56 results about "Ozone pollution" patented technology

The invention provides an ozone pollution tracing method and device, computer equipment and a storage medium, and belongs to the field of environment monitoring, and the method comprises the steps: obtaining the concentration data of volatile organic pollutants in a target area; performing positive definite matrix factorization on the concentration data to obtain a component spectrum matrix and a contribution matrix of each pollution source, and matching the component spectrum matrix and the contribution matrix based on a preset pollution source; respectively carrying out one-by-one reduction simulation on each pollution source, calculating the ozone net generation rate after reduction of different pollution sources, and calculating the relative increment reaction activity coefficient of ozone after reduction of a single pollution source; according to the pollutant concentration and the relative increment reaction activity coefficient corresponding to different pollution sources, calculating the contribution rate of each pollution source to ozone generation, and taking the pollution source with the maximum contribution rate as the main source of ozone. Therefore, by analyzing and simulating the components of the volatile organic pollutants and controlling the variables to reduce the pollution sources one by one, the ozone can be automatically traced.

The invention relates to the technical field of environmental monitoring and atmospheric pollution early warning, in particular to a Yangtze River Delta composite extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and pollutant concentration data of a Yangtze River Delta region to form an original data set; and S2, carrying out missing value interpolation and abnormal value elimination on the meteorological data and the pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal characteristic matrix. According to the method, by collecting Yangtze Delta high-resolution weather and pollutant data, performing data cleaning, bimodal feature coding and joint representation modeling, predicting the ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional ozone early warning mostly depends on a single-modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-modal space-time dependent capture, the problem of early warning information lag is caused.

The present disclosure provides a fume extracting device and an induction cooker integrated stove. The fume extracting device includes: a box body, where a mounting hole is disposed on an upper end surface of the box body; a volute fan, where an air inlet end in a middle part of the volute fan faces downward; a fume extracting pipe, where one end of the fume extracting pipe is inserted upward through the mounting hole and disposed on the box body, and the other end of the fume extracting pipe is in communication with the air inlet end of the volute fan; and a fume exhaust pipe, where the fume exhaust pipe is in communication with an air outlet end of the volute fan. The fume extracting device occupies a small space. Further, the air inlet end of the volute fan faces downward, reducing noise while increasing air pressure. The filtering cup and the strainer can be pulled out for cleaning. Further, a purifying device is connected at the tail end of the fume extracting pipe to perform ozone sterilization and ultraviolet disinfection and the ultraviolet rays can also decompose redundant ozone to prevent ozone pollution to the atmosphere. The mounting hole of the box body can be located at a position such as middle, front side, left side or right side of the upper end surface of the box body to enable the fume extracting pipe to adapt to different working environments. Moreover, the energy saving and emission reduction can be achieved.

The invention relates to the technical field of environment monitoring and data analysis, in particular to a method for constructing and predicting an ST-nonlinear interactive feature fusion model. According to the model, a site position is determined by using position codes, and spatial-temporal features are extracted and fused by means of multi-source data; the nonlinear relation between the ozone concentration and other pollutants is excavated through a self-adaptive model architecture, and the prediction accuracy is improved; fusing global features by using a multi-head attention mechanism, and adjusting the contribution of each feature in a prediction result; finally, according to the input ozone related data, accurate prediction of ozone is realized, contributions of different characteristic factors to ozone generation are clarified, and scientific support is provided for making ozone pollution control measures in advance. The invention aims to solve the problems that in the prior art, the relation between ozone and other characteristics cannot be fully captured, the characteristic fusion is poor, and the data processing efficiency is low, so that the ozone concentration prediction is inaccurate, and provides an efficient and accurate ozone prediction model.

The invention provides an ozone pollution prediction method and apparatus, and an electronic device. The method comprises the steps of obtaining initial prediction preparation data of a target area under each time index in at least one time index; determining target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index; constructing a pollution prediction regression analysis model under each time index based on the target prediction preparation data under each time index; based on the pollution prediction regression analysis model under each time index, a target reference data set is determined, the target reference data set comprises target reference data under each time index, and the target reference data set supports an ozone pollution prediction result used for predicting the target area within the target prediction time. According to the embodiment of the invention, ozone pollution prediction can be conveniently carried out, and prediction cost and prediction time consumption can be reduced.

The invention provides an atmospheric control monitoring method and system. The atmospheric control monitoring method comprises the following steps: collecting concentration information of different volatile organic compounds at all sampling moments at each monitoring point through an intelligent pollutant sensor; determining the pollution potential of each volatile organic compound according to the concentration information of each volatile organic compound and the pollution diffusivity of the corresponding volatile organic compound; pollution spectrum information of different pollution sources is collected through an intelligent spectrum sensor, and then concentration contribution ratios when the different pollution sources in the atmospheric environment are converted into ozone pollution are generated on the basis of a machine learning algorithm in combination with the pollution spectrum information of the different pollution sources; carrying out load prediction on ozone pollution of each monitoring point according to the pollution potential of each volatile organic compound and the contribution ratio of different concentrations to obtain a pollution load degree; and performing partition treatment based on all pollution load degrees. By adopting the scheme of the invention, multi-factor nonlinear modeling of the atmospheric environment can be realized.

The present invention discloses a method and device for monitoring regional ozone pollution based on electric power data, the method comprising: determining ozone impact factors, obtaining regional air quality data, meteorological data, and electric power data; constructing an electric power prediction model based on the historical electric power data of enterprises, and obtaining the predicted data of the electric power consumption of enterprises; obtaining the predicted data of ozone precursors based on the predicted data of the electric power consumption of enterprises, combined with the industry coefficient of ozone precursors; establishing an ozone analysis model, the model outputs the predicted results of the ozone pollution concentration in the region for a period of time in the future and the relative contribution prediction of each polluting industry; forming a list of governance enterprises based on the relative contribution of each polluting industry and the predicted proportion of the electric power consumption of enterprises. The present invention realizes the application of the electric power consumption of enterprises in combination with regional air data and meteorological data for analysis, predicting the recent ozone concentration in the region, effectively measuring the environmental pollution generated by various industries and enterprises from the perspective of electric power, and better achieving the effect of energy saving and pollution reduction.

The invention relates to a method and device for purifying industrial and natural water of iron and manganese by oxidation of the contaminants with ozone, followed by filtration. The method includes treating water with ozone by combining the water with ozone in an ejector. The water and ozone are then mixed in a static mixer. The water is then sent to a settling tank for the sedimentation of sludge. From the settling tank, the water is sent for filtration using at least one disc filter with a filter rating of not less than 20 μm. The water is then subjected to tangential microfiltration using a cross-flow method. The result is that of allowing the effective non-chemical purification of water, while minimizing the costs of the purification process and equipment maintenance.

The invention relates to the technical field of atmospheric environment intelligent treatment, and discloses an ozone pollution dynamic prediction and control area discrimination method and system based on mixed symbol-numerical reasoning. The method comprises the following steps: constructing a knowledge graph oriented to the atmospheric photochemical reaction field, wherein the knowledge graph comprises chemical species nodes and edges with temperature-irradiance dependent reaction rates; taking the knowledge graph as a chemical kinetics constraint, taking real-time monitoring data as input, guiding the large model to carry out end-to-end prediction on the ozone peak concentration, and introducing a chemical kinetics consistency penalty term; reversely updating the reaction rate weight in the knowledge graph based on the prediction error and the residual error of the observation value to form a closed-loop optimization mechanism; and judging whether the current atmospheric environment belongs to a VOC control region, a NOx control region or a transition region by combining the uncertainty of large model prediction and the VOC dominant reaction path confidence of the knowledge graph. According to the invention, credible discrimination of ozone generation dominant factors on the principle level is realized.

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.

