A precise quality control method and system based on dynamic adjustment of ambient air concentration

Abstract

The application provides a precision quality control method and system based on dynamic adjustment of ambient air concentration, comprising the following steps: step one, dynamic single-point quality control, determining the single-point quality control standard concentration based on the actual concentration of target substances in the atmosphere; performing single-point quality control; calculating the response deviation of the monitoring instrument and obtaining the single-point comprehensive uncertainty; step two, multi-point quality control, determining the concentration range of multi-point quality control; setting at least five concentration levels at equal intervals within the concentration range as the theoretical standard concentration of multi-point quality control; step B3, instructing the on-site dynamic calibrator to perform multi-point quality control and calculating the comprehensive uncertainty of each concentration level; performing one-dimensional linear regression fitting on the theoretical standard concentration and the corresponding comprehensive uncertainty. The application innovatively proposes the comprehensive uncertainty, systematically integrates the whole process error sources from the standard gas to the instrument response, and realizes the quantitative evaluation of the overall performance of the monitoring system.

Description

Technical Field

[0001] This invention relates to a precise quality control method based on dynamic adjustment of ambient air concentration, and also to a precise quality control system based on dynamic adjustment of ambient air concentration, belonging to the field of environmental quality monitoring. Background Technology

[0002] Automatic ambient air quality monitoring systems are a crucial technological support for scientific decision-making in air pollution prevention and control. To ensure the accuracy, reliability, and comparability of monitoring data, regular quality control operations must be performed on the monitoring instruments. Standardization, i.e., the introduction of standard gases, is one of the core quality control methods.

[0003] Currently, the main methods of quality control include:

[0004] Fixed concentration standardization: Periodic standardization using one or more pre-set fixed concentration standard gases, such as introducing a medium concentration standard gas once a week.

[0005] Multi-point calibration: Perform multi-point calibration covering low, medium and high concentrations once a quarter or month to verify the linearity of the instrument response.

[0006] Simple deviation calculation: Usually only the relative or absolute deviation between the instrument response value and the theoretical value of the standard gas is calculated, which serves as the basis for judging whether the quality control is qualified.

[0007] However, the above method has the following significant drawbacks:

[0008] Disconnect between quality control concentrations and actual monitored concentrations: Fixed-concentration standards cannot reflect changes in actual pollutant concentration levels at monitoring sites, leading to reduced quality control effectiveness. For example, using medium- to high-concentration standard gases for quality control in areas with long-term low concentrations makes it difficult to accurately reflect the instrument's performance within the actual operating concentration range.

[0009] The frequency and coverage of quality control are mismatched: single-point quality control has a high frequency but little information; multi-point calibration provides comprehensive information but has a long cycle and cannot reflect changes in instrument performance in a timely manner.

[0010] One-sided error assessment: Current quality control systems mostly focus on instrument response deviations without systematically considering uncertainties introduced by multiple factors such as standard gas dilution, flow control, and the accuracy of the calibrator itself, resulting in an incomplete assessment of the overall measurement uncertainty.

[0011] Lack of trend analysis: Multi-point calibration results are mostly evaluated based on single-point pass rates or linear correlation coefficients, lacking quantitative analysis of the trend of uncertainty with concentration.

[0012] Therefore, there is an urgent need for an intelligent and refined quality control method that can dynamically adjust quality control strategies based on actual monitoring data, comprehensively assess measurement uncertainty, and possess trend analysis capabilities. Summary of the Invention

[0013] The purpose of this invention is to provide a precise quality control method and system based on dynamic adjustment of ambient air concentration to solve the above-mentioned problems.

[0014] The present invention adopts the following technical solution:

[0015] A precise quality control method based on dynamic adjustment of ambient air concentration, characterized by the following steps:

[0016] Step 1: Dynamic single-point quality control.

[0017] Step A1: Periodically determine the theoretical standard concentration for single-point quality control based on the monthly average of the actual monitored concentrations of the target substance in the ambient atmosphere over the past month.

