CN110766282A - Wetland purification capacity assessment and improvement method - Google Patents
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Abstract
The invention discloses a wetland purification capacity evaluation and improvement method, which comprises the following steps: (1) constructing a wetland purification model system based on the WASP water quality model; (2) determining model parameters; (3) checking the accuracy of the model; (4) analyzing influence factors of the wetland purification capacity; (5) and (5) improving the purification capacity of the wetland. The method comprises the steps of firstly establishing a wetland purification capacity evaluation model based on a WASP water quality prediction model, then scientifically determining each parameter of the model by combining methods of sensitivity analysis, field actual measurement, reference document inquiry, model standards and the like, and then evaluating the simulation effect of the model by utilizing Nash efficiency to scientifically and reasonably establish a wetland purification capacity evaluation model system.
Description
Technical Field
The invention relates to the field of wetland ecology, in particular to a method for evaluating and improving the purification capacity of a wetland.
Background
The lake wetland, namely the wetland formed by the lake, refers to a transition zone from land to a water body on the surface of the lake, takes hygrophytes as marks, is a substance and energy exchange channel between the lake and the surrounding environment thereof, and plays an extremely important role in the aspects of nutrient balance and biological production of the lake. Researches show that the wetland has the ecological functions of conserving water sources, regulating and storing flood, regulating climate, degrading pollution, fixing C and releasing O2, controlling erosion, protecting soil, realizing nutrient cycle, realizing biological habitat and the like.
The self-purification function of the wetland water quality refers to the capability of the wetland ecosystem for absorbing, converting and redistributing pollutants (such as eutrophication substances like nitrogen, phosphorus and the like) in a water body through the natural ecological process and the material circulation effect of the wetland ecosystem so as to purify the water body. The self-cleaning capability is an important mark for measuring the health of the wetland ecosystem, and the self-cleaning function of the wetland is the most important embodiment of the function of the wetland ecosystem.
Disclosure of Invention
The invention aims to disclose a wetland purification capacity evaluation method, which is used for evaluating the wetland purification capacity of a certain area by constructing a model, further mastering the influence of wetland plants, animals and water conservancy factors on the wetland purification capacity of pollutants and providing a theoretical basis for improving the wetland purification capacity.
The invention comprises the following steps: (1) constructing a wetland purification model system based on the WASP water quality model; (2) determining model parameters; (3) checking the accuracy of the model; (4) analyzing influence factors of the wetland purification capacity; (5) and (5) improving the purification capacity of the wetland.
And (1) selecting a WASP water quality prediction model as a prediction wetland water quality pollutant migration and transformation rule, thereby estimating the pollutant purification capacity of the wetland.
And (2) determining each parameter value in the WASP water quality model.
1. And (3) sensitivity analysis: and determining parameters which have large influence on the simulation result in the simulation process, and accordingly determining which parameters need to be studied in depth.
① sensitivity SsThe relative variation of the state variable caused by the relative variation of the inferior position parameter adopts a calculation formula of
In the formula (I), the compound is shown in the specification,
the relative amount of change in the contamination indicator j caused for a change in the parameter i;
is the value of the contamination index j { △ EiI is 1, 2, 3, 4 … … n, which is the relative change of the parameter i; { EiI-1, 2, 3, 4 … … n, denotes the value of j.
② sensitivity of calculationRepresents the sensitivity of the parameter i to the water quality variable j.
③, the sensitivity of the parameter is graded according to the table, and the parameter is divided into three categories of insensitive, more sensitive and sensitive according to the sensitivity.
The classification criteria are as follows:
| grade | Sensitivity range | Sensitivity of the |
| I | ||
| 0≤|Ss|<0.5 | Insensitivity | |
| II | 0.5≤|Ss|<1 | Is more sensitive |
| III | |Ss|>1 | Sensitivity is high |
2. Parameter value determination: the parameters which are sensitive, namely the parameters with larger influence of simulation results are determined by adopting a method of field actual measurement and local related literature reference; the parameter values with small influence degree refer to the WASP model instruction manual.
The step (3) is to verify the purification capacity of the wetland simulated by the model;
1. and bringing the parameters into a model, and simulating the wetland purification capacity by using the model to obtain the simulated purification capacity.
2. Field measurement: the purification rate is adopted to express the water purification capacity, and the actual purification capacity is evaluated by carrying out field actual measurement. The purification rate calculation formula is as follows:
J(％)＝(Con the upper part-CLower part)/COn the upper part
In the formula COn the upper part-inlet certain water quality parameter concentration value; cLower part-a water quality parameter concentration value of a reservoir core; j-purification rate.
3. And comparing the quality of the simulation result of the model through the Nash efficiency coefficient. The formula is as follows:
in the formula:
is the nth measured value of the sequence;
is the nth analog value of the sequence;
is the average value of the measured values of the sequence; n is the sequence length.
The value range of NSE is minus infinity to 1, and the closer E is to 1, the better the simulation quality is and the higher the model reliability is; e is equal to 1, the model simulation value is completely consistent with the measured value, and the error is 0; the closer E is to 0, the closer the simulation result is to the average value level of the observed value, namely the overall result of the model is credible, but the process simulation error is large; e is much smaller than 0, indicating that the simulation result of the model is not reliable.
In the step (4), other parameters are kept unchanged, a certain parameter is changed, and the influence of the certain parameter on the wetland purification capacity is evaluated;
and (5) according to the influence of a certain parameter on the purification capacity of the wetland, providing a reasonable suggestion to improve the purification capacity of the wetland.
Advantageous effects
The method comprises the steps of firstly establishing a wetland purification capacity evaluation model based on a WASP water quality prediction model, then scientifically determining each parameter of the model by combining a sensitivity analysis method, a field actual measurement method, a reference document query method, a model standard method and the like, then scientifically and reasonably establishing a wetland purification capacity evaluation model system by utilizing a Nash efficiency evaluation model simulation effect, finally simulating the influence of various parameters in the wetland on the wetland purification capacity by utilizing the established wetland purification model, and providing an optimization suggestion to provide a theoretical basis for wetland improvement.
Drawings
Fig. 1 is a flow chart of a wetland purification capacity evaluation model and an evaluation method.
Fig. 2 is a sensitivity value data map.
Fig. 3 is a diagram of the total nitrogen purging capability.
FIG. 4 is a graph showing the purification ability of total phosphorus.
FIG. 5 shows the measured and calculated values of total nitrogen.
FIG. 6 shows the measured values and calculations of total phosphorus.
FIG. 7 shows the capacity of plants to purify nitrogen and phosphorus elements.
FIG. 8 is a graph showing the effect of animal concentration (0.5-4mg/l) on the purification ability of a white lake wetland.
FIG. 9 is the effect of different depths (1m-4.5m) on wetland purification capacity.
Detailed Description
The following provides a practical application example to concretely explain how to apply the wetland purification capacity evaluation and optimization method.
The white lake is the biggest inland freshwater lake and wetland community in North China, and is called as Mingzhu in North China. The Baiyang lake wetland has rich biological resources, supports biological diversity by virtue of a huge food chain system, provides a necessary living environment for a plurality of wild animals and plants, and contains 47 aquatic plants; 54 kinds of fishes; birds of 192 species, thus called "bio-supermarkets". Therefore, abundant biological resources enable the white lake ecosystem to better purify pollutants in water and keep the water quality clear. However, the problem of eutrophication of the white lake has been aggravated by the influence of natural factors and human activities since the 70's of the 20 th century. Research shows that in the 50-60 th of the 20 th century, the water quality of the white lake is clear, and the aquatic resources are rich. After the 70 s of the 20 th century, eutrophication of water began to occur.
