Method, device and equipment for evaluating quality of acid rain observation and storage medium

Abstract

Embodiments of the present disclosure provide an acid rain observation quality evaluation method and device, equipment and a storage medium, which are applied to the meteorological monitoring technical field. The method comprises: obtaining laboratory measurement data and to-be-evaluated measurement data reported by multiple stations; the to-be-evaluated measurement data and the laboratory measurement data comprise multiple pH values and conductivities; the pH values and the conductivities are arranged in ascending order respectively to obtain a pH array and a conductivity array; the standard value and the standard deviation of the pH array and the standard value and the standard deviation of the conductivity array are calculated, and the abnormal value elimination processing is performed on the pH array and the conductivity array respectively, the corresponding standard value and the standard deviation are updated, and the to-be-evaluated measurement data reported by the stations is evaluated. Therefore, the system can be comprehensively evaluated, close to the real natural environment, and unified standard, and problems existing in the station instrument or personnel operation can be accurately found, which is beneficial to improving the acid rain observation quality of the entire observation station network.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of meteorological monitoring, and in particular, to an acid rain observation quality evaluation method and device, equipment and a storage medium. BACKGROUND

[0002] Acid rain is one of the three major environmental hazards in the world. Since the 1970s, China has also had large-scale acid rain, which has continued to the present. Acid rain can harm the terrestrial ecosystem and human health, accelerate the corrosion of materials, and cause widespread and sustained harm and impact on human daily life and production activities. Since the "Seventh Five-Year" period, China has carried out large-scale acid rain observation and research. The Meteorological Bureau of China has included acid rain observation in the basic observation business of meteorological stations since 1992, and currently there are 339 meteorological stations that carry out acid rain observation. In addition, various levels of meteorological departments across the country also provide various forms of observation annual reports, quarterly reports and monthly reports to the government and the public to meet the growing demand for environmental meteorological observation data. Acid rain pollution is extremely harmful and closely related to national economic and social development, ecological system safety, human health, etc. Providing accurate and applicable acid rain observation information products to government departments and the public is of positive significance for environmental protection policy making, implementation, effect testing, and environmental education and environmental diplomacy.

[0003] High-quality acid rain station network observation data is a prerequisite for providing reliable service products, and there are many factors that affect the quality of acid rain observation data, such as the state of the precipitation sample (no pollution, turbidity or flocculation, soil and sand deposits, plant debris such as leaves, biological debris such as insects or bird droppings, and other pollutants), whether the analysis instrument is normal (pH meter electrode aging, exceeding the service life, poor electrode quality, improper maintenance; improper conductivity maintenance resulting in changes in electrode constant, incorrect conductivity electrode constant setting, instrument host performance decline, incorrect electrode model used, etc.), whether the measurement operation is standardized (analysis personnel not wearing powder-free gloves or incorrect glove type, instrument preheating less than half an hour, not calibrating the instrument, not preparing the calibration solution according to the standard process, not following the "first measure K value (conductivity), then measure pH value" operation requirement during the measurement process, improper sample storage, etc.). These problems are small and professional, and it is difficult to discover them in time and comprehensively in daily operations, and when professional personnel analyze and use observation data and find that the station has such problems, it is usually half a year or a year later, so the station may have observation data that is questionable for a long period of time, which is extremely detrimental to the station, the entire observation station network, and the production of service products.

