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Article

Copper Distribution and Binding Affinity to Size-Fractioned Dissolved and Particulate Organic Matter in River Sediment

1
Department of Environmental Science and Engineering, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
2
General Research Service Center, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
3
PanCheng Engineering Consultants Co., Ltd., Kaohsiung 802609, Taiwan
*
Authors to whom correspondence should be addressed.
Environments 2024, 11(6), 129; https://doi.org/10.3390/environments11060129
Submission received: 10 May 2024 / Revised: 8 June 2024 / Accepted: 14 June 2024 / Published: 18 June 2024

Abstract

:
This study investigated the distribution of copper in sediment dissolved and particulate organic matter (DOM and POM) based on their size. The DOM and alkaline extracted POM (AEOM) were separated into five size fractions using a cross-flow ultrafiltration (CFUF) system. The results showed that Cu mass was mainly distributed in the low molecular weight (<1 kDa, LMW) fraction of the DOM with an average range of 78.1–83.1%. Conversely, the high molecular weight (1 kDa–0.45 μm, HMW) AEOM fraction had a higher distribution of Cu mass with an average range of 92.6–93.3%. The Cu and AEOM binding affinity ratios (CuBAAEOM) ranged from 17.0 to 149.6 μmol/g-C in site-1 and from 20.6 to 143.7 μmol/g-C in site-2. The HMW CuBAAEOM ratios were significantly higher than the LMW ratios. The Cu and DOM binding affinity ratios (CuBADOM) ranged from 5.6 to 358.6 μmol/g-C and 17.2 to 126.6 μmol/g-C in site-1 and site-2, respectively. However, the LMW CuBADOM ratios were significantly higher than the HMW ratios. Optical indices suggested that the AEOM had more aromaticity and terrestrial and allochthonous contributions than the DOM. The optical indices were significantly correlated with the CuBAAEOM ratios but weakly correlated with the CuBADOM ratios. Sediment exchange between POM and DOM may affect copper distribution. DOM has a low-molecular-weight composition, while POM retains high-molecular-weight organic matter.

1. Introduction

Rivers that receive wastewater from concentrated animal feeding operations and industrial sources may contain high levels of dissolved and particulate organic matter (DOM and POM) as well as heavy metals (HMs) [1,2,3]. HMs are significant pollutants in aquatic environments and can be absorbed by suspended particulate matter and sediment [4,5]. In sediment, various processes may lead to the binding of dissolved POM with HMs in the aqueous phase, including POM resuspension, hydrolysis, and microbial oxidation cleavage [4,6,7,8]. In the aqueous phase, HMs bind to various DOM molecules, except for free heavy metal ions [9,10,11].
When HMs bind to organic matter of different molecular weights, their biotoxicity and bioavailability vary. HM ions that are free show the highest toxicity towards aquatic organisms, while macromolecule DOM bound-HMs may reduce the bioavailability and toxicity of HMs [12,13]. In an aquatic environment, the distribution of HMs between solid and liquid phases is a dynamic phenomenon [8,14,15,16,17]. The distribution of HMs may be influenced by the exchange of DOM and POM in sediment [4,6,9]. The species and binding of HMs to DOM also impact their transport, distribution, and biological toxicity [18,19,20].
The ratio of heavy metal and DOM binding affinity, known as the HMBA ratio, is a useful indicator in determining the affinity of heavy metals with DOM and POM in the real environment [21,22,23,24,25,26]. Several studies have shown that the HMBA ratios in the environment are highly variable, indicating that the chemical composition, structure, and source of DOM and POM significantly affect the binding affinity of heavy metals [23,24,25]. The concentration of dissolved organic carbon (DOC) and the percentage of fulvic acid are general parameters that determine the behavior of heavy metals and DOM binding [7,10,11,18,19,22]. The chemical composition and structure of DOM and POM are critical factors that affect the binding affinity of heavy metals with DOM [21,22,23,25].
Optical indices are commonly used to analyze the chemical composition and structure of DOM and POM [25,27,28,29,30,31,32]. Specific ultraviolet absorbance at 254 nm (SUVA254) is an optical indicator of DOM, indicating its aromaticity [33]. The fluorescence index (FI) is a measure of the contribution of terrestrial sources to DOM [34], while the biological index (BIX) describes the contribution of autochthonous origin [34,35].
Copper (Cu) is considered a significant pollutant in the environment. Cu binding affinity with DOM influences the fate and biotoxicity of copper. Research has studied the affinity of Cu for DOM and POM in various media. For instance, Cu binding to soil organic matter extracted using different solvents, like water, CaCl2, and NaOH, has been studied [21,22,36,37]. Similarly, in different aquatic environments, Cu binding to DOM has been studied [23,24,25,26]. Additionally, the CuBA ratios of size-fractioned DOM in wastewater treatment have been reported [24,38,39]. The CuBADOM ratios were found to be highly varied across different environmental media.
It has been found that DOM and POM are intricate mixtures of organic substances that come in different molecular weights [24,38,39]. However, only a few studies have examined the HMBA ratios of different molecular weights of DOM and POM, which can provide us with valuable insights into their interaction and exchange and the binding behavior of heavy metals with them in sediment [4,7,8,40]. Knowing how heavy metals bind with DOM and POM of different molecular weights is crucial in understanding their transport, biotoxicity, and environmental fate.
This study aimed to investigate the affinity of Cu binding in different sizes of DOM and AEOM. It also analyzed the correlation between Cu binding affinity ratios and optical indices to identify the critical factors affecting the ratios.

2. Materials and Methods

2.1. Experiment Site and Sample Collecting and Preparation

This study investigated the affinity of Cu binding to DOM and POM in river sediment. The experiments were conducted to collect river sediment and overlying water from areas affected by livestock and industrial wastewater. The liquid and solid phases in the sediment were separated into dissolved and particulate phases. The DOM was obtained from the liquid phase that passed through a filter (<0.45 μm). The AEOM (POM) was extracted from the air-dry solid sediment using an alkaline solution. The DOM and AEOM were sequentially separated into five molecular weights using a crossflow ultrafiltration system. The size-fractionated DOM and AEOM were analyzed for DOC and Cu concentrations. The optical indices were calculated using UV/Vis and fluorescence spectra of DOM and AEOM. The samples were collected from WuLo Creek and Houjing River located in southern Taiwan. The sampling was done at two sites: site-1 (22°46′04.2″ N, 120°32′32.7″ E) and site-2 (22°43′36.6″ N 120°19′06.7″ E). Site-1 is situated at WuLo Creek, which has a catchment area of 54.2 km2, and its main channel is 15 km long. The upstream of the creek is the most concentrated animal feeding area, where around 430,000 pigs are cultivated. Site-2, on the other hand, is located at Houjing River, whose catchment area is 73.45 km2, and its main channel is 13 km long. The river passes through three industrial parks.
The samples were collected from the surface sediment (0–30 cm) using an adjustable long-handle water collector. The liquid water and sediment were separated using a centrifuge at 4500 rpm for 15 min. The liquid phase filtrate was named DOM and stored at 4 °C for further separation. The solid phase was air-dried for a month and then sieved with 2 mm for alkaline extraction.

