Sensitive species screening method and system for soil Cd environment quality benchmark derivation

Abstract

The invention relates to the field of environmental science, and provides a sensitive species screening method and system for soil Cd environmental quality benchmark derivation. The method comprises the following steps: establishing a multi-dimensional evaluation index system, carrying out weighted scoring processing on candidate species, and screening to obtain a preliminary acceptor species directory; for species in the preliminary receptor species directory, acquiring soil Cd toxicity data, and performing soil property normalization processing on the soil Cd toxicity data to obtain a standardized toxicity data set; inputting the standardized toxicity data set into a species sensitivity distribution model for fitting calculation, and generating a species sensitivity distribution curve; according to the species sensitivity distribution curve, sorting the species according to the sensitivity, selecting sensitive species from a sorting result, and forming a sensitive ecological receptor directory. According to the method, scientificity, accuracy and regional applicability of soil Cd environment reference derivation are improved.

Description

Technical Field

[0001] This invention relates to the field of environmental science and technology, and in particular to a method and system for screening sensitive species for deriving soil Cd environmental quality benchmarks. Background Technology

[0002] Soil cadmium pollution, due to its high toxicity, high mobility, and accumulation through the food chain, has become a prominent environmental problem threatening ecosystem security and agricultural product quality. Establishing scientific and reasonable soil Cd environmental quality benchmarks is a prerequisite for effective soil pollution risk management and remediation. The derivation and scientific validity of soil environmental benchmarks highly depend on the representativeness of the selected sensitive ecological receptors, i.e., whether these species can truly reflect the potential adverse effects of Cd pollution on the structure and function of the local ecosystem. The Species Sensitivity Distribution Model (SSD), as a core tool for environmental benchmark derivation, integrates toxicity data from multiple species to construct cumulative probability distribution curves, thereby deriving environmental quality benchmark values ​​that protect a certain proportion of species. This method has been widely applied in benchmark development work in fields such as water and soil environments.

[0003] However, existing methods for selecting sensitive species in the derivation of soil Cd environmental quality benchmarks have significant shortcomings. On the one hand, existing studies often directly use standard test species recommended by standardization organizations. While these species are easy to standardize in laboratories, their distribution, abundance, and ecological functions in native ecosystems are often limited. This leads to the neglect of key native species that are more sensitive to Cd, resulting in poor regional adaptability. On the other hand, the species selection process relies heavily on empirical judgment or data availability, lacking a quantitative, multi-dimensional, and comprehensive evaluation system based on ecological principles. This often results in incomplete trophic level coverage and missing key ecological function groups among the selected species, making it difficult to fully represent the structural and functional integrity of the ecosystem. Furthermore, due to differences in soil properties, the toxicity threshold of Cd for the same species can vary by more than an order of magnitude in different regions. Directly using toxicity data obtained from different soil conditions without standardization inevitably leads to a lack of comparability between toxicity data, ultimately resulting in a serious lack of scientific rigor and accuracy in the derived environmental benchmarks.

[0004] Therefore, there is an urgent need in this field to establish a systematic, objective, and data-driven technology system for screening sensitive ecological receptors, in order to solve the technical problems of insufficient systematic species screening, incomparable toxicity data, and poor regional adaptability in existing methods. Summary of the Invention

[0005] This invention provides a method and system for screening sensitive species for deriving soil Cd environmental quality benchmarks, in order to overcome the shortcomings of existing technologies.

[0006] This invention provides a method for screening sensitive species for deriving soil Cd environmental quality benchmarks, comprising: S1. Establish a multi-dimensional evaluation index system, perform weighted scoring on candidate species, and screen to obtain a preliminary list of recipient species; S2. For the species in the preliminary list of receptor species, obtain soil Cd toxicity data, and perform soil property normalization processing on the soil Cd toxicity data to obtain a standardized toxicity dataset. S3. Input the standardized toxicity dataset into the species sensitivity distribution model for fitting calculation to generate the species sensitivity distribution curve; S4. Sort the species according to their sensitivity based on the species sensitivity distribution curve, and select sensitive species from the sorting results to form a list of sensitive ecological receptors.

[0007] According to the present invention, a method for screening sensitive species for deriving soil Cd environmental quality benchmarks, step S1 further includes: S11. Determine the target ecosystem type and extract candidate species related to the target ecosystem type from the native species database; S12. Establish a multi-dimensional evaluation index system and calculate the multi-index score for each candidate species; S13. Perform a weighted summation calculation based on the preset weight values ​​of the multi-index scores to obtain the comprehensive score of each candidate species. S14. Sort the species from high to low according to the comprehensive scores, select candidate species within the preset ranking range, and form a preliminary list of recipient species.

[0008] According to the present invention, a method for screening sensitive species for deriving soil Cd environmental quality benchmarks is provided. The multidimensional evaluation index system in step S12 includes ecological relevance indexes, trophic level indexes, laboratory culturability indexes, and economic conservation value indexes.

