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Effects of Ulva prolifera dissipation on the offshore environment based on remote sensing images and field monitoring data
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Longxiao Zheng1, Mengquan Wu1, *, Jie Zhao2, Shaopeng Luan2, Dongliang Wang3, Wei Jiang3, Mingyue Xue1, Jiayan Liu1, Yating Cui1
Acta Oceanologica Sinica | 2023, 42(6) : 112 - 120
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Acta Oceanologica Sinica | 2023, 42(6): 112-120
• Marine Information Science •
Effects of Ulva prolifera dissipation on the offshore environment based on remote sensing images and field monitoring data
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Longxiao Zheng1, Mengquan Wu1, *, Jie Zhao2, Shaopeng Luan2, Dongliang Wang3, Wei Jiang3, Mingyue Xue1, Jiayan Liu1, Yating Cui1
Affiliations
  • 1 College of Resources and Environmental Engineering, Ludong University, Yantai 264039, China
  • 2 Yantai Geographic Information Center, Yantai 264025, China
  • 3 Yantai Marine Environment Monitoring and Forecasting Center, Yantai 264040, China
Published: 2023-06-25 doi: 10.1007/s13131-022-2129-7
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Outbreaks of Ulva prolifera have continued in the South Yellow Sea of China (SYS) since 2007, becoming a serious marine ecological disaster. Large amounts of U. prolifera drift to the coast of the Shandong Peninsula to dissipate under the action of southeast monsoons and ocean surface currents. This causes serious harm to the ecological environment and economic activities of coastal cities. To investigate the impact of U. prolifera dissipation, this study extracted the spatiotemporal distribution of U. prolifera in the SYS from 2012 to 2020 based on the Google Earth Engine. The outbreak cycle of U. prolifera was determined by fitting analysis of outbreak time and coverage area through MATLAB. This study also looked at the effect of U. prolifera dissipation on water quality through field monitoring data. The results showed that the growth curve of the U. prolifera has a significant Gaussian distribution. The U. prolifera dissipates in Haiyang, China, in July and August every year and affects the offshore environment. Water quality parameters of seawater at different depths had significant differences after the U. prolifera dissipation. Changes in pH, chemical oxygen demand, nitrite nitrogen, nitrate nitrogen, ammonia nitrogen, chlorophyll a, total phosphorus, and suspended solids were more significant in surface seawater than in deeper water. Changes in the concentrations of dissolved oxygen and total nitrogen were more significant in the deep seawater (1.63 and 1.1 times higher than those in the surface seawater, respectively). The dissipation of U. prolifera releases a large amount of carbon and nitrogen into the seawater, which provides rich nutrients for phytoplankton and may cause secondary disasters such as red tide. These findings are useful for further understanding the rules of U. prolifera dissipation, as well as preventing and controlling green tide disasters.

Ulva prolifera dissipation  /  South Yellow Sea  /  Google Earth Engine  /  water quality  /  remote sensing
Longxiao Zheng, Mengquan Wu, Jie Zhao, Shaopeng Luan, Dongliang Wang, Wei Jiang, Mingyue Xue, Jiayan Liu, Yating Cui. Effects of Ulva prolifera dissipation on the offshore environment based on remote sensing images and field monitoring data[J]. Acta Oceanologica Sinica, 2023 , 42 (6) : 112 -120 . DOI: 10.1007/s13131-022-2129-7
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1 Introduction
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Green tide is a marine ecological disaster caused by the rapid proliferation and accumulation of large amounts of Ulva prolifera under specific environmental conditions (Wu et al., 2014; Li et al., 2021a). The Brittany Coast of France (Schreyers et al., 2021), the Korean Peninsula (Kwon et al., 2017), and other countries and areas around the world have been affected by green tide for many years (Schreyers et al., 2021). It has become a striking recurrent ecological disaster in the South Yellow Sea of China (SYS) over the past 15 a (Wang and Wu, 2021). The outbreak of U. prolifera has had a significant negative impact on the local marine ecosystem and economy (Zhang et al., 2021c). The quantitative study of the dissipation effects of U. prolifera is essential to mitigate its economic and ecological losses.
