A method for measuring algal blooms in urban lakes based on vertical distribution structure analysis of algae

Abstract

The application discloses a kind of urban lake algal bloom measurement method based on algal vertical distribution structure analysis, belong to environmental science field, the application constructs with column plane concentration level, column plane algal community composition, column plane aggregation characteristics and column plane profile form as key dimension, including 14 single index algal vertical distribution structure comprehensive evaluation index system, realizes the quantitative analysis of algal vertical distribution structure.The application establishes a set of fast response and risk classification as core algal bloom measurement process, including algal in-situ stereoscopic observation, algal vertical distribution structure comprehensive evaluation, algal vertical distribution structure classification and algal bloom and risk identification etc.Step.The method breaks through the limitation of traditional method in monitoring dimension, observation data is not fully mined and algal bloom determination standard is fuzzy etc.Limit, lays a theoretical foundation for algal bloom monitoring and evaluation system research, application in urban lake can provide technical means and scientific basis for algal bloom prediction and early warning and algal bloom precision prevention and control.

Description

Technical Field

[0001] This invention belongs to the field of environmental science, specifically relating to a method for measuring algal blooms in urban lakes based on the analysis of the vertical distribution structure of algae. Background Technology

[0002] The increasing global eutrophication and climate change have triggered various types of algal blooms. A deep understanding of the vertical distribution patterns of algal communities is crucial for revealing the mechanisms of algal bloom formation and formulating algal control strategies. However, current algal bloom monitoring systems in my country's lakes and reservoirs mainly rely on fixed-point online monitoring and satellite remote sensing, which can only acquire real-time data on chlorophyll concentration, cyanobacterial biomass, or periodic data on the area of ​​surface algal blooms. There is a lack of high-resolution, continuous vertical observation data of algal communities in urban lakes, and the limited in-depth research on vertical stratification is mostly focused on cyanobacteria. More importantly, current analyses of these three-dimensional observation data are largely limited to acquiring raw observation data and describing trends. Although many studies have pointed out that relying solely on algal biomass as a single indicator to identify algal blooms is scientifically insufficient and have confirmed the indicative value of multi-indicator coupling, current algal bloom determination still does not consider community composition and vertical distribution characteristics. It also lacks in-depth exploration of the potential information after the coupling of ecological mechanisms and indicators. Therefore, it can only indicate the current status of algae in lakes and reservoirs, and it is difficult to predict the future development trend of algae, let alone accurately predict and warn of algal bloom risks. This can lead to the problem of "underprotection" or "overprotection" of lakes, affecting the health and safety of lake ecosystems. Summary of the Invention

[0003] Addressing the urgent need for precise identification of algal dynamics and comprehensive assessment of algal bloom risks in eutrophic lakes, this invention utilizes new in-situ sensing technology and equipment for aquatic algal communities and chlorophyll. It analyzes the vertical distribution structure of algae from a three-dimensional observation perspective and proposes a comprehensive evaluation index system suitable for quantitatively describing the vertical distribution structure of algae in urban lakes. Based on this, the invention establishes an algal bloom measurement process centered on rapid response and risk classification, strengthening the existing algal bloom monitoring and assessment system. This method can rapidly screen algal blooms and their risk levels on-site, providing key technical support for efficient algal bloom management and water quality protection of aquatic ecosystems in my country, and enhancing the existing algal bloom monitoring and assessment system.

[0004] This invention proposes a method for measuring algal blooms in urban lakes based on the analysis of algal vertical distribution structure. This method strengthens the existing algal bloom monitoring and assessment system and, when applied to urban lakes, provides technical means and scientific basis for algal bloom prediction, early warning, and precise control. The key features of this invention are summarized below: 1. This invention constructs a comprehensive evaluation index system for the vertical distribution structure of algae, with key dimensions including columnar concentration level, columnar algal community composition, columnar aggregation characteristics, and columnar profile morphology, encompassing 14 single indicators. This system enables quantitative analysis of the vertical distribution structure of algae. 2. This invention employs an algal bloom measurement process centered on rapid response and risk classification, specifically including steps such as in-situ three-dimensional observation of algae, comprehensive evaluation of the vertical distribution structure of algae, classification of the vertical distribution structure of algae, and identification of algal blooms and risks.

