Nitrate aerosol pollution early warning method and system, electronic equipment and product

Abstract

The invention belongs to the technical field of atmospheric pollution early warning, and aims to provide a nitrate aerosol pollution early warning method and system, electronic equipment and a product. The method comprises the following steps: acquiring nitrate concentration time sequence data, relative humidity time sequence data and temperature time sequence data of a target area in a specified time period; according to the nitrate concentration time sequence data and the relative humidity time sequence data, a critical humidity threshold value of the nitrate concentration sensitive to humidity change is obtained; obtaining a humidity sensitivity index according to the critical humidity threshold value, the relative humidity time sequence data and the temperature time sequence data; accumulating the plurality of humidity sensitive indexes in the sliding time window to obtain a humidity sensitive index accumulated value; and performing pollution early warning on the target area according to the accumulated value of the humidity sensitivity index. According to the invention, nitrate aerosol pollution early warning can be carried out by using conventional meteorological data, the cost is low, and the early warning accuracy and simulation precision of the atmospheric nitrate heavy pollution process can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of air pollution early warning, and specifically relates to a nitrate aerosol pollution early warning method, system, electronic equipment and product. Background Art

[0002] In recent years, the contribution of nitrate aerosols to urban particulate matter pollution has steadily increased. Particularly in low-temperature, high-humidity environments, their explosive growth and accumulation after rapid moisture absorption often become a key factor in the pollution process. However, existing technologies for atmospheric nitrate pollution monitoring and early warning face numerous challenges.

[0003] Aerosol water content (AWC), a key parameter for simulating the hygroscopic reaction of nitrate, is currently a crucial indicator in atmospheric nitrate early warning technology. However, its monitoring process suffers from significant drawbacks. AWC monitoring relies heavily on expensive instruments, such as aerosol mass spectrometers. This not only drives up monitoring costs but also results in sparse monitoring sites and low coverage, making it difficult to obtain comprehensive and detailed regional pollution data. Many small and medium-sized cities and rural areas are unable to conduct effective AWC monitoring due to funding, equipment, and technical limitations, leaving these areas without a clear understanding of nitrate aerosol pollution. During peak pollution periods, the inability to keep abreast of pollution trends presents a significant obstacle to regional joint prevention and control efforts.

[0004] In addition, most existing atmospheric nitrate warning systems use fixed thresholds to trigger alarms, such as when the relative humidity (RH) exceeds 70% and the fine particulate matter (PM 2.5 ) concentration is higher than 75 μg / m 3 An early warning will be issued when the nitrate is generated. However, the sensitivity of nitrate formation is not constant, it will change with the dynamic changes of factors such as temperature. The pollution early warning mechanism based on fixed thresholds in existing technologies is difficult to adapt to complex and changeable meteorological conditions, which often leads to delayed pollution response or false alarms. In the Sichuan-Chongqing Basin, where RH increases frequently in winter, the early warning technology for atmospheric nitrate is unable to capture the explosive growth of nitrate aerosols due to rapid hygroscopic growth, which delays the issuance of early warning signals and makes it impossible to buy valuable time for pollution prevention and control. Therefore, this humidity sensitivity threshold is one of the important meteorological conditions for judging the rapid growth of nitrate.

[0005] In addition, existing technologies do not fully consider the synergistic effects of multiple factors when simulating the rapid growth of nitrate aerosols under high-humidity meteorological conditions. In the actual atmospheric environment, the growth of nitrate aerosols is the result of the interweaving and joint influence of multiple factors such as RH and temperature. However, when constructing existing models, they often focus on a single or a few factors, ignoring the complex dynamic relationship between them, resulting in a large deviation between the simulation results and the actual pollution situation. Especially in high-humidity pollution events, the inaccuracy of the simulation results greatly reduces the pertinence and effectiveness of prevention and control measures, making it difficult to effectively curb the spread of pollution.

[0006] In view of the above problems, there is an urgent need for a low-cost and high-precision nitrate aerosol pollution early warning technology solution that can adapt to complex meteorological conditions, in order to fill the existing technological gaps and improve the level of regional air quality control. Summary of the Invention

[0007] The present invention aims to solve the above technical problems at least to a certain extent, and provides a nitrate aerosol pollution early warning method, system, electronic equipment and product.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a nitrate aerosol pollution early warning method, comprising: Obtain the time series data of nitrate concentration, relative humidity and temperature in the target area within a specified time period; Obtaining a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes based on the nitrate concentration time series data and the relative humidity time series data; Obtaining a humidity sensitivity index according to the critical humidity threshold, the relative humidity time series data, and the temperature time series data; Accumulate multiple humidity sensitivity indices within the sliding time window to obtain a humidity sensitivity index cumulative value; According to the accumulated value of the humidity sensitivity index, a pollution warning is issued to the target area.

