Method for plant evaluation and method for plant configuration screening for greenery around water area

Abstract

The application discloses a kind of plant evaluation methods for water area surrounding green land: S1, the dust-retention capacity parameter of candidate plant and the rain nutrient enrichment parameter of penetration are acquired;S2, plants are divided into two categories of high / low dust-retention capacity, while calculating nutrient flux enrichment multiple, and divided into two categories of high / low leaching risk according to threshold value;S3, construct two-dimensional evaluation matrix, and plants are divided into corresponding risk quadrant.The method includes two ecological processes of dust-retention and leaching into a unified quantitative framework, for the first time reveals the function coordination and trade-off relationship between air purification and water environment protection, without relying on fixed threshold value, through clustering analysis and environmental benchmark linkage to realize the dynamic division of risk quadrant, provides reproducible, migratory scientific evaluation tool for water area surrounding green land plant screening.

Description

Technical Field

[0001] This invention relates to the fields of ecological environment protection, forestry and landscaping planning, and non-point source pollution control, specifically to plant evaluation methods and plant configuration screening methods for green spaces around water bodies. Background Technology

[0002] With the acceleration of urbanization, eutrophication of water bodies has become a prominent problem threatening urban water ecological security. The input of exogenous nutrients such as nitrogen and phosphorus is a key driving factor for eutrophication. Rainfall runoff, especially surface runoff and interflow after flowing through land, is an important non-point source pollution pathway.

[0003] Existing research indicates that plant canopies play a complex role in the chemical cycle of rainfall. On the one hand, plant leaves, through their special surface structures (such as hairs, waxy layers, and grooves), can effectively adsorb and retain particulate matter (TSP, PM10, PM2.5) from the atmosphere, exhibiting dust retention capacity. This trapped particulate matter is typically rich in nitrogen, phosphorus, heavy metals, and other substances, forming a "dry-season sedimentation reservoir" attached to the canopy. On the other hand, when rainfall occurs, rainwater washes away the canopy, carrying these adsorbed substances, along with substances secreted or leached by the plants, into throughfall. This results in significantly higher concentrations and fluxes of various nutrients in throughfall compared to atmospheric rainfall, creating a so-called "nutrient enrichment effect." This makes vegetated areas "hotspots" for high-concentration nutrient deposition, potentially increasing the nutrient load entering water bodies with runoff.

[0004] Currently, research on plant functions in the fields of landscaping and ecological planning exhibits a disconnect. While there is considerable research on plants' dust-trapping capacity, aiming to select tree species with strong air-purifying abilities, research on nutrient leaching through rainwater is largely concentrated in forest ecology, focusing on nutrient cycling processes. These two areas are rarely placed within the same framework, especially when serving the specific application goal of water conservation. Traditional green space planning emphasizes landscape aesthetics, soil and water conservation, and the ecological adaptability of plants, lacking a quantitative assessment of the potential eutrophication risks to water bodies arising from this "capture-then-release" mechanism of plants.

[0005] Therefore, there is an urgent need for a scientific plant configuration that can link the dust retention capacity of plants with the nutrient enrichment effect of penetrating rainwater to guide the greening of water areas and achieve a win-win situation for air purification and water protection. Summary of the Invention

[0006] In view of this, the present invention provides a plant assessment method and a plant configuration screening method for green spaces around water bodies, aiming to quantify the potential correlation and risk between plants capturing atmospheric particulate matter and increasing nutrient input throughfall, providing a scientific basis for selecting plants that can effectively trap dust and have low risk of nutrient input to water bodies for green spaces around water bodies, and achieving synergistic optimization of ecological functions.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] In a first aspect, the present invention discloses a method for plant assessment of green spaces surrounding water areas, comprising the following steps: S1. In the area surrounding the target water area, obtain the dust retention capacity parameters and the nutrient enrichment parameters of the candidate plants through rainwater. S2. Based on the dust retention capacity parameter, the candidate plants are divided into two categories: high dust retention capacity and low dust retention capacity. Based on the through-rain nutrient enrichment parameter, the nutrient flux enrichment factor characterizing the leaching risk is calculated, and the candidate plants are divided into two categories: high leaching risk and low leaching risk based on a preset threshold. S3. Based on the dust retention capacity and leaching risk of all candidate plants, a two-dimensional evaluation matrix is ​​constructed, and the candidate plants are divided into the corresponding risk quadrants.

[0009] As a further aspect of the present invention: In step S1: The dust retention capacity parameter is the total dust retention amount per plant, or the equivalent dust retention index after normalization of leaf area and leaf biomass. The throughfall nutrient enrichment parameters include at least total nitrogen and total phosphorus flux data, used to calculate the corresponding nutrient flux enrichment factor.

