Quantitative evaluation method for environmental monitoring quality

Abstract

The invention relates to the technical field of environmental quality monitoring, and discloses an environmental monitoring quality quantitative evaluation method. According to the environment monitoring quality quantitative evaluation method, high-frequency real-time monitoring is adopted, data timeliness is ensured, environment changes can be rapidly responded, especially effective monitoring and management of sudden pollution events are achieved, then through combination of a standard deviation method and a moving average method, abnormal values are automatically recognized and eliminated, meanwhile, short-term fluctuation is eliminated, and the quality of the environment is effectively evaluated. The method reduces the dependence of manual judgment of environment quality abnormity, improves the objectivity and scientificity of data processing, and can provide a more comprehensive and accurate decision basis for environment management by comparing and evaluating the air quality index and the water quality index obtained by calculation with the industrial standard. The implementation of the method not only improves the efficiency of the monitoring work, but also enhances the comprehensive evaluation capability of the environmental quality, and provides powerful support for improving the environmental quality of the industrial area and guaranteeing the public health.

Description

Technical Field

[0001] The invention relates to the technical field of environmental quality monitoring, and in particular to a method for quantitatively evaluating environmental monitoring quality. Background Art

[0002] Against the backdrop of increasingly serious global environmental problems, accurate and effective environmental monitoring has become an important means to protect public health and ecological security. Currently, many environmental monitoring technologies have been widely used, such as gas analyzers, liquid chromatography, mass spectrometers, etc. These technologies can provide real-time and accurate pollutant concentration data. However, existing technologies still have shortcomings.

[0003] Existing environmental quality monitoring technology has certain limitations in data processing, especially in the process of outlier identification and short-term fluctuation elimination, which often relies on manual judgment. Workers use mechanical equipment to take samples at each sampling point, and then calculate the results to form semi-automatic monitoring. This method leads to inaccurate and unreliable monitoring results. At the same time, many methods lack real-time performance and cannot respond to environmental changes in a timely manner, affecting the effective monitoring and management of sudden pollution incidents. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a method for quantitatively evaluating the quality of environmental monitoring. The method has the advantages of ensuring the timeliness of data by adopting high-frequency real-time monitoring, thereby being able to quickly respond to environmental changes, especially the effective monitoring and management of sudden pollution incidents. Secondly, through the combination of the standard deviation method and the moving average method, outliers are automatically identified and eliminated, while short-term fluctuations are eliminated, reducing the reliance on manual judgment of environmental quality anomalies. Fully automated monitoring improves the objectivity and scientificity of data processing. In addition, the calculated air quality indicators and water quality indicators are compared and evaluated with industry standards, which can provide a more comprehensive and accurate decision-making basis for environmental management. The implementation of this method not only improves the efficiency of monitoring work, but also enhances the comprehensive assessment capability of environmental quality, provides strong support for improving the environmental quality of industrial areas and protecting public health, and solves the above-mentioned problems.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a method for quantitatively evaluating environmental monitoring quality, comprising the following steps:

[0008] S1. Select industrial areas as environmental sampling sites, specify air quality and water quality as evaluation indicators, and monitor every hour every day;

[0009] S2, collecting air composition data through a gas analyzer and water quality component parameters through a water quality monitor, and compiling the above collected data into an air composition data set and a water quality composition data set;

[0010] S3. Use the standard deviation method to identify and remove outliers in the air composition data set and the water quality composition data set, and use the moving average method to eliminate short-term fluctuations in the air composition data and the water quality composition data;

[0011] S4. After the processing of the air component data set and the water quality component data set is completed, the air quality index, the air quality index standard deviation, the water quality index and the water quality index standard deviation are calculated according to the corresponding data set, and the air quality index standard deviation and the water quality index standard deviation are verified. When the calculated deviations of the two are within the normal range, proceed to the next step, otherwise return to S2 to re-collect data;

[0012] S5. Compare and evaluate the calculated air quality index and water quality index with the corresponding industry standards, and generate a comprehensive evaluation report and send it to the communication equipment of the monitoring personnel.

