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Article

Nutrient Distribution Characteristics and Eutrophication Evaluation of Coastal Water near the Yellow River Estuary, China

by
Jing Xiao
1,
Xiang Chen
1,
Li Zhou
1,
Haibo Zhang
2,
Xiaoshuai Hang
1,* and
Yudong Chen
1,*
1
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
2
National Marine Environmental Monitoring Center, 42 Linghe Street, Dalian 116023, China
*
Authors to whom correspondence should be addressed.
Water 2025, 17(16), 2469; https://doi.org/10.3390/w17162469
Submission received: 29 June 2025 / Revised: 18 August 2025 / Accepted: 19 August 2025 / Published: 20 August 2025
(This article belongs to the Special Issue Water Quality Studies: Assessing the Presence of Nutrients and Pollutants)
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Abstract

Coastal ecosystems have faced escalating environmental degradation in recent years, with eutrophication and nutrient imbalances emerging as critical concerns, particularly in estuarine regions. Understanding the spatiotemporal dynamics of key nutrients, including dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and silicate (SiO3-Si), is essential for effective coastal management. This study examines the spatial and seasonal variations in these nutrients across 36 sampling sites in the Yellow River estuary from 2016 to 2018. Results indicate that DIN was the primary contributor to water quality degradation, with more than 27% of sampling sites exceeding the Class II seawater quality standard in 2018. Nutrient concentrations were notably elevated near the estuary. The eutrophication index (EI) revealed predominantly mild-to-moderate eutrophication levels throughout the study area. The study area exhibited a widespread phosphorus (P) limitation, with 44.4–94.4% of coastal waters experiencing P-restricted eutrophication. The N/P ratio significantly exceeded the Redfield ratio (16), indicating a pronounced nutrient imbalance. Furthermore, SiO3-Si concentrations displayed a declining trend, highlighting the need for balanced nutrient management alongside eutrophication mitigation.

1. Introduction

Nitrogen (N), phosphorus (P), and silicon (Si) are essential nutrients for the growth and reproduction of marine organisms [1,2,3]. These nutrients are major factors influencing the dynamics of marine environments [4]. Nutrient enrichment can directly affect the distribution of phytoplankton in water bodies, increase eutrophication levels, and lead to changes in biomes [5,6]. The occurrence of eutrophication can negatively affect the ecological balance of water bodies, as well as marine ecosystem structures and functions [7,8], resulting in frequent red tides [9] and a series of ecological issues, such as reduced dissolved oxygen concentrations in the seabed [10]. In marine ecosystems, eutrophication mainly occurs in coastal areas and semi-enclosed water bodies [11,12]. In fact, human activities can induce the severe eutrophication of inshore waters through the discharge of large nutrient and organic matter amounts into estuaries and inshore waters [13,14], seriously threatening the sustainable development of coastal marine ecology and economy [15,16,17].
Numerous methods have been developed in recent years to evaluate water eutrophication using different parameters depending on the characteristics of the study areas [18,19,20]. The eutrophication index (EI) is, in fact, suitable for assessing eutrophication of seawater in China [21]. The national standard HJ 442.10-2020 explicitly recommends the EI for coastal water quality assessment in China [22]. However, the EI method can only preliminarily investigate the eutrophication status of sea areas but not reflect the specific contribution of nutrients. The eutrophication of marine ecosystems is a complex process, making it challenging to explore using single physical, chemical, or biological indices [23,24]. The Redfield ratio of 16:1 represents the optimal nutrient stoichiometry for phytoplankton growth. In China’s coastal estuaries, nutrient limitation frequently occurs, where the nitrogen-to-phosphorus ratio (N:P = 16:1) serves as a critical indicator for determining the type of nutrient limitation (N-limitation or P-limitation) [25]. Potential eutrophication refers to a water body that has accumulated sufficient nutrient loading to support eutrophication (the material basis) but has not yet exhibited visible algal blooms or other symptomatic manifestations; such systems may rapidly transition to actual eutrophication when triggered by external environmental factors (e.g., temperature, light availability) [26]. The potential eutrophication assessment method developed by Chinese researchers effectively captures these nutrient limitation characteristics [27].
The Yellow River is the largest river in North China. However, it contains large amounts of nutrients and sediments [28]. The Yellow River estuary is located in Dongying City, Shandong Province, China. Besides the Yellow River, this area has a total of 30 drainage channels. The Dongying section of the Yellow River originates from the border of Binzhou, flowing through Dongying City from southwest to northeast before reaching the Bohai Sea, with a total length of 138 km [29]. The Yellow River estuary, an important fishing region in Shandong Province, is characterized by a dense population and rapid economic development. It is significantly impacted by human activities related to production and daily life, highlighting the urgent need for effective management and governance measures [30,31]. Numerous researchers have explored the spatial distribution characteristics and change trends of nutrients in water adjacent to the Yellow River estuary, Bohai Bay, and Laizhou Bay [32]. The Bohai Sea is a semi-enclosed shallow continental shelf sea with an average depth of 18 m, where 95% of its area has a depth less than 30 m. The input of inland pollutants constitutes a key factor affecting coastal water quality in the Bohai Sea [33]. While the Yellow River runoff can reduce water residence time in the Laizhou Bay, its influence on Liaodong Bay and Bohai Bay remains relatively limited. Relevant studies indicate that anthropogenic-induced coastline modifications have weakened the water exchange capacity in Bohai Bay and reduced its pollutant dispersion capability [34]. Therefore, it is crucial to understand the nutrient status and evaluate the levels of eutrophication in the coastal waters of the Yellow River estuary.
This study systematically investigated coastal water quality dynamics in the Yellow River estuary from 2016 to 2018, with the three following primary research objectives: (i) assess the spatiotemporal distribution characteristics of nutrients (DIN, DIP, and SiO3-Si) in the near-shore water; (ii) explore the eutrophication status of the near-shore waters by the EI and potential eutrophication evaluation methods; and (iii) identify the major factors limiting the nutrients in the seawater of the study area. The current research provides a theoretical basis for the environmental management of water in the Yellow River estuary.

