Article pubs.acs.org/est
Cite This: Environ. Sci. Technol. XXXX, XXX, XXX-XXX
Fluorescence and Quenching Assessment (EEM-PARAFAC) of de Facto Potable Reuse in the Neuse River, North Carolina, United States Martha J. M. Wells,*,† Gene A. Mullins,‡ Katherine Y. Bell,§,# Allegra K. Da Silva,∥,∇ and Eileen M. Navarrete⊥ †
EnviroChem Services, Cookeville, Tennessee United States Chemistry Department, Tennessee Technological University, Cookeville, Tennessee United States § CDM Smith, Nashville, Tennessee United States ∥ CDM Smith, Denver, Colorado United States ⊥ Public Utilities Department, City of Raleigh, Raleigh, North Carolina United States ‡
S Supporting Information *
ABSTRACT: The Neuse River, North Carolina, U.S., exemplifies a typical de facto potable reuse scenario, where drinking water sources are located downstream of treated wastewater effluent discharges. The study results imply that planned potable water reuse, whether in an indirect or direct potable reuse scenario, might provide better control over water quality than the status quo conditions. Using fluorescence excitation−emission matrix (EEM) measurements, anthropogenic influence of a wastewater treatment plant (WWTP) discharge was observed in samples near the location of drinking water treatment plant (WTP) intakes, eight or more miles downstream of the WWTP, implying that anthropogenic compounds were not fully removed or degraded by natural processes in this reach of the river. PARAllel FACtor (PARAFAC) analysis supported a two-component model of humic-like and nonhumic-like dissolved organic matter (DOM). A nonmodeled anthropogenic feature was also indicated. Significantly, the quenched fluorescence of humic-like DOM (static and/or dynamic quenching) by nonhumic-like DOMpreviously demonstrated for probe molecules but first reported here in a natural/anthropogenic-influenced systemoffers exciting insight into studies of humic/nonhumic interactions with important implications for pollutant fate and transport, sensing applications, and water treatment. A molecular spectroscopic explanation for dual fluorescing peaks in amino acids and humic substances is postulated.
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INTRODUCTION
coliformin the Neuse River. However, studies of conventional water quality parameters alone are not adequate for consideration of the potential for future potable water reuse but must include examination for the presence of trace wastewaterderived constituents. As early as 1998, the U.S. Environmental Protection Agency (USEPA) sampled the Neuse River Basin chosen for its geographic scale, contaminant spectrum, and potential for human and ecological exposurein an investigative pilot study for the presence of endocrine disrupting compounds (EDCs) as a first step to understanding their impacts.1 A survey of pharmaceuticals in the Neuse River was conducted in conjunction with the USEPA in 2011.7 As a
Water quality in the Neuse River, North Carolina (NC), U.S. similarly to many waterways in North Americais impacted by rural runoff (pesticide application, turkey and hog farms, and septic tanks) and urban inputs (industrial and domestic wastewater treatment (WWTP) effluents as well as nonpoint runoff).1,2 The Neuse River stretches 185 miles through a basin of 6192 square miles.3 The Neuse River water quality is generally satisfactory for most designated uses, including its current use as a drinking water source; however, there is evidence throughout the basin of anthropogenic impacts. The river has areas of low dissolved oxygen (DO), historical polychlorinated biphenyl (PCB) contamination, and elevated nutrient, chlorophyll a, copper, and zinc concentrations.4 Several studies3,5,6 surveyed conventional water quality parametersdissolved oxygen (DO), pH, temperature, salinity, turbidity, total suspended solids (TSS), nutrients, and fecal © XXXX American Chemical Society
Received: July 23, 2017 Revised: October 27, 2017 Accepted: November 7, 2017
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DOI: 10.1021/acs.est.7b03766 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology follow-up, in 2013,4 the City of Raleigh, NC, voluntarily engaged in a study of the fate and transport of trace microconstituents, often referred to as contaminants of emerging concern (CECs), in the Neuse River to support the possible future use of reclaimed water as a source water. The objective of the 2013 Neuse River Water Quality Sampling study4 was to define the baseline water quality in the upper Neuse River by characterizing a selected list of constituents and evaluating the environmental fate and transport of these parameters, in a defined study period that represented the highest contribution of treated effluent in the system. Samples collected three