[0013] Preferably, the S2 air composition data set is as follows:

[0014] {kqyz n ,Kqyz n+1 ,Kqyz n+2 ,...,Kqyz n+4}

[0015] In the dataset, Kqyz n Indicates the first collected air composition data, Kqyz n+1 Indicates the second collected air composition data, Kqyz n+2 Indicates the third collected air composition data, Kqyz n+4 It represents the fourth collected air composition data, and five data constitute a set of data sets;

[0016] The water quality component dataset set is as follows:

[0017] {Szck n ,Szck n+1 ,Szck n+2 ,...,Szck n+4}

[0018] In the dataset, Szck n Indicates the first collected water quality composition data, Szck n+1 Indicates the second collected water quality composition data, Szck n+2 Indicates the third collected water quality composition data, Szck n+4It represents the fourth collected water quality composition data. Five data constitute a set of data sets.

[0019] Preferably, the formula for identifying and removing outliers in a data set using the standard deviation method is as follows:

[0020]

[0021] In the formula, Z i represents the standardized value of the ith data point, Px i represents the i-th measurement value, represents the mean of the data set, σ represents the standard deviation of the data set, and Z i If it is greater than 2, the measured data value is considered to be an outlier, and i represents the position of the data in the data set.

[0022] Preferably, the calculation formula of the abnormal deviation σ of the data set is as follows:

[0023]

[0024] In the formula, σ represents the abnormal deviation of the data set, x i represents the i-th measurement value, Indicates the calculation of the mean of the measurement data set, n represents the total number of data points, and n-1 represents the correction of the deviation caused by the limited sample size through Bessel correction.

[0025] Preferably, the formula for eliminating short-term fluctuations of air component data and water quality component data by the moving average method is as follows:

[0026]

[0027] In the formula, Mk t Represents the moving average at time point t, which is the smoothed value at the current time point, Ys t-j represents the original collected data value at time point tj, n represents the window size of the moving average, that is, the number of time points selected each time the average value is calculated, It means that all the original collected data from j=0 to n-1 are accumulated and summed, and finally divided by the moving average window size n to obtain the moving average.

[0028] Preferably, the air quality index calculation formula is as follows:

[0029]

[0030] In the formula, AQI represents the air quality index, Kqyz i Represents the concentration of the i-th air component in the air composition dataset, obtained through the air composition dataset, Wt irepresents the corresponding weight, i represents the position of the collected air composition data in the data set, and n represents the total number of air composition samples in the collected air composition data set. It means that the air quality index is obtained by multiplying all air component samples by the weight of the corresponding sample and then dividing it by the total number of samples n.

[0031] Preferably, the formula for the standard deviation of the air quality index is as follows:

[0032]

[0033] In the formula, Bzpc represents the standard deviation of air quality index, Kqyz i represents the air component in the i-th air, i represents the counting subscript, Indicates calculating the mean of air composition in all collected air composition data sets, p indicates the total number of data points that need to be calculated, and p-1 indicates that in order to make an unbiased estimate, the calculated standard deviation is closer to the true overall standard deviation. It means that all squared differences are added together using the summation symbol to obtain the total variation between all measurements and the mean, and then the square root is taken to obtain the standard deviation.

[0034] Preferably, the water quality index calculation formula is as follows:

[0035]

[0036] In the formula, WQ represents the water quality index, m represents the total number of ammonia nitrogen samples used to calculate the water quality index, and Hxhy a represents the monitoring value of the a-th water quality ammonia nitrogen sample, obtained through the water quality component dataset, Wx a represents the corresponding weight, It means that the water quality index is obtained by multiplying the sum of all water quality ammonia nitrogen samples by the weight of the corresponding sample and then dividing it by the total number of samples m.

