546 results about "Atmospheric 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.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The invention relates to an air pollution monitoring method based on unmanned aerial vehicle remote sensing and machine learning. According to the method, atmospheric pollutant concentration is monitored in real time by acquiring fixed monitoring stations and satellite remote sensing data, a pollution abnormal signal is generated when the concentration exceeds a preset threshold value, an unmanned aerial vehicle group is scheduled according to the signal to perform three-dimensional pollution data acquisition according to a preset path, and initial pollution distribution data is obtained; and dynamically adjusting a monitoring path and sensor configuration parameters of the unmanned aerial vehicle group by using a reinforcement learning algorithm, collecting real-time monitoring data, performing pollution traceability calculation in combination with a space-time diagram neural network model, generating a pollution source positioning result, then performing a pollution regulation and control instruction, and executing feedback control. By adopting the method, the accuracy of pollution source positioning and the timeliness of regulation and control can be effectively improved, and a scientific basis and technical support are provided for air pollution control.

The invention relates to the technical field of atmospheric environment monitoring, in particular to an atmospheric pollution source tracking and early warning system based on deep learning, which comprises a multi-source data acquisition layer for acquiring multi-dimensional data of atmospheric pollutant concentration, weather, geography and the like through multi-element equipment such as satellite remote sensing and ground sensors; the data preprocessing layer completes data cleaning by using a statistical method and a machine learning algorithm, and realizes heterogeneous data standardization processing through feature extraction, multi-source integration and space-time alignment, and the deep learning model layer realizes heterogeneous data standardization processing by means of a space-time convolutional neural network, a graph neural network and a long-short term memory network in combination with a space-time attention mechanism. The traceability analysis layer is combined with a back diffusion algorithm and a geographic information system to position a pollution source and generate a thermodynamic diagram. And the early warning decision-making layer sets threshold early warning through transfer learning of the adaptive area, and generates an optimal pollution control scheme based on reinforcement learning.

The invention belongs to the technical field of environmental atmospheric pollutant assessment, and discloses a method for assessing the influence of fireworks and crackers on atmospheric PM2.5, which is suitable for assessing the particulate matter pollution influence of a fireworks setting-off point source in a specific area on a surrounding environmental air quality monitoring station, and realizes PM2.5 emission diffusion simulation and traceability of centralized setting-off of fireworks and crackers. The characteristics of Gaussian diffusion, puff diffusion and computational fluid mechanics theories are integrated, PM2.5 influence evaluation of areas and cities can be achieved, firework and cracker pollution contribution key areas can be recognized, rapid response of pollution emission reduction measures can be achieved, and then more effective supervision and control over centralized setting-off of fireworks and crackers can be achieved.

The invention relates to a data calibration method suitable for an atmospheric pollutant sensor deployed in an embedded terminal. The method comprises the following steps: acquiring original response data of different sensors with different concentrations in different atmospheric environments after temperature compensation, and corresponding temperature, humidity, air pressure data and sampling time; collecting real concentration values of detection objects corresponding to different sensors at corresponding moments given by a national control station or a provincial control station; preprocessing the obtained data to obtain a final data set; a random forest algorithm is used as a teacher model, a gradient elevator algorithm is used as a student model, and hyper-parameters of the gradient elevator algorithm and the random forest algorithm are optimized by using a hedera helix algorithm; substituting a hyper-parameter optimal solution found by optimization into a corresponding algorithm, and retraining the teacher model and the student model by using hyper-parameter combinations; and deploying, updating and displaying the model in the embedded equipment. And the model size is reduced to realize the deployment of the embedded end on the premise of ensuring the accuracy.

