RU2755309C1 - Method for managing algoremediation of water bodies - Google Patents

Abstract

FIELD: biotechnology.

SUBSTANCE: invention relates to the field of biotechnology. A method for controlling the algoremediation of water bodies is proposed. The method includes analysis of the chemical composition of water, the first introduction of an algolizant, periodic monitoring of a water body, determination of pollution factors, selection of the most significant pollutants and analysis of the ecosystem conditions of the water body based on the provisions of the multifractal dynamics of hydrobiological indicators. Fractal indices of redundancy, stability and insufficiency are calculated for the most significant pollution factors. In this case, the calculation of the change in the amount of algolizant introduced at the subsequent stages of cultivation is carried out in proportion to the degree of deviation from the stability index D₀.

EFFECT: invention provides increased efficiency of algoremediation of water bodies.

7 cl, 1 dwg, 3 tbl

Description

The technical field to which the invention relates

The invention relates to biotechnology, namely to a method for biological rehabilitation of reservoirs using cultures of microalgae, including chlorella, and relates to the technical field of recovery of eutrophic water.

State of the art

Currently, multifractal methods are actively used in a comprehensive assessment of the state of complex systems. For example, in patent No. CN106290796 A (IPC G01N 33/24, publ. 01/04/2017), the degree of soil salinity is assessed on the basis of multifractal indicators. In patents No. US5456690 A (IPC A61N 1/39, publ. 10.10.1995) it is proposed to use multifractal parameters of the cardiogram to predict heart attacks and sudden cardiac death. In patent No. CN102254095 A (IPC G06F 19/00, 23.11.2011) multifractal parameters are used to differentiate deposits of ore minerals. In patent No. US20110319784 A1 (IPC A61B 5/0476, publ. 12/29/2011), a multifractal calculation unit is described for assessing the emotional state of a person based on an encephalogram. In No. US20160106331 A1 (IPC A61B 5/04, A61B 5/16, publ. 04.21.2016) and No. WO2014176286 A1 (IPC A61B 5/0476; A61B 5/048, publ. 30.10.2014) multifractal methods are proposed to be used for analysis signals of the encephalograph and correction of the course of treatment of patients. In patent No. CN105615879 A (IPC A61B 5/0476; A61B 5/0484; G06F 19/00; G06K 9/00, publ. 01.06.2016) multifractal methods are used in polygraph algorithms. In patent No. US20190282088 A1 (IPC A61B 3/12, publ. 19.09.2019) multifractal parameters are used in retinography.

However, strains of microalgae chlorella are widely used to restore the proper functioning of aquatic ecosystems. In patents No. RU2643256 C1 (IPC A01G 33/00, C12M 1/00, publ. 31.01.2018) and No. RU2192459 (IPC C12N 1/12, C02F 3/34, publ. 10.11.2002), the technology for growing special strains of chlorella and proves the effectiveness of its use for biological rehabilitation of water bodies. Patent No. RU2677983 C1 (IPC A01B 79/02, publ. 01/22/2019) describes a method for biological reclamation of the technogenic landscape of a thermal power plant using microalgae chlorella.

There is a known method of biological rehabilitation of reservoirs with a culture of chlorella, described in patent No. CN110078217 B1 (IPC C02F 3/32; C02F9 / 14, publ. 02.08.2019), in which cultured chlorella is added to the eutrophic water pretreated with a flocculant. Moreover, the process of cultivating chlorella includes a stage at which chlorella is exposed to intense pulsed light radiation.

A known method for reducing the eutrophication of Lake Taihu water using a strain of chlorella, described in patent No. CN102145941 B1 (IPC C02F3 / 34, publ. 10.08.2011).

A known method of cultivating microalgae and a method of processing wastewater using microalgae, described in patent No. KR101549666 B1 (IPC C02F 11/04; C12N 1/12, publ. 03.09.2015). The patent discloses a culture medium suitable for cultivating chlorella in which the ammoniacal nitrogen concentration and color are reduced to 500 mg / L or less and the bacterial count is reduced by sterilization, which gives the effect of stable microalgae production. At the same time, by mixing and cultivating two types of chlorella with different cultivation temperatures, microalgae can be used to reduce water eutrophication throughout the year, even during the low temperature season and in summer.

Also known are methods for controlling the growth of green algae during their cultivation in water bodies, described in patents No. CN101363004 B1 (IPC A01K 61/00; C12N 1/12; C12R 1/89, 09.21.2011) and No. CN105875395 B (IPC C12N 1/12 , 25.01.2019, based on the creation of the algal phase, and in the invention according to patent No. CN105875395, the specific stages of the creation of the algal phase: during the preparation period, maintaining the algal phase at an early stage of cultivation and maintaining the algal phase at the middle or late stage of cultivation.

