Method for dividing risk grade of green land plant around water area based on penetration rain nutrient enrichment characteristics, configuration screening method and system

Abstract

This invention discloses a method, configuration screening method, and system for classifying the risk level of green space plants around water bodies based on the nutrient enrichment characteristics of plants through-rain. The invention obtains the flux enrichment multiples of total nitrogen and total phosphorus (TN) of target plants during precipitation events, and classifies plants into five risk levels based on a comparison of these flux enrichment multiples with preset thresholds: low-risk (both nitrogen and phosphorus), nitrogen-controlled preferred, phosphorus-controlled preferred, medium-risk (both nitrogen and phosphorus), and high-risk (both nitrogen and phosphorus). Furthermore, candidate plants are differentiated and spatially configured according to the risk level, transforming ecological observation indicators into engineering decision-making basis for source environmental risk prevention and control. This achieves a shift from passive management to proactive prevention, providing a quantitative and operable technical solution for reducing the nitrogen and phosphorus input load of green space systems on water bodies and preventing eutrophication.

Description

Technical Field

[0001] This invention relates to the fields of ecological environment engineering, forestry and landscaping planning and eutrophication control of water bodies, specifically a risk level classification method, configuration screening method and system for green space plants around water bodies based on the nutrient enrichment characteristics of penetrating rainwater. Background Technology

[0002] Rainfall is a crucial pathway for water and nutrient input into terrestrial ecosystems. When rainfall passes through the plant canopy, a complex redistribution process occurs, resulting in throughfall, stem flow, and canopy interception. Studies show that throughfall is not only an important component of water input, but its chemical composition is also significantly altered due to its interaction with the canopy. The concentrations of nutrients such as carbon, nitrogen, and phosphorus, as well as various ions and organic matter, in throughfall are typically significantly higher than in atmospheric rainfall. This is because, on the one hand, rainfall can wash away atmospheric dry deposition pollutants and plant secretions adsorbed on the canopy surface; on the other hand, ions from the canopy tissue itself may also be exchanged or leached out. This "enrichment effect" makes vegetated areas, especially under forests, "hotspots" for high-concentration deposition of substances.

[0003] From the perspective of eutrophication control, these hotspots pose a potential risk of non-point source pollution. High concentrations of substances, especially essential elements required by organisms, migrate with surface runoff and interflow, potentially entering nearby lakes, rivers, and reservoirs. This can exacerbate the load on limiting nutrients such as nitrogen and phosphorus, promote the overgrowth of algae and other aquatic organisms, and disrupt the aquatic ecological balance. Traditional green space planning around water bodies focuses primarily on landscape aesthetics, soil and water conservation, and ecological adaptability, with less quantitative consideration of the "amplification" effect of plants on rainfall nutrient input. Therefore, there is an urgent need for a technical method that can quantify the ability of different plants to accumulate nutrients through rainwater and guide plant selection accordingly, in order to reduce nutrient input at the source of the landscape. Summary of the Invention

[0004] In view of this, the present invention provides a method, configuration screening method and system for classifying the risk level of plants in green spaces around water bodies based on the nutrient enrichment characteristics of throughfall. The method aims to quantify the potential risk of different plants exporting nutrients to the environment through throughfall, and to provide a scientific and quantitative basis for screening plants with "low nutrient export risk" in green spaces around water bodies, thereby reducing the nitrogen and phosphorus load entering the water body from the planning source and synergistically realizing the ecological function of green spaces and the protection of water bodies.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, this invention discloses a method for classifying the risk level of green space plants around water areas based on the nutrient enrichment characteristics of throughfall, comprising the following steps: S1. Obtain the flux enrichment factor of one or more nutrients of the target plant in at least one precipitation event, wherein the flux enrichment factor is the ratio of nutrient flux in the through-rain to the corresponding nutrient flux in atmospheric precipitation. S2. Based on the flux enrichment multiples of at least two of the nutrients, classify the target plant into risk levels.

[0007] As a further aspect of the present invention: in step S1, the nutrients include at least one of total nitrogen, dissolved total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus, dissolved total phosphorus, soluble organic carbon, and total organic carbon; Preferably, the nutrients are total phosphorus and total nitrogen.