The invention discloses an atmospheric pollution source intelligent detection system and method, and relates to the technical field of atmospheric pollution detection, and the method comprises the steps: 1, collecting atmospheric pollutant concentration data, space-time position information and meteorological conditions at different time, space and altitude levels; step 2, constructing a pollutant diffusion path prediction model based on the collected pollutant concentration data, space-time position information and meteorological conditions, outputting a pollutant diffusion path prediction value, and predicting the pollutant diffusion path prediction value at a to-be-predicted point; and step 3, constructing a three-dimensional pollution distribution model based on the atmospheric pollutant concentration data, the space-time position information, the meteorological conditions and the derived spatial distance parameters, outputting a pollution concentration prediction value, and predicting the pollution concentration prediction value at the spatial point. According to the method, the risk level of the pollution source is accurately divided, and the problem that the real-time traceability and the classification response capability of the pollution event are limited due to the lack of a systematic data modeling mechanism and a response strategy in the existing method is solved.

The invention discloses an energy pollution reduction and carbon reduction evaluation method, system, medium and equipment, relates to the technical field of environmental protection engineering, systematically covers the whole life cycle of technical operation by constructing a comprehensive technical classification system, and forms an integrated pollution reduction and carbon reduction technical system. Meanwhile, according to the method, through a perfect evaluation mechanism, the comprehensive emission reduction potential of atmospheric pollutants and greenhouse gases is coupled, and policy making of pollution reduction and carbon reduction cooperative treatment can be scientifically supported.

The invention discloses a method and a system for evaluating the pollution reduction and carbon reduction collaboration degree in the coal-fired power generation industry based on online monitoring data, related pollutants are typical atmospheric pollutants, and carbon emission particularly refers to carbon dioxide emission. The method comprises three parts: a subsystem for acquiring and preprocessing data of single atmospheric pollutants such as SO2, particulate matters and NOx, a subsystem for accounting the cooperation degree of single atmospheric pollutants and carbon emission reduction in the coal-fired power generation industry, and a subsystem for comprehensively accounting pollution and carbon reduction indexes in the coal-fired power generation industry. According to the method, the typical representative coal-fired power generation enterprise level data in the region is processed and integrated to evaluate the pollution reduction and carbon reduction level of the coal-fired power generation industry in the region range.

The invention belongs to the field of air pollution prevention and control, and particularly discloses an urban air pollution transmission path and source contribution evaluation system and method based on machine learning and a numerical mode, and the evaluation system comprises a meteorological simulation module, a pollution emission list module, a numerical traceability module and a machine learning transmission path evaluation module. According to the method, machine learning and a numerical mode are combined, the automation degree is high, automatic downloading, automatic running and online data analysis can be achieved without manual data statistics and analysis, operation is convenient, data accuracy is high, the response speed is high, and important support is provided for work such as atmospheric pollutant cause tracing and atmospheric pollution control scheme making.

The invention discloses an OVOC source analysis method, system, medium and equipment in the atmospheric environment, and relates to the technical field of environmental protection, and the method comprises the steps: monitoring the environment, and collecting pollutant concentration data and environmental data during observation; analyzing a plurality of pollution sources of the primary emission part of the OVOC and the concentration of each compound in each pollution source by utilizing a multiple linear regression model and an atmospheric pollutant source analysis receptor model; and inputting into an atmospheric photochemical box-type model to simulate the net generation rate of each oxygen volatile organic compound. The pollution source concentration is quantitatively reduced, the pollution source concentration and other pollution source concentration data which are not reduced are respectively input into an atmospheric photochemical box-type model to simulate the net generation rate and sensitivity of each OVOC, and quantitative analysis of the secondary generation process of the oxygen-containing volatile organic compounds is realized through a relative increment reaction activity coefficient method. Weighted calculation is carried out on the proportion of a primary emission source and a secondary generation source obtained through a multi-line element regression method, and full-process OVOC source quantitative analysis in the atmospheric environment is achieved.

The invention relates to an atmospheric pollutant early warning method and system based on multi-source isomerism, and the method comprises the steps: obtaining multi-source isomerism atmospheric pollutant data, carrying out the data arrangement of the atmospheric pollutant data, and obtaining the arranged atmospheric pollutant data; carrying out unified representation on the sorted air pollutant data to obtain air pollutant data subjected to unified representation, and carrying out intelligent detection by adopting a gridding edge calculation method according to the air pollutant data subjected to unified representation to obtain an air pollutant detection result; according to the detection result of the atmospheric pollutants, performing forecasting and early warning by adopting a method based on big data analysis to obtain a forecasting and early warning result of the atmospheric pollutants; and the forecasting and early warning result of the atmospheric pollutants is displayed through an artificial intelligence GIS platform, so that the purposes of accurately monitoring, forecasting and early warning the atmospheric pollutants, improving the atmospheric pollution treatment level, assisting in environmental protection decision making and promoting development of related technologies are achieved.

