Characterizing Properties and Environmental Behaviors of Dissolved

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Characterizing Properties and Environmental Behaviors of Dissolved Organic Matter Using Two-Dimensional Correlation Spectroscopic Analysis Wei Chen, Chun-Ying Teng, Chen Qian, and Han-Qing Yu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b01103 • Publication Date (Web): 18 Apr 2019 Downloaded from http://pubs.acs.org on April 18, 2019

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Environmental Science & Technology

Characterizing Properties and Environmental Behaviors of Dissolved Organic Matter Using Two-Dimensional Correlation Spectroscopic Analysis

Wei Chena, b, Chun-Ying Tenga, Chen Qianb, Han-Qing Yub, *

aSchool

of Metallurgy and Environment, Central South University, Changsha 410083, China

bCAS

Key Laboratory of Urban Pollutant Conversion, Department of Applied Chemistry, University of Science and Technology of China, Hefei 230026, China

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ABSTRACT: Dissolved organic matter (DOM) exists ubiquitously in environments

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and plays critical roles in pollutant mitigation, transformation and organic

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geochemical cycling. Understanding its properties and environmental behaviors is

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critically important to develop water treatment processes and environmental

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remediation strategies. Generalized two-dimensional correlation spectroscopy

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(2DCOS), which has numerous advantages including enhancing spectral resolution

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and discerning specific order of structural change under an external perturbation,

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could be used as a powerful tool to interpret a wide range of spectroscopic signatures

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relating to DOM. A suite of spectroscopic signatures, such as UV-Vis, fluorescence,

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infrared, and Raman spectra that can be analyzed by 2DCOS, is able to provide

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additional structural information hiding behind the conventional one-dimensional

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spectra. In this paper, the most recent advances in 2DCOS applications for analyzing

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DOM-related environmental processes are reviewed, and the state-of-the-art novel

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spectroscopic techniques in 2DCOS are highlighted. Furthermore, the main

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limitations and requirements of current approaches for exploring DOM-related

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environmental processes and how these limitations and drawbacks can be addressed

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are explored. Finally, suggestions and new approaches are proposed to significantly

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advance the development of 2DCOS in analyzing the properties and behaviors of

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DOM in natural and engineered environments.


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1. INTRODUCTION

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Dissolved organic matter (DOM) is the active organic species and plays the most vital

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roles in environmental and biological processes.1 DOM contains various chemical

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structures, including carboxyl, phenol, quinonyl, aldehyde, ester, ketone, hydroxyl,

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amino, glycosyl and other functional groups. Thus, DOM takes part in numerous

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processes such as electron transfer reactions, adsorption, chelation, and microbial

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metabolisms.2 Interactions of DOM with exogenous nanoparticles, heavy metals, and

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organic pollutants in environments can affect the distribution, migration, toxicity,

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bioavailability, and environmental fates of these species.3 Therefore, understanding

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the environmental processes of DOM is critically important for the development of

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water treatment processes and remediation strategies.4, 5 So far, a variety of techniques

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have been developed to interpret DOM behaviors, and spectroscopic method is one of

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the most efficient tools owing to its unique advantages such as chemical identification

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and high sensitivity. The applicability of spectroscopic method has been greatly

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improved with the integration of two-dimensional correlation analysis.

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First proposed by Noda in 1986, two-dimensional correlation spectroscopy

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(2DCOS) has become a routine analytical technique in spectroscopic studies. In

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2DCOS, subtle key information can be sorted out from systematic variations of

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spectral signals observed with a variety probes under various forms of external

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perturbations.6,

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fluorescence, Fourier-transformed infrared (FTIR), Raman, nuclear magnetic

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A wide range of spectroscopic signatures, such as UV-Vis,



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resonance and X-ray spectra, can be analyzed by 2DCOS. Such an approach can

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provide additional structural change information hiding behind the conventional

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one-dimensional spectra. 2DCOS technique has considerable advantages, including

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enhancing spectral resolution by extending spectra along the second dimension and

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discerning relative directions and specific orders of structural variations. There are

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various types of perturbations available to induce spectral variation for 2DCOS

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analysis, e.g., mechanical, electrical, thermal, chemical, and biological stimulations.

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The application of this versatile 2DCOS technique has been successfully expanded to

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the environmental field since 2009,8 especially in physiochemical and biological

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processes relating to DOM species. Furthermore, new improvements of 2DCOS

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analyses have been carried out in order to specifically deal with complex

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environmental samples.

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Since there is an increasing trend in the area of the exploration of environmental

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processes with the aid of 2DCOS techniques, it is therefore necessary to critically

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summarize and appraise the development of 2DCOS techniques in environmental

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applications. There are some reviews about the applications and some recent

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developments of 2DCOS techniques for various fields.9-11 Also, the advantages of the

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2DCOS approaches used for DOM study have been summarized.12, 13 However, all of

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the existing reviews about DOM characterization or 2DCOS applications fail to

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provide a whole picture of DOM properties/environmental behaviors. As a result,

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evaluation on the applications of 2DCOS techniques in characterizing DOM

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properties and environmental behaviors from various processes is still missing. 4 ACS Paragon Plus Environment

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In this paper, we summarize the state-of-the-art novel spectroscopic techniques in

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2DCOS, and review the most recent advances of 2DCOS applications in the studies of

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environmental behaviors of DOM. We discuss the main limitations and requirements

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of current analytical approaches for the exploration of environmental/microbial

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processes and how these limitations and drawbacks can be addressed, and make

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suggestions where new approaches are needed to significantly advance the

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development of 2DCOS in analyzing DOM-related samples in environments.

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2. 2DCOS APPROACH

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2.1 Generalized 2DCOS. The background and the computational schemes of

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generalized 2DCOS have been previously detailed elsewhere.9,

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summary of the pertinent procedure is provided (Figure 1).

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Here a brief

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As shown in Figure 1, in 2DCOS analysis, the sequentially sampled spectra of the

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system Y(xi, pk) are obtained as a function of a spectral variable detected by an

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electro-magnetic probe (x, representing wavelength, wavenumber, frequency, and so

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on) and an external perturbation variable (p, representing time, temperature, etc.).