[0018] Step A2: Send the theoretical standard concentration command to the on-site dynamic calibrator to perform single-point standardization;

[0019] Step A3: After passing the standard, calculate the response deviation of the monitoring instrument, and combine it with the flow meter deviation of the dynamic calibrator, the uncertainty of the standard gas, the uncertainty of the standard gas dilution process, the sampling flow meter deviation of the monitoring instrument, and the linear fitting degree of the calibration curve to calculate the single-point comprehensive uncertainty.

[0020] Step two, multi-point quality control,

[0021] Step B1: Each quarter, based on the fifth percentile and the 90th percentile of the actual monitored concentration of the target substance in the ambient atmosphere in the past quarter, determine the concentration range for multi-point quality control.

[0022] Step B2: Set at least 5 concentration levels at equal intervals within the concentration range as the theoretical standard concentration for multi-point quality control;

[0023] Step B3 instructs the on-site dynamic calibrator to perform multi-point calibration and calculates the overall uncertainty for each concentration level;

[0024] Step B4: Using the theoretical standard concentration of each concentration level as the abscissa and the corresponding comprehensive uncertainty as the ordinate, perform univariate linear regression fitting to obtain the uncertainty-concentration trend line.

[0025] Furthermore, the precise quality control method based on dynamic adjustment of ambient air concentration of the present invention also has the following feature: the formula for calculating the single-point comprehensive uncertainty is as follows:

[0026] , where u 响应偏差 To monitor instrument response deviation, u 流量计A and u 流量计B For the flow meter deviation of the dynamic calibrator, u标气 For standard gas uncertainty, u 稀释 For the uncertainty of the standard gas dilution process, u 采样流量 To monitor the deviation of the instrument sampling flow meter, u 线性 To calibrate the linearity of the curve.

[0027] Furthermore, the precise quality control method based on dynamic adjustment of ambient air concentration of the present invention also has the following characteristics: the concentration range of multi-point quality control is [P5, P90], where P5 is the fifth percentile and P90 is the ninetieth percentile, and five concentration levels are set at equal intervals within the [P5, P90] interval.

[0028] Furthermore, the precise quality control method based on dynamic adjustment of ambient air concentration of the present invention also has the following characteristics: the frequency of single-point quality control is dynamically adjusted by using the exponentially weighted moving average (EWMA) method. If the result error is small, the quality control frequency can be relaxed; if the error is large, the quality control frequency is increased.

[0029] Furthermore, the precise quality control method based on dynamic adjustment of ambient air concentration in this invention also has the following feature: the exponentially weighted moving average (EWMA) method is used for the frequency of single-point quality control, and the formula is as follows:

[0030] C(t) = α * C(t-1) + (1 - α) * θ(t)

[0031] Where C(t) is the exponentially weighted moving average of the quality control error over t days, α is the weighting coefficient, C(t-1) is the exponentially weighted moving average of the quality control error over t-1 days, and θ(t) represents the actual quality control error over t days.

[0032] Furthermore, the precise quality control method based on dynamic adjustment of ambient air concentration of the present invention also has the following feature: the execution frequency of multi-point quality control is based on the slope of the uncertainty-concentration trend line, and if it exceeds the predetermined range, it prompts that the quality control frequency should be increased.

[0033] Furthermore, the precise quality control method based on dynamic adjustment of ambient air concentration of the present invention also has the following feature, wherein the steps for determining the theoretical standard concentration for single-point quality control are as follows:

[0034] Based on the air quality forecasts released by the China National Environmental Monitoring Centre, cities where monitoring stations are located are selected to obtain the 24-hour forecast range of the Air Quality Index (AQI). The average AQI is calculated, and then, based on the concentrations of various air pollutants and AQI data from the past year, a fitting equation is established using Y=aX+b, where X is the AQI and Y is the concentration of air pollutants. The theoretical standard concentration for each point is dynamically adjusted and determined based on the predicted average AQI.

[0035] Furthermore, the precise quality control method based on dynamic adjustment of ambient air concentration of the present invention also has the following feature: in step A2, the system automatically generates a quality control instruction and sends it to the dynamic calibrator on site. The dynamic calibrator retrieves its internal preset standard sequence according to the received theoretical standard concentration, automatically configures the standard gas dilution ratio, and performs a single-point standard operation.

[0036] This invention also provides a precision quality control system based on dynamic adjustment of ambient air concentration, characterized in that it includes:

[0037] The data acquisition module is used to collect the measured air quality data and sensor data from various air quality monitoring stations.