The invention discloses an urban low-altitude dynamic wind field and air quality refined forecasting method and system in the technical field of environmental weather forecasting, and the method comprises the steps: generating a static sub-urban scale low-altitude wind field map under different weather background wind directions, and carrying out the correction, thereby obtaining a static sub-urban scale low-altitude non-uniform wind field map; generating a sub-city scale low-altitude horizontal wind field dynamic map based on the moment-by-moment time sequence data of the background wind speed and direction and the static sub-city scale low-altitude non-uniform wind field map; calculating a horizontal transmission track and a propulsion dynamic state of the target atmospheric pollutants in the urban low altitude in the dynamic wind field to obtain an external background transmission concentration of the target city; calculating the concentration of atmospheric pollutants formed by local emission in the dynamic wind field; and calculating a high-temporal-spatial-resolution forecasting result of the urban low-altitude atmospheric pollutant concentration. The method can be used for diagnosis, prediction and early warning of ventilation galleries and atmospheric pollutant diffusion and transmission channels in the urban low-altitude environment.

The invention provides a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method, and relates to the technical field of environmental information processing, and the method comprises the steps: constructing a hierarchical grid structure of an urban core domain and a peripheral background domain based on a unified data basic set, forming a gridding baseline emission list through unified activity data and emission factors, establishing a multi-source scale coordination mechanism, fusing ground monitoring and satellite observation information, and generating an observation data set; according to the method, an SMOKE sparse matrix processing chain is embedded to construct a joint estimation model, parameter inversion and consistency constraint optimization are implemented, boundary flux coordination is carried out between a core domain and a background domain, and finally an emission list which has cross-atmospheric pollutant and greenhouse gas consistency and can be used for refined emission inversion is formed. According to the method, the problem of unstable data fusion caused by scale mismatching between the multi-source observation data and the emission model in the prior art is solved.

The invention provides an air quality data quality control method based on time series data, and relates to the technical field of air detection, and the method comprises the steps: obtaining multi-source atmosphere monitoring station data, constructing a spatial topological relation network of monitoring stations, and defining a dynamic hierarchical structure of atmosphere pollutant data under different spatial and temporal scales and a cross-hierarchical association rule of the dynamic hierarchical structure; carrying out anomaly detection and traceability analysis on the obtained monitoring data, and carrying out data correction and filling; performing credibility evaluation and grading on the corrected monitoring data by applying a space-time clustering algorithm and combining a site topological relation and a data association rule, and generating a data credibility grading result of a space-time dimension; when the data credibility is detected to be abnormal or the potential failure risk is detected, a graph neural network model integrated with atmospheric diffusion physical constraints is adopted; and generating a visual atmosphere data quality evaluation and control decision report, and automatically triggering an instrument calibration, maintenance or data review instruction for a specific site or region according to the risk level.

The invention discloses a multi-scale atmospheric pollutant and greenhouse gas emission data assimilation and fusion modeling method, and belongs to the field of atmospheric pollution monitoring. According to the method, the accuracy and physical interpretability of emission estimation are improved through adaptive fusion and high-precision complementation of multi-source observation data in combination with collaborative assimilation of a physical model and a deep intelligent model; and by adopting multi-scale feature dynamic fusion and a hierarchical graph neural network, high-resolution and refined emission spatial-temporal distribution modeling is realized. And meanwhile, Bayesian optimization and transfer learning are adopted to realize continuous adaptive optimization and knowledge generalization of model parameters. The method effectively solves the problems of heterogeneity, data missing, model migration, insufficient generalization ability and the like of multi-source data, and can be widely applied to the fields of atmospheric environment management, carbon emission monitoring and the like.

