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Anthropogenic Influences of Paved Runoff and Sanitary Sewage on the DOM Quality of Wet Weather Overflows: An EEM-PARAFAC Assessment Hao Chen, Zhenliang Liao, Xianyong Gu, Jiaqiang Xie, Huaizheng Li, and Jin Zhang Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 23 Dec 2016 Downloaded from http://pubs.acs.org on December 23, 2016

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Environmental Science & Technology

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Anthropogenic Influences of Paved Runoff and Sanitary Sewage on the DOM Quality of Wet Weather Overflows: An EEM-PARAFAC Assessment

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Hao Chen†‡, Zhen-liang Liao†‡*, Xian-yong Gu†, Jia-qiang Xie†,

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Huai-zheng Li†, and Jin Zhang§

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†

State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China

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‡

UNEP-Tongji Institute of Environment for Sustainable Development, Tongji University, 1239 Siping Road, Shanghai 200092, China

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12 13

§

Institute of Urban Water Management, Technische Universität Dresden, 66 Berg Str., Dresden 01069, Germany *

Corresponding Author:

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Email: [email protected].

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Tel.: +86 (21)65981650; fax: +86 (21)65988292.

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ACS Paragon Plus Environment


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ABSTRACT

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The quality of dissolved organic matter (DOM) in a wet weather overflow (WWF) can be

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broadly influenced by anthropogenic factors, such as non-point sources of paved runoff and point

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sources of sanitary sewage within the drainage networks. This study focused on the anthropogenic

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influences of the paved runoff and sanitary sewage on the DOM quality of WWF using

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Excitation-Emission Matrix - Parallel Factor Analysis (EEM-PARAFAC). Results show that: (1)

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EEM-PARAFAC fitted terrestrial humic-like, anthropogenic humic-like, tryptophan-like and

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tyrosine-like components can be regarded as indicators to identify the types of sewage overflows

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and the illicit connection status of drainage systems. (2) A short emission wavelength (em:

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302~313 nm) peak of the tyrosine-like component occurred in the reserved sanitary sewage, while

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a type of longer emission wavelength (em: 321~325 nm) peak came from the sump deposit. These

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tyrosine-like components were gradually evacuated in the initial phase of the overflow process,

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with the fading of their EEM signals. Fluorescence signal transformations of all the components

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confirmed the potential ability of EEM-PARAFAC to monitor the dynamic changes of the primary

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pollutant sources. (3) The input of the newly increased sanitary sewage had a dominant influence

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on the quality and yield of the WWF DOM.

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TOC/ABSTRACT ART

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INTRODUCTION

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Dissolved organic matter (DOM) is a ubiquitous constituent in aquatic

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environments. High loads of DOM carried by the urban wet weather overflow (WWF)

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cause serious pollution problems in receiving waters and give rise to the degradation

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of the aquatic ecological environment.1 Furthermore, DOM is considered to be a

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carrier of several toxic substances such as heavy metals2,3 and persistent organic

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pollutants (POPs).4,5 It is also regarded as an important precursor of disinfection

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by-products (DBPs).6,7 Hence, to minimize its adverse effect on the ecological

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environment, the study of the source influence and the component distribution of

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DOM in WWF is of great importance.

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Generally, the DOM characteristics are determined by the origins and the

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biogeochemical processes that occur along with their production and transport.8-10

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Since previous studies stated that anthropogenic sewer networks intensely changed

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the urban hydrology11-13 and further affected the biogeochemical transformations of

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the DOM,14-16 their original source could have a great influence on the DOM of WWF.

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Early investigations of urban DOM predominantly focused on their distributions in

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urban stormwater runoff from various landscape sources or the streams that were

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influenced by the stormwater runoff,17-19 but most of the studies paid less attention to

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the DOM of WWF. As terminal water bodies, urban streams also receive substantial

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amounts of WWF influents that are mainly composed of the point source discharge of

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sanitary sewage and the non-point source of paved runoff.20-22 Obviously, it is

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insufficient to study only the urban DOM influenced by the paved runoff. Thus,

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researchers such as McElmurry et al.10 suggested that it is important to stress the

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strong impacts from both of the paved surfaces and sewer sources. Considering that

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most of the freshwater DOM originating from terrestrial ecosystems,23 paved runoff

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and sanitary sewage bring highly bioavailable components24,25 that aggravate the

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ambiguous nature of DOM and pose more severe risks.22,26,27 However, the influences

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of the paved runoff and the sanitary sewage on the WWF DOM yields and

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characteristics are not well known, and this knowledge gap needs to be further filled.

