Comprehensive evaluation method for crust development quality of active algae in different environments

Abstract

The invention belongs to the technical field of biology, and particularly relates to a comprehensive evaluation method for crust development quality of active algae in different environments. The invention creatively constructs a three-dimensional index system containing photosynthetic ability, stress response and structural morphology, and provides a comprehensive evaluation method for the development quality of algae crust based on multi-dimensional environment-biological index integration. According to the method, a three-dimensional index system of photosynthetic capacity, stress response and structural morphology is constructed, and weight calculation of factor analysis-principal component analysis and a membership function quantification mechanism are combined, so that non-destructive, quantitative and comprehensive evaluation on the development level of algae crust is realized; the problems of index singleness, methodology limitation, lack of environment-biological interaction mechanism and the like in the existing biological soil crust evaluation technology are solved, a quantifiable ecological restoration evaluation tool is provided for desertification control, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to a comprehensive evaluation method for the development quality of differential environmental active algal crusts. Background Art

[0002] As a key component of the surface ecosystem in arid regions, soil algal crusts play an important role in sand fixation, soil formation, water and soil conservation, and carbon and nitrogen cycling. In traditional monitoring systems, the crust type, thickness, and coverage are mostly used as the core indicators, but there is a lack of unified technical specifications, and the problem of fragmented monitoring methods is widespread at home and abroad.

[0003] Especially in the early development stage dominated by algae, the existing evaluation system has the following three limitations:

[0004] (1) Defect of single index: Existing studies mostly focus on single biological or physical indicators. For example, the chlorophyll a concentration represents photosynthetic ability, extracellular polysaccharides reflect stress response, and crust thickness evaluates structural stability, but the synergistic mechanism of multi-dimensional indicators is ignored, and a single indicator is difficult to comprehensively reflect the development quality.

[0005] (2) Limitation of methodology: Traditional evaluation relies on destructive sampling or subjective visual grading, lacking a non-destructive and quantitative comprehensive evaluation system.

[0006] (3) Lack of environmental-biological interaction mechanism: Existing technologies do not fully consider the dynamic relationship between biological responses such as photosynthetic capacity (Fv / Fm), oxidative stress, and electrochemical activity and environmental factors. Environmental stress will significantly change the development trajectory, and the evaluation results have large errors.

[0007] Therefore, there is an urgent need to construct a comprehensive evaluation method for the development quality of differential environmental active algal crusts to achieve non-destructive, quantitative, and comprehensive evaluation of the development level of algal crusts. Summary of the Invention

[0008] In view of the problems of single index, methodological limitation, and lack of environmental-biological interaction mechanism in the existing biological soil crust evaluation technology, the present invention innovatively constructs a three-dimensional index system including photosynthetic capacity, stress response, and structural morphology, and proposes a comprehensive evaluation method for the development quality of algal crusts based on the integration of multi-dimensional environmental-biological indicators. By constructing a three-dimensional index system of photosynthetic capacity, stress response, and structural morphology, combining the weight calculation of factor analysis - principal component analysis and the membership function quantification mechanism, the method realizes non-destructive and quantitative evaluation of the development level of algal crusts, overcomes the one-sidedness of traditional single-index evaluation, and provides a quantifiable ecological restoration evaluation method for desertification control.

[0009] To achieve the above object, the present invention adopts the following solutions;

[0010] A method for comprehensively evaluating the development quality of active algal crusts in different environments, specifically including the following steps:

[0011] (1) Cultivation of electroactive microalgae: Use BG11 medium as the microalgae medium; first inoculate the microalgae strain into the microalgae medium, cultivate until the stationary phase, collect the algal solution, and set aside for later use.

[0012] Preferably, the microalgae strain in step (1) is Oscillatoria filamentosa or its genetically modified strain; inoculating the microalgae strain into the microalgae medium uses the chlorophyll a concentration of the inoculated algal solution as the inoculation index, and the chlorophyll a concentration of the algal solution in the medium after inoculation is 5 μg / cm 2 ; the cultivation conditions are light intensity of 3000 lux, temperature of 25 ± 1 °C, and light-dark cycle of 16:8 h.

