Method for generating park component grade vocs dynamic emission inventory based on ambient temperature driving

Abstract

The application provides a park component grading VOCs dynamic emission inventory generation method based on ambient temperature driving. The concentration data of each VOCs component actually measured is decoupled from abnormal production condition factors, photochemical consumption factors and physical diffusion factors, a characteristic value R ij representing only the concentration size change with temperature is constructed; by normalizing the characteristic value of each VOCs component, combining the park VOCs baseline emission total data E all_base , the baseline emission amount data E i_base of each VOCs component is obtained. Thus, an emission inventory containing the individual emission amount of each VOCs component at different temperatures is constructed.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of air pollution control and environmental information technology, and in particular to a method for generating a dynamic VOCs emission inventory of industrial parks based on ambient temperature. Background Technology

[0002] VOCs are fine particulate matter (PM2.5) 2.5 VOCs are important precursors to secondary pollutants such as nitrogen oxides (NOx) and ozone (O3), and some VOC species are also toxic, seriously threatening the ecological environment and human health. Therefore, VOC control is an important task in air pollution prevention and control, and accurate identification and quantification of VOC sources are key to achieving precise control. However, existing VOC monitoring methods cannot dynamically obtain emission inventories containing the emissions of each VOC component under different ambient temperatures. Specifically:

[0003] Firstly, traditional industrial VOCs inventories primarily employ material balance and coefficient methods. The material balance method calculates VOCs emissions based on the formula "Emissions = Total Material - Product Content - Recycling / Treatment Amount." However, since enterprise material statistics are typically managed through accounting (e.g., monthly or quarterly inventory checks), the total material data suffers from significant lag and inaccuracy, making it impossible to obtain accurate real-time data using this method. Similarly, the coefficient method, based on the formula "Emissions = Activity Level × Factor," relies on periodic statistical reports for activity levels (e.g., raw material usage, product output, or fuel consumption), making real-time and granular updates difficult. Furthermore, this method often uses fixed factors, ignoring the real-time impact of meteorological factors (e.g., temperature) and enterprise operating conditions (e.g., start-up, shutdown, maintenance, anomalies) on intensity. Therefore, even though some existing technologies attempt to introduce easily quantifiable production parameters (e.g., electricity consumption) to characterize activity levels, the lack of an effective correction mechanism for temperature changes leads to significant discrepancies between the calculated results and actual fluctuations, failing to meet the needs of refined management.

[0004] Secondly, existing technologies struggle to accurately calculate the independent emissions of each VOC component while considering the effects of temperature. In component analysis, traditional methods often focus only on the total VOC amount, employing simplified merging methods or using fixed source component spectra to proportionally estimate the amounts of each component. However, the physical and chemical properties of different VOC components vary significantly, and temperature changes can cause nonlinear alterations in the volatilization rates of different components, leading to dynamic changes in the actual component ratios and consequently affecting their atmospheric chemical behavior. The severe lack of component-level "temperature-dependent factors" prevents models from characterizing temperature-driven changes in different VOC components, resulting in uncertainties in assessing environmental effects such as atmospheric chemical reactivity (especially ozone formation potential).

[0005] Furthermore, the inversion calculation method based on environmental concentration relies on the formula "monitored concentration C = source strength Q × atmospheric diffusion factor D". It uses known monitoring point concentrations and meteorological diffusion conditions to infer the source strength Q and then calculates emissions based on the source strength. Since changes in monitored concentrations are the result of both changes in emissions (affected by temperature) and changes in atmospheric diffusion conditions (affected by wind speed and atmospheric stability), this method struggles to independently isolate the driving effect of temperature on emissions from the complex atmospheric diffusion background. This makes it impossible to construct an accurate representation of the response relationship between relative emissions and temperature changes, and consequently, it is difficult to achieve precise dynamic prediction of absolute emissions at various times based on baseline emissions. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a method for generating a dynamic VOCs emission inventory of industrial parks based on ambient temperature, in order to solve the aforementioned problems.

[0007] The first aspect of this application provides a method for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature, comprising: acquiring measured concentration data of each VOCs component and its corresponding ambient temperature data at an environmental monitoring point within the target industrial park, abnormal production condition information data within the industrial park, and baseline total VOCs emission data E of the industrial park under a preset temperature range. all_base The concentration data of each VOC component and the ambient temperature data are collected at preset time intervals. Based on the abnormal production condition information data, abnormal data of the concentration of each VOC component and the ambient temperature in the corresponding time period are removed, and the data is aggregated to obtain the VOC component concentration data C after cleaning. ij and its corresponding ambient temperature data T ij Where i is the index identifier of different VOCs components, and j is the index identifier of different ambient temperatures; the concentration data C of each VOCs component after cleaning ijPhotochemical depletion correction and physical diffusion correction were performed sequentially to obtain the characteristic values ​​R corresponding to the concentrations of each VOC component after decoupling from non-temperature-affected factors. ij ; Construct a system based on temperature data T ij As the independent variable, with the eigenvalue R ij For a multiple regression model with the dependent variable, an eigenvalue-temperature response function f is established for each VOC component. i (T); based on the characteristic value R of each VOCs component ij Determine the mass concentration percentage of each VOCs component within a preset temperature range; based on the VOCs baseline emission data E all_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base The baseline emission data E for each VOC component i_base Substitute it into its corresponding response function f i (T) is used to calculate the dynamic emissions of each VOC component at any target temperature and generate a dynamic VOC emission inventory at the component level of the park.