Numerous studies have shown that the U. prolifera in the SYS drifts northward from the northern Jiangsu shoal of China in May and June each year and reaches the coast of Shandong Peninsula in July and August (Zhang et al., 2018; Sun et al., 2018). When the green tide enters the dissipation period, a large amount of U. prolifera begins to dissipate and release toxic substances, which affects the ecological environment of coastal cities (Li et al., 2021b). Many scholars have conducted studies on the influence of the dissipation of U. prolifera, including its effect on cultured abalone, sea cucumber (Wang et al., 2011), and phytoplankton (Xing et al., 2015). Green tide may lead to secondary disasters such as red tide (Kong et al., 2018). The most direct influence of the U. prolifera dissipation is change in the chemical environment of seawater (Li et al., 2021c). Therefore, many scholars have also conducted studies on this issue, including the water quality changes in nearshore waters (Zhang et al., 2020), the characteristic of nutrient release (Feng et al., 2020; Zhang et al., 2021b), and dissolved organic carbon changes during the long-term U. prolifera dissipation process (Chen et al., 2020). These studies have largely revealed the impact of U. prolifera dissipation on the offshore environment. However, most of these studies have been conducted in the laboratory or have explored only numerical changes in water quality. The effects of U. prolifera dissipation on seawater at different depths is not known. Most of the existing studies describe the outbreak cycle of U. prolifera qualitatively (Sun et al., 2018). The dissipation period has been defined as the period of time from the date of outbreak until the date of dissipation (Li et al., 2021b). This method is easily affected by the temporal resolution of satellite images and effects of anomalous years, so it may not provide a representative long-term view of the U. prolifera outbreak cycle.
A U. prolifera outbreak is characterized by wide distribution, long duration, and macroscopic drift of the algae (Xing et al., 2015). The hyperspectral, large-scale, and dynamic nature of remote-sensing technology makes it uniquely suited for long-term series monitoring of a U. prolifera disaster. Traditional remote sensing is limited by the time-consuming and labor-intensive process of a large amount of data screening and processing, so it is difficult to monitor U. prolifera in the long-term and across sensors (Li et al., 2021b). The Google Earth Engine (GEE) platform solves this problem. GEE is a multi-source remote sensing image cloud computing platform supported by Google’s Cloud Infrastructure (Hao et al., 2020). It can automatically visualize and analyze a large number of satellite images online by writing JavaScript code. The GEE platform has changed traditional remote sensing image processing and has been widely used in crop research (Yin et al., 2019), waterbody extraction (Nguyen et al., 2020), and other applications. However, large-scale monitoring of U. prolifera based on GEE has not been widely reported.
Based on the above analysis, this study used GEE and MATLAB fitting analysis to study U. prolifera. Curve models generated by Gaussian functions were used to determine the outbreak cycle of U. prolifera. Water quality data at different depths were used to analyze the impact of U. prolifera dissipation on the offshore environment along a vertical axis. In addition, this study investigated the effects of U. prolifera dissipation on the phytoplankton using polynomial fitting and mathematical analysis. The results of this study provide theoretical support for the early prevention and later control of green tide.
2 Materials and methods
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2.1 Study area
The study area (Fig. 1) is located in the northern regions of the SYS (35°–37°N, 120°–122°E), which is a semi-enclosed shallow sea with an average depth of 44 m (Sun et al., 2018). It is located in a maritime monsoon climate zone with an average annual temperature of 14–20℃ (Zheng et al., 2022a). Major cities in the study area include Rizhao, Qingdao, and Haiyang, all in China. Ulva prolifera continues to have landfall in the southern waters of Haiyang since 2007. Haiyang is located in the northern part of the SYS on the southeastern Shandong Peninsula (36°16′–37°10′N, 120°50′–121°29′E) (Zhang et al., 2022b). It is an important sea cucumber farming base in China. However, the outbreak and dissipation of U. prolifera have seriously endangered the local farming activities and ecological environment.
2.2 Data sources
The satellite data used in this study are Landsat 7 ETM+ (GEEID: LANDSAT/LE07/C01/T1_SR), Landsat 8 OLI (GEEID: LANDSAT/LC08/C01/T1_SR), and Sentinel-2 MSI (GEEID: COPERNICUS/S2_SR) image data from 2012 to 2020 provided by the GEE. The Landsat satellites have a spatial resolution of 30 m and a temporal resolution of 16 d. Sentinel-2 has a spatial resolution of 10 m and a temporal resolution of 5 d. Combining these two kinds of satellite images can reduce the influence of clouds, satellite spatial resolution, and other factors on information extraction.