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Specifically, the spectral data are evenly spaced during the interval between p1 and pm.

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A set of dynamic spectra 𝑌(xi, pk) is defined as:

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𝑌(𝑥𝑖, 𝑝𝑘) =

for 1 ≤ 𝑘 ≤ 𝑚 {𝑌(𝑥 , 𝑝0) - 𝑌(𝑥 )otherwise 𝑖

𝑘

𝑖

(1)

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where 𝑌(𝑥𝑖) denotes to the reference spectrum, typically the m-averaged spectrum

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without a prior knowledge of the reference state. Thus, the portion of the dynamic 5 ACS Paragon Plus Environment


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spectra within the observation interval is essentially equivalent to the mean-centered

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spectra.

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1

𝑚

𝑌(𝑥𝑖) = 𝑚∑𝑘

𝑌(𝑥𝑖, 𝑝𝑘)

(2)

=1

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A pair of data matrixes regarding correlation intensities, i.e., synchronous (Ф)

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and asynchronous (Ψ) correlation spectra, is generated through the discrete

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Hilbert-Noda transform: 1

𝑚

1

𝑚

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Ф(𝑥1,𝑥2) = 𝑚 ― 1∑𝑘 = 1𝑌(𝑥1, 𝑝𝑘) ∙ 𝑌(𝑥2, 𝑝𝑘)

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Ψ(𝑥1,𝑥2) = 𝑚 ― 1∑𝑘 = 1𝑌(𝑥1, 𝑝𝑘) ∙ ∑𝑘 = 1N𝑖𝑘𝑌(𝑥2, 𝑝𝑘)

94 95

𝑚

(3) (4)

where the term Nik is the Hilbert-Noda transformation matrix element, given by N𝑖𝑘 =

{

0 1 π(𝑘 ― 𝑖)

if 𝑖 = 𝑘 otherwise

(5)

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The synchronous and asynchronous 2DCOS maps are generated from the data

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matrixes, and the spectral coordinates, intensities and signs of correlation peaks in the

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2DCOS maps can be interpreted by a set of well-established principles.14

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Synchronous spectra, corresponding to the real part of the cross-correlation function,

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represent the directionality of spectral intensity changes of two spectral variables, x1

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and x2, along the perturbation variable pk. Synchronous 2DCOS map consists of

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auto-peaks located along the main diagonal of the map and cross-peaks symmetrically

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located at the off-diagonal positions. In the synchronous map, auto-peaks are

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non-negative, and their values are related to the susceptibility of spectral intensity

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changes. Cross-peak indicates the coordinated changes of spectral intensities observed

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at spectral coordinates x1 and x2. The sign of the cross-peak becomes positive if the

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spectral intensities measured at x1 and x2 change in the same direction, i.e., either 6 ACS Paragon Plus Environment

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increasing or decreasing. In reverse, if the direction is opposite, the sign of Ф(𝑥1,𝑥2)

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becomes negative. Asynchronous spectra originate from the imaginary part of the

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cross-correlation function with only cross-peaks, and represent the out-of-phase or

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sequential changes of spectral intensities induced by the perturbation. If Ψ(𝑥1,𝑥2) =

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0, the variations of spectral intensities at x1 and x2 are completely synchronized. If the

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signs of Ф(𝑥1,𝑥2) and Ψ(𝑥1,𝑥2) are the same, the overall spectral intensity change

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at x1 occurs predominantly prior to that at x2 along the perturbation variable axis. This

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order is reversed when signs are different. In addition, if Ф(𝑥1,𝑥2) = 0, the sequential

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order of intensity variations becomes indeterminate. It should be noted that 2DCOS

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map only gives the sequential order of spectral intensity variations, but not the order

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of the distributed presence of species contributing to the spectral signals.

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To better demonstrate the basic properties of 2DCOS method, a set of model

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spectra were used for 2DCOS analysis. Figure 2a shows the simulated IR spectra

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evolving under the influence of an arbitrary external perturbation, e.g., contact time

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during a chemical reaction process. The absorption intensities at peaks 1650, 1625,

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1560, and 1420 cm-1 were sensitive to the perturbation variable (time), and their

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values changing with time are shown in Figure 2b. Specifically, the peaks at 1650 and

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1625 cm-1 were too close to be distinguished from the conventional one-dimensional

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spectra. Three stages were divided along the time variable: the intensity of peak 1420

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cm-1 decreased firstly, then the intensities of peaks 1650 and 1560 cm-1 increased

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simultaneously, followed by an intensity increase at peak 1625 cm-1 at the later stage.

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Figures 2c and d show the typical synchronous and asynchronous 2DCOS maps 7 ACS Paragon Plus Environment


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generated from the simulated IR spectra, respectively. The negative sign at the

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cross-peak (1560, 1420 cm-1) in the synchronous map (Figure 2c) suggested opposite

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intensity change of these two peaks, and peaks 1650/1625 and 1560 cm-1 changed in

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the same direction judged by the positive signs at the corresponding cross-peaks. The

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peaks at 1650 and 1625 cm-1 can be clearly divided in the asynchronous map,

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suggesting an enhanced spectral resolution by 2DCOS. Intensity variations at peaks

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1650 and 1560 cm-1 occurred simultaneously as there was no cross-peak. Judged by

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the signs of cross-peaks in the 2DCOS maps, the sequential order of intensity change

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was 1420 → (1650, 1560) → 1625 cm-1. These results are consistent with that in

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Figure 2b, demonstrating the capability of 2DCOS method for the identification of

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subtle spectral intensity variations. These features in 2DCOS analysis make it an

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elegant tool for the interpretation of complicated environmental processes, in which

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the spectral information is substantially interwoven.

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Even if generalized 2DCOS technique can give enhanced spectral resolution and

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the sequential order of peak intensity change, some key information is untapped yet

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on the correlation between the external perturbations and different structural

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variations in environmental samples. Therefore, some derivative tools based on

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2DCOS concepts have been developed to further broaden the utility of 2DCOS

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analysis.10

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2.2 Hetero-2DCOS. In generalized 2DCOS studies, the intensity variations of

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two peaks from a single spectral probe in a specific system are examined to verify the

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presence of simultaneous or sequential changes along an external perturbation. There 8 ACS Paragon Plus Environment

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are various kinds of probes and perturbations reflecting specific aspects of the system.