[0038] The statistical analysis module is used to calculate the correlation error of each sampling parameter;

[0039] The quality control parameter generation module is used to calculate and determine the concentration at a single point based on the predicted average AQI index.

[0040] The instruction issuing module generates an instruction and issues it to the dynamic calibrator on site. The dynamic calibrator retrieves its internal preset standard sequence based on the received theoretical standard concentration, automatically configures the standard gas dilution ratio, and performs a single-point standard operation.

[0041] The uncertainty calculation module calculates the sensor error corresponding to each air quality index based on the actual response value of the monitoring instrument recorded by the data acquisition module during the standardization period, and calculates the comprehensive uncertainty of each quality control point.

[0042] The trend fitting module is used to calculate the comprehensive uncertainty for each point when performing multi-point quality control, obtain a dataset consisting of the quality control points and their corresponding comprehensive uncertainties, and then perform linear fitting on the dataset to obtain the equation: y = kx + d.

[0043] Furthermore, the precision quality control system based on dynamic adjustment of ambient air concentration of the present invention also has the following feature: an uncertainty calculation module, using a formula... Calculate the overall uncertainty, where u 响应偏差 To monitor instrument response deviation, u 流量计A and u 流量计B For the flow meter deviation of the dynamic calibrator, u 标气 For standard gas uncertainty, u 稀释 For the uncertainty of the standard gas dilution process, u 采样流量 To monitor the deviation of the instrument sampling flow meter, u 线性 To calibrate the linearity of the curve.

[0044] Beneficial effects of the invention: The precise quality control method and system based on dynamic adjustment of ambient air concentration of the present invention has the following advantages:

[0045] More targeted quality control: The concentration of single-point quality control is dynamically adjusted based on the monthly average to ensure that the quality control concentration is close to the actual monitoring level and improve the effectiveness of quality control.

[0046] More scientific quality control range: The multi-point quality control range is determined based on P5 and P90, covering the vast majority of actual monitoring concentration ranges, avoiding the subjectivity and irrationality of traditional high and low concentration point selection.

[0047] More comprehensive assessment: The innovative concept of "comprehensive uncertainty" is proposed, which systematically integrates the error sources throughout the entire process from standard gas to instrument response, and realizes a quantitative assessment of the overall performance of the monitoring system.

[0048] It has trend analysis capabilities: by fitting the uncertainty-concentration trend, the performance stability of the instrument can be intuitively judged in different concentration ranges. For example, if the slope is significantly positive, it indicates that the performance may deteriorate in the high concentration range, providing an important basis for instrument maintenance and data review.

[0049] Intelligent and automated: The entire process, including data statistics, concentration determination, instruction issuance, calculation and evaluation, trend fitting, etc., can be completed automatically by the system, reducing human intervention and improving quality control efficiency and standardization.

[0050] Improving data quality and credibility: This method provides a higher level of quality assurance for ambient air quality monitoring data, enhancing the scientific validity and credibility of the monitoring data. Attached Figure Description

[0051] Figure 1 This is a flowchart of a precise quality control method based on dynamic adjustment of ambient air concentration.

[0052] Figure 2 This is a block diagram of a precision quality control system based on dynamic adjustment of ambient air concentration. Detailed Implementation

[0053] The specific embodiments of the present invention will be described below with reference to the accompanying drawings.

[0054] Example 1

[0055] like Figure 2 As shown, the precision quality control system based on dynamic adjustment of ambient air concentration includes a data acquisition module, a statistical analysis module, a quality control parameter generation module, an instruction issuance module, an uncertainty calculation module, and a trend fitting module.

[0056] The data acquisition module collects actual air quality data from sensors at each air quality monitoring station.

[0057] The statistical analysis module calculates the error of each sensor.

[0058] The quality control parameter generation module calculates and determines the concentration of individual points based on the predicted average AQI index. Specifically, the dynamic determination process for individual point concentrations is as follows: based on the air quality forecast released by the China National Environmental Monitoring Centre, the city where the monitoring station is located is selected, the 24-hour forecast range of the air quality AQI index is obtained, the average AQI index is calculated, and then, based on the concentrations of various air pollutants and the AQI index data of the past year, a fitting equation is established using Y=aX+b, where X is the AQI index and Y is the concentration of air pollutants; the concentration of individual points that need to be dynamically adjusted and determined is calculated based on the predicted average AQI index.

