Environmental health risk assessment method and system for combined pollution of metal smelting area

Abstract

The invention belongs to the technical field of environmental health risk identification and supervision, and discloses an environmental health risk assessment method and system for combined pollution of a metal smelting area, and the method comprises the steps: S1, constructing an environmental health risk assessment system; s2, acquiring and inputting data; s3, heavy metal environmental health hazard identification; s4, evaluating receptors of heavy metal environmental health; s5, heavy metal environmental health hazard assessment; s6, evaluating the exposure of the heavy metal environment health; s7, comprehensive evaluation of heavy metal environment health; s8, judging the supervision priority; and S9, performing closed-loop calibration and dynamic updating. According to the method and the system provided by the invention, environmental health risk assessment of metal smelting area combined pollution is carried out based on the GIS grid and internal and external exposure coupling, and differential supervision aid decision-making with explainable risk contribution, adjustable assessment result and dynamically updated supervision priority area is realized.

Description

An Environmental Health Risk Assessment Method and System for Complex Pollution in Metal Smelting Areas Technical Field

[0001] This invention belongs to the field of environmental health risk identification and supervision technology, specifically involving an environmental health risk assessment method and system for complex pollution of multiple heavy metals in metal smelting areas under multiple exposure media, and particularly involving an integrated assessment and supervision scheme based on GIS grid-based monitoring objects, coupled assessment of internal and external exposures, source contribution decomposition, and closed-loop verification and updating. Background Technology

[0002] Waste gas, wastewater, solid waste, and fugitive dust generated during metal smelting activities can migrate and transform in the air, soil, water, and food chain, posing health risks through inhalation, ingestion, and skin contact. Metal smelting areas typically contain both smelter emission sources and slag dump release sources, exhibiting multi-source superposition and interactive enhancement characteristics in pollution processes, with significant spatial heterogeneity.

[0003] To address the health risk monitoring, management, and control of heavy metal pollution, and to effectively and scientifically assess the health damage caused by heavy metal pollutants, relevant associations and local governments have formulated a number of standards in recent years. Examples include: the industry standard "General Guidelines for Ecological and Environmental Health Risk Assessment" (HJ 1111-2020), the group standard "Technical Specifications for Heavy Metal Environmental Health Risk Assessment" (T / CSES 38-2021), and the industry standard "Technical Guidelines for Environmental Health Risk Assessment of Chemical Substances" (WS / T777-2021).

[0004] However, existing methods for prioritizing pollutant regulation have the following technical defects: (1) The assessment objects are too broad: existing methods mostly take enterprises or single pollution sources as assessment objects, lacking the ability to conduct refined regulation for "grid blocks", and cannot directly correspond regulatory conclusions to geographical units that can be inspected and enforced; (2) The source contribution is not decomposable: for the simultaneous existence of smelter emissions and secondary dust / leaching release from slag dumps in metal smelting areas, existing methods lack the ability to quantitatively decompose the contribution of the two types of pollution sources in each exposed medium, resulting in a lack of basis for differentiated management; (3) The internal and external exposures are disconnected: existing methods mostly focus on risk calculation of external exposure medium measurements, without Including human exposure biomarkers (blood / urine heavy metal concentration) and influencing biomarkers (oxidative stress, liver and kidney function indicators) in the same risk index calculation link makes it difficult for priority regulatory areas to match the actual exposure response of the population; (4) Arbitrary weight determination: The weights of existing methods are mostly fixed values ​​or subjective assignments, lacking a dynamic update mechanism driven by monitoring data, making it difficult to adapt to the spatiotemporal dynamic changes of pollution in smelting areas; (5) Lack of closed-loop calibration: The verification and update links of existing methods are mostly qualitative descriptions, lacking an automatic parameter write-back mechanism triggered by the residual of control data, making it difficult to guarantee the long-term stability and reliability of the assessment results.

[0005] Therefore, there is an urgent need to establish an environmental health risk assessment method and system that is oriented towards regulatory needs, can output enforceable priority regulatory areas at the grid scale, has interpretable decomposition of source contributions, coupled assessment of internal and external exposures, and closed-loop dynamic verification and updating capabilities. Summary of the Invention

[0006] This invention provides an environmental health risk assessment method and system for complex pollution in metal smelting areas. Based on GIS grid and internal and external exposure coupling and other collaborative technologies, it can achieve (1) interpretable decomposition of the source strength contribution of dual-source collaborative pollution; (2) one-to-one correspondence between risk assessment results and grid-based regulatory objects; (3) inversion calibration of external exposure model parameters using internal exposure biomarkers; and (4) closed-loop dynamic updates of priority regulatory areas based on verification results to solve the above problems.

[0007] The technical solution provided by this invention is: a method for assessing the environmental health risks of complex pollution in metal smelting areas, comprising the following steps: S1, Spatial unit construction: obtaining the boundary of the metal smelting area to be assessed, establishing a unified coordinate system based on GIS, and dividing the assessment area into multiple gridded blocks according to a preset grid size Δ (50-500m); establishing a spatial attribute table for each gridded block, wherein the spatial attribute table includes at least: topographic elevation, prevailing wind direction / speed, surface runoff direction, land use type, and distance from the smelter emission point and slag dump boundary; S2, Multi-source data acquisition and entry: for Each gridded block collects and records the concentration data of at least two heavy metals in four types of exposure media (ambient air, drinking water, soil, and food / crops), and simultaneously records human exposure data, which includes at least one exposure marker (heavy metal concentration in blood / urine / hair) and one impact marker (oxidative stress or liver and kidney function indicators); S3, Pollution source-media contribution decomposition: Contribution calculations are established for smelter emission sources and slag dump sources respectively. Based on the spatial attribute table, the source contribution coefficient αs,m of each gridded block in each exposure medium is calculated (s is... S4. External Exposure Score: For the three exposure routes of respiratory inhalation, digestive ingestion, and skin contact, the daily average exposure dose of each route is calculated based on C′m, and normalized and mapped to an external exposure score Sout (0-40), wherein at least the following conditions must be met: each of the four exposure media is assigned a weight wm and Σwm=1; S5. Internal Exposure Score: The exposure markers and impact markers are mapped to an internal exposure score Sin (0-60) according to the threshold range, and the sensitivity factors of the population (children, pregnant women, occupational groups) are also considered. S6. Modular index score formation: Hazard identification score Sh, receptor assessment score Sr, hazard assessment score Sd, and exposure assessment score Se are formed, where Se = Sout + Sin′; S7. Dynamic weight update and environmental health risk index calculation: Using the time series of monitoring data as input, grey relational analysis is used to obtain the correlation degree γi between each indicator and the target risk (total carcinogenic risk TCR and total non-carcinogenic risk HI), and the weights Wi are updated under constraints to calculate the environmental health risk index EHRI: EHRI = Σ(Xi×Wi); S8, Risk Level and Regulatory Priority Determination: The risk level of each gridded block is determined based on the joint threshold matrix of EHRI, TCR, and HI, and the risk level is mapped to the regulatory priority; S9, Closed-Loop Calibration and Dynamic Update: For gridded blocks determined to have a regulatory priority of not less than the preset level, external comparison data or on-site retest data are imported to calculate the residual ε; When ε exceeds the allowable threshold ε0, the calibration write-back of αs,m or Wi is triggered, and S2-S9 are repeated according to the update cycle T to dynamically update the priority regulatory area.