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If there is an underlying relationship between the responses or perturbation patterns, it

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can also be compared and revealed by a derived 2D correlation analysis, which is also

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called hetero-2DCOS. Hetero-2DCOS provides complementary information about the

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correlation on the structural responses of the system to the perturbations.15, 16 There

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are three types of hetero-2DCOS methods, i.e., hetero-perturbation, hetero-sample,

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and hetero-spectral correlation analyses. The last one is the most popularly used form.

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Hetero-perturbation 2DCOS compares the spectral responses of the system under

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different external stimulations, such as time versus temperature, verifying similarity

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and difference of such external effects on the system. Hetero-sample 2DCOS explores

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the correlation between samples with different treatments using identical spectral

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probe and perturbation, and uncovers the effect of treatment that cause sample

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difference. Hetero-spectral 2DCOS analysis integrates spectral signals from two

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different types of probes in a system under the same external perturbation.17, 18 Two

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sets of dynamic spectra 𝐴(xi, pk) and 𝐵(yi, pk) are compared along the perturbation

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variable p to generate hetero-spectral 2DCOS maps, given by 1

𝑚

1

𝑚

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Ф(𝑥1,𝑦2) = 𝑚 ― 1∑𝑘 = 1𝐴(𝑥1, 𝑝𝑘) ∙ 𝐵(𝑦2, 𝑝𝑘)

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Ψ(𝑥1,𝑦2) = 𝑚 ― 1∑𝑘 = 1𝐴(𝑥1, 𝑝𝑘) ∙ ∑𝑘 = 1N𝑖𝑘𝐵(𝑦2, 𝑝𝑘)

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𝑚

(6) (7)

Correlation between two complementary spectral signals can help to better

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understand the structural variation of a system under the perturbation.16,

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Moreover, the signals are now not limited to spectroscopy, and other types of probes

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such as chromatography, microscopy and astronomy are also applicable. In general, 9 ACS Paragon Plus Environment

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hetero-2DCOS provides a comprehensive insight into the perturbation-response

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correlation. Specifically, the chemical components of DOM based on their reactivity

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can be classified along the selected perturbation such as salinity using

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hetero-2DCOS.13

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2.3 Moving-Window 2DCOS. In the case of several overlapped spectral

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responses along the perturbation variable, e.g., multiple phase transitions during a

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broad temperature range, determination of sequential order may become ambiguous

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by conventional 2DCOS analysis. Segmentation of dataset into several intervals could

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simplify the analysis, but the results may be subjective and somehow arbitrary

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without an appropriate selection of the segmentation range. Moving-window (MW)

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2DCOS is therefore designed as a systematic approach for identifying the

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characteristic region of spectral variations along the perturbation variable axis.21 In

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MW2DCOS, the data matrix containing N dynamic spectrums is split into a subset

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with 2n+1 spectra along the perturbation variable, and 2n+1 is the window size. The

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subset is analyzed by the standard 2DCOS analysis. Then, a diagonal slice spectrum

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corresponding to the auto-correlation intensity is generated from the synchronous

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2DCOS map. By incrementing the window position along the perturbation axis step

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by step, a total number of N-2n diagonal slice spectrums can be obtained. MW2DCOS

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map is drawn as a waterfall plot of the diagonal slice spectrum versus the perturbation

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variable, which can be used to identify the specific region of pronounced spectral

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intensity variation along the perturbation for unambiguous interpretation. MW2DCOS

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has been widely used in the identification of temperature and pressure-dependent 10 ACS Paragon Plus Environment

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structural transition of polymers.

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To reveal the correlation strength of synchronous and asynchronous spectra, an

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alternative form of MW2DCOS analysis is proposed, namely perturbation-correlation

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moving-window two-dimensional (PCMW2D) analysis.22 In the case of PCMW2D,

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the spectral intensity variation within a small window at a given spectral coordinate is

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correlated against the perturbation variable rather than the intensity variation at

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another spectral coordinate. Then, the window position is incremented to yield

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waterfall plots. Similar to 2DCOS, PCMW2D analysis also yields the synchronous

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and asynchronous maps, both of which are useful for the identification of pronounced

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spectral intensity variation ranges and their spreads. Furthermore, this approach is

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capable of monitoring the complicated spectral variations along the perturbation

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direction and determining the transition points and regions. Therefore, PCMW2D is

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especially an ideal approach for investigating complicated environmental processes.

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2.4 Other Techniques for 2DCOS Analysis. With an increasing attention paid

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on the applications of 2DCOS to various fields, some new concepts of 2DCOS have

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been proposed recently (Figure 3). The probes, perturbations, and samples can be

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various objects, and diverse data processing methods, including hetero correlation,

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multiple perturbation, waterfall plots and data pretreatment, are designed. Specifically,

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chemometric-combined 2DCOS, orthogonal sample design (OSD), projection 2D, and

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2D co-distribution spectroscopy (2DCDS) have been developed for specific purposes,

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highlighting the obscured subtle spectral features by attenuating the dominant signals

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and eliminating unwanted features from congested 2DCOS matrix. Combination of 11 ACS Paragon Plus Environment


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principle component analysis (PCA) and 2DCOS can help to confirm trends in each

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analysis of the data set and provide complementary information about sample

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composition.23 OSD is designed to explore intermolecular interaction between two

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solutes dissolved in the same solvent using concentration as the external

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perturbation.24 Projection 2D correlation analysis is able to simplify highly congested

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2D correlation spectra based on mathematical matrix projection to selectively remove

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the unwanted portion of the spectral data.25 2DCDS focuses on the signal features

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regarding the distributed appearance of species rather than the traditionally analyzed

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deviation patterns from the reference spectrum by 2DCOS under perturbation.26 The

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sequential order of presence of species can thus be determined, which is specifically

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useful for the analysis of intermediates in chemical reactions. 2DCDS can be used

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complementally to make up the deficiency of traditional 2DCOS in directly

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identifying the formation of intermediate species.