[0005] The technical solution of the present invention is as follows:

[0006] A method for measuring algal blooms in urban lakes based on the analysis of the vertical distribution structure of algae includes the following steps:

[0007] Step 1: In-situ three-dimensional observation of algae: Algal biomass data were collected vertically in layers at multiple points in the lake with a step size of 0.05~0.5m;

[0008] Step 2: Comprehensive evaluation of the vertical distribution structure of algae: The vertical distribution structure is quantitatively analyzed based on 14 indicators across 4 dimensions: columnar concentration level, columnar algal community composition, columnar aggregation characteristics, and columnar profile morphology.

[0009] Step 3: Classification of algal vertical distribution structure: Biomass levels are classified according to VAC, which include low / medium / high / extremely high. The dominant algal species, aggregation state and stratification morphology are determined by combining RA, aggregation location and CV.

[0010] Step 4: Algal bloom and risk identification: Algal bloom is determined to have occurred when VAC ≥ 10 μg / L and RA > 50%, and the risk is distinguished as surface-aggregated stratified type / non-aggregated mixed type based on aggregation characteristics.

[0011] In the above technical solution, the column concentration level includes:

[0012] Average concentration per column (VAC): Where n represents the total number of effective water column stratifications at the monitoring point. This represents the algal biomass of the i-th layer, expressed as Chla concentration in μg / L.

[0013] Stratified concentrations (SAC / MAC / BAC): For the surface layer, the concentration is taken from 0.2 to 0.4 m; for the middle layer, the concentration is taken from half the water depth ± adjacent layers; for the bottom layer, the concentration is taken from three consecutive layers upwards after removing the bottom layer. Among these: , , Where SAC represents the surface concentration, MAC represents the mesosphere concentration, BAC represents the bottom concentration, and n s n represents the number of surface layers. m n represents the number of the middle layers. b Indicates the number of the bottom layer. This represents the algal biomass of the i-th layer;

[0014] Total cylindrical algae (TAC): Where n represents the total number of effective water column stratifications at the monitoring point. This represents the algal biomass of the i-th layer.

[0015] In the above technical solution, the columnar algal community composition includes:

[0016] Relative abundance of cylinders (RA): , where VACs represents the average column concentration of a specific phylum of algae, VAC represents the average column concentration, and s represents a specific algal species (phylum or genus).

[0017] Dominant species in the columnar region (DS): Based on RA, the dominant phylum and dominant species within the phylum are determined: DS = max (RA1, RA2, RA3, ..., RAm), where RA1, RA2, RA3, ..., RAm represent the species detected in the dominant phylum, and m is the total number of species.

[0018] In the above technical solution, the cylindrical aggregation characteristic includes:

[0019] Aggregation Location (ZP / ZC): , ZP represents the depth of the water layer where the maximum algal biomass concentration is located, and ZC represents the depth of the centroid of the column. for The corresponding depth, This represents the depth corresponding to the i-th water layer;

[0020] Aggregation layer width (ALD): , , This represents the thickness of the i-th water layer;

[0021] Algal aggregation intensity (AI): When ALD > 0, the aggregation intensity of the aggregated algae is calculated by the ratio of the total algal biomass exceeding the average concentration on the column surface within the aggregation layer to the number of aggregation layers. When ALD = 0, AI is directly defined as 0 because there is no aggregation phenomenon at this time, and the aggregation intensity is 0.

[0022] In the above technical solution, the cylindrical profile shape includes:

[0023] Cylindrical inhomogeneity (CV): , ,in The standard deviation of the vertical concentration distribution;

[0024] Column Concentration Difference (CR): ;

[0025] Cylindrical offset (PC): ZP is the peak depth of the cylinder, and ZC is the centroid depth of the cylinder.

[0026] In the above technical solution, the layering rule is as follows:

[0027] Surface layer: fixed depth 0.2~0.4m;

[0028] Middle layer: The midpoint of the water depth (D / 2) is used as the reference layer, and the adjacent layers above and below it are included;

[0029] Bottom layer: After removing the bottom layer, take three consecutive layers upwards.

[0030] In the above technical solution, the threshold for determining algal blooms is:

[0031] Low biomass: VAC < 10 μg / L; Medium biomass: 10~50 μg / L; High biomass: 50~100 μg / L; Very high biomass: > 100 μg / L;

[0032] Dominant algal species: RA > 50%.