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As the DOM has a complex assemblage of chemical structures, many analytical

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approaches provide only limited proxy information on the quality of DOM.10

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However, Excitation-Emission Matrix - Parallel Factor Analysis (EEM-PARAFAC) is

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a powerful tool to assess the natural DOM dynamics28-30 that can resolve the complex

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mixtures of EEM data sets into several typical components.31,32 Many published

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works had utilized the EEM-PARAFAC method to identify the sources of DOM in

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natural streams or lake ecosystems, such as terrestrial runoff, agricultural runoff,

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forest soil and aquatic production.33-37 In terms of the research matrix of municipal

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water supply, wastewater treatment and groundwater monitoring, this method had also

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been commonly applied.38-41 Furthermore, an increasing number of studies had

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characterized and traced the sources of DOM within urban stormwaters or streams

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using EEM-PARAFAC to explore the influences of urban landscapes, environmental

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factors and urbanization gradients on the urban waterbodies.10,42,43 More recently,

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EEM-PARAFAC had been successfully applied to DOM real-time monitoring of point page 5 / 42



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source contaminations, such as fecal matter in drinking water,44,45 which highlighted

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the capacity and prospect of this technique for online pollutant source identification.

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Therefore, the EEM-PARAFAC method has the potential ability to explain the

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anthropogenic influences of paved runoff and sanitary sewage on the quality of WWF

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DOM.

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Moreover, the relative maximal fluorescence intensity (Fmax) of the fitted

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EEM-PARAFAC components can be used as an indicator to identify the stormwater

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sources or catchment types by the use of a multivariate statistical analysis method,

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such as the principal components analysis (PCA). For example, Hosen et al.42

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conducted PCA on the Fmax rather than the common water quality indicators46 or

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complicated organic compounds47-50 to identify forested and urbanized sample types.

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Hence, by using the DOM source fingerprint with further data mining, it is possible to

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identify the types of drainage system sewage overflows and further identify the point

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source contaminations such as illicit connection discharges within the storm sewers.51

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Nevertheless, there have been few related studies focused on the DOM fluorescence

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characterization of sewer overflows, a fact that may be ascribed to the complexity of

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WWF pollutants.

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Consequently, this study investigated the changes of the WWF DOM

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characteristics and yields in response to the anthropogenic influences of paved runoff

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and sanitary sewage inputs. Samples of terrestrial runoff, paved runoff, sump deposit

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and dry weather flow (DWF) were collected for comparison with the possible sources

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of WWF DOM. DWF samples from pump station sumps were referred as the sanitary page 6 / 42


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sewages that involves the newly increased sewage and the reserved sanitary sewage.22

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It was further hypothesized that: (1) DOM components fitted by EEM-PARAFAC can

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assist the source apportionment of WWF and further identify the types of sewage

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overflow; (2) EEM-PARAFAC is a potential tool to monitor the dynamic changes of

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the primary pollutant sources within urban drainage systems; and (3) the newly

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increased sanitary sewage is a dominant source contributor that influences the

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characteristics and yields of WWF DOM.

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MATERIALS AND METHODS

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Study Sites and Sampling Campaigns

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Sampling campaigns were conducted in 7 residential urban catchments in

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southeastern China. The distributions of the various underlying surfaces are reported

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in SI Figure S1 and Table S1 for each catchment. WWF and DWF were primarily

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collected from 6 forced-pump stations located in densely populated catchments in

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Shanghai, namely, sites COM1 and COM2 (combined sewer systems) and sites SEP1

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to SEP4 (separated sewer systems). Another fraction of the WWF and DWF samples

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were collected from a gravity artesian overflow weir (COM3-OW) within a small

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combined system pump station (COM3) in Changzhou. Paved runoff samples were

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collected from a simplex short-haul rainwater pipe outlet (COM3-RWP) within the

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COM3 catchment that serves a road overpass composed of 92% impervious road

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surface and 8% green land. Terrestrial runoff samples were collected from a sealed

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green land within the COM3 catchment (COM3-green land) that has a surface area of

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15.4 m2 with a lawn cover of more than 98%.

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A total number of 231 samples of WWF, paved runoff and terrestrial runoff, were

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collected from 16 rainfall events that occurred between May and June 2015 using

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automatic samplers (Teledyne ISCO, Portable Sampler 6712, USA). With a 15 min

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interval, the continuous collection of samples was started at the beginning of the

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overflow (stormwater pump launch) or runoff process, and stopped at the end. The

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rain conditions are provided in SI Table S2. A total of 112 DWF samples were page 8 / 42


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continuously collected at 3 h intervals over 48 h under the baseflow conditions. The

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baseflow condition was defined as any dry weather period of more than 48 h after a

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rainfall event.42 Sump deposits from 6 pump stations in Shanghai were sampled under

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the baseflow conditions. Samples were collected in 1 L acid-washed HDPE bottles

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and stored overnight at 4 °C prior to further lab analysis. All the analyses were

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finished within one week.

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The leachates of the sump deposits were the liquid supernatants after 30 min of

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mechanical agitation at 300 rpm. A 2 mg/L humic acid (≥90% purity, Aladdin

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Biochemical Technology Co., Ltd, peat source, CAS No. 1415-93-6) solution was

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prepared with ultrapure water (Millipore).