2.2. AEOM Extraction and Separation

The POM was extracted from sediments using 0.1 N NaOH solution. The NaOH-extracted humic substance was identified as the bulk AEOM solution. The solutions were separated into five different categories: MW-A (10 kDa–0.45 μm), MW-B (3–10 kDa), MW-C (1–3 kDa), MW-D (0.3–1.0 kDa), and MW-E (<0.3 kDa) with nominal molecular weight cutoffs of 10, 3, 1, and 0.3 kDa (TAMI Industries, Noyns, France), 1 kDa~1 nm [41]. In each separation process, the volume concentration factor (Cf) was maintained at 10. The volume ratios were 0.1, 0.09, 0.081, 0.0729, and 0.6561 for the size-fractioned solutions MW-A, MW-B, MW-C, MW-D, and MW-E, respectively.

2.3. Dissolved Organic Carbon and Metals’ Measurement

The concentrations of DOC in both AEOM and DOM solutions were determined using a TOC-V analyzer (Shimadzu, Kyoto, Japan). The concentrations of Cu were measured using either an atomic absorption spectroscope (Hitachi, Z-2300, Tokyo, Japan) or a graphite furnace atomic spectroscope (Hitachi, Z-3000, Tokyo, Japan).

2.4. UV–Vis and Fluorescent Measurement

The AEOM and DOM solutions were diluted with ultrapure water to a concentration of 5.0 and 1.0 mg-C/L, respectively. The absorbance was measured using an ultraviolet/visible spectrophotometer (Hitachi, U-2900, Tokyo, Japan), while fluorescence spectra were measured with a fluorescence spectrometer (Hitachi, F-7000, Tokyo, Japan). The background value was set at a UV–Vis absorbance of 700–800 nm. The sample’s absorbance was then subtracted from the average absorbance at 700–800 nm [42]. The UV–Vis spectrophotometric scanning wavelength was 800–200 nm. The excitation/emission matrixes (EEMs) were generated by recording emission spectra from 250 to 550 nm, in 2.0 nm steps for an excitation wavelength between 200 and 450 nm, in 5 nm increments. The scanning rate was 2400 nm/min.

2.5. Optical Indices and Heavy Metal Binding Affinity Calculation

The specific ultraviolet absorbance at 254 nm (SUVA254, L/mg-C/m) is the result of dividing the UV–Vis absorbance of the sample at 254 nm UV254 (cm−1) by the concentration of dissolved organic carbon (mg-C/L) of the AEOM and DOM samples, then multiplying it by 100 [33]. The fluorescence index (FI) is the emission intensity ratio of 450 nm over 500 nm, both with excitation of 370 nm [34,43]. The biological index (BIX) is the ratio of the intensities at an emission wavelength of 380 nm over 430 nm, both with excitation of 310 nm [34,44]. The copper binding affinity to DOM and AEOM (CuBADOM and CuBAAEOM ratios, μmol/g-C) is calculated by dividing the Cu concentration (μmol/L) by the concentration of dissolved organic carbon (g-C/L) [21,22,23,25].

2.6. Statistic Analysis

The study utilized the S-Plus software version 6.2 to conduct linear regression and various tests, all at a significance level of p < 0.05. To perform two group difference tests between AEOM and DOM, such as concentration, mass percentage, CuBA ratios, and indicators, the t-test method was employed. Additionally, the ANOVA test method was used for three group difference tests.

3. Results

3.1. DOC and Cu Concentrations in the DOM and AEOM

Table 1 lists the concentrations of DOC and Cu for bulk and size-fractioned DOM and AEOM. The average mass recoveries of DOC and Cu in the DOM and AEOM ranged from 92% to 119%, which is within the accepted range of 100 ± 25% [45].
The bulk DOC concentration in the DOM solutions was slightly higher than the DOC concentrations found in general river water [23,25,46,47], and similar to river water that received municipal and industrial wastewater [40,48,49]. The bulk DOM Cu concentrations were 51.2 ± 6.8 and 21.0 ± 6.1 μg L−1, which were higher than the unpolluted river water [23,25,46,48,49,50].
The bulk AEOM DOC was 6.56 ± 0.64 and 3.12 ± 0.11 g kg−1 based on sediment mass, and the AEOC/TOC ratio was 15.4% and 9.2% for site-1 and site-2, respectively. The AEOM DOC levels were higher than DOC contents extracted from soil, which ranged from 274 to 678 mg kg−1 [30,31,36], but similar to sediment (5.94–6.40 g kg−1) [32,40]. The AEOC/TOC ratio was higher than the proportion of alkaline extracted organic carbon from soil [30,31], and similar to that extracted from sediment [32,40].
The bulk AEOM Cu concentrations were 46.1 ± 0.9 and 21.5 ± 0.1 mg kg−1 for site-1 and site-2, respectively. The total Cu concentrations were 111.0 ± 1.92 and 55.2 ± 1.89 mg kg−1 in sediment, based on sediment mass, for site-1 and site-2, respectively. The bulk AEOM Cu concentration accounted for 41.5% and 38.9% of the total Cu concentrations in sediment. Previous research has shown that Cu tends to bind to organic matter in soil, suspended particulate matter, and sediment [31,51,52,53].
Total Cu concentrations in river sediment vary greatly, with unpolluted sediments having levels in the tens of mg kg−1 [54,55], while polluted sediments can have levels over a few hundred mg kg−1 [45,56]. The studied site received livestock wastewater and industrial wastewater discharge, which resulted in high DOC and Cu concentrations in both the liquid and sediment. Moreover, the high levels of Cu and organic matter in sediment were easily extracted by alkaline solution.

3.2. DOC and Cu Mass Percentages in Size-Fractioned DOM/AEOM

The distribution of organic carbon (OC) in different size fractions of DOM and AEOM is crucial for understanding the exchange process between DOM and POM in sediment, as well as the transport and biotoxicity of heavy metals in the environment. Table 1 presents the measured concentrations of DOC and Cu in the fractioned DOM and AEOM. The concentration of each fraction in the bulk solution is multiplied by its corresponding volume ratio to obtain mass percentage. Table 2 lists the OC and Cu mass percentages of the size-fractioned DOM and AEOM. Two sites had a similar order of OC mass distribution. The order of OC mass percentage of the fractioned DOM was MW-E > MW-A > MW-C, MW-B > MW-D. The order of OC mass percentage of fractioned AEOM was MW-A > MW-E > MW-B, MW-C > MW-D. Both DOM and AEOM had higher OC mass percentages in the size fractions of MW-A (>10 kDa) and MW-E (<0.3 kDa) than in other size-fractioned DOM and AEOM. In the DOM solution, the OC mass percentages of high molecular weight (1 kDa–0.45 μm, HMW) were 50.7% and 46.7% for site-1 and site-2, respectively. The HMW OC percentages were in the range of 23 to 80% [57,58,59] of HMW OC in freshwater aquatic environments. In the AEOM solution, the OC mass percentages of HMW (76.5% and 76.0% for site-1 and site-2, respectively) were significantly higher than the low molecular weight OC (<1 kDa, LMW). The HMW OC percentage was close to the OC mass percentage of 79–87% extracted from sediment [32,40,60].
The distribution of Cu in size-fractioned DOM and AEOM showed a significant trend following molecular size. In the AEOM solution, Cu was primarily distributed in the HMW AEOM (93.0%, Table 2). However, in the DOM solution, Cu was primarily distributed in LMW DOM (80.6%, Table 2). The mass percentages of Cu in the HMW DOM varied, for example, Ilina et al. [52] separated soil solution, lake water, and river water into HMW (1 kDa–0.22 μm) and LMW (<1 kDa) fractions. The Cu mass percentages of the HMW were 80%, 53%, and 38%, respectively. Hargreaves et al. [39] separated the DOM into HMW (1 kDa–0.45 μm) and LMW (<1 kDa) fractions of four treatment processes in a municipal wastewater treatment plant. The HMW Cu mass percentages ranged from 69% to 77% in DOM. The present study showed that the size-fractioned DOM and AEOM had different tendencies in Cu distribution following DOM and AEOM molecular weight, which helps understand Cu binding affinity to organic matter.