[0013] (2) Inoculate the algal solution collected in step (1) on the soil surface, and obtain an algal crust sample after cultivation;

[0014] Preferably, the soil in step (2) is collected from the surface layer (1–20 cm) of the reclamation area of an open-pit coal mine, air-dried, and passed through a 2-mm sieve before being used as the inoculation soil.

[0015] Preferably, inoculating the algal solution on the soil surface in step (2) uses the chlorophyll a concentration after inoculation as the inoculation index, and the chlorophyll a concentration of the algal solution in the soil after inoculation is 5–10 μg / cm 2 .

[0016] Preferably, the cultivation conditions in step (2) are: temperature of 15–35 °C, soil water content of 0–10%, light intensity of 25–100 μmol / m 2 / s, light-dark cycle of 16–8 h, cultivation time of 7–14 days, and the thickness of the algal crust sample is 1–3 mm.

[0017] (3) Measure the multi-dimensional indexes of the algal crust sample in step (2) to obtain multi-dimensional index data; the multi-dimensional indexes include photosynthesis ability indexes, stress response indexes, and structural morphology indexes; among them, the photosynthesis ability indexes include chlorophyll a content (chl a) and Fv / Fm value, the stress response indexes include extracellular polysaccharide content, electroactivity, and malondialdehyde (MDA) content, and the structural morphology indexes include crust thickness and coverage;

[0018] Preferably, the chlorophyll a content in step (3) is measured by ethanol extraction-spectrophotometry; the Fv / Fm value is measured using a portable chlorophyll fluorometer.

[0019] Preferably, in step (3), the MDA content is measured using a kit, and the absorbance at 532 nm is calculated by the thiobarbituric acid method; the extracellular polysaccharide is measured by the phenol-sulfuric acid method to determine the polysaccharide content, and a glucose standard curve is used; the electroactivity is measured by cyclic voltammetry (CV) using an electrochemical workstation, with a scanning range of -1.2 to 1 V, and the oxidation-reduction peak current and potential are recorded.

[0020] Preferably, in step (3), for the thickness, a digital vernier caliper is used to randomly select n points to measure the thickness, and the average value is taken as the thickness of the algal crust, with the result expressed in millimeters, where n is a positive integer not less than 3; for the coverage determination, the captured image is imported into image analysis software (ImageJ 1.53), and the coverage area of the algal crust sample is extracted by the color threshold segmentation method, and the coverage percentage is calculated, with the result expressed as a percentage (%).

[0021] (4) Normalize the multi-dimensional index data in step (3) through factor analysis, and calculate the weight of each index based on the principal component analysis method;

[0022] (5) Select the corresponding membership function type according to the positive or negative correlation between each index and the development quality of the algal crust, and calculate the membership degree value of each index.

[0023] (6) Combine the weight value in step (4) with the membership degree value in step (5), and calculate the development quality index (DQI) through the weighted summation formula to realize the quantitative evaluation of the development level of the algal crust.

[0024] Preferably, the Z-score normalization formula for normalizing the multi-dimensional index data in step (4) is:

[0025] Z i =(X i -μ) / σ (Ⅰ)

[0026] where Z i represents the standardized value (Z-Score), X i is the original data value, i is a positive integer; μ is the average value of the population, and σ is the standard deviation of the population.

[0027] Preferably, the steps for calculating the weight value based on the principal component analysis (PCA) in step (4) are: First, extract the principal components with eigenvalues > 1 (the principal components with eigenvalues greater than 1 are considered to be able to explain most of the information of the original data and thus have a high retention value), and then calculate the variance contribution rate C i of each principal component, and calculate the index weight W i according to the principal component load matrix:

[0028] C i =λ i / ∑λ (Ⅱ)

[0029] Among them, λ i is the eigenvalue of the i-th principal component, and ∑λ is the sum of the eigenvalues of all principal components;

[0030]

[0031] Among them, k is the number of principal components, n is the number of indicators, and L ij is the loading value of the i-th indicator on the j-th principal component.

