Water Environment in Macro-Tidal Muddy Sanmen Bay

Abstract

The water environment in estuaries is a crucial factor affecting the biodiversity and self-purification capacity of coastal zones. This study focuses on Sanmen Bay as an example to study the characteristics and temporal variations of the water environment in the turbid coastal waters on the East China Sea coast. The field data of hydrodynamics and water environment from 2018 to 2023 including different seasons in the bay were collected and analyzed. We analyzed the correlation between water environmental factors and sediment and explored the impact of sediment mixing layers on the water environment. Field data indicate that water temperature, dissolved oxygen content, and suspended sediment concentration (SSC) vary seasonally. In summer, the water temperature and SSC are the highest; in autumn, the dissolved oxygen content is the highest. Salinity and pH values showed little variation from 2018 to 2023. The concentration of oils in sediments across the entire area within Sanmen Bay varied from 2018 to 2023, which decreased from (30.6–92.2) × 10⁻⁶ mg/L to below 10⁻⁶ mg/L. Correlational analysis indicates that dissolved oxygen concentration and heavy metal content were correlated with sediment in 2018, with correlation coefficients of approximately 0.5. Sediments impact the water environment through changing stratification and mixing due to suspended particulate matter and through changing water environment parameters (e.g., heavy metal) due to bed sediment erosion. The bulk Richardson number in most areas is larger than 0.25. These results indicate that sediment impacts heavy metals in Sanmen Bay. In highly turbid waters, sediments are more likely to adsorb heavy metals and other pollutants, thereby impacting water quality.

1. Introduction

Estuaries and bays, characterized by intense ocean–land interactions [1], have become hubs of extensive human activities. The rapid expansion of economic endeavors and hydraulic engineering has precipitated the degradation of water quality within estuaries due to the influx of terrestrial and oceanic pollutants [2]. Human interventions, such as thermal discharges, exert profound impacts on the ecosystem and water quality of estuaries and bays, thereby influencing human living conditions [3]. Hydraulic engineering projects, such as dams, alter fluvial water discharges and sediment loads, thereby transforming sediment transport dynamics.

In muddy estuarine environments, sediment plays a pivotal role in material transport, dictating the fate and circulation of substances like pollutants. Most pollutants introduced into natural water bodies, particularly suspended particulate matter, are adsorbed by suspended sediments and transported or deposited alongside sediment particles onto the bed, forming layers laden with various pollutants, acting as sinks for contaminants [4]. Bed sediments can be eroded and re-suspended under energetic tides and waves, leading to the release of a substantial number of pollutants; thus, sediments behave as sources of contamination and trigger secondary pollution of water bodies. For instance, heavy metals from rivers and atmospheric deposition [5,6] undergo transfers between seawater and marine sediments through processes like adsorption and biological pumping, accumulating in the food chain and ultimately posing risks to human health [7]. Intense anthropogenic activities have caused significant alterations in nutrient components (e.g., nitrogen, phosphorus, and carbon) and concentrations in estuaries, resulting in modifications to the estuarine environment [8]. Bed-surface sediments are integral to material cycling in marine environments, playing a crucial role in the transport of heavy metals and nutrients [9]. Polluted sediment represents a potential source of water pollution, with factors influencing the release of contaminated sediment primarily including dissolved oxygen, pH, temperature, and disturbance. Drawing on diffusion theory, Roboert et al. [10] proposed a method to determine exchange processes between sediment and water. Smits and Van der Molen [11] divided the riverbed into an aerobic layer, an oxidation layer, an exchange layer, and a settling layer to determine the nutrient exchange between the sediment and the overlying water, which was applied in the study of nutrient release in Lake Veluwe, Netherlands. DiToro [12] delineated the active sediment layer into anaerobic and aerobic segments, with the determination of release quantities considering complex biological processes.

Sediment flux within estuaries and rivers experiences a decline, leading to a reduction in suspended sediment concentration (SSC) or bed sediment erosion, both of which impact water environments by transporting sediment-related pollutants [13]. The size of sediment particles in the water column plays a critical role in determining the adsorption capacity of pollutants per unit weight of sediment.

Regarding overall adsorption capacity, higher sediment content increases the available surface area for pollutants to interact with, which can enhance the total amount of adsorbed pollutants [14]. Sediment composition further dictates the specific surface area per unit weight, influencing the contact area available for pollutant adsorption. Finer sediment particles, in particular, possess larger specific surface areas, resulting in stronger adsorption capacities under the same sediment content [10].

During bed erosion, pollutants stored in the sediment are released into the water, contributing to the concentrations of nitrogen and phosphorus. This release fosters water quality enrichment, promotes eutrophication, and hinders ecological restoration by stimulating algae growth, sedimentation, decomposition, and the exchange between water and sediment. Factors that influence sediment release include dissolved oxygen, pH, temperature, and physical disturbances [15].

Several methods have been developed to assess water quality and improve environmental conditions in estuarine systems with muddy sediment. These methods include single-factor assessment [16], multi-factor analysis [17], the Analytic Hierarchy Process (AHP) [18], fuzzy comprehensive evaluation [19], principal component analysis (PCA) [20], and artificial neural network (ANN) approaches [21]. Single-factor assessment is a straightforward technique that focuses on the most significantly impaired factor, comparing it against established water quality standards [22].

