{"paper_id":"2bf2255e-dbac-40b8-85c7-7e652f47018e","body_text":"Field Validation of Chromophoric Dissolved Organic Matter (CDOM) Absorbance as a Proxy for Dissolved Organic Carbon (DOC) in Tropical Coastal Waters Influenced by Aquaculture Effluent | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Field Validation of Chromophoric Dissolved Organic Matter (CDOM) Absorbance as a Proxy for Dissolved Organic Carbon (DOC) in Tropical Coastal Waters Influenced by Aquaculture Effluent I Gusti Ngurah Agung Suryaputra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7029413/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aquaculture effluent is a growing source of organic pollution in tropical coastal waters, yet cost-effective and scalable monitoring tools remain limited. This study evaluated the feasibility of using chromophoric dissolved organic matter (CDOM) absorbance, particularly at 254 nm (a₂₅₄), as a proxy for dissolved organic carbon (DOC) in aquaculture-impacted marine environments. Field surveys were conducted at three distinct aquaculture systems in North Bali, Indonesia, including marine finfish cages, shrimp pond discharge, and seaweed farms, using spatial transects, vertical profiling, and temporal sampling across tidal phases and feeding cycles. CDOM absorbance and DOC concentrations were measured at multiple distances from effluent discharge points and depths, while controlled laboratory dilution experiments established quantitative a₂₅₄-DOC relationships for each effluent source. Results showed strong linear correlations between a₂₅₄ and DOC across all systems (R² ≥ 0.99), with relatively consistent regression slopes, indicating a conserved optical yield of DOC among tropical aquaculture effluents. CDOM and DOC concentrations declined with increasing distance from source, and increased following feeding activity, demonstrating responsiveness to operational and hydrodynamic conditions. Spectral slope (S₂₇₅–₂₉₅) and the E₂/E₃ ratio consistently indicated a shift toward higher molecular weight and more aromatic DOM near discharge zones. Principal component analysis further distinguished aquaculture-influenced waters from background conditions based on CDOM-DOC signatures and ammonium loading. These findings confirm that CDOM absorbance is a sensitive, rapid, and cost-effective tool for tracking organic pollution from aquaculture. The integration of field and laboratory data across multiple systems enhances the potential for regional application and supports the adoption of optical proxies in sustainable coastal aquaculture management. CDOM absorbance DOC aquaculture effluent coastal waters monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Coastal aquaculture has emerged as a globally significant sector for food production, contributing substantially to economic growth and food security (FAO, 2022). However, the rapid expansion of this industry, particularly in tropical and subtropical regions, has raised considerable environmental concerns, primarily stemming from the release of nutrient-rich and organically loaded effluents into adjacent coastal waters (Read & Fernandes, 2003 ; Wu, 1995 ). These effluents, derived from uneaten feed, fish or shrimp excreta, and microbial byproducts, can lead to eutrophication, hypoxia, increased microbial activity, and degradation of water quality, thereby threatening the ecological integrity of vulnerable nearshore ecosystems (Turcios & Papenbrock, 2014 ). Effective and frequent monitoring of aquaculture-derived organic pollution is thus essential for sustainable aquaculture development and the protection of coastal environments. Traditional monitoring approaches, such as direct measurements of biochemical oxygen demand (BOD), total organic carbon (TOC), or dissolved organic carbon (DOC), are often time-consuming, costly, and dependent on laboratory infrastructure, limiting their use for high-frequency or broad-scale monitoring programs (Rice et al., 2012 ). In contrast, optical methods based on chromophoric dissolved organic matter (CDOM) absorbance offer a rapid, low-cost, and potentially field-deployable alternative. CDOM, the light-absorbing fraction of dissolved organic matter, exhibits distinct spectral signatures in the ultraviolet (UV) and visible regions, which can serve as sensitive indicators of DOM quantity and compositional shifts (Coble, 1996 ; Helms et al., 2008 ). Several studies have successfully applied CDOM absorbance to trace terrestrial inputs (Spencer et al., 2009 ), monitor wastewater discharges (Imai et al., 2002 ), and estimate DOC concentrations (Fichot & Benner, 2011 ). Despite this growing body of research, the application of CDOM absorbance to characterize and quantify organic pollution from aquaculture remains underdeveloped. Most previous studies have focused on single-source systems, controlled laboratory trials, or limited spatial and temporal contexts. Moreover, few have evaluated the performance of CDOM-based proxies under dynamic field conditions, across multiple aquaculture system types, or over varying tidal and operational cycles. These gaps are particularly relevant in tropical coastal regions, where aquaculture is expanding rapidly, but cost-effective environmental monitoring tools remain limited. This study addresses these knowledge gaps by rigorously assessing the feasibility of using CDOM absorbance as a quantitative proxy for DOC and organic loading from aquaculture effluent in tropical coastal waters. We conducted intensive field sampling across three aquaculture systems in North Bali, Indonesia, including marine finfish cages, shrimp pond discharge, and seaweed farming areas, capturing spatial transects, vertical profiles, and temporal variability associated with tidal phase and feeding events. Laboratory-based dilution experiments were also conducted to calibrate the relationship between CDOM absorbance (notably a₂₅₄) and directly measured DOC concentrations for each effluent type. Spectral indices such as S₂₇₅–₂₉₅ and the E₂/E₃ ratio were evaluated to provide further insight into DOM character and source. The specific objectives of this study were to: (i) characterize spatial and vertical patterns of CDOM and DOC in aquaculture-impacted coastal waters; (ii) determine the quantitative relationships between CDOM absorbance and DOC across different effluent sources and environmental conditions; and (iii) evaluate the consistency and diagnostic utility of CDOM spectral indices as indicators of aquaculture-derived DOM. Through this integrated field and laboratory approach, we aim to enhance the scientific foundation for applying CDOM absorbance as a practical and scalable monitoring tool for organic pollution from aquaculture in tropical marine environments. Materials and Methods This study was conducted along the northern coast of Bali, Indonesia, where three types of aquaculture operations were selected to represent a range of effluent characteristics and management systems. These included a marine finfish cage culture site (Farm A), a shrimp pond outlet discharging into a coastal creek (Farm B), and a seaweed farming area adjacent to a semi-enclosed bay (Farm C). The inclusion of different aquaculture systems allowed for broader insight into the behavior of chromophoric dissolved organic matter (CDOM) and its potential as a monitoring proxy across varying effluent compositions and operational regimes. To capture spatial patterns of CDOM distribution, field sampling was carried out along transects originating at the primary effluent discharge point of each farm. Surface