[0033] In the above technical solution, the aggregation state is determined as follows:

[0034] Surface aggregation: ZP / D≤0.3 and AI observation / AI baseline≥1, where AI baseline is the annual average value of AI;

[0035] Vertical stratification: CV observation / CV baseline ≥ 1, where the CV baseline is the annual average value of CV.

[0036] In the above technical solution, the observation step size is dynamically adjusted according to the season:

[0037] Spring and summer: 0.1m increments, surface layer densification to 0.05m;

[0038] Autumn and winter: 0.2~0.5m step length.

[0039] In the above technical solution, an in-situ algae sensor is used to obtain vertical chlorophyll concentration data, which is applicable to urban shallow lakes with an average water depth of <5m.

[0040] Beneficial effects:

[0041] 1. Break through the limitations of traditional monitoring dimensions.

[0042] This invention constructs 14 vertical distribution structure indicators (including four dimensions: column concentration level, community composition, aggregation characteristics, and profile morphology), achieving for the first time a quantitative analysis of algal vertical distribution. It overcomes the planarity defects of traditional algal bloom monitoring that relies on surface biomass or remote sensing images, and reveals the ecological mechanisms of algal vertical migration and aggregation.

[0043] 2. Resolve the problem of ambiguous criteria for determining algal blooms.

[0044] This invention proposes a combined "biomass-dominant species-aggregation state" judgment rule: algal bloom is judged when VAC≥10μg / L and RA>50%; combining ZP / D≤0.3 with AI threshold to identify surface aggregation risk, avoiding "underprotection" or "overprotection" caused by a single biomass indicator.

[0045] 3. Enhance the adaptability of urban lakes.

[0046] For shallow urban lakes with an average water depth of <5m: dynamic observation step size design (0.1m denser step size in spring and summer to capture details of algal blooms, and 0.2~0.5m step size in autumn and winter to reduce operation and maintenance costs); layer rule optimization (fixed 0.2~0.4m for the surface layer, D / 2 as the benchmark for the middle layer, and removal of disturbed layers for the bottom layer) to solve the problem of bottom sediment suspension interference (such as removing bottom disturbances from BAC layer data).

[0047] 4. Implement risk-based management of algal blooms.

[0048] Based on aggregation characteristics, we can distinguish between surface-aggregated stratified algae and non-aggregated mixed algae, guide differentiated algae control strategies, and improve the accuracy of risk identification.

[0049] 5. Support the collaborative application of in-situ equipment.

[0050] It is compatible with in-situ algae sensors, YSI and other equipment to achieve rapid acquisition of vertical chlorophyll concentration, which improves efficiency compared with traditional laboratory analysis and meets the needs of high-frequency monitoring. Attached Figure Description

[0051] Figure 1 This is a comprehensive evaluation index for the vertical distribution structure of algae in this invention.

[0052] Figure 2 This is a flowchart of a method for measuring algal blooms in urban lakes based on the analysis of the vertical distribution structure of algae, according to the present invention.

[0053] Figure 3 This is a schematic diagram of the normalized vertical profile of algal biomass obtained after cross-sectional observations at 35 points in the lake area in the example. Detailed Implementation

[0054] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. However, the following embodiments are only for explaining the present invention, and the scope of protection of the present invention should include all the contents of the claims. Moreover, through the description of the following embodiments, those skilled in the art can fully implement all the contents of the claims of the present invention.

[0055] Example 1: Key dimensions for comprehensive evaluation of the vertical distribution structure of algae.

[0056] like Figure 1 As shown in the figure, this embodiment is used to describe in detail the key dimensions of the comprehensive evaluation of the vertical distribution structure of algae in this invention, as follows:

[0057] 1. Column surface concentration level:

[0058] 1.1 Average concentration of VAC on the column surface:

[0059] This indicator represents the average algal biomass per unit volume of water from the surface to the lake bottom:

[0060] ,

[0061] n represents the total number of effective water column layers at this monitoring point. This represents the algal biomass of the i-th layer (expressed as Chla concentration, in μg / L).