[0037] Preferably, the calculation formula of the water quality index standard deviation is as follows:

[0038]

[0039] In the formula, Sdpc represents the standard deviation of water quality index, represents the mean of the ammonia nitrogen samples used to calculate water quality, m represents the total number of ammonia nitrogen samples used to calculate water quality indicators, and m-1 represents the correction of the bias caused by the limited number of samples in order to make an unbiased estimate. It means to add up all the square differences to get the total variation between all water quality ammonia nitrogen monitoring values ​​and the mean, and then divide the result by the square root of m-1 to get the standard deviation of the water quality index.

[0040] Preferably, the conditions for verifying the standard deviation of the air quality index and the standard deviation of the water quality index are as follows:

[0041] When the air quality index standard deviation Bzpc exceeds the normal air quality deviation range Km, it means that the air quality index standard deviation is too large. When the water quality index standard deviation Sdpc is greater than the normal water quality index deviation range Kd, it means that the water quality index standard deviation is too large. At this time, return to S2 to re-collect data.

[0042] Compared with the prior art, the present invention provides a method for quantitatively evaluating environmental monitoring quality, which has the following beneficial effects:

[0043] The present invention ensures the timeliness of data by adopting high-frequency real-time monitoring, so that it can quickly respond to environmental changes, especially the effective monitoring and management of sudden pollution incidents. Secondly, through the combination of standard deviation method and moving average method, outliers are automatically identified and eliminated, and short-term fluctuations are eliminated, reducing the reliance on manual judgment of environmental quality anomalies. Fully automated monitoring improves the objectivity and scientificity of data processing. In addition, the calculated air quality indicators and water quality indicators are compared and evaluated with industry standards, which can provide a more comprehensive and accurate decision-making basis for environmental management. The implementation of this method not only improves the efficiency of monitoring work, but also enhances the comprehensive assessment ability of environmental quality, providing strong support for improving the environmental quality of industrial areas and protecting public health. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The existing environmental quality monitoring technology has certain limitations in data processing, especially in the process of outlier identification and short-term fluctuation elimination, which often relies on manual judgment, resulting in inaccurate and unreliable results. At the same time, many methods lack real-time performance and cannot respond to environmental changes in a timely manner, affecting the effective monitoring and management of sudden pollution incidents. Therefore, a quantitative evaluation method for environmental monitoring quality is proposed. Figure 1 , the method comprises the following steps:

[0047] S1. Select industrial areas as environmental sampling sites, specify air quality and water quality as evaluation indicators, and monitor every hour every day;

[0048] S2, collecting air composition data through a gas analyzer and water quality component parameters through a water quality monitor, and compiling the above collected data into an air composition data set and a water quality composition data set;

[0049] Gas analyzers can detect multiple components in the air in real time, including trace gases and volatile organic compounds, by using spectral analysis, gas chromatography and mass spectrometry. These analyzers have high sensitivity and selectivity, and can identify and quantify multiple pollutants such as carbon monoxide, nitrogen dioxide, ozone, air, etc., ensuring the accuracy and reliability of the data;

[0050] At the same time, water quality monitors will be equipped with technologies such as electrochemical sensors, photometry and ion-selective electrodes to monitor important parameters in water bodies in real time, such as chemical oxygen demand, biological oxygen demand, ammonia nitrogen, heavy metal ions, etc. These instruments can quickly provide online monitoring data of water quality by chemically or electrochemically reacting with water samples, ensuring our ability to respond quickly to water quality;

[0051] After data collection is completed, the acquired air composition and water quality composition parameters will be systematically organized, and all collected data will be compiled into structured air composition data sets and water quality composition data sets. In this way, information can not only be stored systematically, but also facilitate subsequent data analysis and processing. In the process of data set management, database technology and data mining methods will also be used to ensure data integrity and traceability;

[0052] S3. Use the standard deviation method to identify and remove outliers in the air composition data set and the water quality composition data set, and use the moving average method to eliminate short-term fluctuations in the air composition data and the water quality composition data;

[0053] The formula for identifying and removing outliers in a data set is as follows:

[0054]

