113 results about "Emission inventory" patented technology

The invention discloses a pollution space analysis method and system based on atmospheric pollution concentration data, and particularly relates to the technical field of data analysis. A high-precision pollutant concentration spatial distribution model is constructed by combining pollutant diffusion characteristics and geographic information, and regional pollution differences are revealed; identifying a pollution source emission mode and analyzing the evolution trends of pollutants in different time and space ranges by matching spatial and temporal change analysis with a pollution source emission list; the influence of real-time meteorological data on pollution diffusion is analyzed, and a pollutant spatial distribution model is adjusted, so that the prediction precision is improved; pollution concentration distribution, pollution source emission and meteorological influence are combined, pollution hot spot areas are accurately identified, future pollution evolution conditions are analyzed and predicted based on historical trends, scientific and reasonable pollution treatment suggestions are provided, the limitation of traditional interpolation and statistical regression models is broken through, dynamic and accurate modeling of pollutant concentration is achieved, and the method is suitable for large-scale popularization and application. And the scientificity of pollution source tracing and treatment decision making is improved.

The embodiment of the invention discloses a causal relationship-based soil pollution tracing method, equipment and medium, and the method comprises the steps: obtaining the soil pollutant concentration detection data of a to-be-traced region, and the data of two emission sources of enterprises and traffic; performing positive matrix factorization on the detection data, and determining contribution of each hidden pollution source to each pollutant; determining at least one pollutant which is contributed by each hidden pollution source and is associated with the enterprise and / or traffic as at least one target pollutant to be traced; aiming at each type of target pollutants, through emission list spatialization and GCCM causal analysis, the causal relationship and the significance degree of the enterprise and the traffic and the target pollutants are verified respectively; and according to a verification result, carrying out auxiliary decision on the preferentially controlled blocks in the region and the types of pollutants and emission sources preferentially controlled by each block. According to the embodiment of the invention, accurate tracing of soil pollution in a complex environment can be realized.

The present application relates to the technical field of heavy-metal pollution remediation, and relates in particular to a soil heavy‐metal accumulation predicting method employing an emission inventory and a receptor model. The method comprises: using atmospheric dust deposition, irrigation water, chemical fertilizers and pesticides as input flux sources of heavy metals to farmland soil, using surface runoff and crops as output fluxes, and constructing an input-output flux inventory of heavy-metal pollutants to clarify a dynamic balance relationship of soil heavy-metal accumulation; and continuously collecting and monitoring of soil samples. The invention provides crucial assistance in determining the transport balance of heavy metals in farmland areas impacted by non-ferrous metal smelting and beneficiation slag sites and in identifying sources of soil heavy-metal pollution, and offers theoretical guidance for subsequent soil heavy-metal remediation and targeted control.

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 discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

The invention relates to the technical field of carbon emission detection, and discloses an urban near-real-time gridding carbon emission measuring and calculating method based on multi-source big data, which comprises the following steps: constructing a gridding carbon emission measuring and calculating database, historical carbon emission data of all departments and industries and multi-source big data capable of reflecting spatial and temporal characteristics of carbon emission intensity are collected and processed into activity level indication variables and spatial allocation weight variables; constructing a relationship between the activity level indication variable and carbon emissions of different departments or industries, and calculating a nearly real-time total daily carbon emission amount based on a grid carbon emission calculation database; and distributing the total day-by-day carbon emission to the 1km grid by using the spatial distribution weight variable, and generating near-real-time day-by-day carbon emission data of the 1km grid. The urban near-real-time gridding carbon emission measuring and calculating method based on the multi-source big data aims at overcoming the defects of an existing carbon emission list in the aspects of temporal-spatial resolution, data updating speed and urban scale applicability.

