Ozone pollution tracing method and device, computer equipment and storage medium

Abstract

The invention provides an ozone pollution tracing method and device, computer equipment and a storage medium, and belongs to the field of environment monitoring, and the method comprises the steps: obtaining the concentration data of volatile organic pollutants in a target area; performing positive definite matrix factorization on the concentration data to obtain a component spectrum matrix and a contribution matrix of each pollution source, and matching the component spectrum matrix and the contribution matrix based on a preset pollution source; respectively carrying out one-by-one reduction simulation on each pollution source, calculating the ozone net generation rate after reduction of different pollution sources, and calculating the relative increment reaction activity coefficient of ozone after reduction of a single pollution source; according to the pollutant concentration and the relative increment reaction activity coefficient corresponding to different pollution sources, calculating the contribution rate of each pollution source to ozone generation, and taking the pollution source with the maximum contribution rate as the main source of ozone. Therefore, by analyzing and simulating the components of the volatile organic pollutants and controlling the variables to reduce the pollution sources one by one, the ozone can be automatically traced.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and specifically relates to an ozone pollution source tracing method, device, computer equipment and storage medium. Background Art

[0002] Ozone pollution has become a prominent issue in recent years. As a significant atmospheric pollutant, ozone has significant impacts on global climate change, regional air quality, human health, and ecosystems. Tropospheric ozone is a typical secondary pollutant, formed by a series of chemical reactions between volatile organic compounds (VOCs) and nitrogen oxides under sunlight. It is chemically active, highly oxidizing, and unstable. Therefore, tracing the source of ozone is extremely difficult, and current ozone source attribution is primarily based on observational or emission data of precursors.

[0003] Ozone source attribution based on precursor emission data involves combining an emissions inventory with an air quality model simulation using meteorological models. This method can simulate the effects of meteorological conditions, chemical generation, and regional transport on ozone. However, air quality models have high technical and cost requirements, and the emissions inventory used is subject to high uncertainty. Ozone source attribution based on observational data primarily involves research based on precursor source attribution results and the atmospheric photochemical reaction box model. This method combines the atmospheric photochemical reaction mechanism, resulting in an attribution that is relatively consistent with the actual atmospheric environment.

[0004] The positive definite matrix factor analysis model is a commonly used method for quantitative source analysis of volatile organic pollutants in the atmospheric environment. Since this method does not require the input of the pollution source component spectrum and does not rely on meteorological conditions, it has been widely used in the source analysis of volatile organic pollutants. However, the positive definite matrix factor analysis model is based on factors extracted in a mathematical sense. During the factor identification process, it is unable to automatically identify the type of pollution source. It is necessary to combine human experience to match factors and pollution sources. The manual identification process is relatively subjective and requires a wealth of theoretical knowledge. It is difficult for novice users of the model to operate. The above problems have led to the current inability to accurately, quickly, and quantitatively trace the source of volatile organic pollutants, and thus the inability to realize the automation of ozone source analysis technology. Therefore, it is an important and urgent issue to quickly and accurately identify the volatile organic pollutant factors extracted by the positive definite matrix factor analysis model. Summary of the Invention

[0005] In order to solve the above-mentioned problem that ozone cannot be traced quickly and accurately, the present invention provides an ozone pollution source tracing method, device, computer equipment and storage medium.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: A method for tracing the source of ozone pollution, the method comprising: Obtaining the concentration data of volatile organic compounds in the target area; Performing positive definite matrix factorization on the concentration data to obtain a component spectrum matrix and a contribution matrix of a plurality of extracted factors; Based on the preset pollution sources, the component spectrum matrix and the contribution matrix of the multiple extracted factors are matched to identify the multiple pollution sources of the volatile organic compounds; Performing reduction simulations on each of the multiple pollution sources, calculating the net ozone generation rate after the reduction of different pollution sources based on the simulations, and then determining the relative incremental reaction activity coefficient based on the net ozone generation rate after the reduction of a single pollution source; The contribution rate of each pollution source to ozone generation is calculated based on the volatile organic compound concentration corresponding to different pollution sources and the relative incremental reaction activity coefficient, and the pollution source with the largest contribution rate is taken as the main source of ozone.

[0007] Optionally, the concentration data is subjected to positive definite matrix factorization to obtain component spectrum matrices and contribution matrices of different pollution sources, including: Cleaning the concentration data to determine uncertain data in the concentration data of different types of volatile organic compounds; The concentration data and the uncertainty data are subjected to positive definite matrix factorization to obtain a component spectrum matrix and a contribution matrix of each pollution source.

