaining permissions, Uta Mallok for nutrient analyses, Claudia Schmalsch for the LC-OCD-OND analysis, Sarah Krocker and Thomas Fuss for the DOC analysis, and Tobias Goldhammer for advice with chemical analyses. Cleo Ninja Stratmann, Lena Meinhold, Ignacio Ajamil, Gonzalo Idoate, Lukas Thuile-Bistarelli, Amina Sultan, Rieke Schulte, Emiliana Tupper, Thomas Fuss, Ruben del Campo, Anna Wieland and Marven Bethke are acknowledged for field assistance. Geert Aschermann and Anke Putschew kindly enabled TrOC analyses. Access to the FT-ICR-MS instrument and associated expertise was generously provided by Thorsten Dittmar during a stay of Gabriel A. Singer at the University of Oldenburg that was funded by the Hanse-Wissenschaftskolleg, Delmenhorst. Thank you also to Kyle Pypkins for support with GIS and to Birgit Kleinschmit for thoughts on the sampling strategy and data analysis.
This research has been supported by the Deutsche Forschungsgemeinschaft (UWI; grant no. GRK 2032).The publication of this article was funded by the Open Access Fund of the Leibniz Association.
This paper was edited by Ji-Hyung Park and reviewed by two anonymous referees.