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EEM-PARAFAC Analysis of the DOM

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EEM data were acquired using a Horiba Fluoromax-4 spectrofluorometer under

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the constant temperature of 20± 2 °C. The excitation wavelengths of the EEM were

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incrementally increased in 5 nm steps from 250 to 450 nm; for each excitation

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wavelength, the emission wavelengths were detected in 2 nm steps from 300 to 550

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nm.42 All samples for EEM determination were diluted 5 times with ultrapure water

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and filtered through 0.45 µm PVDF filters (Durapore, Millipore). The dissolved

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organic carbon (DOC) and ultraviolet absorbance (UV254) of these preprocessed

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samples were in the ranges of 0.9~4.8 mgC/L and 0.011~0.056 cm-1, respectively,

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which met the requirement of UV254 (＜ 0.05 cm-1) for minimizing the inner-filter

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effect.53 Positive linear correlations existed between the DOC and EEM peaks when page 9 / 42



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the DOC was diluted to be below 5 mgC/L (SI Figure S2). This confirmed that the

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inner-filter effect was limited to induce the deviation of the linear correlations when

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the DOC was in the range of 0.9~4.8 mgC/L. Such positive linear correlations also

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guaranteed the comparability of each sample EEM result. The preprocessed samples

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were adjusted to pH 2± 0.2 and 10 mM ionic strength using a KCl solution of 0.1 M

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strength,52,53 aiming to inhibit the fluorescence signal disturbance from aquatic metal

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ions (pH selection shown in SI Figure S3).53 Specifically, ＜ 100 µL of buffer was

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added to each 10 mL preprocessed sample solution. Each EEM sample was scanned

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in triplicate to investigate the reproducibility of the PARAFAC analysis. The inner

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filter correction was performed using a controlled dilution approach with a 2.0 ×

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dilution factor for the diluted sample of which UV254 was still greater than 0.05

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cm-1.53-55 Ultrapure water EEM was subtracted from each sample EEM. Delaunay

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triangulation interpolation algorithm was used for handling the first and second order

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Rayleigh scatter regions before PARAFAC.56,57 This interpolation algorithm was

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applied to address the high signals of the scatter regions in the EEM of samples that

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had low concentrations of DOM, as suggested by previous studies.57,58 The samples

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which had been diluted and adjusted also presented the characteristics that the scatter

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signal orders of magnitude were obviously higher than those of the DOM signals (SI

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Figure S4). The PARAFAC modeling approach (computed by the DOM-Fluor

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Toolbox, www.models.kvl.dk/source) has been described in detail elsewhere.59-62

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Repeated convergence of the model was examined based on the iteration of the

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minimum squares principle. Non-negativity constrains and initialization method of page 10 / 42


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direct trilinear decomposition were applied in the PARAFAC modeling.

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Five components of DOM were retained and validated using the core consistency

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diagnostic (score＞ 90%) and visual inspection (SI Table S3).63 Acidic conditions

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showed a very limited influence on the core consistency diagnostic score compared to

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the neutral pH condition (SI Figure S3d). Split-half analysis was used to validate the

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identified components.64 The source fingerprints of each component are described in

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SI Table S4.42 Briefly, component c1 (ex/em= 250, 325 nm/ 438 nm) is a humic-like

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component of terrestrial origin which is absent from wastewater.25,32 Component c2

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(ex/em= 250, 380 nm/ 510 nm) is a ubiquitous and recalcitrant (terrestrial) humic-like

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and fulvic-like component.25,65 Component c3 (ex/em= 250, 280, 300, 325 nm/ 388

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nm) is an anthropogenic and microbial originated humic-like and fulvic-like

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component.25,28,66 Component c4 (ex/em= 280 nm/ 350 nm) is a recently produced

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tryptophan-like component.32,67 Component c5 (ex/em= 280 nm/ 315, 325 nm) is a

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tyrosine-like component.68,69 The component distribution was calculated based on the

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Fmax (Raman units) of each identified component.66

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Using the distribution proportions of the 5 fitted EEM-PARAFAC components,

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PCAs were conducted on a standard humic acid solution, terrestrial runoff, paved

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runoff, DWF and WWF samples to identify the types of sewage overflows. In

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addition, another PCA was conducted using 5 common water quality indicators as a

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group to compare the advantage of the 5 fitted EEM-PARAFAC components. The 5

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common water quality indicators were total suspended solids (TSS, mg/L), chemical

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oxygen demand (CODCr, mg/L), total nitrogen (TN, mg/L), ammonia nitrogen page 11 / 42



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(NH3-N, mg/L) and total phosphorus (TP, mg/L). Their detection methods are

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described in SI.

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DOM Indicators and Molecular Feature Determination

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The DOC concentration, aromaticity (SUVA254), molecular weight (MW) and

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polydispersity (ρ) are generally utilized as indicators of the DOM yield and

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bioavailability.70-72 Samples were pre-filtered and adjusted to pH 6.7± 0.2 and 10 mM

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ionic strength. The DOC concentration was determined using a Shimadzu V-CPN

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total organic carbon analyzer. SUVA254, which positively correlates to the DOM

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aromaticity, was calculated by dividing the UV254 value by the DOC

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concentration.73,74 UV254 was collected using the ultraviolet spectrum (UV-2550,

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Shimadzu) with 1 cm quartz cuvettes and further corrected for the Fe3+ concentration

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using a previously defined relationship: A254‑corrected= A254‑measured - 0.0687×[Fe3+].75

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The Fe3+ concentration was determined using inductively coupled plasma-optical

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emission spectrometry (ICP-Agilent720ES).

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In addition, the DOM MW and ρ were determined using a gel-filtration

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chromatograph (GFC, LC-10ADVP, Shimadzu). A differential refraction detector was

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coupled with the GFC. (Mobile phase: ultrapure water; flow rate: 0.5 mL/min;

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analysis time: 25 min; column temperature: 40 °C; chromatographic column type:

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TSK gel G4000PWXL; and standard substance: polyethylene oxide/glycol EasiVials,

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4 mL). The MWs discussed here are not the actual values since there are no available

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standards with chemical properties comparable to the organic substances in WWF.76,77 page 12 / 42


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The MW values referenced throughout the text are the weighted averaged MW values.

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Data Analysis

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OriginPro (version 9.0, OriginLab) and SPSS (version 20, IBM) were used for

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the repeated-measure analysis of variance (ANOVA), analysis of covariance

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(ANCOVA), and linear regressions. An alpha-level of 0.05 was used to determine the

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significance. Data were not included in the statistical analyses if they met the outlier

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criterion using Dixon’s Q-test at the 95% confidence level.78 The underlying surface

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characteristics of each sampling catchment were determined using ArcGIS (version

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10.1, ESRI). The EEM data preprocessing, PARAFAC modeling and exported

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contour maps were computed in MATLAB (version R2012a, MathWorks) and

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OriginPro. The PCA results were generated using SPSS.

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RESULTS AND DISCUSSION

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Component Distribution and Possible Sources of WWF DOM

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The distributions of the five DOM components of terrestrial runoff, paved runoff,

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DWF and WWF samples were obtained using the EEM-PARAFAC (Figure 1).

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Compared to the characteristics and original source information described in the

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methods section (and in SI Table S4), the first hypothesis that the DOM analyzed by

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EEM-PARAFAC can assist the source apportionment of WWF has been supported.

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Terrestrial runoff (site COM3-green land surface runoff) contained the same

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components (c1 and c2) as the standard humic acid solution, namely, a predominance

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of terrestrial source humic-like and fulvic-like organic matters.25,32,65,67,79

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Paved runoff input contained a number of anthropogenic humic-like substances

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(c3). As the site COM3-RWP serves a road overpass with paved runoff influents from

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the impervious road surfaces and green land, the DOM in both groups of samples

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from site COM3-RWP contained more than 50% of the c3 + c4 ratios, of which, the

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anthropogenic humic-like substances indicated by c3 made up the largest

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portion.17,25,28,66 The recently produced protein-like components indicated by c4

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accounted for a smaller percentage (approximately 15%), and the natural humic-like

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components (c1 + c2) accounted for only 45%. This result corroborates the previous

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findings that an increase of the impervious surface area is associated with a decreased

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amount of natural humic-like DOM and enriched amount of anthropogenic fulvic-like

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and protein-like DOM.17,42 Actually, as sanitary sewage normally has a low page 14 / 42


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concentration of humic-like substances,80 this finding implies that the anthropogenic

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humic-like substances (c3) in the WWF samples primarily originated from the paved

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runoff.

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DWF, including newly increased and reserved sanitary sewages, carried

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tryptophan-like (c4) and tyrosine-like (c5) components. In the DWF samples from the

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catchments of Shanghai, components c4 + c5 represented approximately 55~75%

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percent of the total, and c1 + c2 always accounted for less than 30%. Component c4

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generally indicates the less degraded and ‘‘fresher’’ proteins, while c5 suggests the

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degraded or ‘‘older’’ proteins.68,81,82 Simultaneously, since the DWF samples were

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collected under baseflow conditions without agitated deposits (no stormwater pump

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launching), the c4 and c5 components might indicate the newly increased and

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reserved sanitary sewage contained in the DWF samples, respectively. This inference

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was further confirmed by the obvious degradation of the c4 component and the

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preservation of the c5 component in freshly collected sanitary sewage having a long

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holding time in the laboratory (SI Figure S5). Furthermore, the compositions of DWF

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DOM from the four separated sewer systems were similar to those from the two

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combined sewer systems, suggesting that there were distinct illicit connections within

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the four separated sewer system networks. However, the degrees of the illicit

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connections seem to be different among these systems, especially the SEP4 system.

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Component distributions of WWF DOM were found as different combinations of

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terrestrial runoff, paved runoff and DWF, which revealed that the sources were clearly

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different. Although the condition parameters of the six rainfall events in sites SEP1 to page 15 / 42



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SEP3 were greatly different (SI Table S2), their WWF DOM component distributions

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were relatively consistent, implying that a similar illicit connection status existed

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among these three drainage networks. The c5 component did not present in the six

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batches of WWF samples, indicating that there was no reserved sanitary sewage.22 All

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of the WWF DOM from the two rainfall events at site SEP4 contained large ratios of

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c4 and c5 components, and such ratios were comparable or even higher than them in

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both the dry and wet weather effluents of the combined sewer system COM2,

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demonstrating that more severe illicit connections remained in the drainage networks

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of site SEP4. The compositions and original sources of WWF DOM from the two

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rainfall events at site COM1 were different. At the 1st rainfall event, c5 components

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composed approximately 40% of the DOM, suggesting that the reserved sanitary

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sewage was the predominant pollutant source. The c3 components were more

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prevalent in the DOM compositions of the 2nd WWF, which was similar to those from

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sites SEP1 to SEP3, thus indicating that paved runoff was their main source.

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Identification of the Types of WWF by PCA

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A PCA was further conducted on the EEM-PARAFAC component (c1 to c5)

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results, as shown in Figure 2. The Fmax percentages of the fitted DOM components,

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rather than the common water quality indicators (SI Figure S6), can efficaciously

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identify the paved runoff-like and sanitary sewage-like types of the sewage overflows

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from drainage systems and notably differentiate the influence of paved runoff and

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sanitary sewage in the WWF samples. page 16 / 42


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To ensure the sufficient influence of the extracted principal component (PC), its

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eigenvalue was required to be greater than 1. Hence, only the first two PCs could be

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retained according to the loading panels in SI Table S5 for the PCA of Figure 2, as

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well as SI Figure S6. In Figure 2, the first component (PC1) accounted for 48.1% of

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the total variance and the second component (PC2) accounted for 27.1% of the total

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variance. Both together explained 75.2% of the total variance of the dataset. Variables

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of c1 and c2 (humic-like substances) primarily contributed to PC1 according to their

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square cosines (SI Table S5), while variable c4 could also be better represented by PC1

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and appeared to be negatively correlated. Variable c3 (anthropogenic humic-like

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substances) continued to have a high loading on PC2, yet variable c5 was also better

300

represented by PC2 and appeared to be negatively correlated.

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The majority of the generalized data points were grouped into 3 distinct clusters

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as shown in Figure 2b. In cluster A, COM3-green land runoff and humic acid solution

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arose as the terrestrial runoff, which contained only 2 types of humic components in

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EEM-PARAFAC (c1 and c2). However, it showed that cluster A correlated not only

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with PC1, but also to a less extend with PC2. The dissimilarity analyzed between

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cluster A and the other clusters justified its unique composition. In cluster B, WWF

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sample points were regularly distributed with relatively similar loadings on both PC1

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and PC2, which explains why the cluster B data points possessed greater positive

309

relationships with the variables of c1, c2 and c3. Simultaneously, paved runoff from

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site COM3-RWP also appeared in this cluster. These findings reveal that the WWF

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samples in cluster B had a distinct type of paved runoff characteristics. The cluster C page 17 / 42



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samples possessed a completely different pollution status and pollutant source

313

compared to those from cluster B. The data points in cluster C presented significantly

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negative correlations with both PC1 and PC2. Nevertheless, they showed positive

315

correlations with variables c4 and c5. DWF samples from the sites COM2 and SEP1

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to SEP3 and WWF samples from the sites COM1-Rf1, COM2 and SEP4, were

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distributed within this cluster, confirming that these WWF samples were a type of

318

sanitary sewage-like overflow.

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Therefore, it can be concluded that separated sewer systems with fewer illicit

320

connections discharged a type of paved runoff-like overflow; while the combined

321

sewer systems, as well as the separated sewer systems with a severe number of illicit

322

connections, discharged a type of sanitary sewage-like overflow. In addition, as the

323

derived indicators of pollutant composition, the EEM-PARAFAC components are not

324

easily disturbed by some uncertain factors, such as their numerical concentration.83

325

Hence, those results were more reliable.

326

By contrast, the data points of the PCA based on the 5 common water quality

327

indicators: TSS, NH3-N, CODCr, TP and TN, presented distinctly different loadings on

328

both PC1 (CODCr and TP) and PC2 (TSS, TN and NH3-N) (SI Figure S6b). There

329

were no clearly formed clusters that were related to the characteristics of the WWF

330

samples. Hence it remains difficult to identify the types of overflow samples using

331

these 5 common water quality indicators.

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A commercial humic acid solution was added into the PCA of Figure 2. However,

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no remarkable influence between the PCAs with and without this humic acid solution page 18 / 42


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was found in the comparison of the loadings of variables and sample data points (SI

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Figure S6c and S6d, SI Table S5). Given that the terrestrial runoff sample added in

336

both PCAs (Figure 2 vs SI Figure S6c and S6d) containing the same EEM-PARAFAC

337

components as the humic acid solution (Figure 1), such similar results were

338

predictable.