[0032] Preferably, in step (5), for the indicators that have a positive correlation with the development quality of algal crusts, such as chlorophyll a content (chl a), Fv / Fm value, extracellular polysaccharide content, electroactivity, crust thickness, and coverage, the ascending trapezoidal membership function is adopted:

[0033] S l =(Z i -Z min ) / (Z max -Z min ), Z min ≤Z i ≤Z max (Ⅳ)

[0034] Among them, S l is the membership value of the l-th indicator, Z i is the standardized value of the i-th indicator data among all indicators, and both l and i are positive integers; Z min and Z max are the minimum and maximum values after data standardization among all indicators;

[0035] Preferably, in step (5), for the indicators that have a negative correlation with the development quality of algal crusts, such as MDA content, the descending trapezoidal membership function is adopted:

[0036] S k =(Z max -Z i ) / (Z max -Z min ), Z min ≤Z i ≤Z max (Ⅴ)

[0037] Among them, S k is the membership value of the k-th indicator, Z i is the standardized value of the i-th indicator data among all indicators, and both k and i are positive integers; Z min and Z max are the minimum and maximum values after data standardization among all indicators;

[0038] Preferably, the formula for calculating the Developmental Quality Index (DQI) in step (6) is as follows:

[0039]

[0040] where n is the total number of indicators, W i is the weight of the i-th indicator, and S i is the membership degree value of the i-th indicator.

[0041] The present invention has at least the following beneficial effects:

[0042] The present invention breaks through the one-sidedness of single-index evaluation, innovatively constructs a three-dimensional index system including photosynthetic capacity, stress response and structural morphology, and proposes a comprehensive evaluation method for the development quality of algal crusts based on the integration of multi-dimensional environment-biological indicators. By constructing a three-dimensional index system of photosynthetic capacity, stress response and structural morphology, combining the weight calculation of factor analysis - principal component analysis and the membership function quantification mechanism, this method reveals the stress regulation law, objective weight distribution and dynamic quantification model, avoids subjective deviation, realizes the non-destructive and quantitative evaluation of the development level of algal crusts, and provides a quantifiable ecological restoration evaluation method for desertification control. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 shows the chlorophyll a content of active algal crusts under different culture conditions in Example 2.

[0044] Figure 2 shows the Fv / Fm of active algal crusts under different culture conditions in Example 2.

[0045] Figure 3 shows the MDA of active algal crusts under different culture conditions in Example 2.

[0046] Figure 4 shows the extracellular polysaccharide of active algal crusts under different culture conditions in Example 2.

[0047] Figure 5 shows the electroactivity of active algal crusts under different culture conditions in Example 2.

[0048] Figure 6 shows the thickness and coverage of active algal crusts under different culture conditions in Example 2.

[0049] Figure 7 shows the characteristic weights of each index of algal crust development in Example 3.

[0050] Figure 8 shows the Developmental Quality Index of algal crusts in Example 3. DETAILED DESCRIPTION OF THE INVENTION

[0051] The various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be construed as a limitation on the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0052] It should be understood that the terms used in the present invention are only for describing specific embodiments and are not intended to limit the present invention.

[0053] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present invention pertains. Although the present invention only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein can also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the said documents. In case of conflict with any incorporated document, the content of this specification shall prevail.

[0054] Without departing from the scope or spirit of the present invention, various improvements and changes can be made to the specific embodiments of the present invention specification, which are obvious to those skilled in the art. Other embodiments obtained from the specification of the present invention are obvious to those skilled in the art. The specification and examples of the present invention are merely exemplary.

[0055] The microalgae used in the present invention is selected as electroactive Leptolyngbya and is purchased from the Freshwater Algae Culture Collection of the Chinese Academy of Sciences.

[0056] The culture nutrient components of BG11 medium are: K2HPO4 10 mL / L, MgSO4·7H2O 10 mL / L, CaCl2·2H2O 10 mL / L, Citric acid 10 mL / L, ammonium ferric citrate 10 mL / L, EDTANa2 10 mL / L, Na2CO3 10 mL / L, H3BO3 2.86 g / L dH2O, MnCl2·4H2O 1.86 g / L dH2O, ZnSO4·7H2O 0.22 g / L dH2O, Na2MoO4·2H2O 0.39 g / L dH2O, CuSO4·5H2O 0.08 g / L dH2O, Co(NO3)2·6H2O 0.05 g / L dH2O.

[0057] The content of malondialdehyde (MDA) is measured using a commercial kit with the catalog number BC002, purchased from Beijing Solarbio Science & Technology Co., Ltd.