[0009] According to the present invention, a method for screening sensitive species for deriving soil Cd environmental quality benchmarks, step S2 further includes: S21. By searching public databases, collect Cd toxicity data of multiple species in the preliminary list of receptor species to obtain raw toxicity data; S22. Perform quality screening on the original toxicity data according to the preset quality standards to obtain qualified toxicity data; S23. Identify species in the preliminary list of receptor species that lack qualified toxicity data, conduct Cd toxicity experiments according to standardized testing methods, and determine experimental toxicity data. S24. Integrate the qualified literature toxicity data and the experimental toxicity data to construct an integrated toxicity dataset; S25. Extract soil property parameters corresponding to multiple toxicity data in the integrated toxicity dataset, and normalize the toxicity values ​​in the integrated toxicity dataset using a bioavailability correction model. Convert the toxicity values ​​under different soil conditions into equivalent toxicity values ​​under standard reference soil conditions to form a standardized toxicity dataset.

[0010] According to the present invention, a method for screening sensitive species for deriving soil Cd environmental quality benchmarks is provided. In step S22, the quality standards include: reasonable control group setting, clear exposure pathway, complete soil property parameters, sufficient number of test repetitions, sufficient test concentration gradient, and presence of compound pollution interference.

[0011] According to the present invention, a method for screening sensitive species for deriving soil Cd environmental quality benchmarks is provided. In step S23, the standardized testing method follows the OECD or ISO standard testing specifications. The test endpoints for Cd toxicity experiments conducted according to the standardized testing method include acute mortality rate, reproductive inhibition rate, growth inhibition rate, and germination inhibition rate.

[0012] According to the present invention, a method for screening sensitive species for deriving soil Cd environmental quality benchmarks is provided. In step S25, the soil property parameters include pH value, organic matter content and cation exchange capacity. The bioavailability correction model adopts a linear regression model or a species sensitivity distribution extrapolation method.

[0013] According to the present invention, a method for screening sensitive species for deriving soil Cd environmental quality benchmarks, step S3 further includes: S31. Select a probability distribution model, wherein the probability distribution model includes the Burr-Type III distribution model and the Log-Normal distribution model; S32. Using the equivalent toxicity values ​​of multiple species in the standardized toxicity dataset as model input, parameter estimation and fitting optimization are performed through the probability distribution model to generate an initial sensitivity distribution curve; S33. Perform a goodness-of-fit test on the initial sensitivity distribution curve to obtain the species sensitivity distribution curve.

[0014] According to the present invention, a method for screening sensitive species for deriving soil Cd environmental quality benchmarks, step S4 further includes: S41. Based on the species sensitivity distribution curve, calculate the cumulative distribution probability value of multiple species on the curve; S42. Sort the species by sensitivity from high to low according to the cumulative distribution probability value, and mark the highly sensitive species near the HC5 protection threshold to obtain the sorting results. S43. Select species within a preset sensitivity range from the sorting results to obtain screened species; S44. Perform a trophic level coverage test on the screened species to confirm that the screened species cover at least three trophic levels among producers, primary consumers, secondary consumers, and decomposers; if the trophic level coverage is insufficient, supplement the selection of sensitive species in the corresponding trophic levels to form the final list of sensitive ecological receptors.

[0015] This invention also provides a sensitive species screening system for deriving soil Cd environmental quality benchmarks, comprising: Weighted module: Used to establish a multi-dimensional evaluation index system, perform weighted scoring on candidate species, and screen to obtain a preliminary list of recipient species; Acquisition module: used to acquire soil Cd toxicity data for species in the preliminary receptor species list, and to perform soil property normalization processing on the soil Cd toxicity data to obtain a standardized toxicity dataset; Fitting module: used to input the standardized toxicity dataset into the species sensitivity distribution model for fitting calculation and generate species sensitivity distribution curve; Output module: used to sort species by sensitivity according to the species sensitivity distribution curve, select sensitive species from the sorting results, and form a list of sensitive ecological receptors.

[0016] This invention provides a method and system for screening sensitive species for deriving soil Cd environmental quality benchmarks. By establishing a multi-dimensional evaluation index system encompassing four dimensions—ecological relevance, trophic hierarchy, laboratory culturability, and economic conservation value—and employing weighted scoring, it changes the traditional arbitrary species selection model relying on experience. This quantifies and standardizes the species screening process, significantly reducing subjectivity and arbitrariness, and ensuring that ecologically representative candidate species groups are identified in the initial screening stage, laying a solid foundation for subsequent precise screening. Secondly, this invention utilizes a dual-source data acquisition method combining literature retrieval and experimental measurements. This fully leverages existing research results to avoid duplication of effort while specifically supplementing data gaps for key native species. It effectively solves the problems of insufficient native species data due to reliance solely on literature data or high costs resulting from complete reliance on experiments, achieving an optimal balance between data quality and acquisition efficiency. Secondly, this invention extracts soil property parameters and uses a bioavailability correction model to normalize toxicity data from different sources, converting toxicity values ​​under different soil conditions into equivalent toxicity values ​​under standard reference soil conditions. This fundamentally eliminates the impact of variations in Cd bioavailability caused by differences in soil pH, organic matter content, and other properties on the comparability of toxicity data, providing a unified comparison benchmark for toxicity data from different studies and regions. Furthermore, this invention uses a standardized toxicity dataset to fit a probability distribution model and generate species sensitivity distribution curves. The cumulative distribution probability values ​​of each species are then calculated based on the curves for objective ranking, avoiding biases caused by subjective human judgment. This ensures that the identification of sensitive species is based on rigorous statistical principles, significantly improving the scientific rigor and reproducibility of the screening results. This invention also ensures that the final receptor list comprehensively represents the complete structure of the ecosystem from producers to consumers to decomposers by verifying the trophic level coverage of selected species and ensuring coverage of at least three trophic levels. This truly reflects the comprehensive impact of Cd pollution on the multi-level and multi-functional ecosystem, making the environmental quality benchmark values ​​derived from this list more comprehensive and effective in ecological protection. Examples show that the confidence interval of the benchmark values ​​is narrower than that of traditional methods, the uncertainty is significantly reduced, and the derived benchmark values ​​can more effectively identify and protect sensitive native species, greatly improving regional applicability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1A schematic diagram of a sensitive species screening method for deriving soil Cd environmental quality benchmarks provided by the present invention; Figure 2 A schematic diagram of a sensitive species screening system for deriving soil Cd environmental quality benchmarks provided by this invention; Figure 3 A schematic diagram of the dose-response relationship curves for typical species provided in the embodiments of the present invention; Figure 4 A schematic diagram of species sensitivity distribution curves provided for embodiments of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0020] The embodiments of the present invention are described below with reference to the figures.