times in the fall of 2013 from eight locations along the Neuse River between Falls Lake and Goldsboro, NCincluding one sampling location at the point of discharge of Raleigh’s Neuse River WWTPwere evaluated for selected microorganisms, chemical constituents, bulk genotoxicity, and fluorescence as part of an assessment to support the potential for future reuse options, including planned potable reuse. The examination of samples for fluorescence was one aspect of the larger study and is discussed here. This project demonstrated the use of excitation−emission matrix (EEM) fluorescence spectroscopy as a tool to assess the extent of anthropogenic influence in a de facto potable reuse context. De facto reuse occurs when a community draws water from a river or reservoir that includes wastewater effluent discharges from upstream communities and subsequently treats the water for potable use.8,9 Samples were collected at strategically located sitesupstream and downstream from WWTP and drinking water treatment plants (WTP)along a 76.5 mile stretch of the upper Neuse River in which de facto reuse already occurs. There are three wastewater treatment plants (WWTPs) and two drinking water treatment plants (WTPs) with permitted discharges or intakes directly to/from this reach of the river. In the overall project, 110 chemical constituents were measured (results are reported elsewhere4). Constrained by considerations of time and expense, it is impossible to analyze for every individual organic compound contributing to the surrogate total organic carbon (TOC) measurement. The objective of the fluorescence portion of the umbrella study was to apply an innovative mapping procedure to provide a gross characterization of organic matter that indicates changes in surface water quality as a result of inputs from various sources into the Neuse River basin. Fluorescence mapping procedures have been applied previously to examine organic matter along reaches in lake, riverine, and estuarine watersheds,10−16 as well as for monitoring anthropogenic impacts and water reuse scenarios.10−12,17,18 Specifically, the use of fluorescence as a potential monitoring tool for recycled water systems was reviewed by Henderson et al.19 Statistical PARAllel FACtor (PARAFAC) analysis decomposes EEMs into components that represent the fluorophores contributing to the overall spectrum and may be attributed to a specific analyte or chemical group.20 However, it is important to emphasize that the presence of anthropogenic markers as indicated by EEMs has not yet been related to any risks to human or ecological health. This research is complementary to that of Osburn et al.2 who reported an EEM-PARAFAC study on the main stem of the Neuse River Basin and its tributaries that investigated dissolved organic nitrogen (DON) sources that may impact the water quality of this estuary. Previously, fluorescent indices21 and EEM-PARAFAC analyses22,23 of dissolved organic matter
(DOM) and base extracted particulate organic matter were reported in the Neuse River Estuary in the Atlantic coastal plain, downstream of the sample sites in this study.
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MATERIALS AND METHODS Sampling. Samples representing eight discrete sites (A−H) in the upper Neuse River main stem (Supporting Information (SI) Tables S1−S2, Figure S1) were collected during three sampling events over a two-week period in October 2013. Site A is the most upstream site, and site H is the most downstream site in the river reach spanning 76.5 river miles of the 185-mile length of the Neuse River. Two of the eight sampling sitesB and Ecorrespond (by comparison of latitude/longitude) to two of the four main stem sites sampled by Osburn et al.2 that they labeled “Raleigh” and “Clayton,″ respectively. Three primary WWTPs are located along this reach of the river: (1) between sites A and B (permitted maximum capacity 2.4 mgd), (2) at site C (60 mgd), and (3) between sites G and H (9.5 mgd). Water samples for EEM analyses were collected in virgin amber 8 oz glass sample containers purchased from ColeParmer (Vernon Hills, IL) from mid-depth, at or as near the center of the stream as possible. Samples were immediately packed on ice in coolers and shipped by priority courier overnight. Standard chain-of-custody procedures were followed. The surface water samples were collected on the upstream side of bridge crossings at all sites except at sites C and H. Site C is in the river directly at the effluent discharge. Samples were collected from the southern bank of the Neuse River at site H. More details of sample collection are available in the Neuse River Water Quality Sampling Final Report.4 River flow and precipitation during sampling events are illustrated in SI Figure S2. Total Organic