Formaldehyde pollution source analysis and ozone influence quantification method and system for coupling receptor model and box-type model

Abstract

The invention relates to the field of pollution source analysis, and discloses a formaldehyde pollution source analysis and ozone influence quantification method and system for coupling a receptor model and a box-type model, and the method comprises the steps: collecting the concentration of formaldehyde and other pollutants in urban atmosphere in real time through online monitoring equipment, and carrying out abnormal value marking and data standardization processing; performing source analysis on the processed data based on a positive definite factor matrix method to generate a pollution source contribution matrix; converting the source contribution matrix into a molecular concentration input file which can be recognized by the box-type model; embedding a free radical circulation mechanism into the box-type model, and executing photochemical simulation under different pollution source scenes; and generating a pollution source contribution heat map based on a simulation result, and outputting management and control suggestions through a deep learning model. The method has the following effects: by coupling the receptor model and the box type model, not only can different pollution sources of formaldehyde be determined, but also the influence of each pollution source on ozone generation through photochemical reaction can be analyzed.

Description

Technical Field

[0001] The present invention relates to the field of pollution source analysis, and in particular to a method and system for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model. Background Art

[0002] In current atmospheric pollution research, formaldehyde (HCHO), a key ozone precursor, is crucial for effective air pollution control and air quality assurance. However, existing technologies have numerous limitations and are unable to meet practical needs.

[0003] How to solve the above technical problems is a technical difficulty that needs to be overcome by those skilled in the art. Summary of the Invention

[0004] The present invention provides a method and system for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model, to at least partially solve the above technical problems.

[0005] In the first aspect, in order to solve the above technical problems, the present invention provides a method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model, comprising:

[0006] S101. Using online monitoring equipment, collect the concentrations of formaldehyde and other pollutants in the urban atmosphere in real time and perform outlier marking and data standardization.

[0007] S102. Perform source analysis on the data processed in S101 based on the positive factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources;

[0008] S103, converting the source contribution matrix into a molecular concentration input file recognizable by a box model;

[0009] S104. Embed the free radical cycle mechanism in the box model to perform photochemical simulation under different pollution source scenarios;

[0010] S105. Generate a pollution source contribution heat map based on the simulation results, and output control recommendations through a deep learning model.

[0011] In an optional embodiment, the concentration of formaldehyde and other pollutants in the urban atmosphere is collected in real time by online monitoring equipment, and abnormal value marking and data standardization are performed, including:

[0012] Collect the concentration of formaldehyde, volatile organic compounds, nitrogen oxides, ozone, carbon monoxide, PM2.5, meteorological parameters and photolysis rate J(N02) in urban atmosphere;

[0013] Abnormal periods are marked based on meteorological parameters and instrument parameters, and outliers are identified using a sliding window method; the instrument parameters include humidity anomalies and flow anomalies;

[0014] Unify the timestamps of different instrument parameters to a common time zone;

[0015] Linear interpolation is used to fill in data segments where the missing rate is greater than a preset value;

[0016] A structured database including an hourly mean matrix is ​​generated; the fields of the mean matrix include: timestamp, formaldehyde, species of volatile organic compounds, nitrogen oxides, and meteorological parameters.

[0017] In an optional embodiment, the data processed in S101 is subjected to source analysis based on the positive factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources, including:

[0018] Set the number of pollution source factors and determine the optimal number of factors;

[0019] The Bootstrap resampling method was used to verify the stability of factor contribution, with the number of iterations not less than 500;

[0020] Input data matrix containing hourly concentrations of formaldehyde and other pollutants and collinear pollutants;

[0021] Based on the correlation analysis of indicator species, the pollution source type corresponding to each factor is determined. The pollution source types include: motor vehicle source, industrial source, and secondary generation source.

[0022] In an optional implementation, determining the optimal number of factors includes:

[0023] Initialize the range of factors and set the value range of pollution source factors to 5 to 8;

[0024] For each factor number within a set range, a corresponding source apportionment model is constructed using the positive factor matrix method; the source apportionment model decomposes the observed pollutant concentration data into the contributions of different factors based on the characteristics of the monitoring data; each factor represents a potential pollution source;

[0025] After constructing the source resolution model corresponding to each factor number, an iterative algorithm is used to solve the model; during the iteration process, the parameters of the source resolution model are continuously adjusted until the preset convergence conditions are met;

[0026] Calculate the Q value for each solved source apportionment model;

[0027] Arrange the calculated Q values ​​corresponding to different numbers of factors into data pairs and use the drawing tool to draw a curve of Q value changing with the number of factors;

[0028] Analyze the changing trend of Q value as the number of factors increases and find the inflection point of the Q value change rate on the curve;

[0029] The number of factors corresponding to the inflection point determined in the curve is taken as the optimal number of factors.

[0030] In an optional embodiment, a free radical cycle mechanism is embedded in the box model to perform photochemical simulations under different pollution source scenarios, including:

[0031] Determine the types of free radicals related to formaldehyde and ozone generation based on atmospheric photochemical principles;

[0032] Construct a free radical cycle reaction equation based on the reaction characteristics of free radicals and atmospheric chemical processes; set initial values ​​for various parameters in the free radical reaction equation;

[0033] According to the pollution source contribution matrix, the pollution sources are divided into motor vehicle sources, industrial sources and secondary generation sources, and

[0034] Design a variety of pollution source scenarios; different pollution source scenarios include: considering only motor vehicle emissions, considering only industrial emissions, considering a combination of motor vehicle and industrial emissions, and considering emissions from all pollution sources but simulating emission reductions for a specific pollution source; each scenario corresponds to different initial pollutant concentrations and emission conditions;

[0035] For each pollution source scenario, the initial conditions for the box model were set based on the HCHO concentrations from different sources and monitoring data analyzed by the positive factor matrix method;

[0036] The photochemical simulation is started in the box model and the simulation process is gradually advanced according to the set time step. During the simulation, the free radical cycle mechanism embedded in the box model and the set reaction equations calculate the concentration changes of various pollutants and free radicals, as well as the generation and consumption of ozone.

