Atmospheric microparticulate matter monitoring device and method based on multi-mode sensing

Abstract

The invention discloses an atmospheric microparticulate monitoring device and method based on multi-mode sensing, and relates to the technical field of ecological environment monitoring. The device comprises a data acquisition system used for carrying out real-time sample acquisition and signal conversion on conventional pollution factors and meteorological parameters in air; the data analysis system is used for carrying out concentration analysis and early warning on the signals converted by the data acquisition system; the error evaluation system is used for performing error evaluation on the key indexes of the data acquisition system according to the acquired samples; and the data transmission system is used for carrying out cross-regional transmission on the sample acquired by the data acquisition system and the analysis result according to a sensitivity quantity algorithm based on an anonymous model. Real-time monitoring of atmospheric micro-particles with high accuracy and strong applicability is realized based on atmospheric monitoring standards independent of a meteorological monitoring framework, and reasonable adjustment of device parameters is realized by cooperation of the atomic fluorescence spectrometer and the enrichment sampling and pretreatment unit.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring technology, and in particular to an atmospheric particulate matter monitoring device and method based on multimodal sensing. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continuous advancement of industrialization and urbanization, the chemical composition emitted into the atmosphere by human activities is becoming increasingly complex, including both solid pollutants such as fine particulate matter (PM2.5) and various gaseous precursors such as volatile organic compounds (VOCs). PM2.5, due to its small particle size, large specific surface area, and long residence time in the atmosphere, can penetrate deep into the respiratory system and carry toxic and harmful substances, exerting long-term, cumulative, and irreversible effects on human health and ecosystems. Simultaneously, these particles alter the Earth's radiation balance by scattering and absorbing solar radiation, and influence regional climate through cloud formation and precipitation processes. In response to these risks, constructing a multi-scale atmospheric particulate matter monitoring network covering urban areas, suburbs, transportation corridors, and industrial clusters is widely regarded as a primary step in risk control. Traditional monitoring methods, mostly relying on gravimetric methods, beta-ray absorption methods, or oscillating microbalance methods, while providing mass concentration information, struggle to capture real-time particle size distribution, chemical composition changes, and secondary formation processes. They also cannot simultaneously analyze the dynamic contribution of gaseous precursors to particulate matter formation, thus exhibiting significant shortcomings in early warning timeliness, pollution source tracing, and health risk assessment.

[0004] Photoionization detection technology, utilizing high-energy photons provided by a vacuum ultraviolet lamp, can ionize molecules with ionization energies lower than the photon energy under ambient temperature and pressure. This allows for online, rapid, and highly sensitive detection of gaseous precursors such as volatile organic compounds through the amplification and analysis of weak current signals. The technology is compact, consumes little power, and has a short response time, making it possible to construct high-density, low-cost monitoring nodes. Integrating photoionization sensors with optical particle size distribution, electrochemical gaseous sensors, and temperature and humidity sensors allows for the simultaneous acquisition of multi-dimensional information such as gaseous precursor concentration, particulate number concentration, particle size distribution, and ambient humidity and temperature, providing data support for understanding the secondary formation mechanism of particulate matter and real-time health risk assessment.

[0005] However, existing atmospheric particulate matter monitoring systems based on photoionization detection technology still face many structural defects. First, the system is incomplete. The lack of unified technical specifications and clear responsibilities in the construction, operation, quality control, and data sharing of the monitoring network leads to incompatibility in equipment selection, calibration procedures, and data formats among different regions and operating entities. Cross-domain data cannot be directly integrated, and data transmission security is insufficient, severely weakening the comparability and traceability of monitoring results. Second, monitoring results are low. Current quality control procedures lack effective correction methods for nonlinear drift and cross-interference caused by photoionization sensors in extreme environments with high humidity, high ozone backgrounds, and the coexistence of complex organic components. This results in frequent signal drift and false peaks in low-concentration ranges, making it difficult to meet the needs of scientific assessment and public awareness regarding data validity and reliability. Third, personnel qualifications are low. Photoionization detection involves high-precision operations such as vacuum ultraviolet light source maintenance, ionization chamber cleaning, and weak signal amplification link calibration. However, grassroots maintenance teams generally lack systematic training and have insufficient capabilities in identifying abnormal equipment conditions, diagnosing faults, and tracing and rectifying them. This results in monitoring stations operating with defects for extended periods, leading to persistently high data gaps and distortion rates. Fourth, the monitoring environment is poor. Many monitoring nodes are deployed in pollution hotspots such as main traffic arteries, industrial boundaries, and construction sites. They not only face sensor aging and performance degradation caused by continuous high concentrations of pollutants but are also subject to physical pollution such as dust, fumes, and water vapor condensation. Deposits on the optical windows and inner walls of the ionization chamber significantly reduce ultraviolet light transmittance and ionization efficiency, further amplifying measurement errors.

[0006] Therefore, how to eliminate the influence of current meteorological monitoring data and establish an atmospheric monitoring standard-setting mechanism independent of the meteorological monitoring framework has become the key point of atmospheric particulate matter monitoring. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the present invention aims to provide an atmospheric particulate matter monitoring device and method based on multimodal sensing. This device and method achieves high accuracy and wide applicability in real-time monitoring of atmospheric particulate matter based on atmospheric monitoring standards independent of meteorological monitoring frameworks. Furthermore, it utilizes an atomic fluorescence spectrometer in conjunction with an enrichment sampling and preprocessing unit to achieve reasonable adjustment of device parameters.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: The first aspect of this invention provides an atmospheric particulate matter monitoring device based on multimodal sensing, comprising: The data acquisition system is used to collect samples and convert signals of conventional pollutants and meteorological parameters in the air in real time, and to perform concentration analysis and early warning on the converted signals. An error assessment system is used to assess the errors of key indicators of a data acquisition system based on collected samples. A data transmission system is used to transmit samples and analysis results collected by a data acquisition system across regions based on a sensitivity measurement algorithm using an anonymity model.

[0009] A second aspect of the present invention provides a monitoring method for an atmospheric particulate matter monitoring device based on multimodal sensing, comprising the following steps: Real-time sample collection and signal conversion of conventional air pollutants and meteorological parameters, followed by concentration analysis and early warning of the converted signals; Error assessment of key indicators of the data acquisition system is performed based on the collected samples; The data acquisition system transmits samples and analysis results across regions using a sensitivity measurement algorithm based on an anonymity model.

[0010] The above one or more technical solutions have the following beneficial effects: This invention discloses an atmospheric particulate matter monitoring device and method based on multimodal sensing. It enables 24-hour continuous online monitoring of conventional air pollutants and meteorological parameters, providing the analyzed data to relevant personnel or institutions as an air quality reference and supporting environmental decision-making. By collecting atmospheric parameters of the urban environment through sampling equipment, monitoring the air quality in the current area, storing and analyzing the monitoring results in real time, and obtaining relevant data, this invention overcomes the shortcomings of existing multi-temporal and long-term time-series monitoring methods, which suffer from limitations in meeting the requirements of single-temporal and spatial conditions and the influence of surrounding environmental factors. Furthermore, this invention ensures data security and reliability through the research of encrypted measurement algorithms for data transmission, achieves two-way data verification, improves the stability of platform services, and provides a solid foundation for data analysis.

[0011] The data acquisition system of this invention achieves long-term continuous monitoring of atmospheric particulate matter through solar power consumption and a special low-power design, resulting in long battery life, low power consumption, and low cost. It also supports pollution source tracing and short-term forecasting, and features dynamic calibration, significantly improving monitoring accuracy and efficiency.

[0012] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1This is a schematic diagram illustrating the working principle of photoionization. Figure 2 This is a schematic diagram of the atmospheric particulate matter monitoring device based on multimodal sensing in Embodiment 1 of the present invention. Figure 3 This is a schematic diagram of the photoionization device in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the hardware structure of the atmospheric particulate matter monitoring device in Embodiment 1 of the present invention; Figure 5 This is a standard curve diagram of the four elements in Embodiment 1 of the present invention; Figure 6 This is a physical schematic diagram of the weather station in Embodiment 1 of the present invention. Detailed Implementation

[0015] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0016] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] Photoionization detection technology is mainly used for the detection of volatile organic compounds (VOCs). The working principle of photoionization is shown in Figure 1. After the gas to be tested enters the photoionization chamber, it is irradiated by a vacuum ultraviolet lamp in the photoionization sensor. When the ionization energy of the gas to be tested is less than that of the ultraviolet lamp, the gas to be tested will undergo an ionization reaction. Under the action of an applied electrode voltage in the ionization chamber, the gas to be tested ionizes into positively charged ions and negatively charged electrons, which move directionally towards both ends of the electrode, thereby generating a weak current signal. This signal is amplified, and the control unit collects and outputs the signal, displaying the concentration of the gas to be tested in real time. Data can also be transmitted to a host computer via an I / O interface. Signals from PM2.5, PM10, NOx, SO2, O3, TVOC sensors, and temperature and humidity sensors are amplified by operational amplifiers, filtered to remove noise interference, acquired by an AD converter, processed by a 32-bit high-precision CPU, and then converted into pollutant concentration values ​​for display.

