# Effects of Nitrogen and Phosphorus Nutrients on the Proliferation of Heterosigma Akashiwo under the Background of Mobile Big Data

Authors: Meina Duan; Jiamin Huang; Erbang Zheng; Yaoqian Liu
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

In the era of information explosion, mobile data is developing rapidly, and many fields are facing the challenges of data collection, analysis and operation. Heterosigma akashiwo is a kind of wide temperature and wide salt algae, belonging to the class Echinococcus and the genus heterobenthos. It is widely distributed in the coastal waters of the world. It has formed harmful red tides for many times in many countries, causing a large number of fish deaths, causing great economic losses and serious damage to aquatic resources. If toxic red tides break out frequently, they will pose a great threat to public health. Seawater eutrophication is the main environmental inducement for the formation of red tide by Heterosigma akashiwo. In particular, the content of nutrients in seawater significantly affects the reproduction of Heterosigma akashiwo and the excessive proliferation of plankton such as algae. At the same time, a large number of planktons will devour the dissolved oxygen in the water, thus affecting the photosynthesis of the water body, resulting in the deterioration of the water quality and the damage to the functions of ecology and water resources. As for the effects of nitrogen, phosphorus, iron and different vitamins on the growth of Heterosigma akashiwo, this paper takes Heterosigma akashiwo as the research object, and focuses on the analysis of the effects of ammonium chloride, urea and sodium dihydrogen phosphate on the proliferation of Heterosigma akashiwo. The results showed that the higher or lower the concentration of nitrogen and phosphorus, the slower the proliferation rate of Heterosigma akashiwo; Heterosigma akashiwo had the maximum proliferation rate under the nitrogen concentration of 300–500μmol/L; At the concentration of 10∼15 μmol/L phosphorus, its growth rate is the fastest and its proliferation level is the highest. The selected nitrogen and phosphorus have a very significant impact on the proliferation of microalgae.


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## Body

## 1. Introduction

Since the twentieth century, the pollution and destruction of the environment by human beings have been increasing day by day, especially the pollution of the marine environment which cannot be ignored. A large amount of industrial and agricultural wastewater, domestic sewage, and aquaculture wastewater produced in the process of industrial and urban development are discharged into the sea, resulting in an increase in the nitrogen, phosphorus content and types of organic pollutants in seawater year by year. It not only changed the nutrient structure of the offshore, but also aggravated the degree of eutrophication of seawater. One of the important problems of water eutrophication is the massive proliferation of algae in the water body, which forms the harmful algal blooms, and affects the ecological environment and water quality of the water body. The harmful algal blooms can inhibit the growth of other biological populations in the water body and reduce biodiversity. In addition, the harmful algal blooms require large amounts of nutrients and dissolved oxygen. Algae blooms refer to a natural ecological phenomenon in which algae multiply in freshwater bodies. Red tide is an abnormal phenomenon in the marine ecosystem. The harmful algal blooms affect the normal exchange of oxygen between the water and the air, resulting in the death of a large number of algae due to the lack of oxygen in the water body. If it is not treated in time, the water body will produce peculiar smell. The overgrowth of plankton forms “harmful algal blooms,” which in severe cases affect and destroy water ecology and water functions. The water source does not meet the national standards for drinking water and cannot meet the supply of raw water for urban development, which affects people’s lives and the sustainable development of the city. Therefore, it is of great significance to study the relationship between nutrients and the growth of marine microalgae.For a long time, the influence of nutrient factors such as nitrogen and phosphorus on the growth of marine microalgae has been a research hotspot in society. In Korea, especially on the southern coast, the marine industry has been gradually damaged by harmful algal blooms. The main way to eliminate harmful algal blooms is to spray red clay. However, it is not only difficult to achieve the elimination effect, but also the phosphorus and trace metals in the laterite can promote the proliferation of red tide algae. Therefore, the use of red clay is not ideal. Kim S conducted laboratory-scale mock-up experiments to predict red tide removal efficiency, water flow, and flow velocity with artificial floats with ceramic carrier membranes installed around offshore underwater fisheries. It was found that artificial floats with ceramic membranes can effectively remove red tides and reduce the flow velocity between the membrane layers to increase the residence time of red tides But his conclusions have not been widely applied [1]. The attachment culture of microalgae is an effective way to reduce the high harvest cost in microalgal biomass production. Nitrogen