A method for dynamically allocating annual atmospheric pollutant targets by stages

Abstract

This invention discloses a method and system for the dynamic allocation of annual air pollutant targets at each level, relating to the field of air pollution control technology. The method includes: acquiring actual data on the number of days that have passed and calculating the completion rate of three assessment indicators; calculating the daily average target concentration for the remaining period based on the total annual target and the total amount already incurred, and introducing a dynamic safety margin coefficient for correction; allocating the remaining cumulative PM2.5 concentration daily using a time decay weight, and decomposing the remaining days of good air quality monthly using a historical weight; calculating the probability of completion and outputting tiered early warnings based on historical best and historical average data; updating all parameters at the end of each day and re-executing the above steps. This invention can reflect the progress of pollutant control in real time, dynamically adjust subsequent targets, and improve the controllability and adaptability of achieving annual targets.

Description

Technical Field

[0001] This invention relates to the field of air pollution control technology, and more specifically to a method and system for the dynamic allocation of annual air pollutant targets at each level. Background Technology

[0002] Currently, urban air quality assessments typically include three components: PM2.5, ... 2.5 Concentration, percentage of days with good air quality, percentage of days with heavy pollution – each province, city, and even county has its own annual assessment indicators. Currently, cities do not manage their assessment targets on a daily basis; instead, they typically use statistics on the completion status over a period of time up to a certain day. This results in a lack of clarity regarding the gap between targets and a lack of precision in formulating control measures.

[0003] Therefore, how to provide a method and system for the dynamic allocation of annual targets for air pollutants is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for dynamic allocation of annual targets for air pollutants at different levels, in order to solve the problems in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: On one hand, this invention discloses a method for the dynamic allocation of annual air pollutant targets at each level, comprising: Step 1: Obtain the actual data of the current number of days that have passed, and calculate the completion rate of the three assessment indicators; Step 2: Based on the annual target total and the total amount already incurred, calculate the daily average target concentration for the remaining period, and introduce a dynamic safety margin coefficient for correction; Step 3: The remaining cumulative PM2.5 concentration is allocated daily using time decay weighting, and the remaining number of days with good air quality is decomposed monthly using historical weighting; Step 4: Based on historical best and historical average data, calculate the completion probability and output a tiered warning. Step 5: After the day ends, update all parameters and repeat steps 1 to 4.

[0006] This invention achieves dynamic tracking and allocation of three assessment indicators—PM2.5, number of days with good air quality, and number of days with heavy pollution—by updating actual data daily and re-executing five steps. Its technical advantages lie in its ability to reflect pollutant control progress in real time, dynamically adjust subsequent targets, and improve the controllability and adaptability of achieving annual goals.

[0007] Preferably, in the above-mentioned method for dynamically allocating annual targets for air pollutants at each level, the specific formula for calculating the completion rate of the three assessment indicators is as follows: PM2.5 concentration completion rate The calculation formula is: ; in, Cumulative PM2.5 concentration over the past few days; Annual PM2.5 target average concentration; The current number of days has elapsed, in days; Good air quality days completion rate The calculation formula is: ; in: The number of days with good air quality completed, in days; Annual target for days with good air quality, in days; The percentage of heavily polluted days completed The calculation formula is: ; in: The number of days with severe pollution that have occurred, in days; Annual target for the number of heavily polluted days, in days.

[0008] By providing specific calculation formulas for the completion rates of PM2.5 concentration, days with good air quality, and days with heavy pollution, the quantitative standards for progress assessment are clarified. The technical benefits are: it standardizes the calculation methods for assessment indicators, facilitates automatic calculation and comparison by the system, and improves the accuracy and operability of progress assessment.

[0009] Preferably, in the above-mentioned method for dynamic allocation of annual air pollutant targets at each level, the formula for the dynamic safety margin coefficient is as follows: ; Among them, , It is an adjustable parameter. This represents the percentage of PM2.5 concentration achieved. The remaining number of days. This represents the total number of days in a year.