The invention discloses an atmospheric pollutant concentration prediction method of a self-organizing recurrent neural network based on Bayesian optimization, and belongs to the technical field of intelligent environmental protection. At present, a method for predicting the concentration of atmospheric pollutants has some problems including complex models, low prediction precision, insufficient space-time correlation, poor real-time performance and the like. Therefore, the invention designs a self-organizing recurrent neural network model based on Bayesian optimization. According to the model, the correlation between various meteorological factors and atmospheric pollutants is deeply mined by using the advantages of a gated circulation unit neural network; an intelligent tuning mechanism based on a Bayesian optimization algorithm is designed to realize preliminary optimization of model parameters and structures, so that the efficiency of the model is improved; and then the contribution degree of each neuron of a hidden layer to output is calculated by adopting sensitivity analysis, so that the network structure and parameters are adaptively adjusted, and the robustness and accuracy of the model are further improved.

The invention provides a discrete cosine transform-based atmospheric pollutant full-coverage data fusion method, which comprises the following steps of: 1, carrying out data preparation and preprocessing, namely adjusting model data and satellite observation data to the same precision by using an area weighted aggregation method, ensuring the consistency of the model data and the satellite observation data in geographic space, and carrying out data fusion on the model data and the satellite observation data; subsequent data fusion processing is facilitated; step 2, performing data fusion processing: performing filling and smoothing operation on three-dimensional space-time proportion parameters of the satellite observation data and the model data based on a space-time discrete cosine transform (DCT) smoothing principle; and step 3, performing correlation analysis on the atmospheric pollutant seamless full-coverage data and the ground station observation data obtained after fusion, and performing advantage and disadvantage judgment on a correlation analysis result according to a set judgment threshold. According to the invention, the model data can be effectively calibrated and the deficiency of satellite observation can be supplemented.

The invention provides an atmospheric pollutant traceability analysis method and system. According to the method, a form classification standard is established through particle shape features, and atmospheric particles are divided into naturally raised dust and industrial emission particle swarms. A dual-wavelength laser technology is adopted for natural flying dust, specifically, the first wavelength is matched with the inherent frequency of mineral crystals, amorphous scattering interference is blocked through polarization beam splitting, and a reflection signal sequence is extracted to generate a mineral migration path; and the second wavelength screens heavy metal scattering signals of the industrial particles, and a heavy metal diffusion track is constructed by utilizing spatial intensity gradient. And superposing the two paths and aerosol three-dimensional distribution data to generate a thermal distribution diagram, and when the space density exceeds a threshold value, calculating a migration path deviation value and a trajectory offset, dynamically distributing pollution source contribution weights, and outputting a pollution source analysis result. According to the invention, dynamic and accurate analysis of natural and industrial pollution sources is realized, and the regional pollution traceability and management and control strategy targeting are improved.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method for determining the pollutant discharge amount of a high-altitude area, which comprises the following steps of: firstly, calculating the pollutant discharge amount in a plain environment, and simulating the pollutant discharge amount of tunnel construction in the plain environment; the basic influence rule of the altitude change on the emission amount is disclosed by establishing the emission increment regression equation, so that accurate evaluation of the pollutant emission amount of tunnel construction in the high-altitude environment is realized, scientific emission evaluation is provided, and making of a more accurate emission reduction strategy is facilitated.

The invention discloses a dangerous goods expressway transportation green path planning method based on multi-source data, and relates to the field of traffic transportation planning and management, and the method comprises the steps: obtaining and processing multi-source data related to a dangerous goods expressway transportation green path planning process; calculating an alarm rate, a dangerous goods transportation accident consequence and a risk mitigation factor of each highway section, and constructing a risk assessment model; calculating greenhouse gas and atmospheric pollutant emissions in the vehicle operation process, and constructing an external cost evaluation model in combination with a unit gas monetization price; constructing a dangerous goods expressway transportation green path planning model by taking the lowest transportation risk and the lowest external cost as double targets; and designing a mixed algorithm combining an ant colony algorithm and a simulated annealing algorithm to solve the model, and outputting a result. According to the method, the highway transportation risk of the dangerous goods can be comprehensively evaluated, and a group of path planning schemes in which the transportation risk and the external cost are balanced can be obtained.