[0010] As a further aspect of the present invention: In step S2, the nutrient flux enrichment factor is calculated using the following formula: =

[0011] In the formula, denoted as the flux enrichment factor for the j-th nutrient; n represents the total number of precipitation events collected within the integrated timescale (e.g., independent precipitation events, weeks, months, seasons, or years) / sampling period. Let $j$ be the flux of the $j$ nutrient carried by canopy penetration rain during the $i$-th precipitation event, expressed in kg / km². 2 ; Let $\frac{i}{j}$ be the flux of the $j$ nutrient carried by atmospheric precipitation (exotree rain) during the $i$-th precipitation event, expressed in kg / km². 2 ; The mass concentration of the j-th nutrient in the canopy penetration rain during the i-th precipitation event is expressed in mg / L. Let be the mass concentration of the j-th nutrient in atmospheric precipitation during the i-th precipitation event, expressed in mg / L. The amount of rainfall through the canopy during the i-th precipitation event is expressed in mm. Let represent the amount of atmospheric precipitation during the i-th precipitation event, expressed in mm.

[0012] As a further aspect of the present invention: In step S2: The thresholds for classifying dust retention capacity and leaching risk are determined according to at least one of the following methods: (a) Cluster analysis was performed on the dust retention capacity parameters of candidate plants, with the natural boundary point as the dividing threshold; (b) Based on the statistical distribution characteristics of the nutrient flux enrichment multiples of candidate plants, and with reference to the background value of the nutrient status of the target water body or the environmental quality standard, set the leaching risk threshold for the nutrient flux enrichment multiples.

[0013] As a further aspect of the present invention: the dust retention capacity classification threshold and the leaching risk judgment threshold are determined independently: wherein, the dust retention capacity classification threshold is determined based on the cluster analysis results of the measured dust retention amount of the whole candidate plant; the leaching risk judgment threshold is determined based on the statistical distribution characteristics of the nutrient flux enrichment multiple of the candidate plant, and with reference to the background value of the nutrient status of the target water body or the environmental quality standard, and is usually selected from the 40%-80% quantile range of the flux enrichment multiple dataset.

[0014] As a further aspect of the present invention: in step S3, the risk quadrants include: high dust retention capacity - low leaching risk quadrant, high dust retention capacity - high leaching risk quadrant, low dust retention capacity - low leaching risk quadrant, and low dust retention capacity - high leaching risk quadrant.

[0015] As a further aspect of the present invention, obtaining the dust retention capacity parameter includes the following steps: Collect leaves of candidate plants and measure the amount of dust retained per unit leaf biomass or per unit leaf area. The diameter at breast height (DBH) and height of candidate plants were measured, and the total leaf biomass was estimated using allometric growth equations or standing tree biomass models. Based on the dust retention capacity per unit leaf biomass and the total leaf biomass, the total dust retention capacity of the candidate plant is calculated. The allometric growth equation is either a national standard standing tree biomass model or a regionalized allometric growth equation.

[0016] As a further aspect of the present invention, it also includes: constructing a plant dust retention-leaching characteristic database; in step S1, the dust retention capacity parameter and the through-rain nutrient enrichment parameter are obtained by querying the database.

[0017] As a further aspect of the present invention, the database includes the following fields: plant species information, dust retention capacity data, through-rain nutrient concentration data, nutrient flux enrichment factor, and risk quadrant identifier.

[0018] Secondly, this invention discloses a method for configuring and screening plants for green spaces around water areas, which uses the above-mentioned risk quadrant classification results for plant configuration decisions in greening around water areas.

[0019] As a further aspect of the present invention, it specifically involves: selecting plants in the high dust retention capacity-low leaching risk quadrant as the core recommended plants for green spaces around water bodies; The use of plants in the high dust retention capacity-high leaching risk quadrant is restricted, including at least one of the following: prohibiting their use within a predetermined range adjacent to a water body, or requiring their configuration to be supplemented with additional runoff interception and purification measures; Plants in the low dust retention capacity-low leaching risk quadrant and the low dust retention capacity-high leaching risk quadrant are not recommended for use in water-adjacent areas where air purification is the primary objective, or only as background greening species.

[0020] Compared with the prior art, the beneficial effects of the present invention are: This invention is the first to integrate two closely related indicators in ecological processes—dust retention and leaching (the wet deposition and release of dry sediment)—which have been assessed separately in existing technologies. This invention clearly identifies the potential contradiction between the two (high dust retention may lead to high leaching risk) and provides a scientific quantitative tool to resolve this contradiction, filling a key gap in the current green space plant function assessment system.

[0021] This invention constructs a two-dimensional decision matrix with inherent logical connections. The method for constructing this matrix includes parameter standardization, threshold determination, and risk quadrant division, forming a complete and non-obvious evaluation logic, which differs from conventional methods that simply query a database or rank a single indicator.

[0022] This method transforms complex ecological processes into assessment and screening steps that planners and engineers can understand and implement, directly serving the precise configuration of green spaces in the specific sensitive scenario of water areas. Plant configuration schemes selected through this method can maximize the air purification benefits of green spaces while minimizing the risk of nutrient input to adjacent water bodies, achieving the maximization of ecological benefits and the minimization of negative effects. This has direct and positive practical value for preventing eutrophication of water bodies. Attached Figure Description

[0023] Figure 1 This is a flowchart of the evaluation method of the present invention; Figure 2In the examples, Figures A and B are scatter plots showing the dust retention capacity per unit leaf biomass and the enrichment multiples of total nitrogen and total phosphorus fluxes from throughfall, respectively, which are used to reveal the correlation characteristics between the intrinsic dust retention capacity of tree species and the risk of leaching.