Sanmen Bay (SMB, Figure 1a) is situated along the turbid coast of the East China Sea, classified as a macro-tidal muddy estuary. It spans an area of approximately 775 km², extending in the northwest to southeast direction, and boasts a water depth largely within 10 m. The bay experiences semi-diurnal tides, with tidal ranges reaching up to 7.2 m [23], indicating its energetic tidal regime. Despite its coastal location, the bay is not fed by large river runoffs, with sediment primarily re-suspended by tidal currents, leading to a correlation between SSC and flow velocity, especially during peak flow moments [24]. The bay’s turbidity is further exacerbated by strong winds and tidal-induced sediment re-suspension, particularly during monsoon and high-tide conditions [25]. On average, the bay maintains a suspended solids concentration of 367 mg/L annually [26]. Seasonal variations in water currents, influenced by the East Asian monsoon, are observed, with colder seasons characterized by the turbid, low-salinity Zhejiang–Fujian Coastal Current, while warmer seasons experience the influence of the clear, high-salinity Taiwan Warm Current and upwelling currents, potentially affecting phytoplankton composition [27].

SMB is renowned for its fisheries and mariculture activities, prompting significant attention to sediment dynamics and pollutant distribution. Previous studies have focused on sediment composition and trace element distribution, highlighting the presence of contaminants in surface sediments and underscoring the need for ongoing monitoring to mitigate potential anthropogenic impacts [28]. Additionally, attention is directed toward understanding the complex interplay between hydrodynamic processes and ecological conditions, exploring relationships between hydrodynamics, sediment composition, and pollutant distribution, particularly in the context of heavy metal pollution in intertidal sediments [29].

SMB serves as a vital ecosystem stabilizer and a significant mariculture base [30]. However, the rapid expansion of industrial activities poses challenges to the water environment and ecosystems in the bay, as evidenced by the presence of contaminants like copper and chromium in sediments and eutrophic zones [7]. The objectives of this study are firstly to investigate the spatio-temporal variations in hydrodynamics, water quality, and sediments in SMB using field data; and secondly to investigate the impacts of sediments on water environmental parameters. Section 2 outlines the materials and methods, while Section 3 presents the results of hydrodynamic, water quality, and sediment measurements. Section 4 is the discussion, and Section 5 is the conclusions.

2. Materials and Methods

2.1. Field Measurements and Sample Collection

Fieldwork was carried out in SMB (Figure 1) in different seasons, including April 2018 (spring), November 2019 (autumn), March 2020 (spring), January 2021 (winter), March 2023 (spring), and June 2023 (summer). The field stations were specifically designed to cover the entire bay horizontally, with a high focus on key coastal areas. During water quality sampling, surface water samples were collected only when the water depth was less than 10 m at the field stations. When the water depth exceeded 10 m, both surface and bottom water samples were collected at the field stations, with oil being collected exclusively from the surface water samples. Surface sediments were collected from the top 0–2 cm layer, and hydrological elements were sampled at various layers: surface, 0.2 H, 0.4 H, 0.6 H, 0.8 H, and bottom. Since the bay is macro-tidal and the water depth varies spatially, using relative depth ensures that comparable samples are collected from various areas, allowing for a more accurate reflection of environmental differences between regions.

The parameters collected during fieldwork were selected to encompass hydrodynamic measurements, in addition to water and sediment quality parameters. Hydrodynamic parameters comprised sea surface elevation, current velocity, and suspended particulate matter (SPM time series) concentration time series of vertical profile. Water quality parameters included water temperature, salinity, SPM concentration (the spatial distribution of suspended sediment concentration at a specific moment), dissolved oxygen content, and pH level. Sedimentation parameters consisted of organic carbon, sulfide, oil content, and heavy metal concentrations (copper, zinc, and mercury) in bed sediments. Field sampling, storage, transportation, analysis, and data processing methods were strictly conducted in accordance with the relevant regulations, including the ‘Specifications for Oceanographic Survey’ [31], ‘Specifications for Marine Monitoring’ [32], and ‘”Technical Specifications for Environmental Monitoring of Coastal Waters’ [33].

Elevation and current data were measured from 7 to 22 January 2021, at the survey stations shown in Figure 1b. Elevation data were obtained using a tide gauge deployed at the stations P12–P19 (Figure 1b), with a sampling frequency of once per hour. For tidal current observation, we used an Acoustic Doppler Current Profiler (ADCP) and a Model 106 propeller-type current meter, with a sampling frequency of 10 min. Water and sediment samples were analyzed in the laboratory. Temperature and salinity analysis was performed using the conductivity temperature depth (CTD) system. Dissolved oxygen analysis was conducted using iodometry, and pH analysis was performed using a pH meter. Heavy metal analysis was conducted using Atomic Absorption Spectrophotometry, and oil substance analysis was performed using a Fluorescence Spectrophotometer at the sampling stations. The sampling methods and configuration of instruments are described in Table 1. Detailed ammeter information and software usage can be found in Ren [34].

2.2. Single-Factor Method

The water quality was evaluated according to the single-factor water quality assessment method [35]. The single-factor evaluation standard of water quality adopts the national standard ‘Seawater Quality Standard’ [36]. The formula for calculating the standard index ($S_{ij}$) of a single water quality parameter $i$ at the point $j$ is

(1)$S_{ij}=C_{ij}/C_{sj}$

where $C_{ij}$ is the concentration of water quality evaluation factor $i$ at sampling point $j$ and the unit is mg/L. $C_{sj}$ is the evaluation standard of the evaluation factor $i$, and the unit is mg/L. The formula is used to calculate heavy metals such as oil, copper, zinc, and mercury.

Due to the different properties of dissolved oxygen and pH from other water quality parameters, a different form of index unit is required. The formula for calculating the standard index ($S_{DO,j}$) of dissolved oxygen at sampling point $j$ is

(2)$\left\{\begin{array}{l}{S}_{DO,j}={\displaystyle \frac{\left|D{O}_f-D{O}_j\right|}{\left|D{O}_f-D{O}_s\right|}},D{O}_j\ge D{O}_s\\ S_{DO,j}=10-9{\displaystyle \frac{D{O}_j}{D{O}_s}},D{O}_j<D{O}_s\end{array}\right.$

where $D{O}_f=468/\left(31.6+T\right)$ (mg/L), T is the temperature (°C), $D{O}_s$ is the evaluation standard of dissolved oxygen (mg/L), and $D{O}_j$ is the monitoring value of dissolved oxygen at the j sampling point (mg/L).