2. Materials and Methods

2.1. Study Area

The study area of the Yellow River estuary is within the longitude and latitude ranges of 118°35′~119°47′ E and 37°45′~38°3′ N, respectively (Figure 1). The study area covers parts of Laizhou Bay and Bohai Bay. The boundary between these two bays is located at the Yellow River estuary. For spatial analysis and cartography, we utilized ArcMap 10.8 (ESRI, Redlands, CA, USA).

2.2. Data Collection

A total of 36 sampling sites were selected around the estuary of the Yellow River in March (winter), May (spring), August (summer), and October (autumn) from 2016 to 2018. The obtained water quality data were supplied by the Department of Ecology and Environment of Shandong Province, China (http://sthj.shandong.gov.cn/, accessed on 18 August 2025). The water quality data are derived from the upper 0.5 m of seawater, and the indicators include NH4+, NO2−, NO3−, PO43−, COD, and SiO3-Si.
In order to analyze the influence of surface runoff on water quality, seawater runoff data of the Lijin Hydrographic Station in the 2016–2018 period were downloaded from the Yellow River Conservancy Commission of the Ministry of Water Resources, China 99 (http://www.yrcc.gov.cn//). It should be noted that Lijin Hydrographic Station is the last hydrographic station on the Yellow River.

2.3. Seawater Eutrophication Evaluation

2.3.1. Eutrophication Index Evaluation Method

The seawater EI is calculated using the formula below (Equation (1)). This formula has undergone multiple revisions over the past 40 years and has been widely used since 2010 [35,36]. Some scholars have evaluated the relationship between the EI and the sea water quality standard (Table S1) [37].
E I = C O D × D I N × D I P 4500 × 10 6
COD denotes the chemical oxygen demand (mg/L); DIN denotes the dissolved inorganic nitrogen concentration (mg/L); and DIP denotes the dissolved inorganic phosphorus concentration (mg/L).
Eutrophication index (EI) values greater than 1 indicate the occurrence of eutrophication. The obtained EI values are usually classified into three eutrophication categories (Table 1). The higher the EI value, the higher the eutrophication level.

2.3.2. Potential Eutrophication Evaluation Method

In this study, the potential eutrophication of offshore seawater was further analyzed using the Redfield ratio. Specifically, a Redfield ratio of 16:1 indicates suitable conditions for phytoplankton growth in water bodies [38]. The eutrophication levels can be classified according to N and P concentrations, as well as the N/P ratios (Table 2).