[0010] A dynamic safety margin coefficient is introduced and adjusted based on the PM2.5 compliance rate and remaining days. The technical effect is that it automatically tightens targets when they are exceeded in the early stages or when there is insufficient remaining time, and moderately relaxes them when they are within limits, thus enhancing the flexibility of target allocation and risk prevention capabilities.

[0011] Preferably, in the above-mentioned method for the step-by-step dynamic allocation of annual air pollutant targets, a dynamic safety margin coefficient is introduced for correction, resulting in the following daily average PM2.5 target: ; in, Daily average target concentration (μg / m³) adjusted by dynamic safety factor 3 ); For dynamic safety margin coefficient, when At that time, the goals were more stringent. Take breaks when appropriate.

[0012] By adjusting the daily average target concentration using a dynamic safety margin coefficient, dynamic correction of the target for the remaining period is achieved. The technical benefits are: when k > 1, the target is more stringent, helping to make up for previous underperformance; when k ≤ 1, the target is moderately relaxed, avoiding over-control and improving the rationality of the control strategy.

[0013] Preferably, in the above-mentioned method for dynamic allocation of annual air pollutant targets at each level, the formula for calculating the time decay weight is as follows: ; in, The attenuation coefficient is... The number of days remaining sky, This represents the number of days remaining.

[0014] The remaining cumulative PM2.5 concentration is allocated daily using a time decay weighting method, with more recent times receiving greater weight. The technical advantage lies in its focus on recent pollution control effectiveness, enhancing the timeliness and responsiveness of the allocation strategy.

[0015] Preferably, in the above-mentioned method for dynamic allocation of annual air pollutant targets at each level, the formula for calculating the daily PM2.5 target concentration is: ; in, For meteorological correction factors; Due to poor diffusion conditions, the daily target should be appropriately relaxed. Diffusion conditions are favorable; the daily target should be appropriately tightened. Remaining allowable cumulative concentration; Time decay weight; This represents the number of days remaining.

[0016] Introducing a meteorological correction factor into the daily PM2.5 target concentration allows for dynamic adjustment of the daily target based on diffusion conditions. The technical benefits are: incorporating meteorological factors into the control strategy, avoiding unintended consequences due to unfavorable weather conditions, and enhancing the scientific rigor and fairness of target setting.

[0017] Preferably, in the above-mentioned method for dynamically allocating annual air pollutant targets at each level, the formula for calculating the probability of completion is: ; in This represents the historical average completion rate for the remaining time period. The required completion rate.

[0018] The probability of completion is calculated based on the historical average completion rate and the required completion rate, providing a quantitative basis for early warning. Its technical effect lies in combining historical data with current goals, thereby improving the predictability of future achievements and the scientific rigor of early warnings.

[0019] Preferably, in the above-mentioned method for dynamic allocation of annual air pollutant targets at each level, the early warning rules for graded early warning are as follows: like The system determined the risk to be "low". like The system issued a "moderate risk warning"; like The system issued a "high-risk warning" and recommended strengthening control measures.

[0020] By setting two thresholds, 80% and 60%, the probability of completion is transformed into a three-level early warning signal. The technical effect is that it achieves a clear classification from "low risk" to "high risk," facilitating differentiated control measures by management departments based on risk levels.

[0021] On the other hand, this invention discloses a hierarchical dynamic allocation system for annual air pollutant targets, employing the above-mentioned method, including: The assessment completion calculation module obtains the actual data of the current number of days that have passed and calculates the completion rate of the three assessment indicators; The calculation and correction module calculates the daily average target concentration for the remaining period based on the total annual target and the total amount already incurred, and introduces a dynamic safety margin coefficient for correction. The decomposition module uses time decay weighting to allocate the remaining cumulative PM2.5 concentration daily and uses historical weighting to decompose the remaining days with good air quality monthly. The early warning module calculates the probability of completion and outputs tiered early warnings based on historical best and historical average data.

[0022] The system modularizes the above method into four functional modules: assessment completion calculation module, calculation correction module, decomposition module, and early warning module. Its technical advantages lie in: automating and systematizing the method, facilitating its deployment and operation in practical environmental protection platforms, and improving its operability and scalability.