water samples were collected at 0 m (directly at the outfall), 25 m, 50 m, 100 m, and 200 m downstream, following the direction of the dominant tidal or current flow. At two key points along each transect (0 m and 50 m), vertical profiling was conducted at surface, 1 m, and 2 m depths using a Van Dorn water sampler. This approach was designed to assess potential stratification of CDOM and associated water quality parameters. Sampling was conducted during both spring and neap tide phases, as well as during pre-feeding (morning) and post-feeding (afternoon) periods, to evaluate temporal and hydrodynamic variability in effluent dispersion. At each sampling station, in situ measurements were recorded using a YSI EXO2 multiparameter sonde equipped with sensors for CDOM fluorescence, temperature, salinity, dissolved oxygen, pH, and turbidity. Water transparency was measured with a Secchi disk, while light attenuation through the water column was assessed at selected points using a LI-COR underwater quantum sensor. These measurements provided context for interpreting the optical properties of dissolved organic matter under varying environmental conditions. Water samples for laboratory analysis were immediately filtered onsite through pre-combusted 0.7 µm Whatman GF/F filters using gentle vacuum filtration (< 100 mmHg). The filtrates were divided for subsequent analysis. For dissolved organic carbon (DOC) measurements, aliquots were acidified to pH < 2 using 2M hydrochloric acid, stored at 4°C, and analyzed within one week using high-temperature catalytic oxidation on a Shimadzu TOC-L CPH/CSN analyzer. For CDOM absorbance measurements, samples were stored in the dark and analyzed within 24 hours using a Shimadzu UV-1800 spectrophotometer, scanning from 200 to 800 nm with a resolution of 0.5 nm. A 1 cm quartz cuvette was used, and Milli-Q ultrapure water served as the blank. Absorbance data were baseline-corrected by subtracting the average value between 700 and 800 nm. From these spectra, specific absorption coefficients (a₂₅₄, a₂₈₀, and a₃₅₀), spectral slopes (S₂₇₅–₂₉₅), absorbance ratios (E₂/E₃, calculated as A₂₅₄/A₃₆₅), and specific UV absorbance (SUVA₂₅₄, calculated as a₂₅₄/DOC) were derived to characterize the composition and aromaticity of DOM. In addition to field sampling, controlled laboratory mixing experiments were performed to calibrate the relationship between CDOM absorbance and DOC concentration under standardized conditions. For each farm, effluent was collected in 10-liter acid-washed HDPE carboys and mixed volumetrically with background seawater (collected 1.5 km upstream from effluent influence) to create a dilution series: 0%, 1%, 5%, 10%, 25%, 50%, 75%, and 100% effluent. Triplicate samples were prepared at each concentration, filtered, and analyzed for CDOM and DOC using the same methods as for field samples. These calibration experiments were designed to establish linear regression models for predicting DOC from absorbance parameters, particularly a₂₅₄, for each aquaculture system. All data were processed using Python and R statistical software. Linear regression analyses were used to evaluate relationships between CDOM optical metrics (a₂₅₄, a₂₈₀, SUVA₂₅₄) and DOC concentrations. Pearson correlation coefficients were calculated to examine associations between CDOM parameters, DOC, and environmental variables. Two-way ANOVA was applied to test for the influence of sampling distance and tidal condition on CDOM absorbance and spectral indices. Principal component analysis (PCA) was used to explore the covariance structure of multiple optical and physicochemical variables and to identify potential patterns associated with aquaculture influence. This combined field-laboratory approach, incorporating spatial, vertical, and temporal dimensions, was designed to provide a robust assessment of the feasibility of CDOM absorbance as a practical and sensitive monitoring tool for different aquaculture systems in tropical coastal environments. Results 1. Spatial Patterns of CDOM and DOC Across Aquaculture Systems Clear spatial gradients in CDOM absorbance and DOC concentration were observed across all three aquaculture sites. At the finfish cage culture site (Farm A), absorbance at 254 nm (a₂₅₄) was highest at the effluent discharge point (3.12 ± 0.08 m⁻¹) and progressively declined with increasing distance, reaching background levels (0.22 ± 0.03 m⁻¹) at 200 m (Fig. 1 ). DOC concentrations followed a similar trend, decreasing from 17.2 ± 0.9 mg L⁻¹ at the outfall to 1.5 ± 0.2 mg L⁻¹ at 200 m (Fig. 2 ; Table 1 ). Spectral slope (S₂₇₅–₂₉₅) increased with distance (Fig. 4 ), suggesting a shift from high-molecular-weight, effluent-derived DOM to more degraded background DOM. Table 1 Initial CDOM absorbance (a₂₅₄) and dissolved organic carbon (DOC) concentrations measured at the effluent discharge point (0 m) for each aquaculture system. Distance (m) Farm a 254 at 0 m (m -1 ) DOC at 0 m (mg/L) 0 Farm A 3.12 17.2 0 Farm B 2.9 15.0 0 Farm C 1.2 4.0 At the shrimp pond outlet (Farm B), elevated a₂₅₄ and DOC values were also detected but exhibited a sharper decline within the first 50 m, indicative of a more localized dispersion pattern (Figs. 1 and 2 ). The seaweed site (Farm C), by contrast, showed modest increases in CDOM and DOC nearshore during ebb tide, likely due to diffuse runoff, but values remained significantly lower than those observed at Farms A and B. Two-way ANOVA revealed that both distance from source and aquaculture type significantly influenced a₂₅₄ (p < 0.001 for both factors), with a significant interaction term (p = 0.03), indicating that the rate of CDOM attenuation varied by system. 2. Vertical Distribution of CDOM and DOC Vertical profiles revealed minor but consistent stratification at the effluent source and 50 m station for Farms A and B. At both sites, surface waters exhibited the highest a₂₅₄ and DOC concentrations, with slight decreases at 1 m and 2 m depths. At Farm A, a₂₅₄ at the surface reached 3.12 ± 0.08 m⁻¹, compared to 2.84 ± 0.10 m⁻¹ at 1 m and 2.61 ± 0.12 m⁻¹ at 2 m. DOC concentrations mirrored this pattern, with a maximum of 17.2 ± 0.9 mg L⁻¹ at the surface and decreasing to 14.8 ± 1.1 mg L⁻¹ at 2 m (Table 1 ). These gradients were less pronounced at Farm B and negligible at Farm C, consistent with surface-dominated effluent delivery and low water column mixing at the former, and low effluent loading at the latter. 3. Temporal Variation During Feeding Cycles and Tidal Phases CDOM concentrations at all sites varied between pre-feeding and post-feeding periods. At Farm A, a₂₅₄ at the discharge point increased from 2.86 ± 0.12 m⁻¹ (pre-feeding) to 3.21 ± 0.10 m⁻¹ (post-feeding), corresponding to a DOC increase from 15.9 ± 0.7 mg L⁻¹ to 17.6 ± 0.9 mg L⁻¹. This pattern suggests a measurable enrichment of chromophoric organic matter following feeding activity (Figs. 1 and 2 ). Similar, albeit smaller, increases were observed at Farm B. Spring tides exhibited greater downstream dilution of CDOM than neap tides, likely due to higher mixing energy and flushing capacity, particularly at Farms A and B. Two-way ANOVA confirmed significant effects of both tidal phase and feeding period on a₂₅₄ and DOC (p < 0.01), with the strongest post-feeding increases recorded during neap tide conditions, when dispersion was lowest. 