Trends Sci. 2026; 23(1): 11197

Introduction

Increasing global population has intensified anthropogenic activities, leading to elevated nutrient loads at river mouths [1]. In South and Southeast Asia, rapid urbanization and economic development have driven the expansion of domestic wastewater discharge and agricultural land [2,3]. Essential macronutrients - dissolved silicate (DSi), nitrogen (N), and phosphorus (P), in both organic and inorganic forms - support phytoplankton growth [4]. A range of anthropogenic activities, including land-use change, infrastructure development, and agriculture, alter nutrient concentrations and their spatial distribution [5]. In densely populated regions, domestic wastewater enhances dissolved inorganic phosphorus (DIP), dissolved inorganic nitrogen (DIN), dissolved organic nitrogen (DON), and phosphorus (DOP) levels [6,7]. Consequently, anthropogenic inputs are fundamentally linked to nutrient variability in river systems.

Estuaries and river mouths serve as critical conduits for transporting terrestrial nutrients to coastal waters [8]. Elevated nutrient inputs often lead to phytoplankton blooms and shifts in community structure [9]. For instance, the Pearl River Estuary in China has experienced eutrophication and increased phytoplankton productivity due to urban and agricultural nutrient loading [10]. Similarly, studies in Jiaozhou Bay highlight the role of bioavailable DON in fueling coastal algal communities.

In Thailand, 4 major river mouths - Maeklong (MK), Thachin (TC), Chaopraya (CPY), and Bangpakong (BPK) - discharge anthropogenic nutrients (especially DIP and DIN) into the inner Gulf of Thailand (inner GoT), fueling eutrophication and phytoplankton blooms reported since 1957 [11,12]. These blooms have led to ecological imbalance, fish kills, aquaculture losses, and economic impacts on tourism and fisheries [11,13,14]. For instance, organic matter from blooms can cause hypoxia and harming marine life. Between 1981 - 1995, fish kills linked to harmful algal blooms (HABs) occurred annually or biannually. Although less frequent between 2000 - 2017, a major 2023 bloom of Noctiluca scintillans in Chonburi caused hypoxia and mussel mortality, with reported losses of USD 14,000 [14-16] . Similar events have caused damages USD 6 and 8.5 million in Malaysia and the Philippines, respectively [14].

Despite long-standing recognition of eutrophication, baseline data on nutrient inputs remain limited. Recent studies [17,18] confirm persistent anthropogenic nutrient loading, but key knowledge gaps remain. Most previous research relied on seasonal sampling (dry/wet monsoons), overlooking potential non-seasonal nutrient fluctuations [19-21] , and often neglected dissolved organic nutrients (DOP and DON), which significantly influence phytoplankton growth and hypoxia [22] . Moreover, studies typically focused on single rivers, limiting cross-system comparisons critical for regional management. To address these gaps, we conducted monthly and seasonal sampling across all 4 river mouths in 2019. Our objectives were to (1) compare spatial and seasonal variations of physicochemical parameters and both inorganic and organic nutrients, and (2) examine nutrient distribution patterns linked to anthropogenic sources. Sampling included vertical salinity and nutrient profiles to assess estuarine mixing. This is among the first studies to provide a year-round comparative assessment of nutrient inputs across all 4 major Thai river systems discharging into the inner GoT. Thus, understanding the characteristics of these nutrients may help mitigate phytoplankton blooms and associated fishery losses in the inner GoT.