The Surface Reflectance (SR) dataset provided by GEE is the atmospherically corrected surface reflectance. However, it does not remove clouds. We need to remove the clouds from the images because the high reflectivity of the clouds may reduce the accuracy of the U. prolifera extraction. We used the simple Cloud Score algorithm provided by the GEE to remove the clouds from Landsat images. The algorithm computes a simple cloud-likelihood score in the range [0, 100] using a combination of brightness, temperature, and normalized difference snow index. According to our experience, the threshold is set to 20 which can mask the cloud very well. We used Bitwise AND calculation to remove clouds of Sentinel-2 images. The bit 10 and bit 11 in the QA60 band in Sentinel-2 represent opaque clouds and cirrus clouds, respectively. In this study, we determined whether the bit 10 and bit 11 of the pixel are covered by clouds at the same time using Bitwise AND calculation. If clouds exist in both the bit 10 and bit 11 of the pixel, then the pixel is considered to be cloud, otherwise, the pixel is considered to be a valid pixel. Finally, the cloud pixels were masked to obtain the cloud-free image of Sentinel-2.
2.3 Methods
2.3.1 Extraction of U. prolifera
For the extraction of U. prolifera, we used the threshold segmentation method (Liu et al., 2009). First, the grayscale image was generated by band operation. Second, the grayscale image was binarized to distinguish the U. prolifera from the seawater. To distinguish U. prolifera from seawater on remote sensing images, we measured their spectral reflectance (Fig. 1d). According to the spectral curves (Fig. 1d), the spectral characteristics of U. prolifera are similar to those of terrestrial vegetation. The reflectance of U. prolifera was generally higher than that of the seawater. Therefore, we calculated the vegetation index to distinguish U. prolifera from seawater. The results show that normalized difference vegetation index (NDVI) can effectively extract U. prolifera in the presence of thin clouds and has the strongest detection ability for each growth stage of U. prolifera (Zhang et al., 2018, 2020). It distinguishes U. prolifera from seawater by normalizing the near-infrared and red bands. The formula for NDVI is as follows:
$ \mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}=(\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R})/(\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{R}) , $
where NIR represents the near-infrared band, and R represents the red band in visible light.
The key of the threshold segmentation method is the choice of threshold value. Many studies determine thresholds through experience or histogram methods that may be subjectively influenced by the researcher. This study calculated the maximum interclass variance of grayscale images by the Otsu algorithm on the GEE to automatically obtain the threshold value (Zhang et al., 2021a). First, the image pixels were divided into 0–255 levels by calculating the histogram of the image. The threshold T was set to start at 0 and iterate to 255. We defined pixels smaller than T as foreground and pixels larger than T as background. The percentage of foreground pixels to the entire image is $ {p}_{0} $, and the average gray scale is $ {g}_{0} $. The percentage of background pixels to the entire image is $ {p}_{1} $, and the average gray scale is $ {g}_{1} $. The mean gray scale of the image is $ {g}_{2} $ and the interclass variance is F. The interclass variance F of the image was calculated by Eqs (2) and (3). T at the maximum interclass variance is the full image threshold.
$ {g}_{2}={p}_{0}\times{g}_{0}+{p}_{1}\times{g}_{1} , $
$ F={p}_{0}{({g}_{0}-{g}_{2})}^{2}+{p}_{1}{({g}_{1}-{g}_{2})}^{2} . $
2.3.2 Processing of water quality data
We went to Haiyang of China twice (in June and August 2020) to collect seawater samples for water quality analysis. The collection dates represent times before and after the dissipation of U. prolifera (Table 1). We set up 3 sections in Haiyang with a total of 15 seawater collection points that cover the sea area affected by U. prolifera in Haiyang (Fig. 1b). Based on the Technical Specification for Offshore Environmental Monitoring, Part 3 Offshore Seawater Quality Monitoring of China (HJ 442.3–2020) and the actual situation of the sea, we collected 15 seawater samples of 0.1–1 m depth and 10 seawater samples of 1–10 m depth in each field survey.
Water samples were passed through a 0.45 µm acetate fiber filter. Acetate fiber filter was first pre-soaked in a 1:1000 hydrochloric acid solution for 24 h, followed by a pH-neutralizing wash in Milli-Q water. The filtrate was stored at −20℃ for nutrient analysis. The filtrate was used to analyze chemical oxygen demand (COD), nitrite nitrogen (${\rm{NO}}^-_2$-N), nitrate nitrogen (${\rm{NO}}^-_3 $-N), ammonia nitrogen (NH3-N), chlorophyll a (Chl a), total nitrogen (TN), total phosphorus (TP), and suspended solids (SS) concentrations by nutrient auto-analyzer. pH and dissolved oxygen (DO) were measured directly in the field by pH meter and a Hydrocat CTD probe.