[0008] Optionally, cleaning the concentration data to obtain uncertain data in the concentration data includes: Arrange the original concentration data according to time series; Eliminate non-numeric data; Calculate the first quartile Q1 and the third quartile Q3 of the concentration data in the time series, calculate the judgment range of outliers based on the interquartile range plus or minus 3 times Q1 and Q3, eliminate outliers and replace them with blanks; Fill in missing periods in the time series; Calculating the uncertainty of the filtered concentration data to obtain uncertainty data of each type of volatile organic compound in the concentration data and its corresponding uncertainty; A signal-to-noise ratio (S / N) of different types of volatile organic compounds is obtained based on the uncertainty data and the concentration data. Based on a numerical range of the S / N, the uncertainty of the uncertainty data of the different types of volatile organic compounds is adjusted, and the uncertainty data of the type of volatile organic compounds is re-determined based on the adjusted uncertainty.

[0009] Optionally, performing positive definite matrix factorization on the concentration data and the uncertainty data to obtain a component spectrum matrix and a contribution matrix of multiple extracted factors include: Set the number of pollution sources as the number of extraction factors; Based on the number of the extraction factors, the concentration data and uncertainty data are input into the PMF model for decomposition to obtain a component spectrum matrix and a contribution matrix of volatile organic compounds on each extraction factor.

[0010] Optionally, setting the number of pollution sources includes: Setting the number of candidate pollution sources, where the number of candidate pollution sources is less than the number of types of volatile organic compounds; Simulating the PMF model output based on the number of candidate pollution sources and calculating the first goodness-of-fit parameter of all input data and the second goodness-of-fit parameter of the input data after removing noise data; The number of candidate pollution sources whose first goodness of fit parameter is closest to the second goodness of fit parameter and is the smallest is determined as the number of pollution sources.

[0011] Optionally, matching the component spectrum matrix and the contribution matrix of the plurality of extracted factors based on the preset pollution sources to identify the plurality of pollution sources of the volatile organic compounds includes: The preset pollution sources and their corresponding pollutant types are matched with the component spectrum matrix, and the component proportions of different pollutant types are matched with the contribution matrix; when the two matching results meet the preset requirements, the pollution source corresponding to the extracted factor is identified.

[0012] Optionally, the relative incremental reactivity coefficient calculation formula is: ; in, is the average relative incremental reactivity coefficient of pollution source X to ozone generation; is the relative incremental reaction activity coefficient of pollution source X on volatile organic compounds in each sample; is the net generation rate of ozone under the initial scenario; n is the number of atmospheric environment samples; The calculation formula for the generation contribution rate is: ; is the contribution of pollution source X to ozone generation; is the concentration of pollution source X; m is the number of pollution sources.

[0013] An ozone pollution source tracing device, comprising: An acquisition module is used to obtain concentration data of volatile organic compounds in a target area; A decomposition module is used to perform positive definite matrix factor decomposition on the concentration data to obtain component spectrum matrices and contribution matrices of different pollution sources; The identification module combines the preset pollution sources, matches the characteristic factors in the component spectrum matrix, identifies multiple pollution sources of volatile organic compounds, and calculates the actual volatile organic compound concentration of each pollution source based on the contribution matrix; An analysis module is configured to simulate reduction of each of the plurality of pollution sources one by one, calculate the net ozone generation rate after reduction of different pollution sources based on the simulation, and further determine a relative incremental reaction activity coefficient based on the net ozone generation rate after reduction of a single pollution source; The source tracing module is used to calculate the contribution rate of each pollution source to ozone generation based on the volatile organic compound concentration corresponding to different pollution sources and the relative incremental reaction activity coefficient, and to take the pollution source with the largest contribution rate as the main source of ozone.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned ozone pollution source tracing method.

[0015] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned ozone pollution source tracing method is implemented.

[0016] The ozone pollution source tracing method provided by the present invention has the following beneficial effects: First, based on the concentration data and PMF decomposition, the component spectrum matrix and contribution matrix of the pollution source can be extracted from the complex volatile organic compound concentration data; the identified pollution component spectrum is matched according to the preset pollution source and pollutant type, which can effectively and quickly identify potential pollution sources; then, by simulating the reduction of multiple pollution sources one by one and calculating the relative incremental reaction activity coefficient of ozone to the pollution source, the interaction between different pollution sources is taken into account. The reduction simulation can more realistically reflect the impact of pollution sources on ozone generation, and quantify the impact, providing a direct basis for the subsequent calculation of pollution source contribution rate. Finally, the contribution rate of each pollution source to ozone generation is calculated, and the pollution source with the largest contribution rate is regarded as the main source of ozone, thus achieving accurate, rapid and quantitative tracing of ozone pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0018] Figure 1 The figure is a flow chart of a method for tracing the source of ozone pollution according to an exemplary embodiment of the present invention.

