85 results about "Emission inventory" patented technology

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 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.

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 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 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 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.

The disclosed methods and systems are directed to mitigating against one or more emissions events at a facility. The method comprises receiving first emissions data associated with the facility and formatting same based on a predefined data structure or a source type associated with the plurality of emissions sources of the facility. The method further comprises tracking using one or more sensors a plurality of emissions records generated using the emissions data. The data from the tracked emissions records may be used to generate an emissions inventory that may be used to generate one or more models which are used in one or more simulations to generate second emissions data. The second emissions data may enable tracking of one or more emissions sources associated with the facility. The second emissions data may enable execution of control operations that mitigate against one or more emissions sources associated with the facility.

The invention discloses a satellite remote sensing inversion method for the emission load of carbon dioxide in a port. The method comprises the following steps: dividing grid regions according to the geographic position of the port by utilizing the hourly NO2 and SO2 column concentrations observed by a GEMS satellite, retaining grid data with strong correlation, performing data reconstruction, taking port CEMS monitoring data as prior emission, and calculating the emission load of NOx and SO2. The emission scale factors of CO2 / NOx and CO2 / SO2 are calculated on the basis of an emission list from bottom to top, downscaling processing is carried out on the emission scale factors of the port through four space proxy indexes taking traffic density as a main part, and the specific emission scale factors of the port are obtained. And calculating to obtain a CO2 emission result under the high temporal-spatial resolution, mutually verifying the CO2 emission result to correct the CO2 emission, and popularizing the CO2 emission result to a long-time sequence result. The method solves the problems that carbon satellite data cannot directly capture port carbon emission, GEMS precision is poor, space loss exists, and long-time sequence observation of port emission cannot be achieved through foundation monitoring values.

This application discloses an agricultural machinery PM based on drone monitoring data. 2.5 This invention addresses the technical challenges of non-overlapping monitoring points and emission sources, as well as poor local adaptability of emission factors in existing agricultural machinery emission monitoring methods and systems. It first utilizes unmanned aerial vehicles (UAVs) to carry PM2.5 data. 2.5 The monitoring system collects pollutant concentration data downwind of the agricultural machinery operation area and preprocesses the data by performing missing data completion, anomaly removal, and duplicate correction. Secondly, based on a Gaussian plume diffusion model, combined with on-site meteorological parameters and agricultural machinery operating parameters, it inversely calculates the emission source intensity of agricultural machinery. Finally, it uses the analytic hierarchy process (AHP) to perform weighted analysis on multiple factors such as agricultural machinery age, operating mode, environmental humidity, and temperature, achieving localized and accurate calibration of emission factors. This application effectively solves the problem of spatial misalignment between monitoring points and emission sources, significantly improving the PM2.5 concentration of agricultural machinery. 2.5 The accuracy and reliability of emission estimates provide a quantitative basis for the compilation of regional emission inventories and the formulation of agricultural machinery renewal policies.

The invention relates to the technical field of water carbon simulation, and discloses a greenhouse gas emission estimation method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a hydrological-hydrodynamic model, simulating hydrological elements of a target watershed, and obtaining hydrological dynamic data of different time scales; constructing greenhouse gas emission amount estimation models of the hydro-fluctuation belt and the water area, and respectively calculating greenhouse gas unit area emission intensity of the hydro-fluctuation belt and the water area according to hydrological elements; the method comprises the following steps: carrying out weighted integration on unit area emission intensity of greenhouse gases in a hydro-fluctuation belt and a water area according to a spatial range to generate a hydro-fluctuation belt emission amount and a water area emission amount, and converting the emission amounts of different greenhouse gases into equivalent CO2 equivalents; and coupling the equivalent CO2 equivalent and hydrological dynamic data, and generating a continuous-time total emission list of the greenhouse gases in the rivers, lakes and reservoirs. Estimation of greenhouse gas emission of rivers, lakes and reservoirs is realized, and the problem of lack of dynamic water level response mechanisms of related methods is solved.

The invention provides a methane observation data high-resolution processing method, system, equipment and medium, and belongs to the field of greenhouse gas satellite remote sensing, and the method comprises the steps: firstly, generating a low-resolution-medium-resolution training pair from medium-resolution data through a physical degradation function, and solving the problem of label missing; then, a physically guided multi-source information fusion super-resolution network is trained, the network is optimