A method and system for ecological risk assessment of waste facility siting

Abstract

This invention discloses a method and system for ecological risk assessment of waste facility site selection, relating to the fields of ecological environment management and spatial planning technology. Addressing the shortcomings of existing methods for ecological risk assessment of waste facility site selection—namely, their inability to quantify dynamic interactions between species, their inability to simulate cascading disturbance effects, and their lack of long-term systematic risk quantification—this invention involves constructing a probabilistic model integrating high-order ecological dynamic processes, multi-source observation data, environmental stress, and spatial effects; utilizing efficient algorithms for parameter estimation; quantifying the multi-dimensional stability of the ecosystem; simulating the dynamic cascading effects caused by facility operation; and constructing a long-term ecological risk index integrating spatiotemporal factors. Finally, combining Pareto front analysis to achieve multi-objective optimized site selection and generate an ecological compensation priority map. This invention overcomes the limitations of traditional static assessments, achieving a closed loop from dynamic simulation of ecological mechanisms to scientific risk decision-making, significantly improving the scientific rigor of site selection assessment and the accuracy of decision support.

Description

Technical Field

[0001] This invention relates to the field of ecological environment management and spatial planning technology, specifically to a method and system for ecological risk assessment of waste facility site selection. Background Technology

[0002] With socio-economic development, the demand for waste treatment facilities is increasing. Their site selection involves not only engineering and economic factors but also poses a long-term, potentially significant risk to the safety of the surrounding ecosystem. Traditional ecological impact assessment methods mainly rely on static, single-factor analysis paradigms, such as delineating buffer zones to circumvent legally mandated ecological protection red lines or comparing pollutant concentrations with environmental quality standards. While these methods are simple to implement, they have significant limitations.

[0003] An ecosystem is a dynamic network composed of multiple species coupled through complex nonlinear relationships. Traditional methods treat species or habitats as isolated units, failing to quantify dynamic interactions between species or simulate how local disturbances can generate cascading amplification effects through the network structure, thus often severely underestimating systemic risks. Furthermore, traditional assessments are mostly based on deterministic point estimates, unable to effectively integrate and handle uncertainties such as noise, missing data, and excessive dispersion prevalent in ecological monitoring data, leading to insufficient reliability of assessment results. Finally, existing methods struggle to dynamically and spatially quantify the entire process of site selection, stress, ecological response, and long-term risks, failing to provide refined decision support for seeking the optimal trade-off between ecological protection and development needs. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method and system for ecological risk assessment of waste facility site selection, which solves the technical problems that existing methods for ecological risk assessment of waste facility site selection are unable to quantify dynamic interactions between species, cannot simulate cascade effects of disturbances, and lack long-term systematic risk quantification basis.

[0005] An ecological risk assessment method for the site selection of waste facilities includes the following steps:

[0006] S110. Obtain ecological monitoring data, basic geographic data, and design parameters of candidate waste facilities for the target area; preprocess them according to data types to form a spatiotemporally consistent multidimensional dataset.

[0007] S120. Based on the multidimensional dataset formed in step S110, a Bayesian hierarchical state-space model is constructed. The model includes: a process model for describing the nonlinear dynamic interaction and hysteresis effects of multiple species, an observation model for fusing multi-source heterogeneous ecological monitoring data, and a random effects module for quantifying spatial dependencies. The Hamiltonian Monte Carlo algorithm or its adaptive variant is used to perform posterior parameter estimation on the model, and the model convergence diagnosis and verification are completed.

[0008] S130. Using the model calibrated in step S120, simulate the long-term dynamic evolution process of the ecosystem under the scenario of no external stress, and determine the equilibrium state of the ecosystem; based on the equilibrium state, quantitatively evaluate the inherent stability state of the ecosystem from three dimensions: local mathematical stability, global network structural stability, and functional stability, and identify key species and vulnerable structures in the ecological network.

[0009] S140. For a specific candidate waste facility site selection scheme, run its physical diffusion model and biotoxicity effect model to generate a multi-dimensional dynamic stress signal with spatiotemporal distribution; use the dynamic stress signal as a time-varying driving factor and input it into the Bayesian hierarchical state space model calibrated in step S120; use the equilibrium state obtained in step S130 as the simulation starting point to conduct a long-term forward-looking simulation to simulate the interference of the facility operation on the ecological network.