4. Calibration of CDOM Absorbance to DOC via Dilution Experiments Controlled laboratory dilution experiments produced strong, positive linear relationships between a₂₅₄ and DOC concentration across all three aquaculture effluents (Fig. 3 ). For Farm A, regression analysis yielded the equation DOC (mg L⁻¹) = 5.12 * a₂₅₄ − 0.18 (R² = 0.996), while Farm B showed DOC = 4.85 * a₂₅₄ − 0.21 (R² = 0.994), and Farm C DOC = 5.04 * a₂₅₄ − 0.15 (R² = 0.991). These results suggest a relatively conserved optical yield across different effluent types, especially for marine-based systems. SUVA₂₅₄ values were highest in Farm A effluent (2.9–3.1 L mg⁻¹ m⁻¹), indicating higher DOM aromaticity compared to Farm B (2.4–2.6) and Farm C (2.0-2.2). Spectral indices such as S₂₇₅–₂₉₅ and E₂/E₃ consistently declined with increasing effluent concentration (Fig. 4 ), reaffirming the shift toward higher molecular weight, less degraded DOM in aquaculture waste. Similarly, SUVA₂₅₄ declined with distance from source (Fig. 5 ). 5. Multivariate Relationships Among CDOM, DOC, and Environmental Variables Principal component analysis (PCA) on combined data from all sites and depths revealed that the first two principal components explained 83.5% of total variance (Fig. 6 ). PC1 was positively loaded with a₂₅₄, a₂₈₀, DOC, and NH₄⁺, and negatively with S₂₇₅–₂₉₅ and E₂/E₃, consistent with a gradient of increasing effluent influence. PC2 captured differences in salinity and turbidity, separating systems based on hydrodynamic and background water characteristics. Farm A and B samples clustered tightly along PC1, while Farm C samples occupied a distinct space with lower CDOM-DOC loading, reinforcing the interpretation of lower anthropogenic impact. Pearson correlation analysis confirmed strong positive correlations between a₂₅₄ and DOC (r = 0.99, p < 0.001), and negative correlations between S₂₇₅–₂₉₅ and both DOC (r = -0.96) and a₂₅₄ (r = -0.97), as shown in Table 2 . These relationships were robust across systems, depths, and tidal conditions, indicating the suitability of CDOM parameters as proxies for organic loading in aquaculture-influenced coastal waters. Table 2 Pearson correlation matrix between CDOM absorbance at 254 nm (a₂₅₄) and DOC concentration across all samples and farms. A strong positive correlation was observed, supporting a₂₅₄ as a reliable DOC proxy. a 254 (m -1 ) DOC (mg/L) a 254 (m -1 ) 1.0 0.99 DOC (mg/L) 0.99 1.0 Discussion This study demonstrates the strong potential of CDOM absorbance, particularly at 254 nm (a₂₅₄), as a robust and sensitive proxy for quantifying dissolved organic carbon (DOC) and assessing organic loading from aquaculture effluent in tropical coastal waters. Through spatial transects (Figs. 1 and 2 ), vertical profiling (Table 1 ), and system-specific laboratory calibrations (Fig. 3 ), this research extends the application of optical DOM monitoring beyond traditional freshwater or wastewater settings and into diverse aquaculture systems under real field conditions. Compared to earlier work, this study builds upon and extends findings from Figueiró et al. ( 2018 ), who used UV-Vis spectroscopy and fluorescence to monitor fish farm water quality. While their study demonstrated the sensitivity of optical methods to organic enrichment, it lacked quantitative linkage to DOC. In contrast, our work incorporates multiple aquaculture types, time periods, and provides direct calibration between a₂₅₄ and DOC (Fig. 3 ; Table 2 ), significantly enhancing the operational applicability of CDOM monitoring. The observed negative correlation between spectral slope S₂₇₅–₂₉₅ and DOC (Fig. 4 ), consistent with Helms et al. ( 2008 ), highlights the prevalence of higher molecular weight DOM near aquaculture sources. The spatial decrease in SUVA₂₅₄ values (Fig. 5 ) aligns with this trend and reinforces the diagnostic utility of simple UV-based indices. These patterns also reflect relatively conserved optical yields across sites (Fig. 3 ), echoing patterns described by Fichot & Benner ( 2011 ), albeit in a more stable marine setting and with anthropogenic point sources. Most previous studies using CDOM absorbance have focused on rivers (Spencer et al., 2009 ), wastewater discharges (Imai et al., 2002 ), or estuarine gradients (Uher et al., 2001 ; Yang et al., 2013 ). Our study, by contrast, demonstrates that aquaculture-derived effluent imparts a reproducible optical signature distinguishable from background seawater across spatial and vertical gradients (Figs. 1 – 5 ). Temporal responses to feeding cycles and tidal phases (Figs. 1 and 2 ) further demonstrate the responsiveness of CDOM absorbance to operational dynamics, aligning with findings from Zhou et al. ( 2024 ) in crab farming systems. However, our simpler absorbance-based approach provides greater accessibility for low-resource regions. The inclusion of PCA (Fig. 6 ) enabled clear differentiation of aquaculture-impacted sites, especially Farms A and B, from background or minimally influenced waters such as Farm C. These results confirm that CDOM optical properties can be employed to identify effluent influence patterns using multivariate tools. Altogether, the findings validate CDOM absorbance, particularly a₂₅₄, SUVA₂₅₄, and spectral slope S₂₇₅–₂₉₅, as practical, low-cost, and scalable proxies for DOC and DOM characterization in aquaculture-impacted environments. These parameters can support early warning systems, regulatory monitoring, and adaptive management for sustainable aquaculture. Conclusion This study provides robust evidence that chromophoric dissolved organic matter (CDOM) absorbance, particularly at 254 nm (a₂₅₄), serves as a reliable, sensitive, and cost-effective proxy for assessing dissolved organic carbon (DOC) and organic enrichment from aquaculture effluent in tropical coastal waters. By combining field transect sampling, vertical profiling, temporal variation analysis, and laboratory calibration experiments across multiple aquaculture systems, including finfish cages, shrimp ponds, and seaweed cultivation areas, we demonstrated that CDOM optical signatures respond consistently and predictably to effluent loading. The strong linear relationships observed between a₂₅₄ and DOC across systems, and the coherence of spectral indices (S₂₇₅–₂₉₅, E₂/E₃, SUVA₂₅₄), highlight the potential of this optical approach to not only quantify DOM concentrations but also to infer compositional characteristics linked to aquaculture activity. Temporal changes tied to feeding schedules and tidal phases further illustrate the responsiveness of CDOM absorbance to operational and environmental dynamics. Compared to previous studies, our findings extend the application of CDOM-based monitoring into tropical aquaculture contexts and confirm that simple UV-Vis absorbance techniques can yield meaningful insights under realistic field conditions. The relatively conserved optical yield per unit DOC across effluent types supports the development of regionally transferable, calibration-based monitoring tools. This work lays a practical foundation for integrating CDOM absorbance into routine environmental monitoring and early warning systems for organic pollution. Future research should expand this framework to include additional aquaculture types, longer-term seasonal variability, and complementary fluorescence or molecular-level analyses to deepen mechanistic understanding. 