Land Use Cover and Flow Condition Affect the Spatial Distribution Characteristics of Fluorescent Dissolved Organic Matter in the Yongding River

Abstract

Dissolved organic matter (DOM) is involved in many biogeochemical processes and plays an important role in aquatic ecosystems. This study integrated three-dimensional fluorescence excitation–emission matrix (EEM), fluorescence regional integration (FRI), and parallel factor analysis (PARAFAC) to better understand the distribution and component characteristics of DOM in the Yongding River and explore the response of DOM to natural and anthropogenic activities. The results showed that the relative abundance of fulvic-like materials of DOM in the river was the highest, with an average of 68.64%. PARAFAC identified three fluorescent components, namely, C1 (microbial humic-like components), C2 (terrestrial humic-like components), and C3 (protein-like components), and their changes with flow confirmed that the riverine DOM was generally influenced by microbial sources and terrestrial inputs. The upper reaches showed strong autochthonous characteristics and a high humification degree of DOM due to a fast flow rate, while the middle reaches showed biological or aquatic bacterial origin due to a moderate flow rate. The lower reaches of the river showed characteristics of biological and bacterial origin, most strongly influenced by human activities. The findings can help provide a basis for identifying DOM characteristics in the Yongding River basin and understanding the geochemical cycle of DOM at a regional scale.

1. Introduction

Dissolved organic matter (DOM) comprises the largest pool of active and exchangeable organic carbon on Earth and is widely found in various aquatic environments. It plays a crucial role in the migration and transformation of heavy metals, as well as the cycling of carbon [1,2,3]. The sources of DOM in aquatic systems are classified into terrestrial sources (e.g., animal and plant residues and human activities) and endogenous sources (e.g., phytoplankton and microbial degradation and metabolites) [4,5]. Therefore, the geochemical characteristics and source identification of aquatic DOM are important prerequisites for further understanding its eco-environmental effects. Previous studies have extensively explored the absorption spectral characteristics, material components, and sources of DOM, focusing primarily on eutrophic lakes, seas, and estuarine waters [6,7,8]. Rivers, the transition zones between continental and oceanic ecosystems, are vital for the transport and transformation of carbon [2,9]. The DOM of rivers can influence the dynamic changes of microorganisms and even aquatic food webs [10]. In river ecosystems, DOM variations are closely related to the surrounding environment, including soil type within the river basin, wetland cover, agricultural land use, and urban sewage discharge [11,12,13]. Currently, the characterization of DOM in major river systems like the Yangtze and Yellow Rivers is well-established. However, there is a lack of in-depth studies on urban rivers that traverse entire cities and undergo complex environmental changes. The Yongding River, an urban river spanning the entire city of Beijing, connects the Haihe River basin and the Bohai Sea basin, thereby functioning as a pivotal conduit for material exchange. Previous studies have reported that diverse land use and human activities (e.g., industrial and agricultural development) significantly influence the ecosystem of the Yongding River. Consequently, investigations on the dynamic characteristics of DOM in complex riparian terrain are essential for enhancing the understanding of DOM transport processes within urban river systems [14].

Three-dimensional fluorescence excitation–emission matrix (EEM) spectroscopy, combined with parallel factor analysis (PARAFAC) and fluorescence regional integration (FRI), has been widely used to characterize the sources, composition, and humification degrees of DOM in aquatic systems [15,16,17]. The EEM provides a comprehensive analytical approach characterized by its high sensitivity and minimal sample requirements while preserving the integrity of the sample structure. This technique enables the determination of both the type and content of fluorescent DOM by analyzing the excitation wavelength, emission wavelength, and fluorescence intensity of specific fluorescence peaks, which makes it a popular method for DOM analysis [18]. The integration of the fluorescence intensity within each defined region of the EEM of the DOM, as well as the quantification of the fluorescence intensity of a specific region, can be accurately achieved by EEM-FRI [19]. Zhao et al. reported that the percentage of fluorescence response calculated from FRI could also be used to assess the humification degree of DOM [20]. Additionally, the method of EEM-PARAFAC can distinguish the changes between different components by analyzing the maximum fluorescence intensity (F_max) based on the excitation load obtained by PARAFAC [21,22]. Given the complex composition of DOM in urban river systems, multiple indicators are needed to evaluate how the humification degree of DOM varies with the surrounding environment from the upper to lower reaches of rivers. To address this complexity, the integrated EEM-FRI/PARAFAC approach has been extensively employed to characterize the composition, distinguish individual components, and explore the spatiotemporal changes of DOM in aquatic ecosystems [16,23]. However, few studies have focused on the subtle changes in source features of riverine DOM and their correlation with fluorescence indicators. Therefore, the integrated approach of the EEM can be used to reveal the dynamic changes and intrinsic properties of DOM in urban river spatial distribution.