[0004] The current evaluation method of acid rain observation quality is single, and is limited to single or multiple site inspection, such as station investigation, checking whether the laboratory of the station is clean and tidy, whether the operation of the personnel is skilled, whether the use of the instrument is professional, whether the rules and regulations are complete, etc., or organizing the station self-checking, etc. Due to the lack of unified evaluation standard, the results of the inspection or self-checking do not have unified comparability. In addition, without the analysis operation of the actual sample, the possible problems of the station, such as the aging of the pH meter electrode, the incorrect setting of the conductivity electrode constant, the non-compliance with the operation requirement of "measuring K value first and then measuring pH value", etc. cannot be found in time and comprehensively. Moreover, the chemical components of the evaluation sample for detecting acid rain in the prior art are too single, only one or two chemical substances, the difference between the chemical components and the natural environment is large, one sample can not only be used as the evaluation sample of pH, but also be used as the evaluation sample of conductivity, and the pH and conductivity evaluation samples in the prior art cannot be shared. At present, the common method is to use different concentrations of acid and base, weak acid or weak alkaline salt to prepare the target pH standard solution, and use different concentrations of acid and base to prepare the target pH value. For example, at 25°C (pH value and conductivity measurement are affected by temperature), potassium biphthalate is used as a standard solution with a pH value of about 4.0 (0.05 mol / L); a mixture of potassium dihydrogen phosphate (0.025 mol / L) and sodium hydrogen phosphate (0.025 mol / L) is used as a standard solution with a pH value of about 7.3; borax (0.01 mol / L) solution is used as a standard solution with a pH value of about 8.9. Different conductivity solutions are prepared by using different concentrations of potassium chloride solution, such as at 25°C, 0.01 mol / L potassium chloride solution is used as a standard solution with a conductivity of 1413 μs / cm, 0.001 mol / L potassium chloride solution is used as a standard solution with a conductivity of 147 μs / cm, and 0.0001 mol / L potassium chloride solution is used as a standard solution with a conductivity of 15 μs / cm. This leads to that in the case of a large number of evaluation sites, the types of evaluation samples that can be provided by the prior art are less, and it is easy to have the same pH value and conductivity value of the evaluation sample of a large number of stations, and because the chemical components are single, and the evaluation is an annual activity, after multiple measurements, according to the working experience, the pH and conductivity values of the evaluation solution prepared by the prior art are easy to be obtained by the station, and the evaluation purpose cannot be achieved.

[0005] The acid rain observation involves many steps, such as the preparation work before sampling (preparation of sampling bag and sampling barrel), sample collection and analysis, instrument standard, etc. The factors affecting the observation data are various, miscellaneous, deep and special, and therefore, how to accurately evaluate the quality of acid rain observation of each observation site and provide a scientific basis for the observation site to further analyze whether the instrument is normal and whether the operation of the analysis personnel is standard according to the evaluation result is a problem to be solved urgently. SUMMARY

[0006] This disclosure provides a method, apparatus, equipment, and storage medium for assessing the quality of acid rain monitoring.

[0007] According to a first aspect of this disclosure, a method for assessing the quality of acid rain observations is provided. The method includes:

[0008] Acquire measurement data to be evaluated and laboratory measurement data reported by multiple stations; the measurement data to be evaluated and laboratory measurement data include multiple pH values ​​and conductivity values;

[0009] The pH value and the conductivity are arranged in ascending order to obtain a pH array corresponding to the pH value and a conductivity array corresponding to the conductivity.

[0010] Calculate the standard values ​​and standard deviations of the pH array and the conductivity array;

[0011] Based on the corresponding standard values ​​and standard deviations, outlier removal is performed on the pH array and the conductivity array, and the corresponding standard values ​​and standard deviations are updated.

[0012] The quality of the measurement data reported by the stations is assessed based on the updated standard values ​​and standard deviations.

[0013] In addition to the aspects and any possible implementations described above, another implementation is provided.

[0014] The outlier removal process for the pH array and the conductivity array based on the corresponding standard values ​​and standard deviations includes:

[0015] Outlier removal is performed on the pH array based on the standard value and standard deviation corresponding to the pH array, and outlier removal is performed on the conductivity array based on the standard value and standard deviation corresponding to the conductivity array.

[0016] The process of removing outliers from the pH array based on the standard value and standard deviation of the pH array includes:

[0017] Calculate the difference between the pH values ​​in the pH array and the standard values, and take the absolute value of the difference; calculate the ratio between the absolute value and the standard deviation;

[0018] The ratio is compared with a critical value. If the ratio is greater than the critical value, the corresponding pH value is identified as an outlier and removed from the pH array.

[0019] The standard values ​​and standard deviations of the pH array are recalculated, and a new round of outlier removal is performed until there are no outliers, at which point the outlier removal process is complete.

[0020] The critical value is determined based on the number of pH values ​​in the pH array and the Grubbs critical table.

[0021] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the updating of the corresponding standard value and standard deviation includes:

[0022] Update the standard values ​​and standard deviations of the pH array, and update the standard values ​​and standard deviations of the conductivity array;

[0023] The updated standard values ​​and standard deviations corresponding to the pH array include:

[0024] The standard value and standard deviation corresponding to the completion of outlier removal are used as the final standard value and standard deviation of the pH array.