[0008] In one embodiment of the first aspect of this application, the photochemical depletion correction method includes: selecting a pair of VOCs components from the same VOCs cluster and having different hydroxyl radical reaction rate constants; calculating the photochemical age τ of the VOCs cluster based on the measured concentration data of the two VOCs components and in conjunction with the photochemical age calculation formula; and calculating the photochemical age τ based on the photochemical age τ and the hydroxyl radical reaction rate constant k of each VOCs component in the VOCs cluster. i The concentration data of VOCs components after cleaning, C ij Photochemical depletion compensation calculations were performed to obtain the initial concentration data C' of each VOC component after compensation. ij .

[0009] In one embodiment of the first aspect of this application, the physical diffusion correction method includes: selecting a VOCs component whose emission is insensitive to changes in ambient temperature as a reference VOCs component; calculating the emission levels of each other VOCs component relative to the reference VOCs component at each ambient temperature T. ij Initial concentration data C' ij The ratio of these values ​​is used as the characteristic value R of the concentration of this VOC component. ij .

[0010] In one embodiment of the first aspect of this application, the reference VOCs component is acetylene or carbon monoxide.

[0011] In one embodiment of the first aspect of this application, the step of basing the VOCs component on the characteristic value R ijThe method for determining the mass concentration percentage of each VOCs component includes: calculating the characteristic value R of each VOCs component at each temperature within a preset temperature range. ij mean The mean values ​​of each VOC component Normalization was performed to obtain the mass concentration percentage of each VOC component.

[0012] In one embodiment of the first aspect of this application, the step of using the VOCs baseline total emission data E all_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base The method includes: multiplying the mass concentration percentage of the component by the baseline total VOC emissions data E. all_base To obtain the baseline emission data E corresponding to this VOCs component. i_base .

[0013] The second aspect of this application provides a system for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature, comprising: a data acquisition module for acquiring measured concentration data of each VOCs component at an environmental monitoring point within the target industrial park and its corresponding ambient temperature data, abnormal production conditions within the industrial park, and baseline total VOCs emission data E of the industrial park within a preset temperature range. all_base The VOCs component concentration data and ambient temperature data are collected at preset time intervals. A data preprocessing module is used to remove abnormal VOCs component concentration and ambient temperature data for the corresponding time period based on the abnormal production condition information data, and to perform data aggregation to obtain the cleaned VOCs component concentration data C. ij and its corresponding ambient temperature data T ij Where i represents the index identifier of different VOCs components, and j represents the index identifier of different ambient temperatures; the data correction module is used to correct the concentration data C of each VOCs component after cleaning. ij Photochemical depletion correction and physical diffusion correction were performed sequentially to obtain the characteristic values ​​R corresponding to the concentrations of each VOC component after decoupling from non-temperature-affected factors. ij The response function construction module is used to construct the response function based on temperature data T. ij As the independent variable, with the eigenvalue R ij For a multiple regression model with the dependent variable, an eigenvalue-temperature response function f is established for each VOC component. i (T); Baseline emission calculation module, used to calculate emissions based on the characteristic values ​​R of each VOCs component. ij Determine the mass concentration percentage of each VOCs component within a preset temperature range; based on the VOCs baseline emission data Eall_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base The emissions inventory generation module is used to generate baseline emission data for each VOCs component. i_base Substitute it into its corresponding response function f i (T) is used to calculate the dynamic emissions of each VOC component at any target temperature and generate a dynamic VOC emission inventory at the component level of the park.

[0014] A third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0015] A fourth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to perform the method described in any of the preceding claims.

[0016] The fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in any of the preceding claims.

[0017] As described above, this application has the following beneficial effects:

[0018] By decoupling the measured concentration data of each VOC component from abnormal production conditions, photochemical consumption, and physical diffusion factors, the characteristic value R, which represents the concentration change with temperature, is obtained after decoupling all non-temperature-affected factors. ij And construct the eigenvalue R ij The response function between concentration and temperature is used to obtain the correlation between concentration and temperature; the characteristic values ​​of each VOCs component are normalized based on a preset temperature range, and combined with the baseline total VOCs emission data E of the industrial park under the preset temperature range. all_base Determine the baseline emission data E for each VOC component within the preset temperature range. i_base For each VOC component, a temperature value within a preset temperature range is selected, and the temperature value is compared with the baseline emission data E. i_base Based on the mapping relationship between the eigenvalue and temperature response function, when a target temperature is input, the corresponding eigenvalue can be found according to the aforementioned eigenvalue-temperature response function. Then, based on the eigenvalue and the aforementioned mapping relationship, the emission amount of the VOC component at that target temperature can be obtained. Thus, an emission inventory containing the individual emissions of each VOC component at different temperatures is generated. Attached Figure Description

[0019] Figure 1 The diagram shown is a flowchart illustrating a method for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature, according to an embodiment of this application.