3.3. Optics Indicators of the DOM and AEOM

Optical indices are widely used to investigate the chemical composition and structure of DOM and AEOM [4,8,21,22,25,32,61].
In Figure 1a–f, the values of the selected indices for bulk and size-fractioned DOM and AEOM are presented. SUVA254 indicates the aromaticity of DOM and AEOM, and a high SUVA254 value has a high aromaticity [29,33]. Figure 1a,b show the SUVA254 values varied with size-fractioned DOM and AEOM. In both sites, the SUVA254 values of the total AEOM were significantly higher than the DOM values (p < 0.01). The SUVA254 values of bulk solutions were 1.88 ± 0.14 and 0.95 ± 0.25 L mg-C−1 m−1 in site-1 and were 3.83 ± 0.15 and 1.80 ± 0.04 L mg-C−1 m−1 in site-2 for AEOM and DOM respectively. The average SUVA254 values of all solutions were 1.51 ± 0.56 and 0.88 ± 0.21 L mg-C−1 m−1 in site-1 and were 3.90 ± 3.71 and 1.62 ± 0.25 L mg-C−1 m−1 in site-2 for AEOM and DOM, respectively. The SUVA254 value suggested the sources of animal wastewater discharge were predominately comprised of hydrophilic substances with poor aromaticity in site-1 and median aromaticity in site-2 [62]. The SUVA254 values decreased with decreasing size-fractioned AEOM. The values (1.87 ± 0.18 L and 5.91 ± 4.22 L mg-C−1 m−1 for site-1 and site-2, respectively) of the HMW AEOM were significantly higher than the values of the LMW AEOM (0.78 ± 0.25 and 0.91 ± 0.39 L mg-C−1 m−1 for site-1 and site-2, respectively) (p < 0.001). However, the DOM SUVA254 values of the HMW and LMW (0.89 ± 0.21, and 0.81 ± 0.22 L mg-C−1 m−1, respectively, for site-1, 1.67 ± 0.28, and 1.44 ± 0.14 L mg-C−1 m−1 for site-2, respectively) were insignificantly different (p = 0.49, and p = 0.06 for site-1 and site-2, respectively).
FI values specify the relative contribution of terrestrial sources in the DOM and AEOM pools [34,43,44]. Figure 1c,d show that the FI values varied with the size-fractioned DOM and AEOM. In both sites, the FI values of AEOM were significantly lower than the values of DOM (p < 0.001). The FI values of AEOM and DOM increased with decreasing size-fractioned DOM and AEOM. In site-1, the HMW AEOM and DOM of the FI values (1.66 ± 0.09, 1.88 ± 0.06, respectively) were significantly lower than the LMW FI values (1.95 ± 0.04, 2.00 ± 0.06, respectively) (p < 0.001, p = 0.004, respectively). In site-2, the HMW AEOM and DOM FI values (1.58 ± 0.05 and 1.91 ± 0.04) were lower than the LMW FI values (1.85 ± 0.08 and 1.95 ± 0.04) (p < 0.001, p = 0.052, respectively). The FI values suggested that the AEOM had more terrestrial origin than DOM. The HMW DOM and AEOM had more terrestrial origin than the LMW DOM and AEOM.
BIX is an index used to assess the relative contribution of the autochthonous sources in the DOM and AEOM pools [34,43,44]. Figure 1e,f shows that the BIX varied with size-fractioned DOM and AEOM. In both sites, the BIX values of the total and bulk AEOM are significantly lower than the DOM (p < 0.001). The AEOM had less autochthonous source contribution than the DOM. In site-1, the BIX values (0.81 ± 0.02 and 1.04 ± 0.03) of the HMW AEOM and DOM were significantly lower than the BIX values (0.98 ± 0.07 and 1.09 ± 0.03) of the LMW AEOM and DOM, respectively (p = 0.002, and p = 0.017). In site-2, the HMW BIX values (0.74 ± 0.01, and 1.07 ± 0.03) of the AEOM and DOM were lower than the LMW BIX values (0.92 ± 0.07, and 1.10 ± 0.03) of the LMW AEOM and DOM (p = 0.002 and p = 0.071). The HMW AEOM contained median autochthonous sources, but the LMW AEOM had strong autochthonous sources. The average BIX values of the size-fractioned DOM ranged from 1.00 to 1.14, indicating a strong autochthonous source [35]. Thus, optical indices offer valuable information to identify the sources and characteristics of DOM and AEOM with different molecular weights.
The optical indices of DOM and AEON can vary in river water and sediment, depending on the sources and biochemical processes of the riverine system. According to previous research, the SUVA254 values for DOM in river water ranged from 1.9 to 3.7 L mg-C−1m−1 [63], while the SUVA254 values for sediment AEOM ranged from 1.4 to 10.7 L mg-C−1m−1 [8,28,32,64]. The BIX values in sediment pore water were found to range from 0.60 to 1.80 [65], while the BIX values of sediment AEOM were between 0.50 and 1.68 [8]. In one study, the AEOM FI value was 1.99 ± 0.41 and BIX was 0.84 ± 0.26 for 43 sediment samples, which were comparable to the present study [8].
Table 3 displays the correlation coefficients for selected indices of both DOM and AEOM at two studied sites. The AEOM indices are found to be significantly correlated with each other. The high aromatic AEOM has a high level of terrestrial and allochthonous sources. However, the selected indices for DOM do not significantly correlate with each other, except for the correlation of FI and BIX at site-1. The composition and structure of AEOM are more homogenized than DOM. The labile and low-molecular-weight particulate organic matter undergoes hydrolysis, and oxidation results in sediment. Hence, the extracted sediment-associated POM produces a stable and homogenized composition of AEOM [4,6]. However, the hydrolyzed and oxidative POM dissolves into the liquid DOM, which also receives complex sources, resulting in weak optical indicator correlation.