This study combined EEM, FRI, and PARAFAC methods to elucidate the relationship between the distribution of DOM and the surrounding environment in the Yongding River. Our objectives were to (1) study the variation in composition and humification of DOM from the upstream sections to the downstream sections of the Yongding River using FRI/PARAFAC and fluorescence indices; (2) explore the correlation between environmental changes along the Yongding River and DOM distributions by examining fluorescence properties; and (3) highlight the significance of DOM cycling in rivers that connect inland and marine water systems.

2. Materials and Methods

2.1. Study Area and Sample Collection

The Yongding River constitutes a pivotal component within the Haihe River basin and holds the distinction of being the most extensive among Beijing’s five primary aquatic systems. Traversing the districts of Mentougou, Shijingshan, Fengtai, Fangshan, and Daxing, the river exhibits a total length of 650 km. Its upstream region bifurcates into two notable tributaries: the Yanghe River, encompassing a drainage area of 16,250 km² and extending 241 km, and the Sanggan River, with a drainage area of 26,000 km² and a total length of 390 km. Within the urban landscape, the primary river channel spans 189 km, while the basin area of the Yongding River encompasses 3168 km², comprising 18.9% of Beijing’s total area, with a mountainous terrain accounting for 2491 km² of this basin.

Water samples were collected from upstream to downstream of the Yongding River, spanning 5 districts with a total of 24 water samples (Figure 1). Surface water samples were collected with a 5 L Niskin bottle (pre-cleaned by pH = 2 HCl-acidified water and ultrapure water) in Jan 2022 (samples Y1 to Y24). The collected samples were immediately shipped to the laboratory and filtered through GF/F filters (Whatman, 47 mm diameter). The filtrates were stored at 4 °C in the dark before analysis. DOC concentration was analyzed using high-temperature combustion on a TOC-V CPN analyzer (Shimadzu, Japan).

2.2. Fluorescence EEM Measurements, FRI Analysis, and Index Calculations

A Hitachi Fluorescence Spectrometer (F-7000, Tokyo, Japan) with a 1 cm path-length quartz cuvette at room temperature was used to measure the EEM of the Yongding River water samples, which were subsequently obtained by scanning emission (Em) wavelengths from 250 to 550 nm and scanning excitation (Ex) wavelengths from 200 to 450 nm with 2 nm intervals. The slit widths of Em and Ex were both 5 nm. The scanning speed was set to 12,000 nm∙min⁻¹, and the PMT voltage was set to 600 V.

The FRI method yields that the integrated standard volumes for different regions represent the corresponding DOM of each region [19,24]. The relevant calculation formula is as follows [17,19]:

$${\mathrm{P}}_{\mathrm{i},\mathrm{n}}={\displaystyle \frac{{\varnothing }_{\mathrm{i},\mathrm{n}}}{{\varnothing }_{\mathrm{t},\mathrm{n}}}}\times 100\%={\displaystyle \frac{{\mathrm{M}\mathrm{F}}_{\mathrm{i}}{\sum }_{\mathrm{e}\mathrm{x}}{\sum }_{\mathrm{e}\mathrm{m}}\mathrm{I}\left({\mathsf{\Delta }\mathsf{\lambda }}_{\mathrm{e}\mathrm{x}}{\mathsf{\Delta }\mathsf{\lambda }}_{\mathrm{e}\mathrm{m}}\right)}{{\int }_{\mathrm{i}=1}^5{\mathsf{\varphi }}_{\mathrm{i},\mathrm{n}}}}\times 100\%,\mathrm{i}=\mathrm{I}-\mathrm{V}$$