[0010] S150. Based on the dynamic simulation results of step S140, calculate the deviation of the ecosystem's state from the inherent stability state described in step S130 in multiple dimensions, and use it as a risk sub-indicator; integrate the risk sub-indicator, the proportion of spatial area affected by stress, and the time cumulative effect to construct a comprehensive long-term ecological risk index; with the dual objectives of minimizing the long-term ecological risk index and minimizing economic costs, conduct Pareto front analysis on all candidate site selection schemes to identify the optimal trade-off solution set; simultaneously, generate an ecological compensation priority zoning map based on the spatial distribution of the risk sub-indicator.

[0011] Furthermore, in step S120, the process model is a high-order generalized Lotka-Volterra model, the observation model is a negative binomial distribution model, and the module for quantifying spatial dependencies is implemented through conditional autoregressive priors.

[0012] Further, in step S130, the local mathematical stability is evaluated by calculating the real part of the largest eigenvalue of the Jacobian matrix at the equilibrium point of the process model; the global network structural stability is evaluated by calculating at least one of the modularity and node centrality indices of the species interaction network; and the functional stability is evaluated by applying perturbations in the simulation and calculating the resistance and resilience of the ecosystem.

[0013] Further, in step S140, the physical diffusion model includes a Gaussian plume model for simulating the diffusion of atmospheric pollutants and / or a convection-diffusion model for simulating the migration of groundwater pollutants; the biotoxicity effect model is based on a dose-response relationship and converts pollutant concentration into stress intensity for a specific species or functional group.

[0014] Further, in step S140, the multi-dimensional dynamic stress signal is generated by nonlinearly weighted fusion of at least two of the chemical toxicity stress factor, physical invasion stress factor, and noise interference stress factor.

[0015] Furthermore, in step S150, the risk sub-indicators include at least two of the following: the cumulative loss rate of key species abundance, the decline rate of ecological network connectivity efficiency, and the increase rate of ecosystem recovery time.

[0016] Further, in step S150, the long-term ecological risk index is the weighted sum of the risk sub-indicators, the proportion of area affected by stress, and the product of the time accumulation factor.

[0017] Further, in step S150, the Pareto front analysis specifically involves: calculating the long-term ecological risk index and unit economic cost of all candidate site selection schemes, identifying all non-dominated solutions in the two-dimensional target space, and the set of non-dominated solutions constitutes the Pareto front, providing a set of optimized schemes for the final decision.

[0018] An ecological risk assessment system for waste facility site selection based on a Bayesian hierarchical state-space model includes functional modules for executing the steps of the method. Specifically, the system includes:

[0019] The data interface and preprocessing module are used to perform step S110.

[0020] A Bayesian hierarchical state-space model engine and parameter inference module are used to execute step S120;

[0021] The module for multi-dimensional quantification of ecological network stability and identification of key elements is used to perform step S130.

[0022] The stress-ecological response dynamic coupling simulation module is used to execute step S140;

[0023] The long-term risk comprehensive assessment and multi-objective decision support module is used to perform step S150.

[0024] The beneficial effects of this invention include:

[0025] 1. By organically unifying high-order ecological dynamics models, observation models for handling discrete and over-discrete data, environmental stress driving mechanisms, and spatial statistical models into a Bayesian hierarchical state space framework, it can simultaneously characterize the endogenous dynamic complexity of ecosystems, the driving effect of exogenous stresses, and spatial heterogeneity, thus achieving holistic probabilistic modeling of the coupling relationship between natural processes and human interference.

[0026] 2. For the constructed complex high-dimensional model, HMC / NUTS is applied for parameter estimation, which effectively solves the problems of slow convergence and low efficiency of traditional methods, and ensures the feasibility and reliability of uncertainty quantification in a large-scale parameter space.

[0027] 3. It breaks through the single stability perspective and establishes a multi-dimensional stability quantification system covering stability, robustness, and resilience. Combined with dynamic simulation to simulate the cascading effect of disturbances, it comprehensively and accurately reveals the generation and transmission mechanism of systemic ecological risks.

[0028] 4. By transforming complex model outputs into intuitive long-term ecological risk indices and Pareto optimal solution sets, a leap has been achieved from esoteric ecological models to a clear management decision-making interface. This supports both scientific site selection and guides ecological compensation, forming a closed-loop decision support capability from risk assessment and optimized site selection to compensation and restoration. Attached Figure Description

[0029] Figure 1 This is an overall flowchart of the ecological risk assessment method for waste facility site selection involved in the embodiments of this application.