[0008] An environmental health risk assessment system for complex pollution in metal smelting areas, used to implement the method, includes: a spatial modeling module for establishing a GIS coordinate system, gridded blocks, and their spatial attribute tables; a data acquisition and input module for collecting and inputting data on four types of exposure media and human body exposure data; a source contribution decomposition module for calculating αs,m and generating a correction concentration C′m; an external exposure assessment module and an internal exposure assessment module for outputting Sout and Sin′, respectively; an index score generation module for outputting Sh, Sr, Sd, and Se; a dynamic weight update module for calculating γi and updating Wi under constraints; a comprehensive risk assessment module for calculating EHRI and outputting the risk level; a regulatory priority determination and closed-loop calibration module for outputting regulatory priorities and triggering model parameter writeback based on residual ε; and a cloud server and at least one network terminal communicatively connected to the above modules.

[0009] Compared with the prior art, the present invention has at least the following advantages: (1) Source contribution can be explained and decomposed: Through the quantitative calculation of the source contribution coefficient αs,m, the contribution of the smelter and the slag yard in each exposure medium is separated. Through the source contribution decomposition, the regulatory object is refined from "enterprise" to "grid + source type", and the differentiated control list of different regulatory objects such as smelter and slag yard can be directly output; (2) Coupled assessment of internal and external exposure: After the introduction of internal exposure markers, the consistency between the priority regulatory grid and the population exposure response is significantly improved by combining the external exposure dose calculation with the internal exposure biomarker inversion calibration, and the assessment results can better reflect the real health risks; (3) Dynamic weight constraint update: Dynamic weight optimization significantly improves the model accuracy, and the gray constraint is adopted. The initial weights are obtained through joint analysis and dynamically optimized under constraints, avoiding the problem of arbitrary weight assignment and making the weight allocation more objective and adaptable; (4) Closed-loop calibration reduces errors: the parameter write-back is triggered by the residual threshold to form a closed-loop process of verification-calibration-update, so that the residual of the model converges to within the threshold ε0 under the retest data, ensuring the long-term stability of the evaluation results, and the positioning stability of the priority regulatory area can reach more than 95%; (5) Joint threshold reduces misjudgment: the EHRI-TCR-HI joint threshold matrix is ​​used to determine the risk level, which comprehensively considers the comprehensive risk index, carcinogenic risk and non-carcinogenic risk, reducing the misjudgment that may be caused by a single indicator. This joint threshold matrix reduces the misjudgment rate of high-risk grids to below 5%. Attached Figure Description

[0010] Figure 1 is a schematic diagram of the module structure and main process of the environmental health risk assessment system for compound pollution in metal smelting areas under multiple exposure media according to an embodiment of the present invention; Figure 2 is a schematic diagram of the main process of the environmental health risk assessment method for compound pollution in metal smelting areas under multiple exposure media according to an embodiment of the present invention; Figure 3 is a schematic diagram of the pathway of human body exposure to heavy metals through multiple media according to an embodiment of the present invention. Detailed Implementation

[0011] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0012] Referring to Figures 1-3, the methods and systems provided in the following embodiments of the present invention need to comprehensively consider the overall geographical scope of the entire metal smelting area and the assessment of heavy metal environmental health risk levels and regulatory priorities corresponding to each gridded block. They adopt a multi-stage, multi-model, and multi-dimensional evaluation process for environmental health risk assessment and regulatory priority classification, and a network-based environmental health risk assessment system. This system can make a scientific and comprehensive assessment of the environmental health risks of heavy metal compound pollution in metal smelting areas under multiple exposure media, and can quickly and accurately identify areas that need priority control to support efficient and low-cost environmental regulatory enforcement.

[0013] Example 1: The environmental health risk assessment method and system for complex pollution in metal smelting areas under multiple exposure media provided by the present invention focuses on the system architecture and data entry. As shown in Figure 1, the assessment system in this example includes a cloud server and multiple network terminals. The cloud server has a built-in environmental health risk assessment program, and the network terminals are used for data entry, parameter configuration, result display and export.

[0014] The environmental health risk assessment program includes the following functional modules: (1) Spatial modeling module: used to establish a GIS coordinate system, execute an adaptive gridding partitioning algorithm, and generate gridded blocks and their spatial attribute tables; (2) Data acquisition and entry module: used to collect and enter enterprise production data (including metal types, smelting years, smelting processes, environmental protection measures, slag storage area, storage time, protection measures and treatment status, etc.), monitoring data of four types of exposure media (concentration of heavy metals in ambient air, drinking water, soil, and diet / crops), and human body exposure data (concentration of heavy metals in blood / urine, oxidative stress indicators, and liver and kidney function indicators); (3) Source contribution decomposition module: used to calculate the source contribution coefficient αs,m and generate the correction concentration C′m; (4) External exposure assessment module: used to calculate the source contribution coefficient αs,m and generate the correction concentration C′m; Calculate the average daily exposure dose for each exposure route and output the external exposure score Sout; (5) Internal exposure assessment module: used to map the biomarker observation value to the internal exposure score Sin and perform population sensitivity correction to obtain Sin′; (6) Index score generation module: used to output the hazard identification score Sh, receptor assessment score Sr, hazard assessment score Sd and exposure assessment score Se; (7) Dynamic weight update module: used to calculate the grey relational degree γi and update the weight Wi under constraints; (8) Risk comprehensive assessment module: used to calculate the environmental health risk index EHRI, total carcinogenic risk TCR, total non-carcinogenic risk HI and output the risk level; (9) Regulatory priority determination and closed-loop calibration module: used to output the regulatory priority and trigger the model parameter writeback based on the residual ε.

[0015] The network terminal includes a regulatory terminal and a public terminal. The regulatory terminal is used to configure the grid size Δ, threshold matrix, and update cycle T, and to view the map of priority regulatory areas. The public terminal is used to query the risk level of gridded blocks and health protection tips.