(1)

where MF_i is a multiplication factor for each region; I(ΔλexΔλem) is the fluorescence intensity at each Ex/Em wavelength pair; and ∆λex and ∆λem are the Ex and Em intervals, respectively. The areas were blocked after pre-processing by MATLAB 2018b, and the area integral was calculated in Excel.

The fluorescence index (FI) is defined as the ratio of the fluorescence intensity of the emission wavelength at 470 nm and 520 nm when the excitation wavelength is 370 nm [25]. It is used to describe the DOM source from terrestrial sources and microbial sources [26,27]. According to the FI, the sources of DOM can be divided into three categories: (1) FI < 1.4, the sources of DOM can be considered as terrigenous input (degraded plant and soil organic matter); (2) FI > 1.9, DOM comes from microbial metabolism (extracellular release and leachate from bacteria and algae); and (3) 1.4 < FI < 1.9, terrigenous input and microbial metabolism both play an important role [25,28]. The humification index (HIX) is the ratio of the area calculated between emission wavelengths of 300 nm and 345 nm and the area calculated between 435 nm and 480 nm when the excitation wavelength is 370 nm [29]. Huguet et al. showed that high HIX values (>16) suggest strong humic characteristics, which implies a high content of humus, indicating a relatively abundant source of organic matter formed through microbial decomposition. Conversely, HIX values less than 4 indicate biological or aquatic bacterial origin [28]. The autochthonous index (BIX) refers to the ratio of the fluorescence intensity of the emission wavelengths at 380 and 430 nm when the excitation wavelength is 310 nm [29]. BIX is an index that measures the contribution of autochthonous DOM production in natural waters [30]. It can be separated into the following categories: (1) low autochthonous component (0.6 < BIX < 0.7); (2) intermediate autochthonous component (0.7 < BIX < 0.8); (3) strong autochthonous component (0.8 < BIX < 1); and (4) biological or aquatic bacterial origin (BIX > 1) [28].

2.3. EEM-PARAFAC Analysis

PARAFAC was used to analyze the 24 samples from the Yongding River. Eliminating Raman scattering and Rayleigh scattering is one of the most important pre-processing steps before performing PARAFAC analysis. Removing the scattering can avoid the generation of spurious peaks and accelerate the analysis process of PARAFAC to obtain more accurate results. Through PARAFAC analysis, multiple component models can be obtained, which can be determined through the OpenFluor database. In the OpenFluor database, similar spectral components were identified on the excitation and emission spectra, respectively, based on the Tucker congruence coefficient of 95%. The maximum fluorescence intensity (F_max), which comes from PARAFAC models, is used to represent the relative concentration or intensity of PARAFAC components [17].

3. Results and Discussion

3.1. EEM-FRI Analysis of DOM

According to the FRI theory proposed by Chen et al., the fluorescence spectra were categorized into five distinct regions, each defined by specific ranges of excitation and emission wavelengths [19]. The ratio of the total area integral of each region represents the degree of humification of organic molecules [31]. For protein-like materials, Region I (Ex/Em: 220–250/250–330 nm) was related to tyrosine-like materials, and Region II (Ex/Em: 220–250/330–380 nm) was related to tryptophan-like materials [19]. Additionally, Region III (Ex/Em: 220–250/380–500 nm), Region IV (Ex/Em: 250–280/280–380 nm), and Region V (Ex/Em: 250–400/380–500 nm) were assigned to fulvic-like materials, soluble microbial metabolites, and humic-like materials, respectively [19].