Carbon Analysis (TOC). TOC samples were collected in 125 mL amber glass bottles containing sample preservative (0.5 mL of H2SO4, 50%) and were analyzed using EPA method 415.1. TOC concentration was reported as mg of carbon/L and data are depicted in SI Figure S3. Fluorescence Analyses. Fluorescence intensities of all samples were corrected for primary and secondary inner filtering effects (IFEs)24−31 using a MATLAB (Natick, MA) program developed in-house. All data, not only samples with high optical densities, were corrected for IFEs in agreement with Murphy et al.20 and Aiken32 because IFEs occur whenever fluorophores are measurable.20 The importance of IFE correction even at low TOC concentrations is discussed and illustrated in SI (Figure S4). Complementary peak picking32 (presenting data as the intensities of individual excitation−emission pairs or peaks) and statistical PARAFAC modeling techniques33 were applied for data analyses, and both techniques were determined to be necessary for complete data interpretation. PARAFAC analysis33 was conducted using the partial least-squares (PLS) Toolbox software (Eigenvector Research Inc., Manson, WA) for use with MATLAB. The data were preprocessed by replacing the first-order elastic Rayleigh scattering with interpolated values (±20 nm) and assigning zero to subRayleigh wavelengths. Wavelengths below EX 218 nm and EM 216 nm were not used to remove inelastic scattering. Statistical analysis of variance (ANOVA) by Duncan’s Separation of Means test was performed using the Statistical Analysis System (SAS, Cary, NC). Additional experimental details of fluorescence analyses are presented in the SI. B
DOI: 10.1021/acs.est.7b03766 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Figure 1. Corrected EEM spectra of (a) event 2 site A and (b) event 2 site C. Peak nomenclatureB: tyrosine-like; T: tryptophan-like; F: fulviclike; H: humic-like. Subscript 1 refers to primary peaks that fluoresce more intensely (yielding better specificity/selectivity and lower limits of detection) at lower excitation wavelength/higher energy than secondary peaks at higher excitation wavelength/lower energy denoted by the subscript 2.
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EEM Spectra. Fluorescence spectroscopy uniquely discriminates between humic and nonhumic organic matter by spectral separation. Representative EEM spectra are compared for the upstream reference location at site A (Figure 1a) and a downstream location at site C (Figure 1b) for sampling event 2. Control site A was selected as a reference site for comparison to downstream sites to ascertain relative anthropogenic inputs to the river system because there were no permitted municipal wastewater treatment plants that discharged upstream of this location. Event 2 is featured in Figure 1 because the sampling occurred when the river flow rate (SI Figure S2) was approximately at the 25th percentile, intermediate between the flow rate during event 1 (between the 25th and 50th percentile) and event 3 (below the 10th percentile). Site C located near the outfall of a WWTP permitted for 60 mgd operationdemonstrated an anthropogenic influence from the WWTP noted in the increased fluorescence at B1, B2, T1, and T2 (Figure 1b) that is absent from site A (Figure 1a). Peak Nomenclature for Fluorescent Fingerprint Regions. EX/EM wavelength pairs specific to this data set
RESULTS AND DISCUSSION Surface water quality is impacted by nonhumic and humic organic matter of allochthonous (leached from soils), autochthonous (formed by aquatic organisms), and anthropogenic (derived from agricultural, industrial, and domestic wastes) origins.34 Here, humic matter is used to refer to both fulvic and humic acids that are acidic, hydrophilic, high molecular weight, amorphous, and polydisperse,34 whereas the nonhumic fraction of organic matter in water has lower aromatic character and molecular size than the humic fraction, and it is primarily composed of hydrophilic acids, proteins, amino acids, and carbohydrates.35 All forms of organic molecules in surface water are collectively indicated by the gross surrogate measurement, TOC. The TOC concentration of the sampled reach of the river varied from approximately 4−6 mg/L (SI Figure S3), which is typical of organic matter levels in fresh waters.36 The TOC concentration at Site C near the WWTP outfall was not statistically significantly different from the other sampling sites. C
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Figure 2. Corrected excitation spectra from sampling event 2 featuring (a) tyrosine-like, (b) tryptophan-like, (c) fulvic-like, and (d) humic-like influences at constant emission wavelengths (a) EM 300 nm, (b) EM 350 nm, (c) EM 424 nm, and (d) EM 474 nm, respectively. Trip blank (TB).