[0020] Figure 2 The diagram shown illustrates the non-temperature-affecting factors in the measured concentration data and their corresponding decoupling methods in one embodiment of this application.

[0021] Figure 3 The diagram shown is a structural schematic of a system for generating a dynamic VOCs emission inventory of industrial parks based on ambient temperature, according to an embodiment of this application.

[0022] Figure 4 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0023] It should be noted first that:

[0024] 1. The following specific embodiments are merely illustrative of the principles and effects of this disclosure and are not intended to limit this disclosure. Any person skilled in the art can make various modifications, additions, changes, or equivalent substitutions to the above embodiments without departing from the spirit and scope of this disclosure. All technical solutions equivalent to those defined in the claims of this disclosure, or changes that a person skilled in the art can conceive of after reading this disclosure without creative effort, should be covered within the protection scope of this disclosure. The protection scope of this disclosure should be determined by the scope of the claims.

[0025] 2. Where there is no conflict, the various embodiments and features in the embodiments of this disclosure can be combined with each other, and the technical solutions formed by the combination are all considered to be the content of this disclosure. For the sake of brevity, this specification will not exhaustively list all possible combinations, but these combinations are also within the protection scope of this disclosure.

[0026] In this disclosure, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0027] 3. The division of modules (or units) in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of this disclosure can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0028] Furthermore, those skilled in the art will recognize that the various illustrative logic blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0029] 4. In the embodiments of this disclosure, terms such as "zeroth" and "first" are used to distinguish identical or similar items with essentially the same function and effect. For example, "first XX" and "second XX" are merely used to distinguish different XXs and do not limit their order, quantity, or execution sequence. Furthermore, terms such as "zeroth" and "first" do not necessarily imply that they are different.

[0030] In this disclosure, the terms "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this disclosure should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0031] The following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed herein:

[0032] like Figure 1 As shown, the first aspect of this application provides a method for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature, comprising:

[0033] S1: Obtain the measured concentration data of each VOC component and its corresponding ambient temperature data at an environmental monitoring point within the target industrial park, information on abnormal production conditions within the park, and baseline total VOC emissions data for the park within a preset temperature range. all_base The concentration data of each VOC component and the ambient temperature data are collected at preset time intervals.

[0034] The environmental monitoring points refer to specific physical locations within the target industrial park that are scientifically selected and deployed for collecting ambient air samples. These physical locations are typically required to represent the air quality of the entire park or a key area, such as the park boundary, downwind of the prevailing wind, or the central area of ​​multiple pollution sources.

[0035] The measured VOCs component concentration data and corresponding ambient temperature data at environmental monitoring stations refer to the VOCs component concentration data collected at different times within a preset time interval, along with the ambient temperature data at that time. In other words, the VOCs component concentration data and ambient temperature data at the same moment correspond one-to-one. It should be understood that VOCs components are the specific constituent species of volatile organic compounds. Common components in industrial parks include dozens or even hundreds of types such as benzene, toluene, xylene, ethylene, acetylene, propylene, formaldehyde, and ethanol. VOCs component concentration refers to the content of various VOCs components in the ambient air near the environmental monitoring station, as measured at that monitoring station.

[0036] The abnormal production status information data within the industrial park refers to the time information corresponding to abnormal, unplanned, or sudden production activities of enterprises within the park. Examples include the start-up or shutdown of production lines (during which emissions are often unstable and high in concentration), equipment leaks, purification facility failures, unusual increases or decreases in production, and operations involving high-risk VOC emissions such as tank cleaning and pipeline purging. This information is crucial for determining whether there are abnormal fluctuations in the concentration data of each VOC component at a given moment.

[0037] Baseline total VOCs emissions data for the industrial park within a preset temperature range (E) all_base This refers to the total emission of all VOCs components emitted by the industrial park within a preset time interval under a preset temperature range. It should be understood that this data is usually not obtained through actual measurement. For example, it is determined based on material balance methods using data such as the consumption of raw and auxiliary materials, product output, and the recovery of VOCs-containing materials reported by various enterprises within the park. For instance, assuming the park's published total annual emission of all VOCs components within the preset temperature range is 365 tons, and assuming the preset time interval for collecting concentration and temperature data at environmental monitoring points is 1 hour, then the baseline total VOCs emission data E of the industrial park within the preset temperature range is... all_base The calculation is 365 tons ÷ 365 days ÷ 10 hours (assuming VOC emissions occur only for 10 hours per day) = 0.1 tons. Since the total annual emissions of all VOC components within the park under the preset temperature range are usually available, E... all_base It can be obtained by performing simple calculations on the data retrieved from the query.

[0038] The preset temperature range refers to the temperature range corresponding to the available VOCs emission data for the industrial park. It should be understood that because emissions fluctuate with temperature, different temperatures correspond to different emissions, and usually only emission data within a certain preset temperature range can be found.

[0039] Preferably, the preset temperature range is a normal temperature range. It should be understood that the preset temperature range can be determined based on the temperature range corresponding to the total VOC emissions data of each VOC in the park that can be queried. The temperature range corresponding to the total VOC emissions data of each VOC in the park is usually a normal temperature range.