[0008] As a further aspect of the present invention: step S2 specifically comprises: A first high-risk threshold and a first low-risk threshold are set for total phosphorus, and a second high-risk threshold and a second low-risk threshold are set for total nitrogen. Based on the comparison between the enrichment factor of total nitrogen flux and total phosphorus flux of the target plant and their respective thresholds, they are classified into risk levels including at least one of the following: Nitrogen and phosphorus dual low-risk type: The flux enrichment factor of TN is not greater than the second low-risk threshold, and the flux enrichment factor of TP is not greater than the first low-risk threshold. Nitrogen control optimization type: The flux enrichment factor of TN is not greater than the second low-risk threshold, and the flux enrichment factor of TP is greater than the first low-risk threshold. Phosphorus control optimization type: The flux enrichment factor of TP is not greater than the first low-risk threshold, and the flux enrichment factor of TN is greater than the second low-risk threshold. High-risk nitrogen and phosphorus dual-risk type: The flux enrichment factor of TN is higher than the second highest risk threshold, and the flux enrichment factor of TP is higher than the first highest risk threshold; Nitrogen and phosphorus dual medium-risk type: The flux enrichment factor of TP is greater than the first low-risk threshold and not greater than the first high-risk threshold, and the flux enrichment factor of TN is greater than the second low-risk threshold and not greater than the second high-risk threshold.

[0009] Furthermore, the first low-risk threshold and the second low-risk threshold are preferably the 40% to 50% percentile values ​​of the corresponding nutrient flux enrichment factor dataset; the first high-risk threshold and the second high-risk threshold are preferably the 80% to 90% percentile values ​​of the corresponding dataset. Specific values ​​can be adaptively adjusted according to the regional water protection targets, environmental background characteristics, and data distribution.

[0010] As a further aspect of the present invention: the flux enrichment factor is calculated according to the following formula:

[0011] in, This represents the flux enrichment factor for the j-th nutrient during the i-th precipitation event; This represents the j-th nutrient flux that enters the Earth's surface through penetration rain during the i-th precipitation event, expressed in kg·km². -2 ; This represents the flux of the j-th nutrient type input to the Earth's surface via atmospheric precipitation during the i-th precipitation event, expressed in kg·km². -2 ; This represents the mass concentration of the j-th nutrient in the rainwater during the i-th precipitation event, expressed in mg·L⁻¹. -1 ; This represents the mass concentration of the j-th nutrient in atmospheric precipitation during the i-th precipitation event, expressed in mg·L⁻¹. -1 ; This represents the depth of rain collected during the i-th precipitation event, measured in mm. This represents the depth of atmospheric precipitation collected during the i-th precipitation event, in mm.

[0012] It should be noted that the i-th precipitation event is a flexible time unit definition, the core of which is to ensure that each assessed sample represents a relatively independent precipitation-throughfall-nutrient output process. In practice, one of the following two commonly used methods can be adopted depending on the research objectives and observation specifications: (1) Independent precipitation event method: Two rainfall events with an interval of more than a certain time (e.g., 6 hours) as defined by meteorology are regarded as independent i-th and i+1-th precipitation events, and samples are collected and measured separately.

[0013] (2) Combined sampling period method: In practice, multiple rainfalls occurring on the same day, or rainfalls occurring over several consecutive days (such as a natural week or ten days), can be combined into a single sampling period for cumulative collection as needed. In remote areas or scenarios where samples cannot be retrieved in a timely manner, the sampling period can be further extended to a month or quarter.

[0014] Regardless of how the sampling period is defined (daily, weekly, monthly, quarterly, or a specific continuous rainfall period), it is treated as a single "i-th precipitation event" during data processing. Samples that cannot be delivered for testing immediately must be stored at low temperature (refrigerated or frozen) and protected from light to prevent sample deterioration and ensure the accuracy of test results.

[0015] Regardless of the method used, the key is to ensure that the same event i in the formula is transmitted through rain samples. Comparison with atmospheric precipitation control samples Completely corresponding in time. The flux enrichment factor. The calculations are based on the flux corresponding to this event, ensuring that the assessment results reflect the net enrichment effect of the plant canopy on nutrients during this (or period) of rainfall.

[0016] Secondly, this invention discloses a method for selecting plant configurations for green spaces around water areas based on the nutrient enrichment characteristics of plants through rainwater, comprising the following steps: A. Using the risk level classification method described above, determine the risk level of one or more candidate plants; B. Select candidate plants based on the risk level for differentiated configuration of green spaces around water areas.

