A nitrate aerosol pollution early warning method, system, electronic device and product

Abstract

The present application belongs to the technical field of atmospheric pollution early warning, and aims to provide a nitrate aerosol pollution early warning method, system, electronic device and product. The method comprises: obtaining time series data of nitrate concentration, relative humidity time series data and temperature time series data of a target area within a specified time period; obtaining a critical humidity threshold value at which the nitrate concentration is sensitive to humidity change according to the nitrate concentration time series data and the relative humidity time series data; obtaining a humidity sensitivity index according to the critical humidity threshold value, the relative humidity time series data and the temperature time series data; accumulating a plurality of humidity sensitivity indexes within a sliding time window to obtain a humidity sensitivity index cumulative value; and performing pollution early warning on the target area according to the humidity sensitivity index cumulative value. The present application can perform nitrate aerosol pollution early warning using conventional meteorological data, has low cost, and can improve the early warning accuracy and simulation precision of the heavy pollution process of atmospheric nitrate.

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, the prior art does not fully consider the synergistic effect of various factors when simulating the rapid growth of nitrate aerosol under high humidity meteorological conditions. In the actual atmospheric environment, the growth of nitrate aerosol is the result of the mutual interweaving and mutual influence of various factors such as RH and temperature. However, when constructing the existing model, attention is often focused on a single or a few factors, and the complex dynamic relationship between them is ignored, 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 the 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 nitrate aerosol pollution early warning technical solution that can adapt to complex meteorological conditions, is low-cost and high-precision, in order to fill the gap in the existing technology and improve the level of regional air quality control. SUMMARY

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

[0008] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0009] In a first aspect, the present application provides a nitrate aerosol pollution early warning method, comprising:

[0010] obtaining nitrate concentration time series data, relative humidity time series data and temperature time series data of a target area within a specified time period;

[0011] obtaining a critical humidity threshold at which nitrate concentration is sensitive to humidity change according to the nitrate concentration time series data and the relative humidity time series data;

[0012] obtaining a humidity sensitivity index according to the critical humidity threshold, the relative humidity time series data and the temperature time series data;

[0013] accumulating a plurality of humidity sensitivity indexes within a sliding time window to obtain a humidity sensitivity index cumulative value;

[0014] performing pollution early warning on the target area according to the humidity sensitivity index cumulative value.

[0015] In one possible design, obtaining a critical humidity threshold at which nitrate concentration is sensitive to humidity change according to the nitrate concentration time series data and the relative humidity time series data comprises:

[0016] traversing relative humidity candidate values in the relative humidity time series data 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;

[0017] linear regression fitting is performed on the relationship between the relative humidity and the corresponding nitrate concentration in the nitrate concentration time series data in the two subsets respectively, and the residual sum of squares of the two subsets is calculated;

[0018] the residual sum of squares of the two subsets is added to obtain the total residual sum of squares corresponding to the relative humidity candidate value, and the relative humidity candidate value that minimizes the total residual sum of squares is selected as the critical humidity threshold at which the nitrate concentration is sensitive to humidity change.

[0019] In one possible design, the critical humidity threshold candidate range is set to 50% to 80%.

[0020] In one possible design, the humidity sensitivity index is:

[0021] , wherein, ;

[0022] In the formula, 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 a preset reaction apparent activation energy, R is a gas constant, T ( t ) is the temperature at time t in the temperature time series data, T 0 is a preset reference temperature.

[0023] In one possible design, the reaction apparent activation energy is set to 40-60 kJ / mol.

[0024] In one possible design, after obtaining the humidity sensitivity index, the method further includes:

[0025] correcting the humidity sensitivity index by multiple regression analysis to obtain a corrected humidity sensitivity index.

[0026] In one possible design, when the target area is subjected to pollution early warning, a hierarchical early warning mechanism is adopted, and correspondingly, the early warning level when the target area is subjected to pollution early warning is:

[0027] ;

[0028] In the formula, is the cumulative value of the humidity sensitivity index.

[0029] In a second aspect, the present application provides a nitrate aerosol pollution early warning system, comprising:

[0030] a data acquisition module configured to acquire time series data of nitrate concentration, time series data of relative humidity and time series data of temperature of a target area within a specified time period;

[0031] a calculation module communicatively connected to the data acquisition module, configured to obtain a critical humidity threshold at which nitrate concentration is sensitive to humidity change according to the time series data of nitrate concentration and the time series data of relative humidity, obtain a humidity sensitivity index according to the critical humidity threshold, the time series data of relative humidity and the time series data of temperature, and accumulate a plurality of humidity sensitivity indexes within a sliding time window to obtain a humidity sensitivity index cumulative value;

[0032] a dynamic early warning module communicatively connected to the calculation module, configured to perform pollution early warning on the target area according to the humidity sensitivity index cumulative value.