and phosphorus, as the two main nutrients for microalgae, are thought to affect the attachment efficiency and growth of microalgae [2]. Zhuang investigated the performance of LX1 in a suspended solid-phase photobioreactor (ssPBR) under different feed conditions of total nitrogen (TN) and total phosphorus (TP) concentrations. At TN = 15 g/m3 and TP = 5, 3, 1.5, and 0.5 g/m3nutrient levels, the average concentrations of TN and TP in the microenvironment around the microalgal biofilm were 2.1 and 15.5 times higher than those in the substrate respectively. Higher nutrient concentrations in the microenvironment surrounding microalgal biofilms improved protein synthesis in attached microalgae, especially in the later stages of batch culture [3]. Biomass is currently receiving much attention, especially in the field of renewable energy technologies, due to its fast production rate, high affinity for nitrogen and phosphorus, and the possibility of co-equilibration of microalgae. Fuels produced from algal biomass could help reduce the consumption of traditional fossil fuels and could alleviate the growing energy crisis and global warming caused by air pollution. Some scholars choose to use sewage as a nutrient medium for algae cultivation. Other scientists have gone a step further, proposing the concept of microalgae systems as components of wastewater treatment plants. The high cost of different microalgae collection methods led to the introduction of the idea of epiphytes with algae immobilized on artificial substrates [4]. Garbowski focused on the possibility of using the waste as a substrate to promote the proliferation of epiphytes in biologically treated wastewater containing a certain amount of nitrogen and phosphorus [5]. These data on the growth of marine microalgae are not properly combined with modern science and technology for auxiliary analysis.With the rapid development of the current network information technology, the number of mobile application software users has increased year by year, which has effectively promoted the development of mobile services and software, and accelerated the early arrival of the era of big data. In the context of big data, Wan analyzed the current situation of mobile application software development, proposed the construction of integrated data model and the design of integrated data model, and proposed the construction path of innovative mobile software development model. On this basis, an optimization model of software development model is established to improve the efficiency and quality of software development. Thereby, it can provide users with better products and services to promote the in-depth development of mobile network applications and software development enterprises [6]. Association rule mining is one of the most commonly used data mining methods. However, mining association rules usually generates a large number of found rules, so the task of the analyzer is to traverse all the rules and discover the rules of interest. Manually sifting through a large number of rules is time-consuming and laborious. Visualization has a long history of using techniques such as selection and scaling to better access large amounts of data, but most association rule visualization techniques still fall short when large numbers of rules are involved. Hahsler introduced a new interactive visualization method, grouped matrix notation, which allowed intuitive exploration and interpretation of highly complex scenes. It also demonstrated how to use R software to analyze large sets of association rules for statistical computation [7]. These methods provide some references for the research, but due to the short time and small sample size of the relevant research, the research has not been recognized by the public. A detailed analysis of marine microalgae and big data technologies is carried out. It is undeniable that these studies have greatly contributed to the development of the corresponding fields, and a lot of experience can be learned from methods and data analysis. However, the research on it in the context of mobile big data is relatively few and not thorough enough, and it is necessary to fully apply these techniques to the research in this field.This paper mainly studies the effects of nitrogen and phosphorus nutrients on the proliferation of Heterosigma akashiwo under the background of mobile big data, and introduces the effects of ammonium chloride, urea and sodium dihydrogen phosphate on the proliferation ofH. akashiwo. Through the analysis of experimental data, under different ammonium chloride and inorganic phosphorus concentration levels, different phosphorus concentrations can promote the growth of H. akashiwo, but the effects are different. Where 10 μmol/L is the greatest and fastest rate of appreciation. Under the condition of high nitrogen concentration, the biomass of H. akashiwo has a downward trend after reaching the maximum value, and the maximum proliferation rate is at the nitrogen level of 500 μmol/L. Under different concentrations of urea and inorganic phosphorus, H. akashiwo had the maximum proliferation rate at the nitrogen level of 300 μmol/L, and the maximum proliferation rate at the phosphorus concentration level of 15 μmol/L, and this level had the best proliferation. It can be found that nitrogen and phosphorus nutrients have a greater effect on the growth of this algae.