Materials and methods

Study area

The Gulf of Thailand (GoT), a shallow inlet of the South China Sea, is an ecologically and economically important semi-enclosed sea in Southeast Asia. Its inner region (~100×100 km ² , ~15 m average depth) receives discharge from 4 major rivers: MK, TC, CPY and BPK, across latitudes 13° 19' 12" N - 13° 45' 36" N and longitudes 99° 55' 12" E - 101° 05' 59" E. According to discharge data from 2014 - 2018 provided by the Royal Irrigation Department (RID), which supported the experimental design, the MK River basin is forested upstream and increasingly populated downstream, regulated by major dams, with an average discharge of 125×10 ⁶ m ³ day ⁻¹ [23-25]. The TC and CPY basins lie in the Chao Phraya Plain, dominated by agriculture and agro-industries; CPY has more intensive land use and urbanization, especially near Bangkok, and a much higher discharge (401×10 ⁶ m ³ day ⁻¹ ) than TC (4.83×10 ⁶ m ³ day ⁻¹ ) [23,24]. The BPK River, formed by the Prachin Buri and Nakhon Nayok Rivers, has diverse land use (paddy, forest, crops, eucalyptus) and faces seawater intrusion up to 52 km inland [23,26-28]. All basins are shaped by the Southeast Asian monsoon, with dry-season winds (mid-October to mid-February) and wet-season rainfall (mid-May to mid-October) driving strong seasonal variation in river discharge. In 2019, discharges during the dry/wet seasons were, respectively, 2,290/11,955 (MK), 119/1,237 (TC), 80/1,700 (CPY), and 701/7,935 MCM (BPK), based on data from RID stations [24-26].

The 4 river basins are strongly influenced by the Southeast Asian summer monsoon. During the dry season, northeasterly winds bring cool, dry air from Mongolia and China, while the wet season is driven by moist southwesterly winds from the Indian Ocean, resulting in heavy rainfall and cloud cover [26]. In 2019, average dry-season discharges for the MK, TC, CPY, and BPK rivers were 2,290, 119, 80, and 701 million cubic meters (MCM), increasing to 11,955, 1,237, 1,700, and 7,935 MCM during the wet season [24]. Discharge data were obtained from RID stations: K.11A (MK), Pholarthep Water Gate (TC), C.13 (CPY), and Ny.1B and Kgt.3 (BPK) [24].

Figure 1 Map of 4 major rivers flowing into the inner Gulf of Thailand (inner GoT); Maeklong (MK), Tachin (TC), Chaophraya (CPY), and Bangpakong (BPK). The maps show (a) average discharge [10 ⁶ m ³ day ⁻¹ ] during 2014 - 2018, (b) the close-up seasonal stations, and (c) the sampling stations. The red diamond points and blue round dot in (c) shows monthly and seasonal sampling stations, respectively.

Sample collection

Surface water samples (0.5 m depth) were collected monthly from January to December 2019 at sites marked in Figure 1(b) . Each river - MK, TC, CPY, and BPK - had 2 fixed stations: upstream (MK-1, TC-1, CPY-1 and BPK-1) located 24.9 - 60.6 km from the coast, and downstream (MK-2, TC-2, CPY-2 and BPK-2) located 3.04 - 8.13 km from the coast. Additional seasonal sampling was conducted during the dry (30 May - 14 June) and wet (25 October - 9 November) seasons of 2019 at 6 - 8 sites along each river’s salinity gradient near the mouth, coinciding with neap tides to limit seawater intrusion. Vertical sampling was performed based on depth (measured using a HONDEX ^® PS-7): 1 near-bottom sample for depths ≤ 4 m; mid- and bottom samples for 5 - 10 m; and 3 layers for depths ≥ 10 m.

All sampling tools were acid-cleaned (10% HCl), rinsed with distilled water, and neutralized. Water samples were collected using a 2-L PTFE sampler and stored in pre-rinsed 1-L LDPE Nalgene ^® bottles at 4 °C until filtration. Filtration used pre-combusted (500 °C, 5 h), pre-weighed GF/F filters; filters were dried (60 °C, 5 h), stored in desiccators, and reweighed for suspended particulate matter (SPM) determination. Filtrates were used to rinse and fill 2 50-mL LDPE bottles: 1 for immediate analysis of dissolved inorganic silicate (DSi), phosphate (DIP), and nitrogen species (NO ₃ ^- , NO ₂ ^- , and NH ₄ ⁺ ), and 1 frozen at –20 °C for later analysis of dissolved organic phosphorus (DOP) and nitrogen (DON).

Physiochemical characteristic

The in situ physicochemical characteristics of the water at the sampling sites were measured using a YSI 650 Multiparameter Display System (650 MDS). Parameters measured included salinity, dissolved oxygen (DO), pH, and temperature ( Tables S1 and S2 ).