2.3.3. Identification of Nutrient-Limiting Factors

In this study, nutrient compositions across sampling sites were assessed using both the relative and absolute limit methods. The evaluation of the nutrient compositions using the relative limit method considered three important indicators, namely N:P, Si:N, and Si:P [39]. Specifically, N:P< 10 and Si:N > 1 indicate that N is the potential limiting nutrient; N:P > 22 and Si:P > 22 indicate that P is the potential limiting nutrient; and Si:P < 10 and Si:N < 1 indicate that Si is the potential limiting nutrient. In contrast, the absolute limitation method is based on minimum uptake thresholds for phytoplankton growth. The uptake threshold values of N, P, and Si are 1, 0.1, and 2 μmol/L, respectively [40].

3. Results and Discussion

3.1. Seasonal Distributions of Nutrients

Figure 2 illustrates seasonal DIN distributions during 2016–2018. The highest average DIN concentration in the coastal waters each year was observed in August. The DIN concentration was the highest in August 2017, with an average of 0.360 mg/L in the study area. DIN was one of the major pollutants in Bohai Bay [41]. During these three years, the average DIN concentration in autumn ranged between 0.200 and 0.300 mg/L, which was lower than the mean values of other seasons. In terms of concentration distribution, DIN levels in winter and spring were relatively clustered, while summer and autumn exhibited more pronounced fluctuations. The lower temperatures in winter and spring limited phytoplankton growth [42], whereas increased rainfall and frequent typhoons in summer and autumn, along with enhanced phytoplankton activity, contributed to the variability in nutrient concentrations [43]. The land-derived inputs became an important factor affecting nutrient concentrations in the seawater, resulting in higher N concentrations than China’s Class II seawater quality standard (0.20 mg/L) [44,45,46]. According to previous observation and simulation results, atmospheric deposition can contribute to DIN concentrations in the ocean by 25 to 54% [47,48]. This is consistent with our findings, showing higher DIN concentrations near the Yellow River estuary in summer due to the river inputs and atmospheric subsidence.
The DIP concentrations in the seawater of the study area were relatively similar in the different seasons of the 2016–2018 period (Figure 3). The DIP concentrations were less than 0.020 mg/L except at some sampling sites. The observed DIP concentrations in 2016 were relatively different, ranging from 0.001 to 0.051 mg/L. In the summer and autumn of 2017 and 2018, on the other hand, the DIP concentrations at each site had small differences. However, comparatively higher DIP concentrations were found in March 2017 and 2018, reaching 0.009 mg/L and 0.010 mg/L, respectively. This phenomenon can be attributed to several seasonal mechanisms. In winter, thorough seawater mixing coupled with temperature-limited primary productivity maintains peak DIP concentrations. During spring, phytoplankton blooms drive substantial phosphate drawdown via biological uptake. This is followed by the autumn regeneration phase, where settling algal detritus undergoes microbial mineralization, subsequently releasing phosphate back into the water column and restoring DIP levels [49]. Moreover, adjacent waters might also contribute to DIP inputs into the Bohai Sea in winter [46]. Indeed, previous related studies on P pollution in the bays of China have also highlighted a high nutrient enrichment of submarine groundwater, especially in areas with intensive human activities [50,51]. Besides external factors such as rainfall and runoff, internal sediment release might also contribute to P pollution in the study area. Previous relevant studies have demonstrated the promoting effect of bacterial behaviors on the release of P from sediments, further aggravating water eutrophication in Bohai Bay [23,52].
Figure 4 shows the seasonal changes in the SiO3-Si concentrations over the 2016–2018 period. The concentration of SiO3-Si was lower in spring and autumn and higher in summer and winter. The highest SiO3-Si concentration in the study area was 0.500 mg/L in March 2017, and the lowest was 0.120 mg/L in October 2018. In this study, the average SiO3-Si concentration was mostly lower than 0.5 mg/L. Previous research has outlined that DIN enrichment is often accompanied by SiO3-Si deficiency [53]. Riverine silica inputs constitute over 50% of the total exogenous silica flux to the global ocean, representing the principal external source of this essential nutrient for marine systems. Longer-term hydrological records demonstrate that the decreasing trend of the SiO3-Si concentration was related to the decrease in runoff into the sea due to the influences of rapid economic development around the Bohai Sea and related human activities, such as dam construction in the Yellow River basin [54,55]. This study exclusively analyzed surface seawater data from 2016 to 2018—a relatively brief observational period during which short-term fluctuations were particularly susceptible to environmental variability. Notably, extreme precipitation events associated with 2018 typhoons generated anomalously elevated SiO3-Si concentrations at discrete sampling stations near the Yellow River estuary. The higher SiO3-Si concentrations in summer than those in the other seasons might be attributed to the weathering of silicate minerals under the effects of surface runoff [56]. Active silicon is an important source element necessary for the growth of siliceous organisms such as diatoms [3]. The phytoplankton community in the Bohai Sea is primarily dominated by diatoms and dinoflagellates. Influenced by the freshwater discharge from the Yellow River estuary, other phytoplankton species such as green algae and cyanobacteria also occur in this region [57]. The concentration of SiO3-Si in the Yellow River estuary has been decreasing over the past three years [58]. This has led to a shift in the dominance of the region from the dominance of diatoms to the co-dominance of diatoms–dinoflagellates, further deteriorating the water quality [59].
The number of sampling sites with water quality below the Class II DIN, DIP, and COD values is shown in Figure S1. According to the results, DIP and COD met the second-class sea water quality standard in the different seasons of the 2016–2018 period, except at some sampling sites. This finding indicates the lack of serious P and organic pollution in offshore seawater [1,60]. In contrast, 80% of the points exceeded the DIN concentration in May 2018. In fact, N pollution was widespread in the in nearshore waters [1]. According to the water flow pattern at the Lijin Station (Figure 5), the annual runoff rates of Lijin Hydrology Station were 81.88, 89.58, and 333.8 billion cubic meters in 2016, 2017, and 2018. The runoff rates of the Yellow River in 2018 were significantly higher than those revealed in 2016 and 2017. As evident from the monthly precipitation data of Dongying City (Figure 6), the highest rainfall consistently occurred during June, July, and August each year, while virtually no precipitation was recorded in winter and spring. The annual precipitation totals for 2016–2018 were 609.8 mm, 546.9 mm, and 987.7 mm, respectively, with 2018 showing a significant increase in rainfall. Based on precipitation and hydrological data analyses, rainfall and associated runoff inputs were identified as key contributing factors to the observed DIN concentrations exceeding Class II seawater quality standards in 2018. The observed algal bloom in summer might be attributed to touristic activities, high precipitation, and high river flows into the sea of the study area [61]. In addition, atmospheric deposition might also contribute significantly to the increase in the N concentrations in the Bohai Sea [62]. The findings of the current study showed low and high DIP and DIN concentrations, respectively, in this study area.