[0018] The primary process of photoionization involves a molecule absorbing a photon, exciting its electrons, and elevating the molecule from its ground state to an excited state. The electronic, vibrational, and rotational states of a molecule are quantized, meaning that energy changes between adjacent states are discontinuous. Therefore, the required photon energy differs depending on the initial and final states of the molecule during excitation, and these energies must be matched as closely as possible. Since the photon energy ε = hv = hc / λ (where h is Planck's constant; v is the frequency of light; λ is the wavelength of light; and c is the speed of light), energy matching is reflected in wavelength matching. Under normal conditions, a molecule exists in a stable state with lower energy, called the ground state. When exposed to light, if the molecule can absorb a photon, it can be elevated to a higher energy state, called an excited state. If the molecule can absorb electromagnetic radiation of different wavelengths, it can reach different excited states. These are named sequentially from the ground state upwards, from first excited state to second excited state, and so on; all excited states higher than the first excited state are collectively called higher excited states.

[0019] Quantum efficiency can be measured using absolute and relative methods. The relative method involves comparison with a system whose absolute yield is known. The absolute method requires directly establishing the functional relationship between the quantum yield of the reaction and wavelength, temperature, light intensity, and the concentrations of various ions (especially hydrogen ions). Systems of this type that have been studied include gaseous systems (such as nitrous oxide, carbon dioxide, hydrogen bromide, and acetone); the instruments used in these methods are collectively called chemical dewmeters.

[0020] When the photoionization detector is working normally, when the analyte gas enters the ionization chamber, the ionization chamber converts the concentration signal of the analyte gas into a weak current signal. To determine the measurement range and design requirements of the weak current amplification module, it is necessary to estimate the weak current signal generated in the ionization chamber. Freedman proved through analysis and testing that the current in the ionization chamber can be expressed as: .

[0021] In the formula, F is the Faraday constant; I0 ​​is the light radiation intensity; η is the absorption cross-section of the gas to be measured; σ is the ionization efficiency of the excited-state molecules; N is Avogadro's constant; L is the thickness of the light absorption layer; and [AB] is the concentration of the analyte. This indicates that once the ultraviolet lamp selection in the ionization chamber is determined, I0 and L will not change. At this point, the ionized current is only related to η and σ of the analyte, and η and σ will change to some extent with the ionization potential (IP) of the substance. Therefore, the ionization potential of the substance affects the response current generated in the ionization chamber.

[0022] Based on the principle of the photoionization detector described above, this invention designs an atmospheric particulate matter monitoring device and method based on multimodal sensing, as detailed in the embodiments.

[0023] Example 1: Embodiment 1 of the present invention provides an atmospheric particulate matter monitoring device based on multimodal sensing, such as... Figure 2 and Figure 4 As shown. Figure 2 The atmospheric particulate matter monitoring device mainly consists of three modular units: a mounting platform system, a sampling system, and an autonomous power supply system. The mounting platform system provides a fixed installation platform for the sampling system and the autonomous power supply system, and also provides a protective barrier for the autonomous power supply system. The sampling system mainly consists of a microgravity sensor, a temperature and humidity sensor, an Arduino platform, and a 4G communication module. The autonomous power supply system consists of a solar panel, a solar power management board, and a battery. The solar power management board comprehensively manages the power input and output. When sunlight is strong, it charges the battery; when sunlight is weak or there is no power input, the battery powers the sampling system.

[0024] Under the influence of sample gravity, the strain bridge inside the microgravity sensor generates a voltage difference signal. This voltage signal is converted into a 24-bit mass digital signal by an A / D module. The mass digital signal and the digital signal from the temperature and humidity sensors are uploaded to the Arduino platform via their respective TTL serial ports. The Arduino platform processes the sampled digital signals, converting them into decimal temperature, humidity, and sample mass data. This data is then sent to the 4G communication module via the TTL serial port. Finally, the temperature, humidity, and sample mass data are converted to MQTT protocol format and uploaded to the Alibaba Cloud platform, enabling real-time acquisition and uploading of sample mass and temperature / humidity data.

[0025] Figure 4 As the core hardware component of the entire device, the HX711 microgravity sensor determines the accuracy and precision of the sampled data. The HX711 uses a strain gauge bridge structure; under gravity, the strain gauge bridge voltage changes, and the mass data voltage signal is converted into a 24-bit digital signal by an A / D converter, then connected to the Arduino platform via a GPIO interface. The SHT30 temperature and humidity sensor communicates with the Arduino platform via an I2C interface, sampling ambient temperature and humidity in real time. The Arduino platform compresses the mass, temperature, and humidity data into JOSN data format, transmits the compressed JOSN data to the 4G communication module via an RS232 interface, and uploads it to the Alibaba Cloud IoT platform for real-time display on a cloud-based big data website. The sampling system is powered by an independent power supply system. The voltage from the 10W solar panel is converted to a stable voltage by a 5V voltage regulator module and connected to the solar management module, which distributes the input power. When sunlight is strong, the power supplies the Arduino and Air724 modules and charges the polymer battery; when sunlight is weak, the polymer battery powers the Arduino and Air724 modules.

[0026] The sampling system specifically includes a data acquisition system, a data analysis system, an error assessment system, and a data transmission system. Details of each system are as follows: The data acquisition system is used to collect samples and convert signals of common air pollutants and meteorological parameters in real time, and to perform concentration analysis and early warning on the converted signals.

[0027] The data acquisition system includes a photoionization device and an enrichment sampling and preprocessing unit. The photoionization device collects samples of the gas to be tested and converts the sample data into electrical signals for concentration analysis. At the same time, the collected samples are processed by the enrichment sampling and preprocessing unit to obtain a digestion solution, which is then input into the error assessment system.

[0028] The principle of photoionization equipment is as follows Figure 3As shown, the device includes an ionization chamber, an amplification module, a display module, and an early warning module. In the test environment, the photoionization device uses the ionization chamber's suction pump to draw the gas to be tested into the PID internal structure of the detection device. The ionization chamber converts the concentration signal into an electrical signal, which is then amplified by the amplification module's circuitry to obtain an amplified voltage signal. The microcontroller acquires and processes this voltage signal, and displays the concentration of the gas to be tested on an OLED screen via the display module. When the concentration exceeds a threshold, the early warning module issues an alarm signal. In the event of gas pollution, this monitoring device can visually reflect the pollution situation.

[0029] In this embodiment, the conventional pollutants in the air include four elements: arsenic, cadmium, mercury, and lead.

[0030] The photoionization equipment, designed for use in harsh environments over extended periods, utilizes special materials for its protective casing. It enables long-term and short-term enhanced monitoring of regional atmospheric quality and pollutants. Specifically, it combines meteorological models such as WRF and MM5 with environmental air quality models like CALPUFF, CMAQ, and WRF-Chem. Data is acquired from meteorological stations, and existing mathematical and physicochemical laws are used to process the data and generate computational models for simulation results. This allows for the simulation of atmospheric environmental quality in typical cities and regions, enabling 24-hour continuous online monitoring and collection of urban atmospheric parameters: wind speed, temperature (-20-60℃), humidity (0-100%RH), and PM2.5 (0-1000ug / m³). 3 PM10 (0-2000ug / m³) 3 The system collects data on various parameters including carbon monoxide (0-50ppm), sulfur dioxide (0-500ppb), nitrogen dioxide (0-500ppb), ozone (0-500ppb), and VOCs (0-20ppm). It boasts high accuracy and a small time constant, and features a modular maintenance design for sensing points. This embodiment of the photoionization equipment features an ultra-low power consumption design and integrates multi-parameter data acquisition capabilities, enabling all-weather, location-independent monitoring.

[0031] The photoionization device also includes a power management module, a signal processing module, a multi-function module, and a temperature detection module.