Figure 3. Corrected intensity for EEM data wavelength pairs comparing all sites and all events at (a) B1: tyrosine-like, (b) T1: tryptophan-like, (c) F1: fulvic-like, and (d) H1: humic-like regions at (a) EX224/EM300, (b) EX224/EM350, (c) EX224/EM424, and (d) EX224/EM474, respectively. Trip blank (TB).
differ. Evolution of the nomenclature for identifying fluorophores in natural waters is discussed in the literature.37−39 Important nonhumic points in the EEM spectra assigned to B1 (EX224/EM300) and B2 (EX275/EM300) are attributed to tyrosine-like compounds,18,40,41 whereas T1 (EX224/EM350), and T2 (EX284/EM350) are attributed to tryptophan-like compounds.18,40,41 Tyrosine and tryptophan are the only amino acids in proteins that fluoresce significantly; however, these peaks may or may not contain tyrosine or tryptophan.
were selected for this study by examining the peak maxima in the EEM spectra of these data. The assignments applied in this research are indicated in Figure 1a. B1, B2, T1, T2, F1, and H1 are observed in the UVC excitation range and F2 and H2 in the UVB and UVA excitation ranges. The exact spectrographic locations of the fluorescent regions within an EEM map can vary spatially between geographic locations if the nature of the organic matter and the characteristics of the water medium D
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also be introduced to the Neuse River downstream from site C cannot be excluded. Statistical evaluation (SI Table S4) of the data at F1 and H1 indicated that minimal specific impact to fulvic/humic fluorescence was noted due to the WWTP outfall. The F2 fulvic-like and H2 humic-like peak maxima for site C were observed to be slightly shifted to a longer excitation wavelength (Figure 2c,d) indicating a difference in the composition of the organic matter. Precipitation. The anthropogenic influence was observed at site C during sampling event 1 (Figure 3a,b), but it was greater at site C during sampling events 2 and 3. For site C, the fluorescence resulting from anthropogenic influence varied most of all sites among sampling events, i.e., exhibited the largest standard deviation (SI Table S3). These temporal differences were attributed to variation in precipitation (SI Figure S2) that would affect concentrations in the wastewater treatment plant as well as in the river itself. During sampling event 1, and during the week before sampling event 1, substantial rainfall was measured that would have a dilution effect on the anthropogenic fluorescent components. Only minor rainfall occurred before sampling event 2, and no measurable precipitation was noted between sampling events 2 and 3. Interestingly, the naturally occurring fulvic/humic acids did not appear to be influenced by rainfall events as were the anthropogenic indicators, indicating a steady state input of fulvic/humic acids during rainfall that balanced the dilution due to increased precipitation. PARAFAC Modeling. Reviews of EEM-PARAFAC techniques20,44 and their applications to DOM in varied water sources45−47 have been published. The PARAFAC technique mathematically extracts information (termed components) contained in three-way EEM data arrays (excitation, emission, and sample modes) that is relatable to fluorescing chemicals/ groups as indicators of water quality.20,47 In this research, two PARAFAC components (nonhumic-like and humic-like) were determined to model most of the data (TOC/Abstract graphic image, Figure 4 and SI Figure S7). An additional wastewater-derived contribution was visually observed not to be as well-modeled by PARAFAC and is discussed in a later section. Plots of the scores and residual sums of squares for the sample number mode and loadings for the emission and excitation modes for the two-component model are illustrated in Figure 5. Modeling of more than two components was unsupported by the core consistency diagnostic.44 The orthogonalized PARAFAC variance captured by a three-component model of all data was tested (component 1 = 49.25%, component 2 = 12.31%; component 3 = 3.60%), but it was less than that captured by the two-component model (component 1 = 74.49%; component 2 = 7.12%) indicating that the two-component model fitted better than the threecomponent model. The two-component model was additionally verified by a split-half assessment (SI Tables S5−S6) of variance (SI Tables S7−S9), residual sums of squares (SI Figures S8−S9), scores (SI Figure S10), and loadings (SI Figure S11); discussion of the data is included in SI. The unusually low number of PARAFAC components is attributed to the small sample size (24 samples) in which only three of 24 samples were collected in the river at the WWTP discharge at site C, and only two of those three samples were collected in reduced flow (lower precipitation) conditions. No samples of the WWTP discharge were collected directly.