[0019] Figure 2 The present invention provides a flowchart of an online tracing process for ambient air ozone pollution according to an exemplary embodiment.

[0020] Figure 3 This is a block diagram of an ozone pollution source tracing device provided according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] First, the present invention provides a method for tracing the source of ozone pollution, specifically: Figure 1 As shown, the following steps are included: S101. Acquire concentration data of volatile organic compounds in a target area.

[0024] In this step, the concentration data of various pollutants such as volatile organic compounds can be obtained from environmental monitoring stations, satellite remote sensing data, mobile monitoring vehicles and other channels, and the data format must be unified, including timestamps, pollutant types (such as PM 2.5 、PM 10 , SO2, NOx, VOCs, etc.), concentration values, etc. In addition, atmospheric environmental conditions data can also be obtained, including: temperature, humidity, pressure, photolysis parameters, etc.

[0025] For example, monitoring points are set up at existing automatic ambient air quality monitoring stations to obtain the concentration of volatile organic compounds in the atmospheric environment; June is selected as the representative period of summer ozone pollution for sampling, and monitoring is carried out continuously for one month; during the monitoring period, concentration data of pollutants such as nitrogen oxides, ozone, CO, as well as meteorological parameters such as temperature, humidity, and pressure are collected simultaneously, and the photolysis rate is calculated using the TUV model.

[0026] S102 , performing positive definite matrix factorization on the concentration data to obtain component spectrum matrices and contribution matrices of multiple extracted factors.

[0027] In this step, the concentration data is first cleaned to determine the uncertainty data in the concentration data of different types of volatile organic compounds; secondly, the concentration data and uncertainty data are subjected to positive definite matrix factorization to obtain the component spectrum matrix and contribution matrix of each pollution source.

[0028] In one embodiment, data cleaning is first required to identify uncertain data. Because the acquired concentration data may contain anomalies, data preprocessing is performed to remove duplicate data and address missing values ​​(e.g., using interpolation or averaging). Machine learning methods can be used to clean species concentration data containing time series. The cleaned data can then be automatically verified and statistically compared. The pollutant types and time periods selected for the study can be adjusted based on the effectiveness of the cleaned data, the missing time periods, and the number of missing values.

[0029] For example, the original concentration data can be sorted according to the time series; non-numeric data can be eliminated; the first quartile Q1 and the third quartile Q3 of the concentration data in the time series are calculated, and the judgment range of the outliers is calculated based on the interquartile range of Q1 and Q3 plus or minus 3 times, and the outliers are eliminated and replaced with blanks; the missing time periods in the time series are filled; the uncertainty of the filtered concentration data is calculated to obtain the uncertainty data of each type of volatile organic compound in the concentration data and its corresponding uncertainty; the signal-to-noise ratio S / N of different types of volatile organic compounds is obtained based on the uncertainty data and the concentration data, and the uncertainty of the uncertainty data of different types of volatile organic compounds is adjusted based on the numerical range of S / N, and the uncertainty data of this type of volatile organic compound is re-determined based on the adjusted uncertainty.

[0030] For example, raw data is organized according to time series, with the species name in the first row and the time series in the first column, formatted as year-month-day-hour-minute-second. Non-numeric data is removed from the species data within the selected time period, and the remaining data is replaced with blanks. For outliers to be removed, the first quartile (Q1) and third quartile (Q3) of the series are first calculated. The outlier range is calculated based on the interquartile range plus or minus three times Q1 and Q3. If the concentration data falls within this range, it is considered non-outlier. If it falls outside this range, it is considered outlier and removed with blanks. Finally, the time series is padded. Step size identification and continuity testing are performed on the time series after outlier removal, and any missing time periods are automatically padded.

