243 results about "Air pollutants" patented technology

The invention belongs to the technical field of environmental science, and relates to a multi-source satellite remote sensing cooperative atmospheric pollution monitoring and dynamic tracing method and system. The method comprises the following steps: collecting, processing and screening multi-source data; constructing a pollutant diffusion model, and generating pollutant concentration distribution data; dynamically adjusting the diffusion coefficient and attenuation rate of the pollutant diffusion model, and generating a pollutant concentration distribution thermodynamic diagram; performing space matching, and iteratively updating the diffusion coefficient and the attenuation rate of the pollutant diffusion model until matching parameters meet set conditions; and verifying pollutant concentration distribution data in the pollutant concentration distribution thermodynamic diagram. According to the invention, parameters of the pollutant diffusion model can be dynamically adjusted and optimized based on real-time meteorological data, the accuracy of pollutant concentration distribution data generated by the pollutant diffusion model is ensured, and real-time tracking and accurate positioning of a pollution source are realized; the monitoring precision is obviously improved; the cooperative monitoring efficiency is improved, and comprehensive monitoring of atmospheric pollutants is realized.

Techniques for operating an environmental sensing system are described. In an example, a cloud-based server system receives a first indication that a first type of air pollutant is present within a first structure from a first plurality of indoor air quality (IAQ) sensing devices positioned within the first structure. A second structure within a predefined distance to the first structure is then identified by the server system. The server system then determines that the first type of air pollutant is not present within the second structure. The server system then causes a second plurality of IAQ sensing devices positioned within the second structure to change an operating mode from a normal sensitivity mode to a high-sensitivity mode.

The invention relates to the technical field of selenium-rich food safety, and discloses a selenium-rich food safety supervision system based on a block chain. The system comprises a production environment monitoring unit which acquires multi-dimensional environment parameters such as soil selenium content, air pollutant index and temperature and humidity of a selenium-rich food raw material planting base and a production and processing place in real time, and generates a comprehensive environment safety index through dynamic acquisition and fusion processing; and when the index is lower than a preset threshold value, a block chain alarm instruction is triggered and a supply chain tracing unit is started. And the supply chain tracing unit responds to the instruction, carries out block chain evidence storage on full-link circulation information (including supplier qualification certificates, logistics temperature control records and the like) of the selenium-rich food from raw material collection to terminal sales, and analyzes the evidence storage information to construct a complete link map. And the quality verification unit synchronously receives the environmental safety index and the supply chain link map, performs multi-source information cross verification in combination with selenium-rich food real-time spectral feature detection data, outputs a quality compliance judgment result and generates a block chain verification label.

The invention discloses a pollutant anomaly detection method based on multi-pollutant collaboration and spatial-temporal feature fusion. The method comprises the following steps: S1, constructing a multi-pollutant sensor network data set; s2, cleaning and preprocessing the multi-pollutant sensor network data set in the research area; s3, learning a directed graph adjacency matrix of the single-pollutant sensor network for the multi-pollutant sensor network data set through Bayesian variation inference, and modeling an asymmetric causal relationship between sensors; s4, processing the sensor nodes of the predicted target location by using a single-pollutant spatial feature extraction module, and performing spatial feature fusion among multiple pollutants based on a hierarchical attention mechanism; s5, performing multi-pollutant data prediction on the multi-pollutant sensor network data of the predicted target location by fusing spatial features and a directed graph by using a spatial-temporal feature fusion module; and S6, performing anomaly detection based on an anomaly score and a dynamic update threshold value of the air pollutant monitoring value generated by cooperation of multiple pollutants.

The invention relates to the technical field of environmental pollutant detection, in particular to a straw open-air incineration pollutant emission quantitative method based on multi-source data fusion, which comprises the following steps: based on air pollutant monitoring data, calling a sensor network to obtain PM2.5, CO and NOX concentrations, analyzing fire point heat radiation intensity, collecting meteorological data, and calculating the concentration of PM2.5, CO and NOX; and obtaining a pollution source data set by combining the time sequence change of the fire points and the meteorological parameters. By integrating an environment monitoring sensor, satellite remote sensing and ground meteorological data, the comprehensiveness and accuracy of pollution source monitoring are improved, the combination of fire point intensity and pollutant concentration time sequence data is optimized, the evaluation of fire point and pollution hot spot areas and the comprehensive analysis of wind directions and meteorological conditions are optimized, the accuracy of a pollutant diffusion model is improved, and the pollution source diffusion efficiency is improved. The pollutant release amount is dynamically adjusted, and the diffusion model is corrected by using a geographic information system, so that the scientificity of pollution prediction and emission intensity evaluation is enhanced, and more accurate pollution prevention and control decision support is provided.