[0009] In one possible design, obtaining a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes based on the nitrate concentration time series data and the relative humidity time series data includes: traversing the relative humidity candidate values ​​in the relative humidity time series data that are within a preset critical humidity threshold candidate range according to a specified step size, and dividing the relative humidity time series data into two subsets based on the relative humidity candidate values; Performing linear regression fitting on the relationship between the relative humidity in the two subsets and the corresponding nitrate concentration in the nitrate concentration time series data, respectively, and calculating the residual sum of squares of the two subsets; The residual square sums of the two subsets are added to obtain the total residual square sum corresponding to the relative humidity candidate value, and the relative humidity candidate value that minimizes the total residual square sum is selected as the critical humidity threshold at which the nitrate concentration is sensitive to humidity changes.

[0010] In one possible design, the candidate range of the critical humidity threshold is set to 50%~80%.

[0011] In one possible design, the humidity sensitivity index is: ,in, ; Where, RH ( t ) is the relative humidity at time t in the relative humidity time series data, RH c is the critical humidity threshold, E a is the preset apparent activation energy of the reaction, R is the gas constant, T ( t ) is the temperature at time t in the temperature time series data, T 0 is the preset reference temperature.

[0012] In one possible design, the apparent activation energy of the reaction is set to 40-60 kJ / mol.

[0013] In one possible design, after obtaining the humidity sensitivity index, the method further includes: The humidity sensitivity index is corrected through multiple regression analysis to obtain a corrected humidity sensitivity index.

[0014] In a possible design, a graded warning mechanism is used to implement pollution warning for the target area. Correspondingly, the warning levels for pollution warning for the target area are: ; Where, is the cumulative value of the humidity sensitivity index.

[0015] In a second aspect, the present invention provides a nitrate aerosol pollution early warning system, comprising: A data acquisition module is used to obtain the time series data of nitrate concentration, relative humidity and temperature in the target area within a specified time period; a calculation module, communicatively connected to the data acquisition module, configured to obtain a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes based on the nitrate concentration time series data and the relative humidity time series data; to obtain a humidity sensitivity index based on the critical humidity threshold value, the relative humidity time series data, and the temperature time series data; and to accumulate multiple humidity sensitivity indices within a sliding time window to obtain a humidity sensitivity index cumulative value; The dynamic early warning module is in communication with the calculation module and is used to issue a pollution early warning to the target area according to the accumulated value of the humidity sensitivity index.

[0016] In a third aspect, the present invention provides an electronic device, comprising: a memory for storing computer program instructions; and A processor is used to execute the computer program instructions to complete the operation of a nitrate aerosol pollution early warning method as described in any one of the above.

[0017] In a fourth aspect, the present invention provides a computer program product, comprising a computer program or instructions, which, when executed by a computer, implements a nitrate aerosol pollution early warning method as described in any one of the above.

[0018] The beneficial effects of the present invention are: The present invention discloses a nitrate aerosol pollution early warning method, system, electronic equipment and product. The present invention can use conventional meteorological data to carry out nitrate aerosol pollution early warning, which is low-cost and can improve the early warning accuracy and simulation accuracy of the atmospheric nitrate heavy pollution process. Specifically, during the implementation of the present invention, first, the nitrate concentration time series data, relative humidity time series data and temperature time series data of the target area within a specified time period are obtained; then, based on the nitrate concentration time series data and the relative humidity time series data, a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes is obtained, and then a humidity sensitivity index is obtained based on the critical humidity threshold value, the relative humidity time series data and the temperature time series data; then, a plurality of humidity sensitivity indices within the sliding time window are accumulated to obtain a humidity sensitivity index cumulative value; finally, based on the humidity sensitivity index cumulative value, a pollution early warning is issued to the target area. Based on this, the present invention replaces the traditional single fixed threshold warning method with the coordinated response relationship of nitrate concentration time series data, relative humidity time series data and temperature time series data. Only conventional meteorological observation data is needed to quantify nitrate humidity sensitivity, and the cumulative value of the humidity sensitivity index based on the sliding time window is used to realize nitrate aerosol pollution warning. The monitoring cost is low, and at the same time, it can solve the problems of large simulation deviation and warning lag in high-humidity pollution events.