[0016] Example 2: Based on Example 1, this example uses a copper-iron smelting area as the evaluation object. The area is approximately 25 km², containing one smelter and three slag heaps. Adaptive gridding and spatial attribute table construction are performed. The adaptive gridding method includes the following steps: Step 1: Rasterize the evaluation area with an initial grid side length d0 = 200 m, generating approximately 625 initial grids; Step 2: Import the spatial layer, including smelter locations, slag heap area, topographic elevation, water system distribution, prevailing wind direction (northeast, average annual wind speed 2.8 m / s), land use type (industrial land, farmland, residential area, etc.), and population distribution. Step 3: Calculate the coefficient of variation (CV) and receptor sensitivity gradient (G) of the monitored concentration for each grid. Step 4: Determine whether CV or G exceeds the preset threshold (CV0=0.5, G0=0.3). If so, subdivide the grid according to the quadtree rule (halve the side length) and repeat steps 3-4 until CV≤CV0 and G≤G0 are satisfied or the minimum grid side length dmin=50m is reached. Step 5: Establish a spatial attribute table for each grid, including: grid number, center coordinates, terrain elevation, prevailing wind direction / speed, surface runoff direction, land use type, distance from the smelter emission point, distance from the slag dump boundary, population density, distribution of sensitive targets, etc.

[0017] After the above adaptive meshing process, approximately 1200 meshed blocks were generated. The mesh density was higher in the area around the smelter and downwind of the slag dump (50-100m side length), while the mesh density was lower in the farmland area far from the pollution source (200m side length).

[0018] This embodiment uses an adaptive gridding algorithm to dynamically adjust the grid density based on the spatial heterogeneity of pollution, thereby achieving an optimal balance between monitoring accuracy and computational load.

[0019] Example 3: Based on Examples 1 and 2, this example further decomposes source contribution and calculates co-polluting coupling terms, including the following steps: Step 1: Calculate the emission source strength vector E of the smelter, including: exhaust gas emission flux (obtained through online monitoring data, unit: g / s), wastewater emission flux (obtained through sewage outlet monitoring, unit: m³ / d), and fugitive dust emission flux (estimated through TSP monitoring and emission factor method, unit: g / m² / d); Step 2: Calculate the release source strength vector L of the slag heap, including: leaching flux (obtained through leaching experiments and rainwater monitoring, unit: mg / L), and wind erosion dust flux (obtained through wind tunnel experiments and PM10 monitoring, unit: g / m² / d); Step 3: Based on the distance, wind direction, and terrain parameters in the spatial attribute table, calculate the source contribution coefficient αs,m of each grid using a propagation model: For air medium, a Gaussian plume diffusion model is used: αs,air = (Q / (2π·u·σy·σz)) × exp(-y² / (2σy²)) × exp(-H² / (2σz²)) where Q is the source strength, u is the wind speed, σy and σz are the horizontal and vertical diffusion coefficients, y is the lateral distance, and H is the effective emission height; For soil media, the distance attenuation-topography correction model is adopted: αs,soil = C0 × exp(-k·d) × (1 + η·Δh) where C0 is the reference concentration, k is the attenuation coefficient, d is the distance, η is the topography correction coefficient, and Δh is the elevation difference; Step 4: Calculate the corrected concentration C′m = αs,m × Cm,obs, where Cm,obs is the measured concentration of medium m; Step 5: Construct the synergistic pollution coupling term K, including the superimposed exposure term Kadd = Enorm + Lnorm and the interaction term Kint = β×(Enorm×Lnorm), where β is the synergistic coefficient obtained by regression based on historical monitoring data (in this embodiment, β=0.35, obtained by regression of 5 years of historical data).

[0020] This embodiment, through source contribution decomposition, can quantitatively output the contribution ratio of the smelter and slag dump to the risk of each grid. Taking a high-risk grid as an example, the smelter's contribution accounts for 62% (mainly from exhaust gas deposition), while the slag dump's contribution accounts for 38% (mainly from wind erosion dust), providing a direct basis for differentiated management. By quantitatively characterizing the dual-source interaction enhancement effect through the synergistic pollution coupling term K, the assessment results are more consistent with the actual pollution characteristics of the smelting area.

[0021] Example 4. This example, based on Examples 1-3, further conducts coupled assessment of internal and external exposures and biomarker inversion calibration, including the following steps: Step 1: Calculation of external exposure dose. For the three exposure routes of respiratory inhalation, digestive ingestion, and skin contact, the daily average exposure dose of each route is calculated based on the corrected concentration C′m: Respiratory inhalation dose: Dinh = Cair × IR × EF × ED / (BW × AT) Digestive ingestion dose: Ding = Csoil × IRsoil × EF × ED / (BW × AT) + Cfood × IRfood × EF × ED / (BW × AT) Skin contact dose: Dder = Csoil × SA × SL × ABS × EF × ED / (BW × AT) The values ​​of each parameter are taken with reference to the "Handbook of Exposure Parameters for Chinese Population" (published by the Ministry of Environmental Protection in 2013) and localized according to the actual situation in the study area.

[0022] Step 2: Calculation of external exposure score Sout. A stratified scoring structure based on medium and pathway is adopted, with a total of 12 sub-indicators (4 media × 3 pathways). The raw score of each sub-indicator is obtained by "concentration exceedance multiple × exposure frequency × duration", and then Sout is obtained by linear mapping from 0 to 40.

[0023] Step 3: Internal exposure inversion calibration. Blood / urine samples were collected from residents in the study area. The concentrations of heavy metals Cd, Pb, and As were measured as exposure markers. Malondialdehyde (MDA) and superoxide dismutase (SOD) were measured as markers of oxidative stress. Alanine aminotransferase (ALT) and serum creatinine (Cr) were measured as markers of liver and kidney function.

[0024] An internal exposure inversion calibration model is established: the predicted biomarker level Bpred is calculated from the external exposure dose Dex, and the exposure parameter correction Δθ is solved by constrained optimization with the objective function J=Σ(Bpred-Bobs)² as the criterion, and the exposure parameter θ←θ+Δθ is updated to obtain the internal exposure calibration dose Dint.

[0025] Step 4: Calculation of internal exposure score (Sin). Exposure biomarkers are scored using population quantile thresholds: below P25 = 1 point, P25-P50 = 2 points, P50-P75 = 3 points, P75-P90 = 4 points, and above P90 = 5 points. Influencing biomarkers are scored using the rate of change from baseline: <20% = 1 point, 20%-50% = 2 points, 50%-100% = 3 points, 100%-200% = 4 points, and >200% = 5 points.

[0026] Step 5: Population Sensitivity Correction. An age correction factor was introduced for children (1.5 for 0-6 years, 1.3 for 6-12 years, and 1.1 for 12-18 years), a pregnancy correction factor (1.4) was introduced for pregnant women, and a length of service correction factor was introduced for occupationally exposed populations (1.2 for <5 years of service, 1.4 for 5-10 years, and 1.6 for >10 years), resulting in the corrected internal exposure score Sin′.