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Practically, the perturbation-induced spectral responses of different bands could

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be varied distinctively, thus weak but important signals may be hidden behind those

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more dominant contributions. Although traditional 2DCOS aids to enhance the

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spectral resolution by spreading the spectra along the second dimension, it also suffers

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from the effect of localized strong responses, and weaker correlation peaks may

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become imperceptible if very strong neighboring correlation peaks exist in the

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2DCOS map. Therefore, pretreatment of data matrix prior to 2DCOS analysis is

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desirable to eliminate the effect of spectral variation magnitude and to extract pure

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correlative information. For this purpose, various methods are developed, including 12 ACS Paragon Plus Environment

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PCA reconstruction, eigenvalue, quadrature signal correction, Pareto scaling, double

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correlation, null-space projection, self-deconvolution, etc.27 After pretreatment, the

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features of 2DCOS maps can be very much simplified, and peak positions can be

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more accurately identified.

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Up to now, generalized 2DCOS, hetero-2DCOS and MW2DCOS are the most

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frequently used methods in DOM studies. The newly developed techniques derived

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from 2DCOS methods have shown promising applications in specific areas of

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materials and chemistry. By analogy, they may also be used to explore the properties

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and environmental behaviors of DOM in environmental processes. For instance,

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2DCDS may be able to determine the sequential appearances of spectral bands to

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identify intermediate species in environmental processes, and the OSD method could

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be used to examine the intermolecular interactions between DOM and exogenous

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substances. Furthermore, some advanced pretreatment methods can eliminate the

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unwanted artificial interferences and magnify the signals in DOM-involved

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environmental interactions.

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3. APPLICATIONS OF 2DCOS IN EXPLORING DOM PROPERTIES

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The chemical composition and distribution of DOM can be significantly altered

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during the complicated environmental processes in natural and artificial systems, such

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as evolution, biochemical reaction, and spatial location transformation. An insightful

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characterization of the chemical nature of DOM can help to clarify these 13 ACS Paragon Plus Environment


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environmental processes. Since DOM contains a variety of organic functional groups,

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its spectrum is the integration of various characteristic absorption/luminescence

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signatures of these functional groups, which would cause severe overlap. Thus, only

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limited structural information can be obtained from the conventional one-dimension

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spectra. As a receipt for unearthing the mystery of structural variation information

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hiding behind the conventional spectra, 2DCOS has been widely used to understand

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the physiochemical properties as well as dynamics of DOM at the molecular level, as

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it changes when subjected to external perturbations. Table 1 summarizes the

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investigations about DOM properties using 2DCOS techniques.

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3.1 Protonation, Thermal, and Redox Properties of DOM. DOM structure is

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very sensitive to ambient pH conditions because it contains various functional groups

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susceptive to protons. The heterogeneous distributions of proton binding sites were

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observed in fulvic-, humic-, tryptophan-, and tyrosine-like moieties of sub-fractions of

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fulvic acid through synchronous fluorescence spectroscopy combined with PCA and

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2DCOS analyses, and the binding of protons to tryptophan-like was preferential.28 As

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revealed by the attenuated total reflection (ATR) FTIR spectra coupled with 2DCOS

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analysis, the C=O groups in DOM had the fastest response to pH variation, while

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polysaccharide C-O and aromatic C=C groups responded the latest.29 Moreover,

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variation of rainwater DOM fluorescence induced by changes in rainfall intensity and

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pH suggested that a decrease in areas affected by acid rain in South China possibly

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resulted in the apparent compositional changes of DOM fluorescence.30

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Temperature variation caused by climate change, seasonal variation and latitudes 14 ACS Paragon Plus Environment

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also affects the physicochemical compositions of DOM, resulting in difference in the

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environmental behaviors of DOM-related pollutants. The temperature-dependent

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structural variation of soil/sediment DOM was examined with the combination of

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fluorescence excitation-emission matrix coupled with parallel factor analysis

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(EEM-PARAFAC) and synchronous fluorescence 2DCOS analysis. The results show

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that the thermal-induced transition priority of DOM structure was protein-like

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component > fulvic-like component > humic-like component.31 In addition, the

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reversible redox sites in solid-phase humic substances were explored by

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electrochemical in situ FTIR and 2DCOS analysis, which showed the presence of

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quinone radicals and dianion intermediates in the redox process and quinone-type

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groups as the predominant redox active sites in DOM.32

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3.2 Origin Variation. There are significant differences in DOM structure as a

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result of spatial location variation due to the change of DOM origins. Application of

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2DCOS to the 13C NMR and FTIR spectra of high molecular-weight DOM from the

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bay salinity transect revealed three major components of the DOM, i.e.,

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heteropolysaccharide compounds, carboxyl-rich compounds, and amide/amino sugar

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compounds, with totally different biogeochemical reactivities.33 An increase in the

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contents of polysaccharides to the high molecular-weight DOM components was

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observed along the salinity transect, suggesting the transformation of DOM structure

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from plant-derived carbohydrates to marine-produced carbohydrates.23

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On the other hand, the fluorescent components of DOM and their spatial

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variations along the urban river can be captured by integration of PCA and 15 ACS Paragon Plus Environment


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synchronous fluorescence 2DCOS. The results showed that the variations of DOM

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components along the river flow could be attributed to different origins of DOM in

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the rural, town, and urban regions.34 In addition, the depth-dependent variations of

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sedimentary DOM composition and metal binding heterogeneity in a eutrophic lake

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could be quantified with the help of EEM-PARAFAC and 2DCOS analyses. The

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analytical results suggested that humic-like component was preferentially biodegraded

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over fulvic-like component, and the composition/release of DOM in sediments was

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highly affected by short-range ordered mineral sorption.35

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3.3 Degradation/Composting of DOM. Tracing the compositional change of

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DOM is essential to understand the degradation/composting process of organic wastes.

