Water quality stability and standard reaching dynamic evaluation method based on multi-dimensional index fusion

Abstract

The invention discloses a water quality stable standard reaching dynamic evaluation method based on multi-dimensional index fusion, and relates to the field of water environment quality evaluation, and the method comprises the following steps: 1, determining an evaluation index; step 2, calculating a single factor SCI of each index; step 3, standardizing a single factor SCI; step 4, carrying out weight distribution according to actual conditions; and 5, performing comprehensive calculation and evaluation. Compared with the prior art, by adopting the technical scheme of the invention, multi-dimensional water quality characteristics are integrated, scientific and accurate water quality standard reaching state judgment is realized, and water environment management is supported to be converted from extensive to refined.

Description

Technical Field

[0001] This invention relates to the field of water environment quality assessment technology, and in particular to a dynamic assessment method for water quality stability and compliance based on the fusion of multi-dimensional indicators. Background Technology

[0002] In water environment management, accurately assessing the stability and compliance of water quality is crucial. Traditional water quality assessment methods often focus on a single indicator or a simple average level, which fails to comprehensively reflect the true state of water quality. For example, judging compliance solely based on annual average water quality values ​​ignores key information such as the volatility of water quality data (e.g., fluctuations reflected by standard deviation and coefficient of variation), instances of exceeding standards (including the frequency and magnitude of exceeding standards), and trends in water quality over time.

[0003] With the increasing demands for water quality, a more systematic and comprehensive assessment method is needed. This method can integrate the multi-dimensional characteristics of multiple water quality indicators, scientifically characterize the stability of water quality compliance from a statistical perspective, provide support for the refinement and scientification of water quality management, and help achieve more effective water resource protection and water pollution prevention and control. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic assessment method for water quality stability and compliance based on the fusion of multi-dimensional indicators, which can be used to comprehensively and scientifically assess the stability and compliance of water quality within an annual cycle, and provide accurate and quantitative decision-making basis for water environment management.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamic assessment method for water quality stability compliance based on multi-dimensional index fusion, comprising the following steps: Step 1: Select key indicators related to stable water quality compliance to determine evaluation indicators; Step 2: Calculate the single-factor SCI for each key indicator; Step 3: Standardize single-factor SCI; Step 4: Assign weights to the standardized single-factor SCI. Step 5: Calculate and evaluate the standardized single-factor SCIs by weighting and summing them according to their weights to obtain the overall multi-index SCIs. Based on the SCI values, propose control recommendations to assess water quality stability.

[0006] In one specific embodiment, the evaluation indicators include total phosphorus, chemical oxygen demand, permanganate index, and ammonia nitrogen content.

[0007] In one specific embodiment, the key indicators include average value, volatility, frequency and magnitude of exceeding limits, and trend of change.

[0008] In one specific embodiment, the SCI value range is (0,1).

[0009] In one specific implementation, the single-factor SCI is calculated using the stable achievement characterization formula:

[0010] In the formula, This is the annual average. Standard error; Corresponding to the selected confidence level Fraction; The standard deviation of water quality monitoring data reflects the dispersion and volatility of the data. These are water quality standard values, used to measure whether water quality meets the standards.

[0011] In a specific embodiment, the calculation expression for the weighted summation of the standardized single-factor SCI is as follows:

[0012] In the formula, This is the average value; The coefficient of variation; This is a frequency exceeding the standard; This is the extent to which the standard is exceeded; For changing trends; , , , , All are coefficients, with values ​​ranging from (0,1).

[0013] In one specific embodiment, the overall multi-index SCI is:

[0014] In the formula, For the number of indicators; For the first Single-factor indicator.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention integrates water quality average level, fluctuation, exceedance characteristics and long-term trends, breaking through the limitations of traditional single-indicator evaluation and adapting to complex water environment management needs (such as the coordinated management of multiple pollutants in a watershed).

[0016] This invention achieves quantitative fusion of multiple indicators through standardization, weighted synthesis, and dynamic adjustment, providing a clear reflection of the degree of stable water quality compliance. The grading rules are clear and provide precise guidance for the formulation of management measures.