[0032] The power management module includes a battery and its charging circuit, as well as a power monitoring and voltage conversion circuit. The monitoring device is powered by a 3.7V lithium battery, which provides centralized power to the ultraviolet lamp, electrode plate, suction pump, STM32F103RCT6 microcontroller, micro-current amplification circuit, and other equipment.

[0033] The signal processing module includes a micro-current amplifier circuit, a filter circuit, a voltage follower circuit before ADC sampling, and an STM32F103RCT6 microcontroller. The STM32F103RCT6 microcontroller is used for receiving and processing photoionization signals, as well as for RGB digital display. The temperature detection module is used for real-time temperature monitoring of the battery. During charging and discharging, the battery undergoes internal oxidation-reduction reactions; oxidation is exothermic, and reduction is endothermic. However, due to internal resistance, heat is generated during both charging and discharging. Valve-regulated batteries operate optimally between 15 and 25°C; excessively high or low temperatures negatively impact overall battery performance. Therefore, real-time monitoring of both the battery itself and the ambient temperature is essential. This temperature detection module uses a temperature sensor. To directly measure the battery's terminal temperature, the module directly measures the terminal temperature. Simultaneously, the temperature detection module employs wireless data transmission, wirelessly transmitting the temperature data collected by the detection unit to the microcontroller.

[0034] For the multifunctional design of the portable photoionization detector, this embodiment integrates various functions into a multifunctional module, including air pump control, alarm function, temperature and humidity acquisition function, SD card storage function, and Wi-Fi communication function.

[0035] To make data viewing more intuitive, data processing more convenient, and operation more user-friendly, this system incorporates a testing software program named "Battery Parameter Comprehensive Tester." This software can perform single measurements of voltage, internal resistance, and temperature, initiate continuous measurements, set unit sampling intervals, set parameter limits, and view historical data. The software interacts with the centralized control unit via a network protocol. Specifically, it primarily utilizes electrical signal measurement and digital processing technologies to achieve its various functions. The core of the measurement section is acquiring basic parameters such as battery voltage, current, and internal resistance, and then calculating advanced indicators such as capacity and health status. Voltage measurement uses a high-precision differential amplifier circuit to directly obtain the potential difference across the battery terminals, which is then converted into a digital signal by an analog-to-digital converter. Current measurement typically uses a precision resistor (called a shunt) connected in series in the circuit to measure the small voltage drop across the resistor as current flows, and then calculates the current value using Ohm's law. Internal resistance measurement often employs the AC injection method, injecting a small-amplitude AC signal of a specific frequency into the battery, and calculating the resistance value through voltage feedback. This method effectively avoids interference from internal battery polarization. The most accurate way to measure capacity is through a discharge test, which involves controlling the internal power devices to discharge the battery at a constant current and then directly calculating the actual capacity based on the product of the current and the time.

[0036] Human-computer interaction is primarily achieved through a combination of hardware interface and software system. Users can input commands via physical buttons or a touchscreen, such as setting parameters, starting or stopping the test. Test results are displayed in real-time on an LCD screen, including not only numerical data but also intuitive formats such as curves and bar charts. The equipment is typically equipped with audible and visual alerts, such as buzzer alarms and LED indicators, for anomaly warnings or status indications. Data communication interfaces include wired methods such as USB and RS232; some high-end devices also support Wi-Fi or 4G wireless transmission, allowing test data to be exported to a computer or cloud platform for further analysis and archiving.

[0037] The centralized control unit is the core control component of the entire testing instrument, typically an embedded computer system. Its hardware core often uses an ARM or DSP processor, responsible for data calculation, logic control, and task scheduling. The control unit also includes various types of memory: FLASH for storing program code and fixed data, RAM for providing temporary space for program execution, and EEPROM for storing calibration parameters and device configuration information. On the software side, it usually runs a real-time operating system (RTOS) or a bare-metal program to ensure that all tasks can be executed promptly and stably.

[0038] The centralized control unit's functions include managing the data acquisition process, executing measurement algorithms, processing user operations, controlling display output, managing data storage and communication, and being responsible for the overall system's safety monitoring. For example, during testing, it can detect battery abnormalities in real time and automatically stop the test if necessary to protect the equipment and battery.

[0039] This centralized control unit is typically installed on the main circuit board inside the device, usually near the back of the panel or in the center of the device, for easy connection to the screen, buttons, and external interfaces. During layout, special care is taken to keep it away from high-current and high-power components to reduce electromagnetic interference, while also considering heat dissipation requirements to ensure stable operation of the device over extended periods.

[0040] In summary, the battery parameter comprehensive tester acquires raw data through precise measurement circuits, performs calculations and controls based on the main control unit, and interacts with users through a user-friendly human-machine interface, forming a complete measurement and control system.

[0041] After designing the various modules and structure of the photoionization device, the outer casing needs to be designed and the mold fabricated using SolidWorks 2019 software. The modules are then sequentially installed into the casing, making the entire photoionization device a handheld device. Simultaneously, the relationship between the concentration of the analyte gas and the detector output signal intensity is analyzed. Origin software is used to fit and determine the calibration curve, and the corresponding algorithm is embedded in the microcontroller program. This allows the display screen to directly show the VOCs concentration values ​​when testing different concentrations of the analyte gas. Finally, the performance of the designed photoionization detector is comprehensively tested, and the impact of humidity on the experimental results is analyzed.

[0042] Specifically, the installation structure of the weather station is as follows: Figure 6 As shown, the system includes a pole connected by two flange sections and secured to the ground with four M14 expansion bolts, making it resistant to strong winds and other severe weather conditions and not easily damaged. The pole houses a solar panel, electrical control box, rain gauge, and weather station. The solar panel is a 60W model. The electrical control box is made of powder-coated metal, resistant to sunlight and corrosion. The cables used for equipment connection are bottom-out, 1-to-1 waterproof plug-in cables to prevent wiring errors. The rain gauge is a 0.3mm resolution national standard pulse rain gauge, mounted on the top of the pole using a bracket to prevent obstruction from other structures. A weather station, an integrated ultrasonic weather station, is also installed on the top of the pole and fixed to the flange. The flange and bracket are mounted on a U-shaped bracket, which is connected to the top of the pole using a clamping seat. All waterproof plug-in cables are located inside the pole, providing an aesthetically pleasing appearance and good protection. The pole also has spare micro-connection holes for wiring.

[0043] Photoionization detectors (PIDs) and weather stations are two completely different types of equipment used in environmental monitoring, but they often work together to provide crucial data for air quality monitoring. The core function of a PID is to detect the concentration of volatile organic compounds (VOCs) in the air. It generates high-energy ultraviolet light through an internal ultraviolet lamp, ionizing gas molecules, and then calculates the VOC concentration by measuring the current generated by ionization. It can sensitively detect organic pollutants at the ppm or even ppb level, essentially acting as a professional "chemical olfactory organ," accurately answering the questions, "What pollutants are in the air? How much?"

[0044] The weather station is responsible for monitoring the physical state parameters of the atmosphere. It measures meteorological elements such as wind speed, wind direction, air temperature, humidity, and atmospheric pressure through a series of sensors. These data reflect the movement state and environmental conditions of the atmosphere and can answer questions such as "How does the air flow? What are the environmental conditions?" Although their functions are independent, in actual environmental monitoring, their data is strongly complementary. When the PID detects abnormal VOCs concentration, the wind direction data provided by the weather station can help determine the direction of the pollution source, the wind speed data can infer the transport speed of pollutants, and the temperature and humidity data can help analyze the conditions for pollutant diffusion and transformation. This combination of data is of great value for pollution source location, emergency response to sudden environmental events, and analysis of environmental quality trends.

[0045] In addition, meteorological data can also be used to improve the accuracy of PID monitoring data. Since meteorological factors such as temperature, pressure, and humidity affect gas density and instrument performance, compensating and correcting the PID readings using the data from the weather station can obtain more reliable monitoring results.

[0046] In summary, the PID device and the weather station, through complementary functions and data fusion, jointly constitute a more complete environmental monitoring solution, providing more scientific and comprehensive technical support for environmental protection decision-making.

[0047] When the microcontroller collects and processes the voltage signal, it conducts concentration monitoring based on the established atmospheric monitoring standard independent of the meteorological monitoring framework. Specifically, in this embodiment, considering professional division of labor and data accuracy, the standard concentration monitoring and meteorological monitoring are kept independent in the framework of atmospheric monitoring. The core objectives of these two types of monitoring are completely different: concentration monitoring uses a photoionization detection device to accurately measure the pollutant content, and its technical core is chemical analysis, focusing on the accuracy and sensitivity of the measurement; meteorological monitoring uses a weather station to record physical parameters such as wind, temperature, humidity, and pressure, and its technical core is atmospheric physics, focusing on the representativeness and timeliness of the data.