[0059] The instruction issuing module generates quality control instructions and issues them to the dynamic calibrator on site. Based on the received theoretical standard concentration, the dynamic calibrator retrieves its internal preset standard sequence, automatically configures the standard gas dilution ratio, and performs a single-point standard operation.

[0060] The uncertainty calculation module calculates the sensor error corresponding to each air quality indicator based on the actual response values ​​of the monitoring instruments recorded by the data acquisition module during the standardization period, and uses the formula... Calculate the overall uncertainty.

[0061] The trend fitting module calculates the comprehensive uncertainty for each point when performing multi-point quality control, obtaining a dataset consisting of the quality control points and their corresponding comprehensive uncertainties. Then, it performs linear fitting on the dataset to obtain the equation: y = kx + d.

[0062] like Figure 1 As shown, the precise quality control method based on dynamic adjustment of ambient air concentration includes the following steps:

[0063] Step 1: Dynamic Single-Point Quality Control Process

[0064] Step 1.1, Dynamic Single-Point Concentration Determination: Based on the air quality forecast released by the China National Environmental Monitoring Centre, select the cities where the monitoring stations are located, obtain the 24-hour forecast range of the Air Quality Index (AQI), calculate the average AQI, and then establish a fitting equation Y=aX+b based on the concentrations of various air pollutants and AQI data from the past year, where X is the AQI and Y is the concentration of air pollutants. The concentration of single-point concentration that needs to be dynamically adjusted and determined is calculated based on the predicted average AQI.

[0065] Step 1.2, Selection of dynamic single-point quality control frequency:

[0066] The frequency of single-point quality control is dynamically adjusted using the exponentially weighted moving average (EWMA) method. If the result error is small, it indicates that the instrument is in good condition and the quality control frequency can be relaxed; if the error is large, the quality control frequency is increased.

[0067] C(t) = α * C(t-1) + (1 - α) * θ(t)

[0068] Where C(t) is the exponentially weighted moving average of the quality control error over t days, α is the weighting coefficient, C(t-1) is the exponentially weighted moving average of the quality control error over t-1 days, and θ(t) represents the actual quality control error over t days.

[0069] The α value is determined based on historical data from the past year, combined with quality control results and current relevant standard limits. Validation is performed first using internal standard data. If the internal standard data is below 30% on a given day, external standard data is not performed that day; if the internal standard data is between 30% and 50%, external standard data is performed normally that day; if it exceeds 50%, instrument maintenance and repair are required.

[0070] Step 1.3, Instruction Issuance and Execution: The system automatically generates quality control instructions and issues them to the dynamic calibrator on site; the dynamic calibrator retrieves its internal preset standard sequence based on the received theoretical standard concentration, automatically configures the standard gas dilution ratio, and performs single-point standard operation.

[0071] Step 1.4, Response Data Acquisition: Record the actual response values ​​of the monitoring instruments during the standardization period.

[0072] Step 1.5: Calculate the overall uncertainty, where u represents the error of each parameter.

[0073] .

[0074] Step 2, Dynamic Multi-point Detection

[0075] Step 2.1, Data Collection and Statistics: The system automatically collects and compiles the actual monitoring data of the target monitoring stations for each target pollutant in the past quarter.

[0076] Step 2.2, determine the concentration range for multi-point quality control: calculate the fifth percentile (P5) and the ninth percentile (P90) of the concentration of each target pollutant in the past quarter, and use this to determine the concentration range [P5, P90] for multi-point quality control.

[0077] Step 2.3, set the standard concentration points: set 5 concentration levels at equal intervals within the range of [P5, P90] as the theoretical standard concentration points for this multi-point quality control.

[0078] Step 2.4, Instruction Issuance and Execution: The system generates multi-point quality control instructions and issues them to the on-site dynamic calibrator to sequentially execute standardization operations at 5 concentration levels.

[0079] Step 2.5, Response Data Acquisition: Record the actual response values ​​of the monitoring instrument at each concentration level. Calculate the overall uncertainty for each sampling point to obtain the dataset.