The disadvantage of the known methods is the insufficient efficiency of biological rehabilitation of water bodies due to the use of an uncontrolled amount of microalgae cultures, including chlorella, in the process of algolization, which prevents the achievement of high indicators for reducing the content of pollutants in water bodies.

Disclosure of invention

When creating the invention, the problem was solved of achieving the desired results for reducing the content of pollutants in water bodies and restoring their ecological balance in the process of algoremediation using, among other things, a planktonic strain of chlorella.

The claimed method for controlling the algoremediation of water bodies is aimed at solving the set problem and achieving a technical result consisting in increasing the efficiency of algoremediation of water bodies by regulating the amount of algolysant introduced.

The task, in contrast to the known analogs, is solved by using the optimal amount of the introduced algolysant, in particular - a suspension of the planktonic strain of chlorella (algolysant) of the appropriate quality (the number of cells per 1 ml of the introduced algolysant is from 50 to 70 million pieces) to restore the coenotic parameters water body in order to ensure the environmental safety of water use.

The method of algolization of water bodies to reduce and neutralize the factor of anthropogenic impact is based on scientific and practical research (Bogdanov N.I. "Biological rehabilitation of reservoirs", Penza, 2008), more intense than natural self-cleaning.

The method for controlling the algoremediation of water bodies consists in periodically introducing the optimally established and calculated amount of algolysant per unit area of the water surface.

When using this method, pollution factors are determined that carry the main threat to the stability of the ecosystem. The selection of the most significant pollutants is performed using the principal component analysis (PCA) using the statistical package SPSS Statistics. The amount of algolysant introduced at stages 2 and 3 is calculated in accordance with the dynamics of the content of pollutants in the water that most affect the ecological state of the water body.

The method is based on the theory of multifractal dynamics, with the help of the instruments of which the time series of concentrations of pollutants and hydrobiological indicators is analyzed as fractal objects. The order of operations for determining the optimal volumes of application of a suspension of a planktonic strain for algoremediation is selected in accordance with the achievement of a stable or unstable state by the ecological system and is determined by the degree of closeness of the fractal parameters of the time series of the components to the base values. This achieves fine control of ecosystem parameters and prevents an excessive increase in the influence of natural and man-made factors on the ecosystem.

Brief Description of Drawings

The essence of the invention is explained by the description of a specific example of execution and illustrated by the accompanying graphic materials, where in Fig. 1 shows a fractal model of dynamics in terms of ecosystem tolerance.

Implementation of a group of inventions

The theoretical basis for algoremediation is a comprehensive solution to the problems of polluted water bodies. Its scheme includes actions aimed at minimizing the content of pollutants, improving sanitary conditions, preventing the "blooming" of water with blue-green algae, and biological reclamation of higher aquatic vegetation.

The method of controlled algoremediation is based on introducing a certain amount of chlorella suspension into a water body, at a certain time, followed by constant monitoring of the dynamics of changes in the chemical composition of water and hydrobiological parameters.

The algolysant is introduced in several stages.

The first application is best done under ice. In the southern regions of the Russian Federation, if freeze-up is not established, the first stage of algoremediation should be carried out at a water temperature of up to 6 ° C. Chlorella multiplies even at this temperature. Since at a water temperature of 8-10 ° C, zooplankton develops, which can prevent the rapid spread of chlorella in the water area of the water body.

In the middle lane and in the north of Russia, the optimal time for the first application is March-May, and it is made under ice. Before the first application, to determine the required amount of algolysant, water samples are taken and the chemical composition is analyzed for the subsequent construction of a multifractal mathematical model.

After the first introduction of the algolysant, the ecological state of the water body is monitored by taking water samples at intervals of 10 to 20 days.

To achieve an optimal analysis result, it is recommended to take the following amount of water samples:

for reservoirs with a water surface area not exceeding 1 sq. km - two samples characterizing the upper and lower reaches of the reservoir;

for reservoirs with a water surface area of 1 to 2 sq. km - three samples taken in the upper, middle and lower reaches of the water body;

for reservoirs with a water surface area from 2 to 20 sq. km - one sample per 100 hectares of water surface;

for reservoirs with a water surface area of 20 to 50 sq. km - one sample per 250 hectares of water surface;

for reservoirs with a water surface area from 50 to 100 sq. km and more - one sample per 500 hectares of water surface.