The standard index ($S_{pH,j}$) of pH value at sampling point j is calculated as

(3)$\left\{\begin{array}{l}{S}_{pH,j}={\displaystyle \frac{7.0-p{H}_j}{7.0-p{H}_u}},p{H}_j\le 7.0\\ S_{pH,j}={\displaystyle \frac{p{H}_j-7.0}{p{H}_{ul}-7.0}},p{H}_j>7.0\end{array}\right.$

where $p{H}_u$ is the lower pH limit specified in the water quality standard, $p{H}_{ul}$ is the upper pH limit specified in the water quality standard, and $p{H}_j$ is the pH monitoring value of sampling point j. If the standard index value of an evaluation factor in a certain water quality is greater than 1, it indicates that the factor exceeds the corresponding water quality evaluation standard and cannot meet the use requirements of the corresponding functional area. Otherwise, it shows that the factor can meet the requirements of the use of functional areas.

2.3. Correlational Analysis Method

The Pearson correlation coefficient $r$ is used to determine the correlation between two variables. A larger $r$ indicates a stronger relationship between two variables:

(4)$r={\displaystyle \frac{{\displaystyle {\sum }_{i=1}^n\left(x_i-\overline{X}\right)\left(y_i-\overline{Y}\right)}}{\sqrt{{\displaystyle {\sum }_{i=1}^n{\left(x_i-\overline{X}\right)}^2}}\sqrt{{\displaystyle {\sum }_{i=1}^n{\left(y_i-\overline{Y}\right)}^2}}}}$

2.4. Richardson Number Calculation

The dimensionless Richardson number (Ri) is used to measure the ratio of potential energy to kinetic energy in the atmosphere [37]. It helps determine how turbulence evolves in the presence of shear and stratification [38]. According to Stacey et al. [39], the Ri is defined as

(5)$Ri={\displaystyle \frac{N^2}{S^2}}$

where N denotes the buoyancy frequency and S is the mean shear. N represents the frequency at which a water parcel would oscillate around its position of neutral density if it was displaced in a region of linear density variation. It is defined as

(6)$N^2=-{\displaystyle \frac{g}{\rho }}{\displaystyle \frac{\partial \rho }{\partial z}}$

The mean shear (S), if assuming that the vertical velocity gradient is much greater than the horizontal velocity gradient, could be defined as

(7)$S^2={\left({\displaystyle \frac{\partial u}{\partial z}}\right)}^2+{\left({\displaystyle \frac{\partial v}{\partial z}}\right)}^2$

Thus, Ri is defined as

(8)$Ri=-{\displaystyle \frac{g}{{\rho }_0}}{\displaystyle \frac{\partial \rho /\partial z}{\left[{\left(\partial u/\partial z\right)}^2+{\left(\partial v/\partial z\right)}^2\right]}}$

Based on the linear stability theory, the critical value for active mixing is $Ri=0.25$. Mixing occurs when $Ri\le 0.25$. Many studies have also utilized $Ri=0.25$ as a critical value for the occurrence of shear instability in estuaries.

Estuarine water density $\rho$ could be estimated using the following method [40]:

(9)$\rho ={\rho }_0\left(1+\beta S_w\right)$

where ${\rho }_0$ is the density of pure water, $\beta =7.8\times {10}^{-4}$ is the haline contractivity [41], and $S_w$ denotes salinity of water.

When SSC is considered, the density of the turbid estuarine water ${\rho }_{ss}$ is calculated by [42]

(10)${\rho }_{ss}=\rho +\left(1-{\displaystyle \frac{\rho }{{\rho }_s}}\right)c$

where ${\rho }_s=2650{\mathrm{kg}/\mathrm{m}}^3$ is the sediment density and $c$ is the SSC (kg/m³).

3. Results

3.1. Hydrodynamics

3.1.1. Tides Time Series

Tide level data were collected during the winter of 2021 (7–22 January), covering the entire spring–neap tidal cycle (Figure 2). The maximum tidal range exceeded 540 cm, with an incremental trend from the open sea to the bay’s shore, peaking at 644 cm in the bay. The average tidal ranges at all stations exceeded 340 cm. The maximum, minimum, and mean tidal ranges gradually enhance from the bay’s mouth to its head. The maximum tidal range was 533 cm at P17, 587 cm at P13, and 644 cm at P14. This propagation showed a 20.8% increase in the maximum tidal range from the bay’s entrance to its head. Both the highest/lowest and mean high/low tide levels exhibited a gradual increase pattern from the bay’s mouth to its head. Compared with P14 and P17, the highest tide level experienced a 20.3% increase. The minimum tidal range and average tidal range show similar spatial distributions.

The average duration of flooding and ebbing tides at each tide station was relatively consistent, with differences ranging from 0 to 5 min. Tidal asymmetry plays a crucial role in the net transport of materials and the morphological evolution of coastal zones [43]. Tidal Duration Asymmetry (TDA) is the unequal duration of flood and ebb tides. When the duration of flood tide is shorter, it is referred to as flood dominance; otherwise, it is termed ebb dominance [44]. TDA can be quantified by the skewness of the tidal elevation time derivative [45]:

(11)${\gamma }_{TDA}={\displaystyle \frac{E\left[{\left(\xi -\overline{\xi }\right)}^3\right]}{{\left(E\left[{\left(\xi -\overline{\xi }\right)}^2\right]\right)}^{3/2}}}$

where $\xi =\partial \eta /\partial t$ and $\eta$ is the tidal elevation. It indicates that the time of the rising tide is less than that of the falling tide if $\gamma >0$, and the waters are probably dominated by flood tides. Conversely, $\gamma <0$ is referred to as ebb dominance. Larger absolute values of γ correspond to more significant asymmetry. The asymmetry in SMB fluctuates distinctly between spring and neap tides. Skewness during spring tides is predominantly larger than 0, indicating shorter flood duration in the bay. During neap tides, ebb dominance prevails with skewness smaller than 0.