3.2. Spatial Distribution Patterns of Nutrients

The observed DIN concentrations in the study area over the 2016–2018 period were high (Figure 7). However, across the entire study area, DIN concentrations showed relatively small variations among sampling sites during both spring and autumn, with the exception of certain locations in Laizhou Bay where more pronounced fluctuations were observed. Notably, the overall DIN levels in autumn were consistently lower than those recorded in spring. In contrast, the DIN concentrations in winter and summer exhibited uneven spatial distributions. In March 2016, areas with elevated DIN concentrations were primarily located within Bohai Bay, while by May 2016, the high-concentration zone had shifted to Laizhou Bay. During August across all study years (2016–2018), sampling stations near river estuaries consistently exhibited higher DIN levels. The DIN concentration in the winter over the 2016–2018 period was not the lowest in the four quarters. The DIN concentration found both exceeded the standard value in the wet season (summer) and dry season (winter) in the Laizhou Bay area [16]. Meteorological records from 2016 revealed distinct seasonal precipitation patterns in the study area. Spring and autumn were characterized by significantly reduced rainfall and prolonged dry periods. While summer exhibited higher precipitation totals, these events displayed marked spatiotemporal variability, alternating between drought conditions and flood episodes. Sampling site 30 at the Yellow River estuary exhibited a significantly higher DIN concentration in August 2018 than those in the other sampling periods. This might be due to the continuous heavy rainfall events in Dongying City as a result of the typhoon in 2018 [63]. In most marine areas of China, particularly the Yellow River estuary, the Yangtze River estuary, and the Pearl River Estuary, nitrogen levels are relatively high and significantly exceed those observed in other regions of China [64].
In this study, the DIP concentrations in the 2016–2018 period were relatively low, except at some sampling sites (Figure 8). However, there was a slight increase in the DIP concentrations in August 2016. It is worth noting that DIP concentrations near sampling sites 20 and 30 were substantially higher than those at the remaining sampling sites. This spatial pattern likely results from distinct environmental conditions at the sampling locations. Station 20 within Bohai Bay experiences significant industrial activity and lies adjacent to wastewater treatment facilities. Meanwhile, Station 30 near the Yellow River estuary exhibits elevated nutrient concentrations due to fluvial inputs from the river’s discharge [65]. Overall, except for these points, DIP concentrations were low throughout in different seasons.
The spatial distributions of the SiO3-Si concentrations were relatively similar in each season of the 2016–2018 period (Figure 9), whereas the highest SiO3-Si concentrations were observed in August 2016 and August 2018 in the Laizhou Bay area and near the Yellow River estuary, with concentrations of 0.480 mg/L and 0.390 mg/L, respectively. The highest SiO3-Si concentrations in March and May 2017 were found near the estuary in the northern part of the study area. Surface SiO3-Si concentrations in the study area exhibit significant spatiotemporal variability due to multiple interacting factors, including riverine inputs, hydrodynamic processes, benthic remineralization, and phytoplankton assimilation [66]. Although the northern region lacks direct influence from the Yellow River estuary, it receives continuous silicate inputs through multiple minor tributaries [67]. Notably, elevated precipitation from winter 2016 through spring 2017, measuring 23% above last year, sustained above-average silicate loading via terrestrial runoff. Concurrently, frequent cold-air outbreaks in late 2016 generated intense wind–wave activity in Bohai Bay, triggering sediment resuspension and subsequent silicate release from the seafloor. Crucially, climate-driven delays in diatom proliferation during this period reduced biological silicate uptake, ultimately promoting its accumulation in surface waters. In 2018, however, there was a significant increase in the number of sampling sites with low SiO3-Si concentrations. Generally, our results showed a seasonal change in the high-SiO3-Si-concentration area from north to south. The significant increases in the SiO3-Si concentrations in summer, particularly near the Yellow River estuary, further demonstrate that precipitation-derived surface runoff was the main source of SiO3-Si in the seawater [66]. Some relevant studies have also pointed out that seaward rivers and black tides are the main sources of SiO3-Si in seawater [68]. In fact, increased SiO3-Si concentrations in recent years led to frequent algal blooms in offshore waters, making it necessary to limit SiO3-Si concentrations [69].