[0058] Example 1:

[0059] (1) Use BG11 medium as the microalgae culture medium for cultivation, and inoculate Oscillatoria filamentosa into BG11 medium; the inoculation index is the chlorophyll a concentration in the algal solution. After inoculation, the chlorophyll a concentration in the algal solution in the medium is 5 μg / cm 2 ;

[0060] The culture conditions are as follows: temperature 25 ± 1 °C, humidity 45%, light intensity 3000 lux, light-dark ratio 16:8 h. Cultivate it for 10 days until the stationary phase to obtain the algal solution in the stationary phase for standby.

[0061] (2) Substrate preparation and inoculation: Take the surface layer (2 cm) soil of the opencast coal mine reclamation area, air-dry it and pass it through a 2-mm sieve, and use it as the inoculation soil. Weigh the inoculation soil and place it at the bottom of the square tank, lay the soil with a thickness of 5 cm, and evenly spray the algal solution in the stationary phase obtained in step (1) on the soil surface (the initial inoculation amount is based on the chlorophyll a content: 5 μg / cm 2 ).

[0062] (3) Adopt a three-factor level orthogonal design, with a total of 6 groups of treatments, and 3 replicates for each group.

[0063] The temperature is set as: 15 °C, 25 °C, 35 °C (controlled by an artificial climate chamber, ±0.5 °C);

[0064] The soil water content is set as: drought treatment (0%), 10% water content (controlled by the gravimetric method, supplemented with water daily);

[0065] The light intensity is set as: 25 μmol / m 2 / s, 50 μmol / m 2 / s, 100 μmol / m 2 / s (LED white light source, light-dark cycle 16:8 h); the culture period is 30 days, and the environmental parameters are recorded regularly and samples are taken.

[0066] The grouping of each experimental group is as follows:

[0067] A: 25 °C - 50 μmol / m 2 / s - 10% water content; B: 15 °C - 50 μmol / m 2 / s - 10% water content; C: 35 °C - 50 μmol / m 2 / s - 10% water content; D: 25 °C - 25 μmol / m 2 / s - 10% water content; E: 25 °C - 100 μmol / m 2 / s - 10% water content; F: 25 °C - 50 μmol / m 2 / s - drought.

[0068] (4) Sampling of algal crust: After cultivation, an algal crust is formed on the soil surface; use a sampler to scrape a 1-mm-thick soil-attached active algal crust with an area of 2 cm × 2 cm on the soil surface. After collection, gently rinse the algal crust with distilled water to remove the attached soil and impurities for detection.

[0069] Example 2:

[0070] The measurement steps of multi-dimensional indexes of active algal crust are as follows

[0071] (1) Each experimental group is as follows: A: 25 °C - 50 μmol / m 2 / s - 10% water content; B: 15 °C - 50 μmol / m 2 / s - 10% water content; C: 35 °C - 50 μmol / m 2 / s - 10% water content; D: 25 °C - 25 μmol / m 2 / s - 10% water content; E: 25 °C - 100 μmol / m 2 / s - 10% water content; F: 25 °C - μmol / m 2 / s - drought.

[0072] (2) Determination of photosynthetic capacity index:

[0073] The content of chlorophyll a (Chl a) is determined by ethanol extraction - spectrophotometry. Specifically, after taking 0.1 g of the sample and grinding it in liquid nitrogen, extract it in the dark with 90% ethanol for 24 h. After centrifugation, measure the absorbance at wavelengths of 665 nm and 649 nm with a violet spectrophotometer, and calculate the content according to the formula:

[0074] Chl a = 1.25(13.95 × OD665 - 6.8OD649) (Ⅶ)

[0075] Among them, Chl a is the content of chlorophyll a, and OD665 and OD649 are the absorbances at wavelengths of 665 nm and 649 nm.

[0076] Figure 1 is the content of chlorophyll a of active algal crust under different culture conditions. The figure shows the trend of the content of chlorophyll a in each experimental group changing with time.

[0077] The value of chlorophyll fluorescence parameter (Fv / Fm) is measured using a portable chlorophyll fluorometer (AquaPen AP 110 / P) on the algal crust sample that has been dark-adapted for 15 minutes.