Tidal types are usually classified by the ratio of the amplitude of the dominant diurnal tides to the dominant semi-diurnal tide [46]: $(H_{K1}+H_{O1})/H_{M2}$. The results of the tidal analyses showed that the ratio was between 0.31 and 0.44 at all stations, and all were less than 0.50. Therefore, the nature of the tides at all stations were regular semi-diurnal tides. The main shallow tides increased from the mouth to the top of the bay. As in winter, the ratio doubles from 0.02 (P12) in the open sea to 0.04 at the top of the bay (P19). The sum of the major shallow-water tides was 0.06 m (P12) at the bay mouth and increased to 0.15 cm at the top of the bay (P19).

3.1.2. Currents Time Series

Based on the current data from 11 tidal stations, the duration of flood and ebb tides just slightly varied throughout the tidal cycles. Temporally, the tidal range at each station fluctuated by approximately 3.5 m during flood tides and around 5 m during ebb tides.

Currents during spring tides were larger than those during neap tides (Figure 3), with neap tidal currents typically amounting to 30–60% of those during spring tidal periods. The maximum current velocity during flood tides was stronger in deep water areas and weaker in shallow water areas, compared with that during ebb tides.

During spring tides (Figure 3(a1–k1)), the maximum current velocity (1.19 m/s) during flood tides exceeded that of ebb tides at almost all the stations (Figure 3(b1–h1)). The ebb currents ranged from 40% to 90% of their peak levels during the flood tides. The ebb currents were stronger than the flood currents in shallow water areas (Figure 3(a1,i1,j1,k1)). During neap tides (Figure 3(a2–k2)), the most intense tidal currents occurred near the surface level of P7 (Figure 3(g2)) during flood tides, reaching 1.2 m/s, closely followed by P1 (1.15 m/s, Figure 3(f2)).

Spatially, the flow velocity at the bay entrance increased, which is 55.5% higher than that near the estuary (P4, P5, and P6). The peak flow velocity at P6 (1.15 m/s) was stronger than that of P4 by 64.3%, indicating a gradual northward increase of flow velocity at the bay’s entrance. Moreover, the flow velocity from the bay’s mouth to its upper reaches (P6, P7, and P9) demonstrates an initial increase followed by a decrease, albeit with minimal differences ranging from 0.05 to 0.2 m/s.

3.1.3. SPM Time Series

Water samples were obtained horizontally at the 11 stations P1–P11, and vertically at six layers (surface, 0.2 H, 0.4 H, 0.6 H, 0.8 H, and bottom layers), during two cruises of a spring tide (27 h) and a neap tide (28 h), with sampling intervals of 1 h.

Based on the measured data, vertical distribution profile diagrams of suspended sediment concentration at different stations during spring tide and neap tide were plotted.

Temporally, during spring tides (Figure 4(a1–k1)), the average SPM during flooding tides at P1, P2, P3, P7, P9, and P11 exceeded those during ebbing tides. Similarly, the average SPM during flooding tides at P4, P5, P6, P8, and P10 were larger than the average SPM during ebbing tides. The highest SPM reaching 1.2 kg/m³ was recorded in the bottom layer during flood tides at P2, while the lowest SPM of 0.101 kg/m³ was observed in the surface layer at P5 during ebb tides.

During neap tides (Figure 4(a2–k2)), the average SPM during flooding tides at P1, P5, P6, P7, P8, P9, P10, and P11 exceeded those during ebbing tides. The highest SPM reaching 0.51 kg/m³ was recorded in the bottom layer during flood tides at P9, while the lowest SPM of 0.04 kg/m³ was observed in the surface layer at P3 during flood tides.

Spatially, SPM along the vertical lines at all sediment stations exhibited broad similarities. Furthermore, the vertical distribution of SPM was characterized by a gradual increase in depth.

3.2. Water Quality

In SMB, the mean and standard deviation of surface temperature, salinity, SPM concentrations, dissolved oxygen concentrations were shown in Table 2. This reveals the variation trend and spatial difference of environmental parameters in Sanmen Bay area. We analyzed the interannual variation in water quality within SMB by comparing environmental factors between March 2020 and March 2023. Additionally, we examined the seasonal variation in water quality by comparing environmental factors, including water temperature and salinity, from April 2018, November 2019, January 2021, and the summer of 2023. We applied linear interpolation to the results from different stations to obtain the spatial variation of different environmental factors.

3.2.1. Water Temperature

Temporally, the sea surface water temperature (SST) in the bay gradually decreases during the spring of 2020 (Figure 5(c1)) and 2023 (Figure 5(e1)). The maximum temperature values decreased by approximately 2 °C, and the minimum temperature values decreased by about 1.8 °C.

As shown in Figure 5, the sea surface temperature (SST) varied seasonally. In June 2023, the SST was 25 °C, indicating a high temperature typical for summer. SSTs in April 2018 (18.7 °C) and November 2019 (19.9 °C) were also relatively high. In contrast, SSTs in March 2020 and March 2023 were lower, with an average of 12 ± 1 °C. Generally, SSTs in April, November, and June exceeded 15 °C. In March, the SST was typically below 15 °C, with the lowest temperature occasionally falling below 10 °C.

In summer, the SST ranged from 23.74 °C to 26.57 °C, with the highest temperature in the western part of SMB and the lowest (23.74 °C) in the northeastern part. The SST gradually increased from northeast to southwest. In March 2020, the SST ranged from 11.8 °C to 14.7 °C, with higher temperatures in the eastern and northwestern parts of SMB and lower temperatures in the northern and southwestern parts. In March 2023, the SST decreased from west to east, ranging from 10 °C to 12.7 °C. SST trends in spring 2018 and autumn 2019 both showed a decrease from northwest to southeast.