[0062] 1.2. Column stratification concentrations: SAC, MAC, BAC:

[0063] Based on the water characteristics of urban shallow lakes, the depth heterogeneity of multiple points in the lake area, and the data quality assessment results, a profile division system suitable for urban lakes was constructed based on hierarchical cluster analysis. The specific stratification method is as follows: First, the water depth range of 0.2~0.4m was defined as the surface layer; second, to address the depth differences at different points, the deepest profile data at each point was removed to eliminate the influence of bottom sediment disturbance, and then three adjacent profile layers were selected upwards as the bottom water layer. For the middle layer, a relative water depth division method was adopted, that is, half the water depth at each point was used as the dividing benchmark, and the benchmark layer and the adjacent profile layers above and below it together constituted the middle water layer.

[0064] Let the total water depth at a certain monitoring point be Dm, the number of effective profile data layers be n, and the depth of each layer be di;

[0065] Surface layer: Within a fixed depth range, all water layers within the range of 0.2m ≤ di ≤ 0.4m are considered. Let the set of surface water layers be S. surface The number of layers is n s ;

[0066] ,

[0067] Middle layer: Using the midpoint of the water depth (D / 2) as a reference, the water layer with the depth closest to D / 2, along with its two adjacent water layers above and below, constitutes the middle layer. Let the set of middle layer water layers be S. middle The number of layers is n m ;

[0068] ,

[0069] Bottom layer: To eliminate the influence of disturbance to the bottom sediment layer, the data from the bottom layer (i.e., the nth layer) is discarded. Three consecutive layers upwards (i.e., the (n-1), (n-2), and (n-3)th layers) are taken as the bottom layer. Let the set of bottom water layers be S. bottom The number of layers is n b ;

[0070] ,

[0071] 1.3 Total Columnar Algae (TAC):

[0072] This indicator represents the absolute total amount of algal biomass within the water column at the monitoring point, comprehensively reflecting the "algal load" of the water column:

[0073] ,

[0074] 2. Composition of columnar algal communities:

[0075] 2.1 Relative abundance of cylindrical surfaces (RA):

[0076] The ratio of the column average concentration (VACs) to the column average concentration (VAC) of a specific algal phylum, expressed as a percentage:

[0077] ,

[0078] Where 's' represents a specific algal species (phylum or genus).

[0079] 2.2 Dominant Species (DS) on the Cylindrical Surface:

[0080] The dominant species (DS) on the column are used to determine the algal taxa that dominate the entire water column. Its core feature is that: first, the dominant phylum is determined by comparing the relative abundance (RA) of all tested algal phyla; then, secondary identification of species within the dominant phylum is performed to determine the species with the highest dominance.

[0081] DS=max (RA1, RA2, RA3,..., RAm),

[0082] Where RA1, RA2, RA3, ..., RAm represent the species detected in the dominant phylum, and m is the total number of species.

[0083] 3. Cylindrical aggregation characteristics:

[0084] 3.1 Gathering Location:

[0085] ZP is the representative depth of the water layer where the maximum algal biomass concentration is located, and ZC is the depth of the centroid of the column. The calculation formula is as follows:

[0086] ,

[0087] for The corresponding depth;

[0088] ,

[0089] This represents the depth corresponding to the i-th water layer.

[0090] 3.2 Aggregation layer width ALD:

[0091] Used to quantify the vertical extent of water layers with above-average algal biomass. The thickness of continuous or discontinuous water layers where the total algal biomass concentration exceeds the column average concentration (VAC).

[0092] ,

[0093] ,

[0094] This represents the thickness of the i-th water layer.

[0095] 3.3 Algal Aggregation Intensity (AI):

[0096] Characterized by the algal biomass load exceeding the average level within a unit width of the aggregation layer, reflecting the vertical aggregation density or enrichment of algae. Its value is the ratio of the total algal biomass exceeding the column average concentration within the aggregation layer to the number of aggregation layers:

[0097] Algal aggregation intensity (AI): When ALD > 0, the aggregation intensity of the aggregated algae is calculated by the ratio of the total algal biomass exceeding the average concentration on the column surface within the aggregation layer to the number of aggregation layers. When ALD = 0, AI is directly defined as 0 because there is no aggregation phenomenon at this time, and the aggregation intensity is 0.

