A dynamic screening method for urban green plants based on multi-dimensional function trade-off

Abstract

The present application belongs to the technical field of urban ecological planning, and relates to a kind of urban green space plant dynamic screening method based on multi-dimensional function trade-off, the present application quantifies ecological function, social demand and stress resistance, combines AHP-entropy weight method, dynamically adjusts weight according to site type, combines nitrogen-fixing bacteria agent and micro-fertilizer, improves leaf nitrogen fixation rate, reduces dependence on chemical fertilizer, compared with traditional method, the present application significantly reduces green space PM 2.5 , improves winter landscape satisfaction, and solves the problem of "spring snow" by using non-flying-snow Salix matsudana and other varieties, providing a universal solution for high-heterogeneous urban environment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban ecological planning, and relates to a dynamic screening method for urban green land plants based on multi-dimensional function trade-off. BACKGROUND

[0002] Urban green land plants, as the core carrier of urban ecological system, have multiple irreplaceable functions. Green land plants can significantly alleviate the heat island effect through transpiration heat absorption and canopy shading, and can fix carbon and release oxygen. The root system can filter rainwater and reduce surface runoff, and can repair contaminated soil through salt-tolerant plants (such as tamarisk) and symbiotic microorganisms. In addition, green land plants effectively support social service functions, and diversified seasonal landscapes (such as forsythia spring flowers and ginkgo autumn leaves) can improve the mental health level of residents and provide habitats for urban biology. Through optimal configuration of vegetation, maintenance costs can be reduced and urban climate resilience can be enhanced. The screening technology of green land plants is directly related to the ecological benefits, social service functions and sustainability of green land. Plant screening needs to follow three basic principles of ecological adaptability, function orientation and landscape aesthetics to cope with the complex environmental challenges brought by urbanization. In modern cities, plant growth faces multiple stress environments, including soil pollution, heat island effect, traffic noise, human disturbance, etc. The traditional ornamental-oriented screening mode has been unable to meet the needs of contemporary cities. Current plant screening research shows a multi-disciplinary intersection, integrating knowledge from ecology, landscape design, environmental psychology and plant physiology, etc. The purpose is to improve the comprehensive service functions of green land through scientific screening, including improving air quality, regulating microclimate, promoting residents' mental health and protecting biodiversity, etc. Traditional screening relies on a single environmental adaptability index (such as salt tolerance), ignoring the coordination of ecological functions (cooling, nitrogen fixation) and social needs (landscape aesthetics), so it is imperative to build a three-level screening technology system of "function quantification-dynamic weight-module configuration". SUMMARY

[0003] To solve the above problems, the application provides a dynamic screening method for urban green land plants based on multi-dimensional function trade-off, which specifically comprises the following steps:

[0004] Step one, ecological function quantification: measure the net photosynthetic rate per unit leaf area, and obtain the cooling index according to the cooling index = 0.38 x photosynthetic rate + 4.2; collect plant leaves, measure the ANF rate of leaf nitrogen-fixing bacteria and the Fe / Mo content in the leaves, and obtain the synergistic coefficient according to the synergistic coefficient = ANF x (0.8 x [Fe] + 0.1 x [Mo]); measure the PM 2.5 adsorption capacity per unit leaf area, and obtain the dust retention capacity;

[0005] Ecological index = (cooling index / 15) + (ANF synergistic coefficient / 2) + (dust retention capacity / 10).

[0006] Preferably, the net photosynthetic rate per unit leaf area is determined by a photosynthesis apparatus.

[0007] Preferably, the ANF rate is determined by the acetylene reduction method.

[0008] Preferably, the Fe / Mo content in the leaf is detected by ICP-MS.

[0009] Preferably, the PM adsorption capacity per unit leaf area is determined by the elution and weighing method. 2.5 adsorption capacity.

[0010] Step two, social demand quantification: evaluate the ornamental value of the plant, combine the flowering period length and color saturation to obtain the visual index; according to the voting frequency and the adoption rate of improved suggestions on the public platform, obtain the participation index.