## 2. The Effect of Nitrogen and Phosphorus Nutrients on the Proliferation of Heterosigma Akashiwo under the Background of Mobile Big Data

### 2.1. Growth of Marine Microalgae

Microalgae are the basis of marine primary productivity, and their species composition, biomass, population growth, and community structure are all directly related to the stability and health of the marine ecological environment. Marine algae are indispensable biological species in the ocean [8, 9]. The carbon and oxygen balance in the ocean is maintained through photosynthesis, and it provides a rich food source for most marine animals [10]. However, the excessive proliferation of algae causes the outbreak of red tide, which endangers the marine ecological environment. The growth and toxin production of toxic red tide algae are affected by a variety of environmental factors (including light, salinity, temperature, and nutrient levels) [11]. Heteroflexus alba is a unicellular organism, slightly oval in shape, without a cell wall, and surrounded by a periplasmic membrane. When the algae are active, the flagella often bend or extend perpendicular to the long axis of the cell. As the material basis of algae growth among various environmental factors, the changes of nitrogen and phosphorus nutrients significantly affect the growth and toxin production of algae, thus indirectly affecting the occurrence of harmful red tides [12].Nutrient salts are important elements needed for the growth of aquatic organisms such as phytoplankton. If the content of nutrients in the water body is lower than the minimum required for phytoplankton growth, algal blooms will be inhibited and the water body may not produce eutrophication [13]. However, high levels of nutrients in water bodies do not necessarily produce eutrophication; eutrophication can only occur under conditions of heavy algal growth [14]. Human activities contribute to accelerated eutrophication through various ways of nutrient input to watershed ecosystems. In natural water ecosystems such as freshwater, estuarine and marine. Nitrogen and phosphorus are the most common limiting factors for phytoplankton. Therefore, the composition, quantity and relative ratio of nutrients such as nitrogen and phosphorus input to water bodies are key factors in determining the eutrophication process [15].The damage caused by eutrophication is serious and it leads to large changes in the water quality of water bodies. Its most direct effect is to disrupt the ecological balance of waters by changing the components, density and overall structure of organisms in the system, causing structural changes in the original ecosystem.Not only the nitrogen nutrient concentration has a great influence on the growth of algae, but also different forms of nitrogen sources can also affect the growth of algae. Algae can not only utilize dissolved inorganic nitrogen (nitrate, ammonium salt, and others) in seawater, but also selectively utilize partially dissolved organic nitrogen (amino acid, urea and others) [16]. Organic nitrogen sources in seawater not only include the organic fertilizers input into the ocean by human production and living, but also the metabolites of marine organisms themselves. Urea is a relatively common nitrogen source in the ocean. After being absorbed by algae, it can usually accumulate in the algae, and rely on the action of urease to decompose urea into CO2, which is then absorbed as a reduced nitrogen source [17].In algae, whether produced by nitrogen metabolism, or by phytoplankton entering the cell directly from the growing environment, they are converted to the organic nitrogen glutamate and glutamine by the GS/GAGOT cycle. In particular, in the GS/GOGAT cycle, it is converted to Gln in the presence of GS/GOGAT. Subsequently, under the catalysis of GOGAT, glutamine andα-ketoglutarate are converted into 2 molecules of Glu. And one molecule of synthesized glutamate continues to participate in the absorption. The other is used to synthesize nitrogen-containing organics in cells such as proteins and nucleic acids [18], as shown in Figure 1. In the GS/GOGAT cycle, the catalytic reaction of GS is(1)L_Glu+ATP+NH3⟶GSL_Gln+ADP+Pi+H2O.Figure 1
Intracellular pathways and enzymes involved in nitrogen transport, reduction, and assimilation.As an important intermediate in amino acid synthesis, glutamine can be further used to synthesize other amino acids, including arginine. In the presence of sufficient phosphorus, the amino acids in the algae can synthesize proteins, thereby supporting the growth and cell division of the algae. However, when phosphorus is deficient, there is an excess of nitrogen in cells, and this excess nitrogen from nitrate reduction and amino acid degradation is mainly in the form of ammonium salts. The accumulated ammonium salts caused a toxic effect on the organism itself (as shown in Figure2). In order to alleviate this toxic effect, the de novo synthesis of arginine is activated, and the toxic effect of intracellular ammonium salts is alleviated by synthesizing arginine with high nitrogen content [19]. During phosphorus limitation, the concentration of glutamine in algal cells increases. This may be due to the inhibition of the pathway to protein synthesis in favor of providing amino groups to generate phosphocarbamates, which are further used to synthesize arginine. Arginine is a precursor for the synthesis of PSP toxin, so that when phosphorus is limited, the synthesis of cytotoxin is accelerated [20].Figure 2
Schematic diagram of the effects of nitrogen and phosphorus on toxic algal toxins.Nitrogen is an important limiting factor for algal growth. It is necessary for the growth and metabolism of algae, and it is also the composition of its own proteins and nucleic acids. N is an important element for the synthesis of proteins, phospholipids, chlorophyll and other substances in plankton. Without the supply of N, algal cells cannot grow and reproduce at all. The growth of seaweed is adversely affected for too low or too high nitrogen content, and the type of nitrogen also has a certain impact on the absorption and utilization of seaweed. Phosphorus is a very important nutrient element, it is the main component of nucleic acid, protein and phospholipid, and it is also a necessary nutrient for the synthesis of chlorophyll [21].There are usually four stages in the growth cycle of microorganisms: adaptation period (latent period), logarithmic period, stationary period, and decay period. During the adaptation period, microorganisms adapt to the surrounding environment and grow slowly. After entering the logarithmic phase, the microorganisms begin to grow rapidly, and the growth shows a logarithmic trend. When the growth of microorganisms reaches a certain level, it enters a stable period due to environmental constraints, and the growth of microorganisms is stable at this time. With the depletion of nutrients, microorganisms tend to decline and finally lose their activity completely [22]. In the logarithmic phase, the algae began to enter a period of rapid exponential growth. After the exponential growth period, the growth rate decreases until the plateau period. At the start point and end point of the exponential growth stage and the corresponding times and, the growth rate of this stage is(2)V=lnX2/X1t2−t1.In the formula, represents the number of cells (or chlorophyll content) on the day; represents the number of cells (or chlorophyll content) on the day.Each growth stage of algae can be fitted by a logistic model. Logistic regression is also known as logistic regression analysis. The logistic regression model is built on the linear regression model of the sigmoid function and independent variables. Through this model, not only the adaptation period, logarithmic period and stable period of microbial growth can be obtained by fitting, but also the turning period of its growth can be determined, that is, the moment when the number of individuals reaches the half-saturated state (k/2). The growth rate of microorganisms is at a maximum at this time, and then begins to slow down until the maximum biomass (or termination biomass) is reached [23]. That is, the SLogistic model introduces the maximum growth rate and the termination biomass, which can accurately and quantitatively describe the biological growth law. The model is derived as follows:The corresponding assumption of the logistic model is: under the condition of limited biological resources, the growth of a single microorganism has the same growth rate, and the required nutrients are the same [24].During the culturing process, microorganisms continue to grow over time, and the microbial growth rate is linked to the population biomass at any time, then the SLogistic model can be expressed as(3)dXdt=cX1−Xk.In the Formula,c is a constant and k is the maximum biological capacity.Its integral form is(4)X=K1+ea−ct.In the Formula, a andc are both constants.The speed of the SLogistic growth model can be known from Formula (4), the maximum value and the time of occurrence are:(5)tmax=ac,(6)vmax=c×k4.Another important parameter of the SLogistic growth rate function, that is, the time when the growth rate increases the fastest is(7)t1=a−1.317b.