[0040] Preferably, the preset time interval is one hour. It should be understood that industrial production activities (such as equipment start-up and shutdown, shift handover, and batch operations) typically exhibit regular fluctuations on an hourly basis. Collecting VOCs concentration and ambient temperature data hourly (while also including E...) all_base Setting the data to the total VOC emissions per hour can effectively capture VOC concentration fluctuations caused by these operational changes. Furthermore, collecting data hourly better reflects VOC concentration fluctuations caused by temperature changes.

[0041] S2: Based on the abnormal production condition information data, remove the abnormal data on the concentration of each VOC component and the ambient temperature for the corresponding time period, and perform data aggregation to obtain the VOC component concentration data C after cleaning. ij and its corresponding ambient temperature data T ij Where i is the index identifier of different VOCs components, and j is the index identifier of different ambient temperatures.

[0042] Based on the abnormal production condition information data, removing abnormal VOCs component concentrations and ambient temperature data for the corresponding time periods means: from all collected VOCs concentration data and corresponding ambient temperature data, removing the VOCs concentration data and ambient temperature data corresponding to the time when these abnormal conditions occurred. For example, the original VOCs component concentration data and corresponding ambient temperature data measured at an environmental monitoring point are shown in Table 1:

[0043]

[0044] Because a company in the park shut down its equipment for maintenance at 12:00, the concentration of VOCs components measured at the environmental monitoring point was greatly affected by the abnormal production conditions. The values ​​could no longer simply reflect the correlation between the characteristic values ​​to be constructed and the temperature. Therefore, it is necessary to remove the VOCs concentration data (and their corresponding ambient temperature data) corresponding to 12:00.

[0045] Preferably, the ambient temperature data is a temperature value or a temperature range. When VOC component concentration data is statistically analyzed according to temperature ranges, it helps to reduce the impact of random errors caused by single or small-scale measurements on the subsequently constructed feature value-temperature correlation, given a relatively small total data volume. For example, assuming that the ambient temperature data is not binned, in the measured data, only one concentration data point is 20 μg / m³ when the ambient temperature is 20.1℃. 3 However, since this concentration number has a large error caused by non-abnormal production conditions (i.e., not caused by abnormal production conditions), directly using this data will lead to a certain deviation in the subsequent emission calculation. After temperature binning, there are multiple concentration data in the temperature range [20℃, 21℃), so the error caused by non-abnormal conditions can be diluted by averaging or other calculation methods that can obtain representative concentration values ​​at this ambient temperature.

[0046] Data aggregation refers to merging multiple concentration data sets corresponding to the same ambient temperature by means of averaging or other calculation methods that can yield representative concentration values ​​at that ambient temperature. This ensures that for a single VOC component, the same ambient temperature data corresponds to a unique concentration. For example, after removing abnormal concentration and ambient temperature data for each VOC component in the corresponding time period, the remaining data for a VOC component shows a concentration of 20.1 μg / m³ at an ambient temperature of 20°C. 3 20.2 μg / m 3 20.6 μg / m 3 There are three sets of data. After averaging the three sets, the resulting concentration value is 20.3 μg / m³. 3 This ensures that a temperature of 20℃ corresponds to a unique concentration value of 20.3 μg / m³. 3 .

[0047] As can be seen from the above, the VOCs component concentration data C obtained after outlier removal and data aggregation is cleaned. ij This means that the VOCs component corresponding to subscript i has only one unique concentration data C at the temperature corresponding to subscript j. ij .

[0048] S3: Concentration data of each VOC component after cleaning C ij Photochemical depletion correction and physical diffusion correction were performed sequentially to obtain the characteristic values ​​R of each VOC component after decoupling from non-temperature-affected factors. ij .

[0049] like Figure 2As shown, the non-temperature-affecting factors include photochemical consumption factors, physical diffusion factors, and the aforementioned abnormal production condition factors. After outlier removal based on abnormal production condition information data, and after photochemical consumption correction and physical diffusion correction, the resulting characteristic value R, correlated with the concentration of each VOC component, is... ij It is only related to temperature.

[0050] In one embodiment of the first aspect of this application, the photochemical depletion correction method includes: selecting a pair of VOCs components from the same VOCs cluster and having different hydroxyl radical reaction rate constants; calculating the photochemical age τ of the VOCs cluster based on the measured concentration data of the two VOCs components and in conjunction with the photochemical age calculation formula; and calculating the photochemical age τ based on the photochemical age τ and the hydroxyl radical reaction rate constant k of each VOCs component in the VOCs cluster. i The concentration data of VOCs components after cleaning, C ij Photochemical depletion compensation calculations were performed to obtain the initial concentration data C' of each VOC component after compensation. ij .

[0051] Photochemical consumption refers to the process by which VOCs components undergo a series of complex photochemical reactions driven by sunlight (especially ultraviolet light), resulting in a certain degree of consumption. Since there is often a certain distance between the environmental monitoring station and the emission location of the VOCs component, a certain amount of photochemical consumption will occur from the time of emission until the time of monitoring.