Chemical analysis

Dissolved inorganic nutrients (DSi, DIP, NO ₃ ⁻ , NO ₂ ⁻ and NH ₄ ⁺ ) were analyzed using spectrophotometric methods based on Strickland and Parsons (1972), a protocol still widely applied in recent studies [27,29] . Dissolved organic phosphorus (DOP) and nitrogen (DON) were calculated by subtracting their respective inorganic concentrations from total dissolved phosphorus (TDP) and total dissolved nitrogen (TDN). TDP and TDN were measured from defrosted samples digested with acidic potassium persulfate, followed by spectrophotometric analysis in accordance with Strickland and Parsons [30] and Grasshoff, Kremling [31]. The accuracy of the experiment was determined using Relative Percent Difference (%RPD). %RPD for DSi, DIP, TDP, TDN, NO ₃ ⁻ , NO ₂ ⁻ , and NH ₄ ⁺ were 1.72%, 1.99%, 3.76%, 4.64%, 4.51%, 3.94%, and 7.94%, respectively. The detection limits were 0.572, 0.965, 0.806, 21.2, and 0.301 µM, respectively. In addition, to ensure data quality and assess analytical precision, both blank and reagent tests were conducted throughout the sampling and laboratory analysis processes. Procedural blanks were included for each nutrient analysis batch to detect any background contamination. Reagent blanks were used to account for potential interferences from chemicals and instruments.

Data analysis

The sampling map shown in this study was plotted using Surfer ^® 16.0.330, a software for 3D data visualization and mapping. Statistical graphs were plotted using Grapher ^® 15.0.259. Vertical profiles of the water column were visualized using Ocean Data View ^® 5.1.7.

Statistical analysis

Pearson’s 2-tailed correlation analysis was applied to evaluate the relationships among physicochemical parameters and nutrient concentrations. Correlations were considered statistically significant at the 95% ( p < 0.05) and 99% ( p < 0.01) confidence levels. All statistical analyses were conducted using IBM SPSS ^® Statistics software version 29.0.0.0 (241).

Results and discussion

Estuarine classification in the inner GoT

Vertical salinity profiles revealed distinct estuarine mixing patterns across the 4 river mouths [32]. MK showed persistent stratification with strong bottom-layer seawater intrusion ( Figure 2 ), attributed to its high discharge, while TC and BPK were well-mixed due to low flow. CPY exhibited partial mixing, consistent with its moderate discharge. Salinity generally declined in the wet season, except at TC, where seawater intrusion extended further inland. TC, a distributary of CPY, had discharges of 119 and 1,237 MCM during the dry and wet seasons, respectively [25]. BPK, affected by dam regulation, had elevated salinity due to limited freshwater input.

Figure 2 Vertical salinity profiles during the dry and wet seasons. Each sub-figure includes a vertical dashed line at 0 km, marking the coastline at the river mouth. “UP” and “DOWN” indicate the landward and seaward directions, respectively.

Influence of water mixing on nutrient distribution at the river mouths

The distributions of DSi, DIP, DIN, and DON across the study area showed complex spatial and seasonal patterns. An in-depth description of each nutrient’s distribution is provided in the Supplementary Section 2 . However, it was clear that water mixing also played a key role in nutrient dynamics. Most nutrient concentrations followed the salinity gradient, with higher levels in freshwater and a gradual decrease toward seawater due to dilution ( Figures S3 and S4 ). In addition, a significant negative Pearson’s correlation between nutrient concentrations and salinity ( Tables S4 and S5 ) - commonly observed at all river mouths - indicated the diluting effect and water mixing influence of seawater [33,34].