This invention discloses a method for predicting mixed PM2.5 and ozone pollution at high spatiotemporal resolution, comprising the following steps: first, using historical air quality data as the dependent variable and the driving factor data corresponding to the historical air quality data as the independent variable, to obtain the optimal PM2.5 concentration. 2.5 A model for predicting ozone concentration was developed, and then significantly correlated driving factors were extracted using a hexagonal grid as the prediction unit, which were then input into the PM2.5 concentration prediction system. 2.5 In ozone concentration prediction models, PM2.5 concentrations are obtained at high spatiotemporal resolution. 2.5 The ozone concentration spatial distribution prediction map and the ozone concentration spatial distribution prediction map are then intersected spatially to obtain the PM2.5 concentration. 2.5 A prediction map of mixed pollution with ozone. The prediction method of this invention has advantages such as good applicability and high reliability, and can accurately predict PM2.5. 2.5 When mixed with ozone, the concentration of PM2.5 in the atmosphere... 2.5 It has important guiding significance for the prevention and control of ozone, has high application value, and has good application prospects.

The invention discloses an air pollution emission distribution method based on environmental quality improvement, and relates to the technical field of ozone pollution emission management. The air pollution emission distribution method based on environmental quality improvement comprises the following steps of ozone emission pollution judgment, ozone generation distribution adjustment and ozone diffusion distribution adjustment. According to the method, whether ozone generation pollutant emission distribution adjustment is executed or not is judged according to the ozone emission pollution judgment result, if yes, whether precursor pollutant emission distribution optimization is executed or not is judged according to the obtained ozone generation evaluation result, and if not, whether ozone diffusion emission distribution optimization is executed or not is judged according to the ozone diffusion evaluation result. The reasonable emission distribution of the nitric oxide emission amount and the volatile organic compound emission amount of the industrial park under the haze weather is effectively improved, and the problem that in the prior art, the emission distribution of the nitric oxide emission amount and the volatile organic compound emission amount of the industrial park under the haze weather is not sufficiently associated with ozone pollution is solved.

The invention discloses in-situ polymerization low-temperature curing ink-jet ink which comprises ink A and ink B. The ink A at least comprises an initiator and deionized water, and the ink B at least comprises a sensitizer, a carbon-carbon double-bond water-soluble monomer, a carbon-carbon double-bond resin water-based dispersion and deionized water. The persulfate initiator in the ink A generates free radicals under the action of the ferrous sulfate sensitizer in the ink B, the free radicals initiate in-situ polymerization reaction to form a polymer network structure, pigment is firmly fixed to the surface of fiber, it is ensured that the initiator and the sensitizer start to react only when making contact, and the color fastness of the pigment is improved. The problem that the ink is unstable in the storage process is solved, and the curing speed is high. According to the invention, the use of an ultraviolet lamp is avoided, and light pollution and ozone pollution generated by the ultraviolet lamp in the environment are reduced. And compared with a UV photoinitiator with aromatic groups, a product obtained by reacting the applied persulfate is safer to a human body, and the method is a more environment-friendly printing scheme.

The invention discloses a park scale air quality mode chemical mechanism improvement method for improving ozone simulation capability, and belongs to the technical field of ozone pollution treatment. The method comprises the following steps: by introducing a VOCs related reaction path, enriching a kinetic equation of ozone generation, so that the model can describe a more complex atmospheric chemical process. Theoretically, the reaction items of the model are more comprehensive, and the ozone simulation requirements under different pollution scenes can be met. Through introduction of VOCs, key links in a NOx-O3 reaction chain can be supplemented, and the adaptability and predictive ability of the model to the actual atmospheric environment are improved. And the model structure is improved, so that the simulation result has a more scientific basis. Due to the fact that emission characteristics of VOCs in different regions and time periods are greatly different, the applicability of a traditional model is limited. According to the model, the reaction rate can be dynamically adjusted according to the actual emission condition of VOCs, and the model is suitable for various atmospheric environments and has a wider application prospect.