3.2.2. Salinity

From 2020 to 2023, the surface salinity of the bay showed a decreasing trend, dropping by approximately 4.3%. In spring 2020, sea surface salinity (SSS) at various stations ranges from 14.1 to 32.8, averaging 29.0 (the average value of each station). In spring 2023, SSS ranged from 22.4 to 29.2, averaging 27.8.

Spatially, SSS in SMB ranged from 14.1 to 35.1. SSS varied in the order of spring (March 2020) > summer (June 2023) > spring (April 2018) > autumn (November 2019) > spring (March 2023). The highest SSS in autumn (35.1) occurred on the south side of the bay mouth. In autumn 2019 (Figure 5(b2)), SSS values (approximately 3–19) were higher compared to those in spring 2020 (Figure 5(c2)). In autumn 2019 and spring 2020, SSS values gradually decreased from the bay mouth to its inlet. In autumn 2019, SSS values were higher at the inlet and lower near the shore. In spring 2020, SSS was higher throughout the bay, with a rapid decline near the runoff area. Comparing SSS in spring 2023 (Figure 5(d2)) with summer 2023, there was little difference, especially at the bay mouth. Further analysis is required to identify the factors influencing these SSS variations, such as precipitation, runoff, and ocean circulation.

Seasonally, SSS in each season exhibited a similar spatial distribution pattern, increasing from northwest to southeast within SMB. In spring 2018, salinity variations in the bay were minimal, with higher SSS observed in the branches. SSS decreased from the bay mouth to its inner branches. In autumn 2019, winter 2021, and spring 2023, SSS values ranged from approximately 1.5 to 6, varying from the bay mouth to its upper reaches. Minor changes in salinity were observed between spring 2020 and summer 2023.

3.2.3. SPM

SPM data in this section were obtained only at the surface level at different stations. The suspended solids data were collected by analyzing water samples in the laboratory. When the water depth was less than 10 m, surface water samples were collected; when the water depth was between 10 and 25 m, both surface and bottom water samples were collected. The suspended solids consist of insoluble sludge, clay, organic matter, algae, and microorganisms and are an indicator for measuring water pollution.

Temporally, the SPM concentration in the bay showed an increasing trend, with the concentration in March 2023 being approximately 8.1 times higher than that in March 2020.

Regarding seasonal variations, a comparison between autumn 2019 (Figure 6(a2)) and spring 2020 was conducted. Spring SPM concentrations were higher (10 to 130 mg/L) than those in autumn in most areas. Comparing SPM concentration in spring 2023 with summer 2023 (Figure 6(a5)), significant differences in SPM concentrations were observed, with summer SPM concentrations approximately four to five times higher than those in spring.

On a spatial scale, SPM concentrations in spring 2018 and spring 2023 increased from the bay mouth to the upper reaches, with higher concentrations near the shore. In autumn 2019, SPM concentrations decreased from the bay mouth to the upper reaches, exhibiting higher values in the south and lower values in the north. During spring 2020, SPM concentrations decreased from northeast to southwest.

3.2.4. Dissolved Oxygen

The surface dissolved oxygen (DO) concentrations in SMB do not exhibit significant variations over time and space. Temporally, DO concentrations in the bay have an increasing trend in spring 2020 and spring 2023.

Seasonally, a comparison between autumn 2019 and spring 2020 was conducted. DO concentrations are approximately 0.2–0.6 mg/L higher in autumn than in spring. Likewise, comparing DO concentrations in spring 2023 with those in summer 2023, a notable difference in DO concentrations is observed, with spring DO concentrations being 3.8–4 mg/L higher than those in summer.

Spatially, DO concentrations in spring 2018 and spring 2020 decreased from the bay’s mouth to its upper reaches, with lower DO concentrations occurring near the shore. In autumn 2019, DO concentrations increased from the bay mouth to the bay head, decreasing from the northwest to the southeast of the bay. In spring 2023, DO concentrations exhibited minimal variation throughout the bay, mostly ranging from 9.4 to 9.6 mg/L. In summer 2023, DO concentrations ranged from approximately 6.2 to 6.6 mg/L over most of the area, with a small area near the northern shore having lower DO concentrations.

3.2.5. pH

In SMB, the pH values ranged from 7.54 to 8.7. The maximum value was observed in spring 2018, while the minimum value was observed in spring 2020. Seasonal variation in pH values was small. Temporally, the pH value of the bay in the spring of 2023 was higher compared to the spring of 2020, but it remained above 7.

When comparing seasonal variations, pH values in autumn 2019 were slightly higher than in spring 2020, and pH levels in summer 2023 exceeded those in spring 2023, though these differences remained minor.

Spatially, in all of spring 2018, autumn 2019, and spring 2023, pH levels tended to be higher near the mouth of the bay, decreasing toward the shore. However, in spring 2020, a different pattern was observed: pH was relatively higher along the shore and in the northern regions, while lower values appeared in the southern parts of the bay.

3.3. Bed Sediment

We collected bed surface sediments (top 0–2 cm) in the bay, froze them for preservation, and then analyzed them in the laboratory to determine the concentrations of organic carbon, sulfides, petroleum, and heavy metals after purification and separation in Table 3.

3.3.1. Organic Carbon

The concentrations of organic carbon in bed sediments showed small temporal but large spatial variations. The organic carbon concentration ranged from 0.15 × 10⁻² mg/L to 1.19 × 10⁻² mg/L. The maximum value was observed in the northern part of SMB in autumn 2019, while the minimum value was observed near the outer sea of SMB in the same season. In spring 2018, the variation in organic carbon concentration within SMB was small (0.2 × 10⁻² mg/L).