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The contribution of phytoplankton degradation to chromophoric dissolved organic matter (CDOM) in eutrophic shallow lakes: Field and experimental evidence. Water Research , 43 (18), 4685–4697. https://doi.org/10.1016/j.watres.2009.07.024 Zhou, R., Hao, Y., Yu, B., Hou, J., Lu, K., Yang, F., & Li, Q. (2024). New Insights into Changes in DOM Fractions in a Crab Farming Park and Key Factors in the Removal Process Using Fluorescence Spectra with MW-2DCOS and SEM. Water , 16 (16), 2249. https://doi.org/10.3390/w16162249 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7029413\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":486457401,\"identity\":\"fdcb7a7d-a970-4994-87c2-42292a99f83b\",\"order_by\":0,\"name\":\"I Gusti Ngurah Agung Suryaputra\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYNCCCjkGPgkGhgOMDUAOM3MDEVrOGDOwQbQYALUwEqGFsQ2ihQGshYGAFvkZuc8kfs4zSGyT7n148OuOP9H87UAtH/fU4tRicCPdTLJ3G1CLzHGDw7JnDHJnHGZsYJzx7DhuLRJpbBK82/4ktkmkMRyWbDPIbQBqYeY5cAyPw9LYJP/OMUBomU9IC8ONNDZp3gaIloMfgVo2QLTU4HbYmWfM1jLHDIzbZI4xHAYGXe5GoJaDMw4cwO2w9jTGm29qDGT7pduYP/5sk8udd/7wwQcfDtThdhgDA4sEjMXMA2UArTiMTwvzBxiL8QdCFK8to2AUjIJRMLIAAIt4WrhjXDP0AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Universitas Pendidikan Ganesha\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"I\",\"middleName\":\"Gusti Ngurah Agung\",\"lastName\":\"Suryaputra\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-07-02 12:38:06\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7029413/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7029413/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":89409804,\"identity\":\"7b2e3a04-8691-4918-b590-41231cd08f43\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 15:54:43\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":104499,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCDOM absorbance at 254 nm (a₂₅₄) versus distance from effluent discharge points across three aquaculture systems in North Bali. Values decreased with distance, reflecting dilution and dispersion of organic matter.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7029413/v1/b4f2e52e4bafbb70e2aa6f11.png\"},{\"id\":89410224,\"identity\":\"54f42b8d-c657-4f40-a390-ac7b302ac95d\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 16:02:43\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":102705,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDissolved organic carbon (DOC) concentrations versus distance from effluent discharge points for each aquaculture site. A clear spatial gradient was observed, especially in finfish and shrimp systems.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7029413/v1/87fe5510c5c36c16884fcbb9.png\"},{\"id\":89410225,\"identity\":\"e60daa85-3cd4-4839-83b4-9e434e505b5f\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 16:02:43\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":98370,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRelationship between a₂₅₄ and DOC concentration across all farms, with linear regression lines. The consistent slope across systems suggests a conserved optical yield of DOC among tropical aquaculture effluents.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7029413/v1/fe6e2109a391973f0ebc41c5.png\"},{\"id\":89409809,\"identity\":\"bbdf2ddf-95f4-4626-9995-2d032ae58f73\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 15:54:43\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":107524,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpectral slope (S₂₇₅–₂₉₅) versus distance from effluent discharge point. Lower slope values near the source indicate the presence of higher molecular weight DOM, with values increasing as distance from the source increases.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7029413/v1/505302b61587aad2831d4fea.png\"},{\"id\":89409807,\"identity\":\"ffe6c552-885b-49d0-940a-34206afa5d3c\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 15:54:43\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":104917,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpecific UV absorbance at 254 nm (SUVA₂₅₄) versus distance from discharge. Higher SUVA₂₅₄ values near aquaculture outfalls reflect increased aromaticity and organic loading, especially in finfish and shrimp systems.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7029413/v1/d1b0aadb4bb6d11f7a80ea1d.png\"},{\"id\":89409810,\"identity\":\"096bd6bf-388e-4457-8812-08e18c06e11a\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 15:54:43\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":87733,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePrincipal component analysis (PCA) biplot showing distribution of samples by aquaculture system. Farm A and B group along PC1 due to higher CDOM and DOC values, while Farm C clusters separately with lower organic loading.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7029413/v1/a0cbe5d40fab75a467dc7ccd.png\"},{\"id\":92663120,\"identity\":\"fc61a6c2-4531-4e40-bb2d-7f583f7d1367\",\"added_by\":\"auto\",\"created_at\":\"2025-10-02 15:31:51\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1051390,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7029413/v1/86754973-f22e-493d-ba05-750fe79e81d3.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Field Validation of Chromophoric Dissolved Organic Matter (CDOM) Absorbance as a Proxy for Dissolved Organic Carbon (DOC) in Tropical Coastal Waters Influenced by Aquaculture Effluent\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCoastal aquaculture has emerged as a globally significant sector for food production, contributing substantially to economic growth and food security (FAO, 2022). However, the rapid expansion of this industry, particularly in tropical and subtropical regions, has raised considerable environmental concerns, primarily stemming from the release of nutrient-rich and organically loaded effluents into adjacent coastal waters (Read \\u0026amp; Fernandes, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Wu, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1995\\u003c/span\\u003e). These effluents, derived from uneaten feed, fish or shrimp excreta, and microbial byproducts, can lead to eutrophication, hypoxia, increased microbial activity, and degradation of water quality, thereby threatening the ecological integrity of vulnerable nearshore ecosystems (Turcios \\u0026amp; Papenbrock, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Effective and frequent monitoring of aquaculture-derived organic pollution is thus essential for sustainable aquaculture development and the protection of coastal environments.\\u003c/p\\u003e\\u003cp\\u003eTraditional monitoring approaches, such as direct measurements of biochemical oxygen demand (BOD), total organic carbon (TOC), or dissolved organic carbon (DOC), are often time-consuming, costly, and dependent on laboratory infrastructure, limiting their use for high-frequency or broad-scale monitoring programs (Rice et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). In contrast, optical methods based on chromophoric dissolved organic matter (CDOM) absorbance offer a rapid, low-cost, and potentially field-deployable alternative. CDOM, the light-absorbing fraction of dissolved organic matter, exhibits distinct spectral signatures in the ultraviolet (UV) and visible regions, which can serve as sensitive indicators of DOM quantity and compositional shifts (Coble, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e; Helms et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). Several studies have successfully applied CDOM absorbance to trace terrestrial inputs (Spencer et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), monitor wastewater discharges (Imai et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e), and estimate DOC concentrations (Fichot \\u0026amp; Benner, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eDespite this growing body of research, the application of CDOM absorbance to characterize and quantify organic pollution from aquaculture remains underdeveloped. Most previous studies have focused on single-source systems, controlled laboratory trials, or limited spatial and temporal contexts. Moreover, few have evaluated the performance of CDOM-based proxies under dynamic field conditions, across multiple aquaculture system types, or over varying tidal and operational cycles. These gaps are particularly relevant in tropical coastal regions, where aquaculture is expanding rapidly, but cost-effective environmental monitoring tools remain limited.\\u003c/p\\u003e\\u003cp\\u003eThis study addresses these knowledge gaps by rigorously assessing the feasibility of using CDOM absorbance as a quantitative proxy for DOC and organic loading from aquaculture effluent in tropical coastal waters. We conducted intensive field sampling across three aquaculture systems in North Bali, Indonesia, including marine finfish cages, shrimp pond discharge, and seaweed farming areas, capturing spatial transects, vertical profiles, and temporal variability associated with tidal phase and feeding events. Laboratory-based dilution experiments were also conducted to calibrate the relationship between CDOM absorbance (notably a₂₅₄) and directly measured DOC concentrations for each effluent type. Spectral indices such as S₂₇₅\\u0026ndash;₂₉₅ and the E₂/E₃ ratio were evaluated to provide further insight into DOM character and source.\\u003c/p\\u003e\\u003cp\\u003eThe specific objectives of this study were to: (i) characterize spatial and vertical patterns of CDOM and DOC in aquaculture-impacted coastal waters; (ii) determine the quantitative relationships between CDOM absorbance and DOC across different effluent sources and environmental conditions; and (iii) evaluate the consistency and diagnostic utility of CDOM spectral indices as indicators of aquaculture-derived DOM. Through this integrated field and laboratory approach, we aim to enhance the scientific foundation for applying CDOM absorbance as a practical and scalable monitoring tool for organic pollution from aquaculture in tropical marine environments.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003eThis study was conducted along the northern coast of Bali, Indonesia, where three types of aquaculture operations were selected to represent a range of effluent characteristics and management systems. These included a marine finfish cage culture site (Farm A), a shrimp pond outlet discharging into a coastal creek (Farm B), and a seaweed farming area adjacent to a semi-enclosed bay (Farm C). The inclusion of different aquaculture systems allowed for broader insight into the behavior of chromophoric dissolved organic matter (CDOM) and its potential as a monitoring proxy across varying effluent compositions and operational regimes.\\u003c/p\\u003e\\u003cp\\u003eTo capture spatial patterns of CDOM distribution, field sampling was carried out along transects originating at the primary effluent discharge point of each farm. Surface water samples were collected at 0 m (directly at the outfall), 25 m, 50 m, 100 m, and 200 m downstream, following the direction of the dominant tidal or current flow. At two key points along each transect (0 m and 50 m), vertical profiling was conducted at surface, 1 m, and 2 m depths using a Van Dorn water sampler. This approach was designed to assess potential stratification of CDOM and associated water quality parameters. Sampling was conducted during both spring and neap tide phases, as well as during pre-feeding (morning) and post-feeding (afternoon) periods, to evaluate temporal and hydrodynamic variability in effluent dispersion.\\u003c/p\\u003e\\u003cp\\u003eAt each sampling station, in situ measurements were recorded using a YSI EXO2 multiparameter sonde equipped with sensors for CDOM fluorescence, temperature, salinity, dissolved oxygen, pH, and turbidity. Water transparency was measured with a Secchi disk, while light attenuation through the water column was assessed at selected points using a LI-COR underwater quantum sensor. These measurements provided context for interpreting the optical properties of dissolved organic matter under varying environmental conditions.\\u003c/p\\u003e\\u003cp\\u003eWater samples for laboratory analysis were immediately filtered onsite through pre-combusted 0.7 \\u0026micro;m Whatman GF/F filters using gentle vacuum filtration (\\u0026lt;\\u0026thinsp;100 mmHg). The filtrates were divided for subsequent analysis. For dissolved organic carbon (DOC) measurements, aliquots were acidified to pH\\u0026thinsp;\\u0026lt;\\u0026thinsp;2 using 2M hydrochloric acid, stored at 4\\u0026deg;C, and analyzed within one week using high-temperature catalytic oxidation on a Shimadzu TOC-L CPH/CSN analyzer. For CDOM absorbance measurements, samples were stored in the dark and analyzed within 24 hours using a Shimadzu UV-1800 spectrophotometer, scanning from 200 to 800 nm with a resolution of 0.5 nm. A 1 cm quartz cuvette was used, and Milli-Q ultrapure water served as the blank. Absorbance data were baseline-corrected by subtracting the average value between 700 and 800 nm. From these spectra, specific absorption coefficients (a₂₅₄, a₂₈₀, and a₃₅₀), spectral slopes (S₂₇₅\\u0026ndash;₂₉₅), absorbance ratios (E₂/E₃, calculated as A₂₅₄/A₃₆₅), and specific UV absorbance (SUVA₂₅₄, calculated as a₂₅₄/DOC) were derived to characterize the composition and aromaticity of DOM.\\u003c/p\\u003e\\u003cp\\u003eIn addition to field sampling, controlled laboratory mixing experiments were performed to calibrate the relationship between CDOM absorbance and DOC concentration under standardized conditions. For each farm, effluent was collected in 10-liter acid-washed HDPE carboys and mixed volumetrically with background seawater (collected 1.5 km upstream from effluent influence) to create a dilution series: 0%, 1%, 5%, 10%, 25%, 50%, 75%, and 100% effluent. Triplicate samples were prepared at each concentration, filtered, and analyzed for CDOM and DOC using the same methods as for field samples. These calibration experiments were designed to establish linear regression models for predicting DOC from absorbance parameters, particularly a₂₅₄, for each aquaculture system.\\u003c/p\\u003e\\u003cp\\u003eAll data were processed using Python and R statistical software. Linear regression analyses were used to evaluate relationships between CDOM optical metrics (a₂₅₄, a₂₈₀, SUVA₂₅₄) and DOC concentrations. Pearson correlation coefficients were calculated to examine associations between CDOM parameters, DOC, and environmental variables. Two-way ANOVA was applied to test for the influence of sampling distance and tidal condition on CDOM absorbance and spectral indices. Principal component analysis (PCA) was used to explore the covariance structure of multiple optical and physicochemical variables and to identify potential patterns associated with aquaculture influence.\\u003c/p\\u003e\\u003cp\\u003eThis combined field-laboratory approach, incorporating spatial, vertical, and temporal dimensions, was designed to provide a robust assessment of the feasibility of CDOM absorbance as a practical and sensitive monitoring tool for different aquaculture systems in tropical coastal environments.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e1. Spatial Patterns of CDOM and DOC Across Aquaculture Systems\\u003c/p\\u003e\\u003cp\\u003eClear spatial gradients in CDOM absorbance and DOC concentration were observed across all three aquaculture sites. At the finfish cage culture site (Farm A), absorbance at 254 nm (a₂₅₄) was highest at the effluent discharge point (3.