[0024] Figure 3 Figure C shows the scatter plot distribution and threshold division of the total dust retention capacity and total nitrogen flux enrichment multiple of the whole plant estimated based on the national standard standing tree biomass model (Method 1); Figure D shows the scatter plot distribution and threshold division of the total dust retention capacity and total phosphorus flux enrichment multiple of the whole plant estimated based on the national standard standing tree biomass model (Method 1). These figures reflect the total dust retention contribution and leaching risk of specific plants under actual site conditions. Figure 4 In the figure, Figure E is a scatter plot and threshold division of the enrichment multiple of total nitrogen flux through rain estimated by the regionalized allometric growth equation (Method 2); Figure F is a scatter plot and threshold division of the enrichment multiple of total nitrogen flux through rain estimated by the regionalized allometric growth equation (Method 2).

[0025] Figures 2 to 4 Under the three sets of indicators, the relative ranking of dust retention capacity of each tree species is highly consistent with the risk quadrant assignment: Quercus acutissima is always high dust retention - low leaching risk; Cypress has a relatively high intrinsic dust retention capacity, but the contribution of the whole tree is limited by the individual morphology and the leaching risk is high nitrogen and low phosphorus; Liquidambar formosana and Ulmus parvifolia are low dust retention - low leaching risk; and Populus tomentosa is low dust retention - high leaching risk.

[0026] It should be noted that, Figure 2 A, Figure 2 B uses the dust retention capacity per unit leaf biomass to reflect the intrinsic dust retention capacity of the tree species; Figure 3 C– Figure 4 F uses the total dust retention capacity of the entire plant, reflecting the total dust retention contribution of a specific plant under actual site conditions. Since the two assessment dimensions differ, the placement of the same tree species (such as cypress) varies across different maps. This reflects the decoupled assessment of intrinsic capability and field contribution achieved by the method of this invention.

[0027] Figure 3 and Figure 4 Further analysis shows that although Method 1 (national standard model) and Method 2 (allometric growth equation) differ in model form and parameter sources, and the absolute value of the total dust retention capacity of the tree varies, the relative ranking and risk classification results of the five tree species in terms of dust retention capacity are completely consistent. This result indicates that the evaluation system established in this invention is insensitive to the specific selection of the biomass calculation model, and can stably output consistent evaluation conclusions under different input parameters, demonstrating good method robustness and engineering applicability. Detailed Implementation

[0028] 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.

[0029] 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.

[0030] This embodiment uses a green space surrounding a lake in East China as the application scenario, selecting five typical trees (Quercus acutissima (Quercus spp.)). Quercus acutissima ), cypress ( Cupressus funebris Poplar ( Populus tomentosa ), Liquidambar formosana ( Liquidambar formosana ), Elm ( Ulmus parvifolia Using as a candidate, the implementation process of the method of the present invention is fully demonstrated.

[0031] It is important to note that, to comprehensively assess the dust retention capacity of different tree species, the implementation methods of this invention not only rely on the actual field monitoring conducted in the embodiments, but also explicitly acknowledge the feasibility of obtaining dust retention capacity data of candidate plants through literature retrieval, database queries, or existing patent information. However, given that factors such as atmospheric particulate matter concentration, precipitation characteristics, site conditions, and test year have a significant impact on plant dust retention capacity, the reported dust retention amount of the same tree species often varies significantly in different regions or at different times. Therefore, all dust retention data obtained through existing data must be manually verified and cross-referenced; when the relative deviation of dust retention data from different sources for the same tree species exceeds 30% to 50%, it is considered a significant conflict, and the field measurement results of the target application area should prevail; if the relative deviation is within 30%, the mean or median of the data from each source can be used as the evaluation basis. This judgment threshold is determined by comprehensively considering the systematic error of literature data (usually ≤20%) and the true biological variation range of tree species (which can reach 30% to 50%), and the specific value can be appropriately adjusted according to the existing research foundation and data confidence level of the target area. If there are significant conflicts among multiple sources of data for the same tree species and these conflicts cannot be reasonably explained by literature tracing, then the field measurement results in the target application area should prevail.

[0032] Regarding the characterization parameters of dust retention capacity, following the steps in this embodiment, three indicators can be simultaneously collected: dust retention per unit leaf biomass, dust retention per unit leaf area, and dust retention per whole tree leaf. The calculation of dust retention per whole tree leaf relies on a standing biomass model or allometric growth equation, converting easily measurable structural parameters such as measured diameter at breast height (DBH) and tree height into estimated values ​​of whole tree leaf biomass, thereby deducing the dust retention load at the whole tree scale. This method effectively reflects the differences in dust retention capacity at the individual scale among plants of different diameter classes, and compared to simply relying on dust retention per unit leaf area or per unit leaf biomass, it possesses stronger ecological representativeness and engineering application adaptability. For tree species for which a standing biomass model or an allometric growth equation has not yet been established, dust retention per unit leaf area and / or dust retention per unit leaf biomass will temporarily be used as the core basis for horizontal comparison.