### 2.2. Data Dependency Model

The excessive proliferation ofH. akashiwo will cause algal blooms, which will lead to the death of fish in a large area and cause huge losses to the aquaculture industry. The data on the proliferation of H. akashiwo is difficult to obtain, but mobile big data technology can be used to obtain and analyze the data on the proliferation of harmful algal blooms at any time through mobile, especially when studying the effect of different nutrient salts on the proliferation of H. akashiwo, big data technology has an important role in monitoring, predicting and analyzing the growth process of H. akashiwo.Mobile big data is a general term for data information that can be interpreted by humans, which mainly refers to the massive data flow obtained from the application process of mobile user terminals using the mobile Internet as the medium, and managed, processed and analyzed within a reasonable time. From the perspective of typical application fields of mobile big data, mobile big data can be classified into entertainment content field, service life field, shopping consumption field, interconnection and collaboration field, and other fields. Typical applications in each field are shown in Figure3.Figure 3
Classification of mobile big data.Most of the information is in the state of original data, and data mining is to reveal the information that has not been found or expressed but has important functions in a large amount of data by automatically discovering patterns or rules by computer software. The performance of data mining technology is generally prediction and description. Among them, data mining techniques that focus on prediction can classify new samples by analyzing historical data. The data sources used for data mining must be real and extensive, and may be incomplete and include some interfering data items. Descriptive focus is on general introduction and partitioning of data. The data mining process is shown in Figure4.Figure 4
Data mining process.Since the development of big data technology, the amount of information data has achieved explosive growth, which has also promoted the development and transformation of the overall structure of the big data era. With the emergence and increase of a large number of document data, image data, network data, and audio data, more and more network data needs to be sorted and analyzed through big data. From this, it is concluded that the characteristics of the big data era are as follows.The first is a large amount of data. In the era of big data, the statistics and measurement of data are no longer limited to the traditional TB capacity, but are more developed into PB and ZB capacity. The amount of data contained in this is huge, which can also be called massive data, and the resulting large amount of information data needs to be summarized and analyzed at a higher level of technology in order to achieve effective data analysis. The data analysis structure diagram is shown in Figure5.Figure 5
Data analysis structure diagram.The second is the diversity of data. A large amount of data contains a variety of data, embodied in the modes of documents, pictures, networks, videos, and audios. Diverse data appears in different structures and forms, and a large amount of data needs to be adjusted and optimized uniformly through big data technology.The third is high-speed circulation. By relying on the advancement of Internet technology and the development of 5G technology, the transmission speed of data in the era of big data is also amazing. And with the continuous improvement of data transmission speed, the security and convenience of information data have also achieved rapid improvement. This is of great benefit to the innovation and progress of information data management tools in the era of big data, which effectively improves the structured development of data information in the era of big data.The fourth is high value. In the era of big data with the rapid development and improvement of information and data, data has become more and more valuable. Many hackers make huge profits by obtaining data, so in the era of big data, data supervision and data security should be strengthened. In the development process of the market economy, the information flow of data is very large, so it is very difficult to ensure the safety and effectiveness of data and realize the efficient use of data. It is also more difficult to find the needed and valuable information in time. But if the information needed can be found, it will effectively increase the overall value of the data.The fifth is authenticity. The greatest value of data is truth. Only real information data can provide an effective basis and support for data analysis work. However, only relying on the scale of the data cannot guarantee the validity and overall quality of the data, and the conclusions drawn from the data analysis and summary cannot provide effective reference and help for decision makers.Association rules can be expressed as:(8)M⇒N.Among them,M is called the domain of the rule, and N is called the domain of the rule. It represents how confidently the elements in N can be obtained when the elements in the M set appear.Rule support is the probability thatm and n appear at the same time.(9)Pm−n=Wm∩​nW.The support for the preceding item is(10)Pm=WmW.The latter support is(11)Pn=WnW.The confidence level of a rule describes the probability of occurrence ofn under the condition that reaction m occurs.(12)Cm−n=Wm∩​nWm.The relationship between the support and confidence of the rule is:(13)Cm−n=Wm∩​nWm=Pm−nPm.Rule lift is the ratio of confidence to consequent support, and the Formula is:(14)Hm−n=Cm−nPn.The degree of lift reflects the degree of influence onn when m appears. If the lift is less than 1, this rule is meaningless. A lift greater than 1 means that m has a promoting effect on the appearance of n.The mathematical expression for the confidence difference is:(15)D=Cm−n−Pn.It can be seen from the Formula that the confidence difference is the absolute difference between the confidence of the rule and the support of the latter item.The main process of data preprocessing is shown in Figure6. The data on the proliferation of H. akashiwo are not ideal. Data that has not been preprocessed cannot be directly mined. In real experimental data, there are some missing data, repeated data or noisy data. The existence of these dirty data affects the effect and efficiency of data mining, so it is necessary to clean these dirty data and fill in the missing data scientifically and reasonably. PCA is a new small variable that converts multiple problems into a low-dimensional space, and then replaces the original small variables with new small variables for subsequent processing, and converts high-dimensional problems into low-dimensional problems. By retaining some of the most important features in high-latitude data, and removing noise and unimportant features, the purpose of improving data processing speed is achieved. The main work of the PCA algorithm is to reduce the indices in the original data, reorganize the relevant indices, and finally obtain a new set of indices that are unrelated to the complex indices in the past. The contribution to cumulative contribution ratio of the principal components refers to the information occupied by the original data X in A after the transformation from high-dimensional to low-dimensional.(1)
Contribution rate: the higher the proportion of the eigenvalue of the i-th principal component in the sum of all eigenvalues of the covariance matrix, the stronger the comprehensive ability of the eigenvalue is. The i-th principal component has an eigenvalue of, and its formula is:(16)ai=ei∑i=1mei.(2)
Cumulative contribution rate: it is the proportion of the sum of the eigenvalues of the firstk principal components in the sum of all eigenvalues. The larger this ratio is, the more the first k principal components can fully represent the information possessed by the original data. The calculation formula is:(17)Sk=∑i=1Vei∑i=1mei.Figure 6
Data preprocessing.In solving practical problems, the firstc (c < m) principal components are generally selected, and the seventeen cumulative variance contribution rate meets certain requirements (usually more than 80%). By using the selected first d principal components to replace the original m variables for analysis, the purpose of data dimensionality reduction can be achieved, which can also be regarded as a feature extraction.First, the mean vector of the samples in the sample data set is calculated, that is:(18)d=1n∑i=1nxi.Each sample is de-averaged, that is the sample data is centered:(19)X˜=X−d.The data matrix is constructed: the covariance matrix R ofX˜:(20)R=1nX˜X˜T.The matrixR is eigen decomposed, and the eigenvalue and the corresponding eigenvector are obtained, and the eigenvalue is arranged in descending order. According to the size of the contribution rate, for the basis of the subspace, the first b eigenvalues is selected:(21)γ=diage1,e2,…,eb.And the corresponding eigenvectors(22)Jb=j1,j2,…,jb.Then theb principal components to be extracted are(23)F=JbTX˜.The original data is reconstructed from the extracted principal components:(24)X=JF+d.