3.3. Seawater Eutrophication Levels

3.3.1. Eutrophication Index

In this study, the eutrophication levels of the 36 stations were evaluated by the EI method in the different years and seasons, with EI values ranging from 0.005 to 6.004 (Table 3). The results revealed a few sampling sites with high eutrophication levels in the near-shore water over the 2016–2018 period. Specifically, 0 to 33.33% of the sampling sites had mild eutrophication levels over the study period, whereas moderate eutrophication was observed at one to two sampling sites in the study period. However, no severe eutrophication levels were found at the sampling sites from 2016 to 2018. A study showed that there was no seasonal eutrophication in the coastal waters in Dongying City, except in autumn, which is consistent with the results of this study [70].
The obtained EI values at sites 15# to 18# in Laizhou Bay are relatively high (Figure 10). This finding might be attributed to the abundant precipitation events in the shore area in summer, promoting the discharge of large amounts of terrestrial nutrients and pollutants into the sea [71]. On the other hand, precipitation events were comparatively less abundant in winter, resulting in lower land-derived pollution levels. The eutrophication levels showed seasonal variation patterns over the 2016–2018 period. Higher EI values were observed in March and August than those in May and October. In 2016, the coastal water was prone to eutrophication in each season. However, there was a significant improvement in the seawater quality in 2017, showing comparatively lower numbers of sampling sites with high eutrophication levels, resulting in a lower average EI value of only 0.421. In 2018, on the other hand, we observed a distinct shift in the seasonal distribution pattern of EI values. Notably, March exhibited significantly higher EI values compared to other seasons, with 11 out of 36 sampling stations (30.6%) showing EI values exceeding the eutrophication threshold of 1.0. Our results demonstrated the significant effects of annual precipitation on the eutrophication levels of the coastal water in the 2016–2017 period. In fact, dry and wet years often alternate in Dongying City, resulting in large differences in the annual precipitation amounts [63]. The relatively low EI values in 2017 might be due to the low precipitation. On the other hand, the high precipitation in 2018 might increase the relatively high EI values.