[0033] In a third aspect, the present application provides an electronic device, comprising:

[0034] a memory configured to store computer program instructions; and

[0035] a processor configured to execute the computer program instructions to complete the operation of the nitrate aerosol pollution early warning method according to any one of the above aspects.

[0036] In a fourth aspect, the present application provides a computer program product comprising computer programs or instructions, which, when executed by a computer, implement the nitrate aerosol pollution early warning method according to any one of the above aspects.

[0037] The present application has the following beneficial effects:

[0038] The application discloses a nitrate aerosol pollution early warning method and system, an electronic device and a product.

[0039] Other beneficial effects of the application will be further described in the specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a flowchart of the nitrate aerosol pollution early warning method in embodiment 1;

[0041] Figure 2 is a module block diagram of the nitrate aerosol pollution early warning system in embodiment 2;

[0042] Figure 3 is a module block diagram of the electronic device in embodiment 3. DETAILED DESCRIPTION

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the present application will be briefly introduced in combination with the drawings and the descriptions of the embodiments or the prior art. Obviously, the following description of the drawings structure is only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor. It should be noted that the description of these embodiment modes is used to help understand the present application, but does not constitute a limitation on the present application.

[0044] Embodiment 1:

[0045] The embodiment discloses a nitrate aerosol pollution early warning method, which can be executed by a computer device or a virtual machine with certain computing resources, such as an electronic device like a personal computer, a smart phone, a personal digital assistant or a wearable device, or a virtual machine.

[0046] As shown in the figure, a nitrate aerosol pollution early warning method can include the following steps: Figure 1

[0047] S1. Obtain 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. It should be noted that the nitrate concentration, relative humidity and temperature are conventional monitoring data, which can be directly obtained from meteorological observation stations and air quality monitoring stations, and the difficulty of obtaining is low.

[0048] S2. Obtain the critical humidity threshold at which the nitrate concentration is sensitive to humidity changes according to the nitrate concentration time series data and the relative humidity time series data.

[0049] In step S2, the critical humidity threshold at which the nitrate concentration is sensitive to humidity changes is identified by using a piecewise regression method. Specifically, the critical humidity threshold at which the nitrate concentration is sensitive to humidity changes is obtained according to the nitrate concentration time series data and the relative humidity time series data, including:

[0050] S201. Traverse the relative humidity candidate values in the preset critical humidity threshold candidate range in the relative humidity time series data according to a specified step length, 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 a high humidity segment subset (relative humidity RH greater than the relative humidity candidate value RH c ). As an example, the specified step length is set to 1%, which is determined according to the number and size of data in the relative humidity time series data, which is not limited here.

[0051] In step S201 of the embodiment, the critical humidity threshold candidate range is set to 50%~80%.

[0052] ​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.

[0053] 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:

[0054] ;

[0055] 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.

[0056] 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.

[0057] 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.

[0058] S3. Obtain a humidity sensitivity index according to the critical humidity threshold, the relative humidity time series data, and the temperature time series data.

[0059] Specifically, the humidity sensitivity index is:

[0060] ,in, ;

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] In step S3, after obtaining the humidity sensitivity index, the method further includes:

[0066] 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.

[0067] 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. εis a preset error term. The optimal value can be obtained by least square fitting or Bayesian parameter inversion method, and the humidity sensitive index is proportionally corrected according to the optimal value, so as to realize more accurate pollution risk measurement. β

[0068] Based on the above correction mechanism, the physical consistency and regional adaptability of the nitrate aerosol pollution early warning performed by the embodiment can be further improved, and the embodiment is suitable for differentiated parameter adjustment requirements of different seasons or cities.