[0078] Figure 2 is the Fv / Fm of active algal crust under different culture conditions, showing the trend of the Fv / Fm value in each experimental group changing with time.

[0079] (3) Oxidative stress measurement:

[0080] Malondialdehyde (MDA) is a product of membrane lipid peroxidation and one of the important indicators of oxidative stress. Therefore, the oxidative stress situation can be reflected by measuring MDA.

[0081] The content of malondialdehyde was determined using a commercial kit, and the absorbance at 532 nm was calculated by the thiobarbituric acid method.

[0082] Figure 3 It is the MDA of the active algal crust under different culture conditions, showing the trend of the MDA content changing with time in each experimental group.

[0083] (4) Detection of stress response index - extracellular polysaccharide (EPS) content

[0084] The pretreated algal crust was extracted in distilled water at 80 °C for 30 min, and then the extracellular polysaccharide extract was centrifuged at 5000 rpm.min -1 in a centrifuge for 10 min. The supernatant was taken to determine the extracellular polysaccharide content by the phenol-sulfuric acid method, and a glucose standard curve was used.

[0085] Figure 4 It is the extracellular polysaccharide of the active algal crust under different culture conditions, and the figure shows the trend of the extracellular polysaccharide content changing with time in each experimental group.

[0086] (5) Measuring electroactivity (EA) by cyclic voltammetry (CV),

[0087] The working electrode (glassy carbon electrode) was directly in contact with the surface of the algal crust layer to ensure full contact with the algal crust. The auxiliary electrode (platinum electrode) was directly inserted into the soil, keeping a distance of 3 cm from the working electrode to ensure that the current could flow evenly through the soil sample. The reference electrode (silver chloride electrode) was inserted into another position 1 cm away from the working electrode in the soil, maintaining electrical contact with the working electrode and the auxiliary electrode. The working electrode, auxiliary electrode, and reference electrode were respectively connected to the corresponding electrode interfaces of the electrochemical analyzer. When connecting the electrode clips, ensure that the metal parts of the electrode clips are in close contact with the metal parts of the electrodes to reduce the contact resistance. The scanning range was -1.2 to 1 V, and the oxidation-reduction peak current and potential were recorded.

[0088] Figure 5 It is the electroactivity of the active algal crust under different culture conditions, and the figure shows the trend of the electroactivity change in each experimental group.

[0089] (6) Quantification of structural and morphological indicators - algal crust thickness (ACT) and coverage (ACC):

[0090] For the thickness of algal crust, a stainless-steel core cutter (5 cm in diameter) was used to collect complete algal crust samples, ensuring a clear interface between the crust and the underlying soil. The collected algal crust samples were rinsed with distilled water to remove residual moisture, and then the thickness was measured at 5 randomly selected points using a digital vernier caliper. The average value was taken as the thickness of the algal crust, and the result was expressed in millimeters (mm).

[0091] For the coverage of algal crust, the captured images were imported into image analysis software (ImageJ 1.53). The algal crust coverage area was extracted by color threshold segmentation, and the coverage percentage was calculated. The measurement was repeated 3 times, and the average value was taken as the coverage of the algal crust, with the result expressed as a percentage (%).

[0092] Figure 6 The thickness and coverage of the active algal crust are shown, presenting the trends of the algal crust thickness and coverage over time in each experimental group.

[0093] Example 3:

[0094] I. Data normalization: Different indicators of algal crust have different dimensions and magnitudes. This difference can lead to some indicators being given excessive weight in the analysis, thus affecting the accuracy of the results. Through Z-score standardization, all indicators can be unified to the same dimension, ensuring the comparability of each indicator in the analysis.

[0095] The data normalization was preprocessed for factor analysis. The operation was as follows: The Z-score standardization was performed on the indicators obtained in Example 2 to eliminate the dimension difference. The formula is as follows:

[0096] Z i =(X i -μ) / σ (Ⅰ)

[0097] Among them, Z i represents the standardized value (Z-Score), X i is the original data value, i is a positive integer; μ is the overall average value, and σ is the overall standard deviation.