The assignments of the fulvic-like F1 (EX224/EM424) and F2 (EX325/EM424), and humic-like H1 (EX224/EM474) and H2 (EX 340/EM474) wavelength pairs are compatible with our previous work25−29,31 and those determined by Holbrook et al.11 in a geographically similar water body. Fulvic acid maxima are observed at emission wavelengths ranging from 400−450 nm, whereas humic acid maxima occur at emission wavelengths ranging from 450−500 nm. F1/F2 are more indicative of fulvic acids than humic acids, and H1/H2 are more indicative of humic acids than fulvic acids, although considerable overlap exists in the spectra.25 Fluorescence emission wavelengths of humic acids are bathochromically (“redshifted”) to higher emission wavelengths with respect to fulvic acids.11,12,25,42,43 Terrestrial humic acids are more red-shifted relative to terrestrial fulvic acids than aquatic humic acids are red-shifted relative to aquatic fulvic acids.25,39,43 The diagonal stripe in the EEM spectra in Figure 1where EX = EMresults not from fluorescence, but from the elastic, first-order Rayleigh scattering of light30 (further discussed in SI and illustrated in Figure S5). Fluorescent Indicators of WWTP Influence. Excitation spectra (Figure 2) and graphs of corrected intensity at specific wavelength pairs (Figure 3) are compared across sampling sites and events. Further discussion regarding the dual fluorescence peaks observed (Figure 2) for amino acids, fulvic acids, and humic acids is presented in a later section. Box and whisker charts, presented and further discussed in SI (Figure S6), were prepared for the data in Figure 3 to evaluate the distribution of variability in the data population. The determination was made to include all data in these analyses; no data were eliminated as outliers. The fluorescence intensities observed for B1 and T1 (Figure 3 a,b) at site A are postulated to result primarily from background allochthonous and autochthonous DOM and not from wastewater-derived anthropogenic influences because there were no permitted municipal wastewater treatment plants that discharged upstream of this location. The background signal, as observed at site A, is concluded to be present in all samples from this waterbody representing a natural state. Nonhumic fluorescence intensity statistically greater than that at reference site A is deemed to be representative of anthropogenic influence. Relative to site A, an increase in tyrosine-like and tryptophan-like fluorescence was observed at site C, which was located near the outfall of a WWTP permitted for 60 mgd operation (Figures 1, 2a,b, and 3a,b). The excess in fluorescencei.e., the intensity at site C minus intensity at site A for B1 or T1is demonstrated visually in Figure 3a,b and statistically for all sites by an ANOVA (SI Tables S3 and S4). The excess fluorescence due to the anthropogenic input of the WWTP is considered to be overlaid on the natural organic matter background signal. The ANOVA (discussed more in detail in SI) is interpreted to mean that fluorescing point source anthropogenic chemicals were introduced to the Neuse River at site C from the WWTP effluent. The increased fluorescence observed at B1 for site C relative to site Acontinued to be statistically detectable in the fluorescence spectra at sites D−G (23.3 river miles downstream from site C). Other anthropogenic chemicals that were likewise introduced to the Neuse River at site C, which fluoresce at T1, similarly continued to be statistically detectable in the fluorescence spectra at sites D−E (8.8 river miles downstream from site C) (SI Table S3). However, the possibility that nonpoint sources of compounds fluorescing at B1 and T1 may E
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S12. Peak nomenclature is indicated in Figure 6a. The reference site A was well-fitted by the PARAFAC model that included all overlapping and negative ( 350 nm, but the B1 and T1 fluorescing regions at EM < 350 nm were not as well fitted (Figure 6c). The score (Figure 5a) and residual sums of squares (Figure 5b) plots also confirm that the least fitted modeling was observed for samples 11 and 19 which were collected from site C near the WWTP outfall during events 2 and 3, respectively (Figure 5c). The less fitted EM < 350 nm region of the spectra was observed for all sites C−H in all sampling events (18 samples) and is indicated in Figure 6c and SI Figure S12 by arrows. Conversely, the EM > 350 nm spectral regions of these samples were very well-fitted by the two-component PARAFAC model (Figure 6c and SI Figure S12). Nonmodeled Wastewater-Derived Contribution. The slight differences between the actual and modeled spectra at EM < 350 nm for sites C−H at events 1, 2, and 3 were designated a nonmodeled contribution. The region of difference between the PARAFAC modeled spectra and the actual measured spectra (denoted by arrows in Figure 6c and SI Figure S12) is here assigned as a wastewater-derived, anthropogenic feature indicated like a “footprint.