In recent years, due to the vigorous development of the breeding industry and the tourism industry, the quality of the white lake water is further deteriorated, and the eutrophication is more serious. According to the invention, the purification capacity of the wetland is evaluated and optimization opinions are provided by establishing a white lake wetland purification capacity model. The method comprises the following specific steps:
1. establishment of white lake wetland model
1.1 purification model of Total Nitrogen and Total phosphorus
① TN the concentration change of total nitrogen mainly comprises the following processes of (1) phytoplankton growth and inorganic nitrogen absorption, (2) phytoplankton death and respiration conversion to non-living organic and inorganic nitrogen, (3) organic nitrogen sedimentation in a granular state, (4) denitrification, (5) organic and inorganic nitrogen generated in the metabolic process of zooplankton, so the nitrogen absorption capacity of the wetland can be expressed as:
in the formula: c4Represents the concentration of chlorophyll a of phytoplankton, mu g/L; gP1Denotes the rate of phytoplankton growth, d-1；DP1Indicating the death rate of phytoplankton, d-1； ancExpressing the carbon-nitrogen ratio of phytoplankton; vs3Represents the settling velocity of the organic matter; f. ofD7Represents the proportion of dissolved organic nitrogen; d represents water depth; k is a radical of20Denotes the denitrification rate at 20 ℃ d-1；θ20Represents k20Temperature coefficient of (d); kNO3Nitrated oxygen limited half-saturation constant, mg/L; c2Represents the concentration of nitrate nitrogen, mg/L; fNH3Expressing the proportion of ammonia nitrogen generated in the metabolic process of zooplankton; fONThe proportion of organic nitrogen produced in the metabolic process of zooplankton.
② TP the change process of total phosphorus mainly comprises the following processes of (1) the phytoplankton grows to absorb inorganic phosphorus, (2) the death and respiration of the phytoplankton are converted into non-living organic and inorganic phosphorus, (3) the sedimentation of organic phosphorus in a granular state, and (4) the organic and inorganic phosphorus generated in the metabolic process of the phytoplankton.
Therefore, the clean absorption of phosphorus by the wetland can be expressed as follows according to the WASP model:
in the formula: a isPcRepresenting the carbon-nitrogen ratio of phytoplankton;
fD8represents the proportion of dissolved organic phosphorus; c8Represents the concentration of organic phosphorus;
FPOexpressing the proportion of inorganic phosphorus generated in the metabolic process of zooplankton; zPCRepresenting the phosphorus to carbon ratio of zooplankton; fOPThe proportion of organic phosphorus generated in the metabolic process of zooplankton;
1.2 model parameters
The value of the purification model parameter is very critical, and the method is related to whether the model can scientifically and accurately reflect the water quality change process and guide the practice. Whether the value of the model parameter is accurate or not needs to make the simulation result and the measured value have higher likelihood, and the actual rule of the water quality change should be reflected in the simulation process. The research firstly carries out sensitivity analysis on each parameter of the model, evaluates the influence degree of each parameter on the model, determines the parameter by adopting a method of field actual measurement and local related literature reference with large influence degree, refers to a WASP model use manual with small influence degree, carries out adjustment in a certain range, and leads the simulation value to be basically consistent with the measured value after repeated fine adjustment.
1.2.1 sensitivity analysis
The parameter sensitivity analysis is used for researching the influence of the change of the model parameters on the simulation output result of the model, so as to determine the parameters which have larger influence on the simulation result in the simulation process, determine which parameters need to be deeply researched, and which parameters are relatively unimportant and can be simplified, and determine the influence trend of the change of the parameters on the model result after the model is applied, so the sensitivity analysis is an important link of the water quality model modeling and the model simulation process. The sensitivity analysis steps are as follows:
1) sensitivity SsThe relative variation of the state variable caused by the relative variation of the inferior position parameter is represented by the following calculation formula. The detection mode is that other parameters are kept unchanged, and the percentage of the change of the water quality variable concentration when the parameters change +/-50% is respectively calculated according to a formula.
In the formula (I), the compound is shown in the specification,
the relative amount of change in the contamination indicator j caused for a change in the parameter i;
is the value of the contamination index j { △ EiI is 1, 2, 3, 4 … … n, is ginsengA relative change amount of the number i; { EiI is 1, 2, 3, 4 … … n, which is the value of the parameter i.
2) Calculating sensitivity
Represents the sensitivity of the parameter i to the water quality variable j.