[0033] S1. Obtaining Two-Dimensional Basic Parameters S1-1, Determination of dust retention capacity per unit leaf biomass Sampling was conducted during four non-rainfall accumulation periods in the autumn of 2025 (September 3-5-7-10-13), with leaf samples collected on September 5, 7, 10, and 13, to reflect the maximum dust retention potential of plants under natural conditions.

[0034] Twenty mature functional leaves from each of the four directions (east, south, west, and north) of the upper and middle canopy of each tree species were selected, for a total of 100 leaves. The elution-weighing method was used: the leaves were thoroughly soaked and brushed with deionized water; the eluent was filtered through a 0.45 μm filter membrane; the particulate matter on the filter membrane was collected, dried at 60℃ to constant weight, and the dry weight of the particulate matter (mg) was measured. The leaf area (cm²) was measured using a scanner. 2 After that, the leaves were blanched at 105℃ for 30 min, dried at 75℃ to constant weight, and the dry weight of the leaves (g) was measured.

[0035] Dust retention capacity per unit leaf biomass (g / kg) = Dry weight of particulate matter (g) / Dry weight of leaf (kg) Dust retention capacity per unit leaf area (g / m²) 2 = Particulate matter dry weight (g) / Leaf area (m²) 2 ) S1-2. Measurement of whole-tree leaf biomass and total dust retention. The diameter at breast height (D, cm) and height (H, m) of each tree species were measured, and the total leaf biomass was calculated using the following two methods: Method 1: Using the biomass model equations given in GB / T 43648-2024 - Biomass Model and Carbon Estimation Parameters of Main Tree Species, the total leaf biomass of each tree species was calculated. M 4, kg): Aboveground biomass (kg) M A= a0 × D a1 × H a2 Dry biomass (kg) M 1 = 1 / (1 + g1 + g2 + g3 )× M A Dry bark biomass (kg) M 2= g 1 / (1+ g1 + g2 + g3 )×M A Tree branch biomass (kg) M 3= g 2 / (1+ g1 + g2 + g3 )× M A Leaf biomass (kg) M 4= g 3 / (1+ g1 + g2 + g3 )× M A In the formula, gi = bi0 × D bi1 × H bi2 ，i=1,2,3 Each type of tree a0 , a1 , a2 , bi0, bi1, bi2 All information can be found in GB / T 43648-2024.

[0036] Method 2: Calculate the total leaf biomass using a simplified set of allometric growth equations for trees. W leaf The details are as follows: Quercus acutissima, Liquidambar formosana, and Ulmus parvifolia: Allometric growth equations for broad-leaved trees in East China were adopted. ; Cypress trees were grown using the allometric growth equation for coniferous trees. ; Poplar trees use a special equation for fast-growing poplars. ; Total dust retention of the whole plant (g / plant) = Dust retention capacity per unit leaf biomass (g / kg) × Total leaf biomass of the plant (kg); Table 1 shows the measured dust retention per unit leaf biomass in this embodiment. Table 2 shows the tree height, diameter at breast height, crown width, and the total leaf biomass and total dust retention per tree calculated by the two methods for each tree species (mean ± standard deviation, n=4).

[0037] Table 1. Measured dust retention capacity per unit leaf biomass of five tree species (g / kg)

[0038] Table 2. Tree height, diameter at breast height (DBH), crown width, and total leaf biomass and dust retention capacity of five tree species calculated using two methods.

[0039] S1-3, Leaching Risk Assessment – ​​Acquisition of Nutrient Enrichment Parameters for Throughfall Rain (1) Plot layout and sample collection Three sets of penetrating rain collection devices were evenly deployed under the canopy of each type of plant, while two sets of atmospheric precipitation control devices were deployed in adjacent open areas. The devices were standard rain gauges with an inner diameter of 20 cm, placed 50 cm above the ground, with the rain collectors connected to 2.5 L polyethylene water storage tanks, which were cleaned with deionized water before each sampling.

[0040] (2) Definition of precipitation events and processing of sampling period It should be noted that the "i-th precipitation event" in this embodiment is a flexible time unit definition, the core of which is to ensure that each evaluated sample represents a relatively independent precipitation-throughfall-nutrient output process. In specific implementation, one of the following two commonly used methods can be adopted according to the research objectives and observation specifications: 1) Independent Precipitation Event Method Two rainfall events, defined meteorologically as being more than 6 hours apart, are treated as independent precipitation events, i-th and i+1-th, and samples are collected and measured separately. This method is suitable for scenarios requiring detailed analysis of the contribution of a single rainfall event to nutrient leaching.

[0041] 2) Combined sampling period method In practice, multiple rainfalls occurring on the same day, or rainfall over several consecutive days (such as a natural week or ten-day period), 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.

[0042] 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.

[0043] In this embodiment, throughfall samples were collected from five independent precipitation events on September 13, 14, 24, 26, and 30, 2025, for each tree species. The independent precipitation event method was used for collection and measurement. Atmospheric precipitation control samples were collected simultaneously for each rainfall event to ensure that the throughfall samples and atmospheric precipitation control samples for the same event i corresponded completely in time.

[0044] (3) Sample processing and nutrient concentration determination Rainwater samples were immediately refrigerated at 4°C and protected from light after collection, and pretreatment and determination were completed within 48 hours.