Comparison of anthropogenic impact on nutrient distribution from 4 main river mouths

The spatial and temporal distribution of nutrient concentrations exhibited considerable variability across the 4 main rivers ( Figure 3 ). DSi in the main rivers ranged from 5.53 - 270 µM. Among rivers, the TC exhibited the highest average DSi concentration at both upstream (205 ± 51.0 µM) and downstream stations (177 ± 66.6 µM). Notably, TC basin presented distinct patterns, with DIP concentrations showing a significant increase from upstream (4.34 ± 0.77 µM) to downstream (11.0 ± 5.74 µM). Furthermore, the highest NH ₄ ⁺ concentration (55.0 ± 50.1 µM) was observed at the downstream station of the TC river (TC-2), contrasting with upstream concentrations that were roughly half. Concurrently, CPY River recorded the highest average NO ₂ ⁻ concentration (~30 µM) among all rivers, and its DON average was twice as high upstream (CPY-1) as downstream (CPY-2), a pattern differing from its DIN levels which remained similar between upstream and downstream.

Each river basin exhibited distinct characteristics. A comprehensive comparison, supported by statistical analysis, is presented in the following sections.

MK river basin

Forest cover and water mixing were the primary factors influencing nutrient distribution in the MK Basin. Forests, particularly in tropical regions, play a crucial role in regulating dissolved inorganic nutrients. For instance, a study in southeastern Brazil found that forested watersheds exhibited significantly lower pollutant levels compared to those dominated by agricultural land use [35]. In the MK Basin, much of the area is covered by evergreen forest and protected areas, including Khao Laem and Erawan National Parks [23]. Consequently, concentrations of nutrients (DIP, DIN, DOP, and DON) were generally low, with the exception of DSi. Pearson’s correlation analysis based on monthly data ( Table S3 ) revealed a moderate positive correlation (~0.5, p < 0.01) between DSi and DIN (including NO ₂ ⁻ and NO ₃ ⁻ ), suggesting a common freshwater origin for both nutrient types [36].

Figure 3 Box plots of nutrient concentrations across the 4 river basins, categorized by monthly (upstream-downstream stations) and seasonal (dry-wet season) variations.

TC river basin

Intensive anthropogenic land use, hypoxia, and limited water mixing are key factors contributing to elevated nutrient concentrations in the TC Basin. Swine farms, agriculture, and urban areas likely drive high inputs of both inorganic and organic nutrients [37,38]. The shift in land use - from fisheries to industrial zones (1982 - 2010) and tourism development post-2015 - aligns with national economic plans prioritizing industrial growth [39], contributing to long-term water quality degradation. The TC River is now classified as having “poor” water quality [40].

Our results showed elevated DIP concentrations and low DO levels, especially in bottom waters - conditions indicative of hypoxia. Under these oxygen-deficient conditions, microbial processes such as ammonification and dissimilatory nitrate reduction likely promote hydrogen sulfide formation and DIP release from SPM [41]. Elevated DOP and DON further contribute to oxygen depletion via microbial decomposition, supported by previously reported high BOD values in the river’s midsection [40,42].

The highest DIP levels occurred downstream, likely reflecting cumulative impacts of non-point source pollution. Agricultural runoff, urban sewage, and industrial effluents are major contributors [43,44], with fertilizers and phosphorus loss from paddy fields playing a substantial role [45-47]. The dense population and intense aquaculture and industrial activity around station TC-2, especially in Samut Sakhon Province, further underscore the anthropogenic pressures driving phosphorus enrichment [23,48]. Pearson’s correlation analysis based on monthly data ( Table S3 ) also revealed a strong positive correlation (0.765, p < 0.01) between DIP and DON, suggesting a common origin possibly linked to anthropogenic sources [43,49].

CPY River Basin

Urbanization, agriculture, and biological processes strongly shaped nutrient distributions in the CPY River. Dissolved nutrients such as DIP, NO ₃ ⁻ , NH ₄ ⁺ , and DON are largely derived from urban wastewater inputs, reflecting inadequate sewage infrastructure and leakage [7,50,51]. Elevated NO ₃ ⁻ and NH ₄ ⁺ concentrations likely result from DON mineralization and ammonification [50,52], consistent with previous findings that domestic sewage dominates nutrient loading in this basin [53]. In this study, the highest concentrations of NO ₃ ⁻ , NO ₂ ⁻ , and DON were observed at site CPY-1 in Bangkok, highlighting the impact of dense urban activity. Pearson’s correlation analysis based on monthly data ( Table S3 ) also revealed a moderate positive correlation (~0.5, p < 0.01) between DIP and DIN (including NO ₂ ⁻ and DON), suggesting a common origin possibly linked to anthropogenic sources [36,54].