[0048] This independence brings many advantages. First, it ensures the professionalism and accuracy of the data, and each type of monitoring can use the most suitable instruments and methods in their respective fields without compromising due to hardware integration. Second, the independent system makes the management responsibility clearer. When there are problems with the data, it can quickly locate whether the concentration monitoring or the meteorological monitoring link has failed. In addition, this architecture also improves the system reliability. The failure of one system will not affect the operation of the other system, and at the same time provides greater flexibility, facilitating the separate optimization and expansion of different types of monitoring points according to actual needs.

[0049] In practical applications, this independence manifests in several aspects. At the hardware level, monitoring stations are equipped with their own dedicated devices; air quality monitors and meteorological sensors are typically installed separately. At the data flow level, the two types of data are collected and transmitted through independent channels, and sent to different management systems. At the operation and maintenance level, concentration monitoring is the responsibility of the environmental protection department, while meteorological monitoring may be maintained by the meteorological department or specialized technical personnel, each following different standards and specifications.

[0050] It is important to emphasize that this independence within the framework does not preclude data integration. In the final data application stage, environmental protection departments will conduct correlation analysis on the two types of independently generated high-quality data to more accurately assess the pollution situation, trace pollution sources, and predict diffusion trends. This "specialized division of labor and data sharing" model ensures the reliability of basic data and provides a scientific basis for decision analysis.

[0051] 1. Determine the parameters that affect atmospheric indicators.

[0052] Atmospheric parameters are typically determined through the following process: 1) Scientific Mechanism Analysis: Based on atmospheric chemistry and physics principles, identify the causes and influencing factors of major pollutants. For example, PM2.5 concentration is closely related to primary emissions (such as industrial smoke and dust) and secondary formation (the chemical reactions of its precursors SO2, NOx, and VOCs in the air). Ozone (O3) is a typical secondary pollutant, formed by the photochemical reaction of NOx and VOCs under sunlight; therefore, its monitoring must be correlated with parameters such as NOx, VOCs, and solar radiation.

[0053] 2) Source apportionment and contribution assessment: By using source apportionment techniques (such as CMB model, PMF model), the relative contribution of different pollution sources (coal combustion, motor vehicles, industrial processes, dust, agricultural ammonia, etc.) to the overall pollution is identified, thereby determining the pollution sources that need to be prioritized for control and their characteristic pollutants (such as SO2 for coal combustion and NOx for motor vehicle exhaust).

[0054] 3) Health and Ecological Effects Studies: Based on epidemiological and toxicological studies, pollutants with the greatest impact on human health (such as respiratory and cardiovascular diseases) and ecosystems (such as acid rain and eutrophication) are selected as core indicators. For example, PM2.5 and ozone are listed as pollutants of global concern due to their clear health risks.

[0055] 4) Technical feasibility and economic cost: Consider whether the monitoring technology for the parameter is mature and stable, and whether the monitoring cost is affordable. Prioritize parameters with existing international or national standard monitoring methods to ensure the accuracy, comparability, and repeatability of the data.

[0056] 5) Policy and management needs: The determination of the indicator system must serve the environmental management objectives. For example, to evaluate the effectiveness of the "Blue Sky Protection Campaign," PM2.5, PM10, O3, and NO2 will be used as core assessment indicators.

[0057] Parameter selection is a dynamic, iterative process, which can be summarized as follows: 1) Problem identification and target definition: Clarify the monitoring purpose (such as urban air quality assessment, pollution source monitoring, background value monitoring, scientific research).

[0058] 2) Literature and case studies: Review scientific literature and mature international and domestic experiences to establish an initial list of alternative parameters.

[0059] 3) Expert consultation and Delphi method: Organizational experts conduct multiple rounds of scoring and evaluation of the importance, representativeness and measurability of the alternative parameters to build consensus.

[0060] 4) Data availability and cost analysis: assess the technical capabilities, equipment costs, and operation and maintenance requirements for monitoring various parameters.

[0061] 5) Preliminary screening and pilot monitoring: Based on the above analysis, narrow down the parameter range, conduct pilot monitoring, and verify the sensitivity, effectiveness, and operability of the parameters.

[0062] 6) Evaluation and optimization: Analyze pilot data, evaluate the correlation between each parameter and the pollution process, further optimize the final determined indicator parameter system, and form standardized technical specifications.

[0063] In summary, determining and selecting atmospheric indicator parameters is a comprehensive decision-making process that integrates scientific understanding, technological capabilities, economic costs, and management needs.

[0064] 2. Research and participate in the formulation of unified standards for atmospheric monitoring based on data.

[0065] Data-driven standards research and development process 1) Strengthening the scientific foundation: Based on a large amount of monitoring and research data Extensive data collection encompasses long-term, massive amounts of data from national and local environmental monitoring networks (urban stations, background stations, regional stations, etc.), scientific research observations (large-scale comprehensive experiments), online pollution source monitoring, and satellite remote sensing retrieval. This data forms the cornerstone of the research.

[0066] By analyzing pollution characteristics and utilizing this data, we can determine the concentration levels, spatiotemporal distribution patterns, and evolution trends of pollutants in different regions and seasons of my country. For example, we can use data to determine whether PM2.5 pollution is mainly caused by coal combustion or mobile sources, and whether ozone pollution is controlled by VOCs or NOx.

[0067] Establishing concentration-effect relationships is the core scientific basis for setting standard values. Through epidemiological big data studies, quantitative relationships need to be established between pollutant concentrations (such as daily and annual average concentrations of PM2.5 and O3) and public health endpoints (such as mortality and hospitalization rates). Similarly, dose-effect relationships regarding the impact on ecosystems (such as crop yield reduction and acid rain) also need to be established.

[0068] 2) Determine benchmark values: based on health and ecological risks Health risks are assessed based on the concentration-effect relationship described above, with scientists and public health experts determining acceptable levels of health risk. The World Health Organization (WHO) air quality guidelines are based on this, representing thresholds for the known relationship between pollutant concentrations and health effects.

[0069] A baseline value should be established, taking into account health risks, ecological effects, and international experience (such as WHO and US EPA standards), to propose a scientific baseline value with health protection as its primary objective. This value is a purely scientific goal.

[0070] 3) Conduct a feasibility study: based on techno-economic assessment data. The technology accessibility analysis assesses whether current mainstream monitoring technologies can stably and accurately measure target pollutants to baseline levels. Simultaneously, it analyzes whether pollution control technologies (such as dust removal, desulfurization, denitrification, and VOCs treatment) can, after large-scale application, generally achieve the expected environmental concentration levels.

[0071] Cost-benefit analysis involves collecting data and simulating to predict the total social costs (government and business inputs) and total benefits (reduced healthcare spending, increased labor productivity, and improved ecosystem services) required to achieve standard goals at different stages. Data-driven cost-benefit analysis is a key basis for decision-making.

[0072] Scenario simulation and impact prediction utilizes air quality models (such as CMAQ) to input different emission control scenarios, simulate and predict future air quality improvement paths, and assess the technical pathways and time required to achieve standard targets.

[0073] In summary, developing air quality standards based on data is a transformation process from "data" to "information" and then to "decision-making." By collecting and analyzing multidimensional data, we can transform it into a profound understanding (information) of pollution characteristics, health risks, and the feasibility of remediation, ultimately supporting scientific and democratic decision-making (standards). Active participation hinges on the ability to generate and provide a solid and reliable chain of data evidence.

[0074] Collecting and monitoring air data, storing and analyzing the results in real time, and obtaining relevant data forms the basis for air quality control and reasonable assessment, as well as the data foundation for the formulation of atmospheric monitoring standards.

[0075] Phase 1: Data Acquisition and Preprocessing The goal at this stage is to ensure that the raw data obtained from the monitoring instruments is accurate, reliable, and usable.

[0076] 1) Data acquisition: Monitoring instruments (such as gas analyzers, particulate matter monitors, and meteorological sensors) continuously generate raw voltage, current, or optical signals at a set frequency (such as once every 1 minute or once every 5 minutes).

[0077] The data acquisition unit automatically reads these signals and converts them into engineering physical quantities, such as concentration (μg / m³), using the instrument's built-in calibration curve (generated by periodic calibration). 3 Data such as ppb, wind speed (m / s), and temperature (°C) are stored with timestamps.