[0055] The standard deviation method can effectively determine which values ​​in the data set deviate from the normal range, thereby identifying possible erroneous data or extreme values. These abnormal values ​​may be caused by equipment failure, sampling errors or emergencies. Eliminating these values ​​can improve the accuracy and credibility of the overall data. In the formula, Z i represents the standardized value of the ith data point, Px i represents the i-th measurement value, represents the mean of the data set, σ represents the standard deviation of the data set, and Z iIf it is greater than 2, the measured data value is considered to be an outlier. i represents the position of the data in the data set. The standard deviation method is used to ensure that the used data set reflects the real environmental conditions. Subsequent analysis and decision-making can be more scientific, helping managers to make more reasonable and effective environmental management measures.

[0056] The formula to eliminate short-term fluctuations in air composition data and water quality composition data is as follows:

[0057]

[0058] By using the moving average method to eliminate short-term fluctuations in air composition data and water quality composition data, the stability and reliability of data analysis can be significantly enhanced. Short-term fluctuations are often affected by instantaneous environmental changes, such as weather conditions and fluctuations in industrial activities. These changes can easily lead to misleading data interpretation. In the formula, Mk t Represents the moving average at time point t, which is the smoothed value at the current time point, Ys t-j represents the original collected data value at time point tj, n represents the window size of the moving average, that is, the number of time points selected each time the average value is calculated, It means that all the original collected data from j=0 to n-1 are accumulated and summed, and finally divided by the moving average window size n to get the moving average. The moving average method can smooth these short-term fluctuations and extract more accurate trends and patterns, making the monitoring results clearer. This method smoothes the values ​​within a period of time so that the monitoring data reflects long-term trends rather than instantaneous changes, which is more conducive to identifying potential environmental problems and providing a basis for formulating long-term environmental management strategies;

[0059] S4. After the processing of the air component data set and the water quality component data set is completed, the air quality index, the air quality index standard deviation, the water quality index and the water quality index standard deviation are calculated according to the corresponding data set, and the air quality index standard deviation and the water quality index standard deviation are verified. When the calculated deviations of the two are within the normal range, proceed to the next step, otherwise return to S2 to re-collect data;

[0060] The air quality index calculation formula is as follows:

[0061]

[0062] Calculating air quality indicators can provide a clear, quantitative standard to assess the severity of air pollution. These indicators can help identify pollution sources and their intensity, and facilitate the implementation of targeted control measures. In the formula, AQI stands for air quality index, Kqyz i Represents the concentration of the i-th air component in the air composition dataset, obtained through the air composition dataset, Wti represents the corresponding weight, i represents the position of the collected air composition data in the data set, and n represents the total number of air composition samples in the collected air composition data set. It means that the sum of all air component samples multiplied by the weight of the corresponding sample is divided by the total number of samples n to obtain the air quality index. The air quality index provides a basis for the formulation of management strategies and public health warnings, so that the public can take necessary protective measures during high pollution periods, such as reducing outdoor activities;

[0063] Weight Wt i The calculation formula is as follows:

[0064]

[0065] Among them, Wt i represents the weight of the i-th air component, H i represents the health impact index associated with the ith air pollutant, and m represents the total number of data on air components;

[0066] The formula for calculating the standard deviation of air quality index is as follows:

[0067]

[0068] Calculating the standard deviation of air quality indicators helps to understand the volatility and reliability of data. The standard deviation can reflect the discreteness of the sampled data. A high standard deviation means that the data has large fluctuations, which may indicate unstable environmental conditions or data quality problems. In the formula, Bzpc represents the standard deviation of air quality indicators, and Kqyz i represents the air composition in the i-th air composition data set, i represents the counting subscript, Indicates calculating the mean of air composition in all collected air composition data sets, p indicates the total number of data points that need to be calculated, and p-1 indicates that in order to make an unbiased estimate, the calculated standard deviation is closer to the true overall standard deviation. It means using the summation symbol to add up all the squared differences to get the total variation between all the measured values ​​and the mean, and then taking the square root to get the standard deviation. By analyzing the standard deviation, the stability of air quality can be evaluated, potential pollution events can be identified, and support can be provided for further environmental management decisions.