[0030] Figure 2 This is a schematic diagram of the architecture of the Bayesian hierarchical state space model involved in the embodiments of this application.

[0031] Figure 3 This is a schematic diagram illustrating multi-objective location optimization using Pareto front analysis as described in the embodiments of this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0033] Example 1

[0034] The following is in conjunction with the appendix Figure 1 Specific embodiments of the present invention will be described in detail;

[0035] like Figure 1 As shown, an ecological risk assessment method for the site selection of waste facilities mainly includes the following steps:

[0036] Step S110, Data Preparation and Preprocessing:

[0037] Collect long-term series ecological monitoring data, basic geographic and infrastructure data for the target area. Preprocess the data according to data type to form a spatiotemporally consistent multidimensional dataset.

[0038] Ecological monitoring data includes species abundance data and environmental ancillary data. The species abundance data is obtained from a long-term ecological monitoring network in the target area. The data format is tabular, containing fields such as: station ID, survey date (year-month-day), species Latin name, observation count, and survey method. For example, data from a 10-year ecological monitoring network of a provincial nature reserve and its surrounding 50-kilometer buffer zone was obtained using the transect method. Example data snippet: Station "A01", Date "2020-05-10", Species "Parus major", Count "15", Method "Transect method".

[0039] The environmental auxiliary data includes remote sensing data products for the corresponding time period and region, including the Normalized Difference Vegetation Index (NDVI, with a value range of [-1,1]) derived from MODIS or Landsat, land cover classification maps, and nighttime light index. The land cover classification map includes forests, grasslands, farmland, water bodies, and built-up areas, and the nighttime light index can be used as a proxy variable for human activity intensity.

[0040] The basic geographic and facility data includes spatial foundation data and candidate facility data. The spatial foundation data includes a digital elevation model (DEM, 30-meter resolution) of the target area, soil type maps, water system distribution maps, and administrative division maps. The candidate facility data includes multiple candidate site selection points P1, P2, ..., Pk for waste treatment facilities. Data for each site selection point includes: site selection point ID, center point coordinates, facility type, designed processing capacity, and key design parameters. For example, a municipal solid waste incineration plant with a designed processing capacity of 1000 tons / day. Key design parameters include a chimney height of 80 meters, a flue gas emission temperature of 140°C, and a designed leachate production of 100 tons / day.

[0041] Data preprocessing includes:

[0042] 1. Define a unified analysis scope and spatial grid:

[0043] Based on the research objectives, a target analysis area encompassing all potential facility site selection locations and their potential impact areas is determined. This area is then uniformly divided into a 1 km × 1 km regular grid (or other suitable scale), generating a total of M spatial analysis units. This grid system will serve as the benchmark framework for all subsequent spatial data aggregation, analysis, and simulation.

[0044] 2. Multi-source data collection and classification preprocessing:

[0045] Ecological monitoring point data primarily consists of discrete point data, such as species survey sampling points and monitoring stations. This type of data is interpolated using Kriging interpolation or inverse distance weighted interpolation to transform the observations of discrete points into a continuous raster surface covering the entire target analysis area. Subsequently, this raster data is aggregated into a predefined grid system, and the average or total value within each grid cell is calculated to form gridded species abundance data.

[0046] Environmental background raster data, such as land use / cover maps, NDVI index maps, nighttime light index maps, and digital elevation models (DEMs), are resampled and aligned to ensure they are completely consistent with the spatial resolution and extent of the aforementioned grid system. For categorical data such as land use, the mode distribution method can be used to determine the dominant type of each grid; for continuous data such as NDVI, the average value is used for aggregation.

[0047] The candidate facility location data is primarily discrete point data, such as the coordinates of planned waste incineration plants and landfills. For each candidate facility point... As a separate analysis object, define the scope of influence: by each facility point. Centered on the data, a spatial impact buffer zone is delineated based on the type, scale, and main environmental impact pathways, such as atmospheric diffusion and groundwater pollution. For example, for the impact of air pollutants, a circular buffer zone with a radius of R kilometers (e.g., R=5 or R=10, depending on specific facility parameters) can be preliminarily estimated using a Gaussian diffusion model. This buffer zone is used in subsequent step S140 to define the specific spatial subset of ecological and environmental data that needs to be extracted and input into the BHSSM for dynamic simulation.