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Dynamic EEM-PARAFAC Monitoring of Overflow Process

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EEM analyses of the continuously collected WWF DOM with PARAFAC

341

processing verified the feasibility of using EEM-PARAFAC for monitoring the

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dynamic changes of the primary pollutant sources within an urban drainage system,

343

such as the runoff, sewage and deposit (Figure 3). This conclusion supports

344

hypothesis 2. Particularly, the obvious “first flush” phenomena of the fluorescence

345

intensity and the Fmax ratio of EEM-PARAFAC fitted c4 component to c3 component

346

were found in WWF batches. A gradually fading of the c5 component signals in the

347

initial phase of the WWF demonstrated an evacuation process of the reserved sanitary

348

sewage (Figure 3 and SI Figure S8). Meanwhile, the agitated sump deposit may also

349

release the c5 component, which presented a longer emission wavelength

350

tyrosine-like peak than that of the reserved sanitary sewage (Figure 4).

351

The fluorophore intensity of DOM has a typical temporal variability that mainly

352

depends on its related component concentrations or yields.63,80 In particular, the DOC

353

concentration showed a directly proportionality to the fluorophore intensity when the

354

DOM content was at a low level (SI Figure S2). However, the shift of fluorophore page 19 / 42



355

wavelength indicates a minor transformation of its molecular structure.84,85 All these

356

are the theoretical basis of dynamic EEM-PARAFAC monitoring.

357

Among all the WWF batches, the c5 components occurred only in COM1-Rf1,

358

COM2-Rf1, COM-Rf2, SEP4-Rf1 and SEP-Rf2 (Figure 1). Hence the monitoring of

359

these five batches demonstrated more completely the dynamic changes of the DOM

360

discharging from various pollutant sources (Figure 3). COM1-Rf1, COM2-Rf1,

361

COM2-Rf2 and SEP4-Rf1 showed obvious “first flush” phenomena in their

362

fluorescence. In particular, considerably strong protein-like peaks (both c4 and c5)

363

suggested that the high DOC concentrations of sanitary sewage were discharged

364

during the initial phase of the overflow process. The dynamic transformation of the

365

Fmax ratio of the EEM-PARAFAC fitted c4 component (newly increased sanitary

366

sewage) to c3 component (paved runoff) also showed clear “first flush” effects (SI

367

Figure S7). The overflow process of SEP4-Rf2 was different from those of the other

368

sites, which had no “first flush” effect. This may suggest that the sanitary sewage

369

influent was an uninterrupted process.

370

A fluorescence fading phenomenon of the tyrosine-like peak (c5) occurred as

371

well with the “first flush” effect in the initial phase of the overflow process (Figure 3),

372

while the tryptophan-like peak (c4) presented throughout the entire overflow period.

373

This phenomenon explains the evacuation process of the reserved sanitary sewage

374

during the initial phase of overflow, which also confirms the use of the c4 and c5

375

components as reasonable and convincing indicators for newly increased and reserved

376

sanitary sewages, respectively. As the c5 component did not appear in the WWF page 20 / 42


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377

batches from sites SEP1 to SEP3 (Figure 1), it may be involved in the dry weather

378

outfall operation of these municipal pumping stations in the separated sewer systems

379

that existed with illicit connection problems.86 The protein-like organics (in sanitary

380

sewage) in these pump sumps were difficult to continuously accumulate and degrade.

381

Thus, it is hard to be transformed into degraded ‘‘older’’ proteins under the frequent

382

dry weather outfall conditions.68

383

However, the tyrosine-like components that presented in the initial phase also

384

revealed a significant qualitative change. Their EEM peaks appeared at both of the

385

short (em: 302~313 nm) and long (em: 321~325 nm) emission wavelength positions,

386

which can be found in the elaborated spectrum (SI Figure S8). Tyrosine-like peaks in

387

COM2-Rf1 and COM2-Rf2 WWF samples were found at long emission wavelengths,

388

while those from the SEP4-Rf1 and SEP-Rf2 WWF samples were found at shorter

389

emission wavelengths. Both short and long emission wavelength tyrosine-like peaks

390

repeatedly occurred in the overflow process of COM1-Rf1. In fact, all of the

391

tyrosine-like peaks in the DWF samples from sites COM1, COM2 and SEP1 to SEP4

392

were found to occur in the short emission wavelength range of 302~313 nm according

393

to the EEM-PARAFAC results shown in Figure 4, and all of the tyrosine-like peaks of

394

the sump deposit leachate samples occurred in the long emission wavelength range of

395

321~325 nm. A possible explanation might be that the tyrosine-like proteins that

396

originated from the sanitary sewage could be further degraded when they came into

397

contact with biofilms on deposits during minor hydraulic disturbance conditions in

398

dry weather.87 Heterotrophic microorganisms preferentially consume carbohydrates page 21 / 42



399

and peptides and release aromatic structures.71 It leads to the tyrosine-like structures

400

having a higher aromaticity, and a red shift of emission wavelength in the EEM

401

plots.10,69,71,88 In addition, the hydraulic disturbance during the overflow process was

402

generally hard to resolve in the dissolved organic components, such as extracellular

403

polymeric substances released from the interior of microorganisms.89,90 This result

404

suggests the longer emission wavelength of the c5 component might be the most

405

degraded tyrosine-like protein that indirectly originated from sanitary sewage.