3.4. CuBA Ratios of DOM and AEOM

The study used the Cu binding affinity to organic matter (CuBA, μmol/g-C) to assess the Cu mobility and binding potential with DOM and AEOM [21,22,23,24,25]. Figure 2a,b shows that the CuBA ratios varied with the size-fractioned AEOM and DOM. The CuBA ratios were found to vary with size-fractioned AEOM and DOM. The CuBAAEOM ratios decreased with the decrease in size-fractioned AEOM, while the ratios of CuBADOM increased with the decrease in size-fractioned DOM.
In site-1, the total and bulk CuBA ratios were insignificantly different between the AEOM and DOM (p = 0.98 for total and p = 0.26 for bulk). However, in site-2, the total and bulk CuBAAEOM ratios were significantly higher than the CuBADOM (p = 0.02 for total and p = 0.03 for bulk). The CuBAAEOM ratios ranged from 17.0 to 149.5 μmol g-C−1 and ranged from 20.6 to 143.7 μmol g-C−1, having 9- and 7-fold variation for site-1 and site-2, respectively. On the other hand, the CuBADOM ratios ranged from 5.6 to 358.3 and ranged from 17.2 to 126.6 μmol g-C−1, having 64- and 7-fold variations for site-1 and site-2, respectively. The ratios of DOM were more varied than the ratios of AEOM in site-1, which is due to the complex composition and sources of the DOM from the feedlot wastewater discharge.
The CuBAAEOM ratios at site-1 and site-2 were 111.2 ± 10.2 and 108.7 ± 8.7 μmol g-C−1, respectively, which were higher than the CuBA ratios of the soil solution (ranging from 3.7 to 20.2 μmol g-C−1), water, and CaCl2 extracted soil organic matter (ranging from BLD to 34.0 μmol g-C−1), and NaOH extracted soil and sediment organic matter (ranging from 13.2 to 56.2 μmol g-C−1) [21,22,37]. The CuBADOM ratios showed a wide variation, as observed during in-field measurements. For site-1 and site-2, the bulk CuBADOM was recorded as 91.1 ± 22.7 μmol g-C−1 and 65.9 ± 34.7 μmol g-C−1, respectively, which was higher than the values observed in river water (ranging from 7.5 to 29.8 μmol g-C−1) [23,24,46]. However, it was also discovered that the high CuBADOM ratios in river water ranged from 6 to 4072 μmol g-C−1 [47]. In Yan et al.’s [47] study, only two out of 53 samples were higher than several thousand μmol g-C−1, with the average being 69.4 ± 68.7 μmol g-C−1 for the remaining 51 samples, which is similar to the CuBADOM ratios observed in the present study. Generally, the CuBA ratios of the DOM were more varied than for soil solutions, which could be due to the complicated source of surface water.
The CuBA ratios varied depending on the size-fractioned AEOM and DOM. In both sites, the CuBAAEOM ratios decreased, while the CuBADOM ratios increased as the molecular weight decreased (Figure 2a,b). Cu preferred to bind with the LMW DOM, but it bound more with the HMW AEOM. For site-1 and site-2, the HMW CuBAAEOM ratios were 138.1 ± 7.1 and 119.4 ± 22.0 μmol g-C−1, respectively, while the LMW CuBAAEOM ratios were 51.4 ± 27.6 and 57.0 ± 42.0 μmol g-C−1, respectively. Moreover, the HMW CuBADOM ratios were 50.3 ± 33.3 and 41.3 ± 19.7 μmol g-C−1, respectively, and the LMW CuBADOM ratios were 191.5 ± 88.5 and 105.9 ± 17.3 μmol g-C−1, respectively. The difference in the CuBA ratios was due to the fact that the DOM had a different composition.

3.5. Correlation between CuBA Ratios with Optical Indices

Table 4 lists the correlation between CuBA ratios and chosen indices for the size-fractioned DOM and AEOM. The CuBAAEOM ratios in both sites exhibited a significant correlation with the selected AEOM indices SUVA254, FI, and BIX. However, the CuBADOM ratios exhibited a weak to medium correlation with the selected DOM optical indices SUVA254, FI, and BIX. With the exception of site-1, the CuBADOM ratios demonstrated a significantly positive correlation with the selected DOM optical index FI. Overall, the correlation indicated that Cu-AEOM binding affinity was favored with a great extent of aromaticity, terrestrial and allochthonous sources of AEOM. On the other hand, the weak correlation of Cu-DOM binding affinity indicated the complex sources of DOM. In site-1, the Cu-DOM binding affinity exhibited a significantly positive correlation with the FI value, which indicated that the Cu binding affinity increased along with a great extent of microbial sources.
Previous studies have reported that the CuBA ratios had a significantly positive correlation with the aromaticity of natural water DOM [23,25] and with the aromaticity of soil solution [21,22,37]. However, when the DOM had complex sources, such as input by wastewater treatment plant effluent [25] and anthropogenic ligand [23], the CuBADOM ratios had a poor correlation with SUVA254. This correlation suggested the complex sources of the DOM in the present study. In a soil NaOH extracted study, the CuBAAEOM ratios of size-fractioned AEOM exhibited a significantly negative correlation with indicators FI and BIX [37]. Hence, high CuBA ratios followed high aromaticity, terrestrial and allochthonous sources in soil and sediment solutions.

3.6. The Exchange of DOM and POM Affect OC and Cu Distribution and Binding Affinity

Based on the exchange conceptual model proposed by Burdige and Komada [6] and He et al. [4], the hydrolysis and oxidation cleavage of sediment POM starts with the low molecular weight and labile POM. As a result of POM decomposition, some of it dissolves into the DOM, leading to the creation of low molecular weight DOM. The high molecular weight organic matter that remains in the sediment is the outcome of hydrolyzed and oxidative POM. Thus, the extracted AEOM consists mainly of high molecular weight organic matter. The mass fractions (>1 kDa) were discovered to be 76.0–76.5% for OC and 92.6–93.3% for Cu. This exchange process results in different chemical compositions and structures in the POM and DOM. The extracted AEOM has higher aromaticity, terrestrial, and allochthonous sources compared to the DOM.
The findings of the current study support the conceptual model. Additionally, the AEOM and DOM that were divided into different sizes had varying chemical characteristics. The HMW AEOM and LMW DOM showed high CuBA ratios. The HMW AEOM had higher aromaticity and originated from terrestrial and allochthonous sources compared to the LMW AEOM. The HMW AEOM also had higher Cu binding affinity compared to the LMW AEOM. The FI and BIX indices revealed that the LMW DOM had higher autochthonous and microbial sources than the HMW DOM. However, the LMW DOM had a higher CuBA ratio than the HMW DOM. This suggests that microbial and autochthonous DOM sources had a strong binding affinity to Cu. Furthermore, there was a positive correlation between DOM CuBA ratios and FI values (Table 3) at site 1. Previous research has demonstrated that Cu has a strong binding capacity to microbial DOM [66,67,68,69]. One limitation of this study is that it only focuses on copper, so further research is needed to understand how other heavy metals and organic pollutants bind to organic matter. Additionally, the study’s focus on low aromatic and terrestrial sources means there is still a need to explore how pollutants bind to organic matter from high aromatic and high terrestrial sources, such as soil.