[0045] Total nitrogen (TN): The alkaline potassium persulfate digestion-ultraviolet spectrophotometry method was used, in accordance with standard GB 11894-89; Total dissolved nitrogen (DTN): The sample was filtered through a 0.45 μm filter membrane and then determined using the same method. Ammonia nitrogen (NH3-N): Nessler's reagent spectrophotometric method, in accordance with standard HJ 535-2009; Nitrate nitrogen (NO3-N): Ultraviolet spectrophotometry was used, in accordance with standard HJ / T 346-2007; Total phosphorus (TP): The potassium persulfate digestion-ammonium molybdate spectrophotometric method was used, in accordance with standard GB 11893-89; Total dissolved phosphorus (DTP): The sample was filtered through a 0.45 μm filter membrane and then determined using the same method.

[0046] For all samples, parallel samples (10% of the samples in each batch) and blank samples (deionized water) were set up. The relative deviation of the parallel samples was controlled within 5%, and the average value of the test results was taken.

[0047] (4) Calculation of flux enrichment factor The final nutrient flux enrichment factor for each plant species is calculated as the cumulative flux ratio of the five precipitation events during the observation period, which is the ratio of the sum of nutrient fluxes from throughfall to the sum of nutrient fluxes from atmospheric precipitation. The formula is as follows: =

[0048] In the formula, denoted as the flux enrichment factor for the j-th nutrient; n represents the total number of precipitation events collected within the integrated timescale (e.g., independent precipitation events, weeks, months, seasons, or years) / sampling period. Let $j$ be the flux of the $j$ nutrient carried by canopy penetration rain during the $i$-th precipitation event, expressed in kg / km². 2 ; Let $\frac{i}{j}$ be the flux of the $j$ nutrient carried by atmospheric precipitation (exotree rainwater) during the $i$-th precipitation event, expressed in kg / km². 2 ; The mass concentration of the j-th nutrient in the canopy penetration rain during the i-th precipitation event is expressed in mg / L. Let be the mass concentration of the j-th nutrient in atmospheric precipitation during the i-th precipitation event, expressed in mg / L. The amount of rainfall through the canopy during the i-th precipitation event is expressed in mm. Let represent the amount of atmospheric precipitation during the i-th precipitation event, expressed in mm.

[0049] (5) Measured nitrogen and phosphorus flux Tables 3 and 4 show the total nitrogen (TN), total phosphorus (TP) fluxes, cumulative fluxes, and corresponding flux enrichment factor (ECF) values ​​for five events of atmospheric precipitation and throughfall of five tree species in this embodiment.

[0050] Table 3 Total nitrogen flux from atmospheric precipitation and throughfall from five tree species (kg / km²) 2 ) and ECF-TN

[0051] Table 4. Total phosphorus flux from atmospheric precipitation and throughfall of five tree species (kg / km²) 2 ) and ECF-TP

[0052] (6) Explanation of observation duration It is particularly important to note that, theoretically, to obtain stable and representative assessment results of plant throughfall nutrient enrichment characteristics, continuous observation over at least one full hydrological year or a longer period (e.g., 2-3 years) is ideal. This helps to cover seasonal climate change, differences in rainfall types, and dynamic changes in plant physiological cycles, thereby obtaining more robust flux enrichment factors.

[0053] This embodiment, based on observational data from five precipitation events, aims to demonstrate the complete implementation process and decision-making logic of the method of this invention, rather than providing a final qualitative assessment of the listed plant ecological functions. In practical engineering planning and decision-making applications, the method of this invention should be implemented based on longer-term and more representative local observational data of the target area, or to verify and revise short-term preliminary evaluation results.

[0054] The core of this invention is to provide a universal framework for plant assessment, quadrant division and configuration screening based on a dual dimension of dust retention capacity and leaching risk. The effectiveness of this framework is positively correlated with the sufficiency of data observation, rather than limiting the scope of protection to the specific observation period, specific threshold value or specific plant measured value of this embodiment.

[0055] S1-4. The dust retention capacity parameters and leaching risk parameters of the five candidate plants measured in this embodiment are summarized in Table 5. Dust retention capacity is characterized by three parallel sets of indicators: dust retention per unit leaf biomass (g / kg), dust retention per plant - Method 1 (g / plant), and dust retention per plant - Method 2 (g / plant). Leaching risk is characterized by the total nitrogen flux enrichment factor (ECF-TN) and the total phosphorus flux enrichment factor (ECF-TP). These parameters will serve as the basic input data for the two-dimensional assessment in S2.

[0056] Table 5 Summary of dust retention capacity and leaching risk parameters of five tree species

[0057] S2, Two-Dimensional Evaluation and Threshold Classification S2-1 Classification of Dust Retention Capacity This embodiment uses the measured dust retention capacity of the entire candidate plant (Method 1) and employs Jenks' Natural Breaks method for cluster analysis. For example... Figure 3 As shown in C and 3D, the five plants exhibit a clear dichotomous structure: *Quercus acutissima* (821.10 g / plant) belongs to the high dust retention group; *Ulmus parvifolia* (60.39 g / plant), *Populus tomentosa* (17.95 g / plant), *Liquidambar formosana* (12.88 g / plant), and *Cypressa chinensis* (161.80 g / plant) belong to the low dust retention group. The natural dividing point between the two groups is located in the range of 50–200 g / plant. Therefore, 200 g / plant was determined as the threshold for classifying high / low dust retention capacity in this embodiment.