[0069] The water quality index calculation formula is as follows:

[0070]

[0071] The calculation of water quality indicators can accurately reflect the pollution status and ecological health of water bodies. These indicators can help determine whether water bodies meet relevant environmental standards and support the sustainable use of water resources. In the formula, WQ represents the water quality index, m represents the total number of ammonia nitrogen samples used to calculate the water quality index, and Hxhy represents the total number of ammonia nitrogen samples used to calculate the water quality index. a represents the monitoring value of the a-th water quality ammonia nitrogen sample, obtained through the water quality component dataset, Wx a represents the corresponding weight, It means that the water quality index is obtained by multiplying all samples of ammonia nitrogen in water quality by the weight of the corresponding sample and then dividing it by the total number of samples m. Regular monitoring of water quality indicators helps to timely detect water pollution and changes, facilitate the implementation of treatment measures, and protect water sources and their ecosystems.

[0072] Wx a The calculation formula is as follows:

[0073]

[0074] Among them, Wx a represents the weight of the i-th water quality ammonia nitrogen, Y i represents the health impact index related to the i-th water quality ammonia nitrogen pollutant, and m represents the total number of water quality ammonia nitrogen data;

[0075] The calculation formula of the standard deviation of water quality index is as follows:

[0076]

[0077] Standard deviation provides a quantitative tool for judging the consistency and stability of water quality data. When the standard deviation is large, it may indicate that the water quality is affected by external factors (such as climate change, industrial emissions, etc.), causing significant fluctuations. In the formula, Sdpc represents the standard deviation of water quality indicators. represents the mean of the ammonia nitrogen samples used to calculate water quality, m represents the total number of ammonia nitrogen samples used to calculate water quality indicators, and m-1 represents the correction of the bias caused by the limited number of samples in order to make an unbiased estimate. It means that all square differences are accumulated to obtain the total variation between all water quality ammonia nitrogen monitoring values ​​and the mean, and then the result is divided by the square root of m-1 to obtain the standard deviation of water quality indicators. The calculation of this deviation is crucial for managers to adjust water quality monitoring strategies and control measures in a timely manner to ensure that water quality remains within a safe and healthy range;

[0078] The conditions for verifying the standard deviation of air quality indicators and water quality indicators are as follows:

[0079] When the air quality index standard deviation Bzpc exceeds the normal air quality deviation range Km, it means that the air quality index standard deviation is too large. When the water quality index standard deviation Sdpc is greater than the normal water quality index deviation range Kd, it means that the water quality index standard deviation is too large. At this time, return to S2 to re-collect data;

[0080] The normal deviation range of air quality Km is in the range of (1,15), and the normal deviation range of water quality index Kd is in the range of (1,8);

[0081] S5. Compare and evaluate the calculated air quality index and water quality index with the corresponding industry standards, and generate a comprehensive evaluation report and send it to the communication equipment of the monitoring personnel;

[0082] By using data visualization tools, the comparison results can be presented in the form of charts or reports, which can intuitively show whether each monitoring indicator meets the standard threshold. After the comprehensive evaluation report is generated, the information management system (a system that supports the Internet of Things, cloud computing and big data analysis) is used to quickly send the report to the monitoring personnel's communication equipment via email or mobile application. This process ensures that relevant personnel can obtain environmental monitoring results in a timely manner, respond quickly to potential environmental problems, and provide a basis for further decision-making. The report will also include warnings on abnormal values, analysis of potential pollution sources, and recommended improvement measures, so that monitoring personnel can effectively carry out subsequent environmental management and governance work.