3) The parameter sensitivities are ranked according to the table. Parameters are classified into three categories, namely insensitive, sensitive and sensitive according to the sensitivity.
The classification criteria are as follows:
meter parameter sensitivity rating
4) And (3) sensitivity calculation: adjusting the variation of each parameter in the model to obtain the sensitivity value of each parameter in the variation of ± 50%, as shown in fig. 2:
as can be seen from the figure, T, TP, TN, fD7、fD8、K1C、C4、Zt、、GP1、DP1、ZPC、Zref、FOP、Vs3H is a sensitive and more sensitive parameter; the parameters have large influence on the purification model, and need to be careful when taking values, and the parameters are determined by field actual measurement and a method of referring to local relevant documents; other parameters are determined with reference to the WASP model.
1.2.2 on-site data acquisition
① test site two sampling points (first sampling point 38 degrees 56 '26' N, 115 degrees 59 '58' E and second sampling point 38 degrees 56 '47' N, 115 degrees 59 '50' E) are selected in the white lake, and the sampling points are continuously collected at the monitoring points one year and one month after another at 2018.07-2019.06.
② sampling method, in-situ measuring surface water temperature, water speed and water depth, and indoor analysis items including total nitrogen, total phosphorus and BOD.
The measurement method is as follows:
③ data were collected as follows:
sample point one:
sampling point II:
1.3 determination of parameter values
With constant adjustment, the final model parameters are shown in the following table:
1.4 cleaning model results
All the parameters are brought into a model, and the model is used for simulating the purification capacity of the white lake for total nitrogen and total phosphorus all the year round to obtain the purification capacity of the white lake for total nitrogen and total phosphorus as shown in figures 3 and 4:
the white lake has certain absorption capacity to nitrogen and phosphorus elements on the whole. The average daily absorption capacity of the white ocean starch for nitrogen elements is 3.0 percent of the total nitrogen concentration bulk value of the white ocean starch; the absorption capacity for phosphorus element is 6.84%/d of the bulk value of the total nitrogen concentration of the white lake. The absorption of nitrogen and phosphorus elements in each month of the year has a big difference, and the seasonal absorption is obvious and is characterized in that: summer > autumn > spring > winter, it is worth noting that in winter, the nitrogen and phosphorus elements in water tend to increase.
1.5 model accuracy verification
1.5.1 in-field measurements
In order to evaluate the accuracy of the model simulation, the purification rate is adopted to express the water purification capacity, and the actual purification capacity is evaluated by performing field actual measurement. The purification rate calculation formula is as follows:
J(％)＝(Con the upper part-CLower part)/COn the upper part
In the formula COn the upper part-inlet certain water quality parameter concentration value; cLower part-a water quality parameter concentration value of a reservoir core; j-purification rate.
1.5.2 accuracy verification
To quantitatively evaluate the reliability of the white lake purification ability model, the simulation accuracy between simulation and actual measurement was characterized by a nash efficiency coefficient. The nash efficiency coefficient is generally used to verify the performance of the hydrological model simulation results. The formula is as follows:
in the formula:
is the nth measured value of the sequence;
is the nth analog value of the sequence;
is the average value of the measured values of the sequence; n is the sequence length.
The value range of NSE is minus infinity to 1, and the closer E is to 1, the better the simulation quality is and the higher the model reliability is; e is equal to 1, the model simulation value is completely consistent with the measured value, and the error is 0; the closer E is to 0, the closer the simulation result is to the average value level of the observed value, namely the overall result of the model is credible, but the process simulation error is large; e is much smaller than 0, indicating that the simulation result of the model is not reliable.
1.5.3 validation results
The reliability of the model is evaluated by using the nash efficiency coefficient, the nash efficiency coefficient between the measured value and the simulated value of the total nitrogen purification model and the total phosphorus purification model is-0.54 and 0.615, which indicates that the more the simulation result is close to the average value level of the observed value, namely the reliability of the overall result of the model, and the measured value and the calculated value are shown in fig. 5 and 6:
2. model analysis
2.1 Effect of aquatic plants on Water quality
In order to research the influence of the aquatic plants on the purification capacity of the wetland, the purification capacity of the annual plants on nitrogen and phosphorus elements is simulated.