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 discloses an atmospheric pollutant and greenhouse gas collaborative emission reduction effect evaluation method, and relates to the technical field of atmospheric pollutant emission reduction, and the method comprises the following steps: at the initial stage of the whole process, setting an initial reference flow rate for flue gas according to a historical average empirical value, and introducing the flue gas into a flue; when flue gas enters a flue at an initial reference flow rate, multiple types of sensors are arranged at key positions of the flue, and real-time monitoring and high-frequency sampling are carried out on the flue gas flow state; and carrying out comprehensive analysis on the collected flue gas flow state data by utilizing a pre-trained deep learning model, and identifying a potential turbulent flow risk in the flue gas. The turbulent flow risk is intelligently identified and regulated through the deep learning model, the flue gas flow rate is dynamically optimized, the SCR reaction efficiency and the flow field uniformity are effectively improved, the problems of NOx exceeding and ammonia escape are solved, and cooperative emission reduction of pollutants and greenhouse gas and multi-target optimization of system operation are achieved.

The invention discloses an atmospheric PM2.5 source analysis and monthly scale prediction method and system based on artificial intelligence, and the method comprises the steps: collecting the data information of the atmospheric PM2.5 pollutant component concentration X and the meteorological element M day by day, and carrying out the preprocessing of the data information; on the basis of an Auto-encoder algorithm, an atmospheric pollutant source analysis model based on the Auto-encoder is constructed, the preprocessed atmospheric PM2.5 pollutant component concentration is input into the model, and the relative contribution of a pollution source is obtained; and constructing a KAN-Transformer model, respectively inputting the preprocessed meteorological element data and the obtained relative contribution of the pollution source into the model, and carrying out multi-step prediction to obtain an absolute value of the monthly-scale atmospheric PM2.5 pollutant component concentration. According to the invention, the complex non-linear relationship among the atmospheric pollution source components can be captured, and the accuracy of PM2.5 component source analysis is improved.

The invention relates to the technical field of ecological environment monitoring, in particular to a multi-rotor unmanned aerial vehicle for cooperatively monitoring greenhouse gas and atmospheric pollutants, which comprises a multi-rotor platform, a three-dimensional ultrasonic anemograph, a comprehensive analyzer, a gas inlet pipeline, a support rod, a main GNSS antenna, an auxiliary GNSS antenna, a temperature and humidity sensor and a damping part, the comprehensive analyzer is arranged below the multi-rotor platform; one end of the damping piece is connected with the multi-rotor platform, and the other end is connected with the comprehensive analyzer; the supporting rod is fixed on the center line of the top end of the comprehensive analyzer, and the supporting rod penetrates out of a gap between the multi-rotor platform and the comprehensive analyzer; the three-dimensional ultrasonic anemograph and the main GNSS antenna are correspondingly arranged on the first end head and the first end rod body of the supporting rod; the auxiliary GNSS antenna and the temperature and humidity sensor are correspondingly arranged on the second end head and the second end rod body of the supporting rod. The air inlet pipeline is fixed below the supporting rod; one end of the air inlet pipeline is arranged below the three-dimensional ultrasonic anemograph, and the other end of the air inlet pipeline is communicated with the comprehensive analyzer.

The invention discloses a preparation method of a low-temperature high-air-velocity VOC (volatile organic compound) catalyst and the catalyst, belongs to the field of industrial waste gas treatment, and aims to solve the problems that volatile organic compounds VOCs as main atmospheric pollutants cause photochemical smoke and harm human health; a VOCs catalyst in the prior art cannot meet catalytic combustion treatment process conditions at a high space velocity of 30000 h <-1 >. The high airspeed VOCs noble metal catalyst is developed aiming at a complex volatile organic compound VOCs catalytic combustion technology and meets industrial requirements, and the maximum airspeed of the catalyst is 30000 h <-1 >. The catalyst is an integral honeycomb catalyst formed by taking a double-effect synergistic two-component noble metal precursor, a transition metal oxide and a rare earth oxide as active components, so that the oxygen vacancy of a carrier and the acidity of a catalyst Bronsted acid are enhanced, and the capabilities of oxygen vacancy flow on the surface of the catalyst and adsorption and oxidative dissociation of functional groups on the surface of VOCs (Volatile Organic Compounds) are enhanced.