[0048] Time alignment and standardization involve uniformly adjusting the time step of all time-series data, such as monthly species observations and quarterly NDVI, to create a complete sequence from time point t=1 to T. T represents the longest monitoring period for the sampled data, such as 120 months. Furthermore, all continuous numerical variables, including standardized species abundance and environmental factors, are z-score standardized to eliminate the influence of dimensions and improve the numerical stability of the model.

[0049] 3. Species selection: To control the complexity of the model, species with a frequency of more than 10% and recorded in at least 5 grids are selected from all observed species to form a species set S, assuming S=50.

[0050] 4. Covariate standardization: All continuous environmental covariates, including NDVI and elevation, are standardized using z-scores to make their mean 0 and standard deviation 1, in order to improve the numerical stability of the model fit.

[0051] Step S120: Construct and calibrate the Bayesian hierarchical state-space model. The specific model architecture is as follows: Figure 2 As shown:

[0052] 1. Build the model.

[0053] Define latent states: Define latent variables , indicating time Grid species The true abundance logarithm of, where , , .

[0054] The process model is a state transition equation, employing a high-order GLV model with first-order lag terms. Specifically, it takes the following form:

[0055]

[0056] Among them, expected value Defined as:

[0057] in, For species The intrinsic growth rate, endowed with a priori ; For species For species The instantaneous interaction effect. Imparting sparse priors. ; For species For species The first-order lag effect imparts sparse priors. ; For species Environmental covariates The response coefficient, prior to which is ; For spatially random effects, subject to conditional autoregressive (ICAR) priors: ,in This is a precision matrix constructed based on grid adjacency relationships. Assign priors to the accuracy parameters. ; Assign a weak prior to the standard deviation of the process error specific to the species. .

[0058] The observation model is a measurement equation, assuming the observed species count. Follows a negative binomial distribution:

[0059]

[0060] in, It is the predicted abundance. For the dispersion parameter (variance = ), giving prior knowledge NegBinomial2 is a built-in parameterization form of Stan.

[0061] 2. Model inference.

[0062] Posterior sampling was performed using Stan's No-U-Turn Sampler (NUTS). Four independent Markov chains were configured, with each chain undergoing a total of 4000 iterations. The first 2000 iterations were used as a warm-up period to adapt parameters such as step size, and the last 2000 iterations were used for sampling.

[0063] After sampling, check all parameters (the key is to check the parameters). )of Statistics. In this embodiment, all parameters are required to be... And effective sample size This ensures sufficient convergence of the sampling. Simultaneously, the parameter trajectory plot is observed to ensure it exhibits no trend drift and is well-mixed.

[0064] Spatiotemporal cross-validation is employed. Specifically, 20% of the grid cells (spatial validation set) and 20% of the time points (temporal validation set) are randomly retained and not used in model training. The trained model is then used to predict these retained data, and the root mean square error (RMSE) and bias between the predicted counts and the actual observed counts are calculated. A qualified model should have a low RMSE (e.g., small relative to the observed mean) and a bias close to 0.

[0065] Step S130: Assess the stability of the baseline ecological network.

[0066] 1. Simulate baseline dynamics: In the absence of external stress (i.e., covariates) Under the condition of taking the long-term average, the process model is run for 500 time steps, simulating approximately 40 years, until the abundance fluctuation of all species, i.e., the standard deviation, is less than 1% of their average. At this point, the system is considered to have reached a dynamic equilibrium state. The abundance vectors of each species at the equilibrium point are recorded. (Length S) and the average state of each grid.

[0067] 2. Evaluate local stability: In each grid Based on the equilibrium point and the estimated interaction matrix Calculate the community Jacobian matrix Matrix elements (when (time), diagonal elements .calculate Find the eigenvalues ​​with the largest real part. Count the results of all M grid cells. If the proportion exceeds 95%, the entire regional ecosystem is considered stable in a local sense.

[0068] 3. Assess global network stability:

[0069] (1) Interspecific interaction matrix based on the average of all grids Take its absolute value and set a threshold, here it means the absolute value is greater than 0.05, generate an S×S adjacency matrix, and use it to define the ecological network.

[0070] (2) Calculate modularity: Use the Louvain algorithm to perform community detection on the network and calculate the network's modularity index. . The higher the value, and the closer it is to 1, the stronger the network's modular structure. (Report) The posterior median and 90% confidence interval.

[0071] (3) Identify key nodes: Calculate the weighted degree centrality and betweenness centrality of each species node in the network. Select the top 5 species in betweenness centrality as key species of the ecological network.