4. Conclusions

The results conclusively support the exchange mechanism between sediment DOM and POM based on the distribution of Cu, binding affinity, and chemical properties of size-fractioned DOM and AEOM in sediment. HMW AEOM has higher aromaticity, terrestrial, and allochthonous sources than LMW AEOM, as confirmed by AEOM optical indices. While OC and Cu are mainly present in LMW DOM, the major fraction is found in HMW AEOM based on the exchange conceptual models of POM and DOM. Consequently, the binding affinity of Cu and LMW DOM is higher than HMW DOM, but Cu and HMW AEOM binding affinity is higher than LMW AEOM. Furthermore, the CuBAAEOM ratios are significantly correlated with selected optical indices, while CuBADOM has a weak association with the indices due to the stable composition of AEOM and the complex sources of the DOM. The significance of this study lies in its exploration of the distribution and binding of copper in interactions between sediment and water and how this changes with different molecular weight organic matter. This helps in understanding how different environmental contaminants bind at different molecular weights, informing future research on the binding of pollutants in different environmental media.

Author Contributions

Conceptualization, C.-Y.H. and T.-C.C.; methodology, M.-Y.H. and W.-H.H.; formal analysis and investigation, M.-Y.H., W.-H.H. and H.-C.T.; writing—original draft preparation, review and editing, C.-Y.H. and T.-C.C.; visualization, M.-Y.H., W.-H.H. and H.-C.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data is available upon reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest and PanCheng Engineering Consultants Co., Ltd. has no conflicts of interest regarding the study.