[0083] Embodiment 1:

[0084] In this experiment, the following five sets of air composition data were collected through the collection equipment. The air components include PM2.5, PM10, NO2, O3, and CO:

[0085] Where Kqyz1 = 37.75, Kqyz2 = 45.00, Kqyz3 = 50.25, Kqyz4 = 30.50, Kqyz5 = 60.00;

[0086] First calculate the mean of these air composition samples:

[0087]

[0088] Calculate the square of the difference between each air composition value and the mean of the air composition data and add them together:

[0089]

[0090] Finally, put this result into the standard deviation formula:

[0091]

[0092] The normal deviation range of air quality Km is in the range of (1,15), and the results calculated by Bzpc do not show excessive deviation, indicating that air quality monitoring is effective;

[0093] Embodiment 2:

[0094] In this experiment, the following five sets of air composition data were collected through the collection equipment. The air components include PM2.5, PM10, NO2, O3, and CO:

[0095] Wherein Kqyz1=35, Kqyz2=40, Kqyz3=38, Kqyz4=37, Kqyz5=120;

[0096] First calculate the mean of these air composition samples:

[0097]

[0098] Calculate the square of the difference between each air composition value and the mean of the air composition data and add them together:

[0099]

[0100] Finally, put this result into the standard deviation formula:

[0101]

[0102] The normal deviation range of air quality Km is in the range of (1,15). The result calculated by Bzpc obviously exceeds this range, indicating that air quality monitoring is invalid and data collection needs to be repeated.

[0103] Through the application of the above-mentioned methods and steps, outliers can be automatically identified and eliminated, short-term fluctuations can be eliminated, reliance on manual judgment of environmental quality anomalies can be reduced, and the objectivity and scientific nature of data processing can be improved. In addition, comparing and evaluating the calculated air quality and water quality indicators with industry standards can provide a more comprehensive and accurate decision-making basis for environmental management.

[0104] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A quantitative evaluation method for environmental monitoring quality, characterized in that: The following steps are involved: S1. Select industrial areas as environmental sampling sites, specify air quality and water quality as evaluation indicators, and monitor every hour every day; S2, collecting air composition data through a gas analyzer and water quality component parameters through a water quality monitor, and compiling the above collected data into an air composition data set and a water quality composition data set; S3. Use the standard deviation method to identify and remove outliers in the air composition data set and the water quality composition data set, and use the moving average method to eliminate short-term fluctuations in the air composition data and the water quality composition data; S4. After the processing of the air component data set and the water quality component data set is completed, the air quality index, the air quality index standard deviation, the water quality index and the water quality index standard deviation are calculated according to the corresponding data set, and the air quality index standard deviation and the water quality index standard deviation are verified. When the calculated deviations of the two are within the normal range, proceed to the next step, otherwise return to S2 to re-collect data; S5. Compare and evaluate the calculated air quality index and water quality index with the corresponding industry standards, and generate a comprehensive evaluation report and send it to the communication equipment of the monitoring personnel.

2. The method for quantitatively evaluating environmental monitoring quality according to claim 1, characterized in that: The S2 air composition dataset is as follows: {Kqyz n ,Kqyz n+1 ,Kqyz n+2 ,...,Kqyz n+4 } In the dataset, Kqyz n Indicates the first collected air composition data, Kqyz n+1 Indicates the second collected air composition data, Kqyz n+2 Indicates the third collected air composition data, Kqyz n+4 It represents the fourth collected air composition data, and five data constitute a set of data sets; The water quality component dataset set is as follows: {Szck n ,Szck n+1 ,Szck n+2 ,...,Szck n+4 } In the dataset, Szck n Indicates the first collected water quality composition data, Szck n+1 Indicates the second collected water quality composition data, Szck n+2 Indicates the third collected water quality composition data, Szck n+4 It represents the fourth collected water quality composition data. Five data constitute a set of data sets.

3. A method for quantitatively evaluating environmental monitoring quality according to claim 2, characterized in that: The formula for the standard deviation method to identify and remove outliers in a data set is as follows: In the formula, Z i represents the standardized value of the ith data point, Px i represents the i-th measurement value, represents the mean of the data set, σ represents the abnormal deviation of the data set, and Z i If it is greater than 2, the measured data value is considered to be an outlier, and i represents the position of the data in the data set.