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The evolution of DOM in composting of biogas residue was examined by FTIR

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spectroscopy coupled with 2DCOS analysis. It was found that the functional groups

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of C equivalent to C/C=N and amide III were preferentially degraded, while the

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aromatic C=C groups were hardly degraded.36 The chemical changes of DOM during

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anaerobic digestion of dewatered sewage sludge and the biodrying process could also

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be analyzed by FTIR-2DCOS, providing new insights into the degradation

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characteristics of individual organic matters in sludge treatment.37, 38 Moreover, the

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evolution of DOM from composting municipal solid wastes suggested that the

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degradation

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substances > proteinaceous compounds > cellulose, hemicellulose and lignin.39

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Particularly, high soil organic carbon content in the chemical and manure treatments

327

during long term fertilization could be attributed to the formation of noncrystalline

of

DOM

components

followed


the

order

of

aliphatic

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micro-particles such as allophane and imogolite.40 Additionally, 2DCOS integrated

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with size exclusion chromatography (SEC) provided clues about the correlation

330

between the molecular weight and polarity of DOM fractions during heterogeneous

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composting.41

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2DCOS was capable of tracing the formation of furfural-like intermediates and

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humic-like products in the simulated formation process of humic-like substances

334

based on the maillard reaction.42 As confirmed by FTIR-2DCOS, destruction of the

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aromatic structures in the DOM was related to the removal of the anti-estrogenic

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activity by ozonation in the tertiary treatment process of domestic wastewater.43 On

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the other hand, the spectral responses relating to chemical structure of humic

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substances in photodegradation can be probed using 2DCOS based on the absorption

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and synchronous fluorescence spectra.44 Moreover, the compositional change of

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DOM in landfill leachates regarding hydrophobicity and polarity as a result of aging

341

showed that the tyrosine-like, humic-like, and fulvic-like substances were the most

342

sensitive moieties.45 EEM-PARAFAC coupled with 2DCOS can offer a better

343

understanding about the key components in DOM and their changes in a partial

344

nitrification sequencing batch biofilm reactor.46 In addition, the dynamics of DOM

345

composition from batch activated sludge bioreactors at different salinities could be

346

revealed, and opposite sequences in fluorescence changes were observed between the

347

low- and the high-saline bioreactors.47

348

The applications of 2DCOS analysis to various spectral signatures of DOM help

349

to better understand the chemical structure, thermal/pH responses, redox activity, 17 ACS Paragon Plus Environment


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350

origin, and composting of DOM, and thus provide significant implications for a

351

comprehensive knowledge about DOM properties. It is noteworthy that these

352

properties may be related to each other. For instance, the protonation/deprotonation

353

state of DOM may affect its redox activity. However, current studies focus on one

354

aspect of DOM properties independently, while the relationship among different

355

DOM properties remains unknown. In 2DCOS techniques, hetero-perturbation

356

analysis may provide some insights on this topic. On the other hand, fluorescence and

357

FTIR spectroscopy are the most commonly used probes for 2DCOS analysis in DOM

358

studies. Since 2DCOS is generally applicable to a wide range of analytical probes in

359

addition to spectroscopy, the integration of 2DCOS analysis with other modern probes,

360

such as dynamic light scattering and chromatography, would provide additional

361

chemical codes regarding DOM characteristics, which remains to be validated.

362 363

4.

364

EXOGENOUS SUBSTANCES

2DCOS ANALYSIS ON THE INTERACTION BETWEEN DOM AND

365 366

4.1 Interaction between DOM and Particles. The occurrence of exogenous particles,

367

such as nanoparticles (NPs), transparent exopolymer particles, and microplastics, has

368

significant impacts on the ecosystem’s characteristics.4,

369

surface properties, biogeochemical cycles and environmental functions of these

370

particles are greatly affected by the ubiquitously existing DOM species in aquatic and

371

terrestrial environments. Therefore, various techniques associated with 2DCOS 18 ACS Paragon Plus Environment

48

Meanwhile, the stability,

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analysis have been developed to untangle the complex interactions between particles

373

and organic DOM species.

374

Since the adsorption of DOM to particles is a heterogeneous process, an online

375

spectral monitoring platform is usually required. Chen et al. designed an ATR-FTIR

376

probe based on a silver halide fiber which allowed increased reflection times so as to

377

enhance spectral signals to persistently monitor the adsorption process of humic acid

378

(HA) onto TiO2 NPs (Figure 4).29 The 2DCOS results showed that the C=O bonds

379

from carboxylate, amide, quinone, or ketone groups and C-O bonds from phenol,

380

aliphatic C-OH, and polysaccharide groups of HA were involved in the interaction

381

between HA and TiO2 NPs. Through SEC-2DCOS and EEM-PARAFAC, the

382

heterogeneous adsorption behaviors of humic substances on TiO2 and ZnO NPs with

383

respect to molecular sizes were also investigated. In this case, the fluorescent

384

components associated with larger molecular sizes in humic substances showed

385

preferential adsorption.49 While in the interactions between bacterial ghosts and TiO2

386

NPs, the functionalities in proteins of bacterial ghosts preferentially interacted with

387

TiO2 NPs.50 2DCOS analysis also suggested that the protein structures of DOM were

388

more favorable for its heterogeneous adsorption onto graphene oxide and reduced

389

graphene oxide.51

390

The effect of cyanobacterial DOM on the stability of ZnO NPs in eutrophic

391

shallow lakes was studied by ATR-FTIR-2DCOS. The results confirmed that both

392

electrostatic attraction and surface complexation were involved in DOM adsorption

393

on ZnO NPs, and DOM adsorption enhanced the stability of ZnO NPs.52 Additionally, 19 ACS Paragon Plus Environment


394

EEM-PARAFAC coupled with FTIR-2DCOS analyses were employed to explore the

395

interaction between HA and polystyrene microplastics. The microplastics with smaller

396

sizes could interact with the aromatic moieties of HA via π-π conjugation, and be

397

entrapped in the HA polymers by the carboxyl groups and C=O bonds, constituting a

398

highly conjugated co-polymer with increased electron density.53 A flow-cell

399

ATR-FTIR setup was designed and coupled with 2DCOS analysis to investigate the

400

adhesion of Shewanella oneidensis MR-1 cells onto goethite mineral surfaces, and

401

bacterial phosphate-moieties were found to play an important role in the formation of

402

mono- and bi-dentate inner-sphere complexes, whereas carboxylic groups on cell

403

surface had a minor contribution to its adhesion.54

404

4.2 Coordination of DOM with Heavy Metals. Insight into the interaction

405

between heavy metals with DOM is beneficial for a better understanding on the

406

toxicity and migration of heavy metals, and thus providing further guidance for heavy

407

metal-containing wastewater treatment.55 In recent years, a variety of studies have

408

been conducted to investigate the heavy metal-DOM interactions using 2DCOS

409

techniques.