[0027] Step 6: Exposure assessment score Se = Sout + Sin′, where Sout ranges from 0 to 40 and Sin′ ranges from 0 to 60.

[0028] In this embodiment, an internal and external exposure coupling assessment was conducted on 120 residents in the study area. The results showed that after introducing internal exposure biomarker inversion calibration, the spatial agreement between the priority monitoring grid and the high-level blood cadmium area (>5 μg / L) of the population increased from 68% in the external exposure model to 89%, and the consistency between the assessment results and the actual population exposure response was significantly improved.

[0029] This embodiment significantly improves the objectivity and accuracy of the assessment results by incorporating internal exposure biomarkers into the risk index calculation chain and correcting the external exposure model parameters through inversion calibration.

[0030] Example 5 This example, based on Examples 1-4, further performs dynamic weight optimization and joint threshold risk classification, including the following steps: Step 1: Grey relational analysis. Using the monitoring data time series (sliding time window L=12 months) as input, calculate the grey relational degree γi between the hazard identification score Sh, receptor assessment score Sr, hazard assessment score Sd, exposure assessment score Se and the target risk quantity (TCR and HI). The grey relational analysis steps include: (1) Data standardization: using initialization or meanization; (2) Calculating the correlation coefficient: ξi(k) = (min+ρ·max) / (|X0(k)-Xi(k)|+ρ·max), where ρ is the resolution coefficient (taken as 0.5); (3) Calculating the correlation degree: γi = (1 / n)×Σξi(k).

[0031] Step 2: Determine the initial weight W0. Normalize the correlation degree γi to obtain the initial weight W0i = γi / Σγi.

[0032] Step 3: Constraint optimization solution. Under the constraints 0.05≤Wi≤0.60 and ΣWi=1, the optimization weight W is solved by minimizing the objective function F=Σ(EHRIpred-EHRIval)²+λ·||W-W0||², where EHRIval is the calibration target value obtained from the validation data, and λ is the regularization coefficient (taken as 0.1).

[0033] Step 4: Calculation of Environmental Health Risk Index. EHRI = Sh×W1 + Sr×W2 + Sd×W3 + Se×W4.

[0034] Step 5: Joint Threshold Risk Classification. Establish an EHRI-TCR-HI joint threshold matrix: EHRI range, TCR range, HI range, risk level <30, <1×10⁻ 6 <0.5 Low risk 30-60 1×10⁻ 6 -1×10⁻ 4 0.5-1 Medium risk 60-80 1×10⁻ 4 -1×10⁻³ 1-2 High risk >80 >1×10⁻³ >2 Very high risk In this embodiment, after dynamic weight optimization, the model's mean absolute percentage error (MAPE) on the validation dataset decreased from 18.5% with fixed weights to 9.2%, improving accuracy by 50%; after using a joint threshold matrix, the misclassification rate of high-risk grids decreased from 23% with a single EHRI threshold to 8%, significantly reducing misclassification; the initial weights were obtained using grey relational analysis and dynamically optimized under constraints, making the weight allocation more objective and adaptable.

[0035] Example 6: Based on Examples 1-5, this example further performs closed-loop calibration and dynamic updates, including the following steps: Step 1: For gridded blocks determined to have a "high" or "extremely high" regulatory priority, import external control data (such as sampling data from higher-level regulatory departments) or organize on-site retesting to obtain verification data Cm,val; Step 2: Calculate the residual ε = |C′m - Cm,val| / Cm,val, where C′m is the model-predicted concentration; Step 3: Determine whether ε exceeds the allowable threshold ε0 (ε0 = 0.30 in this example); if so, trigger the calibration process; Step 4: Use Bayesian update or least squares regression to calibrate the source contribution coefficient αs,m. Taking Bayesian update as an example: P(α|D) ∝ P(D|α) × P(α), where P(α) is the prior distribution, P(D|α) is the likelihood function, and P(α|D) is the posterior distribution; Step 5: Synchronously update the dynamic weights Wi to improve the consistency between the subsequent evaluation results and the verification data to above the preset standard (e.g., MAPE < 10%); Step 6: Repeat S2-S5 according to the update cycle T (T = 30 days in this embodiment) to dynamically update the priority regulatory areas and regulatory priorities.

[0036] During the 6-month operation of this embodiment, a total of 12 calibrations were triggered. The model residual ε converged from the initial 0.42 to 0.18 (<ε0=0.30), and the positioning stability of the priority monitoring area reached more than 95%, ensuring the long-term reliability of the evaluation results. By triggering parameter write-back through residual threshold, a closed-loop process of verification-calibration-update was formed, making the evaluation results calibrable and stable in the long term.

[0037] Example 7: Based on Examples 1-6, this example focuses on the specific implementation of a metal smelting industrial park by a district-level government (regulatory agency: a district people's government; regulatory object: a metal smelting industrial park, including 5 smelting enterprises, 2 centralized slag yards, covering an area of ​​8.6 km²).

[0038] I. Specific Implementation Steps S1. Spatial Unit Construction: Relying on the system of this invention, the People's Government of a certain district takes the boundary of a certain metal smelting industrial park as the assessment scope, establishes a unified coordinate system (WGS84) based on GIS, presets the grid size Δ=200m, and divides it into 215 gridded blocks; establishes a spatial attribute table for each grid, and focuses on supplementing spatial information of core concern to district and county supervision, such as the distribution coordinates of enterprises in the industrial park, the boundary coordinates of slag dumps, the distribution of roads and pipelines in the park, and the distance to surrounding residential areas, and simultaneously incorporates basic information such as terrain elevation and prevailing wind direction (prevailing easterly wind throughout the year, wind speed 2.3m / s).

[0039] S2. Multi-source data acquisition and entry: Data is jointly collected and entered by the District Ecological Environment Bureau and the District Health and Wellness Bureau, including: ① Production data of 5 smelting enterprises (mainly copper, lead and zinc smelting, smelting years of 5-12 years, environmental protection facility operation parameters, slag storage area of ​​12,000 m², etc.); ② Environmental monitoring data (12 monitoring points in the park, covering air, soil and drinking water, monitoring 4 heavy metals Cu, Pb, Zn and Cd, monitoring frequency once a week); ③ Internal exposure data (collecting blood lead, urine cadmium (exposure markers) and serum superoxide dismutase (SOD, influence marker) data of 200 people from 3 residential areas around the park and on-duty employees of enterprises).

[0040] S3. Pollution Source-Media Contribution Decomposition: The Ecological Environment Bureau of a certain district used the source contribution decomposition module of this invention to distinguish the emission sources of 5 enterprises and 2 slag dumps. It used the Gaussian plume diffusion model (air medium) and the distance attenuation-topography correction model (soil medium) to calculate the source contribution coefficient αs,m of each grid. It focused on quantifying the pollution contribution of each enterprise to the surrounding grids and obtained the corrected concentration C′m of each medium, which solved the regulatory pain point of district and county governments "not being able to distinguish which enterprise is heavily polluting".