[0037] The pollutant concentration, free radical concentration, and ozone generation at each time step during the simulation are recorded and the results are output.

[0038] In an optional embodiment, the Bootstrap resampling method is used to verify the stability of factor contributions, including:

[0039] Extract the original sample data containing the contribution value of each pollution source factor from the source apportionment data processed by the positive factor matrix method;

[0040] The number of resampling iterations is set to no less than 500; in each iteration, subsamples with the same number of original samples are randomly selected from the original sample data with replacement;

[0041] For each subsample obtained by resampling, factor analysis is performed using the positive factor matrix method to recalculate the contribution value of each pollution source factor;

[0042] The factor contribution results obtained after each resampling are recorded to form a data set containing multiple iteration results;

[0043] Conduct statistical analysis on the recorded multiple iteration results and calculate the statistical value of the contribution value of each pollution source factor.

[0044] The stability of the contribution of each pollution source factor is judged based on the calculated statistics.

[0045] In an optional embodiment, generating a pollution source contribution heat map based on the simulation results includes:

[0046] Construct a time correlation matrix with the matrix dimension of [pollution source type × time period];

[0047] Calculate the influence weight of each matrix unit based on the change in ozone generation rate:

[0048] The HSV color space is used to map the weight values ​​and generate a dynamic visualization heat map.

[0049] In a second aspect, the present invention provides a method system for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model, comprising:

[0050] The first processing module is used to collect the concentration of formaldehyde and other pollutants in the urban atmosphere in real time through online monitoring equipment and perform abnormal value marking and data standardization processing;

[0051] The second processing module is used to perform source analysis on the data processed by the first processing module based on the positive definite factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources;

[0052] A third processing module is configured to convert the source contribution matrix into a molecular concentration input file recognizable by a box model;

[0053] The fourth processing module is used to: embed the free radical cycle mechanism in the box model and perform photochemical simulations under different pollution source scenarios;

[0054] The fifth processing module is used to generate a pollution source contribution heat map based on the simulation results and output control recommendations through a deep learning model.

[0055] Compared with the existing technology, the present invention has at least the following beneficial effects: through the coupled receptor model (PMF) and the box model (OBM), not only can the different pollution sources of formaldehyde (motor vehicle source, industrial source, secondary generation source, etc.) be determined, but also the impact of each pollution source on ozone generation through photochemical reaction can be analyzed. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of a method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model, provided by the first embodiment of the present invention;

[0057] Figure 2 This is a system block diagram of a formaldehyde pollution source analysis and ozone impact quantification system using a coupled receptor model and a box model, provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] In atmospheric pollution research, receptor models (such as the positive definite factor matrix method PMF) mainly use statistical methods to decompose pollutant concentration data to identify the main contributing sources, such as motor vehicle sources, industrial sources, and secondary generation sources. For example, in a study of the ozone pollution period in Nanjing, the use of PMF analysis found that secondary sources contributed 56.4% to HCHO, and motor vehicle sources accounted for 18.4%. However, this model has obvious defects. It can only give the contribution ratio of different pollution sources, especially in analyzing the contribution of primary emission sources. It cannot quantify the impact of photochemical reactions on HCHO, and it is difficult to evaluate the impact of HCHO on ozone generation. This greatly reduces the control effect when formulating control measures for HCHO and ozone pollution.

[0060] Air quality models, such as CMAQ and WRF-Chem, are important tools for simulating pollutant generation pathways based on physical and chemical mechanisms. These models theoretically provide a detailed understanding of the transformation of pollutants in the atmosphere. However, in practice, their operation requires accurate meteorological data, high-resolution dynamic emissions inventories, and powerful computing resources. For example, a regional O3 pollution simulation in a certain prefecture-level city took over 48 hours to run using a 24-core server. In some complex cases, a single simulation could even take over 72 hours. Such long simulation times fall far short of meeting the demands of real-time pollution control and, in the face of sudden pollution incidents, are unable to provide timely and effective support for decision-making.

[0061] By simplifying the three-dimensional model to simulate a single-point chemical mechanism, the box model (OBM) has unique advantages in analyzing the contribution of photochemical reaction pathways to HCHO, identifying major precursors, and rapidly evaluating the contribution of HCHO photolysis to free radicals (ROx·) and O3. In a study in Changzhou, OBM simulations showed that the RO·+O2 reaction contributed as much as 82.4% to HCHO production. Isoprene and trans-2-butene are key precursors of atmospheric formaldehyde, and when the HCHO concentration dropped to zero, the O3 production concentration decreased by 14.8%. However, traditional OBM has serious shortcomings. It relies on external input HCHO observation data and cannot directly link the source contribution results of PMF analysis. This leads to a break in the complete "source-impact" chain and cannot analyze the impact of HCHO from specific sources on O3, which greatly limits its application in precise pollution control.