[0098] And the data applicability was verified through the KMO test (>0.6) and Bartlett's spherical test (p<0.05). KMO>0.6 is an important criterion for the data to be suitable for factor analysis because it indicates a low partial correlation between variables, and the data has good construct validity, enabling the effective extraction of meaningful factors.

[0099] After standardizing the data of each indicator according to formula (Ⅰ), the factor analysis was carried out. The test results of KMO and Bartlett are shown in the following table:

[0100]

[0101] As can be seen from the above table, the KMO test (value 0.728 > 0.6) and Bartlett's sphericity test (p-value 0.000 < 0.05) verify that the data is suitable for factor analysis. This step effectively avoids weight bias caused by differences in index dimensions and ensures the reliability of subsequent principal component analysis.

[0102] II. Data Dimensionality Reduction and Compression: Principal Component Analysis (PCA).

[0103] PCA determines the importance of principal components through eigenvalues (i.e., characteristic roots). The eigenvalue reflects the explanatory power of the principal component for the original data. The larger the eigenvalue, the stronger the explanatory power of the principal component for the data. Usually, principal components with eigenvalues greater than 1 are considered to be able to explain most of the information in the original data and thus have high retention value.

[0104] The variance contribution rate measures the contribution ratio of each principal component to the total variance and reflects the importance of the principal component in the data dimensionality reduction process. The larger the variance contribution rate, the stronger the influence of the principal component in the data. By calculating the variance contribution rate, the role of each principal component in the overall data can be clarified, providing a basis for subsequent weight calculation. Usually, a cumulative variance contribution rate of 80% - 95% can better balance the needs of information retention and dimensionality reduction.

[0105] Based on PCA for weight calculation: Extract principal components with eigenvalues > 1 and calculate the variance contribution rate C of each principal component i ,

[0106] C i = λ i / ∑λ (II)

[0107] where λ i is the eigenvalue of the i-th principal component, and ∑λ is the sum of the eigenvalues of all principal components.

[0108] As shown in the following table, by extracting principal components with eigenvalues > 1 (cumulative variance contribution rate 88.8%), this method reduces the 7 indicators in Example 2 to 2 principal components, significantly reducing the data complexity. This method dynamically adjusts the weights through the variance contribution rate, more scientifically reflecting the multi-dimensional characteristics of algal crust development.

[0109]

[0110] Calculate the comprehensive weight of the indicators according to the principal component loading matrix:

[0111]

[0112] where k is the number of principal components, n is the number of indicators, L ijis the loading value of the i-th index on the j-th principal component.

[0113] According to formula (Ⅱ) and formula (Ⅲ), the comprehensive weight results of each index of algal crust are as Figure 7 shown. Among them, the highest weight is ACT, accounting for 17.14%, and the lowest weight is Fv / Fm, accounting for 10.06%. The combined weight of photosynthesis-related indexes (Chl a, Fv / Fm) is about 24.40%, the combined weight of structure and morphology-related indexes (ACT, ACC) is 34.23%, and the combined weight of stress response-related indexes (EPS, EA, MDA) is 41.41%.

[0114] This method quantifies the relative importance of different physiological processes through comprehensive weight allocation, overcoming the defect of ignoring the interaction between indexes in traditional evaluation.

[0115] III. Improving the flexibility and accuracy of evaluation

[0116] Selecting the membership function is an essential step in quality evaluation, which can improve the flexibility and accuracy of evaluation.

[0117] Positive and negative correlations directly affect the shape and direction of the membership function, thus determining the calculation of membership degree and the accuracy of evaluation results. This distinction can better reflect the relationship between indexes and evaluation objects, improving the performance and applicability of the fuzzy system.

[0118] For indexes such as chlorophyll a content, Fv / Fm value, extracellular polysaccharide content, electroactivity, crust thickness and coverage that have a positive correlation with the development quality of algal crust, the ascending trapezoidal membership function S l :

[0119] S l =(Z - Z min ) / (Z max - Z min ), Z min ≤Z≤Z max (Ⅳ)

[0120] where S l is the membership degree value of the l-th index, Z i is the standardized value of the i-th data among each index, and Z min and Z max are the minimum and maximum values after data standardization among each index;

[0121] For the index of MDA content that has a negative correlation with the quality of algal crust, the descending trapezoidal membership function S i :