## 2.1. Growth of Marine Microalgae

Microalgae are the basis of marine primary productivity, and their species composition, biomass, population growth, and community structure are all directly related to the stability and health of the marine ecological environment. Marine algae are indispensable biological species in the ocean [8, 9]. The carbon and oxygen balance in the ocean is maintained through photosynthesis, and it provides a rich food source for most marine animals [10]. However, the excessive proliferation of algae causes the outbreak of red tide, which endangers the marine ecological environment. The growth and toxin production of toxic red tide algae are affected by a variety of environmental factors (including light, salinity, temperature, and nutrient levels) [11]. Heteroflexus alba is a unicellular organism, slightly oval in shape, without a cell wall, and surrounded by a periplasmic membrane. When the algae are active, the flagella often bend or extend perpendicular to the long axis of the cell. As the material basis of algae growth among various environmental factors, the changes of nitrogen and phosphorus nutrients significantly affect the growth and toxin production of algae, thus indirectly affecting the occurrence of harmful red tides [12].Nutrient salts are important elements needed for the growth of aquatic organisms such as phytoplankton. If the content of nutrients in the water body is lower than the minimum required for phytoplankton growth, algal blooms will be inhibited and the water body may not produce eutrophication [13]. However, high levels of nutrients in water bodies do not necessarily produce eutrophication; eutrophication can only occur under conditions of heavy algal growth [14]. Human activities contribute to accelerated eutrophication through various ways of nutrient input to watershed ecosystems. In natural water ecosystems such as freshwater, estuarine and marine. Nitrogen and phosphorus are the most common limiting factors for phytoplankton. Therefore, the composition, quantity and relative ratio of nutrients such as nitrogen and phosphorus input to water bodies are key factors in determining the eutrophication process [15].The damage caused by eutrophication is serious and it leads to large changes in the water quality of water bodies. Its most direct effect is to disrupt the ecological balance of waters by changing the components, density and overall structure of organisms in the system, causing structural changes in the original ecosystem.Not only the nitrogen nutrient concentration has a great influence on the growth of algae, but also different forms of nitrogen sources can also affect the growth of algae. Algae can not only utilize dissolved inorganic nitrogen (nitrate, ammonium salt, and others) in seawater, but also selectively utilize partially dissolved organic nitrogen (amino acid, urea and others) [16]. Organic nitrogen sources in seawater not only include the organic fertilizers input into the ocean by human production and living, but also the metabolites of marine organisms themselves. Urea is a relatively common nitrogen source in the ocean. After being absorbed by algae, it can usually accumulate in the algae, and rely on the action of urease to decompose urea into CO2, which is then absorbed as a reduced nitrogen source [17].In algae, whether produced by nitrogen metabolism, or by phytoplankton entering the cell directly from the growing environment, they are converted to the organic nitrogen glutamate and glutamine by the GS/GAGOT cycle. In particular, in the GS/GOGAT cycle, it is converted to Gln in the presence of GS/GOGAT. Subsequently, under the catalysis of GOGAT, glutamine andα-ketoglutarate are converted into 2 molecules of Glu. And one molecule of synthesized glutamate continues to participate in the absorption. The other is used to synthesize nitrogen-containing organics in cells such as proteins and nucleic acids [18], as shown in Figure 1. In the GS/GOGAT cycle, the catalytic reaction of GS is(1)L_Glu+ATP+NH3⟶GSL_Gln+ADP+Pi+H2O.Figure 1
Intracellular pathways and enzymes involved in nitrogen transport, reduction, and assimilation.As an important intermediate in amino acid synthesis, glutamine can be further used to synthesize other amino acids, including arginine. In the presence of sufficient phosphorus, the amino acids in the algae can synthesize proteins, thereby supporting the growth and cell division of the algae. However, when phosphorus is deficient, there is an excess of nitrogen in cells, and this excess nitrogen from nitrate reduction and amino acid degradation is mainly in the form of ammonium salts. The accumulated ammonium salts caused a toxic effect on the organism itself (as shown in Figure2). In order to alleviate this toxic effect, the de novo synthesis of arginine is activated, and the toxic effect of intracellular ammonium salts is alleviated by synthesizing arginine with high nitrogen content [19]. During phosphorus limitation, the concentration of glutamine in algal cells increases. This may be due to the inhibition of the pathway to protein synthesis in favor of providing amino groups to generate phosphocarbamates, which are further used to synthesize arginine. Arginine is a precursor for the synthesis of PSP toxin, so that when phosphorus is limited, the synthesis of cytotoxin is accelerated [20].Figure 2
Schematic diagram of the effects of nitrogen and phosphorus on toxic algal toxins.Nitrogen is an important limiting factor for algal growth. It is necessary for the growth and metabolism of algae, and it is also the composition of its own proteins and nucleic acids. N is an important element for the synthesis of proteins, phospholipids, chlorophyll and other substances in plankton. Without the supply of N, algal cells cannot grow and reproduce at all. The growth of seaweed is adversely affected for too low or too high nitrogen content, and the type of nitrogen also has a certain impact on the absorption and utilization of seaweed. Phosphorus is a very important nutrient element, it is the main component of nucleic acid, protein and phospholipid, and it is also a necessary nutrient for the synthesis of chlorophyll [21].There are usually four stages in the growth cycle of microorganisms: adaptation period (latent period), logarithmic period, stationary period, and decay period. During the adaptation period, microorganisms adapt to the surrounding environment and grow slowly. After entering the logarithmic phase, the microorganisms begin to grow rapidly, and the growth shows a logarithmic trend. When the growth of microorganisms reaches a certain level, it enters a stable period due to environmental constraints, and the growth of microorganisms is stable at this time. With the depletion of nutrients, microorganisms tend to decline and finally lose their activity completely [22]. In the logarithmic phase, the algae began to enter a period of rapid exponential growth. After the exponential growth period, the growth rate decreases until the plateau period. At the start point and end point of the exponential growth stage and the corresponding times and, the growth rate of this stage is(2)V=lnX2/X1t2−t1.In the formula, represents the number of cells (or chlorophyll content) on the day; represents the number of cells (or chlorophyll content) on the day.Each growth stage of algae can be fitted by a logistic model. Logistic regression is also known as logistic regression analysis. The logistic regression model is built on the linear regression model of the sigmoid function and independent variables. Through this model, not only the adaptation period, logarithmic period and stable period of microbial growth can be obtained by fitting, but also the turning period of its growth can be determined, that is, the moment when the number of individuals reaches the half-saturated state (k/2). The growth rate of microorganisms is at a maximum at this time, and then begins to slow down until the maximum biomass (or termination biomass) is reached [23]. That is, the SLogistic model introduces the maximum growth rate and the termination biomass, which can accurately and quantitatively describe the biological growth law. The model is derived as follows:The corresponding assumption of the logistic model is: under the condition of limited biological resources, the growth of a single microorganism has the same growth rate, and the required nutrients are the same [24].During the culturing process, microorganisms continue to grow over time, and the microbial growth rate is linked to the population biomass at any time, then the SLogistic model can be expressed as(3)dXdt=cX1−Xk.In the Formula,c is a constant and k is the maximum biological capacity.Its integral form is(4)X=K1+ea−ct.In the Formula, a andc are both constants.The speed of the SLogistic growth model can be known from Formula (4), the maximum value and the time of occurrence are:(5)tmax=ac,(6)vmax=c×k4.Another important parameter of the SLogistic growth rate function, that is, the time when the growth rate increases the fastest is(7)t1=a−1.317b.