[0062] Furthermore, under existing technology, the output of the pollutant receptor model (PMF) (e.g., source contribution matrix) is incompatible with the input requirements of the oblique box model (OBM) (e.g., pollutant concentrations and meteorological parameters). For example, using PMF output of industrial HCHO time series data for OBM simulation requires tedious manual data cleaning and format conversion, including manual matching of time series and unit formatting. This is not only time-consuming and labor-intensive, but also prone to errors, further reducing research efficiency and data accuracy.

[0063] In summary, existing technologies have key problems in formaldehyde source analysis and environmental impact assessment. They are unable to effectively connect pollution source analysis with the quantification of ozone generation impacts, and are unable to meet the actual needs of real-time and precise control of air pollution. An innovative technical method is urgently needed to overcome these limitations.

[0064] Reference Figure 1 The first embodiment of the present invention provides a method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model, comprising the following steps:

[0065] S101. Collect the concentration of formaldehyde and other pollutants in the urban atmosphere in real time through online monitoring equipment, mark abnormal values ​​and perform data standardization.

[0066] Specifically, online monitoring equipment collects real-time data from urban air, including formaldehyde (HCHO), volatile organic compounds (VOCs, including isoprene, light alkanes, and aromatic hydrocarbons), nitrogen oxides (NOx, ranging from 5-45 ppb), ozone (O3), carbon monoxide (CO), particulate matter (such as PM2.5), and meteorological parameters (temperature, relative humidity, wind speed, and photolysis rate J(NO2)). The ppb here stands for "parts per billion," meaning one part per billion.

[0067] A sliding window method was used to identify outliers in concentration series, marking anomalous periods when humidity levels greater than 100% or flow anomalies were detected. Linear interpolation was used to fill missing data segments with a rate of less than 5%. Timestamps across devices were standardized to the UTC+8 time zone. A structured database containing hourly averages was established, with fields including timestamp, HCHO concentration, VOC species concentration, NOx concentration, temperature, relative humidity, wind speed, and photolysis rate parameters.

[0068] S102. Perform source analysis on the data processed by S101 based on the positive factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources.

[0069] Specifically, the preprocessed data were analyzed for source identification using the positive factor matrix method. The number of pollution source factors was set to 5-8, and the optimal number of factors was determined using the Q-value inflection point method. The Q-value is a measure of the goodness of fit of the model. By plotting the Q-value against the number of factors, the optimal number of factors corresponding to the inflection point of the curve was found to balance the accuracy and complexity of the model.

[0070] The Bootstrap resampling method was used to verify the stability of factor contributions, with no fewer than 500 iterations. The Bootstrap resampling method is a method of extracting subsamples from the original sample with replacement and evaluating the stability of statistics through repeated sampling and analysis. Based on the indicator species correlation analysis, the pollution source type corresponding to each factor was determined, including but not limited to motor vehicle sources, industrial sources, and secondary generation sources. Indicator species refer to substances that are representative of emissions from specific pollution sources. The pollution source type is determined by analyzing their correlation with various factors.

[0071] S103, converting the source contribution matrix into a molecular concentration input file that can be recognized by a box model.

[0072] Specifically, the source contribution matrix was converted into a molecular concentration input file that can be recognized by the box model through an R language script. The source contribution Excel file output by PMF was read and the data starting row containing the "Factor Contributions" field was located. The concentration unit was changed from μg / m 3 Convert to molecules / cm 3 , the conversion formula is: molecular concentration = (observed concentration / 30.03) × 6.022 × 10 2 3 / (1×10 6×24.45), where 30.03 is the molecular weight of formaldehyde (HCHO). Generate a text file that meets OBM input requirements, including a timestamp sequence in seconds and a sequence of molecular concentrations for each pollution source. Set up rules for handling abnormal data. When negative or zero values ​​are detected, replace them with a two-hour sliding average. Missing data after conversion is marked with a -999 placeholder.

[0073] S104. Embed the free radical cycle mechanism in the box model and perform photochemical simulation under different pollution source scenarios.

[0074] Specifically, a free radical cycle mechanism was embedded in the box model to perform photochemical simulations under different pollution source scenarios. A baseline scenario was set, where the actual observed total formaldehyde concentration was input and the contributions of different photochemical pathways were analyzed. A control scenario was set, where the formaldehyde concentrations of each pollution source were input in proportion to the PMF source analysis results. The MCM chemical mechanism simulation was then performed to calculate the ROx free radical concentration and ozone generation rate.

[0075] S105. Generate a pollution source contribution heat map based on the simulation results, and output control recommendations through a deep learning model.

[0076] Specifically, the deep learning model can be Deepseek, ChatGPT, etc.

[0077] In summary, from real-time data acquisition and preprocessing in S101, to source apportionment in S102, to photochemical simulation in S104, and finally to output of results in S105, a complete analytical chain of "pollution source apportionment - photochemical mechanism simulation - quantification of ozone generation impact" has been established. Unlike traditional receptor models (such as PMF), which can only analyze the contribution ratio of pollution sources but cannot quantify the impact of photochemical reactions, this approach, by coupling the receptor model (PMF) with the box-type model (OBM), not only identifies different formaldehyde pollution sources (such as motor vehicles, industrial sources, and secondary sources), but also provides in-depth analysis of the impact of each source on ozone generation through photochemical reactions. Outlier labeling and data normalization of the collected data in S101, and a dedicated R language script in S103 converts the source contribution matrix into a molecular concentration input file that can be recognized by the box-type model, automating and standardizing data processing. This avoids the tedious manual data cleaning and format conversion processes and potential errors associated with traditional methods, significantly improving data processing efficiency. Furthermore, rules for handling abnormal data during the data conversion process ensure data accuracy. S104 embeds a free radical cycle mechanism in the box model and performs photochemical simulations under different pollution source scenarios. By setting the baseline scenario and the control scenario, and combining the MCM chemical mechanism simulation to calculate the ROx radical concentration and ozone generation rate, it can more realistically reflect the complex photochemical reaction process in the atmosphere. S105 generates a pollution source contribution heat map based on the simulation results, which intuitively shows the contribution of different pollution sources to ozone generation in different time periods and spaces. Through deep learning models (such as Deepseek, ChatGPT, etc.), combined with multiple factors such as source contribution ratio, precursor RIR value, meteorological parameter time series, etc., targeted control recommendations are output. Compared with traditional methods, this visual and intelligent decision support method can enable decision makers to understand the pollution situation more quickly and accurately.