Spatially, the concentration of organic carbon was higher near the shore compared to the mouth of SMB, typically ranging from 0.001 to 0.004 mg/L.

3.3.2. Sulfide

In spring 2018 (Figure 7d), the concentration of sulfide in sediments ranged from 2.7 × 10⁻⁶ mg/L to 26.5 × 10⁻⁶ mg/L, with an average of 8.280 × 10⁻⁶ mg/L. In autumn 2019 (Figure 7e), sulfide concentration varied from 4.4 × 10⁻⁶ mg/L to 63.8 × 10⁻⁶ mg/L, averaging 22.756 × 10⁻⁶ mg/L. In summer 2023 (Figure 7f), sulfide concentration ranged from 0.3 × 10⁻⁶ mg/L to 5.8 × 10⁻⁶ mg/L, with a mean value of 0.471 × 10⁻⁶ mg/L.

Spatially, the sulfide concentration in SMB was higher near the shore and in the tributaries, reaching levels that are approximately six to 10 times greater than at the bay’s mouth. Sulfide concentrations decreased from west to east across the entire bay mouth.

3.3.3. Oil

Large spatial variations occurred in the oil concentrations in the bay. In spring 2018, the oil concentrations were relatively high (30.6–92.2 × 10⁻⁶ mg/L), with the highest concentrations observed in the northern part of SMB. In autumn 2019, oil concentrations decreased to between 5.4 × 10⁻⁶ mg/L and 41.3 × 10⁻⁶ mg/L, but their distribution within SMB remained highly variable. By summer 2023, the oil concentrations dropped to below 10 × 10⁻⁶ mg/L, with less variation. Spatially, the distribution of oil in SMB showed higher concentrations in the central area and lower levels in the surrounding regions, with concentrations decreasing closer to the shore. In 2018, the bay had significant spatial fluctuations in oil concentrations, with areas near the mouth having approximately one-third of the concentrations in the bay center. In autumn 2019 and summer 2023, variations in oil concentration within the bay were minimal.

3.3.4. Heavy Metal

The distribution of heavy metals in SMB was focused on copper, zinc, and mercury (Figure 8). In spring 2018 (Figure 8a,d,f), copper, zinc, and mercury concentrations in sediments ranged between 28.1 × 10⁻⁶ mg/L and 37.2 × 10⁻⁶ mg/L, 79.7 × 10⁻⁶ mg/L and 102.6 × 10⁻⁶ mg/L, and 0.042 × 10⁻⁶ mg/L and 0.053 × 10⁻⁶ mg/L, respectively. In autumn 2019 (Figure 8b,e,g), copper, zinc, and mercury concentrations varied between 23.5 × 10⁻⁶ mg/L and 33.5 × 10⁻⁶ mg/L, 80.4 × 10⁻⁶ mg/L and 121 × 10⁻⁶ mg/L, and 0.035 × 10⁻⁶ mg/L and 0.050 × 10⁻⁶ mg/L, respectively. In summer 2023 (Figure 8c,e,i), copper, zinc, and mercury concentration ranged between 17.1 × 10⁻⁶ mg/L and 46.6 × 10⁻⁶ mg/L, 66.5 × 10⁻⁶ mg/L and 127 × 10⁻⁶ mg/L, and 0.025 × 10⁻⁶ mg/L and 0.069 × 10⁻⁶ mg/L, respectively.

Spatially, higher heavy metal concentration occurred near the shore. The concentration of heavy metals was generally 26% to 50% higher in the tributaries than in the bay. Within the bay, heavy metal concentration decreased from the northwest to the southeast by 5% to 25%.

4. Discussion

4.1. Variations of Water Environment

As shown in the single-factor evaluation results (Category I standards) (Table 4), the pH values ranged from 7.93 to 8.3, with the maximum evaluation factor occurring in March 2020, exceeding the Category I standard. The pH evaluation factors across the entire bay remained within the standard range. The DO evaluation factor in the summer 2023 was 1.01, also exceeding the Category I standard. In other years and seasons, the evaluation results remained relatively low. The evaluation factors of heavy metals showed a continuous increase, particularly for zinc and lead. By 2023, their maximum values had exceeded the Category I standard.

From 2018 to 2023, SST and SSS values showed a decreasing trend, while sulfide and DO exhibited an increasing trend. Water quality indicated an acidification trend, with pH values gradually decreasing. The organic carbon concentrations in the bay consistently decreased, while sulfide, oil substances, and heavy metal concentrations increased. Some heavy metals, such as zinc, exceeded the standards of the first category.

4.2. Impacts of Sediments on Water Environment

Spatial and temporal variations in factors such as salinity and SSC are frequent in estuaries. These variations control the phase distribution of chemical elements between particulate and dissolved phases through adsorption and desorption processes. The adsorption strength of metals by suspended sediments is influenced by the properties of the metals themselves. As SSC increases, the adsorption of metals intensifies [47]. Additionally, the adsorption of trace organic elements like phosphorus by sediments is influenced by pH, sediment concentration, and temperature [48].

In estuarine and bay environments, sediment resuspension and diffusive fluxes play significant roles in affecting water quality. Hydrodynamic conditions such as tides and waves can cause bottom sediments to resuspend, increasing SSC and releasing adsorbed pollutants like heavy metals and nutrients into the water column. This process can elevate pollutant concentrations and adversely impact water quality.

Sediment particles serve as carriers for the migration and transformation of substances, affecting the concentration of pollutants in the water. The environmental factors of estuaries and bays are highly correlated with sediments. Our study observed higher SSC in nearshore areas, likely due to resuspension processes. This not only increases water turbidity but also facilitates the transport of pollutants, potentially deteriorating water quality.