The values of P_i,n, which represented the percentage of fluorescence calculated by EEM-FRI analysis, varied significantly among the regions. Mean P_i,n has been used for the identification of specific materials in DOM [24]. The mean P_I,n−P_V,n values for Regions I−V were 0.86% ± 0.18%, 2.26% ± 0.23%, 4.90% ± 0.38%, 23.33% ± 3.07%, and 68.64% ± 3.12%, respectively. These results indicate that humic-like materials were the predominant constituents of DOM in the Yongding River. Humic-like materials are generally produced by the bacterial degradation of some humus species carried by surface runoff and the organic matter released by phytoplankton [32]. The reason for the high relative abundance of P_V,n in the Yongding River may be related to the input of some humus species from the surrounding environment or the decomposition of aquatic plants in rivers. The contents of other fluorescent materials in DOM occurred in the order of soluble microbial metabolites > fulvic-like > tryptophan-like > tyrosine-like materials, according to their mean P_I-V,n values.

The sums of P_I,n and P_II,n values were considered biochemical regions (P_I+II,n), while those of P_III,n and P_V,n were considered geochemical regions (P_III+V,n) [33]. At the spatial scale of the Yongding River, the relative abundance of P_I+II,n (3.36% ± 0.24%) was the highest in the middle reaches (Figure 2a). P_I+II,n exhibited the lowest abundance in the upper reaches (2.90% ± 0.25%). In general, the relative abundance of P_I+II,n initially increased and then decreased. Both P_I,n and P_II,n are classified as protein-like substances, primarily originating from the metabolic processes of aquatic organisms or microorganisms [34]. The elevated metabolic activity of microorganisms likely contributes to the increased presence of tyrosine proteins and tryptophan proteins in the middle reaches. Similarly, the relative abundance of P_IV,n initially increased and then decreased. The proportions of the upper, middle, and lower reaches were 21.55% ± 1.24%, 24.71% ± 1.68%, and 23.73% ± 4.32%, respectively. This result indicates that the midstream had the highest content of soluble microbial metabolites. Conversely, the relative abundance of P_III+V,n initially decreased and then increased, with values of 75.55% ± 1.35% in the upper reaches, 71.92% ± 1.74% in the middle reaches, and 73.92% ± 4.66% in the lower reaches. This result might be attributed to photocatalytic oxidation processes occurring during the migration of fulvic-like materials, which leads to their transformation into smaller molecules [35].

3.2. Evolution of Fluorescence Index of DOM

The spatial distribution of the DOM fluorescence index in the Yongding River reveals the attribute information and possible sources of DOM. The FI values indicate that the DOM of the upper reaches (FI = 1.85 ± 0.08) and the middle reaches (FI = 1.89 ± 0.06) were from the mixture of terrigenous input and microbial metabolism in the Yongding River (Figure 3a). As the river flows downstream, the DOM of the lower reaches (FI = 1.87 ± 0.12) showed stronger characteristics of microbial metabolism sources (Figure 3a). The increase in the FI value from the upper reaches to the lower reaches suggests a diminishing influence of terrigenous sources and a more pronounced impact of microbial metabolism. Previous studies have revealed the negative correlation between the FI index and aromaticity of DOM, which is related to the improvement of DOM saturation and oxidation degree under microbial modification [36]. Meanwhile, the increased rock cover along the riverbank in the lower reaches of the Yongding River reduces the input of terrigenous DOM. Therefore, the cumulative effect of microbial activity and the decrease in terrigenous input led to the increased FI value of DOM in the lower reaches of the Yongding River.