″ This third feature or pseudocomponent may not be modeled in these data because the overall sample size was small (24 samples), only 3 of 24 samples were collected in the river at the WWTP discharge, and no samples of the WWTP discharge were collected directly. Overlapping and Negative PARAFAC Component Values. The physicochemical interpretation of including versus omitting overlapping PARAFAC components and negative contributions of PARAFAC components has not been previously investigated in the literature. Spectral overlap is known to occur between the fluorescence signals for humic substances and lower molecular weight DOM and proteins;32 therefore, overlap should not be eliminated from consideration in PARAFAC models. The consequences of omitting negative values of PARAFAC components is illustrated in Figure 6d. Only positive fluorescence values have scientific meaning, but negative PARAFAC component values have significance to the mathematical model. If nonhumic-like and humic-like negative component contributions are omittedi.e., in the EM 426 nm−EM 692 nm and EM 234 nm−EM 344 nm regions, respectivelyquenching in these regions of each by the other may be overlooked (Figure 6d). Plotting the modeled positive component values only compared to the actual spectrum (Figure 6d) clearly indicates the areas in which fluorescence quenching occurred in these samples. The lack of consideration of fluorescence quenching inaccurately represents the DOM present in the samples. Significance of Static/Collisional Quenching. In Figure 7, IFE-corrected fluorescence intensity is compared to PARAFAC modeled intensity using both components or using the component (1 or 2) that is relevant for a wavelength pair. At B1 (EX224 EM300/tyrosine-like; Figure 7a): Nonanthropogenic tyrosine-like fluorescence was quenched (averaged 26%) by the humic-like component. Anthropogenicinfluenced tyrosine-like fluorescence was not modeled by
Figure 4. Modeled PARAFAC components.
Fluorescence Quenching. Both positive and negative contributions from the fitted components are illustrated in Figures 4, 5, and SI Figure S7. The strong negative contribution to the model by the nonhumic-like component in the fluorescence region of the humic-like component (approximately EM426 nm−EM692 nm) is attributed to the quenching of humic-like fluorescence by association with nonhumic-like compounds. Conversely, a small negative component to the model is also observed in the region of the nonhumic-like component (EM 234 nm−EM 344 nm) that is attributed to the quenching of nonhumic-like compounds by association with humic-like compounds. To our knowledge, this quenching phenomenon has not been previously reported in EEM spectra of natural environmental samples. Mechanisms of fluorescence quenching have been thoroughly discussed.32,48−50 Four types of quenching can influence DOM fluorescence: static quenching, dynamic (collisional) quenching, thermal quenching, and inner filter effects.32 Fluorescence quenching of (a) humic materials by Cu and Al,51 Fe,52 and cationic nitroxides,53 and conversely of (b) specific nonhumic moleculespolycyclic aromatic hydrocarbons (PAHs)54−56 and other organics and nonionic pesticides57upon association of these probe molecules with humic material has been demonstrated. The quenching observed here is not due to thermal quenching or to inner filter effects (which were mathematically removed). Whether these results are due to static or collisional quenching, or both, cannot be determined from these data. Fitting the PARAFAC Model. Representative actual (IFEcorrected) and PARAFAC-modeled emission spectra are compared at a constant excitation wavelength (224 nm) in Figure 6 for sites A and C, and for all 24 samples in SI Figure F
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Figure 5. Two-component PARAFAC model: (a) scores in sample mode, (b) residual sums of squares across the sample mode, (c) sample identification, (d) emission mode loadings, and (e) excitation mode loadings. Component 1 (humic-like) is indicated by solid lines, and component 2 (nonhumic-like) is indicated by dotted lines.