## 2.2. Data Dependency Model

The excessive proliferation ofH. akashiwo will cause algal blooms, which will lead to the death of fish in a large area and cause huge losses to the aquaculture industry. The data on the proliferation of H. akashiwo is difficult to obtain, but mobile big data technology can be used to obtain and analyze the data on the proliferation of harmful algal blooms at any time through mobile, especially when studying the effect of different nutrient salts on the proliferation of H. akashiwo, big data technology has an important role in monitoring, predicting and analyzing the growth process of H. akashiwo.Mobile big data is a general term for data information that can be interpreted by humans, which mainly refers to the massive data flow obtained from the application process of mobile user terminals using the mobile Internet as the medium, and managed, processed and analyzed within a reasonable time. From the perspective of typical application fields of mobile big data, mobile big data can be classified into entertainment content field, service life field, shopping consumption field, interconnection and collaboration field, and other fields. Typical applications in each field are shown in Figure3.Figure 3
Classification of mobile big data.Most of the information is in the state of original data, and data mining is to reveal the information that has not been found or expressed but has important functions in a large amount of data by automatically discovering patterns or rules by computer software. The performance of data mining technology is generally prediction and description. Among them, data mining techniques that focus on prediction can classify new samples by analyzing historical data. The data sources used for data mining must be real and extensive, and may be incomplete and include some interfering data items. Descriptive focus is on general introduction and partitioning of data. The data mining process is shown in Figure4.Figure 4
Data mining process.Since the development of big data technology, the amount of information data has achieved explosive growth, which has also promoted the development and transformation of the overall structure of the big data era. With the emergence and increase of a large number of document data, image data, network data, and audio data, more and more network data needs to be sorted and analyzed through big data. From this, it is concluded that the characteristics of the big data era are as follows.The first is a large amount of data. In the era of big data, the statistics and measurement of data are no longer limited to the traditional TB capacity, but are more developed into PB and ZB capacity. The amount of data contained in this is huge, which can also be called massive data, and the resulting large amount of information data needs to be summarized and analyzed at a higher level of technology in order to achieve effective data analysis. The data analysis structure diagram is shown in Figure5.Figure 5
Data analysis structure diagram.The second is the diversity of data. A large amount of data contains a variety of data, embodied in the modes of documents, pictures, networks, videos, and audios. Diverse data appears in different structures and forms, and a large amount of data needs to be adjusted and optimized uniformly through big data technology.The third is high-speed circulation. By relying on the advancement of Internet technology and the development of 5G technology, the transmission speed of data in the era of big data is also amazing. And with the continuous improvement of data transmission speed, the security and convenience of information data have also achieved rapid improvement. This is of great benefit to the innovation and progress of information data management tools in the era of big data, which effectively improves the structured development of data information in the era of big data.The fourth is high value. In the era of big data with the rapid development and improvement of information and data, data has become more and more valuable. Many hackers make huge profits by obtaining data, so in the era of big data, data supervision and data security should be strengthened. In the development process of the market economy, the information flow of data is very large, so it is very difficult to ensure the safety and effectiveness of data and realize the efficient use of data. It is also more difficult to find the needed and valuable information in time. But if the information needed can be found, it will effectively increase the overall value of the data.The fifth is authenticity. The greatest value of data is truth. Only real information data can provide an effective basis and support for data analysis work. However, only relying on the scale of the data cannot guarantee the validity and overall quality of the data, and the conclusions drawn from the data analysis and summary cannot provide effective reference and help for decision makers.Association rules can be expressed as:(8)M⇒N.Among them,M is called the domain of the rule, and N is called the domain of the rule. It represents how confidently the elements in N can be obtained when the elements in the M set appear.Rule support is the probability thatm and n appear at the same time.(9)Pm−n=Wm∩​nW.The support for the preceding item is(10)Pm=WmW.The latter support is(11)Pn=WnW.The confidence level of a rule describes the probability of occurrence ofn under the condition that reaction m occurs.(12)Cm−n=Wm∩​nWm.The relationship between the support and confidence of the rule is:(13)Cm−n=Wm∩​nWm=Pm−nPm.Rule lift is the ratio of confidence to consequent support, and the Formula is:(14)Hm−n=Cm−nPn.The degree of lift reflects the degree of influence onn when m appears. If the lift is less than 1, this rule is meaningless. A lift greater than 1 means that m has a promoting effect on the appearance of n.The mathematical expression for the confidence difference is:(15)D=Cm−n−Pn.It can be seen from the Formula that the confidence difference is the absolute difference between the confidence of the rule and the support of the latter item.The main process of data preprocessing is shown in Figure6. The data on the proliferation of H. akashiwo are not ideal. Data that has not been preprocessed cannot be directly mined. In real experimental data, there are some missing data, repeated data or noisy data. The existence of these dirty data affects the effect and efficiency of data mining, so it is necessary to clean these dirty data and fill in the missing data scientifically and reasonably. PCA is a new small variable that converts multiple problems into a low-dimensional space, and then replaces the original small variables with new small variables for subsequent processing, and converts high-dimensional problems into low-dimensional problems. By retaining some of the most important features in high-latitude data, and removing noise and unimportant features, the purpose of improving data processing speed is achieved. The main work of the PCA algorithm is to reduce the indices in the original data, reorganize the relevant indices, and finally obtain a new set of indices that are unrelated to the complex indices in the past. The contribution to cumulative contribution ratio of the principal components refers to the information occupied by the original data X in A after the transformation from high-dimensional to low-dimensional.(1)
Contribution rate: the higher the proportion of the eigenvalue of the i-th principal component in the sum of all eigenvalues of the covariance matrix, the stronger the comprehensive ability of the eigenvalue is. The i-th principal component has an eigenvalue of, and its formula is:(16)ai=ei∑i=1mei.(2)
Cumulative contribution rate: it is the proportion of the sum of the eigenvalues of the firstk principal components in the sum of all eigenvalues. The larger this ratio is, the more the first k principal components can fully represent the information possessed by the original data. The calculation formula is:(17)Sk=∑i=1Vei∑i=1mei.Figure 6
Data preprocessing.In solving practical problems, the firstc (c < m) principal components are generally selected, and the seventeen cumulative variance contribution rate meets certain requirements (usually more than 80%). By using the selected first d principal components to replace the original m variables for analysis, the purpose of data dimensionality reduction can be achieved, which can also be regarded as a feature extraction.First, the mean vector of the samples in the sample data set is calculated, that is:(18)d=1n∑i=1nxi.Each sample is de-averaged, that is the sample data is centered:(19)X˜=X−d.The data matrix is constructed: the covariance matrix R ofX˜:(20)R=1nX˜X˜T.The matrixR is eigen decomposed, and the eigenvalue and the corresponding eigenvector are obtained, and the eigenvalue is arranged in descending order. According to the size of the contribution rate, for the basis of the subspace, the first b eigenvalues is selected:(21)γ=diage1,e2,…,eb.And the corresponding eigenvectors(22)Jb=j1,j2,…,jb.Then theb principal components to be extracted are(23)F=JbTX˜.The original data is reconstructed from the extracted principal components:(24)X=JF+d.