3.3.2. Potential Eutrophication

In this study, the nutrient-limiting factors were determined using the N/P, Si/P, and Si/N ratios in combination with the relative and absolute limit methods (Figure 11). The obtained results revealed a lack of relative and absolute limits of N in the study period, except in October 2018 (Table S2). Indeed, there was sufficient N in the coastal waters near the estuary of the Yellow River. However, 44.44 to 94.44% of the sampling sites had insufficient P concentrations. More than 86% of the sites with insufficient P concentrations were found in 2017. On the other hand, over 50% of the sampling sites had insufficient SiO3-Si concentrations in 2018, of which the highest proportions were observed in the autumn of 2018, reaching 41.67%. In addition, there were absolute SiO3-Si limitations in 2016 and 2018, which is consistent with the relative limitation results. It can be seen that the coastal waters near the Yellow River estuary had sufficient N concentrations, with relatively insufficient P and SiO3-Si concentrations.
The N/P ratios of the entire coastal water area in Dongying City were higher than the Redfield ratio (16:1) due to the low P concentrations, especially in autumn. Indeed, 93.3% of the sampling sites were under potential P-limited eutrophication states. Previous related studies have shown that Laizhou Bay had extremely low nutrient concentrations in the 1980s and 1990s before potential eutrophic conditions were induced, with limited P concentrations. In addition, the P-limited conditions has shown an increasing trend in recent years, which is in line with the results of this study [52,72]. The N/P ratios in winter, spring, and autumn were high near the Yellow River estuary and its northern part [73]. According to the investigation, changes in the N/P ratios in the offshore sea area of China were due to the effects of the seasons and geographical locations [38]. In addition to surface runoff impacts, submarine groundwater discharge (SGD) represents a significant and continuous pathway for nutrient transport to coastal ecosystems. SGD delivers substantial nutrient loads characterized by exceptionally high DIN/DIP ratios, which may exacerbate phosphorus limitation and promote eutrophication in receiving waters [53]. Therefore, it is extremely important to control N pollution in land areas to mitigate the transport of this pollutant into the sea. The Si/N ratio showed a decreasing trend from 2016 to 2018. The increase in the N/P ratio is unfavorable for diatoms but promotes the proliferation of dinoflagellates [74]. Meanwhile, the decrease in the Si/N ratio leads to a decrease in large diatoms with high silicon demand and an increase in small diatoms [75]. Previous studies have shown that dissolved Si in coastal seawater mainly comes from runoff-derived surface erosion. However, low precipitation amounts in autumn and winter can consequently lead to low Si concentrations in seawater. Silicate was often a limiting nutrient in other water bodies in China [66]. The eutrophication and imbalanced changes in the nutrients in the coastal waters of Laizhou Bay led to changes in the N/P ratio, affecting phytoplankton communities and inducing frequent harmful algal blooms. These negative effects can, consequently, influence the stability and health of marine ecosystems [8,76]. There was a study that compared the N/P ratios in Bohai Bay between the 2004–2012 and 1978–1980 periods, highlighting an increasing trend of this ratio in recent years [77]. Similarly, our results showed an increasing trend of the N/P ratio in the 2016–2018 period, showing low and high P and N concentrations, respectively, in Bohai Bay.
The potential eutrophication assessment of sampling sites during 2016–2018 (Figure S2) revealed that most locations exhibited either phosphorus-limited moderate nutrition (IVP) or phosphorus-limited potential eutrophication (VIP). P was the main limiting nutrient for eutrophication in a major part of the study area. A similar finding was outlined in other water bodies in China [78]. Due to the low DIP concentration, the seawater eutrophication level in the study area remained relatively low despite the high DIN concentration. Therefore, it is crucial to consider the potential influences of the nutrients in future evaluation studies on seawater eutrophication.
Coastal water quality has improved in recent years, attributable to reduced anthropogenic impacts from dam operations, energy conservation measures, and emission reductions [79,80]. Furthermore, the continuous use of phosphorus-prohibited products has effectively reduced P inputs into the Bohai Sea [81]. However, the intensive applications of N fertilizers in recent years have induced imbalanced nutrients, resulting in the structure of water nutrients gradually changing from nitrogen limitation to phosphorus limitation and silicon limitation [52,77]. Phosphorus limitation arises from both the combined effects of excessive nutrient inputs (particularly nitrogen) and associated eutrophication, as well as being modulated to some extent by changes in coastal biogeochemical cycling processes (primarily driven by variations in phytoplankton biomass and community structure) [60]. The biomass and community structure of phytoplankton respond to the changes in land-derived input, while dinoflagellates continuously increase. This process increases the amount of phosphorus buried in the sediment of the water body and further aggravates the imbalance of nutrients [82,83].