[0122] S k =(Z max - Zi ) / (Z max -Z min ),Z min ≤Z i ≤Z max (Ⅴ)

[0123] where S k is the membership value of the k-th index, Z i is the standardized value of the i-th data among the indices, Z min and Z max are the minimum and maximum values after data standardization among the indices;

[0124] The membership values of each index of the algal crust are obtained according to formulas (Ⅰ) and (Ⅳ, Ⅴ) as shown in the following table:

[0125]

[0126]

[0127] It can be seen that, compared with the traditional linear scoring, this method can more accurately describe the non-linear relationship between the indices and the quality of the algal crust, significantly improving the sensitivity and adaptability of the evaluation system.

[0128] IV. Calculate the algal crust development quality index (DQI) through the weights and membership degrees of each index:

[0129]

[0130] The algal crust quality index is obtained according to formulas (Ⅵ, Ⅴ), and the results are as Figure 8 shown. The DQI score of group A (25°C - 50 μmol / m 2 / s - 10% water content) is the highest (0.73), indicating that this condition is the most suitable for algal crust development. Further analysis reveals that too high temperature (group C at 35°C) or insufficient light (group D at 100 μmol / m 2 / s) will both cause a significant decrease in DQI, and complete drought (group F at 0% water content) has the greatest destructive effect on the algal crust.

[0131] In summary, the present invention proposes a comprehensive evaluation method for the development quality of differential environmental active algal crusts. It integrates statistical dimensionality reduction, dynamic weights, and fuzzy evaluation, overcoming the limitations of simple index superposition in traditional methods. It quantifies the synergistic effect of environmental parameters through DQI, providing a complete technical chain from index screening, data standardization to quantitative evaluation for ecological restoration, significantly improving the accuracy and engineering applicability of the evaluation of algal crust development quality.

[0132] Note: The above embodiments are only used to illustrate the present invention and do not limit the technical solutions described in the present invention; therefore, although the present specification has described the present invention in detail with reference to the above respective embodiments, those of ordinary skill in the art should understand that the present invention can still be modified or equivalently replaced; and all technical solutions and their improvements that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A comprehensive evaluation method for the development quality of active algal crusts in different environments, characterized in that It includes the following steps: (1) Cultivation of electroactive microalgae: Use BG11 medium as the microalgae medium; First, inoculate the microalgae strain into the microalgae medium, cultivate it to the stationary phase, collect the algal liquid, and set it aside for later use; (2) Inoculate the algal liquid collected in step (1) on the soil surface, and obtain an algal crust sample after cultivation; (3) Measure the multi-dimensional indexes of the algal crust sample in step (2) to obtain multi-dimensional index data; The multi-dimensional indexes include photosynthesis ability indexes, stress response indexes, and structural morphology indexes; Among them, the photosynthesis ability indexes include chlorophyll a content and Fv / Fm value, the stress response indexes include extracellular polysaccharide content, electroactivity, and malondialdehyde content, and the structural morphology indexes include crust thickness and coverage; (4) Perform normalization processing on the multi-dimensional index data in step (3) by factor analysis, and calculate the weights of each index based on principal component analysis; (5) Select the corresponding membership function type according to the positive and negative correlations between each index and the development quality of the algal crust, and calculate the membership degree values of each index; (6) Combine the weight values in step (4) with the membership degree values in step (5), and calculate the development quality index through the weighted summation formula to realize the quantitative evaluation of the algal crust development level.

2. The comprehensive evaluation method for the development quality of differential environmental active algal crusts according to claim 1, wherein The microalgae strain described in step (1) is Oedogonium filamentous or its genetically modified strain; inoculating the microalgae strain into the microalgae culture medium uses the chlorophyll a concentration of the inoculated algal solution as the inoculation index, and the chlorophyll a concentration of the algal solution in the culture medium after inoculation is 5 μg / cm 2 ; the culture conditions are a light intensity of 3000 lux, a temperature of 25 ± 1 °C, and a light-dark cycle of 16:8 h.