[0017] Based on the risk levels defined in this invention, candidate plants are subjected to graded screening and differentiated spatial configuration, with the following specific principles: Tiered screening: Plants with low nitrogen and phosphorus risk are the mandatory first choice for water-sensitive areas; plants with nitrogen / phosphorus control are the conditional choice, which should be combined with the water sensitivity attributes and supporting measures; plants with medium / high nitrogen and phosphorus risk should be avoided in principle, or their use should be strictly restricted in areas far away from water bodies.

[0018] Gradual configuration: Based on the stringency of water body protection requirements, green spaces are divided into different risk control zones (such as waterfront areas, buffer zones, and background areas) to form a spatial layout of "low-risk near water and high-risk far water".

[0019] Collaborative decision-making: Taking the aforementioned risk management as the core constraint, and on this basis, taking into account other objectives such as landscape and ecology, a final optimized solution is formed.

[0020] Based on the above principles, this invention directly transforms quantitative risk assessment results into specific engineering decision-making basis for guiding the spatial configuration of green plants around water areas.

[0021] As a further aspect of the present invention, it also includes step C: establishing a plant screening database, wherein the database stores at least the identification information of each plant, the flux enrichment factor (and related source data) calculated as described above, and the corresponding risk level or risk type.

[0022] As a further aspect of the present invention: In step B, plants of different risk types are configured in a differentiated manner, taking into account the characteristics of the water body and the nutrient control targets, as well as the climate, soil and landscape requirements around the water area.

[0023] Thirdly, this invention discloses a system for classifying the risk level of green space plants around water areas based on the above-mentioned method and configuration screening method based on the nutrient enrichment characteristics of plants through-rain, characterized in that it includes: The data input module is used to acquire or receive plant identification information and observation data; The risk assessment module is used to output the risk level or risk type of the plant based on the flux enrichment factor; The screening and output module is used to generate low-risk plant screening recommendations or configuration schemes based on the risk level or risk type.

[0024] As a further aspect of the present invention, it also includes a database module for structured storage of plant identification information, observation data, flux enrichment multiples, and risk levels or risk types, and provides data support for the risk assessment module and the screening and output module.

[0025] To efficiently implement the above-mentioned plant screening method and achieve unified management and intelligent application of multi-source and heterogeneous data, this invention further proposes to construct a dedicated "Plant Through-Rain Nutrient Enrichment Characteristic Database" (hereinafter referred to as the "Database"). This database serves as the preferred data support platform for this screening method, and its construction and query methods are as follows: 1. Database Construction The database is a structured collection designed to systematically record various parameters related to nutrient enrichment through rainwater in plants. One or more core data records are created for each plant species being evaluated, and each record contains the following main fields (modules): Basic plant information module: Species name (including Chinese name and Latin name), life form (tree, shrub, herb, etc.), family and genus, and typical pictures (optional).

[0026] Observation information module: observation location (latitude and longitude, habitat type), observation period (start and end dates), observation frequency (number of sessions, day, week, etc.), climate zone; plant height, diameter at breast height, crown width, and canopy closure of the observed plants.

[0027] Hydrological Parameters Module: Atmospheric Precipitation (V) BP (mm), penetration rainfall (V) TF (mm), average penetration rate (TF, %), average canopy interception rate (CI, %), and trunk stem flow rate (SF, %).

[0028] Nutrient Concentration Module: Stores measured rainwater nutrient concentration data, including at least total nitrogen (TN) and total phosphorus (TP) concentrations. It can be expanded to include indicators such as ammonia nitrogen (NH3-N), nitrate nitrogen (NO3-N), dissolved total nitrogen (DTN), dissolved total phosphorus (DTP), and soluble organic carbon (DOC). Each concentration data point is associated with its corresponding sampling event (i) and nutrient type (j).

[0029] Nutrient enrichment feature module: This is the core derived data field, storing the calculated nutrient enrichment feature parameters, including: Concentration enrichment factor: calculated by session or time period.

[0030] Flux enrichment factor: calculated per session or time period.

[0031] Time-scale integration values: such as monthly average, growing season average, or cumulative flux enrichment factor over a selected observation period.

[0032] Metadata and source module: Records the measurement method, instrument, detection limit, data source (such as "measured in this project", "cited from literature [number]"), data quality label, etc., to ensure the traceability and reliability of the data.