The invention discloses a carbon transaction-oriented biomass gas coupling coal-fired power generation full-life-cycle carbon emission accounting method. The method comprises the steps of determining accounting boundaries of collection and transportation, storage pretreatment, gas production and coupling power generation of covering raw materials; activity data are collected, space-time mapping of upstream batch data and unit side continuous operation data is established, and data quality grading and uncertainty measurement are carried out; when actually measured synthesis gas components or lower calorific values are missing, a soft measurement model is constructed based on operation data, batch characteristic feedforward coupling is introduced, and an estimated value and uncertainty are output; calculating an intermediate quantity based on a material-energy-carbon conservation mechanism model, performing dynamic correction by adopting a residual correction model containing physical consistency constraint, and adjusting a physical constraint weight according to uncertainty; and an emission list and an MRV data packet are generated in combination with the dynamic emission parameters, and are used for monitoring, reporting, checking and transaction settlement, so that the precision and the traceability are improved, and the physical consistency is ensured.

The invention discloses a carbon emission checking method and system based on a multi-mode inversion model, and relates to the technical field of carbon emission checking. Air-based monitoring, foundation monitoring, mobile monitoring and maneuvering monitoring are adopted to form a three-dimensional monitoring network; the intelligent inversion calculation layer fuses global background concentration and regional real-time monitoring data, and adopts a GSI 4DVar algorithm to optimize a three-dimensional concentration field; in the first inversion stage, random disturbance is applied to the prior emission list, multiple emission sets are generated, each emission set corresponds to a grid area of a three-dimensional concentration field, and concentration distribution is simulated through a WRF-CMAQ model; in the second inversion stage, an EnSRF algorithm is adopted to assimilate measured data, and emission parameters are iteratively optimized; according to the invention, various monitoring means, advanced algorithms and data analysis methods are organically integrated, and automation and intelligentization of the whole process from data acquisition, processing and analysis to abnormity determination are realized. The method provides efficient and scientific technical support for carbon emission supervision, and is helpful for promoting the realization of carbon emission fine management and energy conservation and emission reduction targets.

The present application relates to the technical field of the treatment of heavy metal pollution, and in particular, to a method for predicting heavy metal accumulation in soil based on an emission inventory and a receptor model. Atmospheric dust fall, irrigation water, a fertilizer, and a pesticide are used as heavy metal input flux sources of farmland soil, and surface runoff and a crop are used as output fluxes. A heavy metal pollutant input-output flux inventory is established to specify a dynamic equilibrium relationship of heavy metal accumulation in soil, and soil samples are collected and monitored continuously, which provides important help for determining a migration equilibrium of heavy metals in a farmland region affected by a nonferrous metal dressing and smelting slag yard and a source of soil heavy metal pollution, thereby providing theoretical guidance for subsequent prevention and accurate control of heavy metals in soil.

This invention discloses an automatic identification method, system, device, and medium for greenhouse gas point source emissions, relating to the field of environmental monitoring technology. The method includes: acquiring a greenhouse gas emission inventory and satellite remote sensing data of the area to be monitored; performing initial screening of emission sources based on the greenhouse gas emission inventory to determine preliminary greenhouse gas sources; retrieving a greenhouse gas concentration distribution map using the satellite remote sensing data, and employing a Mask R-CNN model to perform target detection and pixel segmentation on the greenhouse gas concentration distribution map to determine greenhouse gas plumes; the greenhouse gas plumes include specific locations and shapes; performing quantitative analysis of the greenhouse gas plumes using the IME algorithm, and combining this with the preliminary screening of greenhouse gas sources to determine the final emission sources. This invention can improve the accuracy and real-time performance of greenhouse gas emission source identification.

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.

The invention discloses a carbon emission accounting method based on an urban sewage treatment plant, and relates to the technical field of sewage treatment carbon emission, the carbon emission accounting method based on the urban sewage treatment plant comprises the following steps: determining an accounting boundary, clarifying a carbon emission activity greenhouse gas generation principle and a carbon emission source, and formulating a greenhouse gas emission list; carbon accounting activities are analyzed, and corresponding accounting methods and accounting principles are confirmed according to different types of carbon emissions; according to an accounting method and principle, an accounting model is built, an accounting system is established, emission factors corresponding to all carbon emission activities are selected, a data monitoring plan is made according to the accounting model / system, carbon emission accounting of all levels is carried out, and the total carbon emission amount is calculated. According to the method, an analysis framework is provided, four carbon reduction types of carbon reduction, carbon substitution, carbon sequestration and endogenous negative carbon are clarified, and a subsequent path scheme is convenient to propose.