[0021] like Figure 1 As shown, this invention provides a method for screening sensitive species for deriving soil Cd environmental quality benchmarks, comprising: S1. Establish a multi-dimensional evaluation index system, perform weighted scoring on candidate species, and screen to obtain a preliminary list of recipient species.

[0022] Step S1 further includes: S11. Determine the target ecosystem type and extract candidate species related to the target ecosystem type from the native species database.

[0023] In step S11, the present invention first identifies the target ecosystem type, for example, determining it to be a southern red soil paddy field ecosystem. Subsequently, the present invention accesses a local species database, which contains information on all recorded species and their ecological characteristics within this ecosystem. The present invention queries the database based on the ecosystem type label, extracting species entries with distribution records within that ecosystem. Specifically, during the extraction process, the present invention obtains basic information for each species, including its scientific name, trophic level, functional role in the ecosystem, distribution range, and population abundance. Through the extraction operation, the present invention selects 35 potential candidate species related to the southern paddy field ecosystem from the database. These species cover multiple trophic levels and functional groups, including producers, consumers, and decomposers, forming an initial candidate species set.

[0024] S12. Establish a multi-dimensional evaluation index system and calculate the multi-index score for each candidate species. The multi-dimensional evaluation index system in step S12 includes ecological relevance index, trophic level index, laboratory culturability index and economic conservation value index.

[0025] In step S12, this invention establishes a screening index system comprising four dimensions: ecological relevance, trophic level, laboratory culturability, and economic / conservation value, and employs a weighted scoring method for quantitative screening. Specifically, for the ecological relevance index, a species is defined as having high abundance, wide distribution, and key function (e.g., decomposer, predator) in the target ecosystem (e.g., farmland, forest), with a weight of 0.3-0.6; for the trophic level index, the selected species must cover at least three trophic levels: producers (e.g., crops), consumers (e.g., herbivores, carnivores), and decomposers (e.g., microorganisms, earthworms), with a weight of 0.2-0.3; for the laboratory culturability index, the species is defined as having easy standardized laboratory culture, a short reproductive cycle, and strong operability in toxicity testing, with a weight of 0.1-0.3; and for the economic / conservation value index, the species has significant economic value (e.g., rice) or is an endangered / endemic species, with a weight of 0.1-0.2.

[0026] Specifically, in the scoring calculation, for the ecological relevance indicator, this invention extracts the abundance, distribution breadth, and functional criticality rating of each candidate species in the target ecosystem from the species database. These three factors are then weighted according to a preset formula to obtain the ecological relevance score, which ranges from 0 to 10. For the trophic level indicator, this invention identifies the trophic level to which a species belongs. Species belonging to producers are assigned 8-10 points, consumers 7-9 points, and decomposers 6-8 points, thus forming the trophic level score. For the laboratory culturability indicator, this invention comprehensively scores based on the species' culturing difficulty coefficient, reproductive cycle length, and the complexity of toxicity testing procedures. Species that are easy to cultivate and have a short cultivation cycle receive higher scores, also ranging from 0 to 10. For the economic conservation value indicator, this invention assigns scores based on the species' economic output data or endangered status assessment results. Crops with significant economic value, such as rice, receive 8-10 points, endangered endemic species receive 7-9 points, and common species receive 3-5 points. Through the above calculations, the present invention generates four separate score data for each candidate species.

[0027] S13. Perform a weighted summation calculation based on the preset weight values ​​of the multi-indicator scores to obtain the comprehensive score of each candidate species.

[0028] In step S13, the present invention sets preset weight values ​​for four evaluation indicators: ecological relevance weight 0.35, trophic hierarchy weight 0.25, laboratory culturability weight 0.25, and economic conservation value weight 0.15. Subsequently, the present invention performs a weighted summation calculation on each candidate species. For example, for rice, assuming its ecological relevance score is 9.2, trophic hierarchy score is 9.0, laboratory culturability score is 7.5, and economic conservation value score is 10.0, then the comprehensive score = 9.2 × 0.35 + 9.0 × 0.25 + 7.5 × 0.25 + 10.0 × 0.15 = 8.845 points. After performing this weighted summation calculation on each of the 35 candidate species, a score dataset containing multiple comprehensive score values ​​is generated.

[0029] S14. Sort the species from high to low according to the comprehensive scores, select candidate species within the preset ranking range, and form a preliminary list of recipient species.