Based on the analysis of the dynamics of multifractal indicators, the second introduction of the algolysant is performed. After the first introduction of the algolysant, the ecological state of the water body is monitored by taking water samples at intervals of 10 to 20 days.

The third application of the algolizant is corrective and is made on the basis of the analysis of the dynamics of multifractal indices made on the basis of a sample from the samples carried out after the second application. Algolysant dose adjustment is made based on the analysis of the response of the aquatic ecosystem. Feedback is provided by analyzing the approach-removal of the state of the hydrobiocenosis to a stable state.

It is proposed to evaluate the ecosystem response to external impact using fractal parameters of the time series of the main pollutants. Involvement of the dynamics of hydrobiological indicators in the analysis increases the sensitivity of the method and increases the accuracy of adjusting the applied dose of the algolysant.

The use of fractal indicators of techno-natural objects as initial statistical data is of significant importance, since it allows analyzing the morphology of an object in connection with a change in the parameters of the external environment. In other words, the complementarity of the hydrobiocenosis and its habitat is assessed. In this sense, a fractal is a fairly universal mathematical tool for describing the structural dynamics of the development of hydrobiocenosis, as a combined action of deterministic and chaotic processes, in which random processes are only a special case. The cross-correlation of processes in the factor state space is described by the fractal relationship:

(1)

(1)

Where: H is Hirst's constant.

It is known that relation (1) can be used to establish the direction of the operating processes by the nature of their correlations: at the values of H = 0.5, C _H = 0, we have random processes with no correlations. With values of H> 0.5, C _H <0, we have persistent (trend-stable) deterministic processes, with values of H <0.5, C _H <0, we have antipersistent (alternating) chaotic processes. It should be noted that the Hirst constant, like the fractal dimension, are quantities that characterize the structural complexity of the temporal dynamics of the analyzed parameter. It is possible to apply the Hirst constant or fractal dimension to the analysis of time series only for a series of observations of the dynamics of a parameter in time.

Thus, the projection of factor loads determined by the correlation relation (1) specifies a system of fractal equations of dynamics according to the conditions of ecosystem tolerance:

(2)

(2)

(3)

(3)

предикаты функциональной целостности экосистемы в виде противоположно направленных векторов техногенного и ресурсного компонентов;

– средневзвешенные (текущие) фрактальные показатели избыточности и дефицита факторов;

– лимитирующие фрактальные показатели устойчивости (толерантности) экосистемы; D_o – фрактальный индекс устойчивости - динамического равновесия (гомеостаза) экосистемы. Where:

predicates of the functional integrity of the ecosystem in the form oppositely directed vectors of technogenic and resource components;

- weighted average (current) fractal indicators of redundancy and deficiency of factors;

- limiting fractal indicators of ecosystem stability (tolerance); D_o - fractal index of stability - dynamic equilibrium (homeostasis) of the ecosystem.

Equations (2-3) substantively determine the direction of development of hydrobiocenosis according to the ratios of fractal measures of determinism and chaos of the constituent components and include a submodel of the "environment-object" type, (2) describing the spatio-temporal dynamics of hydrobiocenosis and a submodel of the "action-response" type, (3) describing the limits of sustainability (tolerance) of the ecosystem, i.e. limitations of the stability of its development, Table 1

Table 1 - structure of the equation of dynamics according to the conditions of ecosystem tolerance.

Levels of description of hydrobiocenosis Model types Functional A set of models of the "environment-object" type that establish cause-and-effect relationships that determine the spatio-temporal dynamics of the ecosystem Logical-functional A set of models of the “impact-response” type, establishing cause-and-effect relationships, determining the limits of stable dynamics in terms of the tolerance of the ecosystem.

In relation (2), the modules of vectors reflect the levels of technogenic load and its resource compensation, and the directions of the vectors determine the time shift (lag) between the action of technogenic and resource components. In this case, the nature of the shift is essential and determines the quality of compensation mechanisms along the feedback loop.

That is, the shift determines how strongly the biocenosis is correlated with the environment of its existence - the higher the correlation, the better the effect of compensation mechanisms, and, conversely, the loss of connection with the environment indicates a complete absence of compensation.

Equations (2-3) also explain the morphology of dynamic systems of a dissipative type, to which the biocenosis belongs. In such systems, each act of structural complication is accompanied by a reciprocal dissipative reaction, which explains the constant alternation of deterministic and chaotic processes that ensure its functional integrity due to fractality.