410

Copper is among the most typical heavy metals and the coordination of DOM

411

with Cu has been extensively studied. A comprehensive illustration of the interaction

412

process between DOM and Cu has been revealed through 2DCOS.55-57 Specifically, a

413

series of heterogeneous binding sites in HA and their subsequent subtle changes

414

during the molecular interactions were elucidated through fluorescence/FTIR

415

hetero-spectral 2DCOS.56 As shown in Figure 5, the transformation sequence of HA 20 ACS Paragon Plus Environment

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groups in aquatic systems during copper binding process and the structural

417

relationship between the fluorescent groups and the corresponding IR groups in HA

418

were clarified. This might open a door to visit the geochemical cycling process of

419

copper in natural environments. Furthermore, the biosorption process of copper onto

420

aerobic granular sludge-derived biochar was evaluated in the presence of effluent

421

organic matter, and a successive fluorescence quenching was observed in various

422

DOM fractions with increasing Cu(II) concentration.58

423

The binding behavior of DOM with Pb(II) in soil sorption process was

424

characterized by EEM-PARAFAC and synchronous fluorescence 2DCOS analysis,

425

and the humic-like fraction showed a superior affinity towards Pb(II) over the

426

protein-like fraction in DOM.59 Additionally, carboxylic and phenolic groups in HA

427

were the predominant binding sites to Pb, and carboxylic groups exhibited a stronger

428

binding ability to Pb compared with phenolic groups, as evidenced by fluorescence

429

and log-transformed UV-Vis absorption spectroscopy coupled with 2DCOS

430

analysis.60

431

2DCOS is also able to accurately simulate the migration and transformation of

432

Cr(VI) in soil environments in the presence of HA.61 According to the FTIR-2DCOS

433

results, HA functional groups located at aromatic domains participated in the reaction

434

with Cr(VI) in the order of chelated carboxyl > phenol > polysaccharide > methyl.62

435

The heterogeneous binding of DOM with mercury in lake sediments was investigated

436

using fluorescence quenching with 2DCOS, demonstrating the impact of different

437

sources of DOM on metal migration.63 With synchronous fluorescence 2DCOS and 21 ACS Paragon Plus Environment


438

FTIR spectroscopy, comparative evaluation of the role of DOM in biosorption of

439

Ni(II) onto aerobic/anaerobic granular sludge was carried out, and the sequence of

440

structural change in DOM induced by Ni(II) was examined.64 Furthermore,

441

application of EEM-PARAFAC and FTIR-2DCOS revealed that divalent Ca2+ and

442

Mg2+ had a strong binding capability with phenolic -OH, aromatic C=C, and

443

polysaccharide C-O groups in DOM, while the monovalent electrolyte exhibited

444

negligible association with these groups. The results indicated that electrolyte cations

445

with an elevated concentration in natural ecosystems may increase roles in

446

Microcystis bloom formation.65

447

As revealed by 2DCOS, functional groups in soil DOM relating to multiple heavy

448

metal binding, such as Cu, Cd and Al, can be modified by fertilization treatments.

449

This result provided implications for the reduced bioavailability of heavy metals in

450

organic fertilized soils.40,

451

deriving from compost and rice straw were compared using EEM-PARAFAC and

452

FTIR-2DCOS analyses, and Cu exhibited a stronger binding affinity towards DOM

453

than Cd, while the phenol-OH, carboxyl and amide groups gave different responses to

454

DOM with different origins.67 Moreover, the binding results of copper and zinc

455

towards DOM, which were extracted from macrophyte- and algal-dominant sediments

456

in a eutrophic shallow lake, showed that more aromatic functional groups and binding

457

sites were responsible for higher binding ability.68 In addition, the complexation

458

characteristics of leachate-derived dissolved organic nitrogen with Mo, Co, Cr, and Ni

459

were investigated using fluorescence and FTIR hetero-spectral 2DCOS, and the

57, 66

The binding characteristics of Cd and Cu to DOM


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460

amines formed strong complexes with Mo, Co, Cr, and Ni, while the proteinaceous

461

matter interacted with the metals Cr and Ni.69

462

4.3 Complexation of DOM with Organic Pollutants. Discharge and occurrence

463

of exogenous organic pollutants, such as antibiotics and pesticides, pose potential

464

threats to the ecological system and human health. DOM may interact with these

465

organic pollutants, and as a result change their migration, toxicity, as well as

466

environmental fate. Thus, prediction of the environmental risks of these organic

467

pollutants is critically important, which drives the research in their environmental

468

behaviors moving forward.

469

An in situ ATR-FTIR-2DCOS approached was developed to study the sorption

470

process of enrofloxacin onto montmorillonite and the impact of DOM on

471

enrofloxacin-montmorillonite interaction.70 The interaction mechanism was mainly

472

cation exchange at acidic condition, charge neutralization at neutral condition, and

473

proton transfer at alkaline condition.70 While electrostatic interaction was the

474

predominant driving force for the interaction between DOM and enrofloxacin, in

475

which H-donor–acceptor interaction and π-π interaction may also be involved.70 The

476

results facilitated the understanding on the mineral–aqueous solution interfacial

477

behaviors of organic contaminants in the presence of DOM.