[0030] In step S14, the present invention sorts the score dataset containing multiple comprehensive score values ​​in descending order according to the numerical value. After sorting, the present invention sets the preset ranking range to the top 40, that is, selects the top 40 species. The present invention forms a preliminary list of recipient species, which is stored in the form of a data table and includes fields such as species number, species name, trophic level category and comprehensive score.

[0031] S2. For the species in the preliminary list of receptor species, obtain soil Cd toxicity data, and perform soil property normalization processing on the soil Cd toxicity data to obtain a standardized toxicity dataset.

[0032] Step S2 further includes: S21. By searching public databases, collect Cd toxicity data of multiple species in the preliminary receptor species list to obtain raw toxicity data.

[0033] In step S21, this invention accesses publicly available toxicology databases both domestically and internationally via a network interface, including the ECOTOX database, EnviroTox database, Web of Science literature database, and CNKI database. Subsequently, this invention constructs a search query, inputting a keyword combination of "species name + Cadmium + toxicity + The search query is either "species name + cadmium + toxicity + inhibitory concentration" or "species name + cadmium + toxicity + inhibitory concentration". Then, a search is performed for each species in the preliminary list of receptor species. After the search is complete, this invention extracts numerical data related to Cd toxicity from the returned results, including... The data includes fields such as pH value, organic matter content, cation exchange capacity (CEC) value, test method description, and test endpoint type. This invention aggregates all extracted data to construct a raw toxicity dataset, which contains several toxicity records from different literature sources.

[0034] S22. The original toxicity data is screened according to the preset quality standards to obtain qualified toxicity data; the quality standards include: reasonable control group setting, clear exposure path, complete soil property parameters, sufficient number of test repetitions, sufficient test concentration gradient, and presence of compound pollution interference.

[0035] Furthermore, this invention establishes a quality standard judgment rule comprising six conditions. For each original toxicity data record, this invention first checks the experimental design description field to determine whether the setting of a blank control group or a solvent control group is clearly recorded; if no control group is recorded, it is marked as unqualified. Subsequently, this invention checks the exposure pathway field to determine whether it clearly states that Cd is exposed to the test species through the soil matrix; if the exposure pathway is ambiguous, it is marked as unqualified. Next, this invention checks the completeness of the soil property parameter field, requiring that the three parameters—pH value, organic matter content, and CEC value—must all be present and have valid values; if any parameter is missing, it is marked as unqualified. Then, this invention continues to check the experimental repetition number field, requiring that the number of repetitions be ≥3; if less than 3, it is marked as unqualified. Next, this invention checks the concentration gradient setting field, requiring that at least 5 concentration gradient points be set; if less than 5, it is marked as unqualified. Finally, this invention checks the pollutant type field; if it records the co-exposure to other heavy metals or organic pollutants, it is marked as unqualified due to compound pollution interference. After the above six criteria are applied, this invention filters out all data records marked as unqualified and retains data records that meet all six criteria to form a qualified toxicity dataset.

[0036] S23. Identify species lacking qualified toxicity data in the preliminary list of receptor species, conduct Cd toxicity experiments according to standardized testing methods, and determine experimental toxicity data; the standardized testing methods follow OECD or ISO standard testing specifications, and the test endpoints for Cd toxicity experiments according to standardized testing methods include acute mortality rate, reproductive inhibition rate, growth inhibition rate, and germination inhibition rate.

[0037] In step S23, the present invention compares the preliminary list of receptor species with the list of species in the qualified toxicity dataset to identify species that exist in the list but lack toxicity data in the qualified dataset. For example, species such as rice, rapeseed, and corn lack literature data that meets quality standards. For these species, the present invention conducts Cd toxicity tests on earthworm species according to ISO-11268, tests on springtail species according to ISO-11267, and tests on plant species according to OECD-208.

[0038] In the experiment, this invention prepared standard artificial soil or collected representative soil from the target area, added Cd solutions of different concentration gradients, and set up 6-8 Cd concentration treatment groups (0, 5, 10, 20, 40, 80 mg / kg) and a blank control group. Subsequently, this invention inoculated or sowed the test species into the treated soil and cultured them in a constant temperature and humidity incubator for 14-28 days. After the culture period, this invention measured the endpoint data of each treatment group, including plant biomass, root length or germination rate, and animal survival rate, reproductive quantity or growth inhibition rate.

[0039] This invention calculates the Cd concentration that results in a 10% inhibition rate based on the percentage of inhibition of each concentration treatment group relative to the control group, and uses nonlinear regression to fit the dose-response curve. Value. Through the above experiments, this invention finally obtained experimental toxicity data for the missing species, including... Numerical values, pH value, organic matter content, and CEC value of the experimental soil, etc.

[0040] S24. Integrate the qualified literature toxicity data and the experimental toxicity data to construct an integrated toxicity dataset.

[0041] Furthermore, this invention merges the qualified toxicity dataset obtained in step S22 with the experimental toxicity data obtained in step S23. During the merging process, this invention unifies the data structure to ensure that each record includes the species name, Fields include numerical values, soil pH, organic matter content, CEC value, and data source labels. This invention concatenates two datasets row-by-row to generate an integrated toxicity dataset, which contains at least one valid toxicity record for all species in the preliminary receptor species list.