[0052] Therefore, after calculating the photochemical age of the VOCs gas cloud using the photochemical age calculation formula, a reaction kinetic equation is used to remove the loss of the original VOCs component concentration caused by photochemical consumption, in order to restore the initial emission concentration of the VOCs component before photochemical consumption. The reaction kinetic equation is as follows: C ij To obtain the concentration values ​​of each VOC component from the concentration data measured at monitoring stations after the aforementioned data aggregation (e.g., averaging), k i Let be the hydroxyl radical reaction rate constant corresponding to the VOC component, and τ be the photochemical age of the VOC cloud. Therefore, based on this formula, combined with k... i τ and C ij The initial concentration C' of the VOC component at the moment of emission can then be calculated. ij .

[0053] It should be understood that the VOCs components within the park are considered to originate from the same air mass.

[0054] In one embodiment of the first aspect of this application, the physical diffusion correction method includes: selecting a VOCs component whose emission is insensitive to changes in ambient temperature as a reference VOCs component; calculating the emission levels of each other VOCs component relative to the reference VOCs component at each ambient temperature T. ij Initial concentration data C' ij The ratio of these values ​​is used as the characteristic value R of the concentration of this VOC component. ij .

[0055] Physical diffusion refers to the diffusion and dilution of the concentration of each VOC component due to meteorological factors such as atmospheric turbulence, wind transport, and vertical deposition. Similarly, since there is often a certain distance between the environmental monitoring point and the emission location of the VOC component, physical diffusion occurs during the transport of VOC clouds from the emission location to the environmental monitoring point, resulting in the measured VOC concentration at the environmental monitoring point being lower than the concentration at the time of emission.

[0056] Since physical diffusion has the same dilution effect on the concentration of all VOC components, this embodiment employs a ratio calculation method that uses a reference VOC component as the denominator and all other VOC components as the numerator to derive a pure correlation between concentration and temperature. A characteristic value (i.e., the concentration ratio) that is concentration-dependent and dynamically reflects temperature changes is constructed to characterize the response function representing the characteristic value-temperature relationship. This effectively eliminates the influence of physical diffusion factors, avoids the introduction of complex fluid dynamics simulations, simplifies the calculation, improves the accuracy of the subsequent dynamic emission inventory, and reduces computational difficulty.

[0057] In one embodiment of the first aspect of this application, the reference VOCs component is acetylene or carbon monoxide.

[0058] It should be understood that acetylene differs from solvent-based or fugitive VOCs, which are primarily controlled by saturated vapor pressure and whose volatilization intensifies with increasing ambient temperature. Acetylene's emission rate is minimally affected by fluctuations in ambient temperature, and therefore can be considered a temperature-insensitive stable emission. Thus, using acetylene as a reference component to normalize other VOCs (calculating concentration ratios) ensures that the calculated ratio R... ij It is only related to temperature changes.

[0059] S4: Construct a system based on temperature data T ij As the independent variable, with the eigenvalue R ij For a multiple regression model with the dependent variable, an eigenvalue-temperature response function f is established for each VOC component. i (T).

[0060] Based on the aforementioned steps, the characteristic value R, which decouples all non-temperature-affecting factors and can characterize the concentration, is obtained. ij Build R ij A response function to temperature, which provides a characteristic value R representing the concentration level for any target temperature input. ij .

[0061] S5: Based on the characteristic value R of each VOCs component ij Determine the percentage of mass concentration of each VOC component within a preset temperature range.

[0062] The percentage of mass concentration of each VOC component refers to the percentage of that VOC component's concentration relative to the total concentration of all VOC components. It should be understood that this percentage is numerically equal to the percentage of the emission of that VOC component relative to the mass of the total emission of all VOC components (within the preset temperature range, this mass percentage is E). i_base E all_base (percentage).

[0063] The reason for pre-defining a temperature range to define the mass concentration percentage is that the mass concentration percentage of each VOC component differs at different ambient temperatures. Setting the specific temperature range within the pre-defined range is to align it with the baseline total VOC emissions data E, which is also defined within the pre-defined temperature range. all_base To ensure temperature uniformity, calculations should be performed to avoid significant discrepancies between calculated emissions and actual emissions caused by inconsistent temperatures.

[0064] In one embodiment of the first aspect of this application, the step of basing the VOCs component on the characteristic value R ij The method for determining the mass concentration percentage of each VOCs component includes: calculating the characteristic value R of each VOCs component at each temperature within a preset temperature range. ij mean The mean values ​​of each VOC component Normalization was performed to obtain the mass concentration percentage of each VOC component.

[0065] It should be understood that, due to the VOCs baseline total emission data E all_base The data is obtained statistically based on a preset temperature range. However, the preset temperature range may contain multiple temperature values ​​or temperature ranges used to characterize the "sampling / data processing granularity." Therefore, it is necessary to analyze the characteristic values ​​R corresponding to these multiple temperature values ​​or temperature ranges within the preset temperature range. ijThe average value is calculated to obtain the characteristic value corresponding to the preset temperature range. Then, the mass concentration percentage of each VOC component is calculated based on its characteristic value within the preset temperature range.

[0066] S6: Based on the aforementioned VOCs baseline total emission data E all_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base .