[0098] 4. Cylindrical profile shape:

[0099] 4.1 Cylindrical Inhomogeneity (CV):

[0100] This indicator characterizes the relative dispersion of algal biomass in the vertical direction, i.e., the non-uniformity of the profile. Based on the concept of the coefficient of variation, it uses the average concentration per cylinder (VAC) to standardize the standard deviation, eliminating the influence of dimensions and ensuring comparability of results between different monitoring points.

[0101] ,

[0102] ,

[0103] in, denoted as the standard deviation of the vertical concentration distribution.

[0104] 4.2. Concentration difference (CR) on the column surface:

[0105] The concentration gradient (CR) of the column surface characterizes the relative fluctuation of algal biomass in the vertical direction. A larger CR value indicates a steeper vertical gradient and a greater concentration difference between the surface and bottom layers; a smaller CR value indicates better vertical mixing. The overall fluctuation of the profile is quantified by the ratio of the concentration gradient (the difference between the maximum and minimum values) to the average concentration of the column surface.

[0106] ,

[0107] C max C represents the maximum concentration at the column surface. min This represents the minimum concentration at the column surface.

[0108] 4.3 Cylindrical offset PC:

[0109] This indicator determines whether algal aggregation is biased towards the water surface or the bottom by measuring the relative position of peak depth (ZP) and centroid depth (ZC). Its value is the ratio of peak depth (ZP) to centroid depth (ZC).

[0110] .

[0111] Example 2: A method for measuring algal blooms in urban lakes based on the analysis of the vertical distribution structure of algae.

[0112] like Figure 2 As shown in the figure, this embodiment is used to describe in detail a method for measuring algal blooms in urban lakes based on the analysis of the vertical distribution structure of algae, as described in the present invention, as follows:

[0113] This invention includes steps such as in-situ three-dimensional observation of algae, comprehensive evaluation of the vertical distribution structure of algae, classification of the vertical distribution structure of algae, and identification of algal blooms and risks.

[0114] 1. First, deploy algal community monitoring equipment in situ. For urban lakes, considering that they are mostly small, shallow, closed bodies of water with stagnant or poor flow (average depth <5m), it is recommended to conduct observations in winter and autumn with a step size of 0.2~0.5m, and in spring and summer with a step size of 0.1m. If obvious stratification is encountered, denser profile observations can be conducted on the surface layer with a step size of 0.05m. Considering the uneven spatial and temporal distribution of algae, it is recommended to conduct observations at multiple points.

[0115] 2. The vertical distribution structure is quantitatively described using the index system described in Example 1 above.

[0116] 3. Based on the evaluation results, select key indicators to quickly determine the vertical distribution structure.

[0117] 3.1 The column concentration level is selected as VAC. When VAC < 10 μg / L, it is defined as low biomass level; when VAC is 10~50 μg / L, it is defined as medium biomass level; when VAC is 50~100 μg / L, it is defined as high biomass level; and when VAC > 100 μg / L, it is defined as very high biomass level.

[0118] 3.2 Selecting RA for columnar algal community composition can determine the dominant algal phyla and their dominance.

[0119] 3.3. The selection of aggregation location and aggregation intensity can determine whether surface aggregation has occurred.

[0120] For clustering location, this method uses the relative position of the peak value in the water column (ZP / D) to determine it. If it is above 0.3, it is considered to be in the surface layer; otherwise, it is considered to be outside the surface layer. The clustering intensity is determined by comparing the annual average with the observation result as the watershed adaptive feature and setting a threshold. If the observation result / benchmark is ≥1, clustering is considered to have occurred; if the observation result / benchmark is <1, clustering is considered not to have occurred. (It should be noted that in practical applications, the annual average can be used as the watershed adaptive feature and compared with the observation result to set a threshold, or clustering can be performed using algorithms such as machine learning.)

[0121] 3.4 Selection of cylindrical profile morphology: Cylindrical non-uniformity is selected. Similarly, this method uses the annual average as the watershed adaptive feature and compares it with the observation results to determine the classification threshold. If the observation result / benchmark ≥ 1, vertical stratification is considered to have occurred. If the observation result / benchmark < 1, vertical stratification is considered not to have occurred. (It should be noted that in practical applications, the annual average can be used as the watershed adaptive feature and compared with the observation results to determine the classification threshold. Alternatively, clustering can be performed using algorithms such as machine learning.)