[0042] S25. Extract soil property parameters corresponding to multiple toxicity data in the integrated toxicity dataset, and normalize the toxicity values ​​in the integrated toxicity dataset using a bioavailability correction model to uniformly convert the toxicity values ​​under different soil conditions into equivalent toxicity values ​​under standard reference soil conditions, thus forming a standardized toxicity dataset. The soil property parameters include pH value, organic matter content, and cation exchange capacity. The bioavailability correction model adopts a linear regression model or a species sensitivity distribution extrapolation method.

[0043] Furthermore, this invention extracts soil property parameters corresponding to each toxicity record from the integrated toxicity dataset, including pH value, organic matter content (OM), and cation exchange capacity (CEC). Subsequently, this invention sets standard reference soil conditions, such as pH=6.5, OM=2.5%, and CEC=15 cmol / kg. Following this, this invention uses a linear regression model to establish a quantitative relationship between Cd bioavailability and soil property parameters.

[0044] Specifically, this invention collects EC10 values ​​and corresponding soil property parameters of the same species under different soil conditions reported in the literature, and constructs a training dataset. After constructing the dataset, this invention uses soil pH, OM content, and CEC value as independent variables, and... As the dependent variable, a multiple linear regression equation is fitted: Where a, b, c, and d are regression coefficients, and after establishment, the present invention estimates the regression coefficient values ​​using the least squares method.

[0045] After fitting, this invention substitutes the actual soil property parameters of each record in the integrated toxicity dataset into the regression equation to calculate the predicted log( ) under that condition. The value was then used, and the property parameters of the standard reference soil were substituted into the equation to calculate the predicted values ​​under standard conditions. Value. Subsequently, this invention calculates the correction factor. and the original Multiplying the value by a correction factor yields the normalized equivalent toxicity value. .

[0046] After performing the above normalization transformation operation on each record in the integrated toxicity dataset, the present invention will transform the data into... value replacement original The values ​​were used to form a standardized toxicity dataset, in which all toxicity values ​​correspond to uniform standard reference soil conditions, thus eliminating the influence of differences in soil properties.

[0047] S3. Input the standardized toxicity dataset into the species sensitivity distribution model for fitting calculation to generate the species sensitivity distribution curve.

[0048] Step S3 further includes: S31. Select a probability distribution model, which includes the Burr-Type III distribution model and the Log-Normal distribution model.

[0049] In step S31, during the species sensitivity distribution model construction stage, this invention selects the Burr-Type III distribution model and the Log-Normal distribution model as candidate probability distribution models. The Burr-Type III distribution model is a three-parameter continuous probability distribution model, while the Log-Normal distribution model is a two-parameter log-normal distribution model. Based on the data volume and distribution characteristics of the standardized toxicity dataset, this invention prioritizes the Burr-Type III distribution model for subsequent fitting operations. If the Burr-Type III model does not perform well, it switches to the Log-Normal distribution model for fitting.

[0050] S32. Using the equivalent toxicity values ​​of multiple species in the standardized toxicity dataset as model input, parameter estimation and fitting optimization are performed through the probability distribution model to generate an initial sensitivity distribution curve.

[0051] In step S32, the present invention extracts the equivalent toxicity values ​​of all species from the standardized toxicity dataset. This process forms a toxicity value array containing multiple values. Subsequently, the present invention sorts the toxicity value array in ascending order to obtain a sorting sequence from the most sensitive species to the least sensitive species.

[0052] Subsequently, this invention assigns a corresponding cumulative probability estimate to each toxicity value, calculated using the empirical cumulative distribution function formula: ,in This represents the species' position in the ordination sequence. Given the total number of species, this invention generates a corresponding cumulative probability value for each toxicity value using this formula, forming a set of coordinate pairs containing multiple pairs of data points. .

[0053] Next, this invention uses the cumulative distribution function of the Burr-Type III distribution model as the fitting objective function, which includes three parameters to be estimated: shape, scale, and location. This invention employs maximum likelihood estimation to estimate these three parameters and minimizes the sum of squared deviations between the observed cumulative probability and the model's predicted cumulative probability through an iterative optimization algorithm. During the iteration process, this invention initializes the parameter values ​​to 2.0 for the shape parameter, 10.0 for the scale parameter, and 0.1 for the location parameter. Then, it updates the parameter values ​​using gradient descent until the sum of squared deviations converges to its minimum or the number of iterations reaches 1000. After optimization, this invention obtains optimal parameter estimates, such as 2.35 for the shape parameter, 8.62 for the scale parameter, and 0.08 for the location parameter. Finally, this invention substitutes the optimal parameters into the Burr-Type III cumulative distribution function to generate a continuous species sensitivity distribution prediction curve, which covers... The concentration range was from 0.1 mg / kg to 50 mg / kg, forming an initial sensitivity distribution curve.

[0054] S33. Perform a goodness-of-fit test on the initial sensitivity distribution curve to obtain the species sensitivity distribution curve.

[0055] Furthermore, this invention performs a goodness-of-fit test on the initial sensitivity distribution curve using the coefficient of determination. As an evaluation metric, this invention calculates the total sum of squares (SST) and the residual sum of squares (SSE) between the observed cumulative probability value and the model predicted cumulative probability value, and then calculates the coefficient of determination.