406

Consequently, in the two batches of WWF from site COM2 may originate from the

407

reserved sanitary sewage, and the two WWF batches from site SEP4 were more likely

408

to be directly released from the sump deposit. However, the tyrosine-like components

409

found in COM-Rf1 may be from the both sources.

410

Dominant Influence of Newly Increased Sanitary Sewage

411

The c3 component from paved runoff and the c4 component from newly

412

increased sanitary sewage were resulted from the anthropogenic influences and were

413

also ubiquitous components of the WWF DOM. Both components presented a certain

414

extent of influence on the DOM quality indicators, such as MW, ρ and aromaticity

415

(SUVA254). Particularly the c4 component, which indicates the newly increased

416

sanitary sewage, had a dominant influence on the WWF DOM quality and yields

417

(Figure 5).

418

MW and ρ were positively correlated with the percent of c4 components at r2=

419

0.63 and 0.66 (Figure 5a and 5b), respectively. It demonstrates that tryptophan-like page 22 / 42


Page 22 of 42

Page 23 of 42


420

fresh proteins from newly increased sanitary sewage were an important influencing

421

factor of the WWF DOM molecular weight and polydispersity. Conversely, there was

422

only a weak negative relationship (r2= 0.49) between the c3 component percentage

423

and the WWF DOM MW (Figure 5e). Similarly, c3 had no obvious regular impact on

424

the molecular polydispersity (SI Figure S9g). As the paved runoff DOM from

425

COM3-RWP consisted of more humic- and fulvic-like components (Figure 1), this

426

runoff was determined to have the lowest molecular weight, and polydispersity and

427

the highest aromaticity (SI Table S6 and Figure 5h), which was consistent with the

428

results of previous studies.10,77,91,92 Simultaneously, no notable correlation (r2= 0.5)

429

was found between SUVA254 and MW as reported by the other studies (Figure 5i).73,74

430

In addition, since the biomolecule-like proteins or polypeptides always have higher

431

molecular weight values,77,91 the point source of sanitary sewage from the various

432

urban underlying surfaces could distinctly increase the molecular weight of the WWF

433

DOM. Hence, the WWF DOM from sites in Shanghai had a higher molecular weight

434

and polydispersity (SI Table S6).

435

Furthermore, the variations of WWF DOM MW and ρ values were not found to

436

be impacted by the ratios of catchment impervious cover (SI Figure S9c and S9d) or

437

the percent of c3 and c4 components (SI Figure S9a and S9b). However, the WWF

438

samples from COM1, COM2, SEP1 to SEP4, and COM3-OW presented a negative

439

correlation between DOM aromaticity and impervious cover ratio (r2= 0.67, Figure

440

5h), which is consistent with the previous conclusions.42 In addition, increasing the c4

441

component would also enhance the WWF DOC and CODCr concentrations (r2= 0.58 page 23 / 42



442

Page 24 of 42

and 0.78, Figure 5d and 5g).

443

On the other hand, SUVA254 presented a descending relationship (r2=0.64) with

444

the percent of the c4 component (Figure 5c). Theoretically, it should be positively

445

correlated to the c3 component, but it was not (SI Figure S9e). The result implies that

446

the c4 component (newly sanitary sewage) may control the holistic quality of the

447

WWF DOM to a greater degree. Similarly, we found that the NH3-N concentration

448

(primarily indicating sanitary sewage) had a strong negative linear relationship

449

(r2=0.92) with the percent of the c3 component (Figure 5f), but not the c4 component

450

(newly increased sanitary sewage, SI Figure S9f). The possible causes of this finding

451

need to be further evaluated.

452

Implications

453

Applying

EEM-PARAFAC

can

directly

distinguish

the

complicated

454

anthropogenic influences of the non-point source of paved runoff and the point source

455

of sanitary sewage on the WWF DOM from the drainage systems in highly urbanized

456

catchments. The presented results support our hypotheses and achieves critical

457

observations as follows. (1) The fitted c3 and c4 components could indicate the paved

458

runoff and newly increased sanitary sewage, respectively. A short emission

459

wavelength (em: 302~313 nm) of the c5 component conspicuously exists in the

460

reserved sanitary sewage, and a type of longer emission wavelength (em: 321~325 nm)

461

of the c5 component is from the agitated sump deposit. (2) These indicated

462

relationships demonstrate the feasibility and capability of analyzing the DOM with page 24 / 42


Page 25 of 42


463

the EEM-PARAFAC method to be used for dynamic monitoring of the primary

464

pollutant sources within urban drainage systems. (3) The input of the newly increased

465

sanitary sewage has a dominant influence on the quality and yield of WWF DOM,

466

compared to the paved runoff. To our knowledge, this work is pioneering the linkage

467

of the WWF DOM with the EEM-PARAFAC method to identify the primary pollution

468

sources, overflow types and illicit connection problems in the completely artificial

469

urban drainage systems. These ideas and approaches regarding the WWF DOM

470

deliver a significant potential to provide supplementary and effective methods for the

471

assessment and reconstruction of urban drainage systems, particularly for those have

472

serious problems regarding sanitary sewage inputs or illicit connections.