[0033] Databases can be built using database management systems (such as MySQL and PostgreSQL), and standardized data dictionaries and entry standards should be established to ensure data consistency and comparability.

[0034] 2. Database querying and application The database provides a query interface that supports the following intelligent filtering in the planning of green spaces around water areas: Combined query conditions: Users can combine query conditions according to their planning needs, for example: Living type = 'tree' AND observation location like '%East China%' Total nitrogen flux enrichment factor (EC~F_TN~) < 2.0 AND total phosphorus flux enrichment factor (EC~F_TP~) < 3.0 Risk type = 'Low nitrogen and phosphorus risk' Spatial and temporal filtering: Supports filtering data by geographical region (such as river basin, province or city) or observation year to obtain the most representative localized data.

[0035] Visualization and Export: Query results can be visualized in the form of lists, charts (such as scatter plots of a two-dimensional risk matrix), and can be exported as reports to directly support planning and design schemes.

[0036] By querying this database, planners can quickly obtain quantitative risk information for candidate plants, thereby efficiently executing the screening steps described in the claims of this invention. The construction of this database makes it possible to move from decentralized observation to centralized application, and from single cases to the summarization of patterns, providing crucial technical support for the large-scale, standardized promotion of this method.

[0037] It should be noted that the database construction and query functions are an efficient and preferred implementation method for the screening method of this invention, and not an essential technical feature of this invention. The core of this invention lies in the plant risk assessment, grading, and screening method itself.

[0038] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention is the first to creatively transform the ecological observation indicator of "nutrient enrichment characteristics of throughfall" into an engineering decision-making indicator for source environmental risk prevention and control. It discovers a neglected technical problem in existing green space planning (plants may exacerbate non-point source pollution) and proposes a completely new solution (quantitative screening based on ECF values), which is fundamentally different from existing water remediation and non-point source pollution control methods in terms of inventive concept.

[0039] 2. This invention provides a complete and operable set of technical steps (parameter acquisition - threshold setting - application screening), solving the technical problem of how to quantify abstract ecological risks and apply them to specific engineering practices. Its core lies in the acquisition and application logic of the nutrient enrichment multiple of plant-penetrated rain relative to atmospheric rainfall, which is not common knowledge or conventional method in this field.

[0040] 3. This method shifts the control of eutrophication from passive treatment to proactive prevention, with clear technical effects. By applying this method, planners can proactively avoid selecting plants with high potential for rainwater nutrient output, thereby theoretically and practically reducing the nutrient input load of green space systems on water bodies. This has direct and positive practical value in controlling eutrophication. This invention has been verified through field observations on more than ten types of trees, including camphor trees, and is feasible. Attached Figure Description

[0041] Figure 1 This is a flowchart of the risk level classification method of the present invention; Figure 2 This is a flowchart of the screening method of the present invention; Figure 3 This is a schematic diagram illustrating the risk screening of total nitrogen flux enrichment factor in plants in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the risk screening of total phosphorus flux enrichment factor in plants in an embodiment of the present invention. Detailed Implementation

[0042] To facilitate understanding of the present invention, a more comprehensive description will be given below with reference to specific embodiments. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0044] This embodiment takes the green space around a drinking water reservoir in the suburbs of Hefei City in the Chaohu Lake Basin and the water system green space of a landscape park in the city as the background. The method of this invention is used to conduct nutrient leaching risk assessment and configuration optimization for 14 common tree and shrub species in the green space.

[0045] Step S1: Rain Penetration Sample Collection and Basic Data Acquisition Through-rain and atmospheric precipitation control collection devices were set up at four representative locations (A, B, C, and D) to conduct on-site observations of 14 plant species, including rainwater sample collection and laboratory nutrient concentration determination.

[0046] Location A: Observation periods from December 2022 to January 2024 and from April 2024 to June 2025, totaling 212 rainfall days. Rainwater samples were collected daily from all occurrences. The tree species was camphor (Cinnamomum camphora). Cinnamomum camphora The atmospheric precipitation flux (TN) during the same period was 5167.92 kg / km². 2 TP 101.43 kg / km 2 .

[0047] Location B: Observation period: March-August 2023. Rainwater samples were collected from the main rainfall events on 23 consecutive days. Tree species included camphor and osmanthus. Osmanthus fragrans ), Photinia ( Photinia serratifolia The atmospheric precipitation flux during the same period was: TN 685.77 kg / km². 2 TP 32.77 kg / km 2 .