[0056] After calculation, the present invention determines Is the value greater than or equal to 0.90? If the fit is deemed good, the initial sensitivity distribution curve is confirmed as the species sensitivity distribution curve. In this invention, the model switches to a Log-Normal distribution, repeats the parameter estimation and fitting optimization operations in step S32, regenerates the initial sensitivity distribution curve, and performs a goodness-of-fit test again. S4. Sort the species according to their sensitivity based on the species sensitivity distribution curve, and select sensitive species from the sorting results to form a list of sensitive ecological receptors.

[0057] Step S4 further includes: S41. Based on the species sensitivity distribution curve, calculate the cumulative distribution probability value of multiple species on the curve.

[0058] In step S41, based on the confirmed species sensitivity distribution curves, the present invention calculates the equivalent toxicity value for each species in the standardized toxicity dataset. Substituting the values ​​into the Burr-Type III cumulative distribution function, this invention obtains the theoretical cumulative distribution probability value for each species through function calculation. For example, rice The value was 3.15 mg / kg. After substituting into the curve function, the cumulative distribution probability value was calculated to be 0.05, indicating that rice is more sensitive than 95% of other species. This invention performs this substitution calculation operation on all species one by one, generating a probability array containing multiple cumulative distribution probability values. This array corresponds one-to-one with the species name, forming a species-probability mapping dataset.

[0059] S42. Sort the species by sensitivity from high to low according to the cumulative distribution probability value, and mark the highly sensitive species near the HC5 protection threshold to obtain the sorting results.

[0060] In step S42, the present invention sorts the species in ascending order based on the cumulative distribution probability values ​​in the species-probability mapping dataset; the smaller the cumulative probability value, the more sensitive the species. After sorting, the present invention generates a sorted result table containing the species number, species name, equivalent toxicity value, and cumulative probability value.

[0061] Subsequently, the present invention calculates the Cd concentration corresponding to the HC5 protection threshold, that is, the concentration value that protects 95% of species. For example, by substituting the cumulative probability of 0.05 into the inverse function of the species sensitivity distribution curve, the HC5 concentration value is calculated to be 0.82 mg / kg.

[0062] Subsequently, this invention marks species with a cumulative probability value ≤0.10 in the ranking results table as highly sensitive species near the HC5 protection threshold. These species have equivalent toxicity values ​​close to or lower than the HC5 concentration, including species such as rice, lettuce, rapeseed, and Chlorella. This invention adds a special marker field to these highly sensitive species in the ranking results table, assigning the value "highly sensitive", thus forming a complete ranking results data table.

[0063] S43. Select species within a preset sensitivity range from the sorting results to obtain screened species.

[0064] In step S43, the present invention sets a preset sensitivity range of cumulative probability value ≤ 0.50, that is, selecting the top 50% of species in terms of sensitivity. Subsequently, the present invention filters species records with cumulative probability values ​​≤ 0.50 from the sorting result data table to obtain the initially selected species. Then, based on actual application needs, the present invention further narrows the selection range to a cumulative probability value ≤ 0.35, corresponding to selecting the top 35% of species in terms of sensitivity, to obtain the final selected species. Finally, the present invention extracts complete information from the 12 selected species, including species name, trophic level, equivalent toxicity value, and cumulative probability value, forming a dataset of selected species.

[0065] S44. Perform a trophic level coverage test on the screened species to confirm that the screened species cover at least three trophic levels among producers, primary consumers, secondary consumers, and decomposers; if the trophic level coverage is insufficient, supplement the selection of sensitive species in the corresponding trophic levels to form the final list of sensitive ecological receptors.

[0066] Further, in step S44, this invention performs trophic level classification statistics on the species in the species dataset obtained in step S43. Specifically, this invention extracts the trophic level category field for each species and counts the number of species in the producer category, primary consumer category, secondary consumer category, and decomposer category. The statistical results show that multiple species contain the number of producers, primary consumers, secondary consumers, and decomposers. Then, this invention determines that at least three of the four trophic level categories have ≥1 species. The test results show that all three categories—producers, primary consumers, and decomposers—have species, for example, the secondary consumer category has only one species, which is too few. This invention determines that the trophic level coverage is basically satisfied, but the secondary consumer representation is insufficient. Subsequently, from the remaining species in the sorted results data table, the invention selects the species in the secondary consumer category with the lowest cumulative probability value, for example, identifying three secondary consumer species: the water spider, the octopus, and the black-shouldered green mirid bug. This invention adds these three species to the selected species dataset, increasing the number of secondary consumers to four. Simultaneously, this invention adjusts the number of species at other trophic levels, removing two species with high cumulative probability values ​​from primary consumers to maintain the total number of species within the range of 15-30, thus finalizing the species range. Subsequently, this invention conducts another trophic level coverage test to confirm that the final selected species cover all four trophic levels and that the number of species in each trophic level is balanced. Finally, this invention summarizes the complete information of the obtained species to form a final list of sensitive ecological receptors. The list is presented in tabular form and includes fields such as species number, scientific name, trophic level category, equivalent toxicity value, and cumulative probability value.