The invention relates to the field of air quality monitoring, and discloses a regional air quality prediction method based on a convolutional network, and the method comprises the steps: carrying out the conjoint analysis of the topographic distribution data of a target region and the real-time air quality monitoring data collected by a plurality of automatic monitoring stations, and dividing the data into a plurality of monitoring sub-regions with homogeneous internal parts and heterogeneous external parts, independently constructing a corresponding interpolation variation model for each monitoring sub-region; for each monitoring sub-region, constructing a corresponding interpolation variation model and an air quality grid map based on the real-time air quality monitoring data of the k automatic monitoring stations in each monitoring sub-region; rasterizing the pollution source emission list to construct a pollution source distribution map; generating a thermodynamic diagram by using satellite remote sensing data; and re-sampling the air quality grid map, the pollution source distribution map and the thermodynamic diagram to a target resolution grid, and inputting the target resolution grid into the air quality prediction model to obtain regional air quality prediction data. Therefore, the prediction precision of the local area is improved.

The application discloses a kind of static water pressure simulation device and pressure sampling method with pressure, it is related to sediment-water interface biogeochemical process simulation technical field, the application includes container cylinder, top sealing installation has sealing cylinder cover;Pressure gauge and temperature table are located on sealing cylinder cover;Air inlet assembly and air release assembly are located on sealing cylinder cover;Discharge pipe is located in the lower part of container cylinder, and first control valve, sampling tube, second control valve are sequentially connected in series.The application has broken through to realize the sampling without pressure relief under the condition of in-situ static water pressure, effectively avoids systematic error caused by dissolved gas due to decompression escape;It is suitable for a variety of sediment types and gas types, and provides a high-fidelity experimental platform for greenhouse gas emission research under extreme conditions of deep water reservoir, thermal stratification area;Provide key data support and methodological basis for constructing pressure-corrected methane flux model and deep water carbon emission inventory, serve the national "double carbon" strategy and global change research needs.

The invention discloses a drainage basin multi-element pollutant quantitative traceability method and device based on a multi-scene water quality model, and relates to the field of pollutant quantitative traceability, and the method comprises the steps: constructing a multi-element pollutant input load emission list in a research region, and setting a plurality of simulation scenes; based on the pollutant input load of each simulation scene, a multi-scene water quality model is used for simulating the migration and transformation process of the drainage basin pollutants, and the output load of each pollutant in each simulation scene is obtained; and calculating the contribution ratio of each pollutant from each emission source and the input load ratio of each pollutant from each pollution source. According to the method, different simulation scenes are set by constructing the multi-element pollutant input load emission list, the emission load capacity of the pollutants is simulated by utilizing the multi-scene water quality model, the emission proportions of the pollutants from all emission sources and pollution sources are accurately and quantitatively analyzed, and the quantitative tracing accuracy of the multi-element pollutants is improved.

The application discloses a ship carbon emission feature prediction method based on a dynamic method and an attention mechanism, adopts an AIS data-based dynamic method and a TFT time sequence prediction model based on the attention mechanism, utilizes ship AIS data, ship Lloyd's file data, pollutant emission factor parameters and geographic information, establishes a high-resolution ship carbon emission list, and realizes multi-period pollutant emission feature prediction of the ship. Through data cleaning, AIS data trajectory integration, data fusion and data completion and other operations, the AIS data is preprocessed, and the accuracy of the AIS data is ensured. According to the ship type and the ship running state, the dynamic method is used to estimate and analyze the pollutant emission amount, and the dynamic ship behavior is considered in real time when analyzing the carbon emission law and predicting the carbon emission feature.