[0031] To calculate the signal-to-noise ratio for different types of VOCs, the bias of the VOC is first determined by the ratio of the difference between the component concentration of the VOC and the uncertainty of the VOC to the uncertainty. The signal-to-noise ratio is then determined based on the ratio of the VOC bias to the number of VOC types. The uncertainty of the species is adjusted based on the signal-to-noise ratio (S / N). Species with an S / N less than 0.2 are classified as "Bad" and removed from the simulation. Species with an S / N greater than 0.2 but less than 2 are classified as "Weak," and their uncertainty is tripled during the simulation. The remaining species are classified as "Strong" by default.

[0032] For example, first, the time series data for each VOC can be visualized and analyzed, such as by plotting time series graphs and boxplots. Trends, periodicity, and seasonality in the time series data can be observed, and correlations between different types of VOCs can be analyzed. Based on the analysis results, the time series characteristics of each VOC can be summarized, such as mean, standard deviation, peaks, valleys, and periodicity. Reasonable outlier determination criteria can be established based on these time series characteristics. Common methods include those based on statistical distribution (e.g., the 3-times standard deviation principle) and prediction residuals based on time series models. The outlier determination criteria can be applied to the time series data for each VOC to determine the range of normal data and identify and mark outlier data points. Based on these marked outlier data points, these outliers can be removed from the time series data. This can be done by directly deleting the outlier data point or by using alternative methods (e.g., interpolation or mean substitution) to fill in the missing data. The time series data after outlier removal can then be reanalyzed to ensure data accuracy and rationality. If it is found that the time series characteristics change significantly after removing abnormal data, it may be necessary to adjust the outlier judgment criteria or re-remove abnormal data.

[0033] In addition, a training sample can be obtained and a CNN network architecture can be constructed. The training sample includes volatile organic compound sample data and corresponding uncertainty true values. The sample data is input into the CNN model to obtain an output predicted value. The CNN is trained with the goal of minimizing the deviation between the predicted value and the uncertainty true value to obtain a trained data screening model. Concentration data is input into the data screening model to obtain the uncertainty of each type of volatile organic compound in the concentration data. Data with uncertainty greater than a first preset value is eliminated as abnormal data, data less than or equal to the first preset value is regarded as high-quality data, and data greater than a second preset value in the high-quality data is regarded as uncertain data. The second preset value is less than the first preset value.

[0034] In another embodiment, it is necessary to perform positive definite matrix factorization on the concentration data and the uncertainty data to determine the component spectrum matrix and contribution matrix of the volatile organic compound at different extraction factors.

[0035] Specifically, the number of pollution sources is set as the number of extraction factors; based on the number of extraction factors, the concentration data and uncertainty data are input into the PMF model for decomposition to obtain the component spectrum matrix and contribution matrix of volatile organic compounds on each extraction factor. Among them, for the input concentration data and uncertainty data, the concentration data and uncertainty data can first be constructed into a concentration matrix and an uncertainty matrix; the concentration matrix and uncertainty matrix are input into the trained PMF model to obtain the component spectrum matrix and contribution matrix of volatile organic compounds on each extraction factor. In addition, in addition to the concentration data and uncertainty data, atmospheric environmental condition data can also be simultaneously input into the PMF model.

[0036] Among them, selecting different numbers of extraction factors will result in different simulation output results, so it is necessary to select an appropriate number of extraction factors. First, set the number of candidate pollution sources, which is less than the number of types of volatile organic compounds; simulate the PMF model output based on the number of candidate pollution sources, and calculate the first goodness-of-fit parameter of all input data and the second goodness-of-fit parameter of the input data after removing the noise data; determine the number of candidate pollution sources with the closest and smallest first goodness-of-fit parameter to the second goodness-of-fit parameter as the number of pollution sources. Among them, the noise data can be data points that contribute little to the model fitting or are considered to be inconsistent with the model assumptions, such as outliers, high uncertainty data points, etc. These data points may be considered to be "noise" or "interference" of the model, and removing them can improve the goodness of fit of the model on "valid" data.

[0037] For example, if the number of extraction factors is set to be smaller than the number of input components, the simulation results will be different. The first goodness-of-fit parameter (e.g., Q(Robust)) is calculated for all input data, and the second goodness-of-fit parameter (e.g., Q(True)) is calculated for input data that do not conform to the model. The optimal number of extraction factors is determined to be the number with the smallest and closest Q(Robust) to Q(True).

[0038] S103 : Based on preset pollution sources, matching the component spectrum matrix and the contribution matrix of the multiple extracted factors is performed to identify the multiple pollution sources of the volatile organic compounds.

[0039] In this step, it is necessary to determine the concentration, type and corresponding pollution source of the volatile organic compound.