[0011] Social index = visual index x 0.6 + participation index x 0.4.

[0012] Preferably, the ornamental value is evaluated: an evaluation team consisting of 5 landscape designers and 5 horticultural experts is formed, and the plants are independently scored using a structured scoring table. The scoring table includes the following dimensions (10 points for each item).

[0013] Preferably, the flowering period length: divide the flowering days by 365 (or 366) to obtain the proportion of the plant's flowering period in the whole year.

[0014] Preferably, the color saturation: the C*ab value output by the color difference meter usually has a large range (such as 0-100+). To unify the dimension, divide the measured CS value by an empirical maximum value (for example, 100), so that its range is between 0-1.

[0015] Preferably, the visual index: the above three standardized indexes (ornamental value, standardized flowering period length, and standardized color saturation) are weighted and summed to obtain the final visual index.

[0016] Preferably, the participation index is calculated: record the number of likes / votes obtained by the plant (or the green space project containing the plant) on the official park management platform or community APP in the specified city:

[0017] Votes (V): the number of public likes / votes (such as monthly / annual).

[0018] Suggestions_Adopted (SA): the number of times that the improved suggestions proposed by the public on the configuration and maintenance of the plant are adopted.

[0019] Suggestions_Total (ST): the total number of improved suggestions proposed by the public on the configuration and maintenance of the plant.

[0020] Calculation formula: Engagement = (V / V Max Region )×0.6+(SA / ST)×0.4.

[0021] Where: V Max Region It is the highest number of votes obtained among all plants / projects in the region during the same period, and SA / ST is the rate of adoption of recommendations.

[0022] Step 3: Quantification of stress resistance: In the gradient salinity hydroponic experiment, the salt tolerance score was calculated with a relative biomass decline rate of ≤20% as the threshold; the electrolyte leakage rate was measured, and the freezing resistance score was assessed based on the leakage rate range;

[0023] Stress resistance index = (salt tolerance score + frost resistance score) / 2.

[0024] Preferably, the gradient salinity hydroponic experiment is to select healthy and uniformly growing one-year-old seedlings, wash the roots and transplant them into a hydroponic device (volume 2L, shading treatment);

[0025] Nutrient solution preparation: Hoagland's nutrient solution was used as the base, and NaCl was added to set the salt gradient: 0 (control), 50, 100, 150, and 200 mmol / L, for a total of 5 groups;

[0026] Culture conditions: temperature 25±2℃, photoperiod 12h / 12h (light intensity 200 μmol·m -2 ·s -1 ), the nutrient solution was replaced every 48 h, and the treatment was continued for 14 days;

[0027] Measurement indicators: At the end of the experiment, the aboveground dry weight of each group of plants was measured (dried at 80°C to constant weight);

[0028] Relative biomass decline rate (%) = [(control dry weight - treatment dry weight) / control dry weight] × 100%.

[0029] Calculate the salt tolerance score: if the decrease rate is ≤10%, it is 1.0; if it is 10-20%, it is 0.8; if it is >20%, it is 0.

[0030] Preferably, according to the electrolyte leakage rate (%) after low temperature stress (-20℃ treatment for 6h), the freezing resistance scoring standard is 1.0 (strong freezing resistance) when the electrolyte leakage rate is ≤20%, FS=0.7 (medium) when it is 20%-40%, and FS=0.3 (weak) when it is >40%.

[0031] Step four, comprehensive score = 0.4 x ecological index + 0.3 x social index + 0.3 x stress resistance index. The comprehensive score is used for priority ranking of plant species and allocation of community roles: the highest score (top 20%) is selected as the core species, the next highest score (specific function prominent) is selected as the functional species, and the rest is dynamically adjusted for use according to social needs, with the specific weight dynamically optimized by the AHP-entropy weight model in step five.

[0032] Step five, use the AHP-entropy weight method double weight model to automatically adjust the index weight according to the site attributes, with stress resistance weight 20%-40%, ecological function weight 30%-40%, and social demand weight 20%-50%, and input climate prediction data to dynamically increase the weight of drought tolerance / waterlogging tolerance traits.