3. The comprehensive evaluation method for the development quality of differential environmental active algal crusts according to claim 1, characterized in that The soil in step (2) was collected from the surface layer of the reclaimed area of the opencast coal mine. After being air-dried and passed through a 2-mm sieve, it was used as the inoculated soil. The algal solution was inoculated on the soil surface with the chlorophyll a concentration after inoculation as the inoculation index. The concentration of chlorophyll a in the algal solution in the soil after inoculation was 5-10 μg / cm 2 ; The cultivation conditions were as follows: temperature 15-35 °C, soil water content 0-10%, light intensity 25-100 μmol / m 2 / s, light-dark cycle 16-8 h, cultivation time 7-14, and the thickness of the algal crust sample was 1-3 mm.

4. The comprehensive evaluation method for the development quality of differential environmental active algal crusts according to claim 1, characterized in that In step (3), the chlorophyll a content is determined by ethanol extraction-spectrophotometry; The Fv / Fm value is measured using a portable chlorophyll fluorometer.

5. The comprehensive evaluation method for the development quality of differential environmental active algal crusts according to claim 1, characterized in that In step (3), the malondialdehyde content is determined using a kit, and the absorbance at 532 nm is calculated by the thiobarbituric acid method; The extracellular polysaccharide content is determined by the phenol-sulfuric acid method to measure the polysaccharide content, and a glucose standard curve is used; The electroactivity is determined by cyclic voltammetry using an electrochemical workstation, with a scanning range of -1.2 to 1 V, and the oxidation-reduction peak current and potential are recorded.

6. The comprehensive evaluation method for the development quality of differential environmental active algal crusts according to claim 1, characterized in that In step (3), the thickness is measured at n random points using a digital vernier caliper, and the average value is taken as the algal crust thickness, and the result is expressed in millimeters, where n is a positive integer not less than 3; The coverage determination is to import the captured image into image analysis software, extract the coverage area of the algal crust sample by the color threshold segmentation method, and calculate the coverage percentage, and the result is expressed as a percentage.

7. The comprehensive evaluation method for the development quality of differential environmental active algal crusts according to claim 1, characterized in that In step (4), the Z-score normalization formula for normalizing the multi-dimensional index data is: Z i = (X i - μ) / σ (Ⅰ) Among them, Z i represents the standardized value, X i is the original data value, i is a positive integer; μ is the mean of the population, and σ is the standard deviation of the population.

8. The comprehensive evaluation method for the development quality of differential environmental active algal crusts according to claim 1, characterized in that The steps of calculating the weight value based on principal component analysis in step (4) are as follows: First, extract the principal components with eigenvalues > 1, and then calculate the variance contribution rate C of each principal component i , and calculate the index weight W according to the principal component load matrix i : C i = λ i / ∑λ (Ⅱ) where λ i is the eigenvalue of the i-th principal component, and ∑λ is the sum of the eigenvalues of all principal components; Among them, k is the number of principal components, n is the number of indicators, and L ij is the loading value of the i-th indicator on the j-th principal component.

9. The comprehensive evaluation method for the development quality of active algal crusts in different environments according to claim 1, characterized in that In step (5), for the indexes with positive correlations between chlorophyll a content, Fv / Fm value, extracellular polysaccharide content, electroactivity, crust thickness, and coverage and the development quality of the algal crust, the ascending semi-trapezoidal membership function is used: S l = (Z i - Z min ) / (Z max - Z min ), Z min ≤ Z i ≤ Z max (IV) Among them, S l is the membership value of the l-th index, and Z i is the standardized value of the data of the i-th index among all indexes. Both l and i are positive integers; Z min and Z max are the minimum and maximum values after data standardization in each index; For the index with a negative correlation between malondialdehyde content and the development quality of the algal crust, the descending semi-trapezoidal membership function is used: S k = (Z max - Z i ) / (Z max - Z min ), Z min ≤ Z i ≤ Z max (Ⅴ) Among them, S k is the membership value of the k-th index, and Z i is the standardized value of the data of the i-th index among all the indexes. Both k and i are positive integers; Z min and Z max are the minimum and maximum values after standardizing the data among all the indexes.

10. The comprehensive evaluation method for the development quality of differential environmental active algal crusts according to claim 1, characterized in that In step (6), the formula for calculating the development quality index is as follows: where n is the total number of indicators, and W i is the weight of the i-th indicator, and S i is the membership degree value of the i-th indicator.

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