[0041] S4. External Exposure Score: The daily average exposure dose is calculated for employees and surrounding residents in the park through three routes: respiratory inhalation, digestive ingestion, and skin contact. A stratified scoring method is used, with 3 media and 3 routes = 9 sub-indicators. The raw score is calculated as "concentration exceeding the standard multiple × exposure frequency × duration", which is linearly mapped to the external exposure score Sout (0-40). The weight of air medium is 0.4, soil medium is 0.3, and drinking water medium is 0.3, which is in line with the district and county supervision requirements of "prioritizing air and soil pollution".

[0042] S5. Internal Exposure Score: Blood lead and urinary cadmium are scored according to population quantile thresholds, and SOD is scored according to the rate of change from baseline. For on-the-job employees (occupational exposure population), a length of service factor (weighted coefficient of 1.2 for length of service ≥ 5 years) is introduced for correction, resulting in the internal exposure correction score Sin′, which focuses on distinguishing the exposure differences between occupational groups and ordinary residents, and is in line with the district and county government's regulatory goal of "taking into account the health of enterprise employees and surrounding people".

[0043] S6. Modular Indicator Score Formation: The scores for each module are determined by the District Ecological Environment Bureau in conjunction with the District Health and Wellness Bureau: ① Hazard Identification Score Sh (combining the emission intensity of 5 enterprises and the diffusion potential of 2 slag dumps); ② Receptor Assessment Score Sr (focusing on sensitive receptors such as residential areas and schools within 1km of the park); ③ Hazard Assessment Score Sd (based on the carcinogenic and non-carcinogenic toxicity parameters of 4 heavy metals); ④ Exposure Assessment Score Se = Sout + Sin′.

[0044] S7. Dynamic Weight Update and EHRI Calculation: A sliding time window of L=6 months (the regular monitoring cycle for district and county supervision) is used to perform grey relational analysis on the monitoring data to obtain the correlation degree γi between each indicator and TCR and HI. The weights are updated under the constraints 0.05≤Wi≤0.60 and ΣWi=1, where Se weight is 0.35, Sh weight is 0.25, Sr weight is 0.20, and Sd weight is 0.20. The EHRI value of each grid is calculated.

[0045] S8. Risk Level and Regulatory Priority Determination: An EHRI-TCR-HI joint threshold matrix (customized for districts and counties) is used, where EHRI ≥ 70 is high risk, 50-69 is medium risk, and < 50 is low risk; TCR ≥ 1 × 10⁻ 4 For significant carcinogenic risk, 1×10⁻ 6 -1×10⁻ 4 For potential carcinogenic risk, <1×10⁻ 6No carcinogenic risk; HI>1 indicates significant non-carcinogenic risk, HI≤1 indicates acceptable non-carcinogenic risk; when TCR reaches the significant risk level, the risk level is upgraded by 1 level, and the 215 grids are finally divided into 18 high-risk, 47 medium-risk, and 150 low-risk grids, which are mapped to a three-level priority of "key supervision, routine supervision, and general supervision", and it is clear which enterprise / slag yard corresponds to the key supervision grid.

[0046] S9. Closed-loop calibration and dynamic update: Set the update cycle T=30 days (the regular frequency of district and county supervision), and allow residual threshold ε0=10%; for 18 high-risk grids, add on-site sampling and retesting every month, calculate residual ε, and when ε>10% of a certain grid, trigger the calibration writeback of source contribution coefficient αs,m and dynamic weight Wi, and update the supervision priority at the same time, forming a closed loop of "monitoring-evaluation-supervision-calibration", which meets the needs of district and county governments for "dynamic control and precise policy implementation".

[0047] II. Main Implementation Results 1. Source Contribution Decomposition: The pollution contribution of 5 enterprises and 2 slag heaps was accurately quantified. Among them, a lead-zinc smelting enterprise contributed 78%-86% of the air medium pollution to the surrounding 3 grids, and the 2 slag heaps contributed 42%-53% of the soil medium pollution to the surrounding 5 grids. The source contribution identification accuracy rate was 92%. Compared with the comparison document 1 (which could not decompose source contributions and could only assess the overall risk of the industrial park), this achieved "precise location of pollution sources" and solved the problem of "unclear responsibility and blind control" in district and county supervision.

[0048] 2. Coupling of internal and external exposure: When only external exposure dose is used, the correlation coefficient between high-risk grids and population exposure response is 0.65; after introducing internal exposure biomarkers, the correlation coefficient increases to 0.91, and the accuracy of high-risk grid identification increases from 73% to 95%. Compared with comparative document 2-3 (which only calculates external exposure risk), the assessment accuracy is improved by 43.2%, avoiding the regulatory loophole of "external exposure meeting the standard but internal exposure exceeding the standard".

[0049] 3. Closed-loop calibration and regulatory efficiency: The initial model had an average residual of 17.8%. After two closed-loop calibrations, the average residual decreased to 7.9% (below ε0=10%). Dynamic updates shortened the response time for key regulatory areas from the original 3 months (traditional regulatory model) to 30 days. The district and county ecological and environmental bureaus reduced their manpower input for the supervision of industrial parks by 45%, and the regulatory efficiency increased by 50%.

[0050] 4. Risk Management Effectiveness: After implementing this invention for 6 months, the number of high-risk grids in the industrial park decreased from 18 to 7; the rate of lead poisoning among residents around the park decreased from 8.5% to 2.1%; the rate of cadmium poisoning in the urine among company employees decreased from 12.3% to 3.7%; the proportion of grids with non-carcinogenic risk (HI>1) decreased from 21.8% to 8.3%; and the carcinogenic risk (TCR≥1×10⁻) decreased.4 All grid cells were cleared.

[0051] Example 8: Based on Examples 1-7, this example further uses the application of heavy metal pollution monitoring in a non-ferrous metal industrial park in a prefecture-level city as an example to conduct a confidential test to verify its effectiveness.

[0052] I. Implementation Background Regulatory Agency: Ecological and Environmental Protection Bureau of a certain district in a certain city Regulatory Target: Non-ferrous Metals Industrial Park of a certain district (approximately 8.5 km², with 23 resident enterprises, including 5 smelting enterprises, 12 processing enterprises, and 6 supporting enterprises); Main pollutants: Cadmium (Cd), Lead (Pb), Arsenic (As), Mercury (Hg) Regulatory Pain Points: The park is densely populated with enterprises, and the traditional enterprise-based regulatory method is difficult to identify areas of overlapping pollution across enterprises, and there is a lack of quantitative assessment methods for secondary release from slag heaps.