In 2018, DO and heavy metals were influenced by SSC, with a correlation coefficient of approximately 0.5. From 2018 to 2023, the correlation coefficients between sediment and various water environmental factors such as pH, organic carbon, sulfide, and oil substances were relatively low (as shown in Table 5). This suggests that sediment resuspension and diffusive fluxes may impact certain water quality parameters, especially DO and heavy metals, while having less effect on others.

4.3. Impacts of Sediment on Stratification

Since most studies use 0.25 as the critical value for mixing, we applied log10(Ri/0.25) in our plots to transform it to a scale with 0 as the threshold, thereby enhancing intuitiveness. The overall Richardson number varied with water depth in spring tides (density calculated considering both salinity and SPM, Figure 9(a1–k1)). During spring tides, the water in the bay was periodically stratified and mixed, with more pronounced stratification at the bottom level. From the head to the mouth of the bay, the stratification became more pronounced. During the spring flood tide, the salinity difference and potential energy difference at the bottom layer gradually increased. During the spring ebb tide, the salinity difference and potential energy difference at the bottom layer gradually decreased, weakening stratification and promoting mixing. Similar changes occurred during neap tides. Spatially, the overall Richardson number was largely smaller near the coast and gradually decreased from northwest to southeast within the bay.

Sediment contributed to stratification by affecting density. When considering only salinity in the density calculation (Figure 10), the overall Richardson number decreased at most stations and times during spring tides (Figure 9(a2–k2)).

5. Conclusions

Field measurements of hydrodynamics, water quality, and bed sediments were carried out in SMB. Data on sea surface level, current velocity, sediment, temperature, salinity, pH, DO, organic carbon, and heavy metals were obtained. These data were used to analyze the spatial and temporal changes in the water environment in the bay. The main conclusions are as follows:

The hydrodynamics in the bay are highly energetic. Tides are macro-tidal and semi-diurnal, with tidal ranges peaking at 644 cm during field measurements. The tidal range increases from the mouth to the upper reaches. Tidal currents strengthen and then diminish along the bay. Tidal asymmetry varies between spring and neap tides. Shorter flood durations occur in spring tides, while stronger ebb dominance occurs in neap tides.

The water environment parameters exhibit both temporal and spatial variability. Influenced by factors such as temperature, salinity, and tidal currents, the water environmental factors vary across different years and seasons. SSC, DO, and oil are particularly affected. Additionally, the spatial distribution of salinity and SSC varies significantly. At the same time, the maximum and minimum values within SMB differ by 18.8 mg/L (14.1–32.8 mg/L) for salinity and 2694 mg/L (61–2755 mg/L) for SSC. The spatial distribution of petroleum substances ((30.6–92.2) × 10⁻⁶ mg/L in spring 2018 and (5.4–41.3) × 10⁻⁶ mg/L in autumn 2019) and sulfides ((2.7–26.5) × 10⁻⁶ mg/L in spring 2018 and (4.4–63.8) × 10⁻⁶ mg/L in autumn 2019) also showed significant differences.

Both bed and suspended sediments impact the water environment in the bay. The SPM impacts water stratification and mixing by impacting water density. Ri values are increased when considering both salinity and SPM in water density. Suspended and bed sediments impact water quality parameters through absorption and desorption in the deposition–erosion process. Sediments correlate with DO and heavy metals, with coefficients around 0.5. The correlations of sediments with pH, organic carbon, sulfide, and oil are small (<0.1).

Author Contributions

Conceptualization, L.L.; investigation and data curation, J.Y.; writing—original draft preparation, L.W.; writing—review and editing, project administration and supervision, L.L., X.Z. and Y.X. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

No applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are unavailable due to privacy or ethical restrictions.

Conflicts of Interest

Jinxiong Yuan was employed by Hangzhou Xi’ao Environment Science and Technology Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential con-flict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. (a) Water depth in SMB. (b) Hydrological stations in 2021 (P1–P11 are the temperature and salinity stations for 2021, and P12–P19 are the tidal level, velocity, and SPM time series change stations.); (c) Water quality stations in 2018; (d) Water quality stations in autumn 2019 and spring 2020; (e) Water quality stations in 2023.

Figure 2. Tidal elevation at the 8 tidal stations and tidal asymmetry (red line).

Figure 3. Flow velocity profiles during spring tides (a1–k1) and neap tides (a2–k2) at the eleven stations P1–P11.

Figure 4. SPM profiles during spring tides ((a1–k1), representing stations P1–P11, respectively) and neap tides ((a2–k2), representing stations P1–P11, respectively).

Figure 5. Sea surface temperature (a1–f1) and sea surface salinity (a2–f2). Spring 2018 (a1,a2); Autumn 2019 (b1,b2); Spring 2020 (c1,c2); Winter 2021 (d1,d2); Spring 2023 (e1,e2); Summer 2023 (f1,f2).

Figure 6. SPM concentration (a1–a5), dissolved oxygen concentration (b1–b5), and pH (c1–c5). Spring 2018 (a1,b1,c1); Autumn 2019 (a2,b2,c2); Spring 2020 (a3,b3,c3); Spring 2023 (a4,b4,c4); Summer 2023 (a5,b5,c5).

Figure 7. Surface organic carbon, sulfide and oil concentration: (a–c) are organic carbon, (d–f) are sulfides, and (g–i) are oils; (a,d,g) are measured data from 2018 (April, spring), (b,e,h) are measured data from 2019 (November, autumn), and (c,f,i) are measured data from 2023 (June, summer).

Figure 8. Heavy metal concentration in surface sediments: cuprum (a–c), zinc (d–f), total mercury (g–i); 2018 (April, spring) (a,d,g), 2019 (November, autumn) (b,e,h), 2023 (June, summer) (c,f,i).

Figure 9. (a1–k1) During spring tides, total Richardson number log10(Ri/0.25) considering the salinity and SSC for calculating water density. (a2–k2) During spring tides, the contribution of SSC to Richardson number log10(Ri/1.25). (a–k) indicate the eleven stations P1–P11.