[0067] In one embodiment of the first aspect of this application, the step of using the VOCs baseline total emission data E all_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base The method includes: multiplying the mass concentration percentage of the component by the baseline total VOC emissions data E. all_base To obtain the baseline emission data E corresponding to this VOCs component. i_base .

[0068] After obtaining the percentage of mass concentration within the preset temperature range, the baseline total VOC emissions E within the preset temperature range are used as the basis. all_base This allows us to calculate the baseline emission data E of each VOC component within a preset temperature range. i_base .

[0069] S7: E represents the baseline emission data for each VOC component. i_base Substitute it into its corresponding response function f i (T) is used to calculate the dynamic emissions of each VOC component at any target temperature and generate a dynamic VOC emission inventory at the component level of the park.

[0070] It should be understood that step S5 is to make the mass concentration percentage (or characteristic value R) ij ) and VOCs baseline total emissions data E all_base Since they are all within the same preset temperature range, the characteristic value R of each VOCs is... ij An average value was taken within a preset temperature range, and this average value was used as a characteristic value representing that preset temperature range. However, the characteristic value-temperature response function f constructed in step S4... i In (T), the temperature does not refer to a preset temperature range. The temperature step size and value are based on the "sampling / data processing granularity". Therefore, in this step, it is necessary to convert it back and select any temperature from the preset temperature range as the baseline emission data E of each VOC component obtained in step S6. i_base The corresponding ambient temperature data for the eigenvalues ​​(constructing the arbitrary temperature and its corresponding eigenvalue E)i_base (The mapping relationship between them). After this mapping relationship is established, the corresponding characteristic value can be found in the corresponding function according to any target temperature, and the emission amount of the VOC component at the target temperature can be calculated proportionally according to the characteristic value and the aforementioned mapping relationship. Furthermore, based on the emission amount of each VOC component at the target temperature, the emission inventory shown in Table 2 is constructed:

[0071]

[0072] The following is a complete and specific example to illustrate the process of generating the aforementioned list (taking a large-scale integrated petrochemical park as the target):

[0073] S1: Obtain the actual concentration data of each VOC component and its corresponding ambient temperature data from one environmental monitoring point (a fixed online GC-MS monitoring station located downwind of the park) for the entire year of 2023, along with abnormal production conditions within the park and the baseline total VOC emissions data for the park under a preset temperature range. all_base :

[0074] VOCs component data: Hourly measured concentration data of 57 PAMS components (including benzene, toluene, xylene, ethylene, propylene, acetylene, etc.).

[0075] Meteorological data: Hourly ambient temperature data.

[0076] Abnormal production conditions data within the park: Export all planned start-up and shutdown, equipment maintenance and repair, and unplanned abnormal operating conditions reported by all enterprises throughout the year from the park's smart environmental protection platform.

[0077] Baseline total VOCs emissions data for the industrial park within a preset temperature range (E) all_base The data used is from the inventory compiled by the park in 2022 in accordance with the Technical Guidelines for the Compilation of Air Pollutant Emission Inventories, which is 8,500 tons (per year).

[0078] S2: Based on the abnormal production condition information data, identify a total of 3 planned full-park shutdowns for maintenance throughout the year (each lasting 3-5 days) and 1 unplanned local equipment failure. Remove all monitoring data corresponding to these four time periods directly from the dataset.

[0079] S3: Concentration data of each VOC component after cleaning C ij Photochemical depletion correction and physical diffusion correction were performed sequentially to obtain the characteristic values ​​R of each VOC component after decoupling from non-temperature-affected factors. ij .

[0080] Photochemical depletion correction: Ethylbenzene (E) and m / p-xylene (X) were selected as a pair of VOC components originating from the same VOC cluster but with different hydroxyl radical reaction rate constants. Their OH reaction rate constants are as follows: and Assuming its initial characteristic value at the time of emission is [value], the photochemical age is calculated using the following formula: Calculate the photochemical age of the air mass at time t. Then, use the formula... The concentrations of all 57 VOC species (including acetylene) were corrected to obtain the initial concentration data C'. ij .

[0081] Physical diffusion correction: Acetylene was selected as the reference VOCs component; calculations were made for the other VOCs components relative to the reference VOCs component at various ambient temperatures T. ij Initial concentration data C' ij The ratio of these values ​​is used as the characteristic value R of the concentration of this VOC component. ij .

[0082] S4: Construct a system based on temperature data T ij As the independent variable, with the eigenvalue R ij For a multiple regression model with the dependent variable, an eigenvalue-temperature response function f is established for each VOC component. i (T). The established response functions show that the response functions of most VOC species can be well fitted using exponential functions. For example, for the key species ethylene, the fitted response function is: Among them, the temperature sensitivity coefficient This indicates that for every 1°C increase in temperature, the emission ratio of ethylene relative to acetylene increases by approximately 6.7%. Similarly, independent functions are established for other species such as benzene, toluene, and propylene. .

[0083] S5: Based on the characteristic value R of each VOCs component ij The mass concentration percentage of each VOCs component is determined within a preset temperature range. Assuming the preset temperature range is 24-26℃, all data from annual temperatures between 24-26℃ are selected, and all characteristic values ​​of each VOCs component within this temperature range are calculated. The average value of all VOCs is then normalized to obtain the mass concentration percentage of each VOC component. For example, the normalized mass concentration percentage of ethylene is 5.2%.