[0040] For example, you can first preset pollution sources and their corresponding pollutant types, set common pollution sources, the first pollution source is set as coal burning source, the second pollution source is set as motor vehicle emission source, the third pollution source is set as industrial source, the fourth pollution source is set as solvent use source, the fifth pollution source is set as oil and gas volatilization source, the sixth pollution source is set as plant source, and other sources are not restricted and are determined according to local characteristic pollution sources. Set the identification species of the pollution source, the identification species of the first pollution source is set as ethane, propane, ethylene, propylene, and benzene; the identification species of the second pollution source is set as ethane, propane, n-butane, isobutane, n-pentane, isopentane, benzene, and toluene; the identification species of the third pollution source is set according to the local dominant industry and is not limited; the identification species of the fourth pollution source is set as benzene, toluene, ethylbenzene, m / p-xylene, and styrene; the identification species of the fifth pollution source is set as ethane, propane, isobutane, n-butane, isopentane, and n-pentane; the identification species of the sixth pollution source is set as isoprene.

[0041] In one embodiment, the preset pollution sources and their corresponding pollutant types can be matched with the component spectrum matrix, and the component proportions of different pollutant types can be matched with the contribution matrix; when the two matching results meet the preset requirements, the pollution source corresponding to the extraction factor is identified.

[0042] Among them, different pollution sources may have the same pollutants, but the proportion of the same pollutants emitted by different pollution sources is different. Therefore, the component spectrum matrix and contribution matrix can be matched to determine the pollution source corresponding to each extracted factor.

[0043] For example, the identification method of the identified species is set. The identification of the first pollution source requires that the proportion of the five VOCs species reaches more than 30%, and the proportion of other VOCs components is less than 20%; the identification of the second pollution source requires that the concentration of the species reaches more than 30%, and the toluene concentration is higher than benzene; the third pollution source identification process is set up according to local characteristic industries and is not limited here; the fourth pollution source requires that the proportion of the aromatic hydrocarbon species reaches more than 40%, and the proportion of alkanes, alkenes and alkynes is less than 10%; the fifth pollution source requires that the proportion of ethane and propane is higher than 20%, and the proportion of isobutane, n-butane, isopentane and n-pentane is higher than 30%, and the proportion of other species is less than 10%; the sixth pollution source requires that the proportion of isoprene is higher than 60%, and the proportion of other VOCs species is less than 10%.

[0044] S104: Simulate reduction of each of the multiple pollution sources one by one, and calculate the relative incremental reaction activity coefficient after reduction of a single pollution source.

[0045] The relative incremental reactivity coefficient characterizes the sensitivity of ozone concentration changes to changes in volatile organic compound (VOC) concentrations. Therefore, this step aims to determine ozone sensitivity to different pollution sources. Each VOC source is simulated for reduction, and the net ozone generation rate after each source reduction is used to determine ozone sensitivity.

[0046] In one embodiment, the multiple pollution sources are simulated for reduction one by one, and the net ozone generation rate after reduction of different pollution sources is calculated based on the simulation, and then the relative incremental reaction activity coefficient is obtained based on the net ozone generation rate after reduction of a single pollution source.

[0047] For example, the ozone source parsing method of the atmospheric photochemical box model coupled with the volatile organic compound source parsing receptor model can be used to input the pollutant concentration data, meteorological parameters, photolysis parameters and simulated pollutant concentrations of each pollution source into the atmospheric photochemical box model to simulate the ozone generation process and calculate the net ozone generation rate; the volatile organic compound (VOCs) concentration of a single pollution source is quantitatively reduced by a certain proportion while maintaining the volatile organic pollutant (VOCs) concentrations of other pollution sources, and then input into the atmospheric photochemical box model again to simulate the ozone generation process and calculate the net ozone generation rate after the single pollution source is reduced; the same calculation method is used to simulate and calculate the net ozone generation rate after different pollution sources are reduced; here, there is no restriction on the reduction ratio of the pollution source, and it is necessary to ensure that the quantitative reduction ratio of different pollution sources is consistent.

[0048] Based on the net ozone generation rate after the reduction of different pollution sources simulated and calculated in the above steps, the ratio of the change rate of the net ozone generation rate after the reduction of a single pollution source to the change rate of the source concentration of the reduced pollution source is calculated respectively. This ratio is used as the relative incremental reaction activity coefficient to characterize the sensitivity of ozone concentration changes to changes in the concentration of volatile organic pollutants (VOCs) from the pollution source.