Systems, methods, and computer-readable media for calibrating air quality sensors are provided. A sensor node includes a sensor node printed circuit board, a sensor module, and a communication module. The sensor node printed circuit board manages power of the sensor node circuitry, the sensor module, and the communication module such that power is provided from a primary power supply supplemented by a secondary power supply. The sensor module includes a plurality of air quality sensors to measure the concentration of air pollutants. The sensor module may be replaceable. The communication module may communicate air quality measurements to and receive configurations from a data management platform, which may perform processes to improve the accuracy of the air quality measurements.

The invention discloses a multi-modal air pollutant prediction method and device, equipment, a medium and a product, and relates to the technical field of environmental monitoring, the method comprises the following steps: carrying out preprocessing and standardization processing on to-be-processed multi-modal data to obtain preprocessed multi-modal data; the preprocessed multi-modal data comprises atmospheric pollutant concentration monitoring station data, emission source data and meteorological field data at the current moment in the target area; splicing the atmospheric pollutant concentration monitoring station data at the previous moment and the atmospheric pollutant concentration monitoring station data at the current moment along the time dimension, and extracting the spliced data features to obtain a station position-time-pollutant concentration feature vector; fusing the extracted feature fusion vector with the feature vector of station position-time-pollutant concentration to obtain a multi-modal feature vector; and inputting the multi-modal feature vector into a pre-trained deep learning network model to obtain a prediction result. According to the invention, the pollutant prediction accuracy can be improved.

The invention relates to the field of pollutant monitoring, in particular to an indoor decoration harmful substance monitoring and early warning method and system based on multi-sensor analysis. The method comprises the following steps that decoration whole-process parameter collection is carried out based on multiple sensor nodes, regional parameter distribution fitting is carried out, and an all-weather monitoring reference database is constructed; performing air pollutant identification marking on the all-weather monitoring reference database, and performing multi-time-point concentration change evolution analysis to obtain a multi-time-point concentration distribution evolution model; carrying out dynamic pollutant diffusion mining on the multi-time-point concentration distribution evolution model, carrying out combined pollution evaluation calculation, and constructing a multi-pollutant combined diffusion risk chain; dynamic interpolation modeling is carried out on the multi-time-point concentration distribution evolution model, micro-scale space-time disturbance reconstruction is carried out, and a pedestrian disturbance space-time diffusion model is constructed. According to the invention, through accurate and efficient risk assessment of indoor decoration harmful substances, dynamic early warning is carried out, and the safety of indoor decoration is improved.

The invention discloses a regional air pollutant traceability monitoring and forecasting model and a regulation and control method. Multi-source data such as meteorology, pollutant concentration and pollution source emission in an area are collected and preprocessed, then a model is constructed and trained based on a machine learning or deep learning algorithm, and performance is improved through feature engineering, model selection and training optimization. The trained model is utilized to forecast pollutant concentration, traceability analysis is carried out, results are visualized, emission reduction targets and regulation and control measures are formulated for a target area according to traceability and forecast simulation data, for example, industrial structure optimization and traffic control measures are implemented for different pollution sources, and the implementation effect is evaluated. Meanwhile, the model is evaluated regularly, the model is updated according to a result, parameters are adjusted, data are increased or an algorithm is improved, so that the accuracy and reliability of the model are continuously improved, accurate monitoring, effective source tracing, accurate forecasting and scientific regulation and control of regional air pollutants are achieved, and air quality improvement is assisted.

The invention belongs to the technical field of environment-friendly treatment, and provides a treatment process for natural gas combustion waste gas generated by zinc chloride production based on zinc ash and zinc slag and acid mist waste gas generated by regenerated acid decoction, which comprises the following steps: S1, introducing the natural gas combustion waste gas and the acid mist waste gas into a spray tower, carrying out dynamic alkali neutralization treatment and cooling treatment on the basis of an intelligent pH value regulation and control method to obtain primary purified gas; s2, the primary purified gas enters primary plasma dedusting and demisting equipment through a pipeline to be treated, and secondary purified gas is obtained; and S3, the secondary purified gas enters secondary plasma dedusting and demisting equipment to be treated, and final treated tail gas is obtained. According to the invention, the chemical neutralization based on the spray tower and the two-stage plasma deep cracking are combined, the treated waste gas meets the emission limit value in the secondary standard in the Integrated Emission Standard of Air Pollutants, the treatment efficiency is high, the medicament consumption is low, the energy consumption is low, and the problems in the prior art are effectively solved.