Agricultural runoff further contributed to nutrient enrichment. Extensive rice cultivation in the CPY basin is a key source of NH ₄ ⁺ and NO ₃ ⁻ , as fertilizer application significantly increases their concentrations in surface waters [38,55,56]. These findings underscore the combined influence of urban and agricultural sources in driving elevated nutrient loads. In addition to external inputs, biological processes may enhance nitrogen availability. The prevalence of NO ₃ ⁻ and NO ₂ ⁻ suggests active nitrogen fixation by cyanobacteria and phytoplankton, particularly where regenerated nitrogen (NH ₄ ⁺ ) is limited - contrasting with conditions observed in the TC system.

BPK River Basin

Seawater intrusion, dam regulation, and water mixing at the river mouth were the primary factors influencing nutrient distribution in the BPK River. In tropical regions, seawater intrusion is typically driven by sea-level rise, cyclones, storm surges, and reduced freshwater discharge due to anthropogenic activities [57]. Such intrusion can degrade drinking water quality and reduce agricultural productivity [58]. The BPK River Basin had experienced these challenges, prompting the Thai government to construct the Bang Pakong Dam, which began operation on 6 January 2000, to limit seawater intrusion [59]. Pearson’s correlation analysis based on monthly data ( Table S3 ) showed no strong correlation between different nutrients types as appeared in other rivers. This could supported various nutrients origin influenced by seawater intrusion.

However, dam operation had altered hydrological dynamics, reduced river discharge and causing several issues at the river mouth: (1) increased tidal fluctuations and severe bank erosion, (2) accumulation of anthropogenic effluents upstream, and (3) decreased seawater availability for upstream aquaculture [59]. Consequently, nutrient distribution in the estuarine zone was heavily influenced by dam regulation.

DON loading from metropolitan into the inner GoT

Metropolitan stations (TC and CPY) were as potentially significant sources of DON to the inner GoT. DON is derived from both natural and anthropogenic sources and often dominates the TDN pool in various ecosystems [60]. For instance, studies have reported that DON comprises over 70% of TDN in South American forests [61], 40% - 90% in southeastern USA watersheds [62] and 75% - 83% in northern Thai forests, indicating a substantial natural contribution [63]. However, anthropogenic activities also play a major role in DON loading globally. Intensive agriculture, particularly with high fertilizer usage, has been shown to elevate fluvial DON concentrations, often with more reactive forms [49]. In northern Thailand, industrial and agricultural activities were also identified as DON sources [64]. Urban infrastructure, including aging sewer systems, can further contribute to DON leakage [7].

Evidence from highly urbanized areas supports this link between human activity and DON contamination. For example, a study in São Paulo, Brazil, found DON loads positively correlated with population density [65]. Similarly, during the COVID-19 pandemic, excessive disinfectant use in Wuhan was associated with elevated DON concentrations in surface waters [66], suggesting that even routine urban chemical use may contribute to DON pollution. Thus, DON observed at TC and CPY likely originates from a combination of natural processes and anthropogenic inputs, including agriculture, industry, and urban infrastructure.

Although often overlooked, DON can play a significant role in stimulating phytoplankton growth in coastal ecosystems. Historically, DON was excluded as a major nutrient source due to assumptions of low bioavailability, particularly in temperate regions [67]. However, several studies have shown that bioactive DON can be rapidly mineralized into inorganic forms, becoming readily available to phytoplankton [67,68]. DON has been implicated in promoting phytoplankton growth and blooms in coastal waters [22]. For example, brown tide events in Long Island, USA, were linked to elevated DON levels [69]. Additionally, dinoflagellates have demonstrated a preference for DON uptake, contributing to bloom formation [70,71].