[0049] The calculation formula of relative incremental reaction activity coefficient is: ; in, is the average relative incremental reactivity coefficient of pollution source X to ozone generation; is the relative incremental reaction activity coefficient of pollution source X on volatile organic pollutants in each sample; is the net generation rate of sampling under the initial scenario; n is the number of atmospheric environment samples; S105. Calculate the contribution rate of each pollution source to ozone generation based on the volatile organic compound concentrations corresponding to different pollution sources and the relative incremental reaction activity coefficients, and take the pollution source with the largest contribution rate as the main source of ozone.

[0050] The sensitivity is weighted according to the pollutant concentrations corresponding to different pollution sources to determine the contribution rate of different pollution sources to the generation of ozone, and the pollution source with the largest contribution rate is taken as the source of the ozone.

[0051] Specifically, based on the obtained sensitivity, the concentration of volatile organic pollutants (VOCs) at the pollution source is used as a weighting coefficient, and the relative incremental reactivity coefficient method calculated in the above steps is used to quantitatively analyze the source of ozone.

[0052] The calculation formula for the generation contribution rate is: ; is the contribution of pollution source X to ozone generation; is the concentration of pollution source X; m is the number of pollution sources.

[0053] Finally, the pollution source with the largest contribution rate will be regarded as the source of ozone.

[0054] Using the above method, first, the PMF decomposition is performed based on the concentration data, and the component spectrum matrix and contribution matrix of the pollution source can be extracted from the complex volatile organic pollutant concentration data; the identified pollution component spectrum is matched according to the preset pollution source and pollutant type, which can effectively identify the potential pollution source; then, by performing reduction simulation on multiple pollution sources one by one and calculating the relative incremental reaction activity coefficient of ozone to the pollution source, the interaction between different pollution sources is taken into account. The reduction simulation can more realistically reflect the impact of pollution sources on ozone generation, and quantify the impact, providing a direct basis for the subsequent calculation of the contribution rate of pollution sources. Finally, the contribution rate of each pollution source to ozone generation is calculated, and the pollution source with the largest contribution rate is regarded as the main source of ozone, thus achieving accurate, rapid and quantitative tracing of ozone pollution.

[0055] Based on the above method steps, the present invention also provides a possible implementation method, taking the online tracing of ozone pollution sources in the atmospheric environment of a certain city as an example, combined with Figure 2 The steps shown specifically include the following steps: First, basic volatile organic pollutant data collection. Monitoring points were set up at existing automatic ambient air quality monitoring stations to obtain the concentration of volatile organic pollutants in the atmospheric environment. The sampling time was selected as the representative period of summer ozone pollution, and monitoring was carried out continuously for one month. During the monitoring period, the concentration data of pollutants such as nitrogen oxides, ozone, and CO were collected, as well as meteorological parameters such as temperature, humidity, and pressure. The photolysis rate was calculated using the TUV model. Machine learning methods were used to clean the species concentration data containing time series. The cleaning results were automatically verified and compared statistically. The species and time periods selected for the study were adjusted based on the species efficiency, missing period, and number of missing items after cleaning.

[0056] Second, the PMF model was set up and the data was adjusted. The cleaned volatile organic pollutant concentration data and uncertainty data were simultaneously input into the PMF model. The species uncertainty was adjusted based on the calculated signal-to-noise ratio (S / N). Species with an S / N less than 0.2 were classified as "Bad" and eliminated from the simulation. Species with an S / N greater than 0.2 but less than 2 were classified as "Weak." During the simulation, the uncertainty was tripled, and the remaining species were classified as "Strong" by default. Different numbers of extraction factors were set based on local characteristics, and the optimal number of factors was selected, with the closest and smallest value between Q (Robust) and Q (True).

[0057] Third, identify the extracted factors and calculate their concentrations. The PMF model is run to extract the profile and contribution matrices of the factors in the simulation results. The factor identification technology developed in this paper is used to match pollution sources with the factors, ultimately identifying the extracted factors as specific pollution sources. The species concentrations of volatile organic pollutants in the factors are calculated based on the PMF principle.