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08 m⁻\\u0026sup1;) and progressively declined with increasing distance, reaching background levels (0.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03 m⁻\\u0026sup1;) at 200 m (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). DOC concentrations followed a similar trend, decreasing from 17.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.9 mg L⁻\\u0026sup1; at the outfall to 1.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2 mg L⁻\\u0026sup1; at 200 m (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Spectral slope (S₂₇₅\\u0026ndash;₂₉₅) increased with distance (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), suggesting a shift from high-molecular-weight, effluent-derived DOM to more degraded background DOM.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eInitial CDOM absorbance (a₂₅₄) and dissolved organic carbon (DOC) concentrations measured at the effluent discharge point (0 m) for each aquaculture system.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistance (m)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFarm\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ea\\u003csub\\u003e254\\u003c/sub\\u003e at 0 m (m\\u003csup\\u003e-1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eDOC at 0 m (mg/L)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFarm A\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFarm B\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFarm C\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eAt the shrimp pond outlet (Farm B), elevated a₂₅₄ and DOC values were also detected but exhibited a sharper decline within the first 50 m, indicative of a more localized dispersion pattern (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The seaweed site (Farm C), by contrast, showed modest increases in CDOM and DOC nearshore during ebb tide, likely due to diffuse runoff, but values remained significantly lower than those observed at Farms A and B.\\u003c/p\\u003e\\u003cp\\u003eTwo-way ANOVA revealed that both distance from source and aquaculture type significantly influenced a₂₅₄ (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 for both factors), with a significant interaction term (p\\u0026thinsp;=\\u0026thinsp;0.03), indicating that the rate of CDOM attenuation varied by system.\\u003c/p\\u003e\\u003cp\\u003e2. Vertical Distribution of CDOM and DOC\\u003c/p\\u003e\\u003cp\\u003eVertical profiles revealed minor but consistent stratification at the effluent source and 50 m station for Farms A and B. At both sites, surface waters exhibited the highest a₂₅₄ and DOC concentrations, with slight decreases at 1 m and 2 m depths. At Farm A, a₂₅₄ at the surface reached 3.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08 m⁻\\u0026sup1;, compared to 2.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10 m⁻\\u0026sup1; at 1 m and 2.61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12 m⁻\\u0026sup1; at 2 m. DOC concentrations mirrored this pattern, with a maximum of 17.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.9 mg L⁻\\u0026sup1; at the surface and decreasing to 14.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.1 mg L⁻\\u0026sup1; at 2 m (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). These gradients were less pronounced at Farm B and negligible at Farm C, consistent with surface-dominated effluent delivery and low water column mixing at the former, and low effluent loading at the latter.\\u003c/p\\u003e\\u003cp\\u003e3. Temporal Variation During Feeding Cycles and Tidal Phases\\u003c/p\\u003e\\u003cp\\u003eCDOM concentrations at all sites varied between pre-feeding and post-feeding periods. At Farm A, a₂₅₄ at the discharge point increased from 2.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12 m⁻\\u0026sup1; (pre-feeding) to 3.21\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10 m⁻\\u0026sup1; (post-feeding), corresponding to a DOC increase from 15.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7 mg L⁻\\u0026sup1; to 17.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.9 mg L⁻\\u0026sup1;. This pattern suggests a measurable enrichment of chromophoric organic matter following feeding activity (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Similar, albeit smaller, increases were observed at Farm B. Spring tides exhibited greater downstream dilution of CDOM than neap tides, likely due to higher mixing energy and flushing capacity, particularly at Farms A and B.\\u003c/p\\u003e\\u003cp\\u003eTwo-way ANOVA confirmed significant effects of both tidal phase and feeding period on a₂₅₄ and DOC (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), with the strongest post-feeding increases recorded during neap tide conditions, when dispersion was lowest.\\u003c/p\\u003e\\u003cp\\u003e4. Calibration of CDOM Absorbance to DOC via Dilution Experiments\\u003c/p\\u003e\\u003cp\\u003eControlled laboratory dilution experiments produced strong, positive linear relationships between a₂₅₄ and DOC concentration across all three aquaculture effluents (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). For Farm A, regression analysis yielded the equation DOC (mg L⁻\\u0026sup1;)\\u0026thinsp;=\\u0026thinsp;5.12 * a₂₅₄ \\u0026minus;\\u0026thinsp;0.18 (R\\u0026sup2; = 0.996), while Farm B showed DOC\\u0026thinsp;=\\u0026thinsp;4.85 * a₂₅₄ \\u0026minus;\\u0026thinsp;0.21 (R\\u0026sup2; = 0.994), and Farm C DOC\\u0026thinsp;=\\u0026thinsp;5.04 * a₂₅₄ \\u0026minus;\\u0026thinsp;0.15 (R\\u0026sup2; = 0.991). These results suggest a relatively conserved optical yield across different effluent types, especially for marine-based systems.\\u003c/p\\u003e\\u003cp\\u003eSUVA₂₅₄ values were highest in Farm A effluent (2.9\\u0026ndash;3.1 L mg⁻\\u0026sup1; m⁻\\u0026sup1;), indicating higher DOM aromaticity compared to Farm B (2.4\\u0026ndash;2.6) and Farm C (2.0-2.2). Spectral indices such as S₂₇₅\\u0026ndash;₂₉₅ and E₂/E₃ consistently declined with increasing effluent concentration (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), reaffirming the shift toward higher molecular weight, less degraded DOM in aquaculture waste. Similarly, SUVA₂₅₄ declined with distance from source (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e5. Multivariate Relationships Among CDOM, DOC, and Environmental Variables\\u003c/p\\u003e\\u003cp\\u003ePrincipal component analysis (PCA) on combined data from all sites and depths revealed that the first two principal components explained 83.5% of total variance (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). PC1 was positively loaded with a₂₅₄, a₂₈₀, DOC, and NH₄⁺, and negatively with S₂₇₅\\u0026ndash;₂₉₅ and E₂/E₃, consistent with a gradient of increasing effluent influence. PC2 captured differences in salinity and turbidity, separating systems based on hydrodynamic and background water characteristics. Farm A and B samples clustered tightly along PC1, while Farm C samples occupied a distinct space with lower CDOM-DOC loading, reinforcing the interpretation of lower anthropogenic impact.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003ePearson correlation analysis confirmed strong positive correlations between a₂₅₄ and DOC (r\\u0026thinsp;=\\u0026thinsp;0.99, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and negative correlations between S₂₇₅\\u0026ndash;₂₉₅ and both DOC (r = -0.96) and a₂₅₄ (r = -0.97), as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. These relationships were robust across systems, depths, and tidal conditions, indicating the suitability of CDOM parameters as proxies for organic loading in aquaculture-influenced coastal waters.