[0078] In one embodiment, the concentration of formaldehyde and other pollutants in urban atmosphere is collected in real time by online monitoring equipment, and abnormal value marking and data standardization are performed, including:

[0079] Collect the concentration of formaldehyde, volatile organic compounds, nitrogen oxides, ozone, carbon monoxide, PM2.5, meteorological parameters and photolysis rate J(N02) in urban atmosphere;

[0080] Abnormal periods are marked based on meteorological parameters and instrument parameters, and outliers are identified using a sliding window method; the instrument parameters include humidity anomalies and flow anomalies;

[0081] Unify the timestamps of different instrument parameters to a common time zone;

[0082] Linear interpolation is used to fill in data segments where the missing rate is greater than a preset value;

[0083] A structured database including an hourly mean matrix is ​​generated; the fields of the mean matrix include: timestamp, formaldehyde, species of volatile organic compounds, nitrogen oxides, and meteorological parameters.

[0084] Specifically, online monitoring equipment is used to collect real-time data on a variety of key pollutants and meteorological parameters in the urban atmospheric environment.

[0085] Formaldehyde (HCHO) is a key precursor to ozone formation. Its concentration changes in the atmosphere have an important impact on the process of photochemical reactions and is one of the key monitoring indicators.

[0086] Volatile organic compounds (VOCs) include various components such as isoprene, lower alkanes, and aromatic hydrocarbons. They participate in complex photochemical reactions in the atmosphere and are closely related to the formation of ozone and secondary organic aerosols. Different VOC components play different roles in these reactions, necessitating precise measurement of their concentrations.

[0087] Nitrogen oxides (NOx) usually refer to nitric oxide (NO) and nitrogen dioxide (NO2). In the atmosphere, NOx is not only an important precursor to ozone formation, but also participates in a variety of complex photochemical reactions. Its concentration range is generally 5-45ppb.

[0088] Ozone (O3) is one of the important indicators of air pollution. High concentrations of ozone can cause harm to human health and the ecological environment.

[0089] Carbon monoxide (CO) is a colorless, odorless, toxic gas primarily derived from the incomplete combustion of carbon-containing substances. Its concentration in the atmosphere reflects the activity of combustion sources, and it also participates in some photochemical reactions, affecting the chemical balance of the atmosphere.

[0090] PM2.5 refers to particulate matter with an aerodynamic equivalent diameter of 2.5 microns or less, also known as fine particulate matter. It contains a large number of toxic and harmful substances, remains in the atmosphere for a long time, and travels long distances, significantly impacting air quality and human health.

[0091] Meteorological conditions significantly influence the diffusion, transport, and chemical reactions of atmospheric pollutants. Meteorological parameters collected include temperature, relative humidity, and wind speed. Temperature and relative humidity influence the physical and chemical properties and reaction rates of pollutants; wind speed determines the diffusion rate and transport direction of pollutants; and the photolysis rate (J(NO2)) is an important indicator of the intensity of photochemical reactions. It reflects the rate at which NO2 decomposes under sunlight to produce free radicals. Free radicals play a key role in initiating and transmitting atmospheric photochemical reactions.

[0092] Meteorological parameters and the instrument's own operating status parameters (i.e., instrument parameters) serve as important indicators for determining data anomalies. A humidity measurement exceeding 100% could indicate a malfunctioning humidity sensor or interference from the monitoring environment, leading to data anomalies. Abnormal flow rates could also indicate a problem with the instrument's gas sampling system, impacting the authenticity and reliability of the collected data. Once these anomalies are detected, the corresponding time period is marked as abnormal, allowing for subsequent special processing or exclusion of data from these periods.

[0093] The sliding window method is a commonly used data processing technique for identifying outliers in time series data. The principle is to set a fixed-length window on the data sequence and slide the window over it. Each time the window is moved, statistical characteristics (such as the mean and standard deviation) of the data within the window are calculated. If a data point within the window differs significantly from these statistical characteristics (for example, by more than a certain multiple of the standard deviation), the data point is considered an outlier.

[0094] Different monitoring instruments may have inconsistent timestamps on their collected data due to differences in clock settings, data recording methods, and other factors. To facilitate subsequent data integration and analysis, it is necessary to unify all data timestamps to the same time zone, typically UTC+8 (GMT+8). This ensures consistency in the temporal dimension of data collected by different instruments, enabling accurate time series analysis and model calculations.