[0025] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the quality assessment of the reported measurement data to be evaluated by the stations based on the updated standard values ​​and standard deviations includes:

[0026] The evaluation gradient value is determined based on the updated standard value and standard deviation;

[0027] The station's measurement data to be evaluated are scored based on the evaluation gradient value;

[0028] The data quality of the measurement data from the corresponding stations is determined based on the scoring results.

[0029] In addition to the aspects described above and any possible implementations, a further implementation is provided in which determining the evaluation gradient value based on the updated standard value and standard deviation includes:

[0030] The evaluation gradient values ​​for pH are determined based on the standard values ​​and standard deviations corresponding to the updated pH array; and the evaluation gradient values ​​for conductivity are determined based on the standard values ​​and standard deviations corresponding to the updated conductivity array.

[0031] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the scoring of the station's measurement data to be evaluated based on the evaluation gradient value includes:

[0032] The measurement data to be evaluated at the station is compared with the corresponding evaluation gradient value to determine the initial score;

[0033] The sum of the first and second highest scores in the initial scores is selected as the score result of the measurement data to be evaluated at the station.

[0034] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the measurement data to be evaluated reported by the station also includes the K value of ultrapure water;

[0035] The method further includes:

[0036] Based on the K-value of the ultrapure water and the preset evaluation standard, determine whether the K-value of the ultrapure water used by the station is qualified.

[0037] According to a second aspect of this disclosure, an acid rain monitoring quality assessment device is provided. The device includes:

[0038] The data acquisition module is used to acquire measurement data to be evaluated and laboratory measurement data reported by multiple stations; the measurement data to be evaluated and laboratory measurement data include multiple pH values ​​and conductivity values;

[0039] An array generation module is used to arrange the pH value and the conductivity in ascending order to obtain a pH array corresponding to the pH value and a conductivity array corresponding to the conductivity.

[0040] An array calculation module is used to calculate the standard value and standard deviation of the pH array, and the standard value and standard deviation of the conductivity array;

[0041] The data removal module is used to remove outliers from the pH array and the conductivity array according to the corresponding standard values ​​and standard deviations, and update the corresponding standard values ​​and standard deviations.

[0042] The quality assessment module is used to assess the quality of the measurement data reported by the stations based on the updated standard values ​​and standard deviations.

[0043] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0044] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method as described in the first aspect of this disclosure.

[0045] The acid rain observation quality assessment method, apparatus, equipment, and storage medium provided in the embodiments of this disclosure calculate the standard values ​​and standard deviations of the pH array and the conductivity array by combining the measurement data to be assessed reported by multiple stations with laboratory measurement data. Outliers are removed from the pH array and the conductivity array respectively, and the corresponding standard values ​​and standard deviations are updated. The standard values ​​and standard deviations after removing outliers are used as the assessment basis for measuring the quality of the measurement data to be assessed reported by the stations, making the assessment results more scientific and accurate, and accurately identifying problems in the operation of station instruments or personnel, which is conducive to improving the acid rain observation quality of the entire observation network.

[0046] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0047] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0048] Figure 1 A flowchart of an acid rain observation quality assessment method according to an embodiment of the present disclosure is shown;

[0049] Figure 2 A block diagram of an acid rain monitoring quality assessment apparatus according to an embodiment of the present disclosure is shown;