[0080] The overall uncertainty is calculated using the following formula, where u represents the error of each parameter.

[0081] .

[0082] Step 2.6: Perform linear fitting on the dataset obtained in Step 3 to obtain the equation: y = kx + d. Under normal circumstances, the value of k is in the range of [0-0.01], and d should be less than 5.

[0083] Example 2

[0084] Dynamic quality control of O3 automatic monitoring stations in a certain city

[0085] Step 1, Single-point quality control, performed daily to weekly:

[0086] The system automatically calculates the average data for the previous month, such as September 1st to September 30th, every Monday at 00:00.

[0087] The calculated monthly average value is 45.6 μg / m3.

[0088] The system sets 45.6 μg / m³ as the target concentration for single-point quality control this week and generates a command to send to the on-site dynamic calibrator.

[0089] The calibrator invoked the preset program, used 1000 μg / m3 standard gas, prepared at a dilution ratio of 21.74, and performed the standard test.

[0090] The recorded response value of the monitoring instrument was 46.2 μg / m3.

[0091] The system retrieves data from each error source:

[0092] Response bias: |(46.2-45.6) / 45.6| = 1.32%

[0093] Flow meter A / B deviation: 0.5%, 0.6%

[0094] Standard gas uncertainty: 2.0%

[0095] Dilution uncertainty: 1.0%

[0096] Sampling flow rate deviation: 0.8%

[0097] Recent multi-point calibration linear residual: 1.2%.

[0098] The comprehensive uncertainty formula provided in Example 1 is used to calculate u. 综合 = 3.1%.

[0099] Step two, multi-point quality control, implemented quarterly:

[0100] The system compiles data for the previous quarter on the first day of the first month of each quarter, such as Q2: April-June.

[0101] The calculated values ​​are P5 = 28.3 μg / m³ and P90 = 75.8 μg / m³.

[0102] Determine the multi-point quality control range [28.3, 75.8] μg / m3.

[0103] Five concentration points were set at equal intervals: 28.3 μg / m3, 40.1 μg / m3, 51.9 μg / m3, 63.7 μg / m3, and 75.8 μg / m3.

[0104] The instruction is issued, and the calibrator performs five consecutive calibrations.

[0105] Step 3: Calculate the comprehensive uncertainty for each point to obtain the dataset:

[0106] (28.3, 3.5%), (40.1, 3.2%), (51.9, 3.1%), (63.7, 3.3%), (75.8, 3.8%).

[0107] Step 4: Perform linear fitting to obtain the equation: y = 0.0072x + 1.56, R² 2 =0.92.

[0108] The slope k = 0.0072, ranging from 0 to 0.01. d = 1.56, less than 5. Overall, the performance is within the normal range. The uncertainty shows a slight upward trend with increasing concentration, and performance in the high-concentration region needs to be monitored.