[0078] 2) Data preprocessing and quality control (QA / QC): This is a crucial step in ensuring data quality. It is usually completed automatically by the data acquisition system or platform, supplemented by manual review.

[0079] 3) Invalid value removal: Automatically identifies and removes invalid or null values ​​caused by instrument power failure, restart, calibration, communication interruption, etc.

[0080] 4) Outlier identification: Identify and mark outliers based on statistical methods (such as the Raida criterion) or physical probability (such as the concentration cannot be negative and the instantaneous fluctuation has a reasonable range).

[0081] 5) Data imputation: For valid data missing within a short period of time, sometimes reasonable imputation is performed based on data from previous and subsequent time points. However, for data missing for a long period of time, it is usually retained as a missing value and not included in the statistics.

[0082] 6) Calibration and correction: Apply parameters that are manually calibrated regularly, or use meteorological data (such as temperature and pressure) from collaborative monitoring to compensate and correct concentration readings so that the data reach standard conditions (such as concentrations under standard temperature and pressure) and ensure data comparability.

[0083] 7) Output of this stage: Continuous, labeled, and valid minute-level or hour-level concentration data that has undergone basic quality control.

[0084] Phase Two: Data Standardization and Statistics The goal of this stage is to process the preprocessed raw data according to the statistical methods specified in national standards to obtain indicators for evaluation.

[0085] 1) Unit Consistency and Conversion: Ensure that all concentration units are consistent with the units of the standard limits. For example, the concentrations of gases such as SO2, NO2, O3, and CO are usually expressed in μg / m³. 3 Sometimes ppb or ppm is used, which needs to be converted according to chemical laws.

[0086] 2) Standardized time-scale statistics: National standard limits are set for different time scales, therefore corresponding statistical calculations must be performed on continuous data. 3) Hourly average concentration: Usually calculated as the arithmetic mean of valid minutes of data within one hour.

[0087] 4) Daily average concentration: Calculate the arithmetic mean of the effective hourly average concentrations over a calendar day (0:00 to 24:00). Typically, at least 20 hours of valid data (i.e., a 75% data capture rate) are required; otherwise, the data for that day is invalid.

[0088] 5) 8-hour moving average (primarily used for O3): Calculates the arithmetic mean of the hourly average concentrations over the previous 8 hours (including the current time). A new 8-hour moving average is calculated every hour.

[0089] 6) Annual average concentration: Calculate the arithmetic mean of the average concentrations of all valid daily days within a calendar year. At least 324 valid daily average data points are required (i.e., approximately 90% data capture rate).

[0090] 7) Outputs at this stage: hourly values, daily averages, 8-hour moving averages, etc., conforming to the statistical methods specified in the standard.

[0091] Phase 3: Concentration Detection, Evaluation, and Application Traditional atmospheric concentration detection and evaluation mainly follow a linear process of "monitoring-statistics-comparison," focusing on the standardized statistical analysis of monitoring data from fixed locations after the fact, and comparing it with national standard limits to determine whether compliance is achieved or to calculate the Air Quality Index (AQI). While this method is standardized, it is somewhat passive and difficult to cope with complex and ever-changing pollution processes.

[0092] The innovative concentration detection procedure aims to construct an intelligent closed-loop system of "sensing-analysis-intervention-verification". Its core is to establish an integrated air-space-ground collaborative sensing network, integrating multi-dimensional data from fixed stations, mobile monitoring vehicles, drones, and micro IoT sensors to achieve precise capture from macro to micro levels, providing a high spatiotemporal resolution data foundation for analysis.

[0093] After acquiring real-time data, the system utilizes machine learning and big data technologies for dynamic source tracing and real-time modeling, rapidly analyzing pollution sources and transport pathways. Simultaneously, based on AI predictive models and scenario simulation tools, it achieves a leap from current status assessment to predictive assessment, enabling quantitative extrapolation of the effectiveness of control measures and providing forward-looking evidence for precise decision-making.

[0094] Ultimately, the system transforms the assessment results into intervention instructions through a cross-departmental collaboration platform, tracks and monitors data changes in real time, and quantitatively verifies the environmental benefits after the implementation of measures, forming a complete data closed loop of "monitoring-evaluation-decision-verification," truly upgrading concentration detection from a static evaluation tool to a dynamic governance engine.

[0095] Phase Four: Data Interpretation and Application Using standards to complete the evaluation is not the end point; what is more important is the in-depth application of data based on the results.

[0096] 1) Compliance Management: Determining whether a city or region meets the standards. For example, a city must meet both the annual average concentration of PM2.5 and the daily average concentration at a specific percentile for it to be considered to have met the annual standards.

[0097] 2) Pollution process analysis: Combining meteorological parameters (wind, temperature, pressure, humidity) data, analyze the process and causes of pollution formation, accumulation, and dissipation, achieving a leap from "what" to "why".

[0098] 3) Source tracing and early warning: Use data to trace the source of pollution and issue early warnings based on meteorological conditions and model predictions before pollution occurs, and initiate emergency response.

[0099] 4) Policy evaluation: Compare long-term monitoring data before and after the implementation of a certain environmental protection policy (such as "coal-to-electricity conversion"), use data to objectively evaluate the environmental benefits of the policy, and provide a basis for future decision-making.

[0100] The entire process relies heavily on standardized operating procedures (SOPs) and quality control (QA / QC) systems to ensure that everything from sampling and analysis to data processing is documented and verifiable, thereby ultimately guaranteeing the accuracy, comparability, and impartiality of the monitoring data so that it can be truly used for scientific research and environmental management.

[0101] The enrichment sampling and pretreatment unit is used to digest the sample using membrane filtration technology to obtain a digestate. In this embodiment, the enrichment sampling and pretreatment unit works in conjunction with the atomic fluorescence spectrometer to perform sample pretreatment and device error assessment.

[0102] The enrichment sampling and pretreatment unit is connected to the atomic fluorescence spectrometer via a corrosion-resistant transmission tube and dual signal lines. The corrosion-resistant tube is used to transport the digested solution after digestion, and the signal lines are responsible for the transmission of control signals between the instruments.

[0103] An error assessment system is used to assess the errors of key indicators of a data acquisition system based on samples collected by the system.

[0104] The error assessment system in this embodiment is an atomic fluorescence spectrometer, used to assess the errors of key indicators of the data acquisition system. These key indicators include the detection limit, calibration correlation coefficient, measurement precision, and data validity rate. The specific steps are as follows: (1) Set the parameters of the atomic fluorescence spectrometer.

[0105] Table 1. Relevant Technical Specifications of Atomic Fluorescence Spectrometer

[0106] 1. Selection of lamp current Atomic fluorescence spectrometers use high-intensity hollow cathode lamps as the light source. Different elements emit specific characteristic spectral lines, and their radiation intensity directly affects the fluorescence signal intensity. This light source supports instantaneous high-current operation without self-absorption and can withstand high-pulse current surges. For high-intensity lamps, energy output can be controlled by adjusting the current ratio of the main cathode to the auxiliary cathode. It is important to note that lamp current adjustment is independent of the high-voltage system. Under specific conditions, both fluorescence intensity and detection sensitivity are positively correlated with lamp current, with the main cathode current having a particularly significant impact on sensitivity. However, it should be noted that if the operating current exceeds the rated range, the lamp life will decrease exponentially. Furthermore, due to the special process and anode lamp structure of the Hg lamp, its lifespan will be significantly shortened if the operating current exceeds the recommended value.

[0107] The maximum average operating current indicated on the lamps or their packaging for each element is the actual current required by the instrument, while the bias current and pulse current should be set accordingly. The optimal current parameters vary depending on the element and the instrument; please refer to the optimized experimental conditions for setting these parameters. The specific lamp current parameter configurations for this instrument are shown in Table 2.

[0108] Table 2. Cathode Lamp Bias Current and Pulse Current Settings

[0109] 2. Photomultiplier tube The atmospheric particulate matter environmental monitoring device uses a solar-blind photomultiplier tube (PMT) as the fluorescence signal detection element. The operating negative high voltage of this device is independent of the adjustment of the light source lamp current. Within a specific operating range, the fluorescence signal intensity is positively correlated with the applied negative high voltage, while the noise level and signal stability are negatively correlated with the negative high voltage. It is particularly important to note that the PMT has a clearly defined withstand voltage range, and its sensitivity varies exponentially with the operating high voltage. In practical operation, when the detection sensitivity meets the testing requirements, a lower negative high voltage operating condition should be preferred to balance instrument performance and stability. The negative high voltage used by this instrument for the four elements to be measured is shown in Table 3.