[0058] Fourth, the ozone generation sensitivity model and the calculation of the proportion of ozone sources. Using the ozone source apportionment method of the atmospheric photochemical box model coupled with the atmospheric pollutant source apportionment receptor model, the pollutant concentration data, meteorological parameters, photolysis parameters, and simulated pollutant concentrations from each pollution source are input into the atmospheric photochemical box model to simulate the ozone generation process and calculate the net ozone generation rate. The volatile organic pollutants (VOCs) concentration of a single pollution source is quantitatively reduced by a certain proportion and added to the volatile organic pollutants (VOCs) concentrations of other pollution sources. This is then input into the atmospheric photochemical box model again to simulate the ozone generation process and calculate the net ozone generation rate after the single pollution source is reduced. The same calculation method is used to simulate and calculate the net ozone generation rate after the reduction of different pollution sources. The relative incremental reaction activity coefficients after the quantitative reduction of different pollution sources are calculated respectively. The volatile organic pollutants (VOCs) concentrations of the pollution sources are used as weighting coefficients to quantitatively calculate the ozone sources.

[0059] based on Figure 1 The method for tracing the source of ambient air ozone pollution based on online precursor observation is described. This method, based on online source analysis of volatile organic pollutants and an atmospheric photochemical box model, ultimately enables automatic ozone source tracing. This method opens up new avenues for rapid, accurate, and quantitative identification of ozone in the environment. This method has significant scientific significance and practical application value, with the following advantages:

[0060] Based on observational data on volatile organic pollutants, the present invention first constructs an online method for tracing the origins of ozone precursors. This method avoids errors in pollution source identification caused by a lack of simulation experience among novice model users, enabling accurate, rapid, and quantitative tracing of precursors. Based on this online precursor tracing technology, the present invention further enables online tracing of ozone pollution sources by combining it with an atmospheric photochemical box model.

[0061] Secondly, the present invention also provides an ozone pollution tracing device, such as Figure 3 Shown, including: The acquisition module 301 is used to acquire the concentration data of volatile organic compounds in the target area.

[0062] The decomposition module 302 is used to perform positive definite matrix factor decomposition on the concentration data to obtain component spectrum matrices and contribution matrices of different pollution sources.

[0063] The identification module 303 is used to identify the module, combine the preset pollution sources, match the characteristic factors in the component spectrum matrix, identify multiple pollution sources of volatile organic compounds, and calculate the actual volatile organic compound concentration of each pollution source according to the contribution matrix.

[0064] The analysis module 304 is used to simulate the reduction of the multiple pollution sources one by one, calculate the net ozone generation rate after the reduction of different pollution sources based on the simulation, and then determine the relative incremental reaction activity coefficient based on the net ozone generation rate after the reduction of a single pollution source.

[0065] The source tracing module 305 is used to calculate the contribution rate of each pollution source to ozone generation based on the volatile organic compound concentrations corresponding to different pollution sources and the relative incremental reaction activity coefficient, and to determine the pollution source with the largest contribution rate as the main source of ozone.

[0066] Using the above-mentioned device, first, the PMF decomposition is performed based on the concentration data, and the component spectrum matrix and contribution matrix of the pollution source can be extracted from the complex volatile organic pollutant concentration data, which can effectively identify potential pollution sources; then, by performing reduction simulations on multiple pollution sources one by one and calculating the relative incremental reaction activity coefficient of ozone to the pollution source, the interaction between different pollution sources is taken into account. The reduction simulation can more realistically reflect the impact of the pollution source on ozone generation, and quantify the impact, providing a direct basis for the subsequent calculation of the contribution rate of the pollution source. Finally, the contribution rate of each pollution source to ozone generation is calculated, and the pollution source with the largest contribution rate is regarded as the main source of ozone, thereby achieving accurate, rapid and quantitative tracing of ozone pollution.

[0067] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 Provide steps for tracing the source of ozone pollution.

[0068] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Provide steps for tracing the source of ozone pollution.

[0069] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0073] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are included in the scope of protection of the patent for the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A method for tracing the source of ozone pollution, characterized in that: The method comprises: Obtaining the concentration data of volatile organic compounds in the target area; Performing positive definite matrix factorization on the concentration data to obtain a component spectrum matrix and a contribution matrix of a plurality of extracted factors; Based on the preset pollution sources, the component spectrum matrix and the contribution matrix of the multiple extracted factors are matched to identify the multiple pollution sources of the volatile organic compounds; Performing reduction simulations on each of the multiple pollution sources, calculating the net ozone generation rate after the reduction of different pollution sources based on the simulations, and then determining the relative incremental reaction activity coefficient based on the net ozone generation rate after the reduction of a single pollution source; The contribution rate of each pollution source to ozone generation is calculated based on the volatile organic compound concentration corresponding to different pollution sources and the relative incremental reaction activity coefficient, and the pollution source with the largest contribution rate is taken as the main source of ozone.