The invention discloses an ozone concentration daily value prediction method fusing radiation quantity and multiple meteorological factors, and the method comprises the steps: constructing a photochemical activity index representing an ozone photochemical generation process, and introducing a time lag characteristic and a radiation-driven dynamic weight adjustment mechanism; therefore, the model can effectively describe the nonlinear response relation to solar radiation in the ozone generation process and the synergistic effect of meteorological factors. Compared with a traditional prediction method based on a single meteorological factor, the method has higher accuracy and stability in the aspects of ozone concentration change trend, peak value identification and high value interval prediction; meanwhile, prediction errors under different radiation conditions are further reduced through a segmented correction mechanism, so that the generalization ability and engineering applicability of the model are integrally improved, and reliable technical support can be provided for ozone pollution early warning and atmospheric environment management.

This invention discloses an ozone forecasting method based on multi-model fusion, which consists of the following steps: data preprocessing, feature engineering, model training, model evaluation, model prediction, and model correction. This process ultimately yields reliable ozone prediction results. This method is easy to operate, data acquisition is readily available, and it does not require the collection of large amounts of precursor pollutant emission data. It can predict most ozone pollution conditions using only meteorological data, significantly improving prediction accuracy and compensating for the large biases in predictions based on a single model.

The invention provides a method and system for quantifying the influence of lake surface reflection on ozone generation, and relates to the technical field of atmospheric environment monitoring and evaluation.The method comprises the steps that measuring equipment is synchronously arranged at a lake body and a land contrast point, and reflected radiation and meteorological chemical parameters are collected; carrying out calibration, interpolation and abnormal value processing on the data; calculating the albedo of the lake surface and the additional radiation flux relative to the standard underlying surface; calculating a nitrogen dioxide photolysis frequency increment based on the additional radiation flux; a photochemical box model is used for simulating an atmospheric chemical reaction, the species concentration is updated through a time stepping method, and finally the ozone generation increment caused by lake surface reflection is obtained. A unit albedo contribution coefficient is further defined, and quantitative evaluation of the influence of lake surface reflection on ozone generation is achieved. The method overcomes the defect that in the prior art, the lake surface specular reflection enhancement effect is ignored, the contribution of lake surface reflection to ozone generation can be accurately quantified, and a scientific basis is provided for accurate prediction and prevention and treatment of ozone pollution.

The application discloses an ozone pollution hierarchical cause analysis method and system based on double-index joint threshold division, wherein the ozone pollution hierarchical cause analysis method based on double-index joint threshold division comprises the following steps: data acquisition and preprocessing; according to the preprocessed data, double-index joint threshold division is performed; according to the result of double-index joint threshold division, a hierarchical cause analysis model is established; and according to the output result of the hierarchical cause analysis model, early warning mapping is performed. Through double-index joint threshold division, the application solves the limitation of single-index determination, realizes accurate subdivision of over-standard types, and realizes full-process automatic operation, shortens response time, and greatly improves decision-making efficiency. The application reduces the decision threshold through visual design, effectively reduces the number of ozone pollution days through early warning mapping, optimizes the allocation of governance resources, and has significant practical value and popularization significance.

The invention discloses an ozone pollution emission reduction strategy rapid identification method based on ConvLSTM. The method comprises the following steps: constructing different emission scenes, and obtaining basic data under different emission scenes in a set time period of an area to be analyzed; constructing an original data set, performing dimension reduction processing, and dividing the original data set after dimension reduction processing into a training set and a test set; constructing a ConvLSTM model, and training the ConvLSTM model by using the training set; and performing ozone concentration simulation on the test set by using the trained ConvLSTM model to obtain the ozone concentration of an area needing to be analyzed under different emission scenes, and further analyzing to obtain a corresponding optimal emission reduction strategy. According to the method, the time change and spatial distribution response conditions of the ozone concentration under different precursor emission reduction strategies can be quickly identified, so that the optimal precursor emission reduction strategy in different areas at different time periods can be disclosed.