2.3.3 Fitting analysis
A large number of studies have demonstrated that the area of U. prolifera shows a trend of increasing and then decreasing with time (Sun et al., 2018; Zhang et al., 2022a). This indicates that U. prolifera has a discrete distribution, and there may not be a linear relationship between the area and time of the U. prolifera outbreak. We found that fitting the area and time of U. prolifera outbreak by Gaussian functions could suitably approximate the growth curve of U. prolifera. The formula of the Gaussian functions is as follows:
$ f\left(t\right)=a\times{{\rm{e}}}^{-{\left(\frac{t-b}{c}\right)}^{2}} , $
where a, b, and c represent constants. $ f\left(t\right) $ and t represent the area and time, respectively, of the U. prolifera outbreak.
Changes in water quality can affect the concentration of Chl a in seawater by influencing the growth of phytoplankton (Zhang et al., 2022b). We explored the relationship between water quality and Chl a by MATLAB multiple regression analysis, and established a model between water quality and Chl a to explore the effect of water quality changes on Chl a. The formula of the multiple regression model is as follows:
$ f\left(w\right)={\rho }_{0}+{\rho }_{1}{w}_{1}+{\rho }_{2}{w}_{2}+\dots +{\rho }_{n}{w}_{n} . $
Equation (5) denotes that the change of n water quality indicators on the linear change of the explanatory variable Chl a. $ {\rho }_{0} $ represents the constant coefficient, $ {\rho }_{n} $ represents the regression coefficient, and $ {w}_{n} $ represents the water quality indicators, including DO, COD, ${\rm{NO}}^-_2 $-N, ${\rm{NO}}^-_3 $-N, NH3-N, Chl a, and SS concentrations. To avoid multiple collinearity effects, the water quality indicators were selected by comparing the parameters of the collinearity diagnostic table. Finally, the accuracy of the model was verified by the coefficient of determination. If R-squared is greater than 0.6, then the model has some credibility.
3 Results
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3.1 Determination of the dissipation time and location of U. prolifera
We needed to determine the time and location of U. prolifera dissipation to study its impact on the environment. First, we extracted the spatiotemporal distribution of U. prolifera in the SYS from 2012 to 2020 based on the GEE platform. We used the Gaussian function to fit the time and area of the U. prolifera outbreak based on MATLAB software, determined the dissipation period of the U. prolifera, and finally obtained a curve model (Eq. (6)):
$ f\left(t\right)=718.2\times{{\rm{e}}}^{-{\left(\frac{t-165.7}{18.74}\right)}^{2}} . $
As illustrated in Fig. 2, the outbreak time and area of the U. prolifera had a Gaussian distribution over the most recent nine years. We can see from the Gaussian curve that the process of a U. prolifera outbreak is divided into I, II, III, IV, and V stages. These five stages can be divided into discovery, development, outbreak, decline, and disappearance. Ulva prolifera entered the growth period in late May and early June. It increased exponentially and reached the outbreak stage in middle and late June. In July, the area of U. prolifera began to decline. In August, it began to dissipate and gradually reached the Haiyang shore with the change of environment and the consumption of nutrients.
It has been demonstrated that U. prolifera is first detected by remote sensing satellites in northern Jiangsu shoal of China and drifts from southeast to northwest under the action of southeast monsoon and surface currents (Zheng et al., 2022b). A large amount of U. prolifera keeps proliferating and gathering together to eventually form the drifting green tide. As shown in Fig. 3, over the most recent nine years, U. prolifera dissipation has occurred mainly in Qingdao and Haiyang. The latest study demonstrated that green tide will continue to persist for about half a month past the date of last monitoring by remote sensing images (Li et al., 2021b). The drift and dissipation of U. prolifera are continuing during this period, so the green tide that occur in Qingdao will eventually have an impact on Haiyang as well.