References

  1. Di Luca, G.A.; Maine, M.A.; Mufarrege, M.; Hadad, H.R.; Sánchez, G.; Bonetto, C.A. Metal retention and distribution in the sediment of a constructed wetland for industrial wastewater treatment. Ecol. Eng. 2011, 37, 1267–1275. [Google Scholar] [CrossRef]
  2. Suzuki, R.; Masuda, T.; Nakatani, H.; Harada, H. A survey of Zinc and Copper contents of wastewater on pig farms in Aichi prefecture. Res. Bull. Aichi Agric. Res. Cent. 2009, 40, 163–169. [Google Scholar]
  3. Suzuki, K.; Waki, M.; Yasuda, T.; Fukumoto, Y.; Kuroda, K.; Sakai, T.; Suzuki, N.; Suzuki, R.; Matsuba, K. Distribution of phosphorus, copper and zinc in activated sludge treatment process of swine wastewater. Bioresour. Technol. 2010, 101, 9399–9404. [Google Scholar] [CrossRef]
  4. He, W.; Chen, M.; Schlautman, M.A.; Hur, J. Dynamic exchanges between DOM and POM pools in coastal and inland aquatic ecosystems: A review. Sci. Total Environ. 2016, 551, 415–428. [Google Scholar] [CrossRef] [PubMed]
  5. Hu, B.; Wang, P.; Wang, C.; Qian, J.; Bao, T.; Shi, Y. Investigating spectroscopic and copper-binding characteristics of organic matter derived from sediments and suspended particles using EEM-PARAFAC combined with two-dimensional fluorescence/FTIR correlation analyses. Chemosphere 2019, 219, 45–53. [Google Scholar] [CrossRef] [PubMed]
  6. Burdige, D.J.; Komada, T. Sediment pore waters. In Biogeochemistry of Marine Dissolved Organic Matter; Elsevier: Amsterdam, The Netherlands, 2015; pp. 535–577. [Google Scholar]
  7. Chen, M.; Hur, J. Pre-treatments, characteristics, and biogeochemical dynamics of dissolved organic matter in sediments: A review. Water Res. 2015, 79, 10–25. [Google Scholar] [CrossRef]
  8. He, W.; Lee, J.-H.; Hur, J. Anthropogenic signature of sediment organic matter probed by UV–Visible and fluorescence spectroscopy and the association with heavy metal enrichment. Chemosphere 2016, 150, 184–193. [Google Scholar] [CrossRef]
  9. Aiken, G.R.; Hsu-Kim, H.; Ryan, J.N. Influence of dissolved organic matter on the environmental fate of metals, nanoparticles, and colloids. ACS Publ. 2011, 45, 3196–3201. [Google Scholar] [CrossRef] [PubMed]
  10. Ahmed, I.A.; Hamilton-Taylor, J.; Lofts, S.; Meeussen, J.C.; Lin, C.; Zhang, H.; Davison, W. Testing copper-speciation predictions in freshwaters over a wide range of metal–organic matter ratios. Environ. Sci. Technol. 2013, 47, 1487–1495. [Google Scholar] [CrossRef]
  11. Ahmed, I.A.; Hamilton-Taylor, J.; Bieroza, M.; Zhang, H.; Davison, W. Improving and testing geochemical speciation predictions of metal ions in natural waters. Water Res. 2014, 67, 276–291. [Google Scholar] [CrossRef]
  12. Shi, W.; Jin, Z.; Hu, S.; Fang, X.; Li, F. Dissolved organic matter affects the bioaccumulation of copper and lead in Chlorella pyrenoidosa: A case of long-term exposure. Chemosphere 2017, 174, 447–455. [Google Scholar] [CrossRef]
  13. Palansooriya, K.N.; Shaheen, S.M.; Chen, S.S.; Tsang, D.C.; Hashimoto, Y.; Hou, D.; Bolan, N.S.; Rinklebe, J.; Ok, Y.S. Soil amendments for immobilization of potentially toxic elements in contaminated soils: A critical review. Environ. Int. 2020, 134, 105046. [Google Scholar] [CrossRef]
  14. Burba, P.; Van den Bergh, J. Transformations of metal species in ageing humic hydrocolloids studied by competitive ligand and metal exchange. Anal. Bioanal. Chem. 2004, 378, 1637–1643. [Google Scholar] [CrossRef] [PubMed]
  15. Zheng, S.; Wang, P.; Wang, C.; Hou, J.; Qian, J. Distribution of metals in water and suspended particulate matter during the resuspension processes in Taihu Lake sediment, China. Quat. Int. 2013, 286, 94–102. [Google Scholar] [CrossRef]
  16. Pourabadehei, M.; Mulligan, C.N. Resuspension of sediment, a new approach for remediation of contaminated sediment. Environ. Pollut. 2016, 213, 63–75. [Google Scholar] [CrossRef]
  17. Feng, C.; Guo, X.; Yin, S.; Tian, C.; Li, Y.; Shen, Z. Heavy metal partitioning of suspended particulate matter-water and sediment-water in the Yangtze Estuary. Chemosphere 2017, 185, 717–725. [Google Scholar] [CrossRef] [PubMed]
  18. Mueller, K.K.; Lofts, S.; Fortin, C.; Campbell, P.G. Trace metal speciation predictions in natural aquatic systems: Incorporation of dissolved organic matter (DOM) spectroscopic quality. Environ. Chem. 2012, 9, 356–368. [Google Scholar] [CrossRef]
  19. Chen, W.; Guéguen, C.L.; Smith, D.S.; Galceran, J.; Puy, J.; Companys, E. Metal (Pb, Cd, and Zn) binding to diverse organic matter samples and implications for speciation modeling. Environ. Sci. Technol. 2018, 52, 4163–4172. [Google Scholar] [CrossRef] [PubMed]
  20. Oleinikova, O.V.; Shirokova, L.S.; Drozdova, O.Y.; Lapitskiy, S.A.; Pokrovsky, O.S. Low biodegradability of dissolved organic matter and trace metals from subarctic waters. Sci. Total Environ. 2018, 618, 174–187. [Google Scholar] [CrossRef]
  21. Amery, F.; Degryse, F.; Degeling, W.; Smolders, E.; Merckx, R. The copper-mobilizing-potential of dissolved organic matter in soils varies 10-fold depending on soil incubation and extraction procedures. Environ. Sci. Technol. 2007, 41, 2277–2281. [Google Scholar] [CrossRef]
  22. Amery, F.; Degryse, F.; Cheyns, K.; De Troyer, I.; Mertens, J.; Merckx, R.; Smolders, E. The UV-absorbance of dissolved organic matter predicts the fivefold variation in its affinity for mobilizing Cu in an agricultural soil horizon. Eur. J. Soil Sci. 2008, 59, 1087–1095. [Google Scholar] [CrossRef]
  23. Baken, S.; Degryse, F.; Verheyen, L.; Merckx, R.; Smolders, E. Metal complexation properties of freshwater dissolved organic matter are explained by its aromaticity and by anthropogenic ligands. Environ. Sci. Technol. 2011, 45, 2584–2590. [Google Scholar] [CrossRef] [PubMed]
  24. Chon, K.; Chon, K.; Cho, J. Characterization of size fractionated dissolved organic matter from river water and wastewater effluent using preparative high performance size exclusion chromatography. Org. Geochem. 2017, 103, 105–112. [Google Scholar] [CrossRef]
  25. Kikuchi, T.; Fujii, M.; Terao, K.; Jiwei, R.; Lee, Y.P.; Yoshimura, C. Correlations between aromaticity of dissolved organic matter and trace metal concentrations in natural and effluent waters: A case study in the Sagami River Basin, Japan. Sci. Total Environ. 2017, 576, 36–45. [Google Scholar] [CrossRef] [PubMed]
  26. Yi, Y.; Xiao, M.; Mostofa, K.M.; Xu, S.; Wang, Z. Spatial Variations of Trace Metals and Their Complexation Behavior with DOM in the Water of Dianchi Lake, China. Int. J. Environ. Res. Public Health 2019, 16, 4919. [Google Scholar] [CrossRef] [PubMed]
  27. Hur, J.; Lee, D.-H.; Shin, H.-S. Comparison of the structural, spectroscopic and phenanthrene binding characteristics of humic acids from soils and lake sediments. Org. Geochem. 2009, 40, 1091–1099. [Google Scholar] [CrossRef]
  28. Hur, J.; Lee, B.-M.; Shin, K.-H. Spectroscopic characterization of dissolved organic matter isolates from sediments and the association with phenanthrene binding affinity. Chemosphere 2014, 111, 450–457. [Google Scholar] [CrossRef] [PubMed]