[0122] 4. Couple community structure, aggregation characteristics, and profile morphology with biomass levels to identify algal blooms and their risks.

[0123] Algal blooms occur when biomass levels are at medium to high levels and a clearly dominant algal species is present (RA > 50%, listed by phylum). If any one of these conditions is not met, it cannot be defined as an algal bloom. Algal blooms can be further subdivided into cyanobacterial blooms, diatom blooms, green algal blooms, and cryptophytic blooms at different biomass levels. Based on this, this method can determine the risk type of algal bloom according to its aggregation characteristics. For stratified algal blooms with surface aggregation, even in shallow lakes, attention should be paid to the risks of bottom oxygen depletion and the accumulation and decomposition of aggregated algae. Furthermore, stratification can lead to underestimation or overestimation of risk, necessitating enhanced profile monitoring. For non-aggregative mixed algal blooms, greater attention should be paid to the risks associated with their formation and dissipation, such as global oxygen depletion and ecosystem collapse.

[0124] Example 3: Experimental verification.

[0125] The data source for this embodiment is: based on the new in-situ sensing technology and equipment for aquatic algal communities and chlorophyll, high-frequency continuous in-situ three-dimensional observations were carried out in the main lake area of ​​West Lake in Tongling City, Anhui Province (a typical eutrophic urban lake) from December 2024 to September 2025 to verify the effectiveness of the invention.

[0126] Four observations at different dominant algal stages are presented for verification. The results are as follows:

[0127] Figure 3 The vertical profile of algal biomass after normalization was obtained from the observation of 35 points in the lake area (the in-situ algal community and chlorophyll sensor (AGHJ-AFS-II) developed by the Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences was used to quickly determine the chlorophyll concentration of different algal phyla in the profile; data can also be obtained using similar equipment such as YSI and BBE).

[0128] The evaluation results of the vertical distribution structure of algae are shown in Table 1 below:

[0129] Table 1

[0130]

[0131] This section only displays data on the dominant gate.

[0132] The algal bloom measurement results are as follows:

[0133] The algal bloom on December 15, 2024, was a mixed type of algal bloom characterized by medium biomass levels of cryptophytes (cryptophytes) and non-aggregative algae.

[0134] February 28, 2025, was a mixed algal bloom of medium biomass diatoms (Cyclophora) and non-aggregating algae.

[0135] April 11, 2025: A layered algal bloom with extremely high biomass levels of green algae (Scenedesmus) - surface aggregation;

[0136] August 19, 2025: A layered algal bloom of cyanobacteria (long-spore algae) with extremely high biomass levels, aggregated on the surface.

[0137] The above results can effectively support the accurate identification of algal bloom risks and efficient algal control.