HIX values of all DOM samples of the Yongding River were less than 4, indicating a low degree of humification in this river. The HIX values of DOM from upper (HIX = 2.62 ± 0.31), middle (HIX = 2.03 ± 0.21), and lower (HIX = 2.24 ± 0.55) reaches first decreased and then increased (Figure 3b). The higher HIX value of DOM in the lower reaches than middle reaches illustrated stronger microbial metabolism in this section of the river, which is consistent with the analysis of the FI values. The trend of the HIX value might be caused by the variation in the microbial modification with the river velocity. In the upper reaches with higher flow rates, shorter water residence time led to shorter time and weaker effects of microbial activity on DOM, while lower flow rates from middle to lower reaches led to more significant microbial modification features of DOM [37,38]. Among all the samples in the lower reaches, sample Y18 showed stronger characteristics of biological or aquatic bacterial origin (FI = 2.30, HIX = 1.21) than other samples (Figure 3a,b). The proximity of the Y18 sampling site to a forest park facilitated the erosion of humus-rich soils and the accumulation of abundant leaf litter, thereby constituting a substantial source of dissolved organic matter (DOM). Consequently, the terrigenous inputs associated with Y18 offered a greater abundance of raw materials for microbial activity than observed in other samples. This enrichment of substrates could potentially serve as a pivotal factor underlying the enhanced humification processes within the riverine DOM. As such, the autochthonous characteristics exhibited by Y18 are intimately intertwined with its terrigenous inputs.

As an indicator of internal contributions to DOM, BIX showed an opposite trend to HIX. The BIX of DOM from upper (BIX = 0.94 ± 0.06), middle (BIX = 1.05 ± 0.03), and lower (BIX = 0.99 ± 0.12) reaches first increased and then decreased (Figure 3c). In the upper reaches and the lower reaches, the DOM exhibited characteristics of autochthonous contributions (BIX = 0.8−1.0). Moreover, in the middle reaches, DOM was mainly from biological or aquatic bacterial origin (BIX > 1.0). The gentle flow of middle reaches provided more time for the activities of microorganisms, especially bacteria, and caused DOM to exhibit stronger autogenous characteristics. In addition, the BIX (BIX = 0.78) and HIX (HIX = 0.75) values of Y21 DOM were remarkably lower and higher than surrounding samples, respectively (Figure 3b,c). This proves the higher degree of humification and lower relative contribution of the endogenous material of Y21 DOM. The domestic sewage and pollutants discharged by human activities in the villages near Y21 have a great impact on DOM. In general, the analysis of the spatial dynamics of DOM, as reflected by the fluorescence index, indicated that landform characteristics and the complex riverbank environment significantly influenced DOM variations along the Yongding River.

3.3. EEM-PARAFAC Analysis of DOM

Residual analysis and split-half analysis are widely used methods for determining the number of PARAFAC components in DOM [17,39], and they were conducted in this study to identify appropriate numbers of individual components of DOM. Three PARAFAC components were successfully identified in the samples of the Yongding River from the upper reaches to the lower reaches, including Component 1 (C1, <251(308)/402 nm), Component 2 (C2, 262(355)/455 nm), and Component 3 (C3, 274/225 nm). C1 and C2 were categorized as humic-like components and could be subdivided into microbial humic-like (C1) and terrestrial humic-like (C2) components according to reported components in Openflour, respectively (Figure 4) [40,41,42]. The microbial humic-like components are usually associated with wastewater and agriculture catchments, and the terrestrial humic-like components are derived from terrestrial inputs by natural or agricultural catchments, which are associated with aromatic molecules of high molecular weight [40,43,44]. C3 was categorized as typical protein-like components, which have been described as tryptophan-like compounds derived from the discharge of effluents from human activities in urban areas [45,46,47].

The F_max values of C1 and C2 related to the humic-like compounds from the Yongding River showed a similar trend, first decreasing and then increasing from the upper reaches (F_max(C1) = 0.36 ± 0.23, F_max(C2) = 0.26 ± 0.19) to the middle reaches (F_max(C1) = 0.25 ± 0.08, F_max(C2) = 0.15 ± 0.05) and the lower reaches (F_max(C1) = 0.42 ± 0.15, F_max(C2) = 0.33 ± 0.17) (Figure 4). This trend explains that both humic-like compounds from the lower reaches exhibited the highest relative abundance, which is consistent with the observed variation in HIX values. The terrestrial humic-like compounds are affected by the surrounding residents’ domestic sewage and the agricultural non-point input of nitrogen and phosphorus [48]. Therefore, more intensive anthropogenic activity in downstream areas partly explained an increase in the abundance of C2 components. Moreover, the increased bioavailability of sediment phosphate led to enhanced endogenous metabolic activity in the river, which may be an important reason for the higher microbial humic-like compounds in this section of the river. The F_max values of C3 exhibited a continuous increase from upstream to downstream. The tryptophan-like compounds of the lower reaches (F_max(C3) = 0.44 ± 0.47) were the most abundant compounds in the river. This phenomenon, which was observed in the downstream section, is likely attributable to the presence of villages and towns in the lower reaches of the river, where domestic sewage and agricultural non-point source pollutants are discharged into the river.