Fractality or large-scale invariance of the ecosystem provides the possibility of dissipation (localization) of technogenic substances of matter and energy due to the development of the structure of the ecosystem, which acts as channels for the flow of these substances. A graphical illustration of the dynamic model according to the conditions of ecosystem tolerance (2-3) and its compensatory scheme with feedback is shown in Fig. 1.

To analyze the multifractal dynamics of the development of hydrobiocenosis, pollutants are selected, the change in concentration of which most significantly affects the overall dynamics. The selection of the most significant pollutants is performed using statistical software based on statistical analysis algorithms with an extensive library of machine learning algorithms, such as SPSS Statistics.

The criteria for the controllability of the development of hydrobiocenosis and the choice of control variables are set by the ecological standardization for the indicators of the stability of the biocenosis. This is necessary to answer the question whether the hydrobiocenosis needs forced restoration, or whether it is possible to restrict oneself to the standard regulations for conducting environmental monitoring.

For ecological standardization to indicators of ecosystem sustainability, the following procedures are carried out:

consider the average value of fractal indicators that form a bias trend towards redundancy of factors

(4)

(4)

calculate the fractal index of redundancy of factors,

(5)

(5)

consider the average value of fractal indicators that form the trend of bias towards the insufficiency of factors

(6)

(6)

calculate the fractal index of factor deficiency

(7)

(7)

The analysis of the ecosystem conditions and the choice of control variables based on the results of environmental regulation are based on the analysis of the calculated data in Table 1, Table 2.

Table 2. Result of environmental regulation.

Fractal index of anthropogenic transformation of biocenosis The level of anthropogenic transformation of biocenosis

Fractal factor redundancy index

<1 Low (normative) 1 Average (maximum allowable) > 1 High (crisis) Factor deficiency fractal index

<1 High (crisis) 1 Average (maximum allowable) > 1 Low (normative)

The main control parameter of the proposed method is the increase / decrease in the amount of algolysant during subsequent applications. The calculation of the change in the amount of algolysant is made in proportion to the degree of deviation from the stability index D ₀ .

The application of the proposed invention was carried out on the basis of a hydrological system of three reservoirs: Verkhny Fermsky pond, Sredny Fermsky pond and Nizhny Fermsky pond.

The first introduction of algolizant - a suspension of microalgae chlorella (strain Chlorella vulgaris BIN with 50-60 million living chlorella cells per 1 ml) was carried out under the ice of Nizhny Fermsky pond in February 2019 in an amount of 60 liters. Before the first introduction of the algolysant, water samples are taken and the chemical composition is analyzed for the subsequent construction of a multifractal mathematical model.

The Sredniy Fermskiy pond was taken as a control reservoir, which was identical in terms of water supply, depth and size to the Nizhniy Fermskiy pond. After the algolysant was added, the water body was monitored by taking water samples for chemical and hydrobiological analysis. Water samples were taken from both ponds at intervals of two weeks from a depth of 0.5 m.

The obtained statistics of the dynamics of changes in the chemical composition of water were analyzed using statistical software that supports data analysis using the method of principal components. Using the principal component analysis (PCA) using the SPSS statistical package, the pollutants that pose the main threat to the sustainability of the ecosystem are selected, from which the group for analysis was composed.

Further calculations were made on the basis of this group of factors. If the weight of the factor is positive, then it negatively affects the ecological situation. If the weight is negative, then the factor has a positive or neutral effect on the environmental situation of the facility, since its growth leads to a decrease in the environmental load.

Using the method of principal components in the process of analyzing water samples from the Nizhny Fermsky pond, four main indicators were selected that affect the change in the ecological state: "Turbidity", "Dissolved oxygen", "Ammonium nitrogen" and "Oil products". The negative value of the "Dissolved oxygen" indicator indicates that the higher its content, the more intensive the oxidation processes of organic and inorganic ecotoxicants take place, as a result of which the level of anthropogenic transformation of the ecosystem of the Lower Fermsky pond decreased during algoremediation and corresponds to a low (normative) level.