Claims

1. A precise quality control method based on dynamic adjustment of ambient air concentration, characterized in that, Includes the following steps: Step 1: Dynamic single-point quality control. Step A1: Periodically determine the theoretical standard concentration for single-point quality control based on the monthly average of the actual monitored concentrations of the target substance in the ambient atmosphere over the past month. Step A2: Send the theoretical standard concentration command to the on-site dynamic calibrator to perform single-point standardization; Step A3: After passing the standard, calculate the response deviation of the monitoring instrument, and combine it with the flow meter deviation of the dynamic calibrator, the uncertainty of the standard gas, the uncertainty of the standard gas dilution process, the sampling flow meter deviation of the monitoring instrument, and the linear fitting degree of the calibration curve to calculate the single-point comprehensive uncertainty. Step two, multi-point quality control, Step B1: Each quarter, based on the fifth percentile and the 90th percentile of the actual monitored concentration of the target substance in the ambient atmosphere in the past quarter, determine the concentration range for multi-point quality control. Step B2: Set at least 5 concentration levels at equal intervals within the concentration range as the theoretical standard concentration for multi-point quality control; Step B3 instructs the on-site dynamic calibrator to perform multi-point calibration and calculates the overall uncertainty for each concentration level; Step B4: Using the theoretical standard concentration of each concentration level as the abscissa and the corresponding comprehensive uncertainty as the ordinate, perform univariate linear regression fitting to obtain the uncertainty-concentration trend line. The formula for calculating the single-point comprehensive uncertainty is as follows: , where u 响应偏差 To monitor instrument response deviation, u 流量计A and u 流量计B For the flow meter deviation of the dynamic calibrator, u 标气 For standard gas uncertainty, u 稀释 For the uncertainty of the standard gas dilution process, u 采样流量 To monitor the deviation of the instrument sampling flow meter, u 线性 To calibrate the linear fit of the curve; The frequency of single-point quality control is dynamically adjusted using the exponentially weighted moving average (EWMA) method. If the error is small, the quality control frequency can be increased; if the error is large, the quality control frequency is increased. The frequency of multi-point quality control is determined based on the slope of the uncertainty-concentration trend line. If the frequency exceeds the predetermined range, it will indicate that the quality control frequency should be increased. The steps for determining the theoretical standard concentration for single-point quality control are as follows: Based on the air quality forecasts released by the China National Environmental Monitoring Centre, cities where monitoring stations are located are selected to obtain the 24-hour forecast range of the Air Quality Index (AQI). The average AQI is calculated, and then, based on the concentrations of various air pollutants and AQI data from the past year, a fitting equation is established using Y=aX+b, where X is the AQI and Y is the concentration of air pollutants. The theoretical standard concentration for each point is dynamically adjusted and determined based on the predicted average AQI.

2. The precise quality control method based on dynamic adjustment of ambient air concentration according to claim 1, characterized in that: The concentration range for the multi-point quality control is [P5, P90], where P5 is the fifth percentile and P90 is the 90th percentile. Five concentration levels are set at equal intervals within the [P5, P90] interval.

3. The precise quality control method based on dynamic adjustment of ambient air concentration according to claim 1, characterized in that: The exponentially weighted moving average (EWMA) method is used to determine the frequency of single-point quality control. The formula is as follows: C(t) = α * C(t-1) + (1 - α) * θ(t) Where C(t) is the exponentially weighted moving average of the quality control error over t days, α is the weighting coefficient, C(t-1) is the exponentially weighted moving average of the quality control error over t-1 days, and θ(t) represents the actual quality control error over t days.

4. The precise quality control method based on dynamic adjustment of ambient air concentration according to claim 1, characterized in that: In step A2, the system automatically generates a quality control instruction and sends it to the dynamic calibrator on site. The dynamic calibrator retrieves its internal preset standard sequence based on the received theoretical standard concentration, automatically configures the standard gas dilution ratio, and performs a single-point standard operation.

5. A dynamic quality control system, based on the precise quality control method for dynamic adjustment of ambient air concentration as described in claim 1, characterized in that, include: The data acquisition module is used to collect the measured air quality data and sensor data from various air quality monitoring stations. The statistical analysis module is used to calculate the correlation error of each sampling parameter; The quality control parameter generation module is used to calculate and determine the concentration at a single point based on the predicted average AQI index. The instruction issuing module generates an instruction and issues it to the dynamic calibrator on site. The dynamic calibrator retrieves its internal preset standard sequence based on the received theoretical standard concentration, automatically configures the standard gas dilution ratio, and performs a single-point standard operation. The uncertainty calculation module calculates the sensor error corresponding to each air quality index based on the actual response value of the monitoring instrument recorded by the data acquisition module during the standardization period, and calculates the comprehensive uncertainty of each quality control point. The trend fitting module is used to calculate the comprehensive uncertainty for each point when performing multi-point quality control, obtain a dataset consisting of the quality control points and their corresponding comprehensive uncertainties, and then perform linear fitting on the dataset to obtain the equation: y = kx + d.

6. The dynamic quality control system as described in claim 5, characterized in that: The uncertainty calculation module uses the formula Calculate the overall uncertainty, where u 响应偏差 To monitor instrument response deviation, u 流量计A and u 流量计B For the flow meter deviation of the dynamic calibrator, u 标气 For standard gas uncertainty, u 稀释 For the uncertainty of the standard gas dilution process, u 采样流量 To monitor the deviation of the instrument sampling flow meter, u 线性 To calibrate the linearity of the curve.

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