The invention relates to a multi-model fusion method for analyzing PM2.5 source contribution and disease burden thereof, which comprises the following steps of: building and verifying an air quality simulation system of a corresponding region, simulating the concentration of air pollutants and PM2.5 components of the corresponding region in recent 10 years by using a WRF-CMAQ model, and discussing the spatial and temporal distribution characteristics of the pollutant concentration; step 2, analyzing a PM2.5 pollution source of a corresponding region, and based on an HYSPLIT model and a high-resolution global emission list; step 3, estimating PM2.5 related disease burden and source contribution thereof, and quantifying premature death and health economic loss thereof caused by PM2.5 in recent ten years in a region by utilizing PM2.5 concentration data simulated by a WRF-CMAQ model and combining a comprehensive exposure-response model and a statistical life value method; the method can help to formulate accurate and effective PM2.5 control countermeasures, improve air quality and protect people's health, and provides a theoretical basis for realizing joint prevention and joint control and sustainable development of polluted areas.

To provide an indoor air cleaning system capable of cleaning an air pollution state of an indoor region.SOLUTION: A gas detection module comprises a microcontroller and a central control communication interface component. The microcontroller performs calculations based on air pollution data, and outputs an adjusted control signal. An air purification device comprises a fan, a filter component, and a drive control assembly. The central control unit provides a control command to the gas detection module via wired or wireless communication, controls the fan to direct air pollution to the filter component, and purifies an air pollution state in an indoor region on the basis of the 24-hour cumulative detection count of PM2.5, which is a floated particulate matter, inhaled by the plurality of gas detection modules, and an indoor area space required for one air purification device, so that the detected air pollution data satisfies clean room ZAPClean room 1 to 9 grade requirements.SELECTED DRAWING: Figure 2A

The invention discloses an air quality data screening method and system based on multi-factor deviation analysis, and solves the problem that complex spatio-temporal data deviation is difficult to process by a traditional method. The method comprises the following steps: acquiring multi-city multi-time-dimension air pollutant monitoring values, meteorological data, topographic data and monitoring station metadata, performing data cleaning through three-level quality control, and constructing a spatio-temporal data cube; dynamically establishing a regional reference value, calculating a combined deviation, and identifying a deviation mode through a space-time DBSCAN method; a city deviation feature vector and a provincial deviation network are constructed, and a graph convolution network is used for sorting; establishing a multi-layer cause and effect graph, and diagnosing deviation root causes by adopting a PC algorithm and a Shapley value decomposition technology; and defining a Markov decision process, and dynamically optimizing a correction strategy. According to the method, the composite deviation can be accurately quantified, root causes such as regional pollution, equipment faults or meteorological interference can be distinguished, and data support is provided for regional collaborative governance.

A computer implemented method of calculating exposure to air pollutants is disclosed. The method comprises receiving a path comprising a sequence of path locations, receiving or retrieving concentration maps corresponding to one or more air pollutants, each concentration map comprising modelled concentration values of the corresponding air pollutant at each of a plurality of model locations, for each given air pollutant, determining a concentration profile of the given air pollutant as a function of distance along the path by: in response to a pair of sequential path locations are separated by more than a target spacing, inserting one or more interpolation locations between that pair of path locations, so every section of the path longer than the target spacing includes at least one path location or interpolation location, for each interpolation location and each path location, interpolating a concentration value corresponding to that interpolation location or path location based on modelled concentration values of the two or more model locations closest to that interpolation location or path location, storing or outputting the concentration profiles of the one or more air pollutants.

The present invention belongs to the technical field of analysis of the causes of air pollution, and discloses a method and system for identifying and classifying risk points of air pollution caused by coal consumption, including obtaining multi-source data; extracting the features of the multi-source data to form data feature samples, using the ground monitoring data at the current moment of the grid point where the data feature sample is located as a label to train a deep learning model; using the trained model to predict the surface concentration of air pollutants; based on the prediction results, calculating the mean and standard deviation of the pollutant concentration of each grid point, screening out risk points; establishing a buffer zone centered on the risk points, analyzing the distribution characteristics of POIs in the buffer zone, and identifying and classifying pollution sources. The present invention uses POI points to achieve accurate identification of risk points of air pollution caused by coal consumption, provides data support for the prevention and control of heavy pollution weather, and promotes scientific emission reduction and refined governance.