[0067] like Figure 2As shown, the present invention also provides a sensitive species screening system for deriving soil Cd environmental quality benchmarks, comprising: Weighted module 100: Used to establish a multi-dimensional evaluation index system, perform weighted scoring on candidate species, and screen to obtain a preliminary list of recipient species; Acquisition module 200: used to acquire soil Cd toxicity data for species in the preliminary receptor species list, and to perform soil property normalization processing on the soil Cd toxicity data to obtain a standardized toxicity dataset; Fitting module 300: used to input the standardized toxicity dataset into the species sensitivity distribution model for fitting calculation and generate species sensitivity distribution curve; Output module 400: used to sort species according to their sensitivity based on the species sensitivity distribution curve, select sensitive species from the sorting results, and form a list of sensitive ecological receptors.

[0068] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0069] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the sensitive species screening method for deriving soil Cd environmental quality benchmarks as described in the various embodiments or some parts of the embodiments.

[0070] The following describes a method for screening sensitive species for deriving soil Cd environmental quality benchmarks, based on specific embodiments of the present invention. In this embodiment, a typical farmland soil ecosystem is used as the application object to specifically illustrate the implementation process and effects of the method. The aim is to address the needs of Cd pollution risk management in farmland soils in my country by using the method of the present invention to screen sensitive ecological receptors that can scientifically represent the structure and function of farmland ecosystems, providing a list of test species for the determination and derivation of farmland soil Cd environmental quality benchmarks.

[0071] The implementation process of this embodiment follows the multidimensional primary screening-toxicity data integration and standardization-SSD model fine screening technology system proposed in this invention. The specific implementation steps are as follows.

[0072] First, using a typical farmland soil ecosystem (southern red paddy soil), 35 potential species covering multiple trophic levels (producers, consumers, decomposers) were initially selected from a native species database. Then, each species was quantitatively scored according to the weighting system established in this invention (ecological relevance 0.35, trophic level 0.25, laboratory culturability 0.25, economic / ecological conservation value 0.15). Based on the total score, the top 40 species were finally selected for the toxicity data integration stage, ensuring the representativeness of the list in terms of trophic level and ecological function. These 40 initially selected species and predictions for some other species are then presented. Reference values ​​are shown in Table 1. Cd toxicity data for the above species were calculated based on literature toxicity data and normalization equations.

[0073] Table 1. Preliminary list of species and EC in the paddy soil ecosystem of southern China 50 value

[0074] Subsequently, Cd toxicity data for the aforementioned species were collected by searching authoritative toxicology databases (such as ECOTOX, Web of Science, and CNKI). The data were screened according to strict quality control standards (such as test specifications, soil property integrity, etc.) to obtain qualified literature data.

[0075] For species lacking reliable literature data, laboratory Cd ecotoxicity tests were conducted according to ISO / OECD standards to obtain their... To eliminate the influence of Cd bioavailability caused by differences in soil properties, a bioavailability correction model based on pH and organic matter content was used to normalize all bioavailability toxicity data, thus obtaining Cd toxicity values ​​for different species. This value ensures the comparability of data.

[0076] Finally, sensitivity ranking and list determination were performed based on the SSD model. Normalized species-equivalent EC50 values ​​were used as the dataset, and a Burr-Type III distribution model was used for fitting, achieving a goodness-of-fit R² of 0.95. The cumulative distribution probability of each species was calculated based on the fitted SSD curves, and sensitivity ranking was performed. Ultimately, the 16 most sensitive species were selected to form the final sensitive ecological receptor list for the derivation of Cd benchmarks in southern paddy soils. This list covers multiple trophic levels and functional groups, including producers, primary consumers, secondary consumers, and decomposers.

[0077] Table 2 List of Sensitive Ecological Species (Receptors)

[0078] To verify the superiority of the method of the present invention, the present invention will compare the list of 16 species selected in this embodiment (the method of the present invention) with the traditional method that uses only 3 common standard species (Eisenia fetida, Leptospira spp., and Maize) to derive the Cd-HC5 (the benchmark value for protecting 95% of ecological species safety) of paddy soil in southern China.

[0079] The results are as follows Figure 3 and Figure 4 As shown, Figure 3 This is a dose-response curve for the toxicity of Cd in a typical species. Figure 4 This paper presents a species sensitivity distribution (SSD) curve obtained using a method for deriving soil Cd environmental baseline values ​​to protect 95% of species. The results show that the HC5 value derived using the method of this invention is 0.82 mg / kg, with a 95% confidence interval of [0.65, 1.02] mg / kg. In contrast, the HC5 value derived using the traditional method is 1.35 mg / kg, with a confidence interval of [0.90, 2.10] mg / kg. This comparison demonstrates that the baseline value derived by the method of this invention (0.82 mg / kg) is more effective in identifying sensitive native species (such as rice), thus providing a higher level of ecological protection. Furthermore, the confidence interval of the HC5 value obtained by the method of this invention is approximately 40% narrower than that obtained by the traditional method, significantly reducing the uncertainty in baseline derivation and improving the scientific validity and reliability of the results. The species in the final list are all common or key species in the southern paddy soil ecosystem, fully demonstrating the advantages of this method in improving the adaptability of the baseline area.