Based on the provisions of multifractal dynamics for the main components, fractal indices of redundancy, stability and insufficiency were calculated. Based on the values of the indices, conclusions were drawn about the proximity of the aquatic ecosystem to one of the three threshold states - depression, stability and crisis. For this, the correspondence of the calculated indices to one of the classified typical scenarios for the development of the ecosystem was established, Table 3

Table 3 - Typical scenarios for ecosystem development

Typical ecosystem development scenarios Name Phase shift of load levels Phase diagram of states Typical conditions of geoecological landscapes Homeostasis (ecological optimum)

Protected reserves, reserves, unique natural complexes with no sources of anthropogenic impact. Techno-natural balance

Urbanized urban and techno-natural landscapes, recreational areas. Unstable balance with the external environment

Violation of technological norms and rules for the development of territories; Deterioration of production, including hazardous production; Violation of technologies for cleaning or utilizing used resources; Violation of regulations for storage, control and transportation of pollutants. Ecological crisis of instability (degradation)

The amount of algolysant introduced at stages 2 and 3 was calculated in accordance with the dynamics of the content of pollutants in the water that most affect the ecological state of the water body. Namely, if the fractal stability indices are less than 0.15, then the amount of algolysant or its concentration can be reduced by 25%, and if less than 0.1, then by 50%. If the stagnation index is less than 0.1, then a complex solution is needed to correct a specific component (except for algolization). If the fractal indices of excess load are less than 0.15, then the amount of algolysant or its concentration should be increased by 25%, and if less than 0.1, then by 50%. Based on a general analysis of the situation, a decision is made on additional measures of algoremediation.

The third introduction of the algolysant was corrective and was carried out during July. The algolysant dose adjustment feedback was made based on the analysis of the response of the aquatic ecosystem. Feedback was provided by analyzing the approach-removal of the ecosystem state to a stable state.

For example, as of September 2019, the ecological situation in Nizhny Fermsky pond can be assessed as favorable. The dynamics of changes in the values of the concentration of indicators has become more stable, which indicates the increased stability of the ecosystem of the Lower Fermsky pond. At the same time, such an important indicator for aquaculture as the level of dissolved oxygen in the water of the Nizhny Fermsky pond has significantly increased, the excess of which in September was 20%.

Proceeding from the fact that the fractal index of redundancy of pollution factors in the Nizhny Fermsky pond is approximately 20% less than the equilibrium value, then the power of influence on the hydrobiocenosis - the volume of algolysant introduced - can be reduced by this value, which was done during the third application.

The effective management of the parameters of the hydrobiocenosis of the Lower Fermsky pond by determining the optimal amount of algolysant was confirmed by comparing the dynamics of the indicators of the ecosystem of the Middle Fermsky pond.

At the end of the season, a general analysis of the time series of pollution and conclusions about the success of algoremediation and further actions were made.

The proposed method provides a scientific and practical apparatus and a method for calculating the amount of a suspension of a planktonic strain of chlorella required for a specific object to achieve an optimal result in reducing the concentration of pollutants in water bodies, bringing them to normal in one season and restoring the ecological balance of a water body. Thus, the efficiency of algoremediation of water bodies with the help of the added algolysant is increased.

Claims (7)

1. A method for managing the algolizant of water bodies, which provides for the natural purification of a water body by rehabilitating a hydrobiocenosis with a phased algolysant, characterized in that before the first introduction of an algolysant, an analysis of the chemical composition of water is carried out, after the first introduction of an algolysant, the water body is periodically monitored by sampling water for a chemical analysis, in the process of analysis, pollution factors are determined, among which the most significant pollutants are selected using the principal component analysis (PCA) using the SPSS statistical package and an analysis of the state of the ecosystem of a water body is carried out based on the provisions of the multifractal dynamics of hydrobiological indicators, in which for the most significant of pollution factors, fractal redundancy indices are calculated (

), stability (D ₀ ) and insufficiency (

), and the calculation of the change in the amount of algolysant introduced in the subsequent stages of cultivation is carried out in proportion to the degree of deviation from the stability index D ₀ . 2. A method for managing algoremediation according to claim 1, characterized in that the first application is carried out when the water temperature reaches 2 to 6 ° WITH. 3. A method for controlling algoremediation according to claim 1, characterized in that at least one culture of microalgae is used as an algolysant. 4. A method for controlling algoremediation according to claim 3, characterized in that the planktonic strain Chlorella vulgaris BIN is used as a culture of microalgae. 5. A method for controlling algoremediation according to claim 1, characterized in that the monitoring of the water body includes hydrobiological analysis of water samples. 6. A method for managing algoremediation according to claim 1 or 5, characterized in that the frequency of monitoring is from 10 to 20 days. 7. A method for controlling algoremediation according to claim 1 or 6, characterized in that for reservoirs with a water surface area exceeding 2 km ² , a water body is monitored on the basis of a sample of at least five water samples at the rate of 1 sample per 50 hectares.

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