[0019] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the nitrate aerosol pollution early warning method in Example 1; Figure 2 This is a module block diagram of the nitrate aerosol pollution early warning system in Example 2; Figure 3 This is a module block diagram of the electronic device in Example 3. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in conjunction with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0022] Example 1: This embodiment discloses a nitrate aerosol pollution early warning method, which can be executed by, but is not limited to, a computer device or a virtual machine with certain computing resources, such as a personal computer, a smart phone, a personal digital assistant, or a wearable device, or by a virtual machine.

[0023] like Figure 1 As shown, a nitrate aerosol pollution early warning method may include, but is not limited to, the following steps: S1. Obtain time-series data on nitrate concentration, relative humidity, and temperature for the target area over a specified time period. It should be noted that nitrate concentration, relative humidity, and temperature are all routine monitoring data that can be directly obtained through meteorological observation stations and air quality monitoring stations, making acquisition relatively easy.

[0024] S2. Obtaining a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes based on the nitrate concentration time series data and the relative humidity time series data.

[0025] In step S2, a segmented regression method is used to identify a critical humidity threshold at which the nitrate concentration is sensitive to humidity changes. Specifically, based on the nitrate concentration time series data and the relative humidity time series data, the critical humidity threshold at which the nitrate concentration is sensitive to humidity changes is obtained, including: S201. Traverse the relative humidity candidate values ​​in the relative humidity time series data that are within the preset critical humidity threshold candidate range according to the specified step size, and divide the relative humidity time series data into two subsets based on the relative humidity candidate values; wherein the two subsets are divided into a low humidity segment subset (relative humidity RH Less than the relative humidity candidate value RH c ) and high humidity subset (relative humidity RH Greater than the relative humidity candidate value RH c As an example, the specified step size is set to 1%, which is determined according to the number and size of data in the relative humidity time series data and is not limited here.

[0026] In step S201 of this embodiment, the candidate range of the critical humidity threshold is set to 50% to 80%.

[0027] It should be noted that, in this embodiment, 50% to 80% is used as the candidate range of the critical humidity threshold, which can effectively cover the most sensitive humidity range in the nitrate aerosol formation process, accurately capture its nonlinear mutation characteristics under medium and high humidity conditions, and avoid interference with critical point identification caused by factors such as low reaction rate and poor signal-to-noise ratio under low humidity (relative humidity RH < 50%) or physical saturation under high humidity (relative humidity RH > 80%), thereby improving the model's recognition accuracy and adaptability to the inflection point of a sudden increase in nitrate concentration, reducing computational complexity, and improving the scientificity and practicality of warning threshold setting.

[0028] S202. Perform linear regression fitting on the relationship between the relative humidity in the two subsets and the corresponding nitrate concentration in the nitrate concentration time series data, and calculate the residual sum of squares of the two subsets; specifically, the residual sum of squares of any subset is: ; Where, is the relative humidity at time t in any subset, RH fit is a regression fitting value obtained by performing linear regression fitting on the relationship between the relative humidity in any subset and the corresponding nitrate concentration in the nitrate concentration time series data.

[0029] S203. Add the residual square sums of the two subsets to obtain the total residual square sum corresponding to the relative humidity candidate value, and select the relative humidity candidate value that minimizes the total residual square sum as the critical humidity threshold for the nitrate concentration to be sensitive to humidity changes.

[0030] It should be noted that in this embodiment, by minimizing the sum of squares of regression residuals, the location where the nitrate concentration undergoes a nonlinear mutation with relative humidity can be optimally identified in a statistical sense, thereby laying an accurate foundation for the subsequent humidity sensitivity index (HSI) modeling and dynamic early warning mechanism.

[0031] S3. Obtain a humidity sensitivity index according to the critical humidity threshold, the relative humidity time series data, and the temperature time series data.

[0032] Specifically, the humidity sensitivity index is: ,in, ; Where, RH ( t ) is the relative humidity at time t in the relative humidity time series data, RH c is the critical humidity threshold, E a is the preset apparent activation energy of the reaction, R is the gas constant. In this embodiment, the gas constant R The value of is 8.314 J / mol·K; T ( t ) is the temperature at time t in the temperature time series data, T 0 is the preset reference temperature. In this embodiment, the reference temperature T The value of 0 is 273K.