[0050] Figure 3 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0052] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0053] In this disclosure, a series of acid rain assessment samples with chemical composition, pH value, and conductivity similar to natural atmospheric precipitation in different regions of northern and southern my country were developed in the laboratory. These samples were uniformly sent to the stations, and the results were analyzed and reported according to requirements. The laboratory used the Grubbs method to statistically analyze the results reported by the stations and judged the assessment results of pH value, conductivity, and ultrapure water K value of the stations as "excellent", "qualified", and "unqualified" according to certain standards. The results were then officially issued to each station, pointing out the existing problems and directions for improvement. This ensured the quality of the acid rain station network observation data of the my country Meteorological Administration, laid the foundation for the development of high-quality service products and decision-making service materials, and achieved application value in operations. Since 1985, the my country Meteorological Administration has been conducting acid rain and precipitation chemical composition observations at atmospheric background stations. There are five background stations: Waliguan in Qinghai, Shangdianzi in Beijing, Lin'an in Zhejiang, Longfengshan in Heilongjiang, and Jinsha in Hubei. Acid rain observations include pH and conductivity. Precipitation chemical composition observations include nine soluble inorganic ions, including four anions: fluoride, chloride, nitrate, and sulfate, and five cations: ammonium, potassium, sodium, calcium, and magnesium ions. Ion chromatography and atomic absorption spectrometry were used for analysis. The samples were analyzed using a photometer. Simultaneously, to further understand the chemical composition of natural precipitation in northern and southern my country, the laboratory conducted a year-long observation of atmospheric precipitation across the country using the National Acid Deposition Monitoring Network. This network has nearly 300 monitoring stations. The selection of station locations ensures sufficient regional representativeness, with at least one monitoring station per 1x1 grid in areas with severe acid rain. In other areas, including the underdeveloped western plateaus and deserts, 3-5 monitoring stations were set up per province, with each station required to avoid the influence of local sources as much as possible. Under strict quality control and assurance, precipitation was collected every 24 hours at the stations, and pH and conductivity were measured at the stations. The precipitation was then sent to the laboratory for analysis of nine soluble inorganic ions (using ion chromatography and atomic absorption spectrophotometry). Therefore, through years of collecting and analyzing samples, the laboratory has gained a thorough understanding of the chemical composition of natural precipitation in northern and southern my country. Based on years of analysis of pH, conductivity, and chemical composition of natural precipitation in northern and southern my country, the laboratory has researched and continuously improved the methods, ultimately determining the ratio and concentration of the test solution. The three samples analyzed at the station have a certain gradient, basically covering the upper and lower limits of pH and conductivity of precipitation in the region. The advantage of this approach is that the pH meter needs to be calibrated with a standard solution before analysis. In northern my country, precipitation is alkaline, i.e., pH greater than 7. Some stations may choose calibration solutions with pH values ​​below 7 when calibrating the pH meter, which can lead to measurement errors.Setting up samples with a certain gradient can verify the accuracy of the station calibration. Furthermore, if the station's pH meter and conductivity electrodes are aging, the analyzed sample results will be extremely similar and low, lacking a gradient distribution characteristic. This allows for the rapid detection of potential electrode problems. Due to rainfall in a particular area, pH and conductivity values ​​may vary very little throughout the year, resulting in relatively consistent values. By using gradient-based sample testing, electrode problems can be identified even when station operators lack sufficient observation experience, preventing significant deviations in the observed data.

[0054] The simulated natural precipitation sample prepared in the laboratory in this disclosure consists of the following three parts:

[0055] (1) Preparation of acid rain test stock solution:

[0056] After multiple laboratory investigations and experiments, following the method shown in Table 1, eight chemical reagents—CaCl2, (NH4)2SO4, NH4HCO3, MgSO4·7H2O, Ca(NO3)2·4H2O, K2SO4, CaSO4·2H2O, and Na2SO4—were weighed using a 0.01% balance. For each type of reagent, the purity was prioritized at the superior purity level, followed by the analytical purity level. The preparation method for the acid rain assessment stock solution was as follows: After weighing the required reagents according to Table 1, they were placed in a clean beaker. 100ml of ultrapure water was slowly added while stirring with a glass rod or using an ultrasonic instrument to ensure complete dissolution. The solution was then transferred to a volumetric flask using a glass rod, and the volume was adjusted to 1L. After thorough mixing, the solution was stored at 4℃ for later use. The other two buffer solutions (CH3COOH and H2SO4) are prepared as follows: 1) Use a pipette to add 4.6 mL and 0.5 mL of CH3COOH to a 1 L volumetric flask to prepare CH3COOH solutions with concentrations of 0.08 mol / L and 0.009 mol / L, respectively, and store them in a refrigerator at 4℃ for later use; 2) Use a pipette to add 0.53 mL of H2SO4 to a 1 L volumetric flask to prepare H2SO4 solution with a concentration of 0.01 mol / L, make two copies, and store them in a refrigerator at 4℃ for later use.

[0057] Table 1. Preparation method of mother liquor for acid rain assessment

[0058]

[0059] (2) Preparation of acid rain assessment stock solution:

[0060] Subsequently, following the method in Table 2, four clean 10L polypropylene (PP) containers were prepared. Nine acid rain assessment stock solutions (CaCl2, (NH4)2SO4, NH4HCO3, MgSO4, Ca(NO3)2, K2SO4, CaSO4, Na2SO4, and CH3COOH) were measured using a graduated cylinder and placed into the 10L PP containers. Ultrapure water was added to approximately bring the volume to 10L, and the solutions were mixed thoroughly to prepare two different 10L stock solutions. The difference lay in the concentration of the acetic acid solution: one stock solution (Stock Solution 1) was prepared with 100mL of 0.08mol / L acetic acid solution, and the other stock solution (Stock Solution 2) was prepared with 100mL of 0.009mol / L acetic acid solution. Considering the risk of insufficient stock solutions in subsequent batch preparations, it was recommended to prepare one extra container of each stock solution. After the stock solutions were allowed to stand and stabilize for 3 days, their pH and conductivity were measured using a pH meter and a conductivity meter.