[0033] Preferably, for industrial areas, the stress resistance weight is 40%, the ecological function weight is 40%, and the social demand weight is 20%; for residential areas, the social demand weight is 50%, the ecological function weight is 30%, and the stress resistance weight is 20%.

[0034] Preferably, extract the drought index (number of consecutive rainless days x average temperature) and the waterlogging index (number of heavy rain days x soil saturation rate) from the climate prediction data

[0035] Standardized calculation:

[0036] Drought threat degree (DT) = (drought index - regional minimum value) / (regional maximum value - regional minimum value)

[0037] Waterlogging threat degree (FT) = (waterlogging index - regional minimum value) / (regional maximum value - regional minimum value)

[0038] Drought tolerance weight increase amount: ΔWd = 0.3 x ln(1 + 10 x DT).

[0039] Wherein, DT should be an index that measures the "drought degree" (such as drought duration, soil moisture deficit, etc.), which can be converted into the weight increase amount through the formula.

[0040] Waterlogging tolerance weight increase amount: ΔWf = 0.3 x ln(1 + 10 x FT).

[0041] Wherein, FT should be an index that measures the "waterlogging degree" (such as water depth, waterlogging time, etc.), which can be converted into the weight increase amount in the same way.

[0042] Adjusted stress resistance weight = basic weight x (1 + ΔWd + ΔWf).

[0043] Step six, select species with an ecological index ≥ 0.7 (full score 1.0) and a lifespan > 10 years as core species, which bear the basic ecological stability function and account for 60% of the community area.

[0044] The high-quantitative-index species are configured according to the dominant ecological demand, the functional species need to reach the index value of the top 30% of a specific function item (such as cooling / diazotization), to ensure that the dominant ecological function is efficiently realized, as the functional species, the area of the community is 30%, and periodic replacement is allowed.

[0045] Fast-growing and seasonal species are selected as dynamic species, and are dynamically adjusted according to social demand, and the area ratio of the community is less than or equal to 10%.

[0046] The core species are evenly distributed in a grid shape, the functional species are adjacent to the pollution source / heat island area, and the dynamic species are embedded in the visual focus area in a cluster mode.

[0047] The application also provides fungus fertilizer management after plant transplanting:

[0048] In step seven, the freeze-dried bacteria powder of the salt-tolerant diazotrophic bacteria is dissolved in sterile water, and a sodium molybdate-iron citrate activating solution is added. The mass ratio of the freeze-dried bacteria powder, the sterile water and the sodium molybdate-iron citrate activating solution is 1:100:0.5 (i.e. 1 g of bacteria powder corresponds to 100 mL of sterile water and 0.5 g of activating solution). The bacteria solution is obtained by placing it in a 24-26℃, 150 rpm shaking culture for 48 hours.

[0049] Preferably, the sodium molybdate-iron citrate activating solution includes 0.05 mmol / L of sodium molybdate and 0.1 mmol / L of iron citrate based on water.

[0050] In step eight, a backpack electric sprayer (atomization particle size ≤100 μm) is used to perform the operation after the morning dew dries or in the evening (air temperature 18-25℃, humidity >60%), the spraying amount is controlled according to the plant type, the core species are 300 mL / plant, and the lower leaves are sprayed as the focus; the functional species / dynamic species are 100 mL / m 2 , and the whole plant is uniformly covered.

[0051] In step nine, within 2 hours after the bacteria agent is sprayed, the same equipment is used to spray the sodium molybdate-iron citrate activating solution, and the spraying amount is 1 / 2 of the bacteria agent.

[0052] Preferably, the pH value of the activating solution is adjusted to 6.0-6.5 to avoid iron ion precipitation.

