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Fluorescent characteristics and metal binding properties of individual molecular weight fractions in municipal solid waste leachate
Environmental Pollution 162 (2012) 63e71
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Environmental Pollution journal homepage: www.elsevier.com/locate/envpol
Fluorescent characteristics and metal binding properties of individual molecular weight fractions in municipal solid waste leachate Jun Wu, Hua Zhang*, Li-Ming Shao, Pin-Jing He* State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, PR China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 May 2011 Received in revised form 11 September 2011 Accepted 7 October 2011
Molecular weight (MW) is a fundamental property of dissolved organic matter (DOM), which potentially affects the binding behavior between DOM and metals. Here, a combined approach of ultrafiltration fractionation, fluorescence excitation-emission matrix quenching and parallel factor analysis (PARAFAC) was employed to elucidate fluorescent characteristics and metal binding properties of individual MW fractions of DOM in landfill leachate. Four humic-like and two protein-like components were identified by PARAFAC. Among them, a fulvic acid-like component was found to be responsible for Cd(II) binding while Cu(II) inclined to complex with humic-like components rather than protein-like ones. Apart from that, MW was found to exert less influence on metal binding than that of specific metals or components. Key components distributed within various fractions of DOM were the main influence on the impact of MW on metal binding. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Landfill leachate Heavy metal binding Molecular weight Fluorescence excitation-emission matrix quenching Parallel factor analysis
1. Introduction Dissolved organic matter (DOM) plays a key role in heavy metal migration from municipal solid waste (MSW) landfill to surrounding aquatic and soil environment via the leachate pathway (Calace et al., 2001a, 2001b; Baumann et al., 2006; Qu et al., 2008). A significant portion of heavy metals in landfill leachate was found to be associated with DOM fraction (Jensen and Christensen, 1999; Baun and Christensen, 2004). Furthermore, the mobility of heavy metals in leachate polluted waters is greatly enhanced by elevated levels of organic matter (Christensen et al., 1996, 1999; Christensen and Christensen, 1999). The DOM in landfill leachate exhibits considerable spatial and temporal variations that are highly dependent on the types of wastes deposited, age of the waste, and hydro-geological factors of the landfill. Using biological assay methods, the binding capacities of Cu(II), Zn(II) and Hg(II) were reported to fluctuate by more than one order of magnitude among various landfill leachate samples (Ward et al., 2005), suggesting that great differences may exist in the binding properties among various individual fractions. Humic substance (HS) is a well-known metal sequester due to its large number of ionizable functional groups, such as carboxylic and
* Corresponding authors. E-mail addresses: [email protected] (H. Zhang), [email protected]. cn (P.-J. He). 0269-7491/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2011.10.017
phenolic groups. Considering the heterogeneity of HS and DOM, the size fractionation method has been widely employed to evaluate the metal distribution in individual fractions. Significant differences observed in heavy metal concentrations among various MW fractions of DOM indicated that MW is another factor potentially affecting the binding behavior between metals and DOM fractions (Baun and Christensen, 2004). Some researchers (Calace et al., 2001a, 2001b; Lou et al., 2009) investigated the effects of individual MW fractions in landfill leachate on the sorption behavior of Cu(II) onto soil and found that high-MW fractions contributed more to the enhancement of Cu(II) sorption than low-MW fractions. In contrast, Knoth de Zarruk et al. (2007) and Chaminda et al. (2008) found that low-MW fractions (MW<500 Da) played an important role in Cu(II) complexing. However, the impact of MW on DOM binding of other metals is less discussed, indicating a need for further research. Ultrafiltration (UF) is widely applied to fractionate DOM derived from various sources (Schafer et al., 2002), including landfill leachate (Li et al., 2009), owing to its versatility and relative simplicity of application, modest equipment requirements and the absence of additional separation media or auxiliary reagents. Meanwhile, fluorescence quenching is a classic method for rapid determination of the binding parameters between fluorescence substances and metal ions. It has been shown that fluorescence excitation-emission matrix (EEM) quenching can provide more detailed information regarding the binding characteristics than single or synchronous fluorescence spectroscopy (Hernandez et al.,
64
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
Table 1 Physicochemical characteristics of the filtered leachate samples. Sample
TOC (mg/L)
TC (mg/L)
IC (mg/L)
pH
Ca (mg/L)
Mg (mg/L)
Al (mg/L)
Fe (mg/L)
Cu (mg/L)
Cd (mg/L)
Fresh leachate Aged leachate
4450 370
6800 1420
2350 1050
8.02 7.87
412 149
163 87.7
1.37 0.449
1.44 0.721
22.7 14.8
1.85 0.844
TOC: total organic carbon; TC: total carbon; IC: inorganic carbon.
2. Materials and methods 2.1. Sample collection and preparation Two leachate samples were collected from two sanitary landfill sites in Shanghai, China. Fresh leachate was taken from a landfill composed of refuse aged 3e5 years, while aged leachate was obtained from a landfill composed of refuge aged 10e15 years. The leachate samples were collected in pre-cleaned brown sampling bottles, after which they were filtered using 0.45-mm membrane filters to separate the solid and soluble phase. The filtrates (DOM fraction) were then stored at 4 C until further use. The physicochemical characteristics of the filtered samples are summarized in Table 1. 2.2. DOM fractionation by the UF technique The UF procedure was conducted using a set of UF membranes (Shanghai Yadong Resin Co., China) with different MW cut-offs of 500 Da, 3 kDa, and 10 kDa in a stirred 300-mL cell (Schafer et al., 2002). Nitrogen gas was applied to pressurize the UF cell at 0.2 MPa. The leachate filtrates were processed at room temperature (25 1 C) by passing aliquots through a series of UF membranes, and the corresponding permeate and retentate were then collected. As a result, each leachate sample was separated into four fractions in terms of MW, (1) <500 Da, (2) 500e3 kDa, (3) 3e10 kDa, and (4) 10 kDae0.45 mm. 2.3. Fluorescence titration Prior to fluorescence titration, all samples were diluted to a low TOC concentration (<10 mg/L) as suggested to minimize the inner filtering effects (Ohno, 2002; Baker and Curry, 2004; Hudson et al., 2007). Aliquots of 25 mL of the diluted solution were titrated in 40-mL brown sealed vials with either Cu(NO3)2 or Cd(NO3)2 using an automatic syringe. The metal concentrations in the final solutions varied from 0 to 100 mmol/L. To maintain a constant pH before and after titration, the metal titrants were adjusted to pH 4.0 for Cu(NO3)2 and to pH 6.0 for Cd(NO3)2 using NaOH and HNO3. During the titration process, no more than 0.025 mL of the metal titrant was
added. All titrated solutions were shaken for 24 h to ensure complexation equilibrium at 25 0.1 C. The titration experiments were conducted in duplicate. Fluorescence EEMs were measured on a Cary Eclipse fluorescence spectrophotometer (Varian Inc., USA) in scan mode. EEM spectra were collected by varying the scanning emission wavelength (Em) from 250 to 500 nm at 2 nm increments and the excitation wavelength (Ex) from 200 to 450 nm at 10 nm increments. Instrumental parameters were as follows: excitation and emission slits, 10 nm; scan speed, 1200 nm/min; photomultiplier tube voltage, 700 V. 2.4. PARAFAC modeling The approach of PARAFAC analysis of EEMs has been well documented in literatures (Bro, 1997; Stedmon and Bro, 2008) and is therefore only briefly described here. PARAFAC is a statistical tool used to decompose N-way arrays into N loading matrices by means of an alternating least squares procedure. In other words, if an EEM dataset is arranged in a three-way array X of dimensions I J K, where I is the number of samples, J is the number of Em, and K is the number of Ex, PARAFAC decomposes them into three matrices labeled A (the score matrix), B and C (the loading matrices) with elements aif, bjf, and ckf. In this study, PARAFAC analysis was conducted using MATLAB 7.0 (Mathworks, Natick, MA) with the DOMFluor toolbox (www.models.life.ku.dk). Prior to the analysis, several preprocessing steps were adopted as follows. (1) The EEM obtained from Milli-Q water (as a control) was subtracted from each EEM of the studied samples. (2) Rayleigh and Raman scatters were removed according to the protocol described by Bahram et al. (2006). (3) The EEMs were normalized by dividing the spectrum by the corresponding TOC concentration to reduce the impact of varying DOM concentrations among different samples. (4) The EEMs were arranged into a three-dimensional data array (210 samples 26 Ex 126 Em). In the PARAFAC analysis, non-negative constraints were applied to the parameters allowing only chemically relevant results. Two to eight components were computed for the EEMs. Determination of the number of components was primarily based on split half analysis and analysis of residuals and loadings (Stedmon and Bro, 2008). 2.5. Complexation modeling The complexation parameters between PARAFAC-derived components and metals were determined by the RyaneWeber model (Ryan and Weber, 1982), based on the assumption of 1:1 complexation between ligands and metals. The complexation parameters, i.e., conditional stability constant KM, binding capacity CL and IML (the limiting value below which the fluorescence intensity does not decrease in response to the addition of metal) could be obtained by the nonlinear regression analysis on a plot of I/I0 vs. CM using eq. (1). I I 1 ¼ 1 þ ML 1 1 þ KM CL þ KM CM I0 2KM CL I0 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2C C ð1 þ KM CL þ KM CM Þ2 4KM L M
(1)
In this equation, I0 and I are the fluorescence intensity at the beginning of the titration (without adding metals) and at the metal concentration of CM, respectively.