[0017] This invention covers scenarios such as watershed assessment, source water monitoring, and emission reduction decision-making, providing a full-process tool for water environment management, encompassing diagnosis, decision-making, and evaluation. It helps identify water quality shortcomings, optimize control measures accordingly, and improve management efficiency. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall process of the present invention; Detailed Implementation The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 A dynamic assessment method for water quality stability compliance based on multi-dimensional index fusion includes the following steps: Step 1: Select key indicators related to stable water quality compliance to determine evaluation indicators; The key indicators include average value, fluctuation, frequency and magnitude of exceedances, and trend of change. The evaluation indicators include water quality parameters such as total phosphorus, chemical oxygen demand, permanganate index, and ammonia nitrogen content.

[0020] The average value represents the average water quality value of 12 monthly monitoring data within a year, reflecting the overall average level of water quality.

[0021] The volatility is measured using the standard deviation σ or the coefficient of variation (CV). The coefficient of variation is suitable for standardized indicators and reflects the relative degree of volatility in water quality data.

[0022] The frequency of exceeding the standard refers to the number of monthly monitoring sessions during the year that exceed the water quality standard value C, reflecting the frequency of water quality exceeding the standard.

[0023] The exceedance range refers to the average exceedance range of the months exceeding the standard within the year. If there is no exceedance, M=0, which reflects the degree to which the water quality deviates from the standard when it exceeds the standard.

[0024] The changing trend represents the annual trend of water quality indicators, which can be measured by the slope of linear regression or the results of the Mann-Kendall trend test, reflecting the trend of water quality improvement or deterioration over time.

[0025] Step 2: Calculate the single-factor SCI for each key indicator, considering the annual average and its volatility. Based on the concept of confidence intervals, establish a stable achievement characteristic formula, and use this formula to calculate the single-factor SCI. The specific steps are as follows: Suppose the monthly monitoring data for a certain water quality indicator (such as COD) is as follows: The standard value C is known, so choose a confidence level (e.g., 95%, corresponding to Z=1.96).

[0026] Calculate the average value μ:

[0027] Substitute the data from the 12 months, sum them, and then take the average to obtain the annual average level of the indicator.

[0028] Calculate the standard deviation σ using the formula:

[0029] First, calculate the sum of squared differences between the monthly data and the average, then take the square root of the average to obtain the data dispersion.

[0030] Calculate the S-value by combining the mean μ and the standard deviation σ. Substituting these values ​​into the single-factor SCI calculation expression yields S.

[0031] To ensure statistically stable compliance with water quality standards, and taking into account both the annual average level and its fluctuations, a single-factor SCI calculation expression is constructed based on the confidence interval concept:

[0032] In the formula, This is the annual average. Standard error; Corresponding to the selected confidence level Fraction; The standard deviation of water quality monitoring data reflects the dispersion and volatility of the data. These are water quality standard values, used to measure whether water quality meets the standards.

[0033] When S≤C, it indicates that the annual average water quality value meets the standard statistically at the selected confidence level. This takes into account both the average compliance and the data fluctuation, and has a high degree of confidence to ensure long-term stable compliance.

[0034] Step 3: Standardize single-factor SCI; To comprehensively evaluate the stability of water quality compliance, a comprehensive Water Quality Compliance Index (SCI) is constructed, which includes the following steps: Determine evaluation indicators: Select key indicators related to stable water quality compliance, covering average value, volatility (measured by standard deviation or coefficient of variation), exceedance status (frequency and magnitude of exceedance), and trend of change, to comprehensively reflect the multi-dimensional characteristics of water quality.

[0035] Standardization process: Standardize indicators with different dimensions and ranges to make them easier to compare and synthesize on the same scale (e.g., from 0 to 1). For indicators that need to be minimized (e.g., coefficient of variation, frequency of exceeding the standard, etc.), reverse the standardization process.

[0036] To standardize all indicators to a range of 0 to 1, a standardization method is adopted: For general indicators (indicators that need to be maximized or have positive significance, such as those whose average value is as high or as close to the standard value as possible under reasonable conditions); for indicators that need to be minimized (such as adverse trends in CV, F, M, T, etc.), standardization is followed by reverse processing. Specifically for each indicator: Average value (μ): Assuming that the lower the water quality index, the better (or set flexibly according to the actual situation, such as the higher the dissolved oxygen, the better).