[0110] Table 3. Negative high pressure used for the measurement of the four elements

[0111] 3. Selection of carrier gas and shielding gas flow rates During digestion, heavy metal elements undergo chemical reactions to form hydrides, which are then transported by carrier gas to the atomizer to complete the atomization process. Experiments show that the atomic concentration, atomization efficiency, and detection sensitivity within the atomizer are inversely proportional to the carrier gas flow rate. However, excessively low carrier gas flow rates lead to increased signal fluctuations and reduced atomization efficiency. After optimization testing, 300 ml / min was ultimately selected as the optimal carrier gas flow rate parameter for this instrument.

[0112] The shielding gas system has a dual function: firstly, it prevents ambient air infiltration to avoid hydride oxidation; secondly, it effectively reduces fluorescence quenching, ensuring the stability and fluorescence efficiency of the detection system. Studies have found that excessively low shielding gas flow rates impair detection sensitivity and stability, while excessively high flow rates reduce atomization efficiency. System experiments have verified that this instrument achieves optimal detection sensitivity and signal stability when using a shielding gas flow rate of 900 ml / min.

[0113] 4. Other configurations and settings The atomizer is a core component of an atomic fluorescence spectrometer, and its furnace height, defined as the vertical distance between the optical axis and the top of the atomizer, is a key parameter affecting detection performance. The fundamental purpose of adjusting the furnace height in this embodiment is to allow the light beam to pass through the region within the atomizer where the concentration of free atoms is highest and most stable, thereby obtaining the optimal absorption signal and improving analytical sensitivity and accuracy. Experiments show that excessively small distances can cause gas-phase interference and increase the detection limit; therefore, this embodiment recommends a standard furnace height setting of 8 mm.

[0114] The atomizer height adjustment process is as follows: 1) Install the special height adjustment tool on the top of the atomizer; 2) Start the instrument's power system; 3) Run the control software; 4) Activate the signal board power supply and pulse power supply sequentially in the debugging interface; 5) Loosen the fixing screws of the atomizer bracket; 6) Adjust the height using the base knob (lower by turning counterclockwise) so that the center of the cathode lamp spot is aligned with the second scale line of the adjustment tool; 7) Adjust the four plastic screws on the lamp holder to precisely align the light spot with the intersection of the second horizontal and vertical lines. Regarding the sample injection system, the injection volume within a fixed time depends on the peristaltic pump speed, pump tubing inner diameter, and compression level. Over-injection will lead to reagent waste and tubing contamination; the optimized flow rate for this instrument is 3.8 ml / min.

[0115] After setting the parameters, the measurement sequence needs to be configured: T1=5s (blank measurement), T2=20s (sample measurement), T3=30s (tubular cleaning). The system will automatically execute the measurement process, and the operator can adjust the subsequent cleaning and acquisition times according to the signal peak shape and sample concentration.

[0116] (2) The enrichment sampling and preprocessing unit was integrated with the atomic fluorescence spectrometer.

[0117] After completing the parameter settings for the atomic fluorescence spectrometer and the configuration of the sampling and digestion parameters, it is necessary to perform joint debugging between the enrichment sampling and preprocessing unit and the atomic fluorescence spectrometer. The collaboration between the two is a systematic project aimed at achieving fully automated and efficient analysis. Its core lies in ensuring that the process from sample enrichment, processing to detection is seamless, stable and reliable.

[0118] Specifically, before integration testing, it is necessary to confirm that each unit operates independently and normally, the interfaces are compatible, and the reagents are qualified. The key to integration testing is timing synchronization, that is, accurately measuring the sample transmission delay time and setting it into the control programs of the AFS and pretreatment units respectively, so that the sample signal peak perfectly matches the data acquisition window of the AFS. At the same time, it is necessary to optimize the carrier gas flow rate and gas-liquid separator parameters to ensure efficient hydride transmission, and optimize the cleaning procedure through cross-contamination testing to eliminate memory effect. Finally, performance verification is completed by creating standard curves and testing precision and detection limits, and all optimized parameters are saved as standardized analytical methods, thereby ensuring that the integrated system meets the detection requirements of high sensitivity, high reproducibility, and automation.

[0119] When the filter membrane begins to undergo agitation and digestion, this process is a crucial pretreatment step in environmental monitoring (such as determining heavy metals and phosphorus in water and atmospheric particulate matter). Its core purpose is to completely and efficiently transfer the analyte retained on the filter membrane into the liquid and transform it into a form detectable by the instrument. The following are the detailed steps and key points of this process: Step 1: Preparation 1) Sample filter membrane: Carefully fold or cut the filter membrane used to collect the sample (such as polytetrafluoroethylene (PTFE) membrane or quartz membrane for atmospheric PM2.5 collection, or mixed cellulose ester membrane for water quality collection, etc.) with clean tweezers (to avoid damaging the sample) and place it in a digestion container (usually a 50ml or 100ml capped polyethylene bottle or glass test tube). 2) Adding Digestion Solution: Add an appropriate amount of strong acid digestion solution based on the target analyte and filter membrane material. Common systems include: Nitric Acid (HNO3) System: The most commonly used, suitable for digesting most metals. Nitric Acid-Hydrogen Peroxide (HNO3-H2O2) System: Stronger oxidizing power, used for digesting difficult-to-decompose organic matter or more stable compounds. Nitric Acid-Perchloric Acid (HNO3-HClO4) System: Extremely strong oxidizing power, highly hazardous, requires operation by qualified personnel in a dedicated fume hood, mainly used for extremely difficult-to-digest samples. Other specific acids: For example, sulfuric acid-potassium persulfate may be needed for total phosphorus determination. 3) Set blanks and standards: Prepare both process blanks (an unused clean filter membrane of the same batch number, treated in the same way) and spiked blanks (add a known amount of standard substance to the blank filter membrane) to monitor contamination and recovery rate during the experiment. Step 2: Dissolving through vibration and heating 1) Sealing: Tighten the cap of the digestion tube to prevent acid from evaporating during heating and shaking, which could lead to concentration changes, cross-contamination, and equipment corrosion.

[0120] 2) Place in a constant temperature shaker: Securely place the digestion tube into the sample rack of a constant temperature shaking water bath or a constant temperature shaker.

[0121] 3) Set parameters and start: Temperature: Usually set at 90°C ~ 100°C. High temperature can greatly accelerate the reaction rate between acid and sample. Shaking frequency: Usually set at 200~300 rpm. Shaking is crucial: Enhances mass transfer: Ensures sufficient and continuous contact between the digest and the filter membrane sample, avoiding excessively high or low local concentrations. Prevents bumping: Smooth shaking prevents the digest from suddenly and violently boiling due to local overheating (bumping), making the reaction more stable. Accelerates disruption: Mechanical force helps to physically disrupt the filter membrane, allowing it to dissolve or disperse more quickly in the acid solution. Time: Usually requires 2~4 hours, the specific time depending on the filter membrane material and the ease of sample loading. Continue until the filter membrane is completely dissolved or almost completely disappeared, and the solution becomes clear or pale yellow.

[0122] Step 3: Cooling and Volume Adjustment 1) Cooling: After digestion, carefully remove the digestion tube and allow it to cool naturally to room temperature in a fume hood. Do not open the lid at high temperatures to avoid burns or the release of acid fumes.

[0123] 2) Transfer and washing: Transfer all the cooled digestion solution to a volumetric flask (e.g., 25 ml or 50 ml). Rinse the inner wall and cap of the digestion tube several times with ultrapure water, and pour all the washing solution into the same volumetric flask to ensure that all analytes are completely transferred and to guarantee the recovery rate.

[0124] 3) Volume Adjustment: Dilute to the mark on the volumetric flask with ultrapure water, tighten the cap, and shake well. At this point, the sample has become a clear liquid sample with consistent acidity, which can be directly analyzed by the instrument (e.g., for metal determination using ICP-MS, AAS, ICP-OES, or for phosphorus determination using spectrophotometry). Key Benefits: Complete Extraction: Ensures that the target pollutants on the filter membrane and encapsulated in the filter matrix are completely released into the liquid. Speciation Conversion: Converts various forms of analytes (e.g., organic mercury, chromium in different valence states) into inorganic ionic states detectable by the instrument. Elimination of Matrix Interference: Destroys organic matter in the filter membrane and sample, preventing clogging of the sample introduction system or matrix interference during subsequent instrument analysis. Concentration Effect: By controlling the initial digestion solution and final volume adjustment, pre-concentration of low-concentration samples can be achieved, improving detection sensitivity.