[0138] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for measuring algal blooms in urban lakes based on the analysis of the vertical distribution structure of algae, characterized in that, Includes the following steps: Step 1: In-situ three-dimensional observation of algae: Algal biomass data were collected vertically in stratification at multiple points in the lake with a step size of 0.05~0.5 m; Step 2: Comprehensive evaluation of the vertical distribution structure of algae: The vertical distribution structure is quantitatively analyzed based on 14 indicators across 4 dimensions: columnar concentration level, columnar algal community composition, columnar aggregation characteristics, and columnar profile morphology. The column surface concentration levels include the average column surface concentration (VAC), surface concentration (SAC), middle layer concentration (MAC), bottom layer concentration (BAC), and total column algae concentration (TAC). The composition of the columnar algal community includes the relative abundance (RA) and the dominant species (DS) on the column. The columnar aggregation characteristics include the representative depth ZP of the water layer where the maximum algal biomass concentration is located, the centroid depth ZC of the columnar column, the aggregation layer width ALD, and the algal aggregation intensity AI. Aggregation location ZP / ZC: , ZP represents the depth of the water layer where the maximum algal biomass concentration is located, and ZC represents the depth of the centroid of the column. for The corresponding depth, This represents the maximum algal biomass concentration. Let represent the depth corresponding to the i-th water layer, and n represent the total number of effective water column layers at the monitoring point. This represents the algal biomass of the i-th layer; Aggregation layer width ALD: , , This represents the thickness of the i-th water layer. For assignment parameters; Algal aggregation intensity AI: When ALD > 0, the aggregation intensity of the aggregated algae is calculated by the ratio of the total algal biomass exceeding the average concentration on the column surface in the aggregation layer to the number of aggregation layers. When ALD = 0, AI is directly defined as 0 because there is no aggregation phenomenon at this time, and the aggregation intensity is 0. The cylindrical profile morphology includes cylindrical non-uniformity (CV), cylindrical concentration difference (CR), and cylindrical offset (PC). Cylindrical inhomogeneity (CV): , ,in The standard deviation of the vertical concentration distribution. This represents the algal biomass of the i-th layer; Column concentration difference CR: C max C represents the maximum concentration at the column surface. min This represents the minimum concentration at the column surface; Cylindrical offset PC: ZP is the representative depth of the water layer where the maximum algal biomass concentration is located, and ZC is the depth of the centroid of the column. Step 3: Classification of vertical distribution structure of algae: Biomass levels are classified according to the average concentration VAC on the column surface. The biomass levels include low / medium / high / extremely high. The dominant algal species, aggregation state and stratification morphology are determined by combining the relative abundance RA on the column surface, aggregation location and column surface heterogeneity CV. Step 4: Algal bloom and risk identification: Algal bloom is determined to have occurred when VAC≥10 μg / L and RA>50%, and the risk is distinguished as surface-aggregated stratified type / non-aggregated mixed type based on the aggregation characteristics.

2. The method according to claim 1, characterized in that: Average concentration of VAC on the column surface: Where n represents the total number of effective water column stratifications at the monitoring point. This represents the algal biomass of the i-th layer, expressed as Chla concentration in μg / L. Stratified concentrations of SAC / MAC / BAC: For the surface layer, the concentration range is 0.2–0.4 m; for the middle layer, the concentration is based on half the water depth ± adjacent layers; for the bottom layer, the concentration is the three consecutive layers above the bottom layer after removing the bottom layer. Among these: , , Where SAC represents the surface concentration, MAC represents the mesosphere concentration, BAC represents the bottom concentration, and n s n represents the number of surface layers. m n represents the number of the middle layers. b Indicates the number of the bottom layer. This represents the algal biomass of the i-th layer; Total TAC of columnar algae: Where n represents the total number of effective water column layers at the monitoring points. This represents the algal biomass of the i-th layer.

3. The method according to claim 2, characterized in that, The hierarchical rules are as follows: Surface layer: fixed depth 0.2~0.4 m; Middle layer: The middle water depth D / 2 is used as the reference layer, and the adjacent layers above and below it are included; Bottom layer: After removing the bottom layer, take three consecutive layers upwards.

4. The method according to claim 1, characterized in that: Relative abundance of cylindrical surfaces (RA): , where VACs represents the column average concentration of a specific algal phylum, VAC represents the column average concentration, and s represents the specific algal species; Dominant species DS in the columnar region: Based on RA, the dominant phylum and dominant species within the phylum are determined: DS = max (RA1, RA2, RA3, ..., RAm), where RA1, RA2, RA3, ..., RAm represent the species detected in the dominant phylum, and m is the total number of species.

5. The method according to claim 1, characterized in that, The threshold for detecting algal blooms is: Low biomass: VAC < 10 μg / L; Medium biomass: 10~50 μg / L; High biomass: 50~100 μg / L; Very high biomass: > 100 μg / L; Dominant algal species: RA > 50%.

6. The method according to claim 1, characterized in that, The aggregation state is determined as follows: Surface aggregation: ZP / D ≤ 0.3 and AI observation / AI baseline ≥ 1, where AI baseline is the annual average value of AI; Vertical stratification: CV observation / CV baseline ≥ 1, where the CV baseline is the annual average value of CV.

7. The method according to claim 1, characterized in that, The observation step size is dynamically adjusted according to the season: Spring and summer: 0.1 m increments, surface layer densification to 0.05 m; Autumn and winter: 0.2~0.5 m step length.

8. The method according to claim 1, characterized in that, In-situ algae sensors are used to obtain vertical chlorophyll concentration data, and the method is applicable to shallow urban lakes with an average water depth of <5 m.

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