[0048] Location C: Observation period: April-July 2023, rainwater samples were collected from the main consecutive rainfall events on 17 rainfall days; tree species included Chinese pistache (Pistacia chinensis). Pistacia chinensis ), Liquidambar formosana ( Liquidambar formosana ), Loropetalum chinense ( Loropetalum chinense ), shrub photinia ( Photinia serratifolia ), Red Photinia ( Photinia × fraseri ), large-leaved boxwood ( Buxus megistophylla ) and holly ( Ilex cornuta The atmospheric precipitation flux during the same period was: TN 345.03 kg / km². 2 TP 39.52 kg / km 2 .

[0049] Location D: Observation period May-November 2025, rainwater samples were collected from the main consecutive rainfall events on 8 rainy days; tree species included camphor and dawn redwood. Metasequoia glyptostroboides ), Osmanthus fragrans, Cherry blossom ( Prunus serrulata ), maple ( Pterocarya stenoptera),cedar( Cedrus deodara The atmospheric precipitation flux during the same period was: TN 137.17 kg / km². 2 TP 22.19 kg / km 2 .

[0050] Regarding the duration of observations: In theory, to obtain stable and representative assessments of plant throughfall nutrient enrichment characteristics, continuous observations over at least one full hydrological year or a longer period (e.g., 2-3 years) are ideal. This helps to cover differences in seasonal climate change, rainfall type, and plant physiological cycles, thus yielding more robust flux enrichment factors.

[0051] In this embodiment, the observation periods at the four locations varied in length (from several months to over a year). This was intended to simulate and demonstrate that in actual research or engineering projects, limited by time and resources, initial assessments might be based on data from a limited timeframe. Although the observation period did not fully cover the entire year, the acquired data included multiple precipitation events of varying intensities, sufficient to preliminarily reveal significant differences and relative risk rankings among different plants in their throughfall nutrient output behavior. This demonstrates the complete process and practical application value of the assessment and screening method described in this invention.

[0052] It should be clearly pointed out that the plant risk level classification results obtained from limited-period data in this embodiment mainly serve the purpose of demonstrating the methodology. In actual engineering planning and decision-making applications, the method of this invention should be implemented based on longer-term and more representative observation data of the target area, or the short-term preliminary assessment results should be verified and revised to ensure the reliability of the risk assessment conclusions. The core of this invention lies in providing a universal methodological framework for quantitative assessment and risk classification based on flux enrichment multiples, the effectiveness of which is positively correlated with the sufficiency of data observation.

[0053] Steps S2-S3: Nutrient flux enrichment factor (ECF) calculation and classification In this embodiment, to demonstrate the risk level classification method, based on the statistical distribution characteristics of the acquired observation dataset and the general principles of ecological risk management, a threshold for the enrichment multiple of total nitrogen (TN) and total phosphorus (TP) fluxes is set.

[0054] The low-risk threshold for total nitrogen (TN) was set at 2.0: this value corresponds approximately to the 47th percentile of the TN flux enrichment factor for all observations (including different plants and locations) in the dataset of this embodiment. This means that in approximately 47% of the observation scenarios, the nitrogen output flux from plant-through-rain did not exceed twice the atmospheric precipitation flux. Setting the threshold here aims to identify plants with relatively mild and manageable increases in nitrogen output.

[0055] The high-risk threshold for total nitrogen (TN) was set at 4.0: this value corresponds approximately to the 88th percentile of the TN flux enrichment factor in the dataset. This indicates that only about 12% of the observed scenarios show nitrogen output flux exceeding four times the atmospheric input. Using this as a high-risk boundary helps to highlight individuals with abnormally high nitrogen leaching potential, requiring strict avoidance around sensitive water bodies.

[0056] The low-risk threshold for total phosphorus (TP) was set to 3.0: this value corresponds to approximately the 41st percentile of the TP flux enrichment factor in the dataset. The logic behind this setting is similar to that of the low-risk threshold for TN, aiming to screen for plants with limited increases in phosphorus output flux.

[0057] The high-risk threshold for total phosphorus (TP) was set at 6.0: this value corresponds to approximately the 82nd percentile of the TP flux enrichment factor in the dataset. Compared to nitrogen, the high-risk threshold for phosphorus corresponds to a lower percentile, which reflects that in the context of this study, the enrichment variability of phosphorus in plant-transited rainwater may be greater, or that plants are more sensitive to phosphorus enrichment from a water conservation perspective, hence the stricter high-risk threshold was set.