[0053] II. Implementation Steps Step 1: System Deployment and Data Initialization (March 2024) (1) Deploy the evaluation system of this invention on the cloud server, configure the grid size Δ=100m, and generate 850 initial grids; (2) Import the GIS layers of the park boundary, enterprise locations, and 3 historical slag heaps (storage area of ​​about 23,000 m²); (3) Collect and enter the monitoring data of four types of media throughout 2023: ambient air (6 monitoring points), soil (45 sampling points), groundwater (8 monitoring wells), and crops (12 sampling points); (4) Collect blood / urine samples from 120 employees in the park and determine the concentrations of Cd, Pb, As and liver and kidney function indicators.

[0054] Step 2: Source contribution decomposition and initial risk assessment (April 2024) (1) Calculate the emission source strength vector E of 5 smelting enterprises and the release source strength vector L of 3 slag dumps; (2) Use the Gaussian plume model and the distance attenuation-terrain correction model to calculate the source contribution coefficient αs,m of each grid; (3) Calculate the environmental health risk index EHRI, total carcinogenic risk TCR, and total non-carcinogenic risk HI, and output the initial risk level distribution.

[0055] Step 3: Internal and external exposure coupling calibration (May 2024) (1) The measured values ​​of blood cadmium and blood lead of 120 employees were used as the exposure marker observation values ​​Bobs; (2) The internal exposure inversion calibration model was run and the exposure parameters were optimized (the oral intake rate IRing was corrected from the default value of 100 mg / d to 156 mg / d, and the skin adhesion factor SL was corrected from 0.2 mg / cm² / d to 0.35 mg / cm² / d); (3) The EHRI was recalculated and the risk level was updated.

[0056] Step 4: Regulatory decision and closed-loop verification (June-December 2024) (1) Based on the EHRI-TCR-HI joint threshold matrix, 23 high-risk grids and 8 extremely high-risk grids were identified as priority regulatory areas; (2) On-site retesting was carried out for the priority regulatory areas, and the residual ε was calculated; (3) The trigger parameters of the grids with ε>ε0 were calibrated, and the priority regulatory areas were dynamically updated.

[0057] III. Comparison data on implementation effects are shown in Table 1 below.

[0058] IV. Verifiable effects (1) Source contribution interpretability: Through source contribution coefficient αs,m analysis, it was identified that the No. 3 slag dump contributed 47% to the eastern area of ​​the park. This dump was not previously included in the key supervision list. After supplementary supervision, the soil Cd concentration in the area decreased by 31%; (2) Consistency between internal and external exposure: After the introduction of internal exposure calibration, the spatial consistency between high-risk grids and people with blood cadmium exceeding the standard (>5μg / L) increased from 62% to 87%, avoiding the possible omissions caused by relying solely on external exposure; (3) Optimization of supervision resources: The area of ​​the priority supervision area accounts for only 18% of the total area of ​​the park, but it covers 73% of the high-risk population, and the supervision efficiency is improved by 4 times.

[0059] Example 9: Based on Examples 1-8, this example further uses the application of precise monitoring of the surrounding area of ​​a copper smelting enterprise in another prefecture-level city as an example to conduct a confidentiality test to verify its effectiveness.

[0060] I. Implementation Background Regulatory Agency: Comprehensive Environmental Protection Law Enforcement Brigade of a certain city; Regulatory Target: A large copper smelting enterprise and its surrounding 2km radius (involving 3 administrative villages, with a permanent population of approximately 4,200); Main Pollutants: Arsenic (As), Cadmium (Cd), Lead (Pb); Regulatory Challenges: The enterprise has long claimed that "the surrounding pollution is not entirely caused by the enterprise itself," and traditional assessment methods cannot distinguish the contribution of the enterprise's emissions from historically accumulated slag, leading to frequent regulatory disputes.

[0061] II. Implementation Steps Step 1: Refined Grid Division (February 2024) (1) Using the enterprise's emission outlet as the origin, a variable density grid is used for division: the grid side length is 50m in the range of 0-500m, the grid side length is 100m in the range of 500-1000m, and the grid side length is 200m in the range of 1000-2000m; (2) A total of 412 grids are generated, and a spatial attribute table containing topography, wind direction, distance, and land use is established.

[0062] Step 2: Quantitative decomposition of dual-source contribution (March 2024) (1) Calculate the enterprise emission source strength vector E (as emission flux of waste gas: 2.3 g / s, cd emission flux of wastewater: 0.8 g / s); (2) Calculate the historical slag dump release source strength vector L (2 dumps, wind erosion dust flux: 0.15 g / m² / d, leaching flux: 3.2 mg / L); (3) Calculate the enterprise contribution coefficient αenterprise and slag contribution coefficient αslag for each grid, and construct the synergistic pollution coupling term K.

[0063] Step 3: Dynamic weight risk assessment (April-June 2024) (1) Using 12 months of monitoring data as the time window, grey relational analysis is used to calculate the correlation between each indicator and TCR / HI; (2) Optimize the weights under the constraint 0.05≤Wi≤0.60 and calculate EHRI; (3) Determine the risk level based on the joint threshold matrix and output the risk distribution map with the contribution ratio of the source.

[0064] Step 4: Differentiated regulatory decision-making (July-December 2024) (1) For enterprise-led high-risk grids (enterprise contribution > 60%), enterprises are required to rectify within a time limit and install pollution control facilities; (2) For slag-led high-risk grids (slag contribution > 50%), the natural resources department is coordinated to carry out slag removal and ecological restoration; (3) For collaborative enhancement grids (interaction item Kint > 0.3), joint governance between enterprises and the government is implemented.

[0065] III. Comparison data on implementation effects are shown in Table 2 below.

[0066] IV. Verifiable Results (1) Clarified Responsibility Definition: Through source contribution decomposition, it was quantitatively identified that enterprise-led grids accounted for 58% of high-risk grids, slag-led grids accounted for 27%, and collaborative enhancement grids accounted for 15%, providing a scientific basis for accurate accountability; (2) Significantly Reduced Regulatory Disputes: Enterprises recognized the objectivity of the assessment results, and the number of administrative review cases dropped from an average of 8 per year to 0; (3) Obvious Governance Effects: After rectification, the air As concentration in enterprise-led grids decreased by 42%; after removal from slag-led grids, the soil Cd concentration decreased by 38%; (4) Optimized Cost-Effectiveness: Over-regulation of non-responsible areas of enterprises was avoided, the environmental protection investment of enterprises was more targeted, and the total governance cost was reduced by about 25%.

[0067] Example 10: Based on Examples 1-9, this example further uses the dynamic monitoring application of heavy metal pollution areas in a certain district as an example to conduct a confidentiality test to verify its effectiveness.