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003ePearson correlation matrix between CDOM absorbance at 254 nm (a₂₅₄) and DOC concentration across all samples and farms. A strong positive correlation was observed, supporting a₂₅₄ as a reliable DOC proxy.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ea\\u003csub\\u003e254\\u003c/sub\\u003e (m\\u003csup\\u003e-1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDOC (mg/L)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ea\\u003csub\\u003e254\\u003c/sub\\u003e (m\\u003csup\\u003e-1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDOC (mg/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study demonstrates the strong potential of CDOM absorbance, particularly at 254 nm (a₂₅₄), as a robust and sensitive proxy for quantifying dissolved organic carbon (DOC) and assessing organic loading from aquaculture effluent in tropical coastal waters. Through spatial transects (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), vertical profiling (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), and system-specific laboratory calibrations (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), this research extends the application of optical DOM monitoring beyond traditional freshwater or wastewater settings and into diverse aquaculture systems under real field conditions.\\u003c/p\\u003e\\u003cp\\u003eCompared to earlier work, this study builds upon and extends findings from Figueir\\u0026oacute; et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), who used UV-Vis spectroscopy and fluorescence to monitor fish farm water quality. While their study demonstrated the sensitivity of optical methods to organic enrichment, it lacked quantitative linkage to DOC. In contrast, our work incorporates multiple aquaculture types, time periods, and provides direct calibration between a₂₅₄ and DOC (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), significantly enhancing the operational applicability of CDOM monitoring.\\u003c/p\\u003e\\u003cp\\u003eThe observed negative correlation between spectral slope S₂₇₅\\u0026ndash;₂₉₅ and DOC (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), consistent with Helms et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e), highlights the prevalence of higher molecular weight DOM near aquaculture sources. The spatial decrease in SUVA₂₅₄ values (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e) aligns with this trend and reinforces the diagnostic utility of simple UV-based indices. These patterns also reflect relatively conserved optical yields across sites (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), echoing patterns described by Fichot \\u0026amp; Benner (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e), albeit in a more stable marine setting and with anthropogenic point sources.\\u003c/p\\u003e\\u003cp\\u003eMost previous studies using CDOM absorbance have focused on rivers (Spencer et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), wastewater discharges (Imai et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e), or estuarine gradients (Uher et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e; Yang et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Our study, by contrast, demonstrates that aquaculture-derived effluent imparts a reproducible optical signature distinguishable from background seawater across spatial and vertical gradients (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eTemporal responses to feeding cycles and tidal phases (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) further demonstrate the responsiveness of CDOM absorbance to operational dynamics, aligning with findings from Zhou et al. (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) in crab farming systems. However, our simpler absorbance-based approach provides greater accessibility for low-resource regions.\\u003c/p\\u003e\\u003cp\\u003eThe inclusion of PCA (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e) enabled clear differentiation of aquaculture-impacted sites, especially Farms A and B, from background or minimally influenced waters such as Farm C. These results confirm that CDOM optical properties can be employed to identify effluent influence patterns using multivariate tools.\\u003c/p\\u003e\\u003cp\\u003eAltogether, the findings validate CDOM absorbance, particularly a₂₅₄, SUVA₂₅₄, and spectral slope S₂₇₅\\u0026ndash;₂₉₅, as practical, low-cost, and scalable proxies for DOC and DOM characterization in aquaculture-impacted environments. These parameters can support early warning systems, regulatory monitoring, and adaptive management for sustainable aquaculture.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study provides robust evidence that chromophoric dissolved organic matter (CDOM) absorbance, particularly at 254 nm (a₂₅₄), serves as a reliable, sensitive, and cost-effective proxy for assessing dissolved organic carbon (DOC) and organic enrichment from aquaculture effluent in tropical coastal waters. By combining field transect sampling, vertical profiling, temporal variation analysis, and laboratory calibration experiments across multiple aquaculture systems, including finfish cages, shrimp ponds, and seaweed cultivation areas, we demonstrated that CDOM optical signatures respond consistently and predictably to effluent loading.\\u003c/p\\u003e\\u003cp\\u003eThe strong linear relationships observed between a₂₅₄ and DOC across systems, and the coherence of spectral indices (S₂₇₅\\u0026ndash;₂₉₅, E₂/E₃, SUVA₂₅₄), highlight the potential of this optical approach to not only quantify DOM concentrations but also to infer compositional characteristics linked to aquaculture activity. Temporal changes tied to feeding schedules and tidal phases further illustrate the responsiveness of CDOM absorbance to operational and environmental dynamics.\\u003c/p\\u003e\\u003cp\\u003eCompared to previous studies, our findings extend the application of CDOM-based monitoring into tropical aquaculture contexts and confirm that simple UV-Vis absorbance techniques can yield meaningful insights under realistic field conditions. The relatively conserved optical yield per unit DOC across effluent types supports the development of regionally transferable, calibration-based monitoring tools.\\u003c/p\\u003e\\u003cp\\u003eThis work lays a practical foundation for integrating CDOM absorbance into routine environmental monitoring and early warning systems for organic pollution. Future research should expand this framework to include additional aquaculture types, longer-term seasonal variability, and complementary fluorescence or molecular-level analyses to deepen mechanistic understanding. Ultimately, the adoption of CDOM absorbance as a rapid diagnostic tool can support more sustainable aquaculture management and help protect the ecological integrity of vulnerable coastal ecosystems.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\u003cp\\u003eThis research was funded by Universitas Pendidikan Ganesha [grant number: 1367/UN48.16/LT/2024].\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eIGNAS prepared all the tables and figures, and wrote the manuscript\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAngelotti de Ponte Rodrigues, N., Carmigniani, R., Guillot-Le Goff, A., Lucas, F. S., Therial, C., Naloufi, M., Janne, A., Piccioni, F., Saad, M., Dubois, P., \\u0026amp; Vin\\u0026ccedil;on-Leite, B. (2024). 