[0125] The enrichment sampling and pretreatment unit sends a liquid extraction command to the atomic fluorescence spectrometer via a signal line. The digestion solution then enters the analysis unit of the atomic fluorescence spectrometer main unit through a corrosion-resistant tube to analyze the chemical reactions (atomization) and physical detection processes that occur after the sample vapor enters. Given that the nanofunctional filter membrane is still in the research and development stage, a standard solution of a specific element will be used instead of the actual digestion solution in subsequent performance evaluations. The advantage of using a standard solution is that its concentration value is known, facilitating accurate assessment of instrument measurement errors and analytical performance indicators.

[0126] (3) Calibrate the conventional pollutants in the air.

[0127] To ensure the accuracy and reliability of the atmospheric heavy metal analyzer in measuring target pollutants, this study employed a standard solution substitution method for systematic instrument calibration. The specific procedure is as follows: using high-purity standard substances of four elements—arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb)—a series of standard solutions with gradient concentrations covering the expected environmental concentration range (e.g., 0.0 μg / L, 1.0 μg / L, 2.0 μg / L, 5.0 μg / L, 10.0 μg / L)—to simulate the final test solution after actual atmospheric particulate matter samples are collected via filter membrane and digested with acid.

[0128] The aforementioned series of standard solutions were sequentially introduced into an atmospheric heavy metal analyzer for measurement. The instrument recorded the characteristic signal response value (y, such as fluorescence intensity or absorbance) corresponding to each known concentration point (x). Subsequently, linear regression analysis was performed using the least squares method to establish a calibration curve with concentration as the x-axis and signal value as the y-axis, and the fitted equation was obtained. The curve is denoted by , where slope 'a' represents the instrument's sensitivity and intercept 'b' reflects the system's background noise level. To confirm the effectiveness of the calibration, the correlation coefficient (R²) of this curve needs to be calculated. 2 Its value needs to be greater than 0.995 to ensure a good linear relationship.

[0129] Finally, this fitted equation is compared with the key parameters (a, b, R). 2 The data is stored in the instrument analysis software and used as the basis for quantitative calculations for subsequent actual sample determinations, thereby enabling accurate calibration and quantitative analysis of these four characteristic heavy metal elements in the air.

[0130] Meanwhile, the standard deviation of the electrical signals and related statistics of the calibration curves of each element, and the test results of multiple measurements of the 80% full-scale standard solution are summarized in Table 4.

[0131] Table 4. Calculation results related to the measurement signal calibration curve

[0132] This embodiment integrates an atmospheric particulate matter environmental monitoring device with an atomic fluorescence spectrometer, achieving online monitoring of the atmospheric particulate matter environment. Gradient concentration standard solutions were used to simulate actual sample digestion, and the key performance indicators of the instrument (including detection limit, calibration correlation coefficient, measurement precision, and data validity rate) were systematically evaluated. The overall instrument error was assessed through a comparative analysis of measured and standard concentrations. Test results show that all indicators of the monitoring system (detection limit, data validity rate, and measurement error) meet the design requirements, and systematic errors and background interference are effectively suppressed. Although a small amount of random error exists, its impact is within an acceptable range.

[0133] A data transmission system is used to transmit samples and analysis results collected by a data acquisition system across regions based on a sensitivity measurement algorithm using an anonymity model.

[0134] This embodiment differentiates the sensitivity requirements for sensitive data, revealing that sensitivity needs vary across different stages of data flow, necessitating different sensitivity measurement methods. Based on these characteristics of sensitive data, this embodiment employs a sensitivity measurement algorithm based on anonymity models for cross-regional data transmission. Its core is the transmission of two types of key data between monitoring nodes in different regions, according to geographical location: first, the collected raw data, including real-time measurements of pollutant concentrations, wind speed, wind direction, and other meteorological parameters at each monitoring point, along with precise time and location tags; second, the evaluated and analyzed results, such as pollution source apportionment contribution rates, cross-border pollution flux estimations, pollution source tracing trajectories, and early warning and forecast information. These data collectively constitute the data foundation for cross-regional pollution assessment and collaborative governance.

[0135] Data transmission primarily occurs between different levels of the monitoring network. Firstly, there is vertical transmission, where data converges from field monitoring equipment (edge ​​nodes) in various regions to regional or national cloud data centers. The aim is to achieve data aggregation, unified quality control, and support for macro-level decision-making. More importantly, there is horizontal transmission, where data is exchanged directly between data centers (clouds) in different regions. For example, data sharing between platforms in various cities within the Beijing-Tianjin-Hebei region is crucial for cross-regional emergency response and collaborative governance. When a region predicts that pollution will spread to neighboring areas, it will immediately transmit early warning information and source tracing data to the other side, facilitating the early initiation of emergency response.

[0136] Therefore, this process is far more than simple data replication; it is an intelligent data flow ecosystem built to achieve precise pollution control. Its ultimate goal is to enable data not only to reveal the local pollution situation but also to clarify the impact of regional transmission, directly supporting collaborative prevention and control decisions and actions across administrative regions.

[0137] Algorithms influence the sensitivity of data by controlling the precision of data generalization. Lower generalization precision results in greater sensitivity, better sensitivity effects, but poorer data usability. Data generalization primarily targets quasi-identifier attributes in relational data tables. Before attribute generalization, a generalization hierarchy tree is constructed based on the possible values ​​of the quasi-identifier attributes. Different values ​​of one or more attributes are then generalized to different depths according to a predetermined generalization path, ensuring that the quasi-identifier attribute values ​​of multiple tuples fall within the same range. For attributes that may need to be generalized to the root of the hierarchy tree, their values ​​are set to null during data publication to suppress their publication. Generalization effectively controls the distribution of sensitive attributes in relational tables; the most common approach is the k-anonymous model.

[0138] The k-anonymity model is a classic method for sensitivity measurement, where k represents the anonymity of the quasi-identifier attribute in the dataset. For each individual data item, there are at least k-1 other data items that are indistinguishable from it. Therefore, an attacker without prior background knowledge has an equal probability of identifying any record among the k anonymized data items, and the probability of identifying sensitive user information is 1 / k. The larger the value of k in k-anonymity, the more difficult it is for an attacker to infer sensitive information, and the lower the risk of sensitive information leakage.

[0139] Sensitivity measurement algorithms based on anonymity models have matured and improved over many years of research. Although certain shortcomings exist in different algorithms, with the resolution of key issues, sensitivity measurement algorithms based on anonymity models have been applied to various fields.

[0140] As can be seen from the above introduction, the sensitivity measurement algorithm designed by constructing anonymization models often depends on the anonymization parameters of the anonymized dataset. However, setting uniform anonymization parameters means providing the same level of security protection for all data, which cannot meet the special needs of some data. This can lead to excessively high or low sensitivity for some specific users, ultimately resulting in excessive data generalization, low usability, or poor resistance to the risk of sensitive data leakage.

[0141] To quantify the privacy leakage risk of sensitive attribute information, the concept of privacy weight is proposed, starting from the correlation between public and sensitive attributes. To reduce the amount of data an attacker can intercept in a single instance and the correlation between public and sensitive attributes, clustering entropy is used as a constraint during data partitioning. Large data tables are split into several equivalence classes with relatively evenly distributed sensitive attribute values, preventing sensitive attributes from clustering in a single data table.

[0142] A relational data table is established, and data-sensitive attributes and execution time are grouped through multiple sets of While loops. This transforms the complex encryption of various metadata types into equivalent attribute transformations. This achieves a low-power, low-performance encrypted transmission method for sensitive data.

[0143] This embodiment ensures data security and reliability through the research of encryption measurement algorithms for data transmission, realizes two-way data verification, improves the stability of platform services, and provides a solid foundation for data analysis.

[0144] 1. Achieve remote encrypted transmission by combining multiple data transmission and communication methods; 2. Implement an optimization algorithm to achieve the optimal matching between device power consumption and transmit / receive frequency; 3. Implement abnormal situation early warning and alarm algorithms to prevent data tampering events from occurring.