[0095] During the data acquisition process, data may be missing due to instrument failure, signal transmission problems, and other reasons. For data segments with a missing rate less than a preset value (generally set to 5%), linear interpolation is used to fill the gaps. Linear interpolation is a simple and commonly used interpolation method that assumes that the data changes linearly between two known data points. For example, for two adjacent known data points A and B in a time series, there is a missing data point C between them. The value of C is calculated by linear interpolation as: C = A + (B-A) * (tC-tA) / (tB-tA), where tA, tB, and tC are the times corresponding to data points A, B, and C, respectively. In this way, missing data can be restored to a certain extent and data continuity can be guaranteed.

[0096] After the aforementioned processing, all data is organized into a structured database containing an hourly mean matrix. This mean matrix includes fields such as timestamp, formaldehyde concentration, concentrations of various volatile organic compound species, nitrogen oxide concentrations, and various meteorological parameters. This structured storage facilitates data management, querying, and analysis, providing an efficient data access interface for subsequent source analysis and model simulations based on this data. For example, when performing source analysis, the required pollutant concentrations and meteorological data can be directly extracted from the database and calculated and analyzed using the corresponding algorithms. For model simulations, the organized data can also be easily input into the model.

[0097] In one embodiment, the data processed by S101 is subjected to source analysis based on the positive factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources, including:

[0098] Set the number of pollution source factors and determine the optimal number of factors;

[0099] The Bootstrap resampling method was used to verify the stability of factor contribution, with the number of iterations not less than 500;

[0100] Input data matrix containing hourly concentrations of formaldehyde and other pollutants and collinear pollutants;

[0101] Based on the correlation analysis of indicator species, the pollution source type corresponding to each factor is determined. The pollution source types include: motor vehicle source, industrial source, and secondary generation source.

[0102] Specifically, when using the positive definite factor matrix method for source apportionment, the first step is to set the number of pollution source factors. Considering the complexity and diversity of urban air pollution sources, the initial range of pollution source factors is set, usually 5-8.

[0103] The Q value is a key indicator for measuring the goodness of fit of the positive factor matrix model, which reflects the degree of difference between the model calculated value and the actual observed value. In order to ensure the reliability of the source apportionment results, the Bootstrap resampling method is used to verify the stability of the factor contribution.

[0104] When performing source apportionment, the input data is a matrix of hourly concentration data for formaldehyde (HCHO) and other pollutants, as well as collinear pollutants, processed through S101. After obtaining the factor results for source apportionment, it is necessary to determine the actual pollution source type corresponding to each factor. This invention primarily focuses on motor vehicle sources, industrial sources, and secondary sources. This goal is achieved through correlation analysis of indicator species. Indicator species are substances that are representative of emissions from specific pollution sources. Their presence and concentration changes can indicate the contribution of a specific pollution source. For example, certain volatile organic compounds (VOCs) can serve as indicator species for motor vehicle emissions. Motor vehicle exhaust emissions contain a variety of unique VOC components, such as aromatic hydrocarbons like benzene, toluene, and xylene, as well as certain specific alkanes and alkenes. By analyzing the correlation between these indicator species and the various factors derived from source apportionment, it is possible to determine which factors are associated with motor vehicle sources. If a factor has a high correlation with an indicator species for motor vehicle emissions, it can be preliminarily determined that the factor represents the contribution of motor vehicle sources to pollutants such as formaldehyde. For industrial sources, different industrial production processes emit specific pollutants. Some sulfur-containing compounds, chlorinated hydrocarbons, etc. may be characteristic pollutants of certain industrial combustion sources or chemical production processes. By analyzing the correlation between these characteristic pollutants and factors, factors representing industrial sources can be identified. Secondary generation sources are more complex. They are generated by primary pollutants in the atmosphere through photochemical reactions, oxidation reactions and other processes. Some secondary generation intermediates or end products, such as peroxyacetyl nitrate (PAN) and secondary organic aerosols, can serve as indicator species of secondary generation sources. By studying the correlation between these indicator species and factors, combined with atmospheric chemistry principles and photochemical reaction mechanisms, factors representing secondary generation sources are determined. Through this method based on correlation analysis of indicator species, the factors obtained from source apportionment can be more accurately matched with the actual pollution source types, and the contribution of different pollution sources to pollutants such as formaldehyde can be clarified.

[0105] In one embodiment, determining the optimal number of factors includes:

[0106] Initialize the range of factors and set the value range of pollution source factors to 5 to 8;

[0107] For each factor number within a set range, a corresponding source apportionment model is constructed using the positive factor matrix method; the source apportionment model decomposes the observed pollutant concentration data into the contributions of different factors based on the characteristics of the monitoring data; each factor represents a potential pollution source;

[0108] After constructing the source resolution model corresponding to each factor number, an iterative algorithm is used to solve the model; during the iteration process, the parameters of the source resolution model are continuously adjusted until the preset convergence conditions are met;

[0109] Calculate the Q value for each solved source apportionment model;

[0110] Arrange the calculated Q values ​​corresponding to different numbers of factors into data pairs and use the drawing tool to draw a curve of Q value changing with the number of factors;

[0111] Analyze the changing trend of Q value as the number of factors increases and find the inflection point of the Q value change rate on the curve;

[0112] The number of factors corresponding to the inflection point determined in the curve is taken as the optimal number of factors.