  29. Li, P.; Hur, J. Utilization of UV-Vis spectroscopy and related data analyses for dissolved organic matter (DOM) studies: A review. Crit. Rev. Environ. Sci. Technol. 2017, 47, 131–154. [Google Scholar] [CrossRef]
  30. Chiu, T.-P.; Huang, W.-S.; Chen, T.-C.; Yeh, Y.-L. Fluorescence characteristics of dissolved organic matter (DOM) in percolation water and lateral seepage affected by soil solution (SS) in a lysimeter test. Sensors 2019, 19, 4016. [Google Scholar] [CrossRef]
  31. Hsieh, S.-H.; Chiu, T.-P.; Huang, W.-S.; Chen, T.-C.; Yeh, Y.-L. Cadmium (Cd) and Nickel (Ni) Distribution on Size-Fractioned Soil Humic Substance (SHS). Int. J. Environ. Res. Public Health 2019, 16, 3398. [Google Scholar] [CrossRef]
  32. Shi, M.-S.; Huang, W.-S.; Hsu, L.-F.; Yeh, Y.-L.; Chen, T.-C. Fluorescence of Size-Fractioned Humic Substance Extracted from Sediment and Its Effect on the Sorption of Phenanthrene. Int. J. Environ. Res. Public Health 2019, 16, 5087. [Google Scholar] [CrossRef] [PubMed]
  33. Weishaar, J.L.; Aiken, G.R.; Bergamaschi, B.A.; Fram, M.S.; Fujii, R.; Mopper, K. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environ. Sci. Technol. 2003, 37, 4702–4708. [Google Scholar] [CrossRef] [PubMed]
  34. Birdwell, J.E.; Engel, A.S. Characterization of dissolved organic matter in cave and spring waters using UV–Vis absorbance and fluorescence spectroscopy. Org. Geochem. 2010, 41, 270–280. [Google Scholar] [CrossRef]
  35. Huguet, A.; Vacher, L.; Relexans, S.; Saubusse, S.; Froidefond, J.-M.; Parlanti, E. Properties of fluorescent dissolved organic matter in the Gironde Estuary. Org. Geochem. 2009, 40, 706–719. [Google Scholar] [CrossRef]
  36. Liu, R.; Lead, J.R.; Baker, A. Fluorescence characterization of cross flow ultrafiltration derived freshwater colloidal and dissolved organic matter. Chemosphere 2007, 68, 1304–1311. [Google Scholar] [CrossRef] [PubMed]
  37. Huang, W.-H.; Lin, T.-C.; Huang, C.-M.; Chen, T.-C.; Yeh, Y.-L. Copper distribution and binding affinity of size-fractioned humic substances taken from paddy soil and correlation with optical characteristics. Agronomy 2022, 12, 1689. [Google Scholar] [CrossRef]
  38. Li, R.; Yue, D.; Liu, J.; Nie, Y. Size fractionation of organic matter and heavy metals in raw and treated leachate. Waste Manag. 2009, 29, 2527–2533. [Google Scholar] [CrossRef] [PubMed]
  39. Hargreaves, A.J.; Vale, P.; Whelan, J.; Constantino, C.; Dotro, G.; Campo, P.; Cartmell, E. Distribution of trace metals (Cu, Pb, Ni, Zn) between particulate, colloidal and truly dissolved fractions in wastewater treatment. Chemosphere 2017, 175, 239–246. [Google Scholar] [CrossRef]
  40. Chuang, C.-W.; Hsu, L.-F.; Tsai, H.-C.; Liu, Y.-Y.; Huang, W.-S.; Chen, T.-C. Nickel Binding Affinity with Size-Fractioned Sediment Dissolved and Particulate Organic Matter and Correlation with Optical Indicators. Appl. Sci. 2020, 10, 8995. [Google Scholar] [CrossRef]
  41. Vasyukova, E.; Pokrovsky, O.S.; Viers, J.; Dupré, B. New operational method of testing colloid complexation with metals in natural waters. Appl. Geochem. 2012, 27, 1226–1237. [Google Scholar] [CrossRef]
  42. Helms, J.R.; Stubbins, A.; Ritchie, J.D.; Minor, E.C.; Kieber, D.J.; Mopper, K. Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. Limnol. Oceanog. 2008, 53, 955–969. [Google Scholar] [CrossRef]
  43. Hansen, A.M.; Kraus, T.E.; Pellerin, B.A.; Fleck, J.A.; Downing, B.D.; Bergamaschi, B.A. Optical properties of dissolved organic matter (DOM): Effects of biological and photolytic degradation. Limnol. Oceanog. 2016, 61, 1015–1032. [Google Scholar] [CrossRef]
  44. Derrien, M.; Yang, L.; Hur, J. Lipid biomarkers and spectroscopic indices for identifying organic matter sources in aquatic environments: A review. Water Res. 2017, 112, 58–71. [Google Scholar] [CrossRef] [PubMed]
  45. Dabrin, A.; Roulier, J.-L.; Coquery, M. Colloidal and truly dissolved metal (oid) fractionation in sediment pore waters using tangential flow filtration. Appl. Geochem. 2013, 31, 25–34. [Google Scholar] [CrossRef]
  46. Park, J.H.; Lee, J.H.; Kang, S.Y.; Kim, S.Y. Hydroclimatic controls on dissolved organic matter (DOM) characteristics and implications for trace metal transport in Hwangryong River Watershed, Korea, during a summer monsoon period. Hydrol. Process. Int. J. 2007, 21, 3025–3034. [Google Scholar] [CrossRef]
  47. Yan, M.; Ma, J.; Zhang, C.; Zhou, Y.; Liu, F.; Han, X.; Li, M.; Ni, J. Optical property of dissolved organic matters (DOMs) and its link to the presence of metal ions in surface freshwaters in China. Chemosphere 2017, 188, 502–509. [Google Scholar] [CrossRef]
  48. Duc, T.A.; Loi, V.D.; Thao, T.T. Partition of heavy metals in a tropical river system impacted by municipal waste. Environ. Monit. Assess. 2013, 185, 1907–1925. [Google Scholar] [CrossRef]
  49. Yu, Y.; Wan, Y.; Camara, A.Y.; Li, H. Effects of the addition and aging of humic acid-based amendments on the solubility of Cd in soil solution and its accumulation in rice. Chemosphere 2018, 196, 303–310. [Google Scholar] [CrossRef]
  50. Zhang, S.; Chen, X.-W.; Ye, Q.; Zhang, Z.-T.; Kong, S.-F.; Cao, C.; Wang, J.-J. Dissolved metal (loid) concentrations and their relations with chromophoric and fluorescent dissolved organic matter in an urban river in Shenzhen, South China. Water 2020, 12, 281. [Google Scholar] [CrossRef]
  51. Hou, D.; He, J.; Lü, C.; Ren, L.; Fan, Q.; Wang, J.; Xie, Z. Distribution characteristics and potential ecological risk assessment of heavy metals (Cu, Pb, Zn, Cd) in water and sediments from Lake Dalinouer, China. Ecotoxicol. Environ. Saf. 2013, 93, 135–144. [Google Scholar] [CrossRef]
  52. Ilina, S.M.; Lapitskiy, S.A.; Alekhin, Y.V.; Viers, J.; Benedetti, M.; Pokrovsky, O.S. Speciation, size fractionation and transport of trace elements in the continuum soil water-mire-humic lake-river-large oligotrophic lake of a Subarctic watershed. Aquat. Geochem. 2016, 22, 65–95. [Google Scholar] [CrossRef]
  53. Ren, Z.-L.; Tella, M.; Bravin, M.N.; Comans, R.N.; Dai, J.; Garnier, J.-M.; Sivry, Y.; Doelsch, E.; Straathof, A.; Benedetti, M.F. Effect of dissolved organic matter composition on metal speciation in soil solutions. Chem. Geol. 2015, 398, 61–69. [Google Scholar] [CrossRef]
  54. Jain, C. Metal fractionation study on bed sediments of River Yamuna, India. Water Res. 2004, 38, 569–578. [Google Scholar] [CrossRef] [PubMed]
  55. Bartoli, G.; Papa, S.; Sagnella, E.; Fioretto, A. Heavy metal content in sediments along the Calore river: Relationships with physical–chemical characteristics. J. Environ. Manag. 2012, 95, S9–S14. [Google Scholar] [CrossRef] [PubMed]
  56. Wang, P.; Hu, B.; Wang, C.; Lei, Y. Phosphorus adsorption and sedimentation by suspended sediments from Zhushan Bay, Taihu Lake. Environ. Sci. Pollut. Res. 2015, 22, 6559–6569. [Google Scholar] [CrossRef]
  57. Hill, J.R.; O’Driscoll, N.J.; Lean, D.R. Size distribution of methylmercury associated with particulate and dissolved organic matter in freshwaters. Sci. Total Environ. 2009, 408, 408–414. [Google Scholar] [CrossRef]