[0037] Coefficient of variation (CV): the lower the better.

[0038] Frequency of exceeding the standard (F): the lower the better.

[0039] Exceeding the limit (M): The lower the better.

[0040] Trend (T): The more stable (no significant trend) or the better the trend (e.g., an improving trend), the better. If the trend is upward (deteriorating), the more stable (smaller absolute slope) the better; if the trend is downward (improving), a reasonable standardization method should be set. After simplification, ZT = 1 − |T| (T is the processed trend value).

[0041] Step 4: Assign weights to the standardized single-factor SCI. Weighting: Based on the importance of each indicator to achieving stable water quality standards, weights are determined through expert evaluation and the Analytic Hierarchy Process (AHP), and then reasonably allocated to reflect the influence of different indicators. The initial weighting is assumed to be as follows (this can be adjusted according to the actual application scenario): Mean (μ): =0.30, because it reflects the overall average level of water quality and is the basis for meeting the standard.

[0042] Coefficient of variation (CV): =0.20, which reflects the degree of water quality fluctuation; larger fluctuations mean greater difficulty in achieving stable standards.

[0043] Exceeding frequency (F): =0.25, the number of times the standard is exceeded directly reflects the compliance status.

[0044] Exceeding the limit (M): =0.15, the severity of exceeding the standard affects the stability of water quality.

[0045] Trend of change (T): =0.10, the long-term trend of water quality is related to the potential for stable compliance in the future.

[0046] Comprehensive calculation: The standardized indicators are weighted and summed to obtain the final SCI, realizing a comprehensive evaluation of multiple indicators.

[0047] Step 5: Calculate and evaluate the standardized single-factor SCIs by weighting and summing them according to their weights to obtain the overall multi-index SCIs. Based on the SCI values, propose control recommendations to assess water quality stability.

[0048] The formula for calculating the weighted sum of the standardized single-factor SCI is as follows:

[0049] In the formula, This is the average value; The coefficient of variation; This is a frequency exceeding the standard; This is the extent to which the standard is exceeded; For changing trends; , , , , All are coefficients, with values ​​ranging from (0,1).

[0050] Among them, the standardized values ​​of each indicator , , , , All values ​​are between 0 and 1, so the SCI value range is also between 0 and 1. The closer the SCI is to 1, the higher the degree of stable water quality compliance; the closer it is to 0, the lower the degree of stable water quality compliance, which can intuitively reflect the overall stability of water quality compliance.

[0051] The overall multi-index SCI is:

[0052] In the formula, For the number of indicators; For the first Single-factor indicator.

[0053] In one specific embodiment, according to The values ​​are used to classify the stable water quality compliance status into three levels: SCI≥0.8: High degree of stable water quality compliance and good management effect (such as long-term stable compliance of drinking water sources).

[0054] 0.6≤SCI<0.8: The level of compliance is moderate, and management needs to be strengthened (focusing on the control of indicators exceeding the standard, such as in the stage of ecological restoration of urban rivers).

[0055] SCI < 0.6: The compliance level is low, and emergency measures need to be taken (such as initiating emergency control and source tracing after a sudden pollution incident).

[0056] The above classification of compliance status can be applied to the following scenarios: River basin management: In cross-administrative region river basin assessment, pollution contribution areas are identified and management responsibilities are clarified through multi-section and multi-index SCI calculations (such as the coordinated management and control of water quality in the main stream and tributaries of the Yangtze River Basin).

[0057] Drinking water source monitoring: Long-term tracking of SCI changes in water sources, early warning of water quality fluctuation risks, and ensuring water supply security (such as monthly SCI assessment of Qiandao Lake water source).

[0058] Pollution reduction decision-making: Based on the weighting and weaknesses of the SCI indicator (e.g., low SCI for total phosphorus (TP) in a certain watershed), precise emission reduction plans are formulated (e.g., reducing TP emissions to address livestock and poultry farming pollution). The following optimization and expansion mechanisms can be adopted: 1. Dynamic weight adjustment Regional adaptation: Adjust the weights according to the characteristics of the watershed. For example, in watersheds dominated by phosphorus pollution, increase the weight of total phosphorus (TP) to 30%-40% to adapt to actual pollution control needs.