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Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. \\u003cem\\u003eLimnology and Oceanography\\u003c/em\\u003e, \\u003cem\\u003e53\\u003c/em\\u003e(3), 955\\u0026ndash;969. https://doi.org/10.4319/lo.2008.53.3.0955\\u003c/li\\u003e\\n\\u003cli\\u003eImai, A., Fukushima, T., Matsushige, K., Kim, Y.-H., \\u0026amp; Choi, K. (2002). Characterization of dissolved organic matter in effluents from wastewater treatment plants. \\u003cem\\u003eWater Research\\u003c/em\\u003e, \\u003cem\\u003e36\\u003c/em\\u003e(4), 859\\u0026ndash;870. https://doi.org/10.1016/S0043-1354(01)00283-4\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, D., Nie, L., Xi, B., Gao, H., Yang, F., \\u0026amp; Yu, H. (2024). A novel-approach for identifying sources of fluvial DOM using fluorescence spectroscopy and machine learning model. \\u003cem\\u003eNpj Clean Water\\u003c/em\\u003e, \\u003cem\\u003e7\\u003c/em\\u003e(1). https://doi.org/10.1038/s41545-024-00370-1\\u003c/li\\u003e\\n\\u003cli\\u003eRead, P., \\u0026amp; Fernandes, T. (2003). Management of environmental impacts of marine aquaculture in Europe. \\u003cem\\u003eAquaculture\\u003c/em\\u003e, \\u003cem\\u003e226\\u003c/em\\u003e(1\\u0026ndash;4), 139\\u0026ndash;163. https://doi.org/10.1016/S0044-8486(03)00474-5\\u003c/li\\u003e\\n\\u003cli\\u003eRice, E. W., Baird, R. B., Eaton, A. D., \\u0026amp; Clesceri, L. S. (2012). \\u003cem\\u003eStandard methods for the examination of water and wastewater\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eSpencer, R. G. M., Aiken, G. R., Butler, K. D., Dornblaser, M. M., Striegl, R. G., \\u0026amp; Hernes, P. J. (2009). Utilizing chromophoric dissolved organic matter measurements to derive export and reactivity of dissolved organic carbon exported to the Arctic Ocean: A case study of the Yukon River, Alaska. \\u003cem\\u003eGeophysical Research Letters\\u003c/em\\u003e, \\u003cem\\u003e36\\u003c/em\\u003e(6). https://doi.org/10.1029/2008GL036831\\u003c/li\\u003e\\n\\u003cli\\u003eStedmon, C. A., \\u0026amp; Cory, R. M. (2014). Biological origins and fate of fluorescent dissolved organic matter in aquatic environments. \\u003cem\\u003eAquatic Organic Matter Fluorescence\\u003c/em\\u003e, 278\\u0026ndash;299.\\u003c/li\\u003e\\n\\u003cli\\u003eStedmon, C. A., \\u0026amp; Markager, S. (2001). The optics of chromophoric dissolved organic matter (CDOM) in the Greenland Sea: An algorithm for differentiation between marine and terrestrially derived organic matter. \\u003cem\\u003eLimnology and Oceanography\\u003c/em\\u003e, \\u003cem\\u003e46\\u003c/em\\u003e(8), 2087\\u0026ndash;2093. https://doi.org/10.4319/lo.2001.46.8.2087\\u003c/li\\u003e\\n\\u003cli\\u003eSun, S. Q., Cai, H. Y., Chang, S. X., \\u0026amp; Bhatti, J. S. (2015). Sample storage-induced changes in the quantity and quality of soil labile organic carbon. \\u003cem\\u003eScientific Reports\\u003c/em\\u003e, \\u003cem\\u003e5\\u003c/em\\u003e. https://doi.org/10.1038/srep17496\\u003c/li\\u003e\\n\\u003cli\\u003eSuryaputra, I. G. N. A., Santos, I. R., Huettel, M., Burnett, W. C., \\u0026amp; Dittmar, T. (2015). 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Non-conservative behaviors of chromophoric dissolved organic matter in a turbid estuary: Roles of multiple biogeochemical processes. \\u003cem\\u003eEstuarine, Coastal and Shelf Science\\u003c/em\\u003e, \\u003cem\\u003e133\\u003c/em\\u003e, 285\\u0026ndash;292. https://doi.org/10.1016/j.ecss.2013.09.007\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, Y., van Dijk, M. A., Liu, M., Zhu, G., \\u0026amp; Qin, B. (2009). The contribution of phytoplankton degradation to chromophoric dissolved organic matter (CDOM) in eutrophic shallow lakes: Field and experimental evidence. \\u003cem\\u003eWater Research\\u003c/em\\u003e, \\u003cem\\u003e43\\u003c/em\\u003e(18), 4685\\u0026ndash;4697. https://doi.org/10.1016/j.watres.2009.07.024\\u003c/li\\u003e\\n\\u003cli\\u003eZhou, R., Hao, Y., Yu, B., Hou, J., Lu, K., Yang, F., \\u0026amp; Li, Q. (2024). New Insights into Changes in DOM Fractions in a Crab Farming Park and Key Factors in the Removal Process Using Fluorescence Spectra with MW-2DCOS and SEM. \\u003cem\\u003eWater\\u003c/em\\u003e, \\u003cem\\u003e16\\u003c/em\\u003e(16), 2249. https://doi.org/10.3390/w16162249\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"CDOM absorbance, DOC, aquaculture effluent, coastal waters, monitoring\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7029413/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7029413/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eAquaculture effluent is a growing source of organic pollution in tropical coastal waters, yet cost-effective and scalable monitoring tools remain limited. This study evaluated the feasibility of using chromophoric dissolved organic matter (CDOM) absorbance, particularly at 254 nm (a₂₅₄), as a proxy for dissolved organic carbon (DOC) in aquaculture-impacted marine environments. Field surveys were conducted at three distinct aquaculture systems in North Bali, Indonesia, including marine finfish cages, shrimp pond discharge, and seaweed farms, using spatial transects, vertical profiling, and temporal sampling across tidal phases and feeding cycles. CDOM absorbance and DOC concentrations were measured at multiple distances from effluent discharge points and depths, while controlled laboratory dilution experiments established quantitative a₂₅₄-DOC relationships for each effluent source.\\u003c/p\\u003e\\u003cp\\u003eResults showed strong linear correlations between a₂₅₄ and DOC across all systems (R\\u0026sup2; \\u0026ge; 0.99), with relatively consistent regression slopes, indicating a conserved optical yield of DOC among tropical aquaculture effluents. CDOM and DOC concentrations declined with increasing distance from source, and increased following feeding activity, demonstrating responsiveness to operational and hydrodynamic conditions. Spectral slope (S₂₇₅\\u0026ndash;₂₉₅) and the E₂/E₃ ratio consistently indicated a shift toward higher molecular weight and more aromatic DOM near discharge zones. Principal component analysis further distinguished aquaculture-influenced waters from background conditions based on CDOM-DOC signatures and ammonium loading.\\u003c/p\\u003e\\u003cp\\u003eThese findings confirm that CDOM absorbance is a sensitive, rapid, and cost-effective tool for tracking organic pollution from aquaculture. The integration of field and laboratory data across multiple systems enhances the potential for regional application and supports the adoption of optical proxies in sustainable coastal aquaculture management.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Field Validation of Chromophoric Dissolved Organic Matter (CDOM) Absorbance as a Proxy for Dissolved Organic Carbon (DOC) in Tropical Coastal Waters Influenced by Aquaculture Effluent\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-19 15:54:39\",\"doi\":\"10.21203/rs.3.rs-7029413/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"12c07ff1-e3b7-41f8-890e-9331b5bfd146\",\"owner\":[],\"postedDate\":\"August 19th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-10-02T15:23:42+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-08-19 15:54:39\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7029413\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7029413\",\"identity\":\"rs-7029413\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}