[0058] It is important to emphasize that the specific threshold values ​​and the quantiles upon which they are based in this embodiment are merely demonstrations of how the method of this invention sets quantitative standards based on data characteristics. In practical applications to different regions or datasets, the threshold values ​​can be adaptively adjusted according to local water protection targets, environmental background, and the specific distribution of the data. The core of this invention lies in providing a methodology for setting thresholds and performing risk classification based on flux enrichment factors, rather than a fixed threshold value itself.

[0059] Table 1: Plant ECF values, risk levels, and screening classifications in this embodiment

[0060] Steps S4-S6: Risk grading assessment of nitrogen and phosphorus dual indicators Based on the above measured data, the TN-TP risk level is classified and screened: Category I: Low-low-content preferred type ( EC F-TN ≤2.0 and EC F-TP ≤3.0), with nitrogen and phosphorus leaching fluxes close to or slightly higher than atmospheric precipitation, resulting in the lowest risk of increased nutrient source load. Selected plants: Buxus macrocarpa, with extremely low risk of nitrogen and phosphorus leaching; shrubs Photinia and Prunus serrulata. EC F-TP The risk level has increased compared to that of Buxus macrocarpa, but it remains in a low-risk area and is classified as excellent overall.

[0061] Category II: Preferred nitrogen-controlled type ( EC F-TN ≤2.0 andEC F-TP >3.0), with a low nitrogen enrichment factor, suitable for areas surrounding nitrogen-sensitive water bodies. Selected plants include camphor tree, osmanthus, photinia, and red photinia. Among them, camphor tree, osmanthus, and photinia all have a high phosphorus risk; therefore, their application should increase the distance from water bodies to reduce the transport of high-load phosphorus from throughfall into the water. Camphor tree: The TP enrichment factors at observation points A, B, and D differ, but all fall within the medium-risk TP level. This phenomenon indicates that the specific values ​​of nutrient enrichment characteristics of the same plant species in throughfall may exhibit heterogeneity due to local atmospheric environment, soil conditions, individual plant differences, and meteorological factors at different locations and observation periods. Therefore, when applying the method of this invention, it is preferable to use observation data from the target planning area or areas with similar environmental conditions for evaluation to obtain a risk assessment that better reflects the actual scenario and ensures the accuracy of the screening decision.

[0062] Category III: Preferred phosphorus-controlled type ( EC F-TN >2.0 and EC F-TP ≤3.0), low phosphorus enrichment factor, suitable for areas surrounding phosphorus-sensitive water bodies. Selected plants: Pistacia chinensis, Liquidambar formosana, Loropetalum chinense, and Cedrus deodara, all with low TP enrichment factors, with Liquidambar formosana showing extremely low values, thus classified as a preferred phosphorus-controlling plant; Cedrus deodara... EC F-TN High-risk, nitrogen-sensitive water bodies require vigilance and can be configured and used in conjunction with engineering or ecological nitrogen load offsetting measures.

[0063] Category IV: Medium-risk (4.0 ≥ EC F-TN >2.0 and 6.0≥ EC F-TP >3.0), Ilex cornuta and Acer palmatum fall into this category. Given their ECF values, they should be placed as far away from water bodies as possible, with further nitrogen and phosphorus trapping measures if necessary. Further observations can be conducted in the future to confirm their suitability.

[0064] Category V: High in both nitrogen and phosphorus ( EC F-TN >4.0 and EC F-TP >6.0), currently only dawn redwood is included in this category among the listed plants. Dawn redwood is common in riparian areas and has the highest observed nitrogen and phosphorus ECF values, indicating an extremely high risk of nitrogen and phosphorus load export.

[0065] Meanwhile, this embodiment also shows that Osmanthus fragrans exhibited low nitrogen and high phosphorus levels at location B and medium risk at location D, respectively. This is because the observation periods for both sets of data were relatively short and not entirely consistent (location A was in summer, location D in autumn), and they were not from the same plant. Summer rainfall is abundant, resulting in a stronger dilution effect on atmospheric nitrogen and a stronger scouring effect on dust, leading to lower nitrogen enrichment and higher phosphorus enrichment in throughfall. From the perspective of nutrient load source control, this invention adopts a strict principle, classifying Osmanthus fragrans as a medium-nitrogen, high-phosphorus type, not belonging to any of the above five categories, and including it in the list of key observation varieties.

[0066] It should be noted that the observation data, risk level classification thresholds, and screening results presented in this embodiment are intended to illustrate the implementation process and application effects of the method of the present invention, and do not constitute a final qualitative assessment of the listed plant ecological functions. In practical applications, the risk thresholds can be adjusted according to factors such as the environmental background and protection objectives of specific regions. Furthermore, the method of the present invention can be combined with other plant function evaluation indicators (such as dust retention capacity and transpiration water consumption) to construct a more comprehensive plant ecological function assessment and screening system.

[0067] As described above, this embodiment classifies plants into five distinct risk types to provide clear decision-making guidance. In actual assessments, the TN and TP flux enrichment factors of a very small number of candidate plants may fall outside the boundary regions of the above five types (e.g., one indicator is "low risk" while another is "medium risk," or one is "medium risk" while another is "high risk," such as Osmanthus fragrans).

[0068] Step S7: Differentiated Configuration Application Strategy Based on Nitrogen and Phosphorus Characteristics In the planning and design of green spaces around water areas, the plants are zoned and configured according to their ECF (electrochemical coefficient) characteristics: Waterfront red line area (0-10m): Strictly control nitrogen and phosphorus, and avoid high nitrogen levels (cedar, dawn redwood). ) and / or high phosphorus (Osmanthus fragrans, Photinia serratifolia, Metasequoia glyptostroboides) Type I plants (low-lying and low-lying) are recommended. Among the examples listed, Buxus macrocarpa is the best, while Photinia serratifolia and Prunus cerasifera are less suitable. EC F_TP Limited use is only possible when the distance threshold is too close.

[0069] Ecological buffer zone (10-30m): Category I dual-superior plants are unrestricted; secondly, medium-risk nitrogen and phosphorus plants (Category IV) can be appropriately configured; alternatively, Category II nitrogen-controlling plants can be selected based on the characteristics of the water body. EC F_TP Among the lower or Class III phosphorus-controlled varieties EC F_TNLower levels of phosphorus accumulation are preferred. For example, given the phosphorus-sensitive nature of Chaohu Lake, low-phosphorus-accumulating species such as Chinese pistache, sweetgum, and redbud are selected. Conversely, for nitrogen-sensitive waters, species with lower nitrogen leaching, such as camphor, cherry blossom, and red photinia, are given priority.

[0070] Background landscape area (>30m): Risk management, Category I dual-excellence type is unrestricted, Category II, III and IV refer to the ecological buffer zone plan; high nitrogen and phosphorus output plants such as osmanthus, photinia, maple and cedar can be used in a limited manner to enrich the landscape, and the soil's interception and adsorption function can be used for retention, filtration and infiltration. At the same time, other measures such as ground cover plants and infiltration strips can be combined to reduce the transfer of nutrients to water bodies.

[0071] For plants with atypical or unconfirmed risk characteristics, the principle of "risk prevention and strict management" should be followed: Prioritize avoidance: In the planning of water-sensitive areas (such as waterfront red line areas and core zones of ecological buffer zones), such plants should be avoided as much as possible because their risk characteristics are unclear and there is potential uncertainty.

[0072] Restricted use: If it is necessary to use it for landscape or ecological function purposes, it should be placed in an area far away from water bodies (such as background landscape area), and ensure that there is sufficient space between the area and the water body and / or effective ecological interception measures (such as deep soil, vegetation filter strip, infiltration ditch, etc.).

[0073] Supplementary observations and labelling: It is recommended to treat this situation as a data gap and conduct supplementary locational observations of this plant to obtain a more definitive risk classification. In databases or plant catalogues, it can be temporarily marked as "Risk Undetermined - Use with Caution".

[0074] This disposal principle embodies the fundamental purpose of the method of this invention in serving water protection, namely, to adopt a conservative strategy for uncertainty based on scientific quantitative assessment, thereby maximizing the protection of aquatic ecological security.

[0075] Although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0076] Therefore, the above description is only a preferred embodiment of this application and is not intended to limit the scope of this application; that is, all equivalent modifications made in accordance with the scope of the claims of this application shall be within the protection scope of the claims of this application.