60 TOC percentage of fraction (%)
2006; Plaza et al., 2006b). However, the EEM spectra of DOM are often composed of various types of overlapping fluorophores (Henderson et al., 2009), which make the quantitative interpretation more complicated. Multivariate analysis methods, such as principal component analysis (PCA), partial least squares (PLS) and parallel factor (PARAFAC) analysis, were introduced to improve the quantitative interpretation of EEMs (Hall et al., 2005). Among them, PARAFAC analysis can decompose fluorescence EEMs into various independent groups of fluorescent components, thereby reducing the interference among fluorescent compounds and allowing a more accurate quantification (Andersen and Bro, 2003). Recent studies have demonstrated that EEM quenching combined with PARAFAC analysis could be a useful tool to characterize the binding properties between metal ions and DOM derived from soil, surface water and MSW leachate (Ohno et al., 2008; Yamashita and Jaffe, 2008; Wu et al., 2011). It can be expected that the combination of UF fractionation, EEM quenching and PARAFAC may provide a deep insight into the binding behavior between heavy metals and individual MW fractions in DOM. In this study, two types of leachates derived from fresh and aged MSW landfills were collected and fractionated into four fractions with different MW ranges by UF. Two heavy metals (Cu(II) and Cd(II)) that are commonly found in MSW and landfill leachate were used as fluorescent quenching agents for titration. Specific fractions involved in heavy metal binding were identified and the differences in the binding characteristics among various individual MW fractions, PARAFAC-derived components, and between two heavy metals were analyzed.
fresh leachate aged leachate
50 40 30 20 10 0 <500
500-3k
3-10k
>10k
Molecular weight cut-offs (Da) Fig. 1. Molecular weight distributions of the two leachate samples.
J. Wu et al. / Environmental Pollution 162 (2012) 63e71 It has been reported that the use of a simplex algorithm to estimate the three fitting parameters (KM, CL, IML) in the RyaneWeber model would lead to unreasonably low values of CL (Luster et al., 1994; Plaza et al., 2006b). Therefore, a pre-fitting step was adopted based on eq. (2) to reduce the number of fitting parameters in eq. (1) from 3 (KM, CL, IML) to 2 (KM, CL,), by which, a constant value of IML/I0 can be acquired from the nonlinear fitting of eq. (2) (Luster et al., 1996). I 1 ¼ I 0
IML aCM I 1 1 e 0
(2)
Where IML/I0 1 and a are the fitting parameters. The quenching degree (f) was calculated by eq. (3).
f ¼
ðI0 IML Þ 100 I0
(3)
65
3. Results and discussion 3.1. MW distribution of the leachate samples The MW distributions of the two leachate samples were demonstrated in Fig. 1 as the percentage of TOC fractionated by the batch UF technique. Significant differences were observed between the two leachate samples. A major part (42.9%) of the organic matters in the fresh leachate had a MW smaller than 500 Da, while high-MW substances >10 kDa were dominated in the aged leachate (54.9%). Apart from that, each leachate sample contained some organic matters with an intermediate-MW range. These overall results were comparable to those reported in many other studies
Fig. 2. Fluorescence excitation-emission matrix spectra of the bulk and fractionated leachate samples with or without titration of Cu(II) and Cd(II) at a total concentration of 50 mmol/L. Blank: no added heavy metals, F: fresh leachate, A: aged leachate, FI/TOC: fluorescence intensity per unit TOC (arbitrary units/(mg/L)).
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
Loadings
.6
Component 1
Component 2
Component 3
Component 4
Component 5
Component 6
.4 .2 0.0 .8 .6
Loadings
The EEM spectra of the bulk and fractionated leachate samples in the absence and presence of Cu(II) or Cd(II) at a total concentration of 50 mmol/L are illustrated in Fig. 2. As expected, the EEM peaks overlapped considerably in the bulk samples, especially in the fresh leachate sample. Although each bulk sample was fractionated into four fractions based on MW by UF, there were no obvious differences in the EEM spectra among the four MW fractions except for the absolute value of the fluorescence intensity per unit of TOC (FI/TOC). In the fresh leachate, the FI/TOC of the intermediate (3e10 kDa) and high-MW (>10 kDa) fractions were stronger than that of the low-MW fractions (<3 kDa), although a major portion of organic matters in the fresh leachate were distributed in the low-MW fractions (<3 kDa) (Fig. 1). This observation suggests that fluorescence substances were more concentrated in intermediate (3e10 kDa) and high-MW (>10 kDa) fractions per unit of TOC. Similarly, fluorescence substances in the aged leachate were more concentrated in low-MW fractions (<3 kDa) per unit of TOC. These results demonstrated that UF combined with EEM spectra could provide additional information regarding the MW distribution of fluorescence substances in DOM, which may be useful for the evaluation of the binding affinities of each fraction toward metal ions. However, no obvious improvement was observed in EEM contours of UF-fractionated samples regarding the overlapping fluorophores. This could be attributed to the wide MW range of the UF technique as well as the ubiquitous distribution of fluorescent substances with various MW ranges in landfill leachate. The addition of Cu(II) induced a marked decrease in fluorescence intensity in both bulk and fractionated leachate samples, while a slight, even negligible effect, was found when Cd(II) was added. No systematic trend was observed in the EEM spectra among individual MW fractions or between the leachate samples in response to the addition of either Cu(II) or Cd(II) based on visual peaking.
.8
.4 .2 0.0 200 250 300 350 400 450 500 200 250 300 350 400 450 500 200 250 300 350 400 450 500 Wavelength (nm) Wavelength (nm) Wavelength (nm)
b Sum of squares
3.2. EEM fluorescence landscape of DOM
Loading
a
Split 1-2 4e+8
3e+8
3e+8
2e+8
2e+8
1e+8
1e+8
250 300 350 400 Exitation wavelength (nm)
450
0 200
4e+7
4e+7
3e+7
3e+7
2e+7
2e+7
1e+7
1e+7
0 250
c
Split 3-4
4e+8
0 200
Sum of squares
representing the typical properties of MW distribution in fresh and aged leachate (Xu et al., 2006; Li et al., 2009).
Sum of squares
66
300 350 400 450 Emission wavelength (nm)
500
0 250
250 300 350 400 Exitation wavelength (nm)
450
300 350 400 450 Emission wavelength (nm)
500
Risidual analysis
5e+6
4-components model 5-components model 6-components model
4e+6 3e+6 2e+6 1e+6
3.3. PARAFAC analysis of EEM spectra 0 200
250
300
350
400
450
Exitation wavelength (nm) 1e+6
Sum of squares
The EEM spectra of the bulk and fractionated leachate samples titrated with Cu(II) and Cd(II) at ten different concentrations were analyzed by PARAFAC. The results revealed that the appropriate number of components was six based on the results of split half analysis as well as residuals and loadings analysis (Fig. 3). All 210 EEMs could be successfully decomposed into a six-component model by PARAFAC analysis despite the differences in fluorescent characteristics among various samples, including bulk and fractionated ones, as well as the different quenching effects of different metals at various concentrations. As illustrated in Fig. 4, the six components identified by the PARAFAC model include four humic-like substances (Component 1, 2, 3, and 6) and two protein-like substances (Component 4 and 5) according to the protocol described by Chen et al. (2003). Comparison of the EEM spectra between the components and other existing similar EEM peaks is the main method used for the interpretation of PARAFAC-derived components (Santin et al., 2009; Baghoth et al., 2011). As summarized in Table 2, all the six components showed peaks similar to those reported in previous studies of landfill leachate. One challenge frequently encountered when using PARAFAC analysis is the interpretation of components with irregular peaks (Yamashita and Jaffe, 2008; Lü et al., 2009; Yu et al., 2010a). The
4-components model 5-components model 6-components model
8e+5 6e+5 4e+5 2e+5 0 250
300
350
400
450
500
Emission wavelength (nm)
Fig. 3. EEM analysis by the DOMFluor-PARAFAC model. a) Excitation (dotted lines) and emission (solid lines) loadings of the six PARAFAC-derived components, b) Split half analysis, c) Residual analysis of components 4e6.
extracting procedure of PARAFAC is mainly based on mathematical calculation, and it is difficult to further characterize the PARAFACderived components using physicochemical tools. In this study, UF fractionation of DOM was adopted prior to PARAFAC analysis, potentially providing additional information regarding the MW distribution of the PARAFAC-derived components. In addition to the distribution of six PARAFAC-derived components in bulk and fractionated leachate samples, Fig. 5
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
67
Fig. 4. Fluorescence excitation-emission matrix contours of the six components identified by the DOMFluor-PARAFAC model.