[0068] I. Implementation Background Regulatory Agency: Ecological and Environmental Bureau of a certain district; Regulatory Target: A certain historical heavy metal pollution area in a certain district (approximately 15 km², including 2 active smelting enterprises, 5 historical slag heaps, 6 administrative villages, and a resident population of approximately 12,000); Main Pollutants: Lead (Pb), Cadmium (Cd), Chromium (Cr); Regulatory Challenges: The pollution sources in the area are complex, including emissions from active enterprises and historical issues. Traditional static assessment methods are difficult to adapt to dynamic changes in pollution, and updates to priority regulatory areas are lagging behind.

[0069] II. Implementation Steps Step 1: System Construction and Baseline Survey (January-March 2024); (1) Deploy the assessment system of this invention, configure the grid size Δ=150m, and generate 667 grids; (2) Conduct baseline surveys: collect 89 soil samples, 24 groundwater samples, 36 crop samples, and air samples (continuous monitoring for 3 months); (3) Collect 280 blood / urine samples from permanent residents and establish a population health baseline database.

[0070] Step 2: Initial risk assessment and priority area delineation (April 2024) (1) Calculate EHRI, TCR, and HI for each grid; (2) Based on the joint threshold matrix, delineate 89 high-risk grids and 34 extremely high-risk grids; (3) Identify the dominant pollution factors in each priority area through source contribution decomposition.

[0071] Step 3: Establishment of closed-loop calibration and dynamic update mechanism (starting from May 2024) (1) Set the update cycle T=30 days and collect environmental media monitoring data once a month; (2) Conduct a sampling test of residents' biomarkers once a quarter (80 people each time); (3) Set the residual threshold ε0=0.25 and trigger parameter calibration when ε>ε0; (4) Use the Bayesian update algorithm to dynamically calibrate the source contribution coefficient αs,m and weight Wi.

[0072] Step 4: Continuous supervision and effectiveness evaluation (June-December 2024) (1) Based on the dynamically updated priority supervision areas, allocate law enforcement forces to carry out differentiated inspections; (2) For newly identified risk grids, promptly include them in the supervision list; (3) For grids with reduced risks, adjust the supervision intensity in a timely manner.

[0073] III. Comparison data on implementation effects are shown in Table 3.

[0074] IV. Verifiable Effects (1) Effectiveness of Dynamic Updates: During the 8-month operation period, parameter calibration was triggered 5 times, priority regulatory areas were dynamically adjusted 12 times, 7 new high-risk grids and 19 risk-downgraded grids were added, and the regulatory list was highly consistent with the actual risk status; (2) Residual Convergence: The initial residual ε mean value was 0.38, which was reduced to 0.19 (<ε0=0.25) after closed-loop calibration, and the model prediction accuracy was significantly improved; (3) Improvement of Population Health: The average level of residents' blood Pb decreased from 8.7μg / dL before implementation to 6.2μg / dL, and the average level of blood Cd decreased from 2.1μg / L to 1.4μg / L; (4) Regulatory Response Speed: The average response time of newly discovered pollution hotspots was shortened from 45 days in the traditional way to 12 days; (5) Resource Utilization Efficiency: The coverage rate of law enforcement patrols increased from 68% to 94%, while the total patrol mileage was reduced by 31%, achieving precise and efficient supervision.

[0075] The above embodiments of the present invention can be used to assess and regulate different regulatory levels (such as district and county governments), different regulatory objects (such as industrial parks, key enterprises, and polluted areas), and different pollution characteristics (multi-enterprise compound pollution, dual-source disputed areas, and historical legacy areas). The feasibility and technical effects of the present invention have been fully verified. Its outstanding technical effects mainly include: 1. Improved grid-based supervision accuracy: Each embodiment adopts a 100m, 50-200m variable density, and 150m grid, respectively, and the supervision accuracy is far higher than the "enterprise-level" coarse assessment of the prior art.

[0076] 2. Practicality of source contribution decomposition: Example 9 successfully resolved the dispute over the definition of enterprise-slag responsibility, reducing administrative reconsideration cases from 8 per year to 0, which is an effect that existing technologies cannot achieve.

[0077] 3. Reliability of closed-loop calibration: During the 8 months of operation of Example 10, the priority monitoring area was adjusted 12 times, always maintaining consistency with the actual risk status, while the existing technology lacks a specific closed-loop implementation mechanism.

[0078] 4. From "risk calculation" to "regulatory decision-making": Existing technologies only output TCR / HI values, while embodiments 7-9 of this invention directly output the boundaries of priority regulatory areas, source contribution ratios, and differentiated control lists, which can directly guide law enforcement; 5. Verification of consistency between internal and external exposure: In embodiment 8, the spatial consistency between the priority regulatory grid and individuals with elevated blood cadmium levels reached 87%, verifying the effectiveness of internal exposure inversion calibration, an effect that existing technologies cannot achieve.

[0079] 6. Quantifiable improvement in population health: In Example 10, the blood Pb and Cd levels of residents decreased significantly, demonstrating the practical effect of this invention in protecting population health.

[0080] The embodiments of this invention all utilize the synergistic coupling of GIS grids and internal / external exposure. First, an assessment database is established based on GIS, and gridded blocks are adaptively generated according to spatial heterogeneity. Monitoring data of external exposure media such as ambient air, drinking water, food, and soil, as well as internal exposure biomarker data such as blood / urine, are collected and entered. For each grid, the emission source strength vector E from metal smelters and the release source strength vector L from slag heaps are calculated, and a synergistic pollution coupling term K is constructed to characterize the dual-source interaction enhancement. A system of hazard identification, receptor assessment, hazard assessment, and exposure assessment indicators is established. External exposure doses are calculated, and internal exposure calibration doses are obtained by inverting exposure parameters using biomarkers. Initial weights are given using grey relational analysis, and dynamic weight optimization is performed under constraints to calculate the Environmental Health Risk Index (EHRI). Simultaneously, the Total Carcinogenic Risk (TCR) and Total Non-Carcinogenic Risk (HI) are calculated. Risk levels are determined based on the EHRI-TCR-HI joint threshold rule, and priority regulatory areas and priorities are automatically delineated by combining grid spatial connectivity and source strength contribution ranking. This invention ultimately achieves differentiated regulatory auxiliary decision-making with interpretable risk contributions, calibrable assessment results, and dynamically updated priority regulatory areas.

[0081] The above embodiments describe only a portion of the embodiments of the present invention, not all of them. In other embodiments, within the scope of the present invention, other similar steps, modules, data, models, information system architectures, etc., can be selected to obtain technical solutions that can also achieve the technical effects described in the present invention, and therefore will not be listed one by one. Furthermore, based on the above embodiments of the present invention, all other modifications or alterations obtained by those skilled in the art without creative effort are within the protection scope of the claims of this application.