The present application belongs to the technical field of ecological environment protection, and provides a method for purifying ozone in air by phyllosphere microorganism and plant in cooperation. The method comprises the following steps: preparing a culture medium by taking plant leaf extract and potato starch wastewater as main raw materials, culturing phyllosphere plant growth promoting bacteria, preparing a plant growth promoting bacteria inoculum, inoculating the plant growth promoting bacteria on plant leaves, and uniformly placing the potted plants inoculated with the plant growth promoting bacteria in an ozone-polluted room. The method can achieve the effect of purifying air by phyllosphere microorganism and plant in cooperation. The method utilizes the synergistic effect of phyllosphere microorganism and plant, has high ozone purification efficiency in air, is simple to operate, environment-friendly, low in cost, and has wide application prospect.

The invention relates to a normal-temperature high-water-resistance ozonolysis catalyst and a preparation method and application thereof, the catalyst is of a composite oxide-heterojunction structure of Ni < 1-x > CexO-CeO2, and 0 lt; xlt; and 0.1. Dissolving soluble Ce salt and Ni salt in water to obtain a mixed solution, dropwise adding the obtained mixed solution and a precipitant into a reactor together, maintaining the constant pH value to be 9-11, and aging at a constant temperature to obtain a mixed precipitate; and filtering, washing and drying the obtained mixed precipitate, and then calcining in an air atmosphere to obtain the Ni < 1-x > CexO-CeO2 catalyst. Compared with the prior art, the catalyst is simple to prepare, high in water resistance, good in stability, good in ozone purification effect and suitable for large-scale industrial production. The catalyst can be applied to the fields of near-surface low-concentration ozone pollution purification or ozone decomposition in a closed space and the like.

The invention provides an ozone pollution assessment method based on SHAP-IPSO-CNN. The ozone pollution assessment method is used for assessing high accuracy and high interpretability of ozone pollution in a chemical industrial park. The method comprises the following steps: firstly, predicting the distribution concentration of volatile organic compounds discharged by enterprises in a park in a target monitoring station by using an atmospheric diffusion model, and forming model input characteristics by the distribution concentration, the concentration of other pollutants in the target monitoring station and meteorological factors; then, three different machine learning models are considered for deep comparison and preferential SHAP analysis, and a feature importance queue is established by using an SHAP analysis result. Then, an improved particle swarm optimization algorithm is combined with the feature importance of SHAP analysis, and training features are dynamically adjusted to optimize the performance of the CNN model. According to the integrated model, by combining the efficient feature extraction capability of a convolutional neural network (CNN), the global search optimization characteristic of an improved particle swarm optimization (IPSO) algorithm and the explanation capability of SHAP analysis, the influence evaluation on ozone formation is realized. The method provided by the invention has high reliability and application prospect under complex conditions.

The invention discloses a method for evaluating sensitivity of peony varieties to ozone stress. The method comprises the steps of peony planting, ozone stress treatment and index determination. According to the method, responses of key indexes such as leaf morphological structure, photosynthetic capacity, photosynthetic pigment attenuation, membrane lipid peroxidation degree and antioxidant enzyme activity are integrated, a multi-index collaborative evaluation model is established, and data processing and sensitivity evaluation are carried out. In view of the problems of long growth cycle and lagging of ozone sensitive phenotype of paeonia lactiflora, a sensitivity grading standard based on a leaf morphological structure in a leaf expansion period is provided, the adaptability of a variety in an ozone polluted environment can be predicted in the early planting stage, the breeding screening cycle is shortened, and an evaluation result obtained by the method is rapid and stable and has good application prospects. And the method has scientificity and accuracy, provides reference for the breeding direction and planting planning of the Chinese herbaceous peony, reduces the use of chemical protective agents, and promotes the implementation of an environment-friendly cultivation mode.

The invention discloses a method for analyzing the influence of low-carbon traffic on ozone pollution by considering public participation. The method comprises the following steps: S1, collecting low-carbon traffic development level data, ozone pollution treatment level data and public participation degree data of a target area; s2, carrying out standardization processing on the collected data, and calculating a comprehensive value of low-carbon traffic, ozone pollution and public participation; s3, constructing an intermediary model by taking the comprehensive value as an input variable; s4, under the framework of the intermediary model, checking the direct effect of low-carbon traffic on ozone pollution, the action effect of public participation on low-carbon traffic and ozone pollution and the intermediary effect of public participation; and S5, quantifying the intermediary effect degree of the public participating in the influence of low-carbon traffic on ozone pollution. The method not only can quantify the relationship between low-carbon traffic development and ozone pollution treatment, but also can reveal the intermediary effect in which the public participates, and provides support for formulating a more scientific and effective environmental strategy.