[0072] 4. Assess functional stability

[0073] (1) Resistance At time step 400 of the baseline simulation, an abundance perturbation of -10% is instantaneously applied to all species across all grids. The total biomass of the system at the next time step after the perturbation is calculated. relative to pre-disturbance biomass The percentage decrease:

[0074] Repeat this perturbation-calculation process 100 times using different posterior samples, and report the results. The median and 95% range.

[0075] (2) Restorative power Following the aforementioned disturbance, the simulation continued to run, monitoring the total biomass to recover to pre-disturbance levels. Required time step within the range . The shorter the length, the stronger the recovery. Repeat 100 times and report. The median and 95% range.

[0076] Step S140: Simulate the interference effects of a specific location scheme:

[0077] 1. Physical diffusion modeling:

[0078] (1) Atmospheric diffusion: The AERMOD model was used. Input parameters included: point source coordinates (latitude and longitude of P1), emission parameters (including...). Emission rate chimney geometric height flue gas outlet velocity ,temperature Local hourly meteorological data for the entire year, including wind speed Wind direction, temperature, cloud cover, etc., are from the nearest weather station. Run the model and output according to step S110. Annual average ground concentration distribution in the gridded area (Unit: μg / m³). The model is mainly based on the Gaussian plume formula:

[0079]

[0080] in, These are the lateral and vertical diffusion parameters, which are functions of atmospheric stability and downwind distance.

[0081] (2) If the waste treatment facility is a landfill, assess the groundwater risk: use MODFLOW / MT3DMS to simulate the migration of ammonia nitrogen (as N) in leachate. Input regional hydrogeological parameters, leachate source strength (100 kg / day), and simulation period of 10 years. Output the spatiotemporal distribution of ammonia nitrogen concentration in the aquifer. Its governing equations are the convection-dispersion equations:

[0082]

[0083] in, The dispersion coefficient is... The pore water flow velocity, For source terms.

[0084] 2. Transformation of ecotoxicological effects:

[0085] Consult ecotoxicology literature to obtain representative species effects The dose-response relationship was investigated. A linear relationship was used to investigate the growth inhibition rate of the native oak species. ,in Threshold concentration, This represents the slope coefficient. The stress factor for each grid cell is calculated. (Value range [0,1]).

[0086] For groundwater pollution, the risk quotient method is used: The PNEC (predicted no-effect concentration) is taken as 1 mg / L (as N). This indicates that there is a risk.

[0087] 3. Construct a comprehensive stress signal:

[0088] For incineration plants, the main considerations are air pollution and physical land occupation (assuming the land occupation results in the loss of 100 hectares of forest land). Define the grid. Comprehensive Stress Index :

[0089]

[0090] in, The weight is 1 within the occupied area, and 0 otherwise; , .Will Normalized to the [0,1] interval, the stress signal for each grid at each time step is obtained. .

[0091] 4. Dynamic ecological response simulation:

[0092] Modify the process model in step S120: As an additional, time-varying stress covariate, it affects the growth rate in a multiplicative manner: .in, It is a species Sensitivity parameters to stress are assigned priors. (Assuming most species are moderately sensitive).

[0093] The stress-free ecosystem balance state, i.e., the species abundance vectors, obtained in step S130 above, are used. The corresponding stability index is used as the initial baseline state (t=0) for dynamic simulation, and the predicted values ​​for the next 50 years (t=1 to 600 months) are used. The first 10 years The curve linearly increases to a maximum and then remains stationary. A modified BHSSM is then run to simulate the trajectory of ecosystem state changes over the next 50 years. Abundance predictions for each time step, each grid cell, and each species, along with their posterior uncertainties, are saved.

[0094] Step S150: Quantify long-term risks and support decision-making:

[0095] 1. Calculate the Long-Term Ecological Risk Index (LTERI):

[0096] Based on the simulation results of step S140, by comparing the system state at the end of the simulation (year 50) with the baseline state established in step S130, the following risk sub-indicators are calculated, including:

[0097] Key species abundance loss rate Calculate the percentage decrease in the average abundance of key species identified in step S130 at the end of the simulation compared to their average abundance at the baseline.

[0098] Network structure degradation rate Based on the species interaction relationships at the end of the simulation, the global efficiency (or modularity) index of the ecological network is recalculated, and the percentage decrease of this index compared to the baseline state (the value calculated in step S130) is calculated.