Claims

1. A method for assessing the environmental health risks of complex pollution in metal smelting areas, characterized in that, This method is executed in an environmental health risk assessment system and includes the following steps: S1, Spatial unit construction: Obtain the boundary of the metal smelting area to be assessed, establish a unified coordinate system based on GIS, and divide the assessment area into multiple gridded blocks according to a preset grid size Δ (50-500m); S1. Establish a spatial attribute table for each gridded block, which includes at least: topographic elevation, prevailing wind direction / speed, surface runoff direction, land use type, and distance from the smelter emission point and slag dump boundary; S2. Multi-source data acquisition and entry: Collect and enter data on four types of exposure media for each gridded block, including concentration data of at least two heavy metals in ambient air, drinking water, soil, and food / crops, and simultaneously enter human exposure data, which includes at least one exposure marker of heavy metal concentration in blood / urine / hair, and one indicator affecting the marker of oxidative stress or liver and kidney function; S3. Pollution source-media contribution decomposition: Establish contribution calculations for smelter emission sources and slag dump sources respectively, and calculate the source contribution coefficient αs,m of each gridded block in each exposure medium based on the spatial attribute table, where s is the pollution source type and m is the medium type, and obtain the corrected concentration C′m of each medium accordingly; S4. External exposure scoring: For respiratory inhalation, digestive ingestion, and skin contact... S5. For each exposure pathway, the daily average exposure dose is calculated based on C′m and normalized to an external exposure score Sout (0-40), where at least the following conditions must be met: weights wm are set for each of the four exposure media and Σwm=1; S6. For internal exposure scores, the exposure markers and impact markers are mapped to internal exposure scores Sin (0-60) according to threshold intervals, and the population sensitivity factor is corrected to obtain Sin′; S7. For modular index score formation, hazard identification score Sh, receptor assessment score Sr, hazard assessment score Sd, and exposure assessment score Se are formed respectively, where Se=Sout+Sin′; S8. For dynamic weight update and calculation of environmental health risk index, the time series of monitoring data is used as input, and grey relational analysis is used to obtain the correlation γi between each index and the target risk quantity, total carcinogenic risk TCR and total non-carcinogenic risk HI, and the weights Wi are updated under the constraints 0.05≤Wi≤0.60 and ΣWi=1, and then the environmental health risk index EHRI is calculated: EHRI = Σ(Xi×Wi), where Xi is the score of at least three of the indicators Sh, Sr, Sd, and Se; S8, Risk level and regulatory priority determination: The risk level of each grid block is determined based on the joint threshold matrix of (i) EHRI, (ii) TCR, and (iii) HI, and the risk level is mapped to the regulatory priority; S9, Closed-loop calibration and dynamic update: For grid blocks that are determined to have a regulatory priority of not less than the preset level, external comparison data or on-site retest data are imported, and the residual ε=|C′m-Cm,obs| / Cm,obs is calculated; When ε exceeds the allowable threshold ε0, the calibration write-back of αs,m or Wi is triggered, and S2-S9 are repeated according to the 7-90 day update cycle T to dynamically update the priority regulatory area.

2. The method according to claim 1, characterized in that: The source contribution coefficient αs,m is calculated by at least one propagation model, which includes at least one of the following: Gaussian plume diffusion model for air medium, runoff confluence model for surface water medium, and distance attenuation-topography correction model for soil medium, and the input parameters of the propagation model are taken from the spatial attribute table.

3. The method according to claim 1, characterized in that: The external exposure score Sout adopts a hierarchical scoring structure based on medium and pathway, containing at least 12 sub-indicators, 4 media × 3 pathways. Each sub-indicator is assigned an initial score based on "concentration exceedance multiple × exposure frequency × duration", and then Sout is obtained through a 0-40 linear mapping.

4. The method according to claim 1, characterized in that: The internal exposure score Sin uses population quantile thresholds or health benchmarks to score exposure biomarkers, and uses the rate of change from baseline to score influencing biomarkers. For occupationally exposed populations, a length of service factor is introduced for weighted correction.

5. The method according to claim 1, characterized in that: The joint threshold matrix includes at least three EHRI thresholds, three TCR thresholds, and two HI thresholds, and stipulates that when the TCR reaches the significant risk level or HI>1, the risk level is increased by at least one level.

6. The method according to claim 1, characterized in that: The dynamic weight update uses a 3-24 month sliding time window L to calculate the monitoring data, and fills in missing data using inverse distance weighted interpolation of adjacent grids or Kalman filtering.

7. The method according to claim 1, characterized in that: In the closed-loop calibration and dynamic update steps, when the residual ε exceeds the threshold ε0, Bayesian update or least squares regression is used to calibrate the source contribution coefficient αs,m, and the dynamic weight Wi is updated simultaneously, so that the consistency between the subsequent evaluation results and the verification data is improved to above the preset standard.

8. The method according to claim 1, characterized in that: The spatial attribute table of the gridded block also includes the coefficient of variation (CV) of the monitored concentration within the grid and the receptor sensitivity gradient (G). When CV or G exceeds a preset threshold, the grid is subdivided according to the quadtree rule until CV≤CV0 and G≤G0 or the minimum grid side length dmin is reached.

9. The method according to claim 1, characterized in that: The calculation of the hazard identification score Sh includes: calculating the emission source strength vector E of the smelter and the release source strength vector L of the slag dump, and constructing a synergistic pollution coupling term K based on the source-receptor distance and the prevailing wind / water flow direction. K includes superimposed exposure terms and interaction terms. The interaction terms satisfy Kint = β × (Enorm × Lnorm), where β is the synergistic coefficient obtained by regression based on historical monitoring data, and Enorm and Lnorm are the normalized results of E and L, respectively.

10. A composite pollution environmental health risk assessment system for metal smelting areas based on GIS grid coupling with internal and external exposure, used to implement the method described in any one of claims 1-9, characterized in that, It includes: a spatial modeling module for establishing a GIS coordinate system, gridded blocks, and their spatial attribute tables; a data acquisition and entry module for collecting and entering data on four types of exposure media and human body exposure data; a source contribution decomposition module for calculating αs,m and generating the corrected concentration C′m; an external exposure assessment module and an internal exposure assessment module for outputting Sout and Sin′, respectively; an indicator score generation module for outputting Sh, Sr, Sd, and Se; a dynamic weight update module for calculating γi and updating Wi under constraints; a comprehensive risk assessment module for calculating EHRI and outputting the risk level; a regulatory priority determination and closed-loop calibration module for outputting regulatory priorities and triggering model parameter writeback based on the residual ε; and a cloud server and at least one network terminal that communicate with the above modules.

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