[0033] It should be noted that, in this embodiment, by combining the relative humidity exceeding the critical humidity threshold with the temperature-corrected Arrhenius formula (an empirical formula for the relationship between the chemical reaction rate constant and temperature), the cumulative effect of nitrate formation under high humidity conditions can be reflected.

[0034] The apparent activation energy of the reaction is set to 40-60 kJ / mol. Specifically, in this embodiment, the apparent activation energy of the reaction is E a The value of is 50kJ / mol. It should be noted that, in this embodiment, setting the apparent activation energy of the reaction to 40-60kJ / mol can effectively reflect the sensitivity changes of the nitrate formation reaction under different temperature conditions, enhance the adaptability of the humidity sensitivity index HSI to seasonal and regional temperature differences, and thus improve the versatility and accuracy of the early warning model under different meteorological backgrounds.

[0035] It should be noted that the above formula integrates the synergistic effect of nitrate concentration RH and temperature T, and can be used to quantify the humidity sensitivity of nitrate formation.

[0036] In step S3, after obtaining the humidity sensitivity index, the method further includes: a. Correcting the humidity sensitivity index through multiple regression analysis to obtain a corrected humidity sensitivity index, so as to calculate the cumulative value of the humidity sensitivity index based on the corrected humidity sensitivity index.

[0037] It should be noted that, in this embodiment, after obtaining the preliminary humidity sensitivity index, a multiple regression analysis can be performed on it and the measured nitrate concentration data, and a response relationship model of the concentration to the humidity sensitivity index can be established, such as: humidity sensitivity index'= β • Humidity Sensitivity Index + ε Where, β is the preset regression coefficient, representing the nitrate concentration increment corresponding to each unit of humidity sensitivity index. ε is the preset error term. Among them, the optimal β The humidity sensitivity index is proportionally corrected based on the value to achieve a more accurate measurement of pollution risk.

[0038] Based on the above correction mechanism, the physical consistency and regional adaptability of the nitrate aerosol pollution early warning in this embodiment can be further improved, and it is suitable for the differentiated parameter adjustment needs in different seasons or cities.

[0039] S4. Accumulate multiple humidity sensitivity indices within the sliding time window to obtain a humidity sensitivity index cumulative value. It should be noted that, if the duration of the sliding time window is set to 24 hours, 48 ​​hours, etc., and the specified time period is set to 1 hour, when the duration of the sliding time window is set to 24 hours and the specified time period is set to 1 hour, the humidity sensitivity index is calculated once every hour. Correspondingly, the humidity sensitivity index cumulative value of the sliding time window is also the sum of the humidity sensitivity indices within the 24 time periods within the sliding time window. Based on this, the fluctuation of a single hour can be smoothed out, and the continuous conditions of pollution formation can be more accurately reflected, avoiding false alarms caused by the humidity sensitivity index being accidentally high in a certain hour, thereby enhancing the stability and timeliness of the early warning.

[0040] S5. Issue pollution warning to the target area based on the accumulated value of the humidity sensitivity index. Specifically, in this embodiment, by establishing a dynamic and hierarchical pollution warning mechanism, the timeliness and accuracy of the prediction of high humidity pollution process are further improved.

[0041] In step S5, when performing pollution warning on the target area, a graded warning mechanism is adopted. Correspondingly, the warning levels when performing pollution warning on the target area are: ; Where, is the cumulative value of the humidity sensitivity index.

[0042] It should be noted that, in this embodiment, the warning level for the pollution warning of the target area indicates the correlation between the cumulative value of the humidity sensitivity index and the pollution event (such as nitrate concentration>15μg / m 3 ), the result can be used as a basis for relevant departments to formulate policies for nitrate aerosol pollution control. As an example, if the duration of the sliding time window is set to 24 hours, then .

[0043] It should be noted that this embodiment sets dynamic, graded warning thresholds based on the cumulative value of the humidity sensitivity index over a certain period, and generates pollution warning information. This effectively captures the cumulative formation characteristics of nitrate aerosol pollution in high-humidity environments. Compared to static, single-point triggering rules, this embodiment can dynamically adapt to different meteorological backgrounds and regional differences, improving the sensitivity and accuracy of the warning system. Furthermore, by setting multi-level warning thresholds such as yellow / orange / red, this embodiment also enables graded responses and differentiated recommendations for remediation measures, providing a more forward-looking and scientific basis for decision-making in environmental management.