also shows the MW distribution of the PARAFAC-derived components. It was expected that the percentages of protein-like components decreased as MW increased, while those of humiclike components increased proportionately. However, it is worth noting that component 1 in the two leachates and component 6 in the fresh leachate decreased as MW increased. These results provided further evidence supporting their lower humification degrees relative to component 2 and 3, which was in good agreement with their EEM spectra (Fig. 4) (a longer excitation wavelength or red shift indicating higher degree of humification (Milori et al., 2002)). 3.4. Behavior of PARAFAC-derived components with the addition of Cu(II) and Cd(II) The binding characteristics of heavy metals and the PARAFACderived components extracted from bulk DOM were investigated
in previous studies (Yamashita and Jaffe, 2008; Wu et al., 2011); however, the differences in heavy metal binding properties among various MW fractions within one sample have not been explored. It is interesting to note that there is no obvious difference in the quenching curves among bulk samples and the various MW fractions of the four humic-like components (Fig. 6). In contrast, marked differences among various components and between the two different metals were observed. These results clearly demonstrated that the impact of MW on the metal binding behavior may be not as great as that of specific components and metals. Although remarkable differences were observed in fluorescent fluorophores among components 1, 2 and 3 (Fig. 4), a similar trend was observed in their fluorescence quenching curves in response to the addition of either Cu(II) or Cd(II). Strong quenching effects for Cu(II) while negligible quenching effects for Cd(II) were observed, showing great similarity to those of commercial humic acid (HA) (Divya et al., 2009), soil- and compost-borne HA (Plaza
Table 2 Comparison of the EEM peaks in this study with those reported in previous studies. This study
Previous studies
Component
Ex/Em
Ex/Em
Sources
References
Fluorescent substance types
1
240,320/400
240,300/420 230,330/410 310e340/420e440 230e245,300e350/400e430
(Lü et al., 2009) (Yu et al., 2010b) (Huo et al., 2008) (Chen et al., 2003)
PARAFAC-derived component PARAFAC-derived component Bulk sample Extracted fraction
2
250,310,390/458
3
220,270,350/452
4
230,300/340
5 6
280/322 220/428
250,310,360/464 250/450 230,260,340/466 244/460,288/450,344/445 220,280/340 220,280/340 220e230/340e370 225e237/340e381,275/340 270e280/340e380 230e250/400e440 220e240/420e440 214e220/440e450
Landfill leachate WEOM derived from compost product Landfill leachate Hydrophobic and hydrophilic fraction of surface water-borne DOM Landfill leachate FA extracted from landfill leachate Landfill leachate WEOM derived from MSW Landfill leachate WEOM derived from compost product Landfill leachate DOM derived from sewage Landfill leachate Landfill leachate Landfill leachate Standard Suwannee River FA
(Lü et al., 2009) (Zheng et al., 2007) (Lü et al., 2009) (Shao et al., 2009) (Lü et al., 2009) (Yu et al., 2010b) (Baker and Curry, 2004) (Henderson et al., 2009) (Huo et al., 2008) (Baker and Curry, 2004) (Huo et al., 2009) (Chen et al., 2003)
PARAFAC-derived component Extracted fraction PARAFAC-derived component Bulk sample PARAFAC-derived component PARAFAC-derived component Bulk sample Bulk sample Bulk sample Bulk sample Bulk sample Standard sample
WEOM: water extractable organic matter.
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
100 80 60 40 20 0
<500
500-3k 3-10k
>10k
40
30
20
10
0
Aged leachate 500 400 300 200 100 0
d Percentage (%)
Percentage (%)
b
Bulk
c arbitrary unit/(mg/L)
Fresh leachate 120
Fluorescence intensity per unit TOC
a (arbitrary unit/(mg/L))
Fluorescence intensity per unit TOC
68
Bulk
<500
500-3k 3-10k
>10k
Bulk
<500
500-3k 3-10k
>10k
40
30
20
10
0
Bulk
<500
500-3k 3-10k
>10k
Component 1
Component 3
Component 5
Component 2
Component 4
Component 6
Fig. 5. Distribution of six PARAFAC-derived components in the bulk and fractionated leachate samples. Fluorescence intensity per unit of TOC (FI/TOC) and the respective percentage for the fresh leachate (a, b) and aged leachate (c, d). The percentage of each component was calculated by dividing its FI/TOC by that of the sum of six components.
et al., 2006a, 2006b). Based on the results of EEM contours (Fig. 4) (including the comparison in Table 2), MW distributions (Fig. 5) and quenching characteristics (Fig. 6), components 1, 2, and 3 are suggested as HA-like substances. Similarly, component 6 is suggested as fulvic acid (FA)-like substance, greatly quenched by either Cu(II) or Cd(II) like commercial FA (Chakraborty and Chakrabarti, 2008) and surface water-borne FA (Xue and Sigg, 1999). It is usually believed that the major ion binding sites in HA and FA are attributed to the carboxylic and phenolic groups, which have different metal affinities. FA contains a larger number of carboxylic-type sites and relatively few phenolic-type sites compared with HA (Milne et al., 2001). The various functional groups in FA and HA might cause the differences of binding behavior between the FA-like and HA-like components. The method of EEM quenching is often limited to examine the binding behavior of metals with no obvious changes in the EEM spectra, such as Cd(II) in this study. The application of PARAFAC analysis helped to identify the critical role of FA-like component in Cd(II) binding. Higher Cd(II) concentration in fresh leachate compared with aged leachate was generally attributed to lower pH value (higher leaching potential) as well as less attenuation by sorption and precipitation in landfill. It is worth noting that the percentages of FA-like component in the fresh leachate were significantly higher than those in the aged leachate (1.51, 2.26, 1.52, 1.45 and 1.33 times for the bulk sample, <500 Da, 500e3 kDa, 3e10 kDa, and >10 kDa fractions, respectively). Considering the great binding potential of Cd(II) by FA-like component, the relatively higher content of FA-like substances (component 6 in this study) in fresh leachate could be another reason for the higher concentration of Cd. Cu(II) quenched the fluorescence intensity of all six PARAFAC-derived components, and it tended to complex with humic-like components rather than protein-like ones (different quenching effects illustrated in Fig. 6). High-MW fractions contained more humic-like substances, suggesting their
more important role in Cu(II) binding than low-MW fractions. Recognizing the different roles of various components played in heavy metal binding, several strategies could be adopted for risk control of heavy metal migration. For example, treatment processes with high removal ratios of HS, especially for FA-like substances, should be encouraged. Based on the above results, it can be suggested that key components distributed within various fractions of DOM were the main influence on the impact of MW on metal binding. Larger fluctuations, but no systematic trends were observed in the quenching curves of protein-like components. Similar phenomena were observed in a previous studies using EEM quenching and PARAFAC (Yamashita and Jaffe, 2008; Wu et al., 2011). These results further confirmed that this method may not be appropriate for evaluation of the binding characteristics of metal ions and protein-like substances. The quenching degrees (f), conditional stability constants (logKM) and complexation capacities (CL) determined by the RyaneWeber model for humic-like components with Cu(II) and Cd(II) are listed in Table 3. Specifically, the f values of FA-like component with Cd(II) were higher than those with Cu(II). For Cu(II) complexation, the quenching effects of FA-like component were stronger than those of HA-like components. And the quenching degrees of three HA-like components were in the order: component 3 > component 1 > component 2. These results provided quantitative information to improve our knowledge on the different quenching effects among various components and between two metals. It should be noted that the f values of humiclike components in high-MW fractions were larger than those of low-MW fractions (except for the component 6), illustrating the impact of MW on quenching effects. On the other hand, the values of logKM and CL for the FA/HA-like components fell in the ranges reported in previous literatures of commercial or extracted FA/HA (Xue and Sigg, 1999; Plaza et al., 2006a, 2006b; Chakraborty and
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
Fresh leachate
69
Component 1
Aged leachate
Component 2
100 75 50 25 0
Component 3
100 75 50 25 0 200
Component 4
I/I0*100 (%)
100 75 50 25 0
150 100 50
Component 5
0 200 150 100 50
Component 6
0 100 75 50 25 0 0 20 40 60 80 100
Cu (µ mol/L) Bulk
0 20 40 60 80 100
Cd (µ mol/L)
<500 Da
0 20 40 60 80 100
0 20 40 60 80 100
Cu (µ mol/L)
Cd (µ mol/L)
500-3k Da
3-10k Da
>10k Da
Fig. 6. Changes in the fluorescence intensity per unit TOC of six PARAFAC-derived components in response to the addition of Cu(II) and Cd(II).
Chakrabarti, 2008; Divya et al., 2009). The lower logKM values of FA-like component with Cd(II) than those with Cu(II) was consistent with IrvingeWilliams series for the binding strength of divalent metal ion complexes (Irving and Williams, 1948). In contrast, the binding capacities (CL) of FA-like component with Cd(II) were larger than those with Cu(II). For Cu(II) complexation, the logKM values were similar among FA-like component (ranging from 5.05 to 5.64) and HA-like components (5.03e5.68), regardless of their differences in molecular structures. Although a prefitting procedure was carried out, quite small CL values (<0.01 mmol/L) were also found in Cu(II) complexation by HA-like components. The CL values of FA-like component were larger than those of HA-like components with the addition of Cu(II). The values of logKM were close to each other among various MW fractions, suggesting that the major composition of binding sites may be similar. Apart from that, no systematic trend was
identified regarding the impact of DOM size on binding characteristics based on logKM and CL values.