[0099] System restoring force decay rate At the end of the simulation, the system recovery time is tested again using the same perturbation method as in step S130 for testing functional stability. .calculate Compared to baseline recovery time (From S130) percentage increase.

[0100] The above indicators represent the degree of deviation of an ecosystem from its inherent health baseline (S130) under stress, thus providing an objective and quantitative definition of risk.

[0101] Expert weighting: The weight vector was determined by soliciting opinions from five ecologists using the Delphi method. .

[0102] Area of ​​influence A: Calculated over the entire 50-year simulation. The proportion is obtained by dividing the total area of ​​grids with a stress level consistently greater than 0.5 (moderate stress) by the total area of ​​the study area. .

[0103] Time accumulation factor : Using exponential decay weighting,

[0104]

[0105] Among them, the decay time constant The year has made the near-term risk weighting higher.

[0106] The Long-Term Ecological Risk Index (LTERI) is as follows:

[0107]

[0108] in, This is the weighted sum of all risk sub-indicators, representing the intensity of the risk; A is the proportion of the area affected by stress; F is the time accumulation factor (e.g., ...). ).

[0109] 2. Multi-objective optimized location selection:

[0110] For K candidate site selection points Repeat steps S140-S150 to calculate the value of each point. and economic cost per unit investment (Including construction, transportation, and environmental taxes, etc., unit: million yuan / year).

[0111] On a two-dimensional plane Plot all points for coordinates. Identify the Pareto optimal front using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) (see...). Figure 3 Points on the frontier (such as points A, B, and C) represent the optimal set of solutions that cannot improve any objective without worsening the other objective.

[0112] Policymakers can choose based on policy preferences: if ecology is a priority, choose the point with the lowest LTERI on the frontier, such as... Figure 3 Midpoint A; if cost is the priority, choose the point with the smallest cost (C); if equilibrium is considered, the middle of the frontier, such as point B, can be chosen.

[0113] 3. Generate an ecological compensation priority map:

[0114] For the finally selected solution, such as Figure 3 Midpoint B, its spatial risk intensity during the LTERI calculation process. Perform kriging spatial interpolation to generate a risk distribution raster map.

[0115] The data is overlaid from a map of priority areas for biodiversity conservation in the regional ecological plan and a map of important water conservation areas calculated based on the InVEST model. The three raster layers are reclassified (high, medium, low) and then overlaid with equal weights for analysis.

[0116] Areas that are simultaneously classified as high-risk, of high biodiversity importance, and of high water conservation importance will be designated as first-level priority compensation zones, providing direct and quantitative spatial guidance for the site selection and funding allocation of ecological restoration projects.

[0117] In summary, step S150 integrates the risk change deviation (result of S140) with reference to the S130 baseline into a comprehensive index and spatial map that decision-makers can understand. Pareto front analysis addresses the multi-objective optimization problem of minimizing LTERI (ecological risk) and minimizing economic costs; while the ecological compensation priority map is directly derived from the risk sub-indicators. Spatial distribution The entire process from S130 to S150 constitutes a complete, closed-loop technical chain, from assessing the baseline to predicting changes, and then to quantifying risks and supporting decision-making.

[0118] In another embodiment, an ecological risk assessment system for the site selection of waste facilities is provided, comprising functional modules for performing the steps of the method, the system specifically including:

[0119] The data interface and preprocessing module are used to perform step S110.

[0120] A Bayesian hierarchical state-space model engine and parameter inference module are used to execute step S120;

[0121] The module for multi-dimensional quantification of ecological network stability and identification of key elements is used to perform step S130.

[0122] The stress-ecological response dynamic coupling simulation module is used to execute step S140;

[0123] The long-term risk comprehensive assessment and multi-objective decision support module is used to perform step S150.