The invention relates to the technical field of atmospheric pollution monitoring, and discloses an atmospheric pollution safety alarm system, which comprises a pollutant monitoring unit used for receiving and analyzing pollutant concentration data of each monitoring point in a plurality of monitoring points in a target area, and determining excessive pollutants and early warning monitoring points. The user management unit is divided into a healthy user portrait data set and a disease user portrait data set according to the health state; obtaining a first user portrait data set according to the age of the healthy user portrait data set; and obtaining a corresponding early warning disease type and a disease severity level according to the disease description information of the disease user portrait data set. In the safety alarm unit, the early warning range is determined according to the standard exceeding grade of the standard exceeding pollutants and the position information of the early warning monitoring points; and screening user portrait data in the early warning range from the first user portrait data group to obtain a fixed user terminal identifier, and sending early warning information to a corresponding user terminal. Therefore, an alarm is given to a user when the atmospheric pollutants exceed the standard.

The invention belongs to the technical field of pollutant diffusion simulation, and particularly relates to a rainfall environment pollutant diffusion analysis method and system based on a Gaussian diffusion model, and the method specifically comprises the steps: 1, obtaining and preprocessing meteorological data; step 2, based on the Gaussian diffusion model of the continuous point source, obtaining a three-dimensional Gaussian diffusion model changing along with time; step 3, calculating precipitation factors based on the meteorological data, and adding the precipitation factors into a three-dimensional Gaussian diffusion model to obtain a precipitation environmental pollutant diffusion model; and step 4, performing diffusion simulation and result visualization processing by using the rainfall environment pollutant diffusion model, and obtaining an atmospheric pollutant diffusion distribution diagram over time in a rainfall environment. According to the method, the condition of the rainfall environment can be considered, so that the established model can better reflect the real pollutant diffusion condition in the rainfall environment.

The present invention provides a method, device, and system for tracing the source of atmospheric pollutant emissions. The method comprises: obtaining tracer observed concentrations and pollutant observed concentrations at multiple observation points within a predetermined time range; determining the atmospheric diffusion coefficient and ground reflectance coefficient of a target Gaussian model based on the tracer observed concentrations at the multiple observation points; generating an initial chromosome population, and determining the fitness of each initial chromosome in the initial chromosome population based on the atmospheric diffusion coefficient, ground reflectance coefficient, the pollutant observed concentrations at the multiple observation points, and the target Gaussian model; determining a target chromosome population based on the fitness of each initial chromosome, and determining the emission source location and / or emission rate of the target pollutant based on the target chromosome population. Embodiments of the present invention can effectively improve the accuracy of tracing the source of atmospheric pollutant emissions.

The invention discloses a cement and coking enterprise atmospheric pollutant emission monitoring method and device based on electric power data. The cement and coking enterprise atmospheric pollutant emission monitoring method based on electric power data comprises the following steps: obtaining the production load of an enterprise; obtaining annual productivity data of different processes of the enterprise; obtaining the daily total output of each process of the enterprise according to the production load of the enterprise and the annual productivity data of different processes of the enterprise; and obtaining one or more of the daily atmospheric pollutant discharge amount of each process of the enterprise, the annual atmospheric pollutant discharge amount of each process of the enterprise and the annual atmospheric pollutant discharge amount of each process of the enterprise. According to the method, the electric power big data is used as a driving factor of the atmospheric pollutant emission amount, so that long-term continuous online monitoring is not needed, additional purchasing of monitoring equipment is not needed, on the other hand, pollutants are obtained through the electric power big data, and due to the fact that the electric power data has the advantages of being high in real-time performance and fast in synchronization, the real-time performance of the pollutants is improved. Therefore, quicker response can be achieved in the aspect of timeliness.