[0044] Based on this embodiment, an example of early warning for winter nitrate aerosol pollution in Chongqing from January 1 to 31, 2022 is as follows: A1. Obtain observation data: hourly NO⁻ concentration (nitrate concentration), relative humidity (RH), and temperature (T) from January 1 to 31, 2022; A2. Identification of critical humidity threshold: determination by segmented regression analysis RH c =72% (minimum point of residual error); A3. Sensitivity Modeling: Parameter Setting: E a =50 kJ / mol, T 0=273 K; calculate HSI and regress it with the observed NO3⁻ concentration to obtain the calibration coefficient β =0.83 μg·m⁻ 3 / HSI-unit(R 2 =0.71).

[0045] A4. Warning verification: On January 15, 2022, HSI_24h=9.5, triggering an orange warning; the actual monitored NO3⁻ concentration was 17.2 μg / m 3 (Exceeding the standard limit of 15 μg / m 3 ).

[0046] This embodiment can use conventional meteorological data to carry out nitrate aerosol pollution warning, which is low-cost and can improve the warning accuracy and simulation accuracy of the atmospheric nitrate heavy pollution process. Specifically, during the implementation of this embodiment, first, the nitrate concentration time series data, relative humidity time series data and temperature time series data of the target area within a specified time period are obtained; then, based on the nitrate concentration time series data and the relative humidity time series data, the critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes is obtained, and then based on the critical humidity threshold value, the relative humidity time series data and the temperature time series data, the humidity sensitivity index is obtained; then, multiple humidity sensitivity indices within the sliding time window are accumulated to obtain a humidity sensitivity index cumulative value; finally, based on the humidity sensitivity index cumulative value, a pollution warning is issued to the target area. Based on this, this embodiment replaces the traditional single fixed threshold warning method with the coordinated response relationship between nitrate concentration time series data, relative humidity time series data and temperature time series data. Only conventional meteorological observation data is needed to quantify nitrate humidity sensitivity, and nitrate aerosol pollution warning is realized based on the cumulative value of the humidity sensitivity index in the sliding time window. The monitoring cost is low, and at the same time, it can solve the problems of large simulation deviation and warning lag in high-humidity pollution events.

[0047] Example 2: This embodiment discloses a nitrate aerosol pollution early warning system for implementing the nitrate aerosol pollution early warning method in Example 1; Figure 2 As shown, the nitrate aerosol pollution early warning system includes: A data acquisition module is used to obtain the time series data of nitrate concentration, relative humidity and temperature in the target area within a specified time period; a calculation module, communicatively connected to the data acquisition module, configured to obtain a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes based on the nitrate concentration time series data and the relative humidity time series data; to obtain a humidity sensitivity index based on the critical humidity threshold value, the relative humidity time series data, and the temperature time series data; and to accumulate multiple humidity sensitivity indices within a sliding time window to obtain a humidity sensitivity index cumulative value; The dynamic early warning module is in communication with the calculation module and is used to issue a pollution early warning to the target area according to the accumulated value of the humidity sensitivity index.

[0048] It should be noted that the working process, working details and technical effects of the nitrate aerosol pollution early warning system provided in this embodiment 2 can be found in embodiment 1 and will not be described in detail here.

[0049] Example 3: Based on the embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a laptop computer or a desktop computer. The electronic device may be called a user terminal, a portable terminal, a desktop terminal, etc. Figure 3 As shown, the electronic equipment includes: a memory for storing computer program instructions; and A processor is used to execute the computer program instructions to complete the operation of a nitrate aerosol pollution early warning method as described in any one of Example 1.

[0050] Specifically, the processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen.

[0051] Memory 302 may include one or more computer-readable storage media, which may be non-transitory. Memory 302 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in memory 302 is used to store at least one instruction, which is executed by processor 301 to implement the nitrate aerosol pollution early warning method provided in Example 1 of the present application.

[0052] In some embodiments, the terminal may optionally include a communication interface 303 and at least one peripheral device. The processor 301, memory 302, and communication interface 303 may be connected via a bus or signal lines. Each peripheral device may be connected to the communication interface 303 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 304, a display screen 305, and a power supply 306.