[0061] Table 2. Preparation method of acid rain assessment reserve solution

[0062]

[0063] (3) Preparation of acid rain assessment solution:

[0064] According to the specific requirements of the target solution's pH value and conductivity (its chemical composition, pH value, and conductivity are similar to those of natural atmospheric precipitation in different regions of northern and southern my country), 10-30L of ultrapure water was added to 15 clean solution containers. At the same time, 5ml-3L of either stock solution No. 1 or No. 2 was added to each container, along with 50-300mL of 0.01mol / L dilute sulfuric acid solution and 0-280mL of a buffer solution of potassium dihydrogen phosphate (0.075mol / L) and disodium hydrogen phosphate (0.025mol / L). The solution was adjusted to the required concentration and allowed to stabilize for 1-3 months. During the stabilization period, the pH value and conductivity of the solution were measured every 7-14 days to assess its stability. After the acid rain test solution was fully stabilized, it was dispensed into 125mL PP vials, sealed tightly, and ready for shipment.

[0065] Before sending samples, the supervising unit sends a notification to the monitoring station in the assessment system, instructing the station to confirm the correctness of its sample delivery address. Once confirmed, the laboratory randomly selects three different acid rain assessment samples and mails them to the station. Following operational guidelines, the station analyzes the pH value, conductivity, and K-value of the ultrapure water used by the received samples (samples 1-3) within the specified timeframe, then completes the analysis in the acid rain assessment system (Tianyuan System). After review by the provincial department, the data is submitted to the supervising unit's laboratory. The test samples obtained through the above-mentioned various ratios have a richer chemical composition, are closer to the real natural environment, have a higher utilization rate, are more scientific and convenient in actual operation, and are more consistent with the actual observation situation (in the observation, the pH value and conductivity value of a natural precipitation sample are measured). The preparation method used in this disclosure can prepare a variety of solutions with different pH and conductivity values, such as pH and conductivity values ​​of 4.25 and 38 for sample 1, and pH and conductivity values ​​of 6.0 and 163.8 for sample 2. The combination of pH and conductivity values ​​is more flexible and varied, more realistic, and its values ​​are not easily obtained by the station, so the test effect is the best.

[0066] The following describes the process of assessing the quality of acid rain observations at each station, using specific examples.

[0067] Figure 1 A flowchart of an acid rain observation quality assessment method 100 according to an embodiment of the present disclosure is shown. Method 100 includes:

[0068] Step 110: Obtain measurement data to be evaluated and laboratory measurement data reported by multiple stations; the measurement data to be evaluated and laboratory measurement data include multiple pH values ​​and conductivity values.

[0069] The measurement data to be evaluated is obtained by the station using test samples sent by the laboratory to measure acid rain; the test samples have a certain gradient, including the upper and lower limits of precipitation pH and conductivity in the area where the station is located.

[0070] In some embodiments, when all stations submit the pH value, conductivity, and K value of the ultrapure water used by the station for assessment samples 1-3 in the Tianyuan system as the measurement data to be evaluated reported by the station, the supervising laboratory uses the Grubbs method to perform statistical analysis on the measurement data to be evaluated reported by the station and the laboratory measurement data. This assumes that the measurement data to be evaluated reported by all stations and the laboratory measurement data (each assessment sample is analyzed twice in the laboratory, providing two results as laboratory measurement data) follow a normal distribution. By fusing the measurement data reported by the station and the laboratory measurement data, outliers can be better eliminated.

[0071] Step 120: Arrange the pH value and the conductivity in ascending order to obtain the pH array corresponding to the pH value and the conductivity array corresponding to the conductivity.

[0072] In some embodiments, the measurement data to be evaluated and the laboratory measurement data reported by the stations in step 110 are classified into pH value, conductivity and ultrapure water K value. The pH value and conductivity are arranged in ascending order to obtain the pH array corresponding to the pH value and the conductivity array corresponding to the conductivity.