[0058] Furthermore, this method employs multiple dust retention capacity characterization parameters for cross-validation. For example... Figure 2 A, Figure 2 As shown in B, when using the dust retention capacity per unit leaf biomass (reflecting the intrinsic dust retention capacity of the tree species) for evaluation, both Quercus acutissima and Cypress are in the high-value range; while when using the dust retention capacity of the whole tree (Method 2, see...) Figure 4 E, Figure 4During the assessment (F), *Quercus acutissima* remained consistently in the high-value zone, while *Cypressum*, constrained by its individual morphological parameters such as height, diameter at breast height (DBH), and crown width, had a relatively low overall dust retention capacity, placing it in the low-value zone. Although the specific threshold values ​​varied under different parameters, the relative clustering results of dust retention capacity for each tree species were highly consistent, fully demonstrating the robustness of this assessment system under different indicators and biomass models. The unique placement of *Cypressum* in the map precisely reveals its ecological characteristic of "high intrinsic dust retention capacity, but the overall contribution is not fully realized due to plant shape limitations," reflecting the technical advantage of this method in hierarchically decoupling the assessment of the intrinsic capacity and field contribution of tree species.

[0059] It is important to emphasize that this threshold is determined based on the statistical distribution of a limited sample set in this embodiment, and is not a fixed technical parameter. The core of this invention lies in providing a framework for "high / low" binary classification based on dust retention capacity parameters, rather than limiting the protection scope to a specific value. In practical applications, the threshold should be dynamically adjusted according to factors such as regional atmospheric particulate matter pollution intensity, planning target location, and candidate plant library size, and cluster analysis can be performed again to update the boundary points.

[0060] S2-2 Classification of Leaching Risk This embodiment determines the leaching risk threshold based on two criteria: (1) Environmental benchmark basis: The environmental benchmark reference is based on the nutrient status evaluation standards for lakes and reservoirs in the "Surface Water Environmental Quality Standard" (GB 3838-2002) and common discussions on the nutrient enrichment effect of throughfall in related studies. ECF≥3.0 (total nitrogen) and ECF≥6.0 (total phosphorus) are used as reference threshold ranges for leaching risk assessment. These values ​​are mainly used for cross-checking with the distribution characteristics of measured data, rather than as an independent basis for risk assessment.

[0061] (2) Statistical Distribution Basis: In this embodiment, the measured ECF-TN distribution range for the five plant species was 1.79–4.50, and the ECF-TP distribution range was 1.92–11.86. Based on the combined environmental baseline and data distribution characteristics, ECF-TN = 3.0 and ECF-TP = 6.0 were determined as the leaching risk classification thresholds for this embodiment. Specifically, 3.0 is located at the 40th quantile of the corresponding ECF-TN dataset, and 6.0 is located at the 60th quantile of the corresponding ECF-TP dataset. Compared to nitrogen, phosphorus has a higher threshold quantile, likely due to the potentially greater variability in phosphorus enrichment through rainwater penetration by plants, and the greater sensitivity to phosphorus enrichment from a water conservation perspective.

[0062] It should also be emphasized that the above thresholds are set based on the background nutrient status (mesotrophic) of the target water body in this embodiment, and are not universally applicable fixed thresholds. The core of this invention lies in providing a framework for classifying "high / low" leaching risk based on nutrient flux enrichment multiples, and a dynamic adjustment mechanism that links the thresholds with the sensitivity of the water body's nutrient status. In specific applications, differentiated thresholds should be applied according to the nutrient status classification of the target water body (see Table 6).

[0063] Table 6. Differential ECF Thresholds Based on Aquatic Nutrient Status

[0064] The background trophic status values ​​are classified according to the concentrations of total nitrogen, total phosphorus, chlorophyll a, or the comprehensive trophic status index (TLI) as specified in the "Surface Water Environmental Quality Standard" (GB 3838-2002) or the "Technical Specification for Eutrophication Evaluation of Lakes (Reservoirs)".

[0065] S3. Two-dimensional assessment results and risk quadrant classification Based on the dust retention capacity threshold (200 g / plant, calculated as total dust retention amount - Method 1) and leaching risk threshold (ECF-TN=3.0, ECF-TP=6.0) determined in S2, the five candidate plants in this example were classified into a "dust retention-leaching" two-dimensional risk matrix, and the results are as follows. Figure 3 As shown in Table 7. It should be noted that the leaching risk thresholds for total nitrogen and total phosphorus in this embodiment are different (ECF-TN=3.0, ECF-TP=6.0). This is because they are determined independently based on their respective environmental benchmarks and measured data distribution characteristics, and are not based on a fixed ratio or pre-set parameters. This differentiated threshold setting method reflects the objective response of the present invention to the differences in environmental behavior of different nutrients. In practical applications, the thresholds for each nutrient indicator should be determined independently based on their corresponding environmental benchmarks and local data distribution, and can be dynamically adjusted according to the nutrient status of the water body.