4. Conclusions

This study investigated the spatiotemporal dynamics of DIP, DIN, and SiO3-Si and eutrophication levels in the Yellow River estuary coastal waters from 2016 to 2018. The highest eutrophication levels in the study area were observed in summer due to the strong effects of land-derived runoff. The obtained EI values indicated the presence of relatively few sampling sites with high eutrophication levels in the 2016–2018 period. These sampling sites were mainly located in the offshore area. However, the EI method might not accurately reflect seawater conditions in the offshore regions. The results of the nutrient limiting factor and potential eutrophication evaluation methods indicated high and low N and P concentrations, respectively, in the coastal area of the Yellow River estuary. Although the observed eutrophication ratios ranged from 2.78 to 38.89%, there was a high risk of future eutrophication in the study area. In order to address the issue of the distorted N/P ratio, appropriate and targeted measures should be taken in the Yellow River estuary area to carry out nitrogen and phosphorus reduction work. Therefore, it is essential to implement location-specific cooperative N-P control strategies for river discharges, establishing multiple management objectives including coastal water quality, riverine nutrient fluxes, and ecosystem stability. Moving forward, the principle of “land–sea coordination” and “integrated land–ocean management” should be consistently upheld.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17162469/s1, Figure S1: The number of DIN, DIP and COD sites exceeding Class II in different seasons; Figure S2: Potential eutrophication at the different sampling stations over the 2016–2018 period. Table S1: Sea water quality standard; Table S2: Relative and absolute limits of nutrients in different seasons.