478

The complexation of tetracycline antibiotics with algae- and macrophyte-derived

479

DOM was probed by a multi-spectroscopic 2DCOS analysis, which suggested that

480

amide, aromatics, and aliphatics in DOM were participated in the complexation.71

481

Moreover, interaction between sodium dodecyl sulfate surfactant and DOM was 23 ACS Paragon Plus Environment


482

validated by EEM-PARAFAC and 2DCOS, and the results showed that HA could not

483

interact with the surfactant but change the binding behaviors of protein-like species.72

484

Furthermore, from EEM-PARAFAC and 2DCOS analyses, soil DOM components

485

was also found to significantly affect the bioavailability, mobility and migration of

486

atrazine pesticide, which was widely used in agriculture.73 Synchronous fluorescence

487

and FTIR spectroscopy coupled with 2DCOS, hetero-2DCOS and PCMW2D analyses

488

were applied to investigate the environmental behaviors of ionic liquids impacted by

489

DOM. This work provided a comprehensive view of the structural changes of DOM

490

during binding with long-chain ionic liquids.74 The interaction between DOM and

491

4-chlorophenol was investigated using fluorescence EEM-PARAFAC coupled with

492

2DCOS, and sequential quenching of DOM fractions during the interaction process

493

was observed.75 With 2DCOS analysis, the production and structural variation of

494

DOM in an aerobic granular sludge system under the stress of 2,4-dichlorophenol was

495

evaluated, and fluorescence 2DCOS showed a potential application in the released

496

DOM assessment in the exposure of toxic compounds in wastewater treatment.76 In

497

addition, the binding process of soil DOM with roxarsone, a widely used

498

organoarsenic feed additive, was revealed by FTIR and fluorescence 2DCOS analyses.

499

It was found that roxarsone interacted with DOM through the hydroxyl, amide II,

500

carboxyl, aliphatic CH, and -NO2 groups, yielding stable DOM- roxarsone

501

complexes.77 Furthermore, through 2DCOS analysis of the ATR-FTIR spectra,

502

DOM-induced membrane fouling was examined, and the results demonstrated the

503

existence of interaction between humic substances and protein components in DOM 24 ACS Paragon Plus Environment

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on/inside the membrane during fouling.78

505

The complex interaction processes between DOM and exogenous substances,

506

including nanoparticles, heavy metals, and organic pollutants, can be clearly revealed

507

under the guidance of 2DCOS analysis (Table 2). Knowledge on the interaction

508

mechanism between DOM and exogenous substances helps to better understand the

509

toxicity, migration, transformation, and environmental fates of these substances when

510

exposed to environment. It should be noted that the interaction processes are closely

511

related to the properties of DOM, i.e., DOM with different origins, pH/thermal

512

responses, or redox reactivities may show distinctive interaction affinities. Moreover,

513

different exogenous substances may coexist and show synergistic or competing

514

effects on the interaction with DOM. Therefore, much attention should be paid on the

515

structural variation of DOM under multiple perturbations, especially in the case of

516

combined pollution. This is beneficial for the development of treatment strategies for

517

the emerging pollutants. Hetero-correlation analysis as derived from 2DCOS may be

518

promising in exploring these complicated and combined processes as it is able to

519

decipher multiple spectral datasets arising from diverse analytical probes, sample

520

systems, and perturbations.

521 522

5. CONCLUSIONS AND OUTLOOK

523 524

During the past decades, there are rapid developments and applications of various

525

types of techniques for the structural characterization of DOM, which contribute to an 25 ACS Paragon Plus Environment


526

improved understanding of environmental processes relating to these species.

527

However, there remains much work left because the characterization of environmental

528

process is very challenging due to the high complexity and heterogeneity of DOM,

529

thus sometimes resulting in controversies on the environmental behaviors of DOM.

530

Compared with other pure samples with known molecular formula as analyzed by

531

2DCOS methods in the fields of materials and chemistry, DOM is a heterogeneous

532

mixture of various organic components. Moreover, the perturbations in DOM-related

533

environmental processes are much more complicated. Thus, the dynamic spectral

534

change of DOM may be resulted from more than one external perturbation. When the

535

2DCOS methods are applied to explore the DOM-related environmental processes,

536

the following aspects should be taken into account: (1) the origins of DOM and their

537

physiochemical properties, (2) multiple external perturbations such as temperature,

538

time, spatial position, and concentration, (3) hydrodynamic conditions, and (4)

539

analytical methods and procedures. Therefore, it is critically important to expand

540

2DCOS techniques and develop novel techniques that allow for an accurate

541

discrimination of DOM composition at the molecular level and monitoring of their

542

structural variations in environmental processes. With such knowledge a direct link to

543

the interaction mechanism can be achieved, which contributes to better insights to

544

understand these complex and challenging environmental processes.

545

Great efforts have been made with 2DCOS techniques involving UV-Vis,

546

fluorescence, and IR spectroscopy for their applications in characterizing DOM

547

-related environmental processes. With the help of 2DCOS analysis, further structure 26 ACS Paragon Plus Environment

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548

information and the variations regarding DOM from the complex spectra in

549

complicated environmental processes can be extracted. However, there are some

550

inherent limitations in 2DCOS and thus very careful attention should be paid when

551

using 2DCOS to interpret the environmental behaviors of DOM. It should be noted

552

that 2DCOS technique provides a connection between perturbation and spectral

553

response only, but further evidences are needed to judge the causal relationship

554

between the two variables. That is, 2DCOS can identify the co-variance of DOM

555

signals induced by perturbations, but cannot actually prove that the variance is

556

directly resulted from the process responsible for each variable independently. For

557

instance, a biological response over time can induce signal changes detected by

558

various analytical instruments, and 2DCOS can identify the co-variance of the

559

measurements, but fails to determine if they are attributed to a specific process,

560

because changes may also occur fortuitously. Moreover, very subtle changes in the

561

dynamic spectra can be magnified by 2DCOS, thus, the selection of the reference

562

spectra and the elimination of interfering signals should be cautious. Additionally, the

563

sequential order judgment may become controversial if the perturbation variable is

564

improperly selected.79, 80

565

To strengthen these approaches in the future, some requirements and challenges

566

in the application of 2DCOS techniques should be considered: (1) capability of online

567

monitoring to reveal the evolution of environmental processes (e.g., modification of

568

sampling equipment); (2) emphasis of obscured subtle spectral features and

569

attenuation of the overwhelmingly dominant signals (e.g., integration of 2DCOS with 27 ACS Paragon Plus Environment


570

chemometrics); (3) elimination of unwanted spectral signatures (with the aid of

571

mathematics); (4) good repeatability/reliability of the analytical results; (5)

572

improvement of detection sensitivity; and (6) reduction in analysis time and

573

simplification of analytical procedure by designing appropriate software. Addressing

574

these issues can definitely improve the availability of 2DCOS methods for the

575

interpretation of DOM structural variations, so as to achieve more comprehensive

576

knowledge about their environmental processes.