Sample Y4 (F_max (C1) = 0.97, F_max (C2) = 0.76, F_max (C2) = 0.81) in the upper reaches of the Yongding River showed obvious differences from the surrounding samples, much higher than the average. This might be caused by the higher DOM content of sample Y4, which also made the TOC values (20.4mg/L) more than other samples. Similar to the FI, HIX, and BIX values, the F_max values of sample Y18 (F_max (C1) = 0.61, F_max (C2) = 0.68, and F_max (C2) = 1.69) were higher than other surrounding samples, confirming the importance of microbial humic-like compounds as well as terrigenous input organic matter. Meanwhile, the F_max value of the downstream DOM exhibited more expected fluctuation due to the obvious effects of domestic sewage and agricultural inputs by human activities (sample Y20, Y21, Y23, and Y24), which is consistent with the low BIX value of downstream DOM.

3.4. Relationships between Fluorescence Indices and Humification Degree of DOM

Various calculation models yield distinct indices for quantifying the humification degree of DOM. In addition to traditional methods that utilize HIX to indicate the humification degree of the DOM, methods based on FRI analysis and PARAFAC analysis are also increasingly being used. Therefore, this study presents a comprehensive humification evaluation method based on the fluorescence feature indices of the EEM, FRI analysis, and PARAFAC analysis. P_I,n/P_II,n and P_I+II+IV,n/P_III+V,n ranged from 0.26 to 0.50 and from 0.27 to 0.60 for all samples, respectively (Figure 2b,c). Additionally, the smallest P_I,n/P_II,n (0.31 ± 0.04) and P_I+II+IV,n/P_III+V,n (0.32 ± 0.02) values were present at the upper reaches. The Spearman correlation analyses were conducted among various fluorescence indicators. Both P_I,n/P_II,n (r = −0.5899, p < 0.01) and P_I+II+IV,n/P_III+V,n (r = −0.9246, p < 0.001) were negatively corrected with the HIX value of DOM (Figure 5), which implied that the ratios of P_I,n/P_II,n and P_I+II+IV,n/P_III+V,n could be the indices of the humification degree of DOM in the Yongding River. The increase in P_I,n/P_II,n, and P_I+II+IV,n/P_III+V,n values represents a reduced humification degree of DOM in the river. Moreover, the (C1 + C2)/C3 and C3/(C1 + C2) ratios showed significant negative (r = −0.9268, p < 0.001) and positive (r = 0.9489, p < 0.001) correlations with P_I+II+IV,n/P_III+V,n values, which meant that biochemical regions (P_I+II+IV,n) represented the relative concentration of the protein-like components and that geochemical regions (P_III+V,n) represented the relative concentration of the humic-like components (Figure 5).

In the Yongding River basin, the microbial humic-like component C1, the terrestrial humic-like component C2, and the typical protein-like component C3 showed very significant positive correlations with each other, indicating that there might be the same source or trend of change between the different components. There were positive (r = 0.6220, p < 0.01) corrections between the FI values and the BIX values, which suggests that the sources of DOM had a great correlation with the autochthonous DOM production in natural waters (Figure 5). The FI values had a positive correlation with the typical protein-like component C3 (r = 0.5375, p < 0.01). The FI values reflect the relative contribution of aromatic amino acids and non-aromatic acids to DOM fluorescence intensity [49]. Therefore, the correlation of FI and the typical protein-like component C3 reflects the contribution of aromatic amino acids to the DOM fluorescence intensity.