amino acid-like regions (B1 and B2; T1 and T2) and humic-like regions (F1 and F2; H1 and H2) have been observed by others,39 but their molecular spectroscopic significance has not previously been understood or described. Coble et al.39 adapted their peak nomenclature to accommodate dual peaks, for example, they denote tryptophan-like dual peaks as T and AT, which correspond, respectively, to the peaks labeled here as T2 and T1. Here, a molecular explanation is offered for the origins of the double peaks, which are oriented parallel to the excitation axis and perpendicular to the emission axis. They are centered about different EX wavelengths, but the same EM wavelengths, and are attributed to phenomena predictable by the Jablonski energy level diagram.58 Absorption of light occurs from the singlet electronic energy ground state (E0) to the singlet electronic excited states (E1...En). Absorption of light to higher level excited states > E1 is nonradiatively converted back to E1 by internal conversion
PARAFAC in these data, and the degree of its potential quenching cannot be estimated. At T1 (EX224 EM 350/ tryptophan-like; Figure 7b): Tryptophan-like fluorescence was not quenched by the humic-like component. Anthropogenicinfluenced tryptophan-like fluorescence was not modeled by PARAFAC in these data. The humic-like component was a minor contributor to fluorescence at this EEM point. At F1 (EX224 EM 424/f ulvic-like; Figure 7c): Fulvic acid-like fluorescence was not quenched by nonhumic-like components. Fulvic acid-like fluorescence was well modeled by PARAFAC in these data. The nonhumic-like component was a minor contributor to fluorescence at this EEM point. At H1 (EX 224 EM 474/humic acid-like; Figure 7d): Humic acid-like fluorescence was quenched (averaged 30%) by the nonhumiclike component. Humic acid-like fluorescence was well modeled by PARAFAC in these data. Molecular Origin of Dual Fluorescing Peaks. The dual (double) peak characteristics detected in Figures 1 and 2 for G
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fluorophores. Double peaks centered at the same emission wavelengths (accounting for polydispersity) can result because fluorophores are excited by absorbed light to various singlet excited states but always fluoresce from the E1 excited state. This phenomenon is not restricted to DOM in natural waters. Recently, dual peaks were reported for mono- and polydisperse systems, that is, 5- and 10 nm citrate-coated gold nanoparticles with and without added organic matter.30,31 Another observation from Figures 1 and 2 is that the separation in excitation energy between the double peaks varies in the order of B2 − B1 ≈ T2 − T1 < F2 − F1 < H2 − H1, which implies that the energy difference between the singlet ground state (E0) and the first singlet excited state (E1) is greater in humic acids than fulvic acids, and less for the tyrosine-like and tryptophan-like peaks. Anthropogenic Influence. The anthropogenic influence was determined to be greatest in samples collected near a WWTP outfall and was also indicated in samples collected eight or more miles downstream from a WWTP outfall near the location of WTP intakes, suggesting that the anthropogenic compounds were not fully removed or degraded by natural processes in this stretch of the river. In complementary work done as part of this study, over 100 specific chemical compounds and microbial indicators were also measured at these sample sites.4 The results of those analyses likewise show that many anthropogenic compounds persist along the stretch of the river and point to nonpoint sources of microbial contamination.59 It is important to emphasize that the Neuse River has acceptable water quality for use as a drinking water supply sourcethe samples collected in this study all had acceptable water quality with respect to the North Carolina Surface Water Quality Standards. Likewise, it is worth noting that the presence of anthropogenic markers as indicated by EEM does not imply a risk to human or ecological health. However, because the Neuse River is an example of de facto potable water reuse, these study results infer that planned potable water reuse, whether in an indirect or direct potable reuse scenario, might provide better control over water quality than the status quo conditions. Cost-Effectiveness Analysis. Wastewater-derived contaminants occur in surface water and subsequently challenge the capabilities of drinking water treatment.60 Definitive quantitative chromatographic analyses of individual trace-level chemical contaminants are labor-intensive, time-consuming, and require trained operators of expensive chromatographic−mass spectrometric equipment that is beyond the economic reach of most municipalities and WWTP facilities. Extensive sample preparation is required for chromatographic−mass spectrometric analyses, whereas for the fluorescence procedures reported here, the only sample pretreatment required was filtration. Purchase of the ultraviolet−visible and fluorescence spectrophotometers needed for EEM analyses (these two capabilities are combined in some instrumentation) is potentially within reach of many WWTP laboratories. Their operators already are trained for rudimentary spectroscopic analyses, such as turbidity and UV254, such that use of this equipment would not impose a large learning curve. Indeed, EEM analyses provide surrogate qualitativenot quantitativeinformation, but they are useful screening procedures that may be used to monitor when there is a need, or not, for more expensive quantitative assessment of water quality.