The simulation results are shown in FIG. 7:
as can be seen from the above figure, the water plants have the highest purification capacity for nitrogen and phosphorus depending on the season, August and February, and the water plants are shown to release nitrogen and phosphorus elements into water from the middle of May to the middle of May of the next year, and are shown to be absorbed from the middle of May to the middle of May. This is because the aquatic plants in the wetland need a lot of nutrient elements during the growth process, and these plants absorb N, P and other nutrient elements in the water body and fix them as a part of their own tissues to achieve the purpose of water purification, while the phytoplankton breathing death releases organic substances into the water body. The growth rate of the plants is greater than the death rate and shows a state of absorbing nitrogen and phosphorus elements, and the growth rate of the plants is less than the death rate from the middle of January to the middle of November and shows a state of releasing nitrogen and phosphorus elements.
2.2 influence of zooplankton on Water quality
Under the condition that other conditions are not changed, changing the concentration parameter of zooplankton, researching the influence of the concentration of animals (0.5-4mg/l) on the purification capacity of the lake wetland, and predicting the change of the purification capacity of the lake as shown in figure 8:
from the above figure, it can be seen that: the purification capacity of the wetland to nitrogen and phosphorus elements is reduced along with the increase of the concentration of zooplankton. This is because a large number of aquatic animals excrete pollutants into water through metabolism, increasing the nitrogen and phosphorus content of the wetland.
2.3 Effect of depth on purification Capacity
Changing the depth parameter of the model without changing other conditions, simulating the influence of different depths (1m-4.5m) on the purification capacity of the wetland, wherein the simulation result is shown in figure 9:
as can be seen from the figure: the purification capacity of the wetland for nitrogen and phosphorus is reduced along with the increase of the depth, the time required for precipitation is increased mainly because of the increase of the depth, and under the same retention time, the precipitation efficiency of suspended particulate matters containing nitrogen and phosphorus is reduced, so the removal efficiency of the wetland for nitrogen and phosphorus is reduced. Therefore, the smaller the depth is, the better the purification capacity of the wetland is, and the removal efficiency of the pollutants is increased more and more along with the reduction of the depth.
3. Advising
3.1 planting Cold-resistant aquatic plants
The Baiyangjiang wetland aquatic plants mainly comprise reed and cattail, and the growth period of the Baiyangjiang wetland aquatic plants is about 4-11 months, so the wetland purification effect is shown as releasing nitrogen and phosphorus elements into water from the middle ten days of the eleventh month to the middle ten days of the next year, and is shown as absorbing nitrogen and phosphorus elements from the middle ten days of the march to the middle ten days of the eleventh month. Therefore, in order to improve the purification effect of the wetland in cold seasons, the aquatic plant with strong cold resistance, such as evergreen aquatic iris, is particularly suitable for cold climates, can keep evergreen and tillere at the low temperature of-9 ℃, and can play an excellent role in absorbing water pollutants throughout the year even in the season of rising winter due to vigorous metabolism of the evergreen aquatic iris. It can be mixed with other aquatic plants.
3.2 timely salvaging aquatic plants
From the above studies, it is known that the lakeflower has a certain purifying effect, and is characterized in that the absorption of nitrogen and phosphorus is mainly performed from middle of March to middle of December, and the release of nitrogen and phosphorus is mainly performed from middle of December to middle of March of the next year. The white ocean lake is a semi-closed shallow water type grass-algae combined lake, and large aquatic plants in the lake are distributed in a large area and in a wide range. If the seeds are not fished and harvested in time, the dissolved oxygen in the water is consumed through microbial decomposition and decay, so that the water body is anoxic, and the anoxic environment will inevitably pollute the water body in the water. Therefore, the aquatic plants are timely salvaged before 11 middle of the month to avoid water body pollution.
3.3 stocking filter feeding benthonic animals
Common filter feeding animals include silver carp, bighead carp, river snail, mussel, loach and the like. Filter feeding benthonic animals ingest zooplankton in water in a filtering manner, and they use gills and teeth in the mouth as a filter screen to filter small-sized zooplankton by sucking and spitting water, thereby controlling the content of zooplankton and controlling the deterioration of water quality caused by the release of endogenous nutrients.