3.2 Effect of U. prolifera dissipation on water quality
As shown in Figs 2 and 3, the time of U. prolifera dissipation is mainly in July and August, and the location of dissipation is mainly in Qingdao and Haiyang of China. We investigated the impact of U. prolifera dissipation on the offshore environment based on the field monitoring data from Haiyang in June and August. According to the Technical Specification for Offshore Environmental Monitoring, Part 3 Offshore Seawater Quality Monitoring of China (HJ 442.3-2020), we took the mean value of the collected seawater data to analyze the impact of U. prolifera dissipation on the surface and deep seawater.
Compared with June, the pH value in the surface seawater decreased by about 0.23 in August after the U. prolifera dissipation (Fig. 4). The concentrations of COD, TP, and Chl a also decreased to different degrees (0.16 mg/L, 0.006 mg/L and 1.34 mg/L, respectively). The concentrations of DO, ${\rm{NO}}^-_2 $-N, ${\rm{NO}}^-_3 $-N, NH3-N, TN, and SS increased by 0.34 mg/L, 0.01 mg/L, 0.09 mg/L, 0.04 mg/L, 4.33 mg/L, and 6.83 mg/L, respectively. In deep seawater, the concentrations of NH3-N, DO, ${\rm{NO}}^-_2 $-N, ${\rm{NO}}^-_3 $-N, TN, and SS increased by 0.03 mg/L, 0.56 mg/L, 0.01 mg/L, 0.07 mg/L, 4.81 mg/L, and 6.26 mg/L, respectively. The values of COD, pH, Chl a, and TP were reduced by 0.04 mg/L, 0.16 mg/L, 0.55 mg/L, and 0.003 mg/L, respectively. These results show that after the U. prolifera dissipation the water quality change trend is the same in deep and surface seawater.
Although water quality has the same change trend in different depths of seawater, there may be differences in the extent of change in different depths of seawater. If this occurs, it reflects the extent of the effect of U. prolifera dissipation on seawater at different depths. We calculated the average rate of change of 12 water quality indicators in different depths of seawater (Table 2). As illustrated in Table 2, the extent of changes in pH, COD, ${\rm{NO}}^-_2 $-N,${\rm{NO}}^-_3 $-N, NH3-N, Chl a, TP and SS were more significant in surface seawater than in deep water. DO and TN changed more significantly in deep seawater (1.63 and 1.1 times, respectively, more than in surface seawater). This indicates a significant difference in the extent of the effect of U. prolifera dissipation on seawater at different depths.
4 Discussions
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4.1 Effects of U. prolifera dissipation on the offshore environment
Macroalgae play an important role in the climate system and global carbon cycle as coastal blue carbon (Zhao et al., 2021). Numerous studies have shown that U. prolifera can absorb inorganic carbon from seawater through photosynthesis, thus forming a carbon sink (Xu et al., 2012; Deng et al., 2018). Ulva prolifera also absorbs a large amount of ${\rm{NO}}^-_3$-N, in the growth process, thus forming a nitrogen sink (Zhang et al., 2021b). The growth of U. prolifera is a process of absorbing carbon and nitrogen, and dissipation is a process of releasing carbon and nitrogen. It has been demonstrated that the nutrient salts released during U. prolifera dissipation are mainly organic (more than 90% of the total nutrient salts), in which the released TN concentration is greater than the standard of eutrophication, reaching 80 µmol/L (Ding, 2014).
The carbon released during the dissipation of U. prolifera causes the pH of seawater to decline, which may lead to seawater acidification (Hu et al., 2015). TN, pH, and SS are closely related to carbon in seawater in our water quality indicators. There is a negative correlation between pH and carbon dioxide in seawater. The organic nitrogen in TN and organic compound in SS both contain large amounts of carbon. So, we discuss the effect of U. prolifera dissipation on the carbon cycle in seawater from these three indicators. Compared with June, the pH of surface seawater decreased by about 0.23 in August, and the concentrations of TN and SS increased by 4.33 mg/L and 6.83 mg/L, respectively (Fig. 4). In deep seawater, the pH decreased by about 0.16 and the concentrations of TN and SS increased by 4.81 mg/L and 6.26 mg/L, respectively. This indicates that the dissipation of U. prolifera released a large amount of carbon into the seawater, resulting in a decline in pH. According to the microbial carbon pump theory (Zhang et al., 2017a, 2017b; Jiao et al., 2018), the dissolved organic carbon (DOC) released during U. prolifera decomposition is partially transformed into refractory dissolved organic carbon (RDOC) via microbial metabolism (Zhang et al., 2019). The RDOC is difficult for microorganisms to utilize or degrade, and thus accumulates in the water for a long time, affecting the carbon cycle and water recalcitrant dissolved organic carbon in the ocean (Deng et al., 2018).