473

Furthermore, there remains limitations of the tool for monitoring paved runoff

474

and sanitary sewage in urban systems, especially the constraining requirement of the

475

fluorescence method on sample nature or content. The Delaunay triangulation

476

interpolation algorithm was used to preprocess the raw EEM data before PARAFAC,

477

yet this algorithm was applicable only to the samples that have relatively high scatter

478

signals in the EEM. Nevertheless, the algorithm can be adapted in terms of the actual

479

nature of the samples. In addition, the tool identifies the overflow pollutant source is

480

in a semi-quantitative way, and a quantitative resolution should be the subject of a

481

future research expansion.

page 25 / 42



482

ACKNOWLEDGMENT

483

This study was financially supported by the National Natural Science Foundation of

484

China (Grant No. 51578396), the National Water Pollution Control and Treatment

485

Science and Technology Major Project (Grant No. 2013ZX07304002-1), and the

486

German BMBF (Bundesministerium für Bildung und Forschung, Federal Ministry of

487

Education and Research) CLIENT project “Managing Water Resources for Urban

488

Catchments” in the framework of the Sino-German “Innovation Cluster Major Water”

489

(Grant NO. 02WCL1337A). We would also like to acknowledge the substantial

490

supports from Shanghai Municipal Sewerage Co. Ltd and Changzhou drainage

491

management office.

page 26 / 42


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492

SUPPORTING INFORMATION

493

Additional details about study sites, raining conditions, condition parameters of

494

methods, plots of DOM component degradation, loading data of PCAs,

495

EEM-PARAFAC dynamic monitoring, Linear regression analysis. This information is

496

available free of charge via the Internet at http://pubs.acs.org.

497 498

page 27 / 42



499

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790

FIGURES

791

Figure 1

792 793

Figure 1. Fmax percentage distributions of the EEM-PARAFAC fitted DOM

794

components (c1, c2, c3, c4 and c5) in a humic acid solution (2 mg/L) and samples of

795

runoff, DWF and WWF. Each batch of samples was totally included for each

796

PARAFAC fitting.

797 798 799 800 801

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802

Figure 2

803 page 36 / 42


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804

Figure 2. PCA of DOM compositions of a humic acid solution (2 mg/L) and samples

805

of terrestrial runoff, paved runoff, DWF and WWF as measured by the Fmax

806

percentages of EEM-PARAFAC fitted components (c1, c2, c3, c4 and c5). (a) square

807

cosines for c1, c2, c3, c4 and c5 on PC1 and PC2; (b) PCA of DOM compositions of a

808

humic acid solution (2 mg/L) and samples of terrestrial runoff, paved runoff, DWF

809

and WWF. Loading panels are described in SI Table S5. The percentages in axis titles

810

indicate the percentage variance explained by each principal component. Coordinate

811

position of each sample point in panel b indicates its loadings on the 2 principal

812

components.

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813

Figure 3

814 815

Figure 3. Dynamic EEM monitoring of the WWF batches of COM1-Rf1, COM2-Rf1, page 38 / 42


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816

COM2-Rf2, SEP4-Rf1 and SEP4-Rf2. Continuous collection of samples was started

817

at the beginning of the overflow process (stormwater pump launch) and stopped at the

818

end with a 15 min interval. Fluorescence intensity: Raman units.

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819

Figure 4

820 page 40 / 42


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821

Figure 4. EEM-PARAFAC fitted tyrosine-like components in DWF samples (left

822

column) and sump deposit leachates (right column) from sites COM1, COM2 and

823

SEP1 to SEP4. Left column: short emission wavelength peak at ex: 280 nm and em:

824

302~313 nm; right column: longer emission wavelength peak at ex: 280 nm and em:

825

321~325 nm. Normalized fluorescence intensity: Raman units.

826 827 828 829 830

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831

Figure 5

832 833

Figure 5. Linear regression analysis of the average values of WWF and paved runoff

834

sample variables. (a) percent c4 to MW (Da), (b) percent c4 to ρ, (c) percent c4 to

835

SUVA254 (L/mgC·m), (d) percent c4 to DOC (mgC/L), (e) percent c3 to MW, (f)

836

percent c3 to NH3-N (mg/L), (g) percent c4 to CODCr (mg/L), (h) impervious cover

837

ratio to SUVA254, (i) SUVA254 to MW. Error bars represent standard error of the

838

average by each batch. Statistical parameters of linear fitted curves are described in SI

839

Table S7.

840

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Anthropogenic Influences of Paved Runoff and ... - ACS Publications

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