[0053] The application has the following advantages:

[0054] Multi-dimensional quantification and dynamic weighting: the ecological functions (cooling, diazotization, dust retention), social needs (vision, participation) and stress resistance (salt tolerance, frost resistance) are comprehensively quantified, the weights are dynamically adjusted by using the AHP-entropy weight method combined with the site attributes, scientific and accurate screening is realized, and the one-sidedness of traditional methods is overcome.

[0055] Modular configuration improves comprehensive performance: Through the core species-function species-dynamic species modular configuration strategy (60%:30%:≤10%), the ecological stability, dominant function demand and social landscape dynamic response are considered, and the green PM 2.5 ability, winter landscape satisfaction and nitrogen fixation efficiency are reduced.

[0056] Microbial-plant synergistic effect: Combined with specific salt-tolerant nitrogen-fixing bacteria and molybdenum-iron micro-fertilizer activation liquid spraying technology, the leaf nitrogen fixation rate is significantly improved (Example 4 reaches 146%), the dependence on chemical fertilizers is reduced (Example 2 reduces the cost of fertilization by 35%, and Example 4 reduces the cost of soil improvement by 62%), and the adaptability of plants in stress environments such as salinity is enhanced (Example 4 survival rate increases to 92%).

[0057] Universality and high efficiency: Customized solutions are provided for high-heterogeneity urban environments (such as cold industrial areas, dust areas, waterfront areas, and coastal saline areas), and through four city verifications, while improving core functions (dust retention +37%, PM 10 -45%, cooling +3.1℃), the cost is significantly reduced (irrigation-28%, soil improvement-40~62%) and the plant survival rate is increased (89~97%), and through the application of preferred varieties (such as non-flying snow Salix matsudana), urban environmental problems such as “spring snow” are effectively solved. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be described below in a clear and complete manner. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0059] Example 1: Changchun cold industrial area (anti-freezing-dust retention dominant)

[0060] Environmental constraints: annual mean temperature 4.6℃, frozen soil depth 1.8m, PM 2.5 Annual average 78 μg / m 3 .

[0061] Function weight: stress resistance 50% (anti-freezing + pollution resistance), ecological function 40% (dust retention), social demand 10%

[0062] Modular configuration:

[0063]

[0064] Example 2: Beijing dust pollution area (dust retention-landscape synergy)

[0065] Environmental constraints: spring PM 10 peak 800 μg / m3 , community requires more color and green.

[0066] Technical measures: leaf inoculation of nitrogen-fixing bacteria, spraying 0.05 mmol / L sodium molybdate.

[0067] Effect comparison:

[0068]

[0069] Example 3: Hangzhou waterfront area (cooling-landscape dominated)

[0070] Dynamic weight adjustment:

[0071] Summer (June-September) ecological function weight rises to 60% (focus on cooling), social demand 40%;

[0072] Winter (December-February) social demand weight rises to 55% (focus on flowering).

[0073] Configuration scheme:

[0074] Core species: Cinnamomum camphora (crown shading rate 85%).

[0075] Functional species: Weeping willow (transpiration rate 8.2 mmol / m² / s, cooling 4.3℃).

[0076] Dynamic species: Summer: Lotus (visual index 0.93); Winter: Winter-flowering tea (visual index 0.88).

[0077] Example 4: Haikou coastal saline area (salt resistance-ecological restoration)

[0078] Microbial synergism:

[0079] Inoculation of salt-tolerant nitrogen-fixing bacteria, combined with 0.1 mmol / L ferric citrate

[0080] Quantitative results:

[0081]

[0082] Effect comprehensive comparison data (four cities summary)

[0083]