4. Conclusions MW was found to exert a less important impact on metal binding than that of specific metals or components. An FA-like component was identified to be responsible for Cd(II) binding while Cu(II) tended to complex with humic-like components (highMW fractions) rather than protein-like ones (low-MW fractions). Based on the quantitative distribution of the key components (responsible for metal binding) in various MW fractions, it was suggested that MW is an indirect factor for the interactions of DOM with metals. Overall, the combined approach of UF fractionation, EEM quenching and PARAFAC analysis could be applied as
1.00 0.96 0.99 0.99 0.98 1.00 0.99 1.00 0.99 0.98
Acknowledgments This study was financially supported by the National Basic Research Program of China (973 Program No. 2011CB201500), the National Natural Science Foundation of China (No. 20807031), Ministry of Education (No. 20090072120068), and Program of Shanghai Subject Chief Scientist (No. 10XD1404200).
100 75.4 100 100 91.6 100 100 100 100 100 0.97 0.96 0.98 0.96 0.99 0.96 0.98 0.95 1.00 1.00 8.22 9.74 15.25 14.61 22.87 10.01 19.15 5.63 21.88 24.68
4.78 5.17 4.66 4.79 4.68 4.37 4.69 4.72 4.51 4.71
61.12 9.74 51.33 46.71 43.02 83.16 65.53 55.49 75.19 65.37
a valuable tool for fingerprinting individual MW fractions in DOM as well as characterizing their binding behavior of heavy metal.
References
0.90
0.92 0.94 0.96 0.93 0.99 0.94 0.97 0.97
<0.01
<0.01 0.19 2.95 <0.01 8.66 1.35 3.80 3.06 5.68 5.29 5.39 5.32 5.45 5.54 5.38 5.48 “e”: The fluorescence intensity per unit of TOC was too low (Fig. 4) to be modeled, CL: mmol/L, f: %.
Aged leachate
Fresh leachate
Bulk sample <500 Da 500e3 kDa 3e10 kDa 10 kDae0.45 mm Bulk sample <500 Da 500e3 kDa 3e10 kDa 10 kDae0.45 mm
39.8 33.1 33.7 38.6 49.6 43.5 28.4 41.5 55.1 62.7
5.34 5.24 5.52 5.35 5.34 5.13 5.35 5.36 5.44 5.58
2.75 4.44 <0.01 3.04 6.39 <0.01 0.11 <0.01 0.19 1.29
0.96 0.95 0.93 0.95 0.97 0.91 0.94 0.89 0.95 0.94
23.9 25.4 21.3 23.0 28.2 35.3 28.0 28.2 29.1 41.2
5.31 5.12 5.40 5.18 5.21 5.03 5.18 5.20 5.21 5.25
1.31 <0.01 <0.01 4.16 2.79 <0.01 6.30 <0.01 2.41 <0.01
0.92 0.88 0.90 0.90 0.96 0.92 0.96 0.93 0.95 0.89
45.8 e 49.8 45.9 57.9 61.3 59.2 47.5 67.9 74.1
5.45
73.6 72.1 78.4 84.8 53.5 72.3 57.7 77.2 86.3 94.7
5.18 5.17 5.21 5.05 5.50 5.21 5.30 5.19 5.64 5.57
f R2 CL f R2 CL logKM f f
logKM
CL
R2
f
logKM
CL
R2
Comp 3 Comp 2 Comp 1
Cu Sample
Table 3 The f, logKM and CL values of the humic-like components in landfill leachate with Cu(II) and Cd(II) determined by the RyaneWeber model.
Comp 6
logKM
Cd
Comp 6
logKM
R2
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
CL
70
Andersen, C.M., Bro, R., 2003. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data. Journal of Chemometrics 17 (4), 200e215. Baghoth, S.A., Sharmaa, S.K., Amy, G.L., 2011. Tracking natural organic matter (NOM) in a drinking water treatment plant using fluorescence excitation-emission matrices and PARAFAC. Water Research 45 (2), 797e809. Bahram, M., Bro, R., Stedmon, C., Afkhami, A., 2006. Handling of Rayleigh and Raman scatter for PARAFAC modeling of fluorescence data using interpolation. Journal of Chemometrics 20 (3e4), 99e105. Baker, A., Curry, M., 2004. Fluorescence of leachates from three contrasting landfills. Water Research 38 (10), 2605e2613. Baumann, T., Fruhstorfer, P., Klein, T., Niessner, R., 2006. Colloid and heavy metal transport at landfill sites in direct contact with groundwater. Water Research 40 (14), 2776e2786. Baun, D.L., Christensen, T.H., 2004. Speciation of heavy metals in landfill leachate: a review. Waste Management & Research 22 (1), 3e23. Bro, R., 1997. PARAFAC. Tutorial and applications. Chemometrics and Intelligent Laboratory Systems 38 (2), 149e171. Calace, N., Liberatori, A., Petronio, B.M., Pietroletti, M., 2001a. Characteristics of different molecular weight fractions of organic matter in landfill leachate and their role in soil sorption of heavy metals. Environmental Pollution 113 (3), 331e339. Calace, N., Massimiani, A., Petronio, B.M., Pietroletti, M., 2001b. Municipal landfill leachate-soil interactions: a kinetic approach. Chemosphere 44 (5), 1025e1031. Chakraborty, P., Chakrabarti, C.L., 2008. Competition from Cu(II), Zn(II) and Cd(II) in Pb(II) binding to Suwannee River fulvic acid. Water Air and Soil Pollution 195 (1e4), 63e71. Chaminda, G.G.T., Nakajima, F., Furumai, H., 2008. Heavy metal (Zn and Cu) complexation and molecular size distribution in wastewater treatment plant effluent. Water Science & Technology 58 (6), 1207e1213. Chen, W., Westerhoff, P., Leenheer, J.A., Booksh, K., 2003. Fluorescence excitationemission matrix regional integration to quantify spectra for dissolved organic matter. Environmental Science & Technology 37 (24), 5701e5710. Christensen, J.B., Christensen, T.H., 1999. Complexation of Cd, Ni, and Zn by DOC in polluted groundwater: a comparison of approaches using resin exchange, aquifer material sorption, and computer speciation models (WHAM and MINTEQA2). Environmental Science & Technology 33 (21), 3857e3863. Christensen, J.B., Jensen, D.L., Christensen, T.H., 1996. Effect of dissolved organic carbon on the mobility of cadmium, nickel and zinc in leachate polluted groundwater. Water Research 30 (12), 3037e3049. Christensen, J.B., Botma, J.J., Christensen, T.H., 1999. Complexation of Cu and Pb by DOC in polluted groundwater: a comparison of experimental data and predictions by computer speciation models (WHAM and MINTEQA2). Water Research 33 (15), 3231e3238. Divya, O., Venkataraman, V., Mishra, A.K., 2009. Analysis of metal ion concentration in humic acid by excitation-emission matrix fluorescence and chemometric methods. Journal of Applied Spectroscopy 76 (6), 864e875. Hall, G.J., Clow, K.E., Kenny, J.E., 2005. Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis. Environmental Science & Technology 39 (19), 7560e7567. Henderson, R.K., Baker, A., Murphy, K.R., Hamblya, A., Stuetz, R.M., Khan, S.J., 2009. Fluorescence as a potential monitoring tool for recycled water systems: a review. Water Research 43 (4), 863e881. Hernandez, D., Plaza, C., Senesi, N., Polo, A., 2006. Detection of copper(II) and zinc(II) binding to humic acids from pig slurry and amended soils by fluorescence spectroscopy. Environmental Pollution 143 (2), 212e220. Hudson, N., Baker, A., Reynolds, D., 2007. Fluorescence analysis of dissolved organic matter in natural, waste and polluted waters - a review. River Research and Application 23 (6), 631e649. Huo, S.-L., Xi, B.-D., Yu, H.-C., He, L.-S., Fan, S.-L., Liu, H.-L., 2008. Characteristics of dissolved organic matter (DOM) in leachate with different landfill ages. Journal of Environmental Sciences-China 20 (4), 492e498. Huo, S.-L., Xi, B.-D., Yu, H.-C., Liu, H.-L., 2009. Dissolved organic matter in leachate from different treatment processes. Water and Environment Journal 23 (1), 15e22. Irving, H., Williams, R.J.P., 1948. Order of stability of metal complexes. Nature 162 (4123), 746e747. Jensen, D.L., Christensen, T.H., 1999. Colloidal and dissolved metals in leachates from four Danish landfills. Water Research 33 (9), 2139e2147.