[0145] Because the hardware involved in the project requires long-term, high-frequency data transmission, the total data transmission time is limited after large-scale deployment. The given time is insufficient for encrypting all data and performing secondary verification, meaning some data will inevitably be exposed in plaintext during transmission. This study utilizes mutual information to measure the correlation between public and sensitive attribute information, and proposes a quantification method based on information entropy to distribute sensitive attribute information as evenly as possible across equivalence classes, reducing the risks of privacy leaks and power consumption for sensitive user data. The following are the detailed steps and key points of the encryption algorithm applied in this embodiment: Step 1: Data Sensitivity Grading and Quantitative Evaluation System To achieve refined security control, this embodiment establishes a data sensitivity quantification model based on multi-factor weighted evaluation. This model comprehensively judges data from three dimensions: the consequence impact factor (C) evaluates the degree of social and economic impact that may be caused by data leakage (such as being rated 9 points for causing public panic or cross-provincial disputes); the responsibility correlation factor (I) determines the tightness of the association between the data and a specific entity (such as a source analysis result that precisely points to a certain enterprise being rated 10 points); the compliance factor (R) sets confidentiality requirements based on regulations such as the Environmental Protection Law and the Data Security Law (such as core monitoring data required by the state being rated 8 points). The sensitivity score is calculated through the formula S=(C+I+R) / 3, and the data is divided into four security levels: Level 1 (0<S≤3 is the original monitoring data), Level 2 (3<S≤7) is the regular warning information, Level 3 (7<S≤10 covers the source analysis result and cross-border flux data), and Level 4 (S=10) corresponds to top-secret information such as the core algorithm model.

[0146] Step 2: Hierarchical encryption transmission and key management mechanism For different sensitivity levels, the system implements differential protection using encryption algorithms certified by the National Cryptography Administration: Level 1 / 2 data uses the SM4 symmetric encryption algorithm and is transmitted through an IPSec VPN tunnel. The key is uniformly distributed by the provincial environmental monitoring center and rotated quarterly; for Level 3 highly sensitive data, the "digital envelope" technology is enabled. The temporarily generated SM4 session key is encrypted using the SM2 asymmetric algorithm and then transmitted through a two-way authentication TLS 1.3 channel; for Level 4 top-secret data, a quantum key distribution (QKD) network based on physical isolation is deployed, and the key is transmitted through quantum states to achieve unconditional security. The entire key lifecycle is managed by a hardware security module (HSM) to ensure that the entire process of generation, storage, distribution, and destruction is auditable and non-exportable.

[0147] Step 3: Secure transmission and integrity guarantee process End-to-end protection is implemented during the data transmission process: The sending end first calculates the SM3 hash value of the data packet, signs it with the sender's private key, and transmits it together with the encrypted data; after the receiving end verifies the signature to confirm the authenticity of the data source, Level 3 / 4 data requires a two-level decryption process - first, the session key is decrypted using the receiving end's private key, and then the data content is decrypted using the session key. After decryption, the hash value is recalculated and compared with the hash in the signature. Any tampering will trigger an audit alarm. The entire process complies with the third-level security requirements of the "Basic Requirements for Information Security Technology Network Security Classification Protection" (GB / T 22239-2019). Each transmission generates a digital fingerprint and stores it in the blockchain evidence storage system to provide a technical basis for subsequent liability tracing.

[0148] Step 4: Post-decryption data processing and application governance Decrypted data is processed in a secure sandbox environment: First, it is integrated with the data lineage management system, automatically adding a "decrypted" status tag and transmission logs; then, it undergoes anonymization processing according to the "Environmental Monitoring Data Open Sharing Specification" (e.g., masking precise enterprise coordinates while retaining administrative division information), and recipient identification information is implanted using data watermarking technology. Processed data is then pushed to different application scenarios based on access levels: Level 1-2 data is directly integrated into the regional visualization cloud platform system; Level 3 data requires multi-party secure computation (MPC) before participating in regional pollution source tracing analysis; Level 4 data is only allowed to participate in model optimization in encrypted form through federated learning mode. All data usage is recorded and synchronized to the national environmental data source tracing platform, forming a closed-loop governance system.

[0149] This system, through the deep integration of quantitative grading, cryptographic technology, and process control, not only meets the real-time requirements of cross-regional data sharing, but also achieves controllability and manageability of sensitive data throughout its entire lifecycle, providing a safe and reliable data circulation infrastructure for collaborative governance of the ecological environment.

[0150] Example 2: Embodiment 2 of the present invention provides a monitoring method for an atmospheric particulate matter monitoring device based on multimodal sensing, comprising the following steps: Real-time sample collection and signal conversion of conventional air pollutants and meteorological parameters; The signal converted by the data acquisition system is used for concentration analysis and early warning. Error assessment of key indicators of the data acquisition system is performed based on the collected samples; The sensitivity measurement algorithm based on the anonymity model enables cross-regional transmission of samples collected by the data acquisition system and analysis results from the data analysis system.

[0151] The steps involved in the above embodiment two correspond to those in embodiment one. For specific implementation details, please refer to the relevant description section of embodiment one.

[0152] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application 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 application. In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)). The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An atmospheric particulate matter monitoring device based on multimodal sensing, characterized in that, include: The data acquisition system is used to collect samples and convert signals in real time from common air pollutants and meteorological parameters. The data analysis system is used to perform concentration analysis and early warning on the signals converted by the data acquisition system; An error assessment system is used to assess the errors of key indicators of a data acquisition system based on collected samples. A data transmission system is used to transmit samples and analysis results collected by a data acquisition system across regions based on a sensitivity measurement algorithm using an anonymity model.

2. The atmospheric particulate matter monitoring device based on multimodal sensing as described in claim 1, characterized in that, Common air pollutants include four elements: arsenic, cadmium, mercury, and lead.

3. The atmospheric particulate matter monitoring device based on multimodal sensing as described in claim 1, characterized in that, The data acquisition system includes a photoionization device and an enrichment sampling and preprocessing unit. The photoionization device collects samples of the gas to be tested and converts the sample data into electrical signals for concentration analysis. At the same time, the collected samples are processed by the enrichment sampling and preprocessing unit to obtain a digestion solution, which is then input into the error assessment system.

4. The atmospheric particulate matter monitoring device based on multimodal sensing as described in claim 1, characterized in that, The photoionization device includes an ionization chamber, an amplification module, a display module, and an alarm module. In the test environment, the photoionization device uses the suction pump of the ionization chamber to draw the gas to be tested into the internal structure of the PID controller of the detection device. The ionization chamber converts the concentration signal into an electrical signal, which is then converted into an IV signal by the amplification circuit of the amplification module to obtain an amplified voltage signal. The microcontroller collects the voltage signal, processes and analyzes it, and displays the concentration of the gas to be tested on an OLED screen through the display module. When the concentration exceeds the threshold, the alarm module issues an alarm signal.

5. The atmospheric particulate matter monitoring device based on multimodal sensing as described in claim 1, characterized in that, The photoionization device also includes a power management module, a signal processing module, and a temperature detection module. The power management module includes a battery and its charging circuit, a power monitoring and voltage conversion circuit, a signal processing module including a micro-current amplification circuit, a filtering circuit, a voltage follower circuit before ADC sampling, and an STM32F103RCT6 microcontroller, and the temperature detection module is used to perform real-time temperature detection of the battery.

6. The atmospheric particulate matter monitoring device based on multimodal sensing as described in claim 1, characterized in that, The error assessment system is an atomic fluorescence spectrometer.

7. The atmospheric particulate matter monitoring device based on multimodal sensing as described in claim 6, characterized in that, The enrichment sampling and pretreatment unit is connected to the atomic fluorescence spectrometer via a corrosion-resistant transmission tube and dual signal lines. The corrosion-resistant tube is used to transport the digested solution after digestion, and the signal lines are responsible for the transmission of control signals between the instruments.

8. The atmospheric particulate matter monitoring device based on multimodal sensing as described in claim 1, characterized in that, Key performance indicators include detection limit, calibration correlation coefficient, measurement precision, and data validity.

9. The atmospheric particulate matter monitoring device based on multimodal sensing as described in claim 6, characterized in that, The specific steps for error assessment of key indicators of the data acquisition system based on the collected samples are as follows: Set the parameters for the atomic fluorescence spectrometer; The enrichment sampling and preprocessing unit was integrated with the atomic fluorescence spectrometer. To calibrate conventional pollutants in the air.

10. A monitoring method for atmospheric particulate matter based on a multimodal sensing device, characterized in that, Includes the following steps: Real-time sample collection and signal conversion of conventional air pollutants and meteorological parameters; The signal converted by the data acquisition system is used for concentration analysis and early warning. Error assessment of key indicators of the data acquisition system is performed based on the collected samples; The sensitivity measurement algorithm based on the anonymity model enables cross-regional transmission of samples collected by the data acquisition system and analysis results from the data analysis system.

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