[0073] Step 130: Calculate the standard value and standard deviation of the pH array, and the standard value and standard deviation of the conductivity array.

[0074] In some embodiments, standard values ​​and standard deviations of the pH array and the conductivity array are calculated.

[0075] Step 140: Based on the corresponding standard values ​​and standard deviations, perform outlier removal processing on the pH array and the conductivity array respectively, and update the corresponding standard values ​​and standard deviations.

[0076] In some embodiments, step 140 specifically includes: performing outlier removal processing on the pH array based on the standard value and standard deviation corresponding to the pH array, and performing outlier removal processing on the conductivity array based on the standard value and standard deviation corresponding to the conductivity array. Specifically, performing outlier removal processing on the pH array based on the standard value and standard deviation corresponding to the pH array includes: calculating the difference between the pH values ​​in the pH array and the standard values, and taking the absolute value of the difference; calculating the ratio between the absolute value and the standard deviation; comparing the ratio with a critical value; if the ratio is greater than the critical value, then identifying the corresponding pH value as an outlier and removing it from the pH array; recalculating the standard value and standard deviation of the pH array, and performing a new round of outlier removal processing until there are no outliers, thus completing the outlier removal processing; wherein the critical value is determined based on the number of pH values ​​in the pH array and the Grubbs critical table.

[0077] In some embodiments, the difference between each pH value in the pH array and its corresponding standard value is calculated. The absolute value of the difference is then taken, and the ratio between the absolute value and the standard deviation is calculated. A critical value (within a certain confidence interval and degrees of freedom) is determined by consulting the Grubbs table based on the number of pH values ​​in the pH array. If this ratio is greater than the critical value, the pH value is considered an outlier and is removed from the pH array. A new round of calculation is then started, recalculating the standard values ​​and standard deviations of the new pH array, and outliers are removed until no outliers remain. Finally, the standard values ​​and standard deviations of the pH array are updated based on the results of the last round of calculations. Similarly, the conductivity array is also subjected to outlier removal until no outliers remain. The standard values ​​and standard deviations after outlier removal are then used as the evaluation criteria for assessing the quality of the measurement data reported by the stations, making the evaluation results more scientific and accurate.

[0078] Step 150: Perform a quality assessment on the measurement data to be evaluated reported by the stations based on the updated standard values ​​and standard deviations.

[0079] In some embodiments, an evaluation gradient value is determined based on updated standard values ​​and standard deviations; the measurement data to be evaluated at the station is scored based on the evaluation gradient value; and the data quality of the measurement data at the corresponding station is determined based on the scoring results. Specifically, determining the evaluation gradient value based on updated standard values ​​and standard deviations includes: determining the evaluation gradient value for pH based on the updated standard values ​​and standard deviations corresponding to the pH array; and determining the evaluation gradient value for conductivity based on the updated standard values ​​and standard deviations corresponding to the conductivity array. The evaluation gradient value is the standard value ± n times the standard deviation, where n = 1, 2, 3.

[0080] In some embodiments, scoring the measurement data to be evaluated at a station based on the evaluation gradient value includes: comparing the measurement data to be evaluated at the station with the corresponding evaluation gradient value to determine an initial score; selecting the sum of the first and second highest scores in the initial score as the score result of the measurement data to be evaluated at the station. For example, if the pH value and conductivity of a station are within ±1 standard deviation of the standard value, 5 points are awarded; within ±2 standard deviations, 4 points are awarded; within ±3 standard deviations, 3 points are awarded; and exceeding 3 standard deviations, 0 points are awarded. Then, the specific scores for pH value and conductivity of each station are determined as the initial scores. Then, the first and second highest scores in the initial scores are summed, and the score result of the measurement data to be evaluated at the corresponding station is determined based on the obtained score, i.e., excellent, qualified, and unqualified. Among them, a cumulative score of 9 or 10 points is excellent, a cumulative score of 6 to 8 points is qualified, and a cumulative score of 5 points or less (including 5 points) is unqualified.