[0059] Methodological Upgrade: An analytic hierarchy process (AHP) combined with entropy weighting is used to construct a weighted system that integrates subjective and objective factors. AHP determines the weights of expert experience, while entropy weighting adjusts the weights based on the degree of data dispersion, thereby improving the scientific rigor.

[0060] 2. Expansion of the indicator system Supplementing standard indicators: Adding dissolved oxygen (DO), total nitrogen (TN), and heavy metals (Hg, As, etc.) indicators to improve pollution characterization. For example, in the assessment of lake eutrophication, adding TN and chlorophyll a indicators provides a more comprehensive reflection of water quality status.

[0061] Ecological indicators are incorporated: Aquatic biodiversity indices (such as the Shannon-Wiener index) are introduced to reflect ecosystem health. After river ecological restoration, benthic animal diversity assessments are used to supplement the water quality ecological dimension evaluation.

[0062] 3. Data Fusion and Dynamic Evaluation Multi-source data integration: Integrating remote sensing inversion water quality data (such as satellite monitoring of chlorophyll a) and model prediction data (such as SWAT model simulation of non-point source pollution) improves the timeliness and coverage of SCI. During heavy rainfall, SCI is dynamically updated by combining radar rainfall measurements and model predictions to reflect water quality changes in a timely manner.

[0063] Enhanced spatiotemporal analysis: Conduct SCI assessments at multiple time scales (monthly, quarterly, annual) and spatial scales (section, sub-basin, whole basin) to identify water quality change trends and spatial heterogeneity. For example, analyze seasonal SCI differences in different areas of Taihu Lake to accurately locate pollution sources and ecologically vulnerable areas.

[0064] 4. Dynamic evaluation and feedback Regular updates: Monitoring data are updated quarterly, SCI is recalculated and compared with historical results to analyze long-term water quality trends and provide a basis for adjusting management measures.

[0065] Management Feedback: Adjust water environment management strategies promptly based on changes in SCI. If the SCI of a particular indicator continues to decrease, strengthen targeted emission reduction and monitoring of that pollutant to achieve dynamic control.

Claims

1. A dynamic assessment method for water quality stability and compliance based on multi-dimensional index fusion, characterized in that, Includes the following steps: Step 1: Select key indicators related to stable water quality compliance to determine evaluation indicators; Step 2: Calculate the single-factor SCI for each key indicator; Step 3: Standardize single-factor SCI; Step 4: Assign weights to the standardized single-factor SCI. Step 5: Calculate and evaluate the standardized single-factor SCIs by weighting and summing them according to their weights to obtain the overall multi-index SCIs. Based on the SCI values, propose control recommendations to assess water quality stability.

2. The method for dynamic evaluation of stable water quality compliance based on multi-dimensional index fusion according to claim 1, characterized in that, The evaluation indicators include total phosphorus, chemical oxygen demand, permanganate index, and ammonia nitrogen content.

3. The method for dynamic evaluation of stable water quality compliance based on multi-dimensional index fusion according to claim 1, characterized in that, The key indicators include average value, volatility, frequency and magnitude of exceeding the limit, and trend of change.

4. The method for dynamic evaluation of water quality stability and compliance based on multi-dimensional index fusion as described in claim 1, characterized in that, The SCI value range is (0,1).

5. The method for dynamic evaluation of stable water quality compliance based on multi-dimensional index fusion according to claim 1, characterized in that, Calculation of single-factor SCI using the stable achievement characterization formula: In the formula, This is the annual average. Standard error; Corresponding to the selected confidence level Fraction; The standard deviation of water quality monitoring data reflects the dispersion and volatility of the data. These are water quality standard values, used to measure whether water quality meets the standards.

6. The method for dynamic evaluation of stable water quality compliance based on multi-dimensional index fusion according to claim 1, characterized in that, The formula for calculating the weighted sum of the standardized single-factor SCI is as follows: In the formula, This is the average value; The coefficient of variation; This is a frequency exceeding the standard; The extent exceeding the standard; For changing trends; , , , , All are coefficients, with values ​​ranging from (0,1).

7. The method for dynamic evaluation of water quality stability and compliance based on multi-dimensional index fusion according to claim 1, characterized in that, The overall multi-index SCI is: In the formula, For the number of indicators; For the first Single-factor indicator.

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