[0023] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for the hierarchical dynamic allocation of annual targets for air pollutants. The present invention achieves hierarchical dynamic management of the three assessment targets of PM2.5 concentration, number of days with good air quality, and number of days with heavy pollution by dynamically updating actual data daily and re-executing the allocation process. It introduces a dynamic safety margin coefficient to automatically tighten or loosen the remaining targets based on the previous completion status and the number of remaining days, adopts a time decay weight to enhance the focus on recent pollution control, and combines meteorological correction factors to make the daily target setting more in line with the actual diffusion conditions. At the same time, it calculates the probability of completion based on the historical average completion rate and the required completion rate and outputs a three-level graded early warning, thereby significantly improving the scientific nature, flexibility and timeliness of the annual target allocation and the early warning, and ultimately improving the actual achievement rate of the annual assessment targets for air pollutants. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 A flowchart of the method provided by the present invention; Figure 2 The structural block diagram provided for this invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1: Dynamic allocation of annual air quality assessment targets for a certain city; Based on the basic data settings, taking a northern city as an example, the city's air quality assessment targets for 2025 are as follows:

[0028] As of July 19, 2025 (the 200th day of that year), the actual air quality data for the city is as follows:

[0029] Step 1: Calculate the completion rate of the three assessment indicators. The specific formula is as follows: PM2.5 concentration completion rate The calculation formula is: ; Good air quality days completion rate The calculation formula is: ; The percentage of heavily polluted days completed The calculation formula is: ; Analysis of the calculation results: The PM2.5 concentration has reached 107.14% of the annual target, exceeding it by 7.14 percentage points, indicating that the pressure to control PM2.5 in the first 200 days was relatively high, and the cumulative concentration has exceeded the corresponding proportion of the annual allowable total. The number of days with good air quality and the number of days with heavy pollution have both reached 60%, both of which are behind schedule (200 / 365 ≈ 54.8%), requiring increased control efforts in the remaining 165 days.

[0030] Step 2: Calculation of basic margin; Total permissible cumulative concentration throughout the year: ; Remaining allowable cumulative concentration: ; Remaining days: ; Uncorrected remaining daily average target concentration: ; Remaining days with good air quality: ; Remaining days of heavy pollution: ; The dynamic safety margin factor is calculated using default parameters. , ; ; ; A dynamic safety margin coefficient is introduced for correction, resulting in the corrected daily average PM2.5 target: ; Since PM2.5 concentrations exceeded the standard in the previous 200 days, the dynamic safety margin factor... This led to the revised standard Below the uncorrected This means that a stricter daily average concentration target (30.79 μg / m³) needs to be implemented for the remaining period. 3 < 31.97 μg / m 3 ); Step 3: Dynamically decompose the objectives level by level; Daily PM2.5 concentration distribution; time-decrease weighting is used, and the decay coefficient is taken. Remaining days Calculate the daily weights: For the remaining number Days (t=1,2,...,165): Substitute the number of days to obtain the daily weight; Calculate the sum of all weights (approximated here by numerical integration): ; Assuming weather correction factor The values ​​for the remaining time period are as follows: First 30 days (summer): Diffusion conditions are relatively good. =-0.1; The middle 90 days (autumn): diffusion conditions are generally good. =0; The following 45 days (winter): Diffusion conditions are poor. =0.15.

[0031] Taking the remaining 1 day as an example, t=1; ; ; With the remaining 100 days (t=100, autumn, For example: ; ; With the remaining 165 days (t=165, winter), For example: ; ; Analysis of allocation results: Due to the influence of time decay weighting, the target concentration was higher in the early stage (summer) (approximately 35.86 μg / m³). 3 The mid-season (autumn) temperature is moderate (approximately 29.52 μg / m³). 3 The concentration was lower in the later stages (winter) (approximately 27.88 μg / m³). 3 This achieves a "tight at the beginning and loose at the end" allocation strategy. The meteorological correction factor further fine-tunes the daily targets based on seasonal diffusion conditions.