[0084] S6: Based on the aforementioned VOCs baseline total emission data Eall_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base Since the mass concentration percentage of ethylene is 5.2%, the baseline total VOC emissions data is E. all_base The baseline emission figure for ethylene is 8,500 tons, assuming 365 days a year and 10 hours of emissions per day. i_base =5.2%×(8500÷365÷10)=0.121 tons.

[0085] S7: E represents the baseline emission data for each VOC component. i_base Substitute it into its corresponding response function f i (T) is used to calculate the dynamic emissions of each VOC component at any target temperature and generate a dynamic VOC emission inventory at the component level of the park.

[0086] Choose any temperature from the preset temperature range. As a baseline emission data E for each VOC component obtained in step S6 i_base The corresponding ambient temperature data for the feature values. This temperature T ref Substituting into the response function of ethylene, we obtain its corresponding characteristic values: Assuming the characteristic value for a temperature of 20℃ in the response function is 0.1, then the hourly ethylene emission at 20℃ is: 0.121 tons × 0.1 ÷ 0.15 = 0.081 tons. Emissions for all VOCs species at all target temperatures are calculated, and an emission inventory reflecting different ambient temperatures and including the individual emissions of each VOC component is constructed accordingly.

[0087] like Figure 3 As shown, the second aspect of this application provides a system for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature, comprising: a data acquisition module for acquiring measured concentration data of each VOCs component at an environmental monitoring point within the target industrial park and its corresponding ambient temperature data, abnormal production conditions within the industrial park, and baseline total VOCs emission data E of the industrial park under a preset temperature range. all_base The VOCs component concentration data and ambient temperature data are collected at preset time intervals. A data preprocessing module is used to remove abnormal VOCs component concentration and ambient temperature data for the corresponding time period based on the abnormal production condition information data, and to perform data aggregation to obtain the cleaned VOCs component concentration data C. ij and its corresponding ambient temperature data T ijWhere i represents the index identifier of different VOCs components, and j represents the index identifier of different ambient temperatures; the data correction module is used to correct the concentration data C of each VOCs component after cleaning. ij Photochemical depletion correction and physical diffusion factor correction were performed sequentially to obtain the characteristic values ​​R corresponding to the concentrations of each VOC component after decoupling from non-temperature-affected factors. ij The response function construction module is used to construct the response function based on temperature data T. ij As the independent variable, with the eigenvalue R ij For a multiple regression model with the dependent variable, an eigenvalue-temperature response function f is established for each VOC component. i (T); Baseline emission calculation module, used to calculate emissions based on the characteristic values ​​R of each VOCs component. ij Determine the mass concentration percentage of each VOCs component within a preset temperature range; based on the VOCs baseline emission data E all_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base The emissions inventory generation module is used to generate baseline emission data for each VOCs component. i_base Substitute it into its corresponding response function f i (T) is used to calculate the dynamic emissions of each VOC component at any target temperature and generate a dynamic VOC emission inventory at the component level of the park.

[0088] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0089] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0090] A third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0091] A fourth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to perform the method described in any of the preceding claims.

[0092] like Figure 4As shown, a fifth aspect of this application provides an electronic terminal 100, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in any of the preceding claims. The electronic terminal 100 includes at least one processor 101, a memory 102, at least one network interface 103, and a user interface 105. The various components in the electronic terminal 100 are coupled together via a bus system 104. It is understood that the bus system 104 is used to implement communication between these components. In addition to a data bus, the bus system 104 also includes a power bus, a control bus, and a status signal bus.

[0093] The user interface 105 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0094] It is understood that memory 102 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0095] In this embodiment of the invention, the memory 102 is used to store various types of data to support the operation of the electronic terminal 100. Examples of this data include: any executable program for operation on the electronic terminal 100, such as the operating system 1021 and application programs 1022; the operating system 1021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 1022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The methods provided in this embodiment of the invention may be included in the application program 1022.

[0096] The methods disclosed in the above embodiments of the present invention can be applied to processor 101, or implemented by processor 101. Processor 101 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 101 or by instructions in the form of software. The processor 101 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 101 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 101 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0097] In an exemplary embodiment, the electronic terminal 100 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0098] In summary, this application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