[0069] S4. The humidity sensitive indexes in the sliding time window are accumulated to obtain a humidity sensitive index cumulative value. It should be noted that the length of the sliding time window is set to be 24 hours, 48 hours, etc., and the specified time period is set to be 1 hour. In the scenario where the length of the sliding time window is set to be 24 hours and the specified time period is set to be 1 hour, the humidity sensitive index is calculated every hour. Correspondingly, the sliding time window humidity sensitive index cumulative value is the sum of the humidity sensitive indexes in the 24 time periods in the sliding time window. Based on this, the fluctuation of a single hour can be smoothed out, the continuity of pollution formation can be more accurately reflected, false positives caused by accidental high humidity sensitive index in a single hour can be avoided, and the stability and timeliness of the early warning can be enhanced.

[0070] S5. Pollution early warning is performed on the target area according to the humidity sensitive index cumulative value. Specifically, in the embodiment, a dynamic and hierarchical pollution early warning mechanism is established to further improve the prediction timeliness and accuracy of high-humidity pollution processes.

[0071] In step S5, when the target area is subjected to pollution early warning, a hierarchical early warning mechanism is used. Correspondingly, the early warning level when the target area is subjected to pollution early warning is:

[0072]

[0073] In the formula, H is the humidity sensitive index cumulative value.

[0074] It should be noted that the early warning level when the target area is subjected to pollution early warning in the embodiment indicates the corresponding relationship between the humidity sensitive index cumulative value and a pollution event (such as nitrate concentration > 15 μg / m 3 .

[0075] ​​​​It should be noted that in the present embodiment, the dynamic hierarchical early warning threshold is set according to the humidity sensitivity index cumulative value in a certain period, and the pollution early warning information is generated, based on which the cumulative formation characteristics of nitrate aerosol pollution in a high humidity environment can be effectively captured. Compared with the static single-point trigger rule, the present embodiment can dynamically adapt to different meteorological backgrounds and regional differences, and improve the sensitivity and accuracy of the early warning system. In addition, in the present embodiment, by setting multi-level early warning thresholds such as yellow / orange / red, hierarchical response and differentiated treatment measures can be realized, thereby providing more forward-looking and scientific decision basis for environmental management.

[0076] Based on the present embodiment, the following is an example of nitrate aerosol pollution early warning in Chongqing from January 1 to 31, 2022:

[0077] A1. Obtain observation data: hourly NO3⁻ concentration (nitrate concentration), relative humidity RH and temperature T from January 1 to 31, 2022;

[0078] A2. Critical humidity threshold identification: piecewise regression analysis determines RH c =72% (minimum residual point);

[0079] A3. Sensitivity modeling: parameter setting: E a =50 kJ / mol, T 0=273 K; calculate HSI and regress with observed NO3⁻ concentration to obtain calibration coefficient β =0.83 μg·m⁻ 3 / HSI-unit (R 2 =0.71).

[0080] A4. Early warning verification: on January 15, 2022, HSI_24h=9.5, triggering orange early warning; actual monitoring NO3⁻ concentration is 17.2 μg / m 3 (exceeding the standard limit of 15 μg / m 3 ).

[0081] The embodiment can be used for nitrate aerosol pollution early warning using conventional meteorological data, has low cost, and can improve the early warning accuracy and simulation precision of the heavy pollution process of atmospheric nitrate. Specifically, in the implementation process of the embodiment, first, time series data of nitrate concentration, time series data of relative humidity, and time series data of temperature of a target region in a specified time period are obtained; then, a critical humidity threshold sensitive to the change of nitrate concentration and humidity is obtained according to the time series data of nitrate concentration and the time series data of relative humidity, and a humidity sensitivity index is obtained according to the critical humidity threshold, the time series data of relative humidity, and the time series data of temperature; subsequently, a humidity sensitivity index cumulative value is obtained by accumulating a plurality of humidity sensitivity indexes in a sliding time window; finally, pollution early warning is performed on the target region according to the humidity sensitivity index cumulative value. Based on this, the embodiment replaces the traditional single fixed threshold early warning method by using the cooperative response relationship of the time series data of nitrate concentration, the time series data of relative humidity, and the time series data of temperature, can quantify the humidity sensitivity of nitrate only by using conventional meteorological observation data, and can realize nitrate aerosol pollution early warning based on the humidity sensitivity index cumulative value of the sliding time window, thereby reducing the monitoring cost and solving the problems of large simulation deviation and early warning lag in high-humidity pollution events.

[0082] Embodiment 2

[0083] The embodiment discloses a nitrate aerosol pollution early warning system for implementing the nitrate aerosol pollution early warning method in embodiment 1. Figure 2 As shown in the figure, the nitrate aerosol pollution early warning system comprises:

[0084] A data acquisition module is configured to obtain time series data of nitrate concentration, time series data of relative humidity, and time series data of temperature of a target region in a specified time period.