The application discloses a method for ozone and particulate matter prediction and early warning based on a multi-mode air quality model, and the method comprises the following steps: obtaining meteorological prediction data, satellite remote sensing data, pollution source emission inventory and air quality monitoring data of a target region; inputting the obtained data into a CMAQ air quality model, a CAMx air quality model, a particulate matter ozone neural network prediction model and a satellite remote sensing aerosol inversion model to obtain model prediction values; adopting a neural network model architecture, and on the basis of long-term historical related data, cyclically training the model prediction values and actual monitoring values to establish a multi-mode air quality model; inputting the latest related data of the target region into the multi-mode air quality model, and predicting and early warning the ozone and particulate matter of the target region according to the output results of the multi-mode air quality model. The method has good effects on particulate matter and ozone prediction and early warning.

The invention discloses a hectometer-level greenhouse gas emission list generation method fused with vehicle-mounted underway observation, which comprises the following steps of: acquiring data through vehicle-mounted underway observation, eliminating background influence in observation data, performing quality control and preprocessing, determining an optimal space average scale and characteristic variables, dividing data subsets based on a spatial clustering analysis method, and generating a hectometer-level greenhouse gas emission list. A model is constructed and evaluated, and the rationality of characteristic values is checked; and constructing a model to simulate regional concentration, and carrying out emission list downscaling on the basis of a concentration quantification weight coefficient. According to the method, the problems of light population misallocation, heterogeneous interpolation extrapolation distortion and lack of high-resolution input of machine learning in a hectometer method in the prior art are solved; a set of greenhouse gas emission list with high spatial resolution is generated by fusing the emission list with kilometer-level or even higher resolution with vehicle-mounted underway observation data, and hectometer-level greenhouse gas emission data can be provided for cities.

The invention relates to the technical field of water pollution treatment, and discloses a multi-scale coupled drainage basin high-resolution pollution source emission list construction method, which comprises the following steps: acquiring multi-scale administrative division statistical data, and preprocessing the multi-scale administrative division statistical data to obtain multi-scale pollution source basic data; converting the basic data space of the multi-scale pollution source into gridding space distribution data of each pollution source in a target drainage basin range by utilizing a space mapping relation between an administrative scale and a gridding drainage basin scale; calculating multi-source pollution emission data based on the gridding spatial distribution data of each pollution source in the target drainage basin range; and superposing the multi-source pollution emission data on the watershed grid scale, and constructing a watershed high-resolution pollution source emission list. According to the method, accurate description of drainage basin pollution source emission and identification of key pollution units are realized, and the scientization, refinement and datamation levels of regional water environment treatment can be effectively improved.

This application provides a method, apparatus, device, and medium for optimizing greenhouse gas emission inventory data. The method acquires emission inventory data, multi-source observation concentration data, and auxiliary data for a target greenhouse gas in a target area. The emission inventory data includes the emission amount of the target greenhouse gas in the spatiotemporal dimensions within the target area. Using an atmospheric transport model, gas concentration simulation is performed based on the emission inventory data to obtain simulated gas concentration data in the spatiotemporal dimensions. The emission inventory data, multi-source observation concentration data, and auxiliary data are spatiotemporally aligned and correlated with the simulated gas concentration data to obtain correlated data. The correlated data is input into a trained physical-guided neural network to obtain an optimization coefficient sequence. The emission inventory data is then optimized based on the optimization coefficient sequence to obtain optimized target emission inventory data. Embodiments of this application can improve the accuracy of the emission inventory.

The present invention discloses a method, system, and device for inverting atmospheric pollutant emissions based on machine learning. The method comprises: obtaining pollutant concentration observation data within a regional grid; obtaining an influence function and simulated concentration values ​​within the regional grid; establishing an emissions inversion model within the regional grid based on the pollutant concentration observation data, the simulated concentration values ​​within the regional grid, the influence function of the regional grid, and prior emission information; and iteratively calculating the inverted atmospheric pollutant emissions within the regional grid based on the loss function of the emissions inversion model. The technical solution provided by the present invention solves the current problems of low accuracy and resolution of atmospheric pollutant emission inventory data. Furthermore, the atmospheric pollutant emission inversion equipment used is inexpensive, solving the problem of high construction costs for current inversion systems and equipment used.