The nitrogen released during the decomposition of U. prolifera is mainly nitrate and ammonium (Liu et al., 2016). As illustrated in Fig. 4, the concentrations of ${\rm{NO}}^-_2 $-N, ${\rm{NO}}^-_3 $-N, and NH3-N in the surface seawater increased by 0.01 mg/L, 0.09 mg/L, and 0.04 mg/L, respectively, after the dissipation of U. prolifera. In deep seawater, the concentrations of ${\rm{NO}}^-_2$-N, ${\rm{NO}}^-_3 $-N, and NH3-N increased by 0.01 mg/L, 0.07 mg/L, and 0.03 mg/L, respectively. This, to a certain extent, indicates that a large amount of nitrogen was released into seawater during the process of U. prolifera decomposition. Ulva prolifera decomposition also affects the abundance of bacterial microorganisms in seawater. The abundance of specific bacterial groups such as heterotrophic nitrogen fixing bacteria increased significantly (Zhang et al., 2015). This indicates that U. prolifera decomposition affected the nitrogen concentration in seawater through the release of nitrogen and the increase of nitrogen fixing bacteria.
4.2 Effects of U. prolifera dissipation on phytoplankton
Ulva prolifera decomposition will release a large number of nutrients, which are absorbed and used by phytoplankton in seawater and will affect the growth and reproduction of phytoplankton (Zhang et al., 2017a). The concentration of Chl a is an indicator of phytoplankton biomass. There is a positive correlation between them (Zhang et al., 2022b). The accumulation of phytoplankton on the sea surface will lead to a significant increase in the concentration of Chl a. The biomass of phytoplankton is affected by environmental factors, such as temperature and nutrients (Zhang et al., 2022b). Therefore, the effect of water quality change on Chl a after U. prolifera decomposition can reflect the effect of U. prolifera decomposition on phytoplankton.
First, we performed multiple linear regression analyses in MATLAB for the concentration of Chl a and the measured water quality data. Then, a linear model was developed to explore the effect of water quality changes on Chl a. We found a significant correlation between Chl a concentration and pH, DO, ${\rm{NO}}^-_3 $-N, NH3-N, and SS after repeated trials. Finally, we built the model as shown in Eq. (7). The R-squared of the model is 0.75 and the p-value is 1.63×10−6, which is statistically significant. As illustrated in Table 3, Chl a was positively correlated with pH and negatively correlated with the concentrations of DO, ${\rm{NO}}^-_3 $-N, NH3-N, and SS in this model. This indicates that the changes in pH, DO, ${\rm{NO}}^-_3 $-N, NH3-N, and SS will significantly affect the concentration of Chl a.
$ \begin{split} f\left(w\right)=& 10.05{w}_{1}{w}_{2}-91.63{w}_{1}{w}_{3}+159.72{w}_{1}{w}_{4}+0.37{w}_{1}{w}_{5}+\\& 171.33{w}_{2}{w}_{3}-209.52{w}_{2}{w}_{4}+0.05{w}_{2}{w}_{5}+30\;417{w}_{3}{w}_{4}+\\& 29.5{w}_{3}{w}_{5}-3.55{w}_{4}{w}_{5}-27.48{w}_{1}^{2}-0.16{w}_{2}^{2}+\\& 12\;580{w}_{3}^{2}-12\;089{w}_{4}^{2}-0.003{w}_{5}^{2}-1\;271.9 ,\\[-10pt]\end{split}$
where $ f\left(w\right) $represents the concentration of Chl a. $ {w}_{1} $, $ {w}_{2} $, $ {w}_{3} $, $ {w}_{4} $, and $ {w}_{5} $ represent the values of pH, DO, ${\rm{NO}}^-_3 $-N, NH3-N, and SS concentrations, respectively.