[0032] Breakdown of days with good air quality by month: The remaining months are July to December, a total of 6 months. Based on the city's historical data from the past 5 years, the average number of days with good air quality in each month is as follows:

[0033] Calculate the weights, the total = 25 + 26 + 24 + 22 + 18 + 16 = 131; Monthly weights =0.1908; =0.1985; calculate sequentially , , , ; Monthly Excellent Targets (120 days remaining): ; and so on, calculate the excellent targets for each month; Daily Excellent Goals (taking July as an example): The requirement is that each day in July must have a 74.2% probability of being rated as good or excellent, meaning that 23 out of 31 days must meet the good or excellent standard.

[0034] Control of heavily polluted days: Heavy pollution days are not broken down to daily levels, but a daily warning threshold is set: the predicted PM2.5 concentration for the day > 150 μg / m³. 3 At that time, a "heavy pollution risk warning" was triggered.

[0035] Step four aims to complete the stress analysis; ; Historical data statistics: Based on the city's historical data from 2020 to 2024, the completion rate of calculating the average PM2.5 concentration for the remaining period (days 201-365) is as follows:

[0036] Statistical results: The best historical completion rate was 100.1%; the historical average completion rate was 96.02%. ; Tiered early warning judgment due to If the rate is ≥80%, the system determines it as "low risk" and outputs the message: "At the current pace, the annual target can be achieved based on the historical average level. It is recommended to maintain the current level of control." It also includes: dynamic updates and closed-loop feedback; Assuming that after the 201st day (July 20th), the actual air quality data for that day is as follows:

[0037] Update parameters:

[0038] Repeat steps one through four; these steps are executed automatically once daily, forming a closed-loop management system. Every morning: Based on the latest actual data, the system outputs the daily PM2.5 target concentration, target number of days with good air quality, and heavy pollution warning threshold for the current day and the remaining time period; Daily Implementation: Environmental management departments formulate control measures based on the daily targets; At the end of each day: the system collects the actual data for that day, updates the parameters, and recalculates the subsequent targets; Abnormal warning: When the actual performance deviates significantly from the target (such as PM2.5 exceeding the standard for several consecutive days), the system will automatically tighten subsequent targets and issue recommendations for enhanced control.

[0039] Example 2: This embodiment discloses a hierarchical dynamic allocation system for annual air pollutant targets, including: The assessment completion calculation module obtains the actual data of the current number of days that have passed and calculates the completion rate of the three assessment indicators; The calculation and correction module calculates the daily average target concentration for the remaining period based on the total annual target and the total amount already incurred, and introduces a dynamic safety margin coefficient for correction. The decomposition module uses time decay weighting to allocate the remaining cumulative PM2.5 concentration daily and uses historical weighting to decompose the remaining days with good air quality monthly. The early warning module calculates the probability of completion and outputs tiered early warnings based on historical best and historical average data.

[0040] It also includes: a visualization module that displays the following in chart form: an annual target completion progress bar; daily PM2.5 target curves for the remaining period; monthly excellent target bar charts; and a stress analysis dashboard (risk level, probability of completion).

[0041] Example 3: Dynamic safety factor parameters , The system is adaptive; however, in practical applications, different cities have different pollution characteristics and control capabilities. The system supports automatic optimization based on historical backtesting. , Parameters: Optimization objective: Minimize the deviation between the predicted objective and the actual achieved objective.

[0042] Method: Using historical data from the past 3 years, we iterate through α∈[0.1,0.5] and β∈[0.01,0.1] to select the parameter combination that minimizes the mean absolute percentage error (MAPE).

[0043] The attenuation coefficient γ is adaptive; γ controls the intensity of time-based attenuation (the degree of tightening at the beginning and loosening at the end). The system can automatically set γ based on the historical seasonal distribution of pollution in the city: if pollution is usually heavier in the second half of the year than in the first half (such as during the winter heating season in northern cities), a smaller γ (such as 0.3) is used to make the target allocation more gradual; if pollution is usually lighter in the second half of the year than in the first half (such as during the summer ozone pollution season in southern cities), a larger γ (such as 0.7) is used to make the tightening at the beginning and loosening at the end more obvious. The default value γ=0.5 is suitable for most cities.