[0081] In some embodiments, such as in an acid rain assessment, the pH values ​​of the three test samples from station A were 4.38, 4.23, and 4.84, respectively, with differences from the corresponding standard values ​​ranging from -6.1 to -12.7 times the standard deviation. The assessment result was deemed unqualified. This was because the pH values ​​were significantly low, indicating that the electrodes of the pH meter at station A were aging, resulting in reduced electrode performance and significantly lower measured pH values. Similarly, the pH values ​​of the three test samples from station B were 4.68, 5.94, and 6.33, with differences from the corresponding standard values ​​ranging from 3.8 to 17.3 times the standard deviation. The assessment result was also unqualified because the pH values ​​were high, indicating that the temperature electrode in the pH meter at station B was rusted, leading to sample contamination and higher measured pH values. The three pH samples from station C... The conductivity of the water samples tested were 84.7, 103.0, and 165.7, respectively. The differences between these and the corresponding standard values ​​were 4.1 to 50.8 times the standard deviation, resulting in unqualified test results. This was due to the high conductivity, which was caused by the station not following the operational requirement of "measuring K value first, then pH value" during the measurement process, leading to higher measured conductivity. The conductivity of the three water samples tested at station D were 38.9, 48.2, and 113.0, respectively. The differences between these and the corresponding standard values ​​were -4.2 to -4.8 times the standard deviation, resulting in unqualified test results. This was caused by improper measurement operation at the station. During the measurement process, the conductivity electrodes were cleaned with pure water but not rinsed with the test samples before being directly analyzed, resulting in lower conductivity. Based on this, the specific cause of the situation can be determined by the ratio of the difference between the measured data reported by the stations and the corresponding standard values ​​to the standard deviation. This allows for the issuance of a detailed assessment report, which can then be formally issued to each station, pointing out existing problems and directions for improvement. This ensures the quality of the meteorological bureau's acid rain station network observation data and achieves its application value in operational settings. Furthermore, by having a unified management department formulate and operate the sample configuration and quality assessment processes, a comprehensive, realistic, and standardized assessment system can accurately identify problems in station instrument or personnel operation. This improves the overall observation quality of the network, makes data more comparable between stations, and ensures more scientific and accurate assessment results.

[0082] Based on the above embodiments, the acid rain observation quality assessment method in another embodiment provided in this disclosure further includes: the measurement data to be assessed reported by the station also includes the ultrapure water K value; and the ultrapure water K value used by the station is determined to be qualified according to the ultrapure water K value and the preset evaluation standard.

[0083] In some embodiments, the evaluation criteria for the K-value of ultrapure water are as follows: K-value ≤ 5 μS / cm indicates excellent performance; 5 μS / cm < K-value ≤ 10 μS / cm indicates acceptable performance; and > 10 μS / cm indicates unacceptable performance. Based on the evaluation results, the corresponding level of the K-value of the ultrapure water used by the station can be determined, and the situation can be written into a specific evaluation report and officially issued to each station to facilitate improvement.

[0084] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0085] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0086] Figure 2 A block diagram of an acid rain monitoring quality assessment device 200 according to an embodiment of the present disclosure is shown. Figure 2 As shown, the device 200 includes:

[0087] Data acquisition module 210 is used to acquire measurement data to be evaluated and laboratory measurement data reported by multiple stations; the measurement data to be evaluated and laboratory measurement data include multiple pH values ​​and conductivity values;

[0088] The array generation module 220 is used to arrange the pH value and the conductivity in ascending order to obtain a pH array corresponding to the pH value and a conductivity array corresponding to the conductivity.

[0089] The array calculation module 230 is used to calculate the standard value and standard deviation of the pH array, and the standard value and standard deviation of the conductivity array;

[0090] The data removal module 240 is used to remove outliers from the pH array and the conductivity array according to the corresponding standard values ​​and standard deviations, and update the corresponding standard values ​​and standard deviations.

[0091] The quality assessment module 250 is used to assess the quality of the measurement data reported by the stations based on the updated standard values ​​and standard deviations.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0093] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0094] Figure 3 A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0095] Electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in ROM 302 or a computer program loaded into RAM 303 from storage unit 308. RAM 303 can also store various programs and data required for the operation of electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0096] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0097] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the acid rain observation quality assessment method. For example, in some embodiments, the acid rain observation quality assessment method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the acid rain observation quality assessment method described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the acid rain observation quality assessment method by any other suitable means (e.g., by means of firmware).