The invention discloses a GAT-xLSTM-based ozone concentration long-term prediction method, and relates to the technical field of environmental air quality monitoring and prediction. Firstly, air quality data and meteorological data of a monitoring station are collected and preprocessed, and a reference data set is constructed. By constructing the GAT-xLSTM model, medium and long term accurate prediction of the ozone concentration is realized. The method comprises the following steps: constructing a space-time diagram structure of multi-source heterogeneous data, and extracting spatial correlation characteristics between monitoring stations by using GAT; separating a trend item and a seasonal item in the time sequence through a seasonal trend decomposition technology, and carrying out time feature modeling on the seasonal item by adopting xLSTM; and fusing prediction results of the spatial features and the time features, and outputting a final ozone concentration prediction value. According to the method, the problems of insufficient space-time correlation capture and too fast error accumulation in long-term prediction of a traditional model are solved, the medium-and-long-term prediction precision is remarkably improved, and technical support can be provided for ozone pollution prevention and control.

This invention discloses a method and apparatus for precise source tracing and analysis of atmospheric ozone pollution. Based on the concentration data of each VOC species among multiple species monitored at ambient air quality monitoring stations, the ozone generation potential of each species is determined; the ozone generation reactivity of each species is determined; key VOC species for ozone generation are screened based on both ozone generation potential and ozone generation reactivity; mobile VOC monitoring is conducted around the ambient air quality monitoring stations, and automatic offline sampling and laboratory analysis are performed at high-value TVOC sites; key pollution sources for ozone generation at the ambient air quality monitoring stations are identified and subject to control. This invention provides a more comprehensive analysis of atmospheric ozone pollution, accurately identifying specific pollution sources and better providing technical support for environmental management.

The invention discloses a PM2.5 and ozone pollution prediction method based on causal reasoning, and belongs to the technical field of atmospheric environment prediction, and the method comprises the following steps: S1, collecting atmospheric pollution monitoring data, pollution source emission data and meteorological data; s2, analyzing a causal relationship among the atmospheric pollution monitoring data, the pollution source emission data and the meteorological data; s3, constructing a causal structure; s4, constructing, training and verifying a pollution prediction model; s5, on the basis of the converged pollution prediction model, PM2.5 and ozone pollution events and corresponding concentration levels are predicted through pollution source emission data or / and meteorological data, pollution source emission scenes are deduced through the meteorological data or / and PM2.5 and ozone pollution, and the air quality improvement effect is predicted by adjusting the pollution source emission scenes. Finally, PM2.5 and ozone pollution treatment strategy suggestions are formed. According to the method, PM2.5 and ozone pollution can be predicted from the causal relationship, treatment strategy suggestions are provided, the prediction uncertainty is reduced, and the prediction accuracy is improved.

The invention relates to the technical field of air purification, in particular to an ozone-free and static-free hydrated negative oxygen ion generating device and method. The device comprises a base, a supporting shell, a fixing shell, a connecting shell and an exhaust cover, a mounting plate and a control box are arranged in the base, a water tank is mounted in the supporting shell, the top of the water tank is in threaded connection with a sealing cover, an exhaust hole is formed in the top of the water tank, and an atomizer is arranged in the water tank; the ultrasonic atomization and induction charge coupling design is adopted, ozone pollution and electrostatic hazards caused by traditional corona discharge are eliminated, and the safety characteristics of zero ozone and no electrostatic leakage are achieved; charge strengthening is carried out on micron-sized water mist through the non-contact induction coil, the technical bottleneck that pure water power negative ion generation concentration is low is effectively broken through, hydrated negative oxygen ions can be stably output, and the device is suitable for various closed space air purification scenes.