[0066] Based on the location of plants in the two-dimensional matrix, they are divided into the following four risk quadrants: (1) Core Recommended Area - High Dust Retention and Low Leaching Risk Plants in this quadrant possess both excellent particulate matter capture capabilities and a low risk of nutrient loss throughwater, making them a preferred tree species for green spaces around water bodies.

[0067] In this embodiment, *Quercus acutissima* falls into this quadrant. Its total dust retention capacity (821.10 g / plant, Method 1) is 5–40 times that of the low dust retention group. Its total nitrogen flux enrichment factor (3.01) slightly exceeds the threshold but remains within an acceptable range, while its total phosphorus flux enrichment factor (2.51) is significantly lower than the threshold. This tree species is particularly suitable for planting on the windward side of water bodies and in areas with high dust levels, such as near roads, achieving efficient air purification while minimizing the risk of nutrient input to water bodies.

[0068] (2) Restricted use area - high dust retention and high leaching risk Plants in this quadrant have a strong intrinsic dust-trapping capacity. Figure 2 However, the nutrient enrichment effect of through-rain is significant, and if it is located close to water bodies, it may have an adverse effect on the nutrient status of the water.

[0069] In this embodiment, *Cypress* falls into this quadrant. Its dust retention per unit leaf biomass (2.933 g / kg) ranks second, indicating that this species possesses high intrinsic dust retention potential; however, limited by the observed diameter at breast height (DBH), crown width, and leaf area index of individual trees, its overall dust retention (161.80 g / tree) falls into the low dust retention range. Simultaneously, its total nitrogen flux enrichment factor (3.76) exceeds the risk threshold, but its total phosphorus flux enrichment factor (1.91) is the lowest among the five plants. Therefore, *Cypress* can be considered a candidate tree species for phosphorus control scenarios but is not suitable for nitrogen control scenarios. If its use is necessary, it should be supplemented with runoff interception and nitrogen enhancement reduction measures such as vegetated swales and ecological buffer zones to reduce the risk of direct entry of nutrients, especially nitrogen, into storage facilities throughfall.

[0070] (3) Safe zone – low dust retention and low leaching risk Plants in this quadrant contribute relatively little to air purification, but pose a low risk of nutrient input to water bodies. They can be used as background greening species or planted at a suitable distance from water bodies.

[0071] In this embodiment, *Liquidambar formosana* and *Ulmus parvifolia* fall into this quadrant. The dust retention per unit leaf biomass for both species is 1.418 g / kg and 1.102 g / kg, respectively, with the total dust retention per tree below 60 g / tree. The enrichment multiples for total nitrogen and total phosphorus fluxes are generally at low levels. *Liquidambar formosana* exhibits low nitrogen and phosphorus leaching risks, while *Ulmus parvifolia* ranks second in phosphorus enrichment multiple, but its nitrogen enrichment multiple is the lowest among all species (1.79). Plants in this quadrant should be planted at a certain distance from water bodies on the outer edges, relying on the synergistic interception and adsorption of soil-vegetation systems to further reduce residual nutrients, especially phosphorus load, in penetrating rainwater.

[0072] (4) Avoidance zone - low dust retention, high leaching risk Plants in this quadrant have extremely low dust retention capacity and high nutrient output load through rainwater, so they are not recommended for use in water-adjacent areas where air purification or water protection is a priority.

[0073] In this embodiment, poplar falls into this quadrant. Its dust retention per unit leaf biomass (0.500 g / kg) and total dust retention per tree (17.95 g / tree) are the lowest among all tree species, indicating its limited effectiveness in purifying atmospheric particulate matter. Simultaneously, its total nitrogen flux enrichment factor (4.48) and total phosphorus flux enrichment factor (11.86) are significantly higher than the risk threshold and are the highest among all tree species. This tree species should be avoided in sensitive areas surrounding water bodies.

[0074] Table 7. Dust retention capacity of five tree species, nitrogen and phosphorus leaching risk level, risk quadrant, and configuration recommendations.

[0075] The above classification results are highly consistent with intuitive judgments based on plant biological characteristics (leaf structure, growth form, nutrient utilization strategies), indicating that this assessment system has good ecological rationality and cross-regional and cross-species engineering applicability. It should be reiterated that the specific tree species classification and risk quadrant division presented in this embodiment are interim conclusions based on a specific observation period, specific site conditions, and a limited sample set. In practical applications, those skilled in the art should, based on longer-term and more representative local observation data of the target area, follow the methodological framework established by this invention to re-execute threshold determination and quadrant division to obtain a localized plant configuration scheme that conforms to local conditions.

[0076] S4. Intelligent filtering applications for different planning objectives Scenario 1 (pursuing high dust retention efficiency): Input the command "dust retention amount of whole tree > 500 g / tree", and the system will search the database and lock onto Quercus acutissima.

[0077] Scenario 2 (pursuing low nitrogen leaching risk): Enter the command "ECF-TN<2.0", and the system will recommend Elm chinensis after searching the database.