[0044] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0045] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamic allocation of annual air pollutant targets at each level, characterized in that, include: Step 1: Obtain the actual data of the current number of days that have passed, and calculate the completion rate of the three assessment indicators; Step 2: Based on the annual target total and the total amount already incurred, calculate the daily average target concentration for the remaining period, and introduce a dynamic safety margin coefficient for correction; Step 3: The remaining cumulative PM2.5 concentration is allocated daily using time decay weighting, and the remaining number of days with good air quality is decomposed monthly using historical weighting; Step 4: Based on historical best and historical average data, calculate the completion probability and output a tiered warning. Step 5: After the day ends, update all parameters and repeat steps 1 to 4.

2. The method for dynamic allocation of annual air pollutant targets at each level according to claim 1, characterized in that, The specific formula for calculating the completion rate of the three assessment indicators is as follows: PM2.5 concentration completion rate The calculation formula is: ； in, Cumulative PM2.5 concentration over the past few days; Annual PM2.5 target average concentration; The current number of days has elapsed, in days; Good air quality days completion rate The calculation formula is: ； in: The number of days with good air quality completed, in days; Annual target for days with good air quality, in days; The percentage of heavily polluted days completed The calculation formula is: ； in: The number of days with severe pollution that have occurred, in days; Annual target for the number of heavily polluted days, in days.

3. The method for dynamic allocation of annual air pollutant targets at each level according to claim 1, characterized in that, The formula for the dynamic safety margin factor is as follows: ； Among them, , It is an adjustable parameter. This represents the percentage of PM2.5 concentration achieved. The remaining number of days. This represents the total number of days in a year.

4. The method for dynamic allocation of annual air pollutant targets at each level according to claim 3, characterized in that, A dynamic safety margin coefficient is introduced for correction, resulting in the corrected daily average PM2.5 target: ； in, Daily average target concentration (μg / m³) adjusted by dynamic safety factor 3 ); For dynamic safety margin coefficient, when At that time, the goals were more stringent. Take breaks when appropriate.

5. The method for dynamic allocation of annual air pollutant targets at each level according to claim 1, characterized in that, The formula for calculating the time decay weight is as follows: ； in, The attenuation coefficient is... The number of days remaining sky, This represents the number of days remaining.

6. The method for dynamic allocation of annual air pollutant targets at each level according to claim 5, characterized in that, The formula for calculating the daily PM2.5 target concentration is as follows: ； in, For meteorological correction factors; Due to poor diffusion conditions, the daily target should be appropriately relaxed. Diffusion conditions are favorable; the daily target should be appropriately tightened. Remaining allowable cumulative concentration; Time decay weight; This represents the number of days remaining.

7. The method for dynamic allocation of annual air pollutant targets at each level according to claim 1, characterized in that, The formula for calculating the completion probability is: ； in This represents the historical average completion rate for the remaining time period. The required completion rate.

8. The method for dynamic allocation of annual air pollutant targets at each level according to claim 7, characterized in that, The early warning rules for tiered early warning systems are as follows: like The system determined the risk to be "low". like The system issued a "moderate risk warning"; like The system issued a "high-risk warning" and recommended strengthening control measures.

9. A hierarchical dynamic allocation system for annual air pollutant targets, characterized in that, The method for dynamic allocation of annual air pollutant targets by level according to any one of claims 1-8 includes: The assessment completion calculation module obtains the actual data of the current number of days that have passed and calculates the completion rate of the three assessment indicators; The calculation and correction module calculates the daily average target concentration for the remaining period based on the total annual target and the total amount already incurred, and introduces a dynamic safety margin coefficient for correction. The decomposition module uses time decay weighting to allocate the remaining cumulative PM2.5 concentration daily and uses historical weighting to decompose the remaining days with good air quality monthly. The early warning module calculates the probability of completion and outputs tiered early warnings based on historical best and historical average data.

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