## 3. Experimental Preparation for the Effect of Nitrogen and Phosphorus Nutrients on the Proliferation ofH. Akashiwo

In this paper,H. akashiwo is the research object, and by setting different nitrogen and phosphorus nutrient concentrations, the effect of nitrogen and phosphorus concentrations on the growth of H. akashiwo is discussed. H. akashiwo was cultured in a BG-11 medium (as shown in Table 1 for the composition) with a conical flask at a 25°C incubator, where the light intensity was set to 3000 lux, and the light cycle was the ratio of light to non-light 12 hours to 12 hours. After culturing a certain number of single cells, two cells, and a small group of H. akashiwo in BG-11 medium, the formal experiment was carried out (22.1% of the small group in the algal species, with an average of 3.8 cells per group).Table 1
Composition and concentration of BG-11 medium in the experiment
ComponentConsumptionMother liquor concentrationMgSO4⋅7H2O10 mL/L3.50 g/500 mL dH2OCaCl2⋅2H2O10 mL/L1.7 g/500 mL dH2OEDTANa210 mL/L0.05 g/500 mL dH2ONa2CO35 mL/L1.0 g/500 mL dH2OAll experimental equipment were sterilized in an autoclave at 121°C for 30 min before the experiment, and cooled before use.100 mL of N-free and P-free BG-11 culture medium were added to the selected cultured algae species, and the nitrogen and phosphorus concentration settings were shown in Table2. The concentration of nitrogen and phosphorus in A5 was the concentration of nitrogen and phosphorus in normal BG-11 medium.Table 2
Nitrogen and phosphorus concentrations in the culture medium of each group.
Processing groupA1A2A3A4A5Total nitrogen (mg/L)0.1110100200Total phosphorus (mg/L)0.0050.050.555.5Culture mediumBG-11BG-11BG-11BG-11BG-11Three parallel samples were set up for each group in the experiment, which were placed in an incubator for 14 days under the above conditions, and the Erlenmeyer flask was shaken once a day. The number of algal cells was measured under a binocular dissecting microscope, and the cell concentration was calculated, while the survival of the algae was observed.The natural seawater was used for cultivation and experiments, and the content of nitrogen and phosphorus in the water was reduced by means of biological adsorption. After dark settling, filtering, boiling, it was ready for use, and it was boiled and cooled before testing.Among the different test factors, 6 different concentrations were selected and different concentration values were given. N is used to indicate the concentration calculation of ammonium chloride and urea, and P is used to indicate the concentration calculation of sodium dihydrogen phosphate. As shown in Table3, the experiment is divided into two stages: sodium dihydrogen phosphate is mixed with ammonium chloride and urea respectively.Table 3
Initial concentrations of nitrogen and phosphorus used in the experiment.
Factor level phosphorusConcentration123456Nitrogen0.51.52.551015Factor level2550100200300500