[0053] The communication interface 303 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board. In other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0054] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices via electromagnetic signals.

[0055] The display screen 305 is used to display a UI (User Interface). The UI may include any combination of graphics, text, icons, and videos.

[0056] The power supply 306 is used to supply power to various components in the electronic device.

[0057] Example 4: Based on any one of Examples 1 to 3, this embodiment discloses a computer program product, including a computer program or instructions. When executed by a computer, the computer program or instructions implement the nitrate aerosol pollution early warning method described in any one of Example 1. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0058] Obviously, those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0059] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that modifications may be made to the technical solutions described in the above embodiments, or that some of the technical features may be replaced with equivalents. Such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A nitrate aerosol pollution early warning method, characterized in that: include: Obtain the time series data of nitrate concentration, relative humidity and temperature in the target area within a specified time period; Obtaining a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes based on the nitrate concentration time series data and the relative humidity time series data; Obtaining a humidity sensitivity index according to the critical humidity threshold, the relative humidity time series data, and the temperature time series data; Accumulate multiple humidity sensitivity indices within the sliding time window to obtain a humidity sensitivity index cumulative value; According to the accumulated value of the humidity sensitivity index, a pollution warning is issued to the target area.

2. A nitrate aerosol pollution early warning method according to claim 1, characterized in that: Obtaining a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes based on the nitrate concentration time series data and the relative humidity time series data, including: traversing the relative humidity candidate values ​​in the relative humidity time series data that are within a preset critical humidity threshold candidate range according to a specified step size, and dividing the relative humidity time series data into two subsets based on the relative humidity candidate values; Performing linear regression fitting on the relationship between the relative humidity in the two subsets and the corresponding nitrate concentration in the nitrate concentration time series data, respectively, and calculating the residual sum of squares of the two subsets; The residual square sums of the two subsets are added to obtain the total residual square sum corresponding to the relative humidity candidate value, and the relative humidity candidate value that minimizes the total residual square sum is selected as the critical humidity threshold at which the nitrate concentration is sensitive to humidity changes.

3. A nitrate aerosol pollution early warning method according to claim 2, characterized in that: The candidate range of the critical humidity threshold is set to 50%~80%.

4. The nitrate aerosol pollution early warning method according to claim 1, characterized in that: The humidity sensitivity index is: ,in, ; Where, RH ( t ) is the relative humidity at time t in the relative humidity time series data, RH c is the critical humidity threshold, E a is the preset apparent activation energy of the reaction, R is the gas constant, T ( t ) is the temperature at time t in the temperature time series data, T 0 is the preset reference temperature.

5. A nitrate aerosol pollution early warning method according to claim 4, characterized in that: The apparent activation energy of the reaction is set to 40-60 kJ / mol.

6. The nitrate aerosol pollution early warning method according to claim 1, characterized in that: After obtaining the humidity sensitivity index, the method further includes: The humidity sensitivity index is corrected through multiple regression analysis to obtain a corrected humidity sensitivity index.

7. The nitrate aerosol pollution early warning method according to claim 1, characterized in that: When performing pollution warning for the target area, a graded warning mechanism is adopted. Correspondingly, the warning levels for performing pollution warning for the target area are: ； Where, is the cumulative value of the humidity sensitivity index.

8. A nitrate aerosol pollution early warning system, characterized in that: include: A data acquisition module is used to obtain the time series data of nitrate concentration, relative humidity and temperature in the target area within a specified time period; a calculation module, communicatively connected to the data acquisition module, configured to obtain a critical humidity threshold value at which the nitrate concentration is sensitive to humidity changes based on the nitrate concentration time series data and the relative humidity time series data; to obtain a humidity sensitivity index based on the critical humidity threshold value, the relative humidity time series data, and the temperature time series data; and to accumulate multiple humidity sensitivity indices within a sliding time window to obtain a humidity sensitivity index cumulative value; The dynamic early warning module is in communication with the calculation module and is used to issue a pollution early warning to the target area according to the accumulated value of the humidity sensitivity index.

9. An electronic device, characterized in that: include: a memory for storing computer program instructions; as well as, A processor is configured to execute the computer program instructions to thereby complete the operation of the nitrate aerosol pollution early warning method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When executed by a computer, the computer program or the instructions implements a nitrate aerosol pollution early warning method according to any one of claims 1 to 7.

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