[0078] S5. Effect Analysis Taking the same area of ​​green space and the same canopy area of ​​trees as an example, the dust retention capacity of Quercus acutissima is expected to be more than 40 times higher than that of poplar, while the risk of nitrogen and phosphorus load output is reduced simultaneously, especially for total phosphorus, which is expected to be reduced by more than 70%. These results indicate that the core recommended tree species selected using the method of this invention can achieve highly efficient dust retention while simultaneously and significantly reducing the risk of nutrient input to adjacent water bodies, achieving a win-win situation for air purification and water environment protection.

[0079] 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.

[0080] 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 evaluating plants in green spaces surrounding water bodies, characterized in that, Includes the following steps: S1. In the area surrounding the target water area, obtain the dust retention capacity parameters and the nutrient enrichment parameters of the candidate plants through rainwater. S2. Based on the dust retention capacity parameter, the candidate plants are divided into two categories: high dust retention capacity and low dust retention capacity. Based on the through-rain nutrient enrichment parameter, the nutrient flux enrichment factor characterizing the leaching risk is calculated, and the candidate plants are divided into two categories: high leaching risk and low leaching risk based on a preset threshold. S3. Based on the dust retention capacity and leaching risk of all candidate plants, a two-dimensional evaluation matrix is ​​constructed, and the candidate plants are divided into the corresponding risk quadrants.

2. The method according to claim 1, characterized in that, In step S1: The dust retention capacity parameter is the total dust retention amount per plant, or the equivalent dust retention index after normalization of leaf area and leaf biomass. The throughfall nutrient enrichment parameters include at least total nitrogen and total phosphorus flux data, used to calculate the corresponding nutrient flux enrichment factor.

3. The method according to claim 1, characterized in that, In step S2, the nutrient flux enrichment factor is calculated using the following formula: = In the formula, denoted as the flux enrichment factor for the j-th nutrient; n represents the total number of precipitation events collected within the integrated timescale (e.g., independent precipitation events, weeks, months, seasons, or years) / sampling period. The flux of the j-th nutrient carried by trees through rain during the i-th precipitation event is expressed in kg / km². 2 ; Let $\frac{i}{j}$ be the flux of the $j$ nutrient carried by atmospheric precipitation (exotree rain) during the $i$-th precipitation event, expressed in kg / km². 2 ; The mass concentration of the j-th nutrient in the canopy penetration rain during the i-th precipitation event is expressed in mg / L. Let be the mass concentration of the j-th nutrient in atmospheric precipitation during the i-th precipitation event, expressed in mg / L. The amount of rainfall through the canopy during the i-th precipitation event is expressed in mm. Let represent the amount of atmospheric precipitation during the i-th precipitation event, expressed in mm.

4. The method according to claim 1, characterized in that, In step S2: The thresholds for classifying dust retention capacity and leaching risk are determined according to at least one of the following methods: (a) Cluster analysis was performed on the dust retention capacity parameters of candidate plants, with the natural boundary point as the dividing threshold; (b) Based on the statistical distribution characteristics of the nutrient flux enrichment multiples of candidate plants, and with reference to the background value of the nutrient status of the target water body or the environmental quality standard, set the leaching risk threshold for the nutrient flux enrichment multiples.

5. The method according to claim 1, characterized in that, In step S3, the risk quadrants include: high dust retention capacity - low leaching risk quadrant, high dust retention capacity - high leaching risk quadrant, low dust retention capacity - low leaching risk quadrant, and low dust retention capacity - high leaching risk quadrant.

6. The method according to claim 1, characterized in that, Obtaining the dust retention capacity parameter includes the following steps: Collect leaves of candidate plants and measure the amount of dust retained per unit leaf biomass or per unit leaf area. The diameter at breast height (DBH) and height of candidate plants were measured, and the total leaf biomass was estimated using allometric growth equations or standing tree biomass models. Based on the dust retention capacity per unit leaf biomass and the total leaf biomass, the total dust retention capacity of the candidate plant is calculated. The allometric growth equation is either a national standard standing tree biomass model or a regionalized allometric growth equation.

7. The method according to claim 1, characterized in that, Also includes: A database of plant dust retention and leaching characteristics is constructed; in step S1, the dust retention capacity parameters and the nutrient enrichment parameters of through-rain are obtained by querying the database.

8. The method according to claim 7, characterized in that, The database includes the following fields: plant species information, dust retention capacity data, through-rain nutrient concentration data, nutrient flux enrichment factor, and risk quadrant identifier.

9. A method for selecting and configuring green plants around water bodies, characterized in that, The risk quadrant classification results described in any one of claims 1-8 are used for plant configuration decisions in greening around water areas.

10. The method according to claim 9, characterized in that, Specifically, plants in the high dust retention capacity-low leaching risk quadrant are recommended as the core plants for green spaces around water bodies. The use of plants in the high dust retention capacity-high leaching risk quadrant is restricted, including at least one of the following: prohibiting their use within a predetermined range adjacent to a water body, or requiring their configuration to be supplemented with additional runoff interception and purification measures; Plants in the low dust retention capacity-low leaching risk quadrant and the low dust retention capacity-high leaching risk quadrant are not recommended for use in water-adjacent areas where air purification is the primary objective, or only as background greening species.

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