  58. Jarvie, H.; Neal, C.; Rowland, A.; Neal, M.; Morris, P.; Lead, J.; Lawlor, A.; Woods, C.; Vincent, C.; Guyatt, H. Role of riverine colloids in macronutrient and metal partitioning and transport, along an upland–lowland land-use continuum, under low-flow conditions. Sci. Total Environ. 2012, 434, 171–185. [Google Scholar] [CrossRef] [PubMed]
  59. Xu, H.; Guo, L. Intriguing changes in molecular size and composition of dissolved organic matter induced by microbial degradation and self-assembly. Water Res. 2018, 135, 187–194. [Google Scholar] [CrossRef] [PubMed]
  60. Yeh, Y.-L.; Yeh, K.-J.; Hsu, L.-F.; Yu, W.-C.; Lee, M.-H.; Chen, T.-C. Use of fluorescence quenching method to measure sorption constants of phenolic xenoestrogens onto humic fractions from sediment. J. Hazard. Mater. 2014, 277, 27–33. [Google Scholar] [CrossRef]
  61. Hur, J.; Park, M.-H.; Schlautman, M.A. Microbial transformation of dissolved leaf litter organic matter and its effects on selected organic matter operational descriptors. Environ. Sci. Technol. 2009, 43, 2315–2321. [Google Scholar] [CrossRef]
  62. Matilainen, A.; Gjessing, E.T.; Lahtinen, T.; Hed, L.; Bhatnagar, A.; Sillanpää, M. An overview of the methods used in the characterisation of natural organic matter (NOM) in relation to drinking water treatment. Chemosphere 2011, 83, 1431–1442. [Google Scholar] [CrossRef] [PubMed]
  63. Yang, Z.; Xia, X.; Wang, Y.; Ji, J.; Wang, D.; Hou, Q.; Yu, T. Dissolved and particulate partitioning of trace elements and their spatial-temporal distribution in the Changjiang River. J. Geochem. Explor. 2014, 145, 114–123. [Google Scholar] [CrossRef]
  64. Hur, J.; Kim, G. Comparison of the heterogeneity within bulk sediment humic substances from a stream and reservoir via selected operational descriptors. Chemosphere 2009, 75, 483–490. [Google Scholar] [CrossRef] [PubMed]
  65. Jiang, T.; Bravo, A.G.; Skyllberg, U.; Björn, E.; Wang, D.; Yan, H.; Green, N.W. Influence of dissolved organic matter (DOM) characteristics on dissolved mercury (Hg) species composition in sediment porewater of lakes from southwest China. Water Res. 2018, 146, 146–158. [Google Scholar] [CrossRef] [PubMed]
  66. Hur, J.; Lee, B.-M. Characterization of copper binding properties of extracellular polymeric substances using a fluorescence quenching approach combining two-dimensional correlation spectroscopy. J. Mol. Struct. 2014, 1069, 79–84. [Google Scholar] [CrossRef]
  67. Hur, J.; Lee, B.-M. Characterization of binding site heterogeneity for copper within dissolved organic matter fractions using two-dimensional correlation fluorescence spectroscopy. Chemosphere 2011, 83, 1603–1611. [Google Scholar] [CrossRef] [PubMed]
  68. Zhang, F.; Li, Y.; Xiong, X.; Yang, M.; Li, W. Effect of composting on dissolved organic matter in animal manure and its binding with Cu. Sci. World J. 2012, 289896. [Google Scholar] [CrossRef]
  69. Xu, J.; Luo, H.-W.; Wang, Y.-K.; Sheng, G.-P. Fluorescence approach for investigating binding properties between metals and soluble microbial products from a biological wastewater treatment plant. Process Biochem. 2015, 50, 636–642. [Google Scholar] [CrossRef]
Figure 1. Optical indicators of size-fractioned DOM/AEOM (a,b) SUVA254, (c,d) FI, and (e,f) BIX.
Figure 1. Optical indicators of size-fractioned DOM/AEOM (a,b) SUVA254, (c,d) FI, and (e,f) BIX.
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Figure 2. CuBA ratios of size-fractioned DOM and AEOM (a) site-1, (b) site-2.
Figure 2. CuBA ratios of size-fractioned DOM and AEOM (a) site-1, (b) site-2.
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Table 1. The measured DOC and Cu concentrations of bulk and size-fractioned DOM and AEOM.
Table 1. The measured DOC and Cu concentrations of bulk and size-fractioned DOM and AEOM.
FractionDOCCu
DOM (mg/L)AEOM (mg/L)DOM (μg/L)AEOM (mg/L)
Site-1
Bulk9.1 ± 1.6328 ± 3251.2 ± 6.82.30 ± 0.05
MW-A23.1 ± 4.21848 ± 6418.4 ± 7.517.00 ± 1.00
MW-B9.1 ± 2.0416 ± 6832.1 ± 11.03.55 ± 0.64
MW-C12.0 ± 1.4341 ± 5662.2 ± 20.32.93 ± 0.39
MW-D7.0 ± 1.1164 ± 5470.0 ± 10.90.73 ± 0.14
MW-E5.5 ± 1.1103 ± 5668.1 ± 15.30.17 ± 0.02
Site-2
Bulk5.6 ± 0.2156 ± 1221.0 ± 6.11.07 ± 0.01
MW-A16.1 ± 1.9976 ± 5821.0 ± 3.68.30 ± 0.10
MW-B5.8 ± 0.7176 ± 3917.8 ± 5.31.38 ± 0.24
MW-C5.9 ± 0.3175 ± 2120.0 ± 3.51.08 ± 0.17
MW-D6.0 ± 0.569 ± 1243.2 ± 9.60.41 ± 0.22
MW-E3.9 ± 0.553 ± 624.1 ± 2.70.08 ± 0.02
Volume ratio: MW-A (0.1), MW-B (0.09), MW-C (0.081), MW-D (0.0729), MW-E (0.6561).
Table 2. The mass percentages of DOC and Cu for size-fractioned DOM and AEOM.
Table 2. The mass percentages of DOC and Cu for size-fractioned DOM and AEOM.
FractionDOCCu
DOM (mg/L)AEOM (mg/L)DOM (μg/L)AEOM (mg/L)
Site-1
MW-A28.9 ± 8.756.5 ± 5.63.3 ± 1.770.3 ± 3.7
MW-B10.1 ± 1.911.5 ± 2.64.8 ± 1.113.2 ± 2.5
MW-C11.8 ± 0.98.5 ± 1.78.9 ± 4.19.8 ± 1.3
MW-D6.2 ± 0.73.6 ± 1.38.6 ± 0.12.2 ± 0.4
MW-E43.1 ± 9.219.9 ± 8.574.5 ± 5.14.5 ± 0.4
Site-2
MW-A28.8 ± 4.158.2 ± 2.18.7 ± 1.373.8 ± 1.6
MW-B9.4 ± 0.79.4 ± 1.86.5 ± 1.111.1 ± 1.8
MW-C8.6 ± 0.58.4 ± 0.86.7 ± 0.77.8 ± 1.1
MW-D7.8 ± 0.23.0 ± 0.412.9 ± 2.02.7 ± 1.4
MW-E45.5 ± 4.421.0 ± 2.865.2 ± 1.44.7 ± 1.1
Table 3. Correlation coefficients of optical indices for size-fractioned DOM and AEOM.
Table 3. Correlation coefficients of optical indices for size-fractioned DOM and AEOM.
SUVA254FIBIX
Site-1
SUVA254 −0.92 ***−0.92 ***
FI−0.05 0.88 ***
BIX−0.440.58 *
Site-2
SUVA254 −0.71 **−0.57 *
FI−0.29 0.89 ***
BIX−0.370.24
Note: right upper triangle represents AEOM coefficients of three selected indices. Left lower triangle represents DOM coefficients of three selected indices. *** p < 0.001, ** p < 0.01, * p < 0.05.
Table 4. Correlation coefficients of optical indices with (Cu)/(DOC) ratios for DOM and AEOM.
Table 4. Correlation coefficients of optical indices with (Cu)/(DOC) ratios for DOM and AEOM.
SUVA254FIBIX
Site-1Site2Site-1Site2Site-1Site2
CuBAAEOM0.94 ***0.63 *−0.87 ***−0.82 ***−0.96 ***−0.87 ***
CuBADOM0.26−0.320.70 **0.530.410.39
*** p < 0.001, ** p < 0.01, * p < 0.05
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Hung, M.-Y.; Huang, W.-H.; Tsai, H.-C.; Hsieh, C.-Y.; Chen, T.-C. Copper Distribution and Binding Affinity to Size-Fractioned Dissolved and Particulate Organic Matter in River Sediment. Environments 2024, 11, 129. https://doi.org/10.3390/environments11060129

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Hung M-Y, Huang W-H, Tsai H-C, Hsieh C-Y, Chen T-C. Copper Distribution and Binding Affinity to Size-Fractioned Dissolved and Particulate Organic Matter in River Sediment. Environments. 2024; 11(6):129. https://doi.org/10.3390/environments11060129

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Hung, Ming-Yuan, Wei-Hsiang Huang, Hsiang-Chun Tsai, Chi-Ying Hsieh, and Ting-Chien Chen. 2024. "Copper Distribution and Binding Affinity to Size-Fractioned Dissolved and Particulate Organic Matter in River Sediment" Environments 11, no. 6: 129. https://doi.org/10.3390/environments11060129

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