577

In summary, 2DCOS methods have a great potential in exploring environmental

578

processes with unique advantages. Particularly, the flexibility of platforms for

579

perturbation-induced structural variation recognition makes it possible for real-time

580

measurement of the environmental processes, and the anti-interference nature allows

581

for the accurate differentiation and quantification of different DOM components.

582

Moreover, with the basis of generalized 2DCOS method, it can also be applied to

583

analyze the dynamic data of other probes (such as chromatography), not limited to

584

spectra, which may reflect the properties and environmental behaviors of DOM from

585

another point of view. Finally, integration of 2DCOS with other analytical methods,

586

such as multivariate curve resolution analysis and microspectroscopic imaging, is

587

beneficial for unearthing the mystery of complex and heterogeneous environmental

588

processes with high temporal/spatial resolution and designing favorable water

589

treatment and remediation strategies.

590 591

AUTHOR INFORMATION


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592


*Corresponding author: Prof. Han-Qing Yu, E–mail: [email protected]

593 594

ACKNOWLEDGEMENTS

595

The authors thank the National Key R&D Program of China (2018YFC0406303),

596

the National Natural Science Foundation of China (21707167, 51538011, 21590812

597

and 51821006) and the Fundamental Research Funds for the Central Universities of

598

Central South University for supporting this work.

599 600

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Table 1. Summary of DOM properties reported in 2DCOS studies. Property

pH response

Method

Highlight

Fluorescence +

Tryptophan-like component bind to

PCA&2DCOS

protons preferentially

ATR-FTIR + 2DCOS

Fluorescence 2DCOS

C=O groups had the fastest response

Acid rain changed the apparent

The transition priority of DOM

response

2DCOS

structure was determined

Electrochemical FTIR

Quinone-type groups were the

+ 2DCOS

predominant redox active sites

Origin

NMR + FTIR+

along the salinity transect

Fluorescence 2DCOS

DOM components varied along the

+ PCA

river flow

EEM + Fluorescence

DOM composition in sediment was

2DCOS

highly affected by mineral sorption

2DCOS

Degradation/ Composting

polysaccharide content increased

2DCOS

EEM + FTIR +

Evolution of DOM was clarified molecular weight and polarity

SEC + 2DCOS EEM + SEC + 2DCOS

29

30

composition of DOM

EEM + Fluorescence

13C

28

to pH variation

Thermal

Redox

Ref.

31

32

23, 33

34

35

36-40

41

evolution characteristics Formation of humic substances

Fluorescence/UV-Vis/

Characterization of DOM

FTIR + 2DCOS

degradation


42

43-47


Page 40 of 47

Table 2. Summary of the interaction processes between DOM and exogenous substances probed by 2DCOS. Exogenous substance

Particles

Contents

Features

TiO2/ZnO NPs,

Heterogeneous process;

graphenes, microplastics,

preferential functional groups

minerals

dependent of particle type

Heavy

Cu, Pb, Cr, Ni, Cd, Zn,

metals

Mo, Co, etc.

surfactant, ionic liquids,

pollutants

disinfection byproducts,

27, 47-52

DOM groups bind to heavy metals in order; binding

38, 53-67

affinity differs

Antibiotics, pesticides, Organic

Ref.

Electrostatic and covalent interaction; complex compounds were formed

addictive


68-76

Page 41 of 47


Figure captions

Figure 1. General scheme of 2DCOS analysis procedure.

Figure 2. (a) Simulated IR spectra of a system with contact time as the perturbation. (b) Intensity changes of four peaks (1650, 1625, 1560, and 1420 cm-1) with time. (c-d) Synchronous (c) and asynchronous (d) 2DCOS maps. Color changing from blue to red indicates correlation intensity changing from negative to positive.

Figure 3. Diverse concepts of 2DCOS methods based on correlation analysis.

Figure 4. Adsorption of HA onto TiO2 NPs.29 (a) schematic illustration of the ATR-FTIR measurement. (b-c) synchronous and asynchronous 2DCOS maps generated from the dynamic ATR-FTIR spectra during adsorption.

Figure 5. Hetero-spectral 2DCOS plots generated from the dynamic synchronous fluorescence and IR spectra of HA upon copper addition.56 (a) synchronous map; (b) asynchronous map; (c) illustration of HA-copper binding affinity. Colors in a and b from blue to red represent signs from negative to positive.



Figure 1. General scheme of 2DCOS analysis procedure.


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Figure 2. (a) Simulated IR spectra of a system with contact time as the perturbation. (b) Intensity changes of four peaks (1650, 1625, 1560, and 1420 cm-1) with time. (c-d) Synchronous (c) and asynchronous (d) 2DCOS maps. Color changing from blue to red indicates correlation intensity changing from negative to positive.



Figure 3. Diverse concepts of 2DCOS methods based on correlation analysis.


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Figure 4. Adsorption of HA onto TiO2 NPs.29 (a) schematic illustration of the ATR-FTIR measurement. (b-c) synchronous and asynchronous 2DCOS maps generated from the dynamic ATR-FTIR spectra during adsorption.



Figure 5. Hetero-spectral 2DCOS plots generated from the dynamic synchronous fluorescence and IR spectra of HA upon copper addition.56 (a) synchronous map; (b) asynchronous map; (c) illustration of HA-copper binding affinity. Colors in a and b from blue to red represent signs from negative to positive.


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Table of Contents (TOC) art


Characterizing Properties and Environmental Behaviors of Dissolved

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