A system for enabling enterprise decarbonization is disclosed. The system includes three platforms that can operate on a standalone basis, or in conjunction with the other platforms as Climate Suite. The three platforms include a Sustain platform configured to streamline emissions inventory, analytics and reporting and enable creation of emissions reductions that become deals in the PPA or Carbon marketplaces; a PPA platform configured to provide digital transaction services for clean power purchases and sales; and a Carbon platform configured to provide digital transaction services for purchases and sale of carbon credits and renewable energy certificates.

The invention relates to the technical field of pollutant emission evaluation, in particular to a polycyclic aromatic hydrocarbon emission and environmental health evaluation method based on a carbon emission scene. According to the method provided by the invention, the energy activity data under different scenes are constructed through the multi-climate comprehensive evaluation model GCAM-China, the polycyclic aromatic hydrocarbon emission lists under various carbon emission scenes are constructed based on the energy activity data under different scenes and the polycyclic aromatic hydrocarbon emission factors, and the polycyclic aromatic hydrocarbon emission lists under various carbon emission scenes are constructed based on the polycyclic aromatic hydrocarbon emission lists under various carbon emission scenes. And outputting a pollutant concentration space-time distribution diagram under multiple scenes, and based on the pollutant concentration space-time distribution diagram under multiple scenes, evaluating collaborative emission reduction benefits of climate policy implementation on pollution control and health improvement. The method can realize quantitative evaluation of carbon dioxide and polycyclic aromatic hydrocarbon cooperative emission reduction benefits, environmental medium concentration simulation and crowd health risk priority identification under different climate policy paths, and breaks through the limitation of a traditional method in the aspect of systematic evaluation.

This invention relates to the fields of data processing and remote sensing information processing technology, specifically to a remote sensing carbon source and sink monitoring and assessment method based on a multimodal hybrid expert model. The method includes: feature extraction from multi-source remote sensing data (hyperspectral, visible, and infrared); multimodal fusion of the extracted features; construction of a large-scale multimodal hybrid expert carbon source and sink model using a hybrid expert partitioning sparsity processing mechanism to detect and segment carbon source and sink changes, obtaining a carbon source and sink distribution map; assessment of annual carbon emissions / absorption rates for different types of land cover using a combination of source and sink distribution and ground measurement data; and calculation of regional carbon emissions and absorption based on land cover type area and annual carbon emissions / absorption rates to obtain carbon flux assessment results. This invention can be applied to carbon metering, regional carbon emission inventory compilation, and carbon neutrality effectiveness assessment, providing technical support for achieving the "dual carbon" goal and improving the effectiveness of remote sensing carbon source and sink monitoring and assessment.

The present application relates to the technical field of model construction, and more particularly to a method and system for constructing a carbon peak and carbon neutral path evaluation model and a medium. The method comprises the following steps: obtaining regional energy consumption, industrial structure and carbon emission source monitoring data, constructing a basic data set, and forming a source-by-source carbon emission inventory through item-by-item measurement; correcting the inventory based on an emission factor library to obtain standard carbon emission data; constructing a parameter matrix including energy substitution rate, industrial adjustment coefficient and emission reduction technology penetration rate, and performing coupled operation to generate multi-scenario carbon emission evolution results; determining the carbon peak time node and carbon neutral path through time series fitting analysis, and finally constructing a path evaluation model and outputting the evaluation results. Through source-by-source correction, disturbance modulation and multi-dimensional parameter coupling analysis, the present application realizes multi-scenario accurate simulation and path optimization evaluation of carbon emission evolution, thereby significantly improving the accuracy of carbon peak and carbon neutral path prediction and decision support capability.