[0084] The above description of disclosed examples enables a person skilled in the art to implement or use the present application. Various modifications to these examples will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other examples without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these examples shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic screening method for urban green space plants based on multi-dimensional functional trade-offs, characterized in that: The following steps are involved: Step 1: Quantification of ecological functions: Measure the net photosynthetic rate per unit leaf area and calculate the cooling index according to the cooling index = 0.38 × photosynthetic rate + 4.2; collect plant leaves, measure the ANF rate of diazotrophic bacteria in the leaf space and the Fe / Mo content in the leaves, and calculate the synergistic coefficient; measure the PM per unit leaf area. 2.5 The adsorption capacity is the dust retention capacity; Ecological index = (cooling index / 15) + (synergy coefficient / 2) + dust retention score; Step 2: Quantify social needs: Evaluate the ornamental value of plants, combining the length of flowering period and color saturation to obtain a visual index; Participation is determined by recording the frequency of voting on the public platform and the adoption rate of improvement suggestions; Social index = visual index × 0.6 + participation × 0.4; Step 3: Quantification of stress resistance: In the gradient salinity hydroponic experiment, the relative biomass decline rate ≤ 20% was used as the threshold to obtain salt tolerance; the electrolyte leakage rate was measured to obtain frost resistance; Stress resistance index = (salt tolerance score + frost resistance score) / 2; Step 4: Comprehensive score = 0.4 × ecological index + 0.3 × social index + 0.3 × resilience index; Step 5: Use the AHP-entropy weight method dual-weight model to automatically adjust the indicator weights according to site attributes, with stress resistance weighting 20%-40%, ecological function weighting 30%-40%, and social demand weighting 20%-50%; Step 6: Select species with an ecological index ≥ 0.7 and a lifespan > 10 years as core species, fulfilling the basic ecological stability function and occupying 60% of the community area; select species with a cooling index in the top 30% of the index value as functional species, occupying 30% of the community area; select fast-growing species with significant seasonal changes as dynamic species, dynamically adjusted according to social needs, and occupying ≤ 10% of the community area; core species are evenly distributed in a grid pattern, functional species are adjacent to pollution sources / urban islands, and dynamic species are clustered and embedded in visual focal areas; Step 7: Mix the salt-tolerant nitrogen-fixing bacteria with sterile water, add sodium molybdate-ferric citrate activation solution, and culture at 24-26° C. and 150 rpm for 48 hours to obtain a bacterial solution; Step 8: Spray the bacterial solution, 300 mL / plant for core species, focusing on spraying the middle and lower leaves; 100 mL / m 2 , the whole plant is evenly covered; Step 9: Within 2 hours after spraying the microbial agent, use the same equipment to spray sodium molybdate-ferric citrate activation solution, with the spraying amount being 1 / 2 of the microbial agent.

2. The method for dynamic screening of urban green space plants based on multi-dimensional functional trade-offs according to claim 1, characterized in that: The sodium molybdate-ferric citrate activation solution is based on water and includes 0.05 mmol / L sodium molybdate and 0.1 mmol / L ferric citrate.

3. The method for dynamic screening of urban green space plants based on multi-dimensional functional trade-offs according to claim 1, characterized in that: The pH value of the activation solution in step nine is adjusted to 6.0-6.

5.

4. The method for dynamic screening of urban green space plants based on multi-dimensional functional trade-offs according to claim 1, characterized in that: In step 1, the net photosynthetic rate per unit leaf area was measured by a photosynthetic meter, the ANF rate of chlorophyll was measured by acetylene reduction method, the Fe / Mo content in the leaves was detected by ICP-MS, and the PM per unit leaf area was measured by elution weighing method. 2.5 Adsorption amount.

5. The method for dynamic screening of urban green space plants based on multi-dimensional functional trade-offs according to claim 1 is characterized in that: Industrial areas: stress resistance weight 40%, ecological function weight 40%, social demand weight 20%; residential areas: social demand weight 50%, ecological function weight 30%, stress resistance weight 20%.

6. The method for dynamic screening of urban green space plants based on multi-dimensional functional trade-offs according to claim 1, characterized in that: The salt tolerance scoring standard described in step 3 is 1.0 for a decrease rate of <10%, 0.8 for 10-20%, and 0 for >20%.

7. The method for dynamic screening of urban green space plants based on multi-dimensional functional trade-offs according to claim 1, characterized in that: The frost resistance scoring standard described in step 3 is 1.0 for an exudation rate of <20%, 0.7 for 20-40%, and 0.3 for >40%.

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