The invention belongs to the technical field of air pollutant traceability. The invention provides an air pollution prediction method and system based on artificial intelligence and a mechanism model, and the method comprises the steps: extracting geographic features and time dependency relationship features, fusing the geographic features and the time dependency relationship features, outputting potential calculation factors, and calculating the weight of each factor; a high weight factor with the absolute value of the weight larger than a set threshold value is fused into the mechanism model, the reference emission rate of the mechanism model serves as basic input data, the selected high weight factor and the weight are combined to calculate the corrected gridding emission rate, and an improved mechanism model with a dynamic emission source list is formed; the pollutant concentration is predicted through the improved mechanism model, and the main pollution source is determined based on pollution source data and potential calculation factors in the database; according to the method, various factors involved in the atmospheric pollution forming process are fully considered, and the prediction precision is improved.

The invention relates to the technical field of atmospheric pollutant removal, in particular to an atmospheric pollutant removal device which comprises a removal tank and an impurity removal plate arranged on the inner side of the removal tank, one side of the removal tank is provided with an air inlet pipe, one end of the air inlet pipe is provided with a pressurizing pipe, the pressurizing pipe is connected with the removal tank, and the impurity removal plate is arranged on the inner side of the removal tank. And a preliminary filtering plate is arranged on the inner side of the air inlet pipe. Through the arrangement of the preliminary filter plate, preliminary impurity filtration can be performed on atmosphere conveyed into the removal tank, so that the replacement time of the impurity removal plate can be prolonged, the use cost of the device is reduced, the disassembly and assembly steps between the preliminary filter plate and the removal tank are simplified through the arrangement of the connecting assembly, the replacement efficiency of the preliminary filter plate is improved, and the use cost of the device is reduced. By arranging the filter plate cleaning assembly, dust impurities filtered on the preliminary filter plate can be cleaned, so that the replacement time of the preliminary filter plate can be prolonged, and the removal cost of atmospheric pollutants is reduced.

This invention discloses an outdoor air pollutant treatment device, belonging to the field of air pollutant treatment technology. It includes a mounting frame, on the upper side of which multiple motor protective shells are fixedly mounted. A power motor is fixedly connected inside each motor protective shell, and multiple axial flow fans are fixedly connected to the outer side of the output shaft of each power motor. A fixed frame is located on the outer side of the motor protective shells, and a filter element is fixedly mounted within the fixed frame. A cleaning assembly for the filter element is also installed within the fixed frame, and the cleaning assembly includes a positioning plate fixedly mounted on the outer side of the filter element. During the air pollutant treatment process, the cleaning box and cleaning plate can clean the pollutants accumulated on the outer side of the filter element, improving the filter element's service life. Furthermore, during this process, the cleaning plate moves up and down relative to the cleaning box, allowing for better cleaning of the pollutants accumulated on the outer side of the filter element.

The invention provides an air pollutant intelligent monitoring system and monitoring method, and relates to the field of environment air quality detection.The system comprises a basic information module used for obtaining basic user information of a user and the area category of a target monitoring area; the determining and monitoring module is used for determining monitoring pollutants according to the basic user information and the region category; the real-time monitoring module is used for acquiring the pollutant content of the monitoring pollutants in each target monitoring area; the real-time information module is used for acquiring real-time user information of the user; the pollution index module is used for determining the air pollution index of each target monitoring area according to the real-time user information and the pollutant content; the change index module is used for determining an air pollution change index according to the air pollution index; and the monitoring report module is used for generating a monitoring report according to the air pollution index and the air pollution change index. According to the invention, the intelligence and accuracy of air pollutant monitoring can be improved.

This invention relates to the field of air pollutant detection technology and discloses a method for detecting excessive emissions of air pollutants. The method includes: acquiring pollutant concentration data from multiple monitoring points in a target area to form a raw dataset; performing outlier detection and correction, removing sensor malfunction outliers to obtain a preprocessed dataset; extracting concentration change trend characteristics within a preset time window for each monitoring point to construct a spatiotemporal feature matrix; inputting the matrix into a pollutant diffusion model to calculate the transmission paths and intensities between monitoring points, generating a regional transmission network; identifying potential source areas of abnormal pollutant concentration fluctuations based on the network and marking them as candidate areas for verification; performing multi-scale concentration gradient analysis on the candidate areas to determine key monitoring areas; deploying mobile equipment in the key monitoring areas to collect high-precision component data; comparing this data with a standard emission source feature library, matching emission source types with similarity exceeding a threshold, determining whether emissions exceed standards, and generating a detection report.