The present invention relates to the technical field of atmospheric pollution source tracing, and specifically to a pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution, comprising: constructing a deep neural network simulation model for the spatial distribution of fine particulate matter concentration; determining the sites and time periods where the fine particulate matter monitoring concentration exceeds the standard according to the air quality monitoring standard, and generating a gridded fine particulate matter concentration distribution within the exceeding time period by the deep neural network model for the spatial distribution of fine particulate matter concentration; cyclically stepping to generate pollution transmission trajectories, and when the tracing termination condition is met, superimposing the pollution source coordinates in the emission inventory data to achieve the final pollution tracing. The present invention, from the perspective of geographic spatial association, based on the gridded fine particulate matter concentration in continuous time periods, iteratively traces back the pollution diffusion path time by time period, obtains the fine particulate matter transmission and diffusion trajectory, intuitively reflects the atmospheric pollution diffusion process, provides directional guidance for fine particulate matter pollution source tracing, and after superimposing the spatial distribution of pollution sources, can lock the atmospheric pollution emission source.

The invention discloses a coupling emission accounting method and system based on a whole process chain greenhouse gas and atmospheric heavy metal, and relates to the technical field of cement industry emission estimation. Through multi-source data fusion, a set of unified emission list covering geographic parameters, raw material / fuel information, kiln types, pollution control facility types and other multi-level information is constructed, and the refined accounting requirement of collaborative emission reduction of greenhouse gases and atmospheric heavy metals is met. An upstream and downstream full-flow chain greenhouse gas and atmosphere heavy metal emission accounting method in the cement industry is provided, greenhouse gas and atmosphere heavy metal emission in the upstream raw material and fuel exploitation and transportation process is included, and the problem that due to the lack of emission links, the emission list of the cement industry is large in uncertainty is solved. And the underestimation problem caused by lack of emission links in the traditional method is reduced.

The invention relates to a ruminant methane emission estimation method, system and device and a storage medium, and the method comprises the steps: obtaining real-time activity data and environmental factor data of a ruminant, collecting animal individual data, and constructing an input data set according to the environmental factor data and the animal individual data; inputting the input data set into a preset methane emission estimation model, and estimating the methane emission factor of the ruminant to obtain a spatio-temporal change result of the emission factor; based on the estimation result of the emission factors, combining real-time activity data to quantify methane emissions of different regions, analyzing key factors for determining regional distribution characteristics, and generating a regional difference analysis result; and performing visualization processing on the spatio-temporal change result of the emission factor and the regional difference analysis result, displaying spatial distribution and time change of the spatio-temporal change result of the emission factor, and generating a corresponding regional emission list. The method has the effect of improving the methane emission accuracy of the ruminant.

The invention discloses an air quality and climate collaborative emission reduction path optimization method based on multi-dimensional integration. The method comprises the following steps: generating an emission list under a city and department scale carbon emission reduction scene by using a comprehensive evaluation model and a downscaling model; constructing a dynamic marginal emission reduction cost curve covering multiple departments and multiple technology types, and converting the discrete stepped MACC into a continuous total emission reduction cost function; constructing a collaborative path dynamic optimization model which takes minimization of the total emission reduction cost of the region as a target function and takes the air quality standard reaching, the technical application rate and the emission reduction potential of the receptor city as constraint conditions; and solving the optimization model to obtain an optimal collaborative emission reduction path, and coupling an air quality model to carry out space-time evolution and benefit evaluation. According to the method, a national-province-city policy conduction path is constructed, so that the city-level optimal collaborative governance strategy taking economic feasibility and environmental benefits into consideration can be identified.

The present application is a pollution forecasting and tracing method based on Lagrangian particle transport, which is based on the source-receptor relationship (SRR) simulated by the FLEXPART model, combined with the station observation data to correct the emission inventory of pollutants by using the Bayesian optimization method. The real-time prediction of pollutants is carried out by using the corrected emission inventory, which can not only improve the real-time prediction accuracy of pollutants, but also obtain the contribution rate and spatial distribution of each type of pollution source (industry, power, residents and transportation), which can provide corresponding reference information for environmental management, public health protection and national and provincial scale emission reduction decision making.