J. Wu et al. / Environmental Pollution 162 (2012) 63e71 Li, R., Yue, D.-B., Liu, J.-G., Nie, Y.-F., 2009. Size fractionation of organic matter and heavy metals in raw and treated leachate. Waste Management 29 (9), 2527e2533. Lou, Z.-Y., Chai, X.-L., Niu, D.-J., Ou, Y.-Y., Zhao, Y.-C., 2009. Size-fractionation and characterization of landfill leachate and the improvement of Cu2þ adsorption capacity in soil and aged refuse. Waste Management 29 (1), 143e152. Lü, F., Chang, C.-H., Lee, D.-J., He, P.-J., Shao, L.-M., Su, A., 2009. Dissolved organic matter with multi-peak fluorophores in landfill leachate. Chemosphere 74 (4), 575e582. Luster, J., Blasera, P., Magyar, B., 1994. Equilibrium ion exchange method: methodology at low ionic strength and copper(II) complexation by dissolved organic matter in a leaf litter extract. Talanta 41 (11), 1873e1880. Luster, J., Lloyd, T., Sposito, G., Fry, I.V., 1996. Multi-wavelength molecular fluorescence spectrometry for quantitative characterization of copper(II) and aluminum(III) complexation by dissolved organic matter. Environmental Science & Technology 30 (5), 1565e1574. Milne, C.J., Kinniburgh, D.G., Tipping, E., 2001. Generic NICA-Donnan model parameters for proton binding by humic substances. Environmental Science & Technology 35 (10), 2049e2059. Milori, D.M.B.P., Martin-Neto, L., Bayer, C., Mielniczuk, J., Bagnato, V.S., 2002. Humification degree of soil humic acids determined by fluorescence spectroscopy. Soil Science 167 (11), 739e749. Ohno, T., 2002. Fluorescence inner-filtering correction for determining the humification index of dissolved organic matter. Environmental Science & Technology 36 (4), 742e746. Ohno, T., Amirbahman, A., Bro, R., 2008. Parallel factor analysis of excitationemission matrix fluorescence spectra of water soluble soil organic matter as basis for the determination of conditional metal binding parameters. Environmental Science & Technology 42 (1), 186e192. Plaza, C., Brunetti, G., Senesi, N., Polo, A., 2006a. Fluorescence characterization of metal ion-humic acid interactions in soils amended with composted municipal solid wastes. Analytical and Bioanalytical Chemistry 386 (7e8), 2133e2140. Plaza, C., Brunetti, G., Senesi, N., Polo, A., 2006b. Molecular and quantitative analysis of metal ion binding to humic acids from sewage sludge and sludge-amended soils by fluorescence spectroscopy. Environmental Science & Technology 40 (3), 917e923. Qu, X., He, P.-J., Shao, L.-M., Lee, D.-J., 2008. Heavy metals mobility in full-scale bioreactor landfill: initial stage. Chemosphere 70 (5), 769e777. Ryan, D.K., Weber, J.H., 1982. Fluorescence quenching titration for determination of complexing capacities and stability constants of fulvic acid. Analytical Chemistry 54 (6), 986e990.
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Santin, C., Yamashita, Y., Otero, X.L., Alvarez, M.A., Jaffe, R., 2009. Characterizing humic substances from estuarine soils and sediments by excitation-emission matrix spectroscopy and parallel factor analysis. Biogeochemistry 96 (1e3), 131e147. Schafer, A.I., Mauch, R., Waite, T.D., Fane, A.G., 2002. Charge effects in the fractionation of natural organics using ultrafiltration. Environmental Science & Technology 36 (12), 2572e2580. Shao, Z.-H., He, P.-J., Zhang, D.-Q., Shao, L.-M., 2009. Characterization of waterextractable organic matter during the biostabilization of municipal solid waste. Journal of Hazardous Materials 164 (2e3), 1191e1197. Stedmon, C.A., Bro, R., 2008. Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnology and Oceanography-Methods 6, 572e579. Ward, M.L., Bitton, G., Townsend, T., 2005. Heavy metal binding capacity (HMBC) of municipal solid waste landfill leachates. Chemosphere 60 (2), 206e215. Wu, J., Zhang, H., He, P.-J., Shao, L.-M., 2011. Insight into the heavy metal binding potential of dissolved organic matter in MSW leachate using EEM quenching combined with PARAFAC analysis. Water Research 45 (4), 1711e1719. Xu, Y.-D., Yue, D.-B., Zhu, Y., Nie, Y.-F., 2006. Fractionation of dissolved organic matter in mature landfill leachate and its recycling by ultrafiltration and evaporation combined processes. Chemosphere 64 (6), 903e911. Xue, H.-B., Sigg, L., 1999. Comparison of the complexation of Cu and Cd by humic or fulvic acids and by ligands observed in lake waters. Aquatic Geochemistry 5 (4), 313e335. Yamashita, Y., Jaffe, R., 2008. Characterizing the interactions between trace metals and dissolved organic matter using excitation-emission matrix and parallel factor analysis. Environmental Science & Technology 42 (19), 7374e7379. Yu, G.-H., He, P.-J., Shao, L.-M., 2010a. Novel insights into sludge dewaterability by fluorescence excitation-emission matrix combined with parallel factor analysis. Water Research 44 (3), 797e806. Yu, G.-H., Luo, Y.-H., Wu, M.-J., Tang, Z., Liu, D.-Y., Yang, X.-M., Shen, Q.-R., 2010b. PARAFAC modeling of fluorescence excitation-emission spectra for rapid assessment of compost maturity. Bioresource Technology 101 (21), 8244e8251. Knoth de Zarruk, K., Scholer, G., Dudal, Y., 2007. Fluorescence fingerprints and Cu2þcomplexing ability of individual molecular size fractions in soil- and wasteborne DOM. Chemosphere 69 (4), 540e548. Zheng, Z., He, P.-J., Shao, L.-M., Lee, D.-J., 2007. Phthalic acid esters in dissolved fractions of landfill leachates. Water Research 41 (20), 4696e4702.
Contents lists available at SciVerse ScienceDirect
Environmental Pollution journal homepage: www.elsevier.com/locate/envpol
Fluorescent characteristics and metal binding properties of individual molecular weight fractions in municipal solid waste leachate Jun Wu, Hua Zhang*, Li-Ming Shao, Pin-Jing He* State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, PR China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 May 2011 Received in revised form 11 September 2011 Accepted 7 October 2011
Molecular weight (MW) is a fundamental property of dissolved organic matter (DOM), which potentially affects the binding behavior between DOM and metals. Here, a combined approach of ultrafiltration fractionation, fluorescence excitation-emission matrix quenching and parallel factor analysis (PARAFAC) was employed to elucidate fluorescent characteristics and metal binding properties of individual MW fractions of DOM in landfill leachate. Four humic-like and two protein-like components were identified by PARAFAC. Among them, a fulvic acid-like component was found to be responsible for Cd(II) binding while Cu(II) inclined to complex with humic-like components rather than protein-like ones. Apart from that, MW was found to exert less influence on metal binding than that of specific metals or components. Key components distributed within various fractions of DOM were the main influence on the impact of MW on metal binding. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Landfill leachate Heavy metal binding Molecular weight Fluorescence excitation-emission matrix quenching Parallel factor analysis
1. Introduction Dissolved organic matter (DOM) plays a key role in heavy metal migration from municipal solid waste (MSW) landfill to surrounding aquatic and soil environment via the leachate pathway (Calace et al., 2001a, 2001b; Baumann et al., 2006; Qu et al., 2008). A significant portion of heavy metals in landfill leachate was found to be associated with DOM fraction (Jensen and Christensen, 1999; Baun and Christensen, 2004). Furthermore, the mobility of heavy metals in leachate polluted waters is greatly enhanced by elevated levels of organic matter (Christensen et al., 1996, 1999; Christensen and Christensen, 1999). The DOM in landfill leachate exhibits considerable spatial and temporal variations that are highly dependent on the types of wastes deposited, age of the waste, and hydro-geological factors of the landfill. Using biological assay methods, the binding capacities of Cu(II), Zn(II) and Hg(II) were reported to fluctuate by more than one order of magnitude among various landfill leachate samples (Ward et al., 2005), suggesting that great differences may exist in the binding properties among various individual fractions. Humic substance (HS) is a well-known metal sequester due to its large number of ionizable functional groups, such as carboxylic and
* Corresponding authors. E-mail addresses: [email protected] (H. Zhang), [email protected]. cn (P.-J. He). 0269-7491/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2011.10.017
phenolic groups. Considering the heterogeneity of HS and DOM, the size fractionation method has been widely employed to evaluate the metal distribution in individual fractions. Significant differences observed in heavy metal concentrations among various MW fractions of DOM indicated that MW is another factor potentially affecting the binding behavior between metals and DOM fractions (Baun and Christensen, 2004). Some researchers (Calace et al., 2001a, 2001b; Lou et al., 2009) investigated the effects of individual MW fractions in landfill leachate on the sorption behavior of Cu(II) onto soil and found that high-MW fractions contributed more to the enhancement of Cu(II) sorption than low-MW fractions. In contrast, Knoth de Zarruk et al. (2007) and Chaminda et al. (2008) found that low-MW fractions (MW<500 Da) played an important role in Cu(II) complexing. However, the impact of MW on DOM binding of other metals is less discussed, indicating a need for further research. Ultrafiltration (UF) is widely applied to fractionate DOM derived from various sources (Schafer et al., 2002), including landfill leachate (Li et al., 2009), owing to its versatility and relative simplicity of application, modest equipment requirements and the absence of additional separation media or auxiliary reagents. Meanwhile, fluorescence quenching is a classic method for rapid determination of the binding parameters between fluorescence substances and metal ions. It has been shown that fluorescence excitation-emission matrix (EEM) quenching can provide more detailed information regarding the binding characteristics than single or synchronous fluorescence spectroscopy (Hernandez et al.,
64
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
Table 1 Physicochemical characteristics of the filtered leachate samples. Sample
TOC (mg/L)
TC (mg/L)
IC (mg/L)
pH
Ca (mg/L)
Mg (mg/L)
Al (mg/L)
Fe (mg/L)
Cu (mg/L)
Cd (mg/L)
Fresh leachate Aged leachate
4450 370
6800 1420
2350 1050
8.02 7.87
412 149
163 87.7
1.37 0.449
1.44 0.721
22.7 14.8
1.85 0.844
TOC: total organic carbon; TC: total carbon; IC: inorganic carbon.