As shown in Fig. 4, the decomposition of U. prolifera will decrease the pH of seawater. From an ecological point of view, the decrease in pH may lead to seawater acidification, affecting the respiration rate (Gao et al., 2019; Liu et al., 2020), lipid metabolism (Zheng et al., 2009), and nucleic acid metabolism of marine organisms (Franke and Clemmesen, 2011). Seawater acidification may also reduce the abundance of certain phytoplankton species, with cascading effects on the ecosystem (Gao et al., 2021). The U. prolifera dissipation leads to the increase of DO, ${\rm{NO}}^-_3 $-N, NH3-N, and SS concentrations in the water, which provides phytoplankton with the nutrients required for growth and development. However, excessive nutrients in seawater can lead to deterioration of water quality. The U. prolifera dissipation releases high concentrations of transudates containing abundant nitrogen and phosphorus that may inhibit the growth of some phytoplankton species (Zhang et al., 2019). Meanwhile, allelopathy is one of the most important effects of U. prolifera on surrounding organisms. The fatty acid secreted by U. prolifera will inhibit the population growth of phytoplankton (Lv et al., 2021). These may be the reasons for the decrease of seawater Chl a concentration after the U. prolifera dissipation.
During U. prolifera dissipation, the concentration of nutrients in seawater is abnormally high and the water quality is poor (Ogawa et al., 2015). Seawater has a certain self-purification ability. The seawater quality will return to its original level within a certain period after the end of the U. prolifera dissipation period. The water quality will have a phase suitable for phytoplankton growth during this process. Suitable physicochemical conditions of seawater will promote the growth of phytoplankton. Ulva prolifera dissipation releases large amounts of nitrogen into seawater. NH3-N can promote the growth of planktonic algae, which will easily cause secondary disasters such as red tide (Wang et al., 2007, 2012).
We investigated the red tide around the south of the Shandong Peninsula during 2000–2019. As shown in Fig. 5, a total of 19 large-scale red tide disasters occurred in the south of the Shandong Peninsula over the most recent 20 years. There were 13 red tide disasters after the 2008 Qingdao green tide disaster. Five red tide disasters occurred in Haiyang after 2008. The area of the 2008 red tide disaster in Haiyang reached 300 km2. The red tide occurring in Haiyang are caused by phytoplankton, such as Noctiluca scintillans, Chattonella marina, and Gnoyaulax spinifera (Zhou et al., 2022). This indicates that the green tide in Qingdao may have some correlations with the red tide in Haiyang. To a certain extent, this reveals the relationship between green tide and red tide: the nutrients in seawater returns to suitable levels after the U. prolifera dissipation, which for a while will promote the growth of phytoplankton and may trigger red tide. This is also consistent with the results of laboratory simulation and field investigations (Wang et al., 2012; Kong et al., 2018).
5 Conclusions
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We explored the effects of U. prolifera dissipation on the offshore environment based on data from the GEE platform, MATLAB fitting analysis, and measured seawater data. The results of the study show that the U. prolifera dissipates in Haiyang in July and August every year and has an impact on the offshore environment. By measuring the water quality data from Haiyang, we found that the dissipation of U. prolifera leads to different changes in water quality at different depths. The dissipation of U. prolifera can lead to changes in water quality and trigger secondary disasters. Our results show that the dissipation of U. prolifera releases large amounts of carbon and nitrogen into seawate. With the recovery of water quality, the large amount of nitrogen present in seawater will promote the growth of phytoplankton, which may cause secondary disasters such as red tide. In the future, we will further explore the pattern of U. prolifera outbreak and the impact of U. prolifera dissipation on the offshore environment to provide a theoretical basis for the prevention and control of green tide.
Funding
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  • The National Natural Science Foundation of China under contract No. 42071385; the Shandong Natural Science Foundation under contract No. ZR2019MD041; the Open Project Program of Shandong Marine Aerospace Equipment Technological Innovation Center, Ludong University under contract No. MAETIC2021-12; the Yantai Science and Technology Innovation Development Plan Project under contract No. 2022MSGY062.
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doi: 10.1007/s13131-022-2129-7
  • Receive Date：2022-04-27
  • Online Date：2025-11-21
  • Published：2023-06-25
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  • Received：2022-04-27
  • Accepted：2022-10-12
Funding
The National Natural Science Foundation of China under contract No. 42071385; the Shandong Natural Science Foundation under contract No. ZR2019MD041; the Open Project Program of Shandong Marine Aerospace Equipment Technological Innovation Center, Ludong University under contract No. MAETIC2021-12; the Yantai Science and Technology Innovation Development Plan Project under contract No. 2022MSGY062.
Affiliations
    1 College of Resources and Environmental Engineering, Ludong University, Yantai 264039, China
    2 Yantai Geographic Information Center, Yantai 264025, China
    3 Yantai Marine Environment Monitoring and Forecasting Center, Yantai 264040, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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