[0113] Specifically, after constructing the source apportionment model corresponding to each factor number, an iterative algorithm needs to be used to solve the model. An iterative algorithm is a method that gradually approaches the optimal solution through repeated calculations. In the process of solving this model, starting from an initial parameter estimate, the parameters of the source apportionment model, such as factor contribution coefficients and error terms, are continuously adjusted. In each iteration, the output of the model is calculated based on the current parameter values ​​and compared with the actual observed data. The parameters are adjusted based on the difference between the two. This adjustment process will continue until the preset convergence conditions are met. The preset convergence conditions are usually set based on experience and actual needs. For example, it can be set that when the error between the model prediction value and the actual observation value is less than a specific threshold, or the rate of change of the error is less than a certain value, the model is considered to have reached convergence. When the convergence conditions are met, it means that the model has found a relatively optimal set of parameters that can better fit the actual observed data.

[0114] For each solved source apportionment model, its Q value is calculated. The Q value is an important indicator to measure the goodness of model fit, which reflects the degree of difference between the model prediction value and the actual observation value. The specific calculation method is to sum the squares of the residuals between the model prediction value and the actual observation value (that is, the difference between the prediction value and the observation value). The smaller the Q value, the closer the model prediction value is to the actual observation value, and the better the model fits the data; conversely, the larger the Q value, the worse the model fits the data. By calculating the Q value, the model fitting effect under different numbers of factors can be quantitatively compared, providing an objective basis for selecting the optimal number of factors.

[0115] The calculated Q values ​​corresponding to different numbers of factors are organized into data pairs (number of factors, Q values). Then, using a plotting tool, a curve is plotted showing how the Q value changes with the number of factors. When plotting the curve, the horizontal axis represents the number of factors, and the vertical axis represents the corresponding Q value. This intuitive curve display clearly shows how the Q value changes as the number of factors increases. Common plotting tools such as the matplotlib library in Python and the ggplot2 package in R can achieve this function. The purpose of plotting the curve is to more intuitively analyze the relationship between the Q value and the number of factors, making it easier to find the optimal number of factors.

[0116] Analyze the Q-value trend as the number of factors increases, and identify the inflection point on the curve where the Q-value changes. Generally, as the number of factors increases, the model's ability to fit the data improves, and the Q-value gradually decreases. However, after the number of factors reaches a certain level, the improvement in model fit with further increases gradually diminishes, and the rate of decrease in the Q-value slows. The inflection point is the point where the rate of change of the Q-value changes significantly, marking the transition from a significant improvement in model fit with increasing the number of factors to a minimal improvement. The inflection point can be identified by calculating the first or second derivative of the curve. The first derivative reflects the rate of change of the Q-value with the number of factors, while the second derivative reflects the change in the rate of change. When the second derivative changes significantly, the corresponding point is the inflection point. The number of factors corresponding to the inflection point identified on the curve is considered the optimal number of factors. The source apportionment model constructed with this number of factors fully reflects the actual pollution sources while ensuring model stability and interpretability. Using the optimal number of factors for subsequent source analysis can produce a more accurate and reliable pollution source contribution matrix.

[0117] In one embodiment, a free radical cycle mechanism is embedded in a box model to perform photochemical simulations under different pollution source scenarios, including:

[0118] Determine the types of free radicals related to formaldehyde and ozone generation based on atmospheric photochemical principles;

[0119] Construct a free radical cycle reaction equation based on the reaction characteristics of free radicals and atmospheric chemical processes; set initial values ​​for various parameters in the free radical reaction equation;

[0120] According to the pollution source contribution matrix, the pollution sources are divided into motor vehicle sources, industrial sources and secondary generation sources, and

[0121] Design a variety of pollution source scenarios; different pollution source scenarios include: considering only motor vehicle emissions, considering only industrial emissions, considering a combination of motor vehicle and industrial emissions, and considering emissions from all pollution sources but simulating emission reductions for a specific pollution source; each scenario corresponds to different initial pollutant concentrations and emission conditions;

[0122] For each pollution source scenario, the initial conditions for the box model were set based on the HCHO concentrations from different sources and monitoring data analyzed by the positive factor matrix method;

[0123] The photochemical simulation is started in the box model and the simulation process is gradually advanced according to the set time step. During the simulation, the free radical cycle mechanism embedded in the box model and the set reaction equations calculate the concentration changes of various pollutants and free radicals, as well as the generation and consumption of ozone.

[0124] The pollutant concentration, free radical concentration, and ozone generation at each time step during the simulation are recorded and the results are output.

[0125] Specifically, based on the principles of atmospheric photochemistry, the main free radical species related to the formation of formaldehyde and ozone were identified. In the atmospheric environment, free radicals such as hydroxyl radicals and peroxyl radicals play a crucial role in the photochemical reaction process.

[0126] Based on the reaction characteristics of free radicals and atmospheric chemical processes, detailed free radical cycle reaction equations are constructed. These reaction equations describe the chemical reaction processes between free radicals and other pollutants. After constructing the reaction equations, initial values ​​are set for the various parameters in the equations (such as reaction rate constants, quantum yields, etc.). Based on the pollution source contribution matrix obtained through the positive factor matrix method, pollution sources are divided into motor vehicle sources, industrial sources, and secondary generation sources.

[0127] To comprehensively analyze the impact of different pollution sources on ozone production, various pollution source scenarios were designed. Considering only motor vehicle emissions: In this scenario, it is assumed that atmospheric pollutants originate solely from motor vehicle emissions. Through simulation, the contribution of motor vehicle emissions to formaldehyde conversion and ozone production can be individually assessed, understanding the direct impact of motor vehicle exhaust emissions on the atmospheric environment. In some implementations, pollution sources can also be categorized into six types of HCHO sources: motor vehicle sources, industrial sources, secondary generation sources, combustion sources, solvent use sources, and plant emissions sources.