3.4 stocking mollusks
The snail, the freshwater mussel and the like are common freshwater benthonic animals in China, the feeding habits of the snail, the freshwater mussel and the like are wide, and higher plants, algae, bacteria, small animals and dead bodies or rotten debris of the small animals are taken as food, so that the concentration of zooplankton can be effectively controlled. Meanwhile, filter feeding benthonic animals and soft animals are main consumers, are combined with algae and bacteria microorganisms to form a main biological community and a nutrition structure of the composite ecological system, remove pollutants in eutrophic water through interdependence, and maintain water ecological balance.
Claims (4)
1. A wetland purification capacity evaluation and improvement method is characterized by comprising the following steps:
(1) constructing a wetland purification model system based on the WASP water quality model;
(2) determining model parameters;
(3) checking the accuracy of the model;
(4) analyzing influence factors of the wetland purification capacity;
(5) a proposal for improving the purification capacity of the wetland;
the step (1) selects a WASP water quality prediction model as a prediction wetland water quality pollutant migration and transformation rule, so as to estimate the pollutant purification capacity of the wetland;
determining each parameter value in the WASP water quality model in the step (2):
and (3) sensitivity analysis: determining parameters which have great influence on the simulation result in the simulation process, and accordingly determining which parameters need to be studied deeply:
① sensitivity SsThe relative variation of the state variable caused by the relative variation of the inferior position parameter is as follows:
in the formula (I), the compound is shown in the specification,
the relative variation of the pollution index j caused by the variation of the parameter i;
is the value of the contamination index j { △ EiI is 1, 2, 3, 4 … … n, which is the relative change of the parameter i; { EiI ═ 1, 2, 3, 4 … … n, the value of j is denoted;
② sensitivity of calculation
Represents the sensitivity of the parameter i to the water quality variable j;
③ grading the sensitivity of the parameters according to the table, and classifying the parameters into insensitive, sensitive and sensitive types according to the sensitivity;
parameter value determination: the parameters which are sensitive, namely the parameters with larger influence of simulation results are determined by adopting a method of field actual measurement and local related literature reference; the parameter values with small influence degree refer to the WASP model instruction manual.
2. The wetland purification capacity evaluation and improvement method according to claim 1, wherein the step (3) is used for verifying the purification capacity of the wetland simulated by the model;
bringing each parameter into a model, and simulating the wetland purification capacity by using the model to obtain simulated purification capacity;
field measurement: the purification rate is adopted to express the water purification capacity, and the actual purification capacity is evaluated by performing field actual measurement according to the expression:
the purification rate calculation formula is as follows:
J(％)＝(Con the upper part-CLower part)/COn the upper part
In the formula COn the upper part-inlet certain water quality parameter concentration value; cLower part-a water quality parameter concentration value of a reservoir core; j-purification rate.
The Nash efficiency coefficient is used for comparing the quality of the simulation result of the model, and the formula is as follows:
in the formula:
is the nth measured value of the sequence;
is the nth analog value of the sequence;
is the average value of the measured values of the sequence; n is the sequence length;
the value range of NSE is minus infinity to 1, and the closer E is to 1, the better the simulation quality is and the higher the model reliability is; e is equal to 1, the model simulation value is completely consistent with the measured value, and the error is 0; the closer E is to 0, the closer the simulation result is to the average value level of the observed value, namely the overall model result is credible, but the process simulation error is large; e is much less than 0, indicating that the simulation results for the model are not authentic.
3. The wetland purification capacity evaluation and improvement method according to claim 1, wherein in the step (4), other parameters are kept unchanged, one parameter is changed, and the influence of the one parameter on the purification capacity of the wetland is evaluated.
4. The wetland purification capacity evaluation and improvement method according to claim 1, wherein in the step (5), a rationalization proposal is made to improve the purification capacity of the wetland according to the influence of a certain parameter on the purification capacity of the wetland.
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