2. Materials and methods 2.1. Sample collection and preparation Two leachate samples were collected from two sanitary landfill sites in Shanghai, China. Fresh leachate was taken from a landfill composed of refuse aged 3e5 years, while aged leachate was obtained from a landfill composed of refuge aged 10e15 years. The leachate samples were collected in pre-cleaned brown sampling bottles, after which they were filtered using 0.45-mm membrane filters to separate the solid and soluble phase. The filtrates (DOM fraction) were then stored at 4 C until further use. The physicochemical characteristics of the filtered samples are summarized in Table 1. 2.2. DOM fractionation by the UF technique The UF procedure was conducted using a set of UF membranes (Shanghai Yadong Resin Co., China) with different MW cut-offs of 500 Da, 3 kDa, and 10 kDa in a stirred 300-mL cell (Schafer et al., 2002). Nitrogen gas was applied to pressurize the UF cell at 0.2 MPa. The leachate filtrates were processed at room temperature (25 1 C) by passing aliquots through a series of UF membranes, and the corresponding permeate and retentate were then collected. As a result, each leachate sample was separated into four fractions in terms of MW, (1) <500 Da, (2) 500e3 kDa, (3) 3e10 kDa, and (4) 10 kDae0.45 mm. 2.3. Fluorescence titration Prior to fluorescence titration, all samples were diluted to a low TOC concentration (<10 mg/L) as suggested to minimize the inner filtering effects (Ohno, 2002; Baker and Curry, 2004; Hudson et al., 2007). Aliquots of 25 mL of the diluted solution were titrated in 40-mL brown sealed vials with either Cu(NO3)2 or Cd(NO3)2 using an automatic syringe. The metal concentrations in the final solutions varied from 0 to 100 mmol/L. To maintain a constant pH before and after titration, the metal titrants were adjusted to pH 4.0 for Cu(NO3)2 and to pH 6.0 for Cd(NO3)2 using NaOH and HNO3. During the titration process, no more than 0.025 mL of the metal titrant was
added. All titrated solutions were shaken for 24 h to ensure complexation equilibrium at 25 0.1 C. The titration experiments were conducted in duplicate. Fluorescence EEMs were measured on a Cary Eclipse fluorescence spectrophotometer (Varian Inc., USA) in scan mode. EEM spectra were collected by varying the scanning emission wavelength (Em) from 250 to 500 nm at 2 nm increments and the excitation wavelength (Ex) from 200 to 450 nm at 10 nm increments. Instrumental parameters were as follows: excitation and emission slits, 10 nm; scan speed, 1200 nm/min; photomultiplier tube voltage, 700 V. 2.4. PARAFAC modeling The approach of PARAFAC analysis of EEMs has been well documented in literatures (Bro, 1997; Stedmon and Bro, 2008) and is therefore only briefly described here. PARAFAC is a statistical tool used to decompose N-way arrays into N loading matrices by means of an alternating least squares procedure. In other words, if an EEM dataset is arranged in a three-way array X of dimensions I J K, where I is the number of samples, J is the number of Em, and K is the number of Ex, PARAFAC decomposes them into three matrices labeled A (the score matrix), B and C (the loading matrices) with elements aif, bjf, and ckf. In this study, PARAFAC analysis was conducted using MATLAB 7.0 (Mathworks, Natick, MA) with the DOMFluor toolbox (www.models.life.ku.dk). Prior to the analysis, several preprocessing steps were adopted as follows. (1) The EEM obtained from Milli-Q water (as a control) was subtracted from each EEM of the studied samples. (2) Rayleigh and Raman scatters were removed according to the protocol described by Bahram et al. (2006). (3) The EEMs were normalized by dividing the spectrum by the corresponding TOC concentration to reduce the impact of varying DOM concentrations among different samples. (4) The EEMs were arranged into a three-dimensional data array (210 samples 26 Ex 126 Em). In the PARAFAC analysis, non-negative constraints were applied to the parameters allowing only chemically relevant results. Two to eight components were computed for the EEMs. Determination of the number of components was primarily based on split half analysis and analysis of residuals and loadings (Stedmon and Bro, 2008). 2.5. Complexation modeling The complexation parameters between PARAFAC-derived components and metals were determined by the RyaneWeber model (Ryan and Weber, 1982), based on the assumption of 1:1 complexation between ligands and metals. The complexation parameters, i.e., conditional stability constant KM, binding capacity CL and IML (the limiting value below which the fluorescence intensity does not decrease in response to the addition of metal) could be obtained by the nonlinear regression analysis on a plot of I/I0 vs. CM using eq. (1). I I 1 ¼ 1 þ ML 1 1 þ KM CL þ KM CM I0 2KM CL I0 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2C C ð1 þ KM CL þ KM CM Þ2 4KM L M
(1)
In this equation, I0 and I are the fluorescence intensity at the beginning of the titration (without adding metals) and at the metal concentration of CM, respectively.
60 TOC percentage of fraction (%)
2006; Plaza et al., 2006b). However, the EEM spectra of DOM are often composed of various types of overlapping fluorophores (Henderson et al., 2009), which make the quantitative interpretation more complicated. Multivariate analysis methods, such as principal component analysis (PCA), partial least squares (PLS) and parallel factor (PARAFAC) analysis, were introduced to improve the quantitative interpretation of EEMs (Hall et al., 2005). Among them, PARAFAC analysis can decompose fluorescence EEMs into various independent groups of fluorescent components, thereby reducing the interference among fluorescent compounds and allowing a more accurate quantification (Andersen and Bro, 2003). Recent studies have demonstrated that EEM quenching combined with PARAFAC analysis could be a useful tool to characterize the binding properties between metal ions and DOM derived from soil, surface water and MSW leachate (Ohno et al., 2008; Yamashita and Jaffe, 2008; Wu et al., 2011). It can be expected that the combination of UF fractionation, EEM quenching and PARAFAC may provide a deep insight into the binding behavior between heavy metals and individual MW fractions in DOM. In this study, two types of leachates derived from fresh and aged MSW landfills were collected and fractionated into four fractions with different MW ranges by UF. Two heavy metals (Cu(II) and Cd(II)) that are commonly found in MSW and landfill leachate were used as fluorescent quenching agents for titration. Specific fractions involved in heavy metal binding were identified and the differences in the binding characteristics among various individual MW fractions, PARAFAC-derived components, and between two heavy metals were analyzed.
fresh leachate aged leachate
50 40 30 20 10 0 <500
500-3k
3-10k
>10k
Molecular weight cut-offs (Da) Fig. 1. Molecular weight distributions of the two leachate samples.
J. Wu et al. / Environmental Pollution 162 (2012) 63e71 It has been reported that the use of a simplex algorithm to estimate the three fitting parameters (KM, CL, IML) in the RyaneWeber model would lead to unreasonably low values of CL (Luster et al., 1994; Plaza et al., 2006b). Therefore, a pre-fitting step was adopted based on eq. (2) to reduce the number of fitting parameters in eq. (1) from 3 (KM, CL, IML) to 2 (KM, CL,), by which, a constant value of IML/I0 can be acquired from the nonlinear fitting of eq. (2) (Luster et al., 1996). I 1 ¼ I 0
IML aCM I 1 1 e 0
(2)
Where IML/I0 1 and a are the fitting parameters. The quenching degree (f) was calculated by eq. (3).
f ¼
ðI0 IML Þ 100 I0
(3)
65
3. Results and discussion 3.1. MW distribution of the leachate samples The MW distributions of the two leachate samples were demonstrated in Fig. 1 as the percentage of TOC fractionated by the batch UF technique. Significant differences were observed between the two leachate samples. A major part (42.9%) of the organic matters in the fresh leachate had a MW smaller than 500 Da, while high-MW substances >10 kDa were dominated in the aged leachate (54.9%). Apart from that, each leachate sample contained some organic matters with an intermediate-MW range. These overall results were comparable to those reported in many other studies
Fig. 2. Fluorescence excitation-emission matrix spectra of the bulk and fractionated leachate samples with or without titration of Cu(II) and Cd(II) at a total concentration of 50 mmol/L. Blank: no added heavy metals, F: fresh leachate, A: aged leachate, FI/TOC: fluorescence intensity per unit TOC (arbitrary units/(mg/L)).