[0128] Only industrial source emissions are considered: eliminating interference from other pollution sources, focusing on studying the photochemical behavior of pollutants emitted by industrial sources in the atmosphere and their effect on ozone production, providing targeted basis for industrial pollution control.

[0129] Considering the combined emissions from motor vehicle and industrial sources: simulating the combined effects of these two major pollution sources in the actual atmospheric environment, analyzing their mutual influence and synergistic effect on ozone generation, which is closer to the actual atmospheric pollution situation.

[0130] Consider emissions from all pollution sources but simulate emission reduction for a certain pollution source: On the basis of all pollution sources including motor vehicle sources, industrial sources, secondary generation sources, etc., set emission reduction operations for a certain pollution source (such as motor vehicle sources or industrial sources), and evaluate the effectiveness of emission reduction measures by simulating and comparing the changes in ozone generation and pollutant concentrations before and after emission reduction.

[0131] Each scenario corresponds to different initial pollutant concentrations and emission conditions to accurately simulate atmospheric photochemical processes under various conditions.

[0132] For each designed pollution source scenario, initial conditions are set for the box model based on actual monitoring data and pollution source emission characteristics. The initial conditions mainly include the initial concentrations of various pollutants and free radicals, and meteorological conditions. Based on the atmospheric pollutant concentration data obtained from actual monitoring and combined with the emission characteristics of the pollution sources, the initial concentrations of each pollutant and free radical in the atmosphere at the beginning of the simulation are determined. These initial concentrations are the starting point of the simulation, and their accuracy directly affects the reliability of the simulation results. Meteorological conditions have a significant impact on atmospheric photochemical reactions. Meteorological parameters such as temperature, relative humidity, wind speed, and light intensity need to be set at the beginning of the simulation. Temperature affects the rate of chemical reactions, light intensity determines the intensity of photochemical reactions, and wind speed and relative humidity affect the diffusion and transmission of pollutants.

[0133] Start the photochemical simulation in the box model and gradually advance the simulation process according to the set time step (such as 1 minute, 5 minutes, etc.). During the simulation process, the box model calculates the concentration changes of various pollutants and free radicals, as well as the generation and consumption of ozone in real time based on the embedded free radical cycle mechanism and the set reaction equation. As the simulation time goes by, the model continuously updates the concentration data of each substance. Record key data such as pollutant concentration, free radical concentration, ozone generation, etc. at each time step during the simulation process. Output these data, and the output format can use common data file formats such as CSV, TXT, etc., so that the simulation results can be analyzed and processed later.

[0134] In one embodiment, the Bootstrap resampling method is used to verify the stability of factor contributions, including:

[0135] Extract the original sample data containing the contribution value of each pollution source factor from the source apportionment data processed by the positive factor matrix method;

[0136] The number of resampling iterations is set to no less than 500; in each iteration, subsamples with the same number of original samples are randomly selected from the original sample data with replacement;

[0137] For each subsample obtained by resampling, factor analysis is performed using the positive factor matrix method to recalculate the contribution value of each pollution source factor;

[0138] The factor contribution results obtained after each resampling are recorded to form a data set containing multiple iteration results;

[0139] Conduct statistical analysis on the recorded multiple iteration results and calculate the statistical value of the contribution value of each pollution source factor.

[0140] The stability of the contribution of each pollution source factor is judged based on the calculated statistics.

[0141] In one embodiment, generating a pollution source contribution heat map based on simulation results includes:

[0142] Construct a time correlation matrix with the matrix dimension of [pollution source type × time period];

[0143] Calculate the influence weight of each matrix unit based on the change in ozone generation rate:

[0144] The HSV color space is used to map the weight values ​​and generate a dynamic visualization heat map.

[0145] Reference Figure 2 The second embodiment of the present invention provides a method system for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model, including:

[0146] The first processing module is used to collect the concentration of formaldehyde and other pollutants in the urban atmosphere in real time through online monitoring equipment and perform abnormal value marking and data standardization processing;

[0147] The second processing module is used to perform source analysis on the data processed by the first processing module based on the positive definite factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources;

[0148] A third processing module is configured to convert the source contribution matrix into a molecular concentration input file recognizable by a box model;

[0149] The fourth processing module is used to: embed the free radical cycle mechanism in the box model and perform photochemical simulations under different pollution source scenarios;

[0150] The fifth processing module is used to generate a pollution source contribution heat map based on the simulation results and output control recommendations through a deep learning model.

[0151] It should be noted that the formaldehyde pollution source analysis and ozone impact quantification method system of a coupled receptor model and a box-type model provided in an embodiment of the present invention is used to execute all the process steps of the formaldehyde pollution source analysis and ozone impact quantification method of a coupled receptor model and a box-type model in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0152] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model, characterized in that: include: S101. Using online monitoring equipment, collect the concentrations of formaldehyde and other pollutants in the urban atmosphere in real time and perform outlier marking and data standardization. S102. Perform source analysis on the data processed in S101 based on the positive factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources; S103, converting the source contribution matrix into a molecular concentration input file recognizable by a box model; S104. Embed the free radical cycle mechanism in the box model to perform photochemical simulation under different pollution source scenarios; S105. Generate a pollution source contribution heat map based on the simulation results, and output control recommendations through a deep learning model.