Figure 10. (a1–k1) During spring tides, total Richardson number log10(Ri/0.25) considering the salinity for calculating water density. (a1–k1) indicate the eleven stations P1–P11.

Table 1

The configurations of the instruments and the sampling methods.

Parameters	Instruments and Sampling Method	Field Work Time and Stations
Elevation	Tidal gauge, 1 h sampling frequency.	7–22 January 2021, at stations in Figure 1b.
Current	Current meter, 10 min sampling frequency, vertical 6 layer.	Same as Elevation.
SPM	Horizontal water sampler, 1 h sampling frequency, vertical 6 layer (Section 3.1.3).	Same as Elevation (Section 3.1.3).
Water temperature	Conductivity, Temperature, and Depth meter (CTD); fixed point at surface level.	17–26 April 2018, at stations in Figure 1c; 4–13 November 2019 and 24–31 March 2020, at stations in Figure 1d; 7–22 January 2021, at stations in Figure 1c; March and June 2023, at stations in Figure 1e.
Salinity	Salinometer DQ2017-LH31 (2019 autumn and 2020 spring); CTD (2018 and 2023).	Same as water temperature.
pH	pH meter method.	17–26 April 2018, at stations in Figure 1c; N4–13 November 2019 and 24–31 March 2020, at stations in Figure 1d; March and June, 2023, at stations in Figure 1e.
Dissolved oxygen	Burette 25 mL A class.	Same as pH.
SPM	Gravimetric methods, water depth < 10 m, fixed point at surface level; water depth > 10 m, fixed point at surface and bottom levels (Section 3.2.3).	Same as pH.
Organic carbon	Potassium dichromate oxidation-reduction volumetric method.	17–26 April 2018, at stations in Figure 1c; 2019 and 24–31 March 2020, at stations in Figure 1d; June 2023, at stations in Figure 1e.
Sulfide	Sulfur ion-selective electrode method.	Same as organic carbon.
Oil	Fluorescence spectrophotometry.	Same as organic carbon.
Heavy mental	Inductively coupled plasma mass spectrometry.	Same as organic carbon.

Table 2

The concentration of each substance in SMB during each cruise (mean ± standard deviation).

Parameters	April 2018	November 2019	March 2020	January 2021	March 2023	June 2023
Water temperature	18.7 ± 0.9 °C	19.9 ± 0.6 °C	13.3 ± 0.7 °C	7.23 ± 0.3 °C	11.2 ± 0.7 °C	25 ± 0.7 °C
Salinity	26.7 ± 1.2	31.5 ± 1.7	29.0 ± 5.0	25.9 ± 0.4	27.8 ± 0.8	28.6 ± 1.6
SPM	140.2 ± 96.7 mg/L	78.2 ± 42.5 mg/L	33.9 ± 18.2 mg/L	/	275.9 ± 143.8 mg/L	245.2 ± 390.9 mg/L
Dissolved oxygen	8.2 ± 0.3mg/L	8.5 ± 0.7 mg/L	8.3 ± 0.4 mg/L	/	9.5 ± 0.3 mg/L	6.1 ± 0.4 mg/L
pH	8.2 ± 0.1	8.0 ± 0.05	7.8 ± 0.1	/	8.0 ± 0.02	8.1 ± 0.07

Table 3

The concentration of each substance in sediments in SMB during each cruise (mean ± standard deviation).

Parameters	April 2018	November 2019	June 2023
Organic carbon (×10−2mg/L)	0.907 ± 0.067	0.579 ± 0.256	0.467 ± 0.145
Sulfide (×10−6 mg/L)	8.280 ± 6.549	22.756 ± 17.754	0.471 ± 1.2371
Oil (×10−6 mg/L)	55.693 ± 15.898	16.564 ± 9.691	5.733 ± 0.986
Copper (×10−6 mg/L)	32.007 ± 2.547	29.724 ± 2.879	33.938 ± 6.943
Zinc (×10−6 mg/L)	88.680 ± 6.983	102.060 ± 8.034	103.067 ± 14.841
Mercury (×10−6 mg/L)	0.047 ± 0.003	0.043 ± 0.004	0.0484 ± 0.009

Table 4

Single-factor evaluation results.

Year and Month		pH	DO	Oils	Cu	Zn	Hg	Pb
April 2018	Mean	0.33	0.34	0.26	0.43	0.52	0.44	0.32
	Maximum	0.63	0.49	0.68	0.88	0.96	0.52	0.72
November 2019	Mean	0.41	0.25	0.20	0.37	0.67	0.82	0.74
	Maximum	0.89	0.56	0.34	0.54	0.78	1.04	0.85
March 2020	Mean	0.76	0.48	0.39	0.22	0.76	0.79	0.70
	Maximum	1.74	0.69	0.7	0.44	0.86	1	1.06
March 2023	Mean	0.70	0.07	0.15	0.17	0.90	0.52	0.20
	Maximum	0.72	0.43	0.38	0.32	1.4	0.68	1.99
June 2023	Mean	0.74	1.01	0.14	0.30	0.78	0.38	0.37
	maximum	0.87	1.33	0.25	0.58	1.17	0.72	0.93

Table 5

Sediment–ecological factor correlation coefficient.

Year and Month	Dissolved Oxygen	pH	Organic Carbon	Sulfide	Oils	Cu	Zn	Hg
April 2018	0.67	0.42	0.22	0.2	0.03	0.52	0.47	0.2
November 2019	0.35	0.14	0.08	0.18	0.01	0.07	0.34	0.05
March 2020	0.36	0.06	/	/	/	/	/	/
March 2023	0.14	0.19	0.17	0.03	0.03	0.32	0.34	0.22
June 2023	0.25	0.04	/	/	/	/	/	/

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