[0080] This embodiment demonstrates that the sensitive ecological receptor screening technology system provided by the present invention can systematically and objectively screen out a list of highly representative sensitive species. The environmental benchmark values ​​derived from this list are more scientific and accurate, and are significantly superior to traditional empirical methods.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for screening sensitive species for deriving soil Cd environmental quality benchmarks, characterized in that, include: S1. Establish a multi-dimensional evaluation index system, perform weighted scoring on candidate species, and screen to obtain a preliminary list of recipient species; S2. For the species in the preliminary list of receptor species, obtain soil Cd toxicity data, and perform soil property normalization processing on the soil Cd toxicity data to obtain a standardized toxicity dataset. S3. Input the standardized toxicity dataset into the species sensitivity distribution model for fitting calculation to generate the species sensitivity distribution curve; S4. Sort the species according to their sensitivity based on the species sensitivity distribution curve, and select sensitive species from the sorting results to form a list of sensitive ecological receptors.

2. The method for screening sensitive species for deriving soil Cd environmental quality standards according to claim 1, characterized in that, Step S1 further includes: S11. Determine the target ecosystem type and extract candidate species related to the target ecosystem type from the native species database; S12. Establish a multi-dimensional evaluation index system and calculate the multi-index score for each candidate species; S13. Perform a weighted summation calculation based on the preset weight values ​​of the multi-indicator scores to obtain the comprehensive score of each candidate species; S14. Sort the species from high to low according to the comprehensive scores, select candidate species within the preset ranking range, and form a preliminary list of recipient species.

3. The method for screening sensitive species for deriving soil Cd environmental quality benchmarks according to claim 2, characterized in that, The multidimensional evaluation index system in step S12 includes ecological relevance indicators, trophic level indicators, laboratory culturability indicators, and economic conservation value indicators.

4. The method for screening sensitive species for deriving soil Cd environmental quality benchmarks according to claim 1, characterized in that, Step S2 further includes: S21. By searching public databases, collect Cd toxicity data of multiple species in the preliminary list of receptor species to obtain raw toxicity data; S22. Perform quality screening on the original toxicity data according to the preset quality standards to obtain qualified toxicity data; S23. Identify species in the preliminary list of receptor species that lack qualified toxicity data, conduct Cd toxicity experiments according to standardized testing methods, and determine experimental toxicity data. S24. Integrate the qualified literature toxicity data and the experimental toxicity data to construct an integrated toxicity dataset; S25. Extract soil property parameters corresponding to multiple toxicity data in the integrated toxicity dataset, and normalize the toxicity values ​​in the integrated toxicity dataset using a bioavailability correction model. Convert the toxicity values ​​under different soil conditions into equivalent toxicity values ​​under standard reference soil conditions to form a standardized toxicity dataset.

5. The method for screening sensitive species for deriving soil Cd environmental quality benchmarks according to claim 4, characterized in that, In step S22, the quality standards include: a reasonable control group for the experiment, a clear exposure pathway, complete soil property parameters, sufficient number of experiment repetitions, sufficient concentration gradients, and the presence of compound pollution interference.

6. The method for screening sensitive species for deriving soil Cd environmental quality benchmarks according to claim 4, characterized in that, In step S23, the standardized testing method follows the OECD or ISO standard testing specifications. The test endpoints for Cd toxicity experiments conducted according to the standardized testing method include acute mortality rate, reproductive inhibition rate, growth inhibition rate, and germination inhibition rate.

7. The method for screening sensitive species for deriving soil Cd environmental quality standards according to claim 4, characterized in that, In step S25, the soil property parameters include pH value, organic matter content and cation exchange capacity, and the bioavailability correction model adopts a linear regression model or species sensitivity distribution extrapolation method.

8. The method for screening sensitive species for deriving soil Cd environmental quality benchmarks according to claim 1, characterized in that, Step S3 further includes: S31. Select a probability distribution model, wherein the probability distribution model includes the Burr-Type III distribution model and the Log-Normal distribution model; S32. Using the equivalent toxicity values ​​of multiple species in the standardized toxicity dataset as model input, parameter estimation and fitting optimization are performed through the probability distribution model to generate an initial sensitivity distribution curve; S33. Perform a goodness-of-fit test on the initial sensitivity distribution curve to obtain the species sensitivity distribution curve.

9. The method for screening sensitive species for deriving soil Cd environmental quality standards according to claim 1, characterized in that, Step S4 further includes: S41. Based on the species sensitivity distribution curve, calculate the cumulative distribution probability value of multiple species on the curve; S42. Sort the species by sensitivity from high to low according to the cumulative distribution probability value, and mark the highly sensitive species near the HC5 protection threshold to obtain the sorting results. S43. Select species within a preset sensitivity range from the sorting results to obtain screened species; S44. Perform a trophic level coverage test on the screened species to confirm that the screened species cover at least three trophic levels among producers, primary consumers, secondary consumers, and decomposers; if the trophic level coverage is insufficient, supplement the selection of sensitive species in the corresponding trophic levels to form the final list of sensitive ecological receptors.

10. A sensitive species screening system for deriving soil Cd environmental quality benchmarks, characterized in that, include: Weighted module: Used to establish a multi-dimensional evaluation index system, perform weighted scoring on candidate species, and screen to obtain a preliminary list of recipient species; Acquisition module: used to acquire soil Cd toxicity data for species in the preliminary receptor species list, and to perform soil property normalization processing on the soil Cd toxicity data to obtain a standardized toxicity dataset; Fitting module: used to input the standardized toxicity dataset into the species sensitivity distribution model for fitting calculation and generate species sensitivity distribution curve; Output module: used to sort species by sensitivity according to the species sensitivity distribution curve, select sensitive species from the sorting results, and form a list of sensitive ecological receptors.

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