The embodiment of the utility model relates to the technical field of air pollutant or nuisance source identification and detection, and discloses air pollution and nuisance investigation equipment and system. The investigation equipment comprises at least two air inlet units and sensor units correspondingly connected with the air inlet units. A central processing unit; and a motion unit; wherein the at least two air inlet units are inclined in different directions and collect at least two air samples from different directions and different positions, and the central processing unit is connected with each sensor unit and the motion unit. Through the mode, the embodiment of the utility model can collect at least two air samples from different directions and different positions, accurately and efficiently detect the concentration data of various air pollutants in the air samples, and utilize the concentration data to control the equipment to effectively move towards a pollution source so as to quickly position the position of the pollution source.

The invention belongs to the technical field of air quality management, and discloses a pollutant source analysis method and system for establishing coupling of an air quality mode and a receptor model, and the method comprises the steps: carrying out three-dimensional regional air quality mode simulation on the basis of an air pollutant emission list and a meteorological field based on meteorological mode simulation; through verification of observation data, an adopted three-dimensional regional air quality mode system comprising a meteorological mode and an air quality mode has reliable simulation performance; a source analysis module embedded in a three-dimensional area air quality mode is used for carrying out source analysis on atmospheric pollutants. According to the method, respective advantages of a three-dimensional regional air quality mode and a receptor model in the aspect of source analysis are integrated, the accuracy of refined source analysis of atmospheric pollutants composed of multiple components such as PM2.5, PM10 and VOCs in the atmosphere of a single site or multiple sites is remarkably improved, and refined and scientific development of urban and regional atmospheric pollution prevention and control is promoted; therefore, the health of people is better protected.

The invention belongs to the technical field of atmospheric pollutant control, and relates to a polyacid functionalized guanidine salt ionic liquid system for removing hydrogen sulfide and dimethyl sulfide. The harm of hydrogen sulfide and dimethyl sulfide to a human body and the environment is elaborated, and the limitations of secondary pollution, high energy consumption and the like of the existing removal method are indicated. According to the system, 1, 1, 3, 3-tetramethylguanidine salt and amino acid ionic liquid are dissolved in water according to the ratio of 1: 1 to prepare tetramethylguanidine amino acid ionic liquid, and then Keggin type polyacid is dissolved in the tetramethylguanidine amino acid ionic liquid. The method comprises the following steps: respectively carrying out removal experiments on hydrogen sulfide with the concentration of 500-1000mg / m < 3 > and sulfur-containing VOC (Volatile Organic Compounds) with the concentration of 120-13000mg / ml by using the system at the temperature of 25-45 DEG C by taking N2 as carrier gas, and monitoring tail gas by using a specific instrument. Embodiments show that the system has a good removal effect on both hydrogen sulfide and sulfur-containing VOC, and has application potential in related fields.

The invention provides a near-real-time ozone numerical prediction error evaluation method, which comprises the following steps of: acquiring meteorological monitoring data and air pollutant monitoring data at the night of the previous day and the morning of the day in near real time, and acquiring meteorological prediction data and air pollutant prediction data at the night of the previous day and the morning of the day at the same time; fusing and using an ozone nowcasting model based on the monitoring data, an ozone nowcasting model based on the forecasting data and a zero-dimensional box model which are obtained through machine learning training according to the monitoring data and the forecasting data; calculating a system error of the forecasting model, an accumulative error of air pollutant simulation, an ozone forecasting error caused by a weather forecasting error, and a contribution error of local air pollutant chemical generation; after the current-day ozone pollution forecast error is estimated and analyzed, support can be provided for automatic decision-making of ozone pollution early warning and management and control measures.

The invention discloses a geographic meteorological multi-mode fusion air pollutant monitoring data completion method and device, and belongs to the technical field of deep learning and data processing. The method comprises the following steps: acquiring geographic space information, meteorological information and air pollutant monitoring data of a target area; performing space-time alignment and cleaning on the multi-modal data; constructing a space-time diagram structure, extracting spatial features by using a diagram convolutional network, and enhancing time features in combination with a long-short term memory network and an attention mechanism; multi-scale features such as space, time, weather and time embedding are fused through a multi-layer perceptron, and site spatial correlation and time dynamics are captured; according to the method, a condition diffusion model conforming to multi-modal information feature constraints is constructed, and high-precision complementation of missing data is realized through an iteration process of forward noise addition and reverse denoising under the guidance of fusion features. According to the method, geographic and meteorological multi-modal information is fully fused, and the space-time consistency and the physical rationality of a completion result are remarkably improved.