[0124] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

Claims

1. A method and system for ecological risk assessment for waste facility siting, characterized in that, Includes the following steps: S110. Obtain ecological monitoring data, basic geographic data, and design parameters of candidate waste facilities for the target area; preprocess them according to data types to form a spatiotemporally consistent multidimensional dataset. S120. Based on the multidimensional dataset formed in step S110, a Bayesian hierarchical state-space model is constructed. The model includes: a process model for describing the nonlinear dynamic interaction and hysteresis effects of multiple species, an observation model for fusing multi-source heterogeneous ecological monitoring data, and a random effects module for quantifying spatial dependencies. The Hamiltonian Monte Carlo algorithm or its adaptive variant is used to perform posterior parameter estimation on the model, and the model convergence diagnosis and verification are completed. S130. Using the model validated in step S120, simulate the long-term dynamic evolution of the ecosystem under no external stress scenario to determine the equilibrium state of the ecosystem; based on the equilibrium state, quantitatively evaluate the inherent stability state of the ecosystem from three dimensions: local mathematical stability, global network structural stability, and functional stability, and identify key species and vulnerable structures in the ecological network. S140. For a specific candidate waste facility site selection scheme, run its physical diffusion model and biotoxicity effect model to generate a multi-dimensional dynamic stress signal with spatiotemporal distribution; use the dynamic stress signal as a time-varying driving factor and input it into the Bayesian hierarchical state space model calibrated in step S120; use the equilibrium state obtained in step S130 as the simulation starting point to conduct a long-term forward-looking simulation to simulate the interference of the facility operation on the ecological network. S150. Based on the dynamic simulation results of step S140, calculate the deviation of the ecosystem's state from the inherent stability state described in step S130 in multiple dimensions, and use it as a risk sub-indicator; integrate the risk sub-indicator, the proportion of spatial area affected by stress, and the time cumulative effect to construct a comprehensive long-term ecological risk index; with the dual objectives of minimizing the long-term ecological risk index and minimizing economic costs, conduct Pareto front analysis on all candidate site selection schemes to identify the optimal trade-off solution set; simultaneously, generate an ecological compensation priority zoning map based on the spatial distribution of the risk sub-indicator.

2. The method and system for ecological risk assessment of waste facility site selection according to claim 1, characterized in that, In step S120, the process model is a high-order generalized Lotka-Volterra model, the observation model is a negative binomial distribution model, and the module for quantifying spatial dependencies is implemented through conditional autoregressive priors.

3. The method and system for ecological risk assessment of waste facility site selection according to claim 1, characterized in that, In step S130, the local mathematical stability is evaluated by calculating the real part of the largest eigenvalue of the Jacobian matrix at the equilibrium point of the process model; the global network structure stability is evaluated by calculating at least one of the modularity and node centrality indices of the species interaction network. The functional stability is assessed by applying perturbations in a simulation and calculating the resistance and resilience of the ecosystem.

4. The method and system for ecological risk assessment of waste facility site selection according to claim 1, characterized in that, In step S140, the physical diffusion model includes a Gaussian plume model for simulating the diffusion of atmospheric pollutants and / or a convection-diffusion model for simulating the migration of groundwater pollutants; the biotoxicity effect model is based on a dose-response relationship and converts pollutant concentration into stress intensity for a specific species or functional group.

5. The method and system for ecological risk assessment of waste facility site selection according to claim 1, characterized in that, In step S140, the multidimensional dynamic stress signal is generated by nonlinearly weighted fusion of at least two of the chemical toxicity stress factor, physical invasion stress factor, and noise interference stress factor.

6. The method and system for ecological risk assessment of waste facility site selection according to claim 1, characterized in that, In step S150, the risk sub-indicators include at least two of the following: the cumulative loss rate of key species abundance, the decline rate of ecological network connectivity efficiency, and the increase rate of ecosystem recovery time.

7. The method and system for ecological risk assessment of waste facility site selection according to claim 1, characterized in that, In step S150, the long-term ecological risk index is the weighted sum of the risk sub-indicators, the proportion of area affected by stress, and the product of the time accumulation factor.

8. The method and system for ecological risk assessment of waste facility site selection according to claim 1, characterized in that, In step S150, the Pareto front analysis specifically involves: calculating the long-term ecological risk index and unit economic cost of all candidate site selection schemes, identifying all non-dominated solutions in the two-dimensional target space, and the set of non-dominated solutions constitutes the Pareto front, providing a set of optimized schemes for the final decision.

9. An ecological risk assessment system for waste facility site selection based on a Bayesian hierarchical state-space model, characterized in that, The system includes functional modules for performing the steps of the method as described in any one of claims 1 to 8, and specifically includes: The data interface and preprocessing module are used to perform step S110. A Bayesian hierarchical state-space model engine and parameter inference module are used to execute step S120; The module for multi-dimensional quantification of ecological network stability and identification of key elements is used to perform step S130. The stress-ecological response dynamic coupling simulation module is used to execute step S140; The long-term risk comprehensive assessment and multi-objective decision support module is used to perform step S150.

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