There was no significant correlation between the HIX values and F_max values of PARAFAC components (C1, C2). The mean values of C1/C3, C2/C3, (C1 + C2)/C3, and C3/(C1 + C2) in the Yongding River were 1.39 ± 0.36, 0.95 ± 0.26, 2.35 ± 0.61, and 0.47 ± 0.20, respectively. The C1/C3 (r = 0.8828, p < 0.001) and C2/C3 (r = 0.9585, p < 0.001) ratios exhibited positive correlations with HIX values, indicating that the ratio of microbial humic-like versus typical protein-like components, as well as terrestrial humic-like versus typical protein-like components, might be causing the changes in the humification degree of the DOM in the upper, middle, and lower reaches of the Yongding River. The (C1 + C2)/C3 and C3/(C1 + C2) ratios showed significant positive (r = 0.9408, p < 0.001) and negative (r = −0.8059, p < 0.001) correlations with HIX values, respectively (Figure 5). This result indicated that both humic-like and protein-like components significantly influenced the humification degree of DOM in the river. Specifically, a higher proportion of humic-like components and a lower proportion of protein-like components are associated with a greater degree of humification of the DOM. Additionally, the significant negative correlation between C3/(C1 + C2) ratios and HIX confirmed that protein-like components mainly originate from the metabolic activities of microorganisms, while the HIX value mainly reflects the terrestrial influence of organic matter [50]. Therefore, for HIX values, the interrelationship between the characteristic indices derived from feature indices of EEMs and PARAFAC offers a more comprehensive portrayal of the DOM’s humification degree than either EEMs’ characteristic indices or PARAFAC in isolation.

Delving into the origins and extent of humification in dissolved organic matter (DOM) within the Yongding River holds paramount significance for enhancing our comprehension of DOM dynamics and its intricate environmental interactions within urban riverine systems. The optical properties of DOM were determined by FRI analysis, feature indices of EEMs, and PARAFAC to clarify the spatial distribution of DOM from upstream to downstream in the Yongding River basin and its influencing factors. Variations in the ambient environmental characteristics serve as pivotal modulators of DOM sources, necessitating a nuanced understanding of their interplay. Human activities impact the ecological quality of the watershed, altering its natural ecological state and obscuring the effects of natural factors on the watershed’s ecological quality [26].

4. Conclusions

This study presents a pivotal reference for elucidating the spatial distribution of fluorescent DOM in the Yongding River. By analyzing variations in the fluorescence characteristics of DOM from the river’s upper reaches to lower reaches, this study elucidates how environmental factors influence these changes. Notably, the variability in DOM properties within riverine systems is intricately linked to the land use patterns adjacent to the riverbanks, where coastal rock cover and human activities play key roles. Beyond the surrounding environmental determinants, the hydrological dynamics of the river itself significantly modulate the distribution of DOM. Specifically, higher flow rates diminish the influence of microbial activity on DOM, whereas lower flow rates enhance this effect. Thus, this study provides a novel perspective and direction for exploring the environmental behavior of DOM in complex inland water systems like the Yongding River and highlights the crucial role of DOM in the carbon cycle. In addition, the EEM method, combined with FRI and PARAFAC, as presented in this study, constitutes a valuable methodological framework for investigating the geochemical behavior of fluorescent DOM and assessing its degree of humification. Based on the relationship between various fluorescence indices, P_I,n/P_II,n, P_I+II+IV,n/P_III+V,n, (C1 + C2)/C3, and C3/(C1 + C2) ratios were more suitable to for the evaluation of DOM properties in the Yongding River, reflecting the increasing humification degree of riverine DOM from upper reaches to lower reaches. However, this study’s focus was limited to the Yongding River and fluorescent DOM, necessitating a more extensive DOM database at the molecular level to establish a comprehensive system for understanding the environmental behavior of DOM.