[0085] A calculation module is in communication connection with the data acquisition module and is configured to obtain a critical humidity threshold sensitive to the change of nitrate concentration and humidity according to the time series data of nitrate concentration and the time series data of relative humidity, to obtain a humidity sensitivity index according to the critical humidity threshold, the time series data of relative humidity, and the time series data of temperature, and to accumulate a plurality of humidity sensitivity indexes in a sliding time window to obtain a humidity sensitivity index cumulative value.

[0086] A dynamic early warning module is in communication connection with the calculation module and is configured to perform pollution early warning on the target region according to the humidity sensitivity index cumulative value.

[0087] It should be noted that the working process, working details, and technical effects of the nitrate aerosol pollution early warning system provided in the embodiment 2 can be referred to the embodiment 1, which will not be repeated here.

[0088] Embodiment 3:

[0089] Based on the embodiments 1 or 2, the present embodiment discloses an electronic device, which can be a smartphone, a tablet computer, a notebook computer or a desktop computer, etc. The electronic device can be referred to as a user terminal, a portable terminal, a desktop terminal, etc., as shown in Figure 3 The electronic device includes:

[0090] a memory for storing computer program instructions; and

[0091] a processor for executing the computer program instructions to complete the operations of the nitrate aerosol pollution early warning method according to any one of the embodiments 1.

[0092] Specifically, the processor 301 can include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), a FPGA (Field-Programmable Gate Array), a PLA (Programmable Logic Array). The processor 301 can also include a main processor and a coprocessor, the main processor is a processor for processing data in a wake-up state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 301 can be integrated with a GPU (Graphics Processing Unit) for rendering and drawing the content required to be displayed by the display screen.

[0093] The memory 302 can include one or more computer-readable storage media, which can be non-transitory. The memory 302 can also include a high-speed random access memory, and a non-volatile memory such as one or more disk storage devices, flash storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction for being executed by the processor 301 to implement the nitrate aerosol pollution early warning method provided in the embodiment 1 of the present application.

[0094] In some embodiments, the terminal can further optionally comprise a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 can be connected through a bus or a signal line. Each peripheral device can be connected to the communication interface 303 through a bus, a signal line or a circuit board. Specifically, the peripheral device comprises at least one of a radio frequency circuit 304, a display screen 305 and a power supply 306.

[0095] The communication interface 303 can be used to connect at least one peripheral device related to I / O (Input / Output) 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 some other embodiments, any one or two of the processor 301, the memory 302 and the communication interface 303 can be implemented on a separate chip or circuit board, and the present embodiments are not limited in this regard.

[0096] 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 through electromagnetic signals.

[0097] The display screen 305 is used to display a UI (User Interface). The UI can include any combination of graphics, text, icons and videos.

[0098] The power supply 306 is used to supply power to various components in the electronic device.

[0099] Embodiment 4:

[0100] On the basis of any one of embodiments 1 to 3, the present embodiment discloses a computer program product comprising a computer program or instructions, which, when executed by a computer, implement a nitrate aerosol pollution early warning method as described in any one of embodiment 1. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0101] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be realized by general computing devices, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and optionally, they can be realized by program codes executable by computing devices, so that they can be stored in storage devices and executed by computing devices, or they can be respectively manufactured into individual integrated circuit modules, or multiple modules or steps among them can be manufactured into a single integrated circuit module to realize. Thus, the present application is not limited to any specific combination of hardware and software.

[0102] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application, and are not intended to limit the same; although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for some of the technical features. Such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

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; issuing a pollution warning to the target area according to the accumulated value of the humidity sensitivity index; 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; Adding the residual sums of squares of the two subsets to obtain a total residual sum of squares corresponding to the relative humidity candidate value, and selecting the relative humidity candidate value that minimizes the total residual sum of squares as the critical humidity threshold at which the nitrate concentration is sensitive to humidity changes; The candidate range of the critical humidity threshold is set to 50% to 80%; 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; 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.

2. A nitrate aerosol pollution early warning method according to claim 1, characterized in that: The apparent activation energy of the reaction is set to 40-60 kJ / mol.

3. 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.

4. A nitrate aerosol pollution early warning system for implementing the nitrate aerosol pollution early warning method according to any one of claims 1 to 3, 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.

5. 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 3.

6. 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 3.

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