The utility model discloses an air supply filtering system of a large constant-temperature and constant-humidity unit, and relates to the field of synthetic leather production, in particular to the air supply filtering system of the large constant-temperature and constant-humidity unit. Comprising centralized air conditioner refrigeration, electrode humidifier humidification and winter auxiliary heating pipe auxiliary heating. The auxiliary heating pipe introduces heat through an induced draft fan, filter cotton is laid on an air inlet in the outer side of the auxiliary heating pipe, an air pressure sensor is installed beside the filter cotton and connected to an air pressure controller, and an audible and visual alarm is arranged on the upper side of the controller. The electrode humidifier is provided with a humidity induction meter to regulate and control indoor humidity, and the air pressure controller can store alarm information. The system can effectively filter air pollutants, timely discover the blockage problem of the filter cotton, accurately control the indoor humidity, improve the intelligent degree and the management efficiency, and reduce the energy consumption and the cost.

1. Name of the design product: waste gas electric purification bin. 2. Use of the design product: used for purifying air pollutants such as dust, PM2.5, malodor, odor, organic volatile matter, VOCs, etc. generated in industrial or commercial activities. 3. Design points of the design product: in shape. 4. Picture or photo that best indicates the design points: perspective view.

A computer implemented method of calculating exposure to air pollutants is disclosed. The method comprises receiving a path comprising a sequence of path locations, receiving or retrieving concentration maps corresponding to one or more air pollutants, each concentration map comprising modelled concentration values of the corresponding air pollutant at each of a plurality of model locations, for each given air pollutant, determining a concentration profile of the given air pollutant as a function of distance along the path by: in response to a pair of sequential path locations are separated by more than a target spacing, inserting one or more interpolation locations between that pair of path locations, so every section of the path longer than the target spacing includes at least one path location or interpolation location, for each interpolation location and each path location, interpolating a concentration value corresponding to that interpolation location or path location based on modelled concentration values of the two or more model locations closest to that interpolation location or path location, storing or outputting the concentration profiles of the one or more air pollutants.

The invention discloses a VOCs waste gas continuous treatment device and method based on secondary activated carbon adsorption in the field of air pollution treatment, and aims to solve the technical problems that an existing VOCs waste gas treatment process depends on a boiler, treatment is discontinuous, the investment cost is high, and adaptability is poor. According to the device, an original gas collection pipeline and a fan of an enterprise are reformed, and a first-stage granular activated carbon adsorption unit and a second-stage fine-particle-size activated carbon adsorption unit are connected in series to form a closed-loop treatment system of collection, graded adsorption, pressure-boosting discharge and maintenance and update; according to the treatment method, continuous deep purification of the VOCs waste gas with medium and low concentration is realized by controlling the retention time of the waste gas in the two-stage adsorption unit and the particle size gradient of the activated carbon. The investment is less than or equal to 10 thousand yuan, the treatment gas amount is 8000m / h, the NMHC emission concentration is less than or equal to 50mg / m, the secondary standard of the table 2 of Integrated Emission Standard of Air Pollutants (GB16297-1996) is met, the device can continuously and stably run for more than or equal to 6 months in 24 hours, and the device is adaptive to the waste gas treatment requirements of chemical devices such as acetic acid, methanol and the like.

The application discloses a central air conditioner air pipe disinfection device and disinfection method, and belongs to the technical field of central air conditioner air pipe disinfection, which comprises: sequentially connected air inlet pipeline, disinfection buffer pipeline and air outlet pipeline; a filter adsorption unit is detachably arranged at the air inlet of the air inlet pipeline; a disinfection unit and an air flow buffer unit are sequentially arranged in the disinfection buffer pipeline along the flow direction of air; a rotating plate is arranged at the air outlet of the air outlet pipeline; and the rotating plate is in damping rotary connection with the inner wall of the air outlet pipeline, so as to adjust the air outlet size at the air outlet of the air outlet pipeline. The central air conditioner air pipe disinfection device and disinfection method are convenient to use, can treat the particulate pollutants and toxic air pollutants in air, and can purify and disinfect the air.