Phytoplankton blooms in the inner GoT reflected persistent nutrient imbalances, with 97 events recorded between 1957 and 2001 [11]. Noctiluca scintillans and Trichodesmium erythraeum dominate these blooms. MODIS data (2003 - 2017) show chlorophyll-a concentrations closely tied to discharges from the TC and CPY rivers, indicating urban nutrient sources [72]. High DSi, DIP, and DIN levels enhance diatom prey, indirectly fueling N. scintillans blooms [73,74], particularly near TC [15], where blooms are linked to hypoxia and fish kills [14].

Effective nutrient management must consider all dissolved forms, particularly DON. The Coordinating Body on the Seas of East Asia (COBSEA) is one of the key international organizations addressing marine pollution and nutrient management. According to the “Strategic Directions 2023 - 2027,” anthropogenic pollution remains a priority under the Regional Seas Strategic Direction 2022 - 2025. Enhancing nutrient runoff data is essential to support evidence-based and sustainable coastal management [75].

Conclusions

This study presents a comprehensive comparison of 4 major watersheds discharging nutrients into the inner GoT during a synchronized sampling period. The results confirmed that anthropogenic activities and water mixing are key drivers of dissolved nutrient distributions at river mouths. The MK Basin, the most pristine site, showed the lowest concentrations of DIP, DIN, DOP, and DON, highlighting the critical role of forested areas in regulating nutrient loads. In contrast, TC, characterized by intensive domestic and industrial activity, exhibited the highest DIP concentrations and the lowest DO levels, indicating substantial organic pollution and possible hypoxia-induced DIP release from sediments. CPY, with similar land use to TC, showed a different DIN composition dominated by NO ₃ ⁻ and NO ₂ ⁻ , likely due to intensive paddy cultivation, wastewater discharge, DON remineralization, and elevated nitrogen fixation. Exceptionally high DON concentrations in TC and CPY suggested a significant, underrecognized nitrogen source from metropolitan areas to the inner GoT, potentially fueling episodic phytoplankton blooms along the eastern coast. Nutrient distribution in BPK was notably influenced by dam regulation. The Bang Pakong Dam, built to limit seawater intrusion, altered tidal dynamics, and trapped upstream domestic waste, resulting in a non-uniform nutrient pattern. Across all sites, nutrient concentrations showed strong negative correlations with salinity, and vertical profiles revealed decreasing nutrient levels with increasing seawater influence - both indicating estuarine mixing as the dominant process at the river mouths. Future nutrient policies must address all dissolved forms, especially DON, as drivers of coastal eutrophication. Regional efforts like COBSEA’s 2023 - 2027 strategy highlight marine pollution, underscoring the need for robust runoff data to inform sustainable management.

Acknowledgements

This study was supported by “The 100 ^th Anniversary Chulalongkorn University Fund, Thailand for Doctoral Scholarship” and “The 90 ^th Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund).

Declaration of Generative AI in Scientific Writing

Generative AI tools (e.g., ChatGPT by OpenAI) were used solely to improve the readability and language of the manuscript. All contents (graphical abstracts, graphs, maps, contour profile) was created and verified by the authors, who take full responsibility for its accuracy and integrity. No AI tools were used for data analysis, interpretation, or scientific conclusions. The tools were not listed as authors or co-authors.

CRediT author statement

Suparat Srisaard : Conceptualization, Writing - original draft, Data collection, Data curation, Methodology, Visualization, Formal analysis, Investigation. Penjai Sompongchaiyakul : Conceptualization, Supervision, Data curation, Writing - reviewing and editing, Funding acquisition. Tanakorn Ubonyaem : Field sampling, Formal analysis, Writing - reviewing and editing. Chawalit Charoenpong : Writing - reviewing and editing, Data curation. Narainrit Chinfak : Conceptualization, Supervision, Writing - reviewing and editing, Data collection. Sujaree Bureekul : Conceptualization, Supervision, Data curation, Writing - reviewing and editing.

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Supplementary Material

Supplementary section 1: Table

Table S1 Range and average from monthly data (upstream and downstream stations).

Table 2 Range and average from seasonal data (dry and wet season).

Table S3 Pearson’s correlation from monthly data.