Claims

1. A method for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature-driven tiers, characterized in that, include: Acquire the measured concentration data of each VOC component and its corresponding ambient temperature data at an environmental monitoring point within the target industrial park, information on abnormal production conditions within the park, and baseline total VOC emissions data for the park under a preset temperature range. all_base The concentration data of each VOC component and the ambient temperature data are collected at preset time intervals. Based on the abnormal production condition information data, abnormal data on the concentration of each VOC component and ambient temperature in the corresponding time period are removed, and the data is aggregated to obtain the VOC component concentration data C after cleaning. ij and its corresponding ambient temperature data T ij Where i is the index identifier of different VOCs components, and j is the index identifier of different ambient temperatures; The concentration data of each VOC component after cleaning, C ij Photochemical depletion correction and physical diffusion correction were performed sequentially to obtain the characteristic values ​​R corresponding to the concentrations of each VOC component after decoupling from non-temperature-affected factors. ij ; Constructing temperature data T ij As the independent variable, with the eigenvalue R ij For a multiple regression model with the dependent variable, an eigenvalue-temperature response function f is established for each VOC component. i (T); Based on the characteristic value R of each VOCs component ij Determine the percentage of mass concentration of each VOCs component within the preset temperature range; Based on the aforementioned VOCs baseline total emissions data E all_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base ; The baseline emission data E of each VOCs component i_base Substitute it into its corresponding response function f i (T) is used to calculate the dynamic emissions of each VOC component at any target temperature and generate a dynamic VOC emission inventory at the component level of the park. The photochemical depletion correction method includes: selecting a pair of VOCs components from the same VOCs cluster and having different hydroxyl radical reaction rate constants; calculating the photochemical age τ of the VOCs cluster based on the measured concentration data of the two VOCs components and the photochemical age calculation formula; and calculating the photochemical age τ based on the photochemical age τ and the hydroxyl radical reaction rate constant k of each VOCs component in the VOCs cluster. i The concentration data of VOCs components after cleaning, C ij Photochemical depletion compensation calculations were performed to obtain the initial concentration data C' of each VOC component after compensation. ij ; The physical diffusion correction method includes: selecting a VOCs component whose emission is insensitive to changes in ambient temperature as a reference VOCs component; calculating the emission levels of each other VOCs component relative to the reference VOCs component at various ambient temperatures T. ij Initial concentration data C' ij The ratio of these values ​​is used as the characteristic value R of the concentration of this VOC component. ij .

2. The method for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature as described in claim 1, characterized in that, The reference VOCs component is acetylene or carbon monoxide.

3. The method for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature-driven classification, as described in claim 1, is characterized in that... The characteristic value R of each VOCs component is used. ij Methods for determining the mass concentration percentage of each VOC component include: Calculate the characteristic value R of each VOC component at each temperature within the preset temperature range. ij mean The mean values ​​of each VOC component Normalization was performed to obtain the mass concentration percentage of each VOC component.

4. The method for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature-driven classification, as described in claim 1, is characterized in that... The total VOCs emissions data E based on the VOCs baseline data all_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base The method includes: multiplying the mass concentration percentage of the component by the baseline total VOC emissions data E. all_base To obtain the baseline emission data E corresponding to this VOCs component. i_base .

5. A system for generating a dynamic VOCs emission inventory for industrial parks based on ambient temperature, characterized in that, include: The data acquisition module is used to acquire the measured concentration data of each VOC component and its corresponding ambient temperature data at an environmental monitoring point within the target industrial park, information on abnormal production conditions within the park, and the baseline total VOC emissions data for the park under a preset temperature range. all_base The concentration data of each VOC component and the ambient temperature data are collected at preset time intervals. The data preprocessing module is used to remove abnormal data on the concentration of each VOC component and ambient temperature in the corresponding time period based on the abnormal production condition information data, and to perform data aggregation to obtain the VOC component concentration data C after cleaning. ij and its corresponding ambient temperature data T ij Where i is the index identifier of different VOCs components, and j is the index identifier of different ambient temperatures; The data correction module is used to correct the concentration data C of each VOC component after cleaning. ij Photochemical depletion correction and physical diffusion correction were performed sequentially to obtain the characteristic values ​​R corresponding to the concentrations of each VOC component after decoupling from non-temperature-affected factors. ij The photochemical depletion correction method includes: selecting a pair of VOCs components from the same VOCs cluster with different hydroxyl radical reaction rate constants; calculating the photochemical age τ of the VOCs cluster based on the measured concentration data of the two VOCs components and the photochemical age calculation formula; and calculating the photochemical age τ based on the photochemical age τ and the hydroxyl radical reaction rate constant k of each VOCs component in the VOCs cluster. i The concentration data of VOCs components after cleaning, C ij Photochemical depletion compensation calculations were performed to obtain the initial concentration data C' of each VOC component after compensation. ij The physical diffusion correction method includes: selecting a VOCs component whose emission is insensitive to changes in ambient temperature as a reference VOCs component; calculating the emission levels of each other VOCs component relative to the reference VOCs component at each ambient temperature T. ij Initial concentration data C' ij The ratio of these values ​​is used as the characteristic value R of the concentration of this VOC component. ij ; The response function building module is used to construct the response function based on temperature data T. ij As the independent variable, with the eigenvalue R ij For a multiple regression model with the dependent variable, an eigenvalue-temperature response function f is established for each VOC component. i (T); The baseline emission calculation module is used to calculate emissions based on the characteristic values ​​R of each VOC component. ij Determine the mass concentration percentage of each VOCs component within a preset temperature range; based on the VOCs baseline emission data E all_base Based on the mass concentration percentage of each VOCs component, determine the baseline emission data E of each VOCs component within the preset temperature range. i_base ; The emissions inventory generation module is used to generate baseline emission data for each VOC component. i_base Substitute it into its corresponding response function f i (T) is used to calculate the dynamic emissions of each VOC component at any target temperature and generate a dynamic VOC emission inventory at the component level of the park.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-4.

7. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to implement the method as described in any one of claims 1-4.

8. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1-4.

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