4. The method for quantitatively evaluating environmental monitoring quality according to claim 3 is characterized in that: The calculation formula of the abnormal deviation σ of the data set is as follows: In the formula, σ represents the abnormal deviation of the data set, x i represents the i-th measurement value, Indicates the calculation of the mean of the measurement data set, n represents the total number of data points, and n-1 represents the correction of the deviation caused by the limited sample size through Bessel correction.

5. The method for quantitatively evaluating environmental monitoring quality according to claim 4, characterized in that: The formula for the moving average method to eliminate short-term fluctuations in air composition data and water quality composition data is as follows: In the formula, Mk t Represents the moving average at time point t, which is the smoothed value at the current time point, Ys t-j represents the original collected data value at time point tj, n represents the window size of the moving average, that is, the number of time points selected each time the average value is calculated, It means that all the original collected data from j=0 to n-1 are accumulated and summed, and finally divided by the moving average window size n to obtain the moving average.

6. A method for quantitatively evaluating environmental monitoring quality according to claim 5, characterized in that: The air quality index calculation formula is as follows: In the formula, AQI represents the air quality index, Kqyz i Represents the concentration of the i-th air component in the air composition dataset, obtained through the air composition dataset, Wt i represents the corresponding weight, i represents the position of the collected air composition data in the data set, and n represents the total number of air composition samples in the collected air composition data set. It means that all air component samples are multiplied by the weight of the corresponding sample, and then the sum is divided by the total number of samples n to obtain the air quality index.

7. A method for quantitatively evaluating environmental monitoring quality according to claim 6, characterized in that: The formula for the standard deviation of the air quality index is as follows: In the formula, Bzpc represents the standard deviation of air quality index, Kqyz i represents the air component in the i-th air, i represents the counting subscript, Indicates calculating the mean of air composition in all collected air composition data sets, p indicates the total number of data points that need to be calculated, and p-1 indicates that in order to make an unbiased estimate, the calculated standard deviation is closer to the true overall standard deviation. It means that all squared differences are added together using the summation symbol to obtain the total variation between all measurements and the mean, and then the square root is taken to obtain the standard deviation.

8. A method for quantitative evaluation of environmental monitoring quality according to claim 7, characterized in that: The water quality index calculation formula is as follows: In the formula, WQ represents the water quality index, m represents the total number of ammonia nitrogen samples used to calculate the water quality index, and Hxhy a represents the monitoring value of the a-th water quality ammonia nitrogen sample, obtained through the water quality component dataset, Wx a represents the corresponding weight, It means that the water quality index is obtained by multiplying the sum of all water quality ammonia nitrogen samples by the weight of the corresponding sample and then dividing it by the total number of samples m.

9. A method for quantitatively evaluating environmental monitoring quality according to claim 8, characterized in that: The calculation formula of the standard deviation of the water quality index is as follows: In the formula, Sdpc represents the standard deviation of water quality index, represents the mean of the ammonia nitrogen samples used to calculate water quality, m represents the total number of ammonia nitrogen samples used to calculate water quality indicators, and m-1 represents the correction of the bias caused by the limited number of samples in order to make an unbiased estimate. It means adding up all the square differences to get the total variation between all water quality ammonia nitrogen monitoring values ​​and the mean, and then dividing the result by the square root of m-1 to get the standard deviation of the water quality index.

10. A method for quantitative evaluation of environmental monitoring quality according to claim 9, characterized in that: The conditions for verifying the standard deviation of the air quality index and the standard deviation of the water quality index are as follows: When the air quality index standard deviation Bzpc exceeds the normal air quality deviation range Km, it means that the air quality index standard deviation is too large. When the water quality index standard deviation Sdpc is greater than the normal water quality index deviation range Kd, it means that the water quality index standard deviation is too large. At this time, return to S2 to re-collect data.

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