Figure 6. Representative emission spectra at constant excitation wavelength (224 nm) for event 2: (a) actual (IFE corrected) spectra for sites A and C, (b) actual and modeled spectra based on positive and negative PARAFAC component values for Site A, (c) actual and modeled spectra for site C, and (d) actual and modeled spectra based on positive PARAFAC component values only for Site A.
such that fluorescence always occurs from E1 to E0 regardless of the singlet electronic excited state to which the photon was promoted. Therefore, we propose that for the double peaks observed (a) the more intense primary peaks (subscript 1) represent absorbance from the E0 to the E2...En electronic states that require shorter excitation wavelengths/greater energy followed by fluorescence from the E1 to the E0 electronic states and (b) the less intense secondary peaks (subscript 2) represent transitions for absorbance from the E0 to the E1 electronic states generated by longer excitation wavelengths/ less energy followed by fluorescence from the E1 to the E0 electronic states. The double peaks are centered at the same EM wavelengthas predicted by the Jablonski energy level diagram58because molecules relax by internal conversion to the lowest vibrational level of E1 and subsequently, fluorescence emission typically results from the lowest vibrational energy state of E1 to the ground state.58 Variability in the emission wavelength at a specific excitation wavelength is proposed to result from two sources: (a) fluorescence occurring from the lowest vibrational level of the E1 excited state to various vibrational levels of the E0 electronic ground state and (b) polydispersity of the fluorescing molecules, such as the amino acid tyrosine located in varied molecular positions in varied proteins. The explanation supports the proposition that the double peaksB1 and B2, T1 and T2, F1 and F2, H1 and H2arise from the same fluorophore or fluorophore group, but does not negate the possibility that they may result from different H
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Figure 7. Fluorescence intensities for (a) B1 (EX224 EM300), (b) T1 (EX224 EM350), (c) F1 (EX224 EM424), and (d) H1 (EX224 EM474). Sample number designations are identified in Figure 5c. For each sample, red (leftmost) bars represent IFE-corrected intensity; black (middle) bars represent intensity calculated by adding PARAFAC component 1 plus component 2; blue bars (rightmost) represent intensity based on PARAFAC component 2 for B1 and T1 and based on PARAFAC component 1 for F1 and H1.
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ASSOCIATED CONTENT
supply options. In addition, this project would not have been possible without the work conducted by the CDM Smith team lead by the project manager Sheryl Smith with significant contributions from Janelle Amador who coordinated the analytical work for this project. Support provided by Mike Renfro of the Center for Manufacturing Research at Tennessee Technological University in assisting in preparing MATLAB programs is gratefully acknowledged.
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b03766. Additional data are presented including sample location maps of the study site, river flow and precipitation, TOC concentration, experimental methods, IFEs correction, first-order Rayleigh scattering spectra, ANOVA comparisons, 2D and 3D PARAFAC components, model fitting, and split-half analysis (PDF)
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REFERENCES
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AUTHOR INFORMATION
Corresponding Author
*Phone: 931-979-6808; e-mail:
[email protected] or
[email protected]. ORCID
Martha J. M. Wells: 0000-0002-2230-3630 Present Addresses
# (K.Y.B.) MWH Global/Stantec, Brentwood, Tennessee, United States. ∇ (A.K.D.S.) MWH Global/Stantec, Denver, Colorado, United States.
Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank the City of Raleigh Public Utilities Department for funding this research and having the foresight to plan ahead and think outside of the box in terms of identifying future water I
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