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
Loadings
.6
Component 1
Component 2
Component 3
Component 4
Component 5
Component 6
.4 .2 0.0 .8 .6
Loadings
The EEM spectra of the bulk and fractionated leachate samples in the absence and presence of Cu(II) or Cd(II) at a total concentration of 50 mmol/L are illustrated in Fig. 2. As expected, the EEM peaks overlapped considerably in the bulk samples, especially in the fresh leachate sample. Although each bulk sample was fractionated into four fractions based on MW by UF, there were no obvious differences in the EEM spectra among the four MW fractions except for the absolute value of the fluorescence intensity per unit of TOC (FI/TOC). In the fresh leachate, the FI/TOC of the intermediate (3e10 kDa) and high-MW (>10 kDa) fractions were stronger than that of the low-MW fractions (<3 kDa), although a major portion of organic matters in the fresh leachate were distributed in the low-MW fractions (<3 kDa) (Fig. 1). This observation suggests that fluorescence substances were more concentrated in intermediate (3e10 kDa) and high-MW (>10 kDa) fractions per unit of TOC. Similarly, fluorescence substances in the aged leachate were more concentrated in low-MW fractions (<3 kDa) per unit of TOC. These results demonstrated that UF combined with EEM spectra could provide additional information regarding the MW distribution of fluorescence substances in DOM, which may be useful for the evaluation of the binding affinities of each fraction toward metal ions. However, no obvious improvement was observed in EEM contours of UF-fractionated samples regarding the overlapping fluorophores. This could be attributed to the wide MW range of the UF technique as well as the ubiquitous distribution of fluorescent substances with various MW ranges in landfill leachate. The addition of Cu(II) induced a marked decrease in fluorescence intensity in both bulk and fractionated leachate samples, while a slight, even negligible effect, was found when Cd(II) was added. No systematic trend was observed in the EEM spectra among individual MW fractions or between the leachate samples in response to the addition of either Cu(II) or Cd(II) based on visual peaking.
.8
.4 .2 0.0 200 250 300 350 400 450 500 200 250 300 350 400 450 500 200 250 300 350 400 450 500 Wavelength (nm) Wavelength (nm) Wavelength (nm)
b Sum of squares
3.2. EEM fluorescence landscape of DOM
Loading
a
Split 1-2 4e+8
3e+8
3e+8
2e+8
2e+8
1e+8
1e+8
250 300 350 400 Exitation wavelength (nm)
450
0 200
4e+7
4e+7
3e+7
3e+7
2e+7
2e+7
1e+7
1e+7
0 250
c
Split 3-4
4e+8
0 200
Sum of squares
representing the typical properties of MW distribution in fresh and aged leachate (Xu et al., 2006; Li et al., 2009).
Sum of squares
66
300 350 400 450 Emission wavelength (nm)
500
0 250
250 300 350 400 Exitation wavelength (nm)
450
300 350 400 450 Emission wavelength (nm)
500
Risidual analysis
5e+6
4-components model 5-components model 6-components model
4e+6 3e+6 2e+6 1e+6
3.3. PARAFAC analysis of EEM spectra 0 200
250
300
350
400
450
Exitation wavelength (nm) 1e+6
Sum of squares
The EEM spectra of the bulk and fractionated leachate samples titrated with Cu(II) and Cd(II) at ten different concentrations were analyzed by PARAFAC. The results revealed that the appropriate number of components was six based on the results of split half analysis as well as residuals and loadings analysis (Fig. 3). All 210 EEMs could be successfully decomposed into a six-component model by PARAFAC analysis despite the differences in fluorescent characteristics among various samples, including bulk and fractionated ones, as well as the different quenching effects of different metals at various concentrations. As illustrated in Fig. 4, the six components identified by the PARAFAC model include four humic-like substances (Component 1, 2, 3, and 6) and two protein-like substances (Component 4 and 5) according to the protocol described by Chen et al. (2003). Comparison of the EEM spectra between the components and other existing similar EEM peaks is the main method used for the interpretation of PARAFAC-derived components (Santin et al., 2009; Baghoth et al., 2011). As summarized in Table 2, all the six components showed peaks similar to those reported in previous studies of landfill leachate. One challenge frequently encountered when using PARAFAC analysis is the interpretation of components with irregular peaks (Yamashita and Jaffe, 2008; Lü et al., 2009; Yu et al., 2010a). The
4-components model 5-components model 6-components model
8e+5 6e+5 4e+5 2e+5 0 250
300
350
400
450
500
Emission wavelength (nm)
Fig. 3. EEM analysis by the DOMFluor-PARAFAC model. a) Excitation (dotted lines) and emission (solid lines) loadings of the six PARAFAC-derived components, b) Split half analysis, c) Residual analysis of components 4e6.
extracting procedure of PARAFAC is mainly based on mathematical calculation, and it is difficult to further characterize the PARAFACderived components using physicochemical tools. In this study, UF fractionation of DOM was adopted prior to PARAFAC analysis, potentially providing additional information regarding the MW distribution of the PARAFAC-derived components. In addition to the distribution of six PARAFAC-derived components in bulk and fractionated leachate samples, Fig. 5
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
67
Fig. 4. Fluorescence excitation-emission matrix contours of the six components identified by the DOMFluor-PARAFAC model.
also shows the MW distribution of the PARAFAC-derived components. It was expected that the percentages of protein-like components decreased as MW increased, while those of humiclike components increased proportionately. However, it is worth noting that component 1 in the two leachates and component 6 in the fresh leachate decreased as MW increased. These results provided further evidence supporting their lower humification degrees relative to component 2 and 3, which was in good agreement with their EEM spectra (Fig. 4) (a longer excitation wavelength or red shift indicating higher degree of humification (Milori et al., 2002)). 3.4. Behavior of PARAFAC-derived components with the addition of Cu(II) and Cd(II) The binding characteristics of heavy metals and the PARAFACderived components extracted from bulk DOM were investigated
in previous studies (Yamashita and Jaffe, 2008; Wu et al., 2011); however, the differences in heavy metal binding properties among various MW fractions within one sample have not been explored. It is interesting to note that there is no obvious difference in the quenching curves among bulk samples and the various MW fractions of the four humic-like components (Fig. 6). In contrast, marked differences among various components and between the two different metals were observed. These results clearly demonstrated that the impact of MW on the metal binding behavior may be not as great as that of specific components and metals. Although remarkable differences were observed in fluorescent fluorophores among components 1, 2 and 3 (Fig. 4), a similar trend was observed in their fluorescence quenching curves in response to the addition of either Cu(II) or Cd(II). Strong quenching effects for Cu(II) while negligible quenching effects for Cd(II) were observed, showing great similarity to those of commercial humic acid (HA) (Divya et al., 2009), soil- and compost-borne HA (Plaza
Table 2 Comparison of the EEM peaks in this study with those reported in previous studies. This study
Previous studies
Component
Ex/Em
Ex/Em
Sources
References
Fluorescent substance types
1
240,320/400
240,300/420 230,330/410 310e340/420e440 230e245,300e350/400e430
(Lü et al., 2009) (Yu et al., 2010b) (Huo et al., 2008) (Chen et al., 2003)
PARAFAC-derived component PARAFAC-derived component Bulk sample Extracted fraction
2
250,310,390/458
3
220,270,350/452
4
230,300/340
5 6
280/322 220/428
250,310,360/464 250/450 230,260,340/466 244/460,288/450,344/445 220,280/340 220,280/340 220e230/340e370 225e237/340e381,275/340 270e280/340e380 230e250/400e440 220e240/420e440 214e220/440e450
Landfill leachate WEOM derived from compost product Landfill leachate Hydrophobic and hydrophilic fraction of surface water-borne DOM Landfill leachate FA extracted from landfill leachate Landfill leachate WEOM derived from MSW Landfill leachate WEOM derived from compost product Landfill leachate DOM derived from sewage Landfill leachate Landfill leachate Landfill leachate Standard Suwannee River FA
(Lü et al., 2009) (Zheng et al., 2007) (Lü et al., 2009) (Shao et al., 2009) (Lü et al., 2009) (Yu et al., 2010b) (Baker and Curry, 2004) (Henderson et al., 2009) (Huo et al., 2008) (Baker and Curry, 2004) (Huo et al., 2009) (Chen et al., 2003)
PARAFAC-derived component Extracted fraction PARAFAC-derived component Bulk sample PARAFAC-derived component PARAFAC-derived component Bulk sample Bulk sample Bulk sample Bulk sample Bulk sample Standard sample
WEOM: water extractable organic matter.
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
100 80 60 40 20 0
<500
500-3k 3-10k
>10k
40
30
20
10
0
Aged leachate 500 400 300 200 100 0
d Percentage (%)
Percentage (%)
b
Bulk
c arbitrary unit/(mg/L)
Fresh leachate 120
Fluorescence intensity per unit TOC
a (arbitrary unit/(mg/L))
Fluorescence intensity per unit TOC
68
Bulk
<500
500-3k 3-10k
>10k
Bulk
<500
500-3k 3-10k
>10k
40
30
20
10
0
Bulk
<500
500-3k 3-10k
>10k
Component 1
Component 3
Component 5
Component 2
Component 4
Component 6
Fig. 5. Distribution of six PARAFAC-derived components in the bulk and fractionated leachate samples. Fluorescence intensity per unit of TOC (FI/TOC) and the respective percentage for the fresh leachate (a, b) and aged leachate (c, d). The percentage of each component was calculated by dividing its FI/TOC by that of the sum of six components.
et al., 2006a, 2006b). Based on the results of EEM contours (Fig. 4) (including the comparison in Table 2), MW distributions (Fig. 5) and quenching characteristics (Fig. 6), components 1, 2, and 3 are suggested as HA-like substances. Similarly, component 6 is suggested as fulvic acid (FA)-like substance, greatly quenched by either Cu(II) or Cd(II) like commercial FA (Chakraborty and Chakrabarti, 2008) and surface water-borne FA (Xue and Sigg, 1999). It is usually believed that the major ion binding sites in HA and FA are attributed to the carboxylic and phenolic groups, which have different metal affinities. FA contains a larger number of carboxylic-type sites and relatively few phenolic-type sites compared with HA (Milne et al., 2001). The various functional groups in FA and HA might cause the differences of binding behavior between the FA-like and HA-like components. The method of EEM quenching is often limited to examine the binding behavior of metals with no obvious changes in the EEM spectra, such as Cd(II) in this study. The application of PARAFAC analysis helped to identify the critical role of FA-like component in Cd(II) binding. Higher Cd(II) concentration in fresh leachate compared with aged leachate was generally attributed to lower pH value (higher leaching potential) as well as less attenuation by sorption and precipitation in landfill. It is worth noting that the percentages of FA-like component in the fresh leachate were significantly higher than those in the aged leachate (1.51, 2.26, 1.52, 1.45 and 1.33 times for the bulk sample, <500 Da, 500e3 kDa, 3e10 kDa, and >10 kDa fractions, respectively). Considering the great binding potential of Cd(II) by FA-like component, the relatively higher content of FA-like substances (component 6 in this study) in fresh leachate could be another reason for the higher concentration of Cd. Cu(II) quenched the fluorescence intensity of all six PARAFAC-derived components, and it tended to complex with humic-like components rather than protein-like ones (different quenching effects illustrated in Fig. 6). High-MW fractions contained more humic-like substances, suggesting their
more important role in Cu(II) binding than low-MW fractions. Recognizing the different roles of various components played in heavy metal binding, several strategies could be adopted for risk control of heavy metal migration. For example, treatment processes with high removal ratios of HS, especially for FA-like substances, should be encouraged. Based on the above results, it can be suggested that key components distributed within various fractions of DOM were the main influence on the impact of MW on metal binding. Larger fluctuations, but no systematic trends were observed in the quenching curves of protein-like components. Similar phenomena were observed in a previous studies using EEM quenching and PARAFAC (Yamashita and Jaffe, 2008; Wu et al., 2011). These results further confirmed that this method may not be appropriate for evaluation of the binding characteristics of metal ions and protein-like substances. The quenching degrees (f), conditional stability constants (logKM) and complexation capacities (CL) determined by the RyaneWeber model for humic-like components with Cu(II) and Cd(II) are listed in Table 3. Specifically, the f values of FA-like component with Cd(II) were higher than those with Cu(II). For Cu(II) complexation, the quenching effects of FA-like component were stronger than those of HA-like components. And the quenching degrees of three HA-like components were in the order: component 3 > component 1 > component 2. These results provided quantitative information to improve our knowledge on the different quenching effects among various components and between two metals. It should be noted that the f values of humiclike components in high-MW fractions were larger than those of low-MW fractions (except for the component 6), illustrating the impact of MW on quenching effects. On the other hand, the values of logKM and CL for the FA/HA-like components fell in the ranges reported in previous literatures of commercial or extracted FA/HA (Xue and Sigg, 1999; Plaza et al., 2006a, 2006b; Chakraborty and
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
Fresh leachate
69
Component 1
Aged leachate
Component 2
100 75 50 25 0
Component 3
100 75 50 25 0 200
Component 4
I/I0*100 (%)
100 75 50 25 0
150 100 50
Component 5
0 200 150 100 50
Component 6
0 100 75 50 25 0 0 20 40 60 80 100
Cu (µ mol/L) Bulk
0 20 40 60 80 100
Cd (µ mol/L)
<500 Da
0 20 40 60 80 100
0 20 40 60 80 100
Cu (µ mol/L)
Cd (µ mol/L)
500-3k Da
3-10k Da
>10k Da
Fig. 6. Changes in the fluorescence intensity per unit TOC of six PARAFAC-derived components in response to the addition of Cu(II) and Cd(II).
Chakrabarti, 2008; Divya et al., 2009). The lower logKM values of FA-like component with Cd(II) than those with Cu(II) was consistent with IrvingeWilliams series for the binding strength of divalent metal ion complexes (Irving and Williams, 1948). In contrast, the binding capacities (CL) of FA-like component with Cd(II) were larger than those with Cu(II). For Cu(II) complexation, the logKM values were similar among FA-like component (ranging from 5.05 to 5.64) and HA-like components (5.03e5.68), regardless of their differences in molecular structures. Although a prefitting procedure was carried out, quite small CL values (<0.01 mmol/L) were also found in Cu(II) complexation by HA-like components. The CL values of FA-like component were larger than those of HA-like components with the addition of Cu(II). The values of logKM were close to each other among various MW fractions, suggesting that the major composition of binding sites may be similar. Apart from that, no systematic trend was
identified regarding the impact of DOM size on binding characteristics based on logKM and CL values.
4. Conclusions MW was found to exert a less important impact on metal binding than that of specific metals or components. An FA-like component was identified to be responsible for Cd(II) binding while Cu(II) tended to complex with humic-like components (highMW fractions) rather than protein-like ones (low-MW fractions). Based on the quantitative distribution of the key components (responsible for metal binding) in various MW fractions, it was suggested that MW is an indirect factor for the interactions of DOM with metals. Overall, the combined approach of UF fractionation, EEM quenching and PARAFAC analysis could be applied as
1.00 0.96 0.99 0.99 0.98 1.00 0.99 1.00 0.99 0.98
Acknowledgments This study was financially supported by the National Basic Research Program of China (973 Program No. 2011CB201500), the National Natural Science Foundation of China (No. 20807031), Ministry of Education (No. 20090072120068), and Program of Shanghai Subject Chief Scientist (No. 10XD1404200).
100 75.4 100 100 91.6 100 100 100 100 100 0.97 0.96 0.98 0.96 0.99 0.96 0.98 0.95 1.00 1.00 8.22 9.74 15.25 14.61 22.87 10.01 19.15 5.63 21.88 24.68
4.78 5.17 4.66 4.79 4.68 4.37 4.69 4.72 4.51 4.71
61.12 9.74 51.33 46.71 43.02 83.16 65.53 55.49 75.19 65.37
a valuable tool for fingerprinting individual MW fractions in DOM as well as characterizing their binding behavior of heavy metal.
References
0.90
0.92 0.94 0.96 0.93 0.99 0.94 0.97 0.97
<0.01
<0.01 0.19 2.95 <0.01 8.66 1.35 3.80 3.06 5.68 5.29 5.39 5.32 5.45 5.54 5.38 5.48 “e”: The fluorescence intensity per unit of TOC was too low (Fig. 4) to be modeled, CL: mmol/L, f: %.
Aged leachate
Fresh leachate
Bulk sample <500 Da 500e3 kDa 3e10 kDa 10 kDae0.45 mm Bulk sample <500 Da 500e3 kDa 3e10 kDa 10 kDae0.45 mm
39.8 33.1 33.7 38.6 49.6 43.5 28.4 41.5 55.1 62.7
5.34 5.24 5.52 5.35 5.34 5.13 5.35 5.36 5.44 5.58
2.75 4.44 <0.01 3.04 6.39 <0.01 0.11 <0.01 0.19 1.29
0.96 0.95 0.93 0.95 0.97 0.91 0.94 0.89 0.95 0.94
23.9 25.4 21.3 23.0 28.2 35.3 28.0 28.2 29.1 41.2
5.31 5.12 5.40 5.18 5.21 5.03 5.18 5.20 5.21 5.25
1.31 <0.01 <0.01 4.16 2.79 <0.01 6.30 <0.01 2.41 <0.01
0.92 0.88 0.90 0.90 0.96 0.92 0.96 0.93 0.95 0.89
45.8 e 49.8 45.9 57.9 61.3 59.2 47.5 67.9 74.1
5.45
73.6 72.1 78.4 84.8 53.5 72.3 57.7 77.2 86.3 94.7
5.18 5.17 5.21 5.05 5.50 5.21 5.30 5.19 5.64 5.57
f R2 CL f R2 CL logKM f f
logKM
CL
R2
f
logKM
CL
R2
Comp 3 Comp 2 Comp 1
Cu Sample
Table 3 The f, logKM and CL values of the humic-like components in landfill leachate with Cu(II) and Cd(II) determined by the RyaneWeber model.
Comp 6
logKM
Cd
Comp 6
logKM
R2
J. Wu et al. / Environmental Pollution 162 (2012) 63e71
CL
70
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