2. The method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model according to claim 1, characterized in that: Online monitoring equipment is used to collect the concentration of formaldehyde and other pollutants in urban atmosphere in real time, and perform outlier marking and data standardization, including: Collect the concentration of formaldehyde, volatile organic compounds, nitrogen oxides, ozone, carbon monoxide, PM2.5, meteorological parameters and photolysis rate J(NO2) in urban atmosphere; Abnormal periods are marked based on meteorological parameters and instrument parameters, and outliers are identified using a sliding window method; the instrument parameters include humidity anomalies and flow anomalies; Unify the timestamps of different instrument parameters to a common time zone; Linear interpolation is used to fill in data segments where the missing rate is greater than a preset value; A structured database including an hourly mean matrix is ​​generated; the fields of the mean matrix include: timestamp, formaldehyde, species of volatile organic compounds, nitrogen oxides, and meteorological parameters.

3. The method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model according to claim 2, characterized in that: The data processed by S101 are analyzed based on the positive factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources, including: Set the number of pollution source factors and determine the optimal number of factors; The Bootstrap resampling method was used to verify the stability of factor contribution, with the number of iterations not less than 500; Input data matrix containing hourly concentrations of formaldehyde and other pollutants and collinear pollutants; Based on the correlation analysis of indicator species, the pollution source type corresponding to each factor is determined. The pollution source types include: motor vehicle source, industrial source, and secondary generation source.

4. The method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model according to claim 3, characterized in that: Determine the optimal number of factors, including: Initialize the range of factors and set the value range of pollution source factors to 5 to 8; For each factor number within a set range, a corresponding source apportionment model is constructed using the positive factor matrix method; the source apportionment model decomposes the observed pollutant concentration data into the contributions of different factors based on the characteristics of the monitoring data; each factor represents a potential pollution source; After constructing the source resolution model corresponding to each factor number, an iterative algorithm is used to solve the model; during the iteration process, the parameters of the source resolution model are continuously adjusted until the preset convergence conditions are met; Calculate the Q value for each solved source apportionment model; Arrange the calculated Q values ​​corresponding to different numbers of factors into data pairs and use the drawing tool to draw a curve of Q value changing with the number of factors; Analyze the changing trend of Q value as the number of factors increases and find the inflection point of the Q value change rate on the curve; The number of factors corresponding to the inflection point determined in the curve is taken as the optimal number of factors.

5. The method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model according to claim 4, characterized in that: Embed free radical cycle mechanisms in box models to perform photochemical simulations under different pollution source scenarios, including: Determine the types of free radicals related to formaldehyde and ozone generation based on atmospheric photochemical principles; Construct a free radical cycle reaction equation based on the reaction characteristics of free radicals and atmospheric chemical processes; set initial values ​​for various parameters in the free radical reaction equation; Based on the pollution source contribution matrix, pollution sources are divided into motor vehicle sources, industrial sources, and secondary generation sources, and a variety of pollution source scenarios are designed. These include: considering only motor vehicle emissions, only industrial source emissions, considering a combination of motor vehicle and industrial sources, and considering emissions from all pollution sources but simulating emission reductions for a specific source. Each scenario corresponds to a different initial pollutant concentration. For each pollution source scenario, the initial conditions for the box model were set based on the HCHO concentrations from different sources and monitoring data analyzed by the positive factor matrix method; The photochemical simulation is started in the box model and the simulation process is gradually advanced according to the set time step. During the simulation, the free radical cycle mechanism embedded in the box model and the set reaction equations calculate the concentration changes of various pollutants and free radicals, as well as the generation and consumption of ozone. The pollutant concentration, free radical concentration, and ozone generation at each time step during the simulation are recorded and the results are output.

6. The method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model according to claim 5, characterized in that: The Bootstrap resampling method is used to verify the stability of factor contributions, including: Extract the original sample data containing the contribution value of each pollution source factor from the source apportionment data processed by the positive factor matrix method; The number of resampling iterations is set to no less than 500; in each iteration, subsamples with the same number of original samples are randomly selected from the original sample data with replacement; For each subsample obtained by resampling, factor analysis is performed using the positive factor matrix method to recalculate the contribution value of each pollution source factor; The factor contribution results obtained after each resampling are recorded to form a data set containing multiple iteration results; Perform statistical analysis on the recorded results of multiple iterations, calculate the statistical quantity of the contribution value of each pollution source factor, and judge the stability of the contribution of each pollution source factor based on the calculated statistical quantity.

7. The method for formaldehyde pollution source analysis and ozone impact quantification using a coupled receptor model and a box model according to claim 1, characterized in that: Generate pollution source contribution heat maps based on simulation results, including: Construct a time correlation matrix with the matrix dimension of [pollution source type × time period]; Calculate the influence weight of each matrix unit based on the change in ozone generation rate: The HSV color space is used to map the weight values ​​and generate a dynamic visualization heat map.

8. A method system for formaldehyde pollution source analysis and ozone impact quantification based on a coupled receptor model and a box model, characterized in that: include: The first processing module is used to collect the concentration of formaldehyde and other pollutants in the urban atmosphere in real time through online monitoring equipment and perform abnormal value marking and data standardization processing; The second processing module is used to perform source analysis on the data processed by the first processing module based on the positive definite factor matrix method to generate a pollution source contribution matrix including motor vehicle sources, industrial sources, and secondary generation sources; A third processing module is configured to convert the source contribution matrix into a molecular concentration input file recognizable by a box model; The fourth processing module is used to: embed the free radical cycle mechanism in the box model and perform photochemical simulations under different pollution source scenarios; The fifth processing module is used to generate a pollution source contribution heat map based on the simulation results and output control recommendations through a deep learning model.

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