Author Contributions

Conceptualization, J.X.; data curation, X.C., L.Z., and H.Z.; formal analysis, X.C., L.Z., and H.Z.; investigation, J.X.; methodology, J.X.; project administration, J.X., X.H., and Y.C.; software, J.X.; supervision, X.H., and Y.C.; writing—review and editing, X.C., L.Z., and H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the Project of Ecological and Environmental Protection Integration Research Institute in Yangtze River Delta (ZX2023SZY118) and the National Natural Science Foundation of China (42407557).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographic locations of the study area and the sampling sites. The study area is a coastal area near the Yellow River estuary, mainly located along the coast of Dongying City. The boundary between Laizhou Bay and Bohai Bay is located at the Yellow River estuary. Among the 36 sampling sites, (1) sites #1–11 and #19–30 are situated within Bohai Bay; (2) sites #12–18 and #31–36 are situated within Laizhou Bay; and (3) sites #29, #30, and #31 are positioned adjacent to the Yellow River estuary.
Figure 1. Geographic locations of the study area and the sampling sites. The study area is a coastal area near the Yellow River estuary, mainly located along the coast of Dongying City. The boundary between Laizhou Bay and Bohai Bay is located at the Yellow River estuary. Among the 36 sampling sites, (1) sites #1–11 and #19–30 are situated within Bohai Bay; (2) sites #12–18 and #31–36 are situated within Laizhou Bay; and (3) sites #29, #30, and #31 are positioned adjacent to the Yellow River estuary.
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Figure 2. Seasonal changes in the DIN concentrations over the 2016–2018 period.
Figure 2. Seasonal changes in the DIN concentrations over the 2016–2018 period.
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Figure 3. Seasonal changes in the DIP concentrations over the 2016–2018 period.
Figure 3. Seasonal changes in the DIP concentrations over the 2016–2018 period.
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Figure 4. Seasonal changes in the SiO3-Si concentrations over the 2016–2018 period.
Figure 4. Seasonal changes in the SiO3-Si concentrations over the 2016–2018 period.
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Figure 5. Annual runoff of Lijin Station at the Yellow River estuary over the 2016–2018 period.
Figure 5. Annual runoff of Lijin Station at the Yellow River estuary over the 2016–2018 period.
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Figure 6. Monthly precipitation in Dongying City over the 2016–2018 period.
Figure 6. Monthly precipitation in Dongying City over the 2016–2018 period.
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Figure 7. Spatiotemporal distributions of the DIN concentrations over the 2016–2018 period. In this study, the inverse distance weight method was used to interpolate DIN concentrations at 36 points. DIN concentrations across the study area ranged from 0.021 to 1.036 mg/L, with colors closer to blue indicating lower concentrations and redder colors indicating higher concentrations.
Figure 7. Spatiotemporal distributions of the DIN concentrations over the 2016–2018 period. In this study, the inverse distance weight method was used to interpolate DIN concentrations at 36 points. DIN concentrations across the study area ranged from 0.021 to 1.036 mg/L, with colors closer to blue indicating lower concentrations and redder colors indicating higher concentrations.
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Figure 8. Spatiotemporal distributions of the DIP concentrations over the 2016–2018 period.
Figure 8. Spatiotemporal distributions of the DIP concentrations over the 2016–2018 period.
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Figure 9. Spatiotemporal distributions of the SiO3-Si concentrations over the 2016–2018 period.
Figure 9. Spatiotemporal distributions of the SiO3-Si concentrations over the 2016–2018 period.
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Figure 10. Seasonal variations in the EI at the different sampling from 2016 to 2018. Each point represents the EI value at a different time at this site. Black represents March, red represents May, blue represents August, and green represents October.
Figure 10. Seasonal variations in the EI at the different sampling from 2016 to 2018. Each point represents the EI value at a different time at this site. Black represents March, red represents May, blue represents August, and green represents October.
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Figure 11. Quarterly variation in nutrient ratio in coastal waters. The data on the left corresponds to the ratio of N/P and Si/P, and the data on the right corresponds to the ratio of Si/N.
Figure 11. Quarterly variation in nutrient ratio in coastal waters. The data on the left corresponds to the ratio of N/P and Si/P, and the data on the right corresponds to the ratio of Si/N.
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Table 1. The degree of eutrophication corresponding to the EI value.
Table 1. The degree of eutrophication corresponding to the EI value.
Range of EIDegree of Eutrophication
EI ≤ 3.0Mild eutrophication
3.0 < EI ≤ 9.0Moderate eutrophication
EI > 9.0Severe eutrophication
Table 2. Evaluation model of potential eutrophication and principles for classification of trophic levels.
Table 2. Evaluation model of potential eutrophication and principles for classification of trophic levels.
Eutrophication LevelsTrophic LevelDIN/
μmol·L−1		DIP/
μmol·L−1		N/P
(Molar Ratio)
IOligotrophy<14.28<0.978~30
IIModerate nutrition14.28~21.410.97~1.458~30
IIIEutrophy>21.41>1.458~30
IVPPhosphorus limits moderate nutrition14.28~21.41->30
VPPhosphorus moderately limits potential eutrophication>21.41-30~60
VIPPhosphorus limits potential eutrophication>21.41->60
IVNNitrogen-restricted moderate nutrition-0.97~1.45<8
VNNitrogen moderately limits potential eutrophication->1.454~8
VINNitrogen-limiting potential eutrophication->1.45<4
Table 3. Evaluation of eutrophication in coastal waters over the 2016–2018 period.
Table 3. Evaluation of eutrophication in coastal waters over the 2016–2018 period.
YearMonthEIStation Proportion of Eutrophication Severity
Mild EutrophicationModerate
Eutrophication		Severe
Eutrophication
2016March0.055~2.30816.67%--
May0.054~4.42911.11%5.56%-
August0.116~2.54425.00%--
October0.055~2.19211.11%--
2017March0.028~3.059-2.78%-
May0.125~1.4502.78%--
August0.075~1.53811.11%--
October0.009~1.0482.78%--
2018March0.120~3.76133.33%5.56%-
May0.023~3.7888.33%2.78%-
August0.063~3.72111.11%2.78%-
October0.017~6.004-2.78%-
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Xiao, J.; Chen, X.; Zhou, L.; Zhang, H.; Hang, X.; Chen, Y. Nutrient Distribution Characteristics and Eutrophication Evaluation of Coastal Water near the Yellow River Estuary, China. Water 2025, 17, 2469. https://doi.org/10.3390/w17162469

AMA Style

Xiao J, Chen X, Zhou L, Zhang H, Hang X, Chen Y. Nutrient Distribution Characteristics and Eutrophication Evaluation of Coastal Water near the Yellow River Estuary, China. Water. 2025; 17(16):2469. https://doi.org/10.3390/w17162469

Chicago/Turabian Style

Xiao, Jing, Xiang Chen, Li Zhou, Haibo Zhang, Xiaoshuai Hang, and Yudong Chen. 2025. "Nutrient Distribution Characteristics and Eutrophication Evaluation of Coastal Water near the Yellow River Estuary, China" Water 17, no. 16: 2469. https://doi.org/10.3390/w17162469

APA Style

Xiao, J., Chen, X., Zhou, L., Zhang, H., Hang, X., & Chen, Y. (2025). Nutrient Distribution Characteristics and Eutrophication Evaluation of Coastal Water near the Yellow River Estuary, China. Water, 17(16), 2469. https://doi.org/10.3390/w17162469

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