2. The method for tracing the source of ozone pollution according to claim 1, characterized in that: The concentration data is subjected to positive definite matrix factorization to obtain component spectrum matrices and contribution matrices of different pollution sources, including: Cleaning the concentration data to determine uncertain data in the concentration data of different types of volatile organic compounds; The concentration data and the uncertainty data are subjected to positive definite matrix factorization to obtain a component spectrum matrix and a contribution matrix of each pollution source.

3. The method for tracing the source of ozone pollution according to claim 2, characterized in that: The concentration data is cleaned to obtain uncertain data in the concentration data including: Arrange the original concentration data according to time series; Eliminate non-numeric data; Calculate the first quartile Q1 and the third quartile Q3 of the concentration data in the time series, calculate the judgment range of outliers based on the interquartile range plus or minus 3 times Q1 and Q3, eliminate outliers and replace them with blanks; Fill in missing periods in the time series; Calculating the uncertainty of the filtered concentration data to obtain uncertainty data of each type of volatile organic compound in the concentration data and its corresponding uncertainty; A signal-to-noise ratio (S / N) of different types of volatile organic compounds is obtained based on the uncertainty data and the concentration data. Based on a numerical range of the S / N, the uncertainty of the uncertainty data of the different types of volatile organic compounds is adjusted, and the uncertainty data of the type of volatile organic compounds is re-determined based on the adjusted uncertainty.

4. The method for tracing the source of ozone pollution according to claim 2, characterized in that: The concentration data and the uncertainty data are subjected to positive definite matrix factorization to obtain component spectrum matrices and contribution matrices of multiple extracted factors, including: Set the number of pollution sources as the number of extraction factors; Based on the number of the extraction factors, the concentration data and uncertainty data are input into the PMF model for decomposition to obtain a component spectrum matrix and a contribution matrix of volatile organic compounds on each extraction factor.

5. The method for tracing the source of ozone pollution according to claim 4, characterized in that: The number of pollution sources to be set includes: Setting the number of candidate pollution sources, where the number of candidate pollution sources is less than the number of types of volatile organic compounds; Simulating the PMF model output based on the number of candidate pollution sources and calculating the first goodness-of-fit parameter of all input data and the second goodness-of-fit parameter of the input data after removing noise data; The number of candidate pollution sources whose first goodness of fit parameter is closest to the second goodness of fit parameter and is the smallest is determined as the number of pollution sources.

6. The method for tracing the source of ozone pollution according to claim 4, characterized in that: The matching of the component spectrum matrix and the contribution matrix of the plurality of extracted factors based on the preset pollution sources to identify the plurality of pollution sources of the volatile organic compounds includes: Matching the preset pollution sources and their corresponding pollutant types with the component spectrum matrix, and matching the component proportions of different pollutant types with the contribution matrix; When the two matching results meet the preset requirements, the pollution source corresponding to the extracted factor is identified.

7. The method for tracing the source of ozone pollution according to claim 1, characterized in that: The relative incremental reaction activity coefficient calculation formula is: ； in, is the average relative incremental reactivity coefficient of pollution source X to ozone generation; is the relative incremental reaction activity coefficient of pollution source X on volatile organic compounds in each sample; is the net generation rate of ozone under the initial scenario; n is the number of atmospheric environment samples; The calculation formula for the generation contribution rate is: ； is the contribution of pollution source X to ozone generation; is the concentration of pollution source X; m is the number of pollution sources.

8. An ozone pollution tracing device, characterized in that: The device comprises: An acquisition module is used to obtain concentration data of volatile organic compounds in a target area; A decomposition module is used to perform positive definite matrix factor decomposition on the concentration data to obtain component spectrum matrices and contribution matrices of different pollution sources; The identification module combines the preset pollution sources, matches the characteristic factors in the component spectrum matrix, identifies multiple pollution sources of volatile organic compounds, and calculates the actual volatile organic compound concentration of each pollution source based on the contribution matrix; An analysis module is configured to simulate reduction of each of the plurality of pollution sources one by one, calculate the net ozone generation rate after reduction of different pollution sources based on the simulation, and further determine a relative incremental reaction activity coefficient based on the net ozone generation rate after reduction of a single pollution source; The source tracing module is used to calculate the contribution rate of each pollution source to ozone generation based on the volatile organic compound concentration corresponding to different pollution sources and the relative incremental reaction activity coefficient, and to take the pollution source with the largest contribution rate as the main source of ozone.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

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