## 4. Data on the Proliferation ofH. Akashiwo

Nitrogen and phosphorus nutrients in the ocean can exist in various forms and in different forms. The composition of nitrogen mainly includes inorganic nitrogen such as nitrate, ammonium salt, urea, amino acid, and so on. At the same time, the ocean contains a large number of phosphorus nutrients, which exist in the form of inorganic and organic matter. Some microalgae obtain both inorganic and organic nutrients from the ocean. The different forms of nitrogen and phosphorus nutrients in the ocean have certain effects on their growth, and the existence of organic nutrients is the main factor leading to the occurrence of some marine microalgae. Different concentrations of nutrients have significant effects on the growth of algae. Overall, the growth rate of algae increased with increasing nutrient levels, while excess nutrient levels inhibited their growth. In different seaweeds, the nutrient content required for the maximum growth rate of different seaweeds is different, and even the same seaweeds have different nutrient levels.Under different ammonium chloride and inorganic P concentrations, the changes of biomass ofH. akashiwo with time are shown in Figures 7 and 8. The horizontal axis is the experimental time, and the vertical axis is the concentration of H. akashiwo (per ml) after proliferation.Figure 7
Growth Curves ofH. akashiwo with Different Phosphorus Concentrations.
(a)(b)Figure 8
Growth Curves ofH. akashiwo with Different Ammonium Chloride Concentrations.
(a)(b)It can be seen from Figure7 that different phosphorus concentrations have a promoting effect on the growth of H. akashiwo, but the effect is different. Among them, 10 μmol/L is with the largest value-added value and the fastest speed, and there is a long stable period. However, the high concentration has a great influence on its growth rate. When the phosphorus concentration is 15 μmol/L, the growth rate of H. akashiwo is greatly slowed down, which is similar to the effect at 5 μmol/L.It can be seen from Figure8 that different concentrations of nitrogen sources can promote the rapid reproduction of H. akashiwo. At low concentrations (100 μmol/L), the degree of proliferation was decreased, but the duration was prolonged. When the concentration of N was high, the biomass of H. akashiwo had a tendency to decrease at the maximum, especially when the concentration was 200 μmol/L, the decrease of biomass was more significant. Under the condition of low nitrogen fertilizer, its decrease was not significant.The results showed that different concentrations of N and P had obvious effects on different biomass, and the higher and lower the concentration of nitrogen and phosphorus, the slower the proliferation rate was. The above phenomenon indicated that the growth rate ofH. akashiwo was significantly positively correlated with the nutrient concentration before reaching the optimum concentration. With the substantial increase in the nutrient concentration, the proliferation rate did not increase, but instead decreased the growth rate. When the N content was 500 μmol/L, the proliferation rate of H. akashiwo was the highest. When the phosphorus concentration was 10 μmol/L, the proliferation rate was the fastest, and the cells at this level proliferated were the best.With the increasing use of organic N fertilizers such as urea, a large amount of urea enters the coastal waters. During the experiment, under different concentrations of urea and inorganic phosphorus, the growth trend ofH. akashiwo with time is shown in Figures 9 and 10. The horizontal axis is the experimental time, and the vertical axis is the concentration of microalgae (units/ml).Figure 9
Growth Curves ofH. akashiwo with Different Urea Concentrations.
(a)(b)Figure 10
Growth curves ofH. Akashiwo at different phosphorus concentrations.
(a)(b)It can be seen from Figure9 that the effect of different urea concentrations on the proliferation of H. akashiwo is similar to the effect of ammonium chloride on the proliferation of H. akashiwo. When the concentration was 300 μmol/L, the proliferation rate was the fastest, and when the concentration was 50 μmol/L, the proliferation of H. akashiwo showed a significant decrease.It can be seen from Figure10 that the concentration of sodium dihydrogen phosphate has a great influence on the growth rate of H. akashiwo. Compared with the concentrations of 0.5 μmol/L and 1.5 μmol/L, the proliferation rate of H. akashiwo at the concentration of 2.5 μmol/L was significantly increased. The growth effect of H. akashiwo increased with the increase of concentration, and the growth number was the highest at 15 μmol/L. When the N concentration was 300 μmol/L, the growth rate of H. akashiwo was the highest. At the concentration of 15 μmol/L, the growth rate was the fastest, and the proliferation at this level was the best.The results showed that nitrogen and phosphorus nutrients could significantly promote the growth ofH. akashiwo. The algae is a marine organism. Therefore, a large number of survey results support the obvious effects of nitrogen and phosphorus nutrients on the proliferation of marine microalgae.


## 5. Conclusion

This paper takesH. akashiwo as the research object, and conducts experiments on the effects of nitrogen and phosphorus nutrients on marine microalgae. Nitrogen is an important element in the biochemical process of the Earth. For marine life, nitrogen is seen as a limiting factor in the primary productivity of the global ocean. Nitrogen, as an essential element for phytoplankton growth, has an important impact on the abundance and community composition of phytoplankton in specific sea areas. In the face of the growing mass of data in many fields such as biology, people are increasingly relying on computers to intelligently obtain useful information that needed to solve problems from the mass of data. The kinetic parameters of the growth of H. akashiwo at different concentrations of N and P are obtained through experiments, which provides basic data for studying the relationship between the formation of H. akashiwo and the eutrophication of seawater. The relationship between nutrients and the population of H. akashiwo is revealed. Nitrogen and phosphorus nutrients, as the main limiting factor for the growth of H. akashiwo population, could restrict the growth of marine algae. At present, red tides mostly occur in eutrophic water bodies, and nitrogen and phosphorus are the main nutrient input of eutrophic water bodies. The outbreak of marine microalgae has become a common problem in the world. The outbreak modes and types vary from place to place, so it is necessary to control harmful algae according to the actual situation.



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*Source: 2484938-2022-09-15.xml*