[0098] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0100] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0102] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0103] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0104] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0105] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for assessing the quality of acid rain observations, characterized in that, include: Acquire measurement data to be evaluated and laboratory measurement data reported by multiple stations; the measurement data to be evaluated and laboratory measurement data include multiple pH values ​​and conductivity values; The pH value and the conductivity are arranged in ascending order to obtain a pH array corresponding to the pH value and a conductivity array corresponding to the conductivity. Calculate the standard values ​​and standard deviations of the pH array and the conductivity array; Based on the corresponding standard values ​​and standard deviations, outlier removal is performed on the pH array and the conductivity array, and the corresponding standard values ​​and standard deviations are updated. The outlier removal process for the pH array and the conductivity array based on their corresponding standard values ​​and standard deviations includes: removing outliers from the pH array and the conductivity array based on their respective standard values ​​and standard deviations; wherein removing outliers from the pH array based on their respective standard values ​​and standard deviations includes: calculating the difference between the pH values ​​in the pH array and the standard values, and taking the absolute value of the difference; calculating the ratio between the absolute value and the standard deviation; comparing the ratio with a critical value, and if the ratio is greater than the critical value, identifying the corresponding pH value as an outlier and removing it from the pH array; recalculating the standard values ​​and standard deviations of the pH array and performing a new round of outlier removal until no outliers are found, thus completing the outlier removal process; wherein the critical value is determined based on the number of pH values ​​in the pH array and the Grubbs critical table; The quality of the measurement data reported by the stations is assessed based on the updated standard values ​​and standard deviations.

2. The method according to claim 1, characterized in that, The updated standard value and standard deviation include: Update the standard values ​​and standard deviations of the pH array, and update the standard values ​​and standard deviations of the conductivity array; The updated standard values ​​and standard deviations corresponding to the pH array include: The standard value and standard deviation corresponding to the completion of outlier removal are used as the final standard value and standard deviation of the pH array.

3. The method according to claim 1, characterized in that, The quality assessment of the measurement data reported by the stations based on the updated standard values ​​and standard deviations includes: The evaluation gradient value is determined based on the updated standard value and standard deviation; The station's measurement data to be evaluated are scored based on the evaluation gradient value; The data quality of the measurement data from the corresponding stations is determined based on the scoring results.

4. The method according to claim 3, characterized in that, The step of determining the evaluation gradient value based on the updated standard value and standard deviation includes: The evaluation gradient values ​​for pH are determined based on the standard values ​​and standard deviations corresponding to the updated pH array; and the evaluation gradient values ​​for conductivity are determined based on the standard values ​​and standard deviations corresponding to the updated conductivity array.

5. The method according to claim 4, characterized in that, The scoring of the station's measurement data based on the evaluation gradient value includes: The measurement data to be evaluated at the station is compared with the corresponding evaluation gradient value to determine the initial score; The sum of the first and second highest scores in the initial scores is selected as the score result of the measurement data to be evaluated at the station.

6. The method according to claim 1, characterized in that, The measurement data to be evaluated reported by the stations also includes the K value of ultrapure water; The method further includes: Based on the K-value of the ultrapure water and the preset evaluation standard, determine whether the K-value of the ultrapure water used by the station is qualified.

7. An acid rain monitoring quality assessment device, characterized in that, include: The data acquisition module is used to acquire measurement data to be evaluated and laboratory measurement data reported by multiple stations; the measurement data to be evaluated and laboratory measurement data include multiple pH values ​​and conductivity values; An array generation module is used to arrange the pH value and the conductivity in ascending order to obtain a pH array corresponding to the pH value and a conductivity array corresponding to the conductivity. An array calculation module is used to calculate the standard value and standard deviation of the pH array, and the standard value and standard deviation of the conductivity array; The data removal module is used to remove outliers from the pH array and the conductivity array according to the corresponding standard values ​​and standard deviations, and update the corresponding standard values ​​and standard deviations. The outlier removal process for the pH array and the conductivity array based on their corresponding standard values ​​and standard deviations includes: removing outliers from the pH array and the conductivity array based on their respective standard values ​​and standard deviations; wherein removing outliers from the pH array based on their respective standard values ​​and standard deviations includes: calculating the difference between the pH values ​​in the pH array and the standard values, and taking the absolute value of the difference; calculating the ratio between the absolute value and the standard deviation; comparing the ratio with a critical value, and if the ratio is greater than the critical value, identifying the corresponding pH value as an outlier and removing it from the pH array; recalculating the standard values ​​and standard deviations of the pH array and performing a new round of outlier removal until no outliers are found, thus completing the outlier removal process; wherein the critical value is determined based on the number of pH values ​​in the pH array and the Grubbs critical table; The quality assessment module is used to assess the quality of the measurement data reported by the stations based on the updated standard values ​​and standard deviations.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

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