Claims

1. A method for classifying the risk level of vegetation in green spaces surrounding water areas based on the nutrient enrichment characteristics of throughfall, characterized in that, Includes the following steps: S1. Obtain the flux enrichment factor of one or more nutrients of the target plant in at least one precipitation event, wherein the flux enrichment factor is the ratio of nutrient flux in the through-rain to the corresponding nutrient flux in atmospheric precipitation. S2. Based on the flux enrichment multiples of at least two of the nutrients, classify the target plant into risk levels.

2. The grading method according to claim 1, characterized in that, In step S1, the nutrients include at least one of total nitrogen, dissolved total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus, dissolved total phosphorus, soluble organic carbon, and total organic carbon. Preferably, the nutrients are total phosphorus and total nitrogen.

3. The grading method according to claim 2, characterized in that, Step S2 is as follows: A first high-risk threshold and a first low-risk threshold are set for total phosphorus, and a second high-risk threshold and a second low-risk threshold are set for total nitrogen. Based on the comparison between the enrichment factor of total nitrogen flux and total phosphorus flux of the target plant and their respective thresholds, they are classified into risk levels including at least one of the following: Nitrogen and phosphorus dual low-risk type: The flux enrichment factor of TN is not greater than the second low-risk threshold, and the flux enrichment factor of TP is not greater than the first low-risk threshold. Nitrogen control optimization type: The flux enrichment factor of TN is not greater than the second low-risk threshold, and the flux enrichment factor of TP is greater than the first low-risk threshold. Phosphorus control optimization type: The flux enrichment factor of TP is not greater than the first low-risk threshold, and the flux enrichment factor of TN is greater than the second low-risk threshold. High-risk nitrogen and phosphorus dual-risk type: The flux enrichment factor of TN is higher than the second highest risk threshold, and the flux enrichment factor of TP is higher than the first highest risk threshold; Nitrogen and phosphorus dual medium-risk type: The flux enrichment factor of TP is greater than the first low-risk threshold and not greater than the first high-risk threshold, and the flux enrichment factor of TN is greater than the second low-risk threshold and not greater than the second high-risk threshold.

4. The grading method according to any one of claims 1-3, characterized in that, The flux enrichment factor is calculated using the following formula: in, This represents the flux enrichment factor for the j-th nutrient during the i-th precipitation event; This represents the j-th nutrient flux that enters the Earth's surface through penetration rain during the i-th precipitation event, expressed in kg·km². -2 ; This represents the flux of the j-th nutrient type input to the Earth's surface via atmospheric precipitation during the i-th precipitation event, expressed in kg·km². -2 ; This represents the mass concentration of the j-th nutrient in the rainwater during the i-th precipitation event, expressed in mg·L⁻¹. -1 ; This represents the mass concentration of the j-th nutrient in atmospheric precipitation during the i-th precipitation event, expressed in mg·L⁻¹. -1 ; This represents the depth of rain collected during the i-th precipitation event, measured in mm. This represents the depth of atmospheric precipitation collected during the i-th precipitation event, in mm.

5. A method for selecting plant configurations for green spaces around water areas based on the nutrient enrichment characteristics of plants through-rain, characterized in that, Includes the following steps: A. Using the risk level classification method as described in any one of claims 1-4, determine the risk level of one or more candidate plants; B. Select candidate plants based on the risk level for differentiated configuration of green spaces around water areas.

6. The configuration filtering method according to claim 5, characterized in that, In step B, plants of different risk types are configured in a differentiated manner, taking into account the characteristics of the water body, the nutrient control targets, and the climate, soil and landscape requirements around the water area.

7. The configuration filtering method according to claim 5, characterized in that, It also includes step C: establishing a plant screening database, wherein the database stores at least the identification information of each plant, the flux enrichment factor calculated as described in any one of claims 1-4, and the corresponding risk level or risk type.

8. A system for performing the method for screening plant configuration in green spaces around water areas based on the nutrient enrichment characteristics of plant through-rain as described in any one of claims 1-4, characterized in that, include: The data input module is used to acquire or receive plant identification information and the flux enrichment factor of one or more nutrients; The risk assessment module is used to output the risk level or risk type of the plant based on the flux enrichment factor; The screening and output module is used to generate plant screening recommendations or configuration schemes based on the risk level or risk type.

9. The system according to claim 8, characterized in that, It also includes a database module for structured storage of plant identification information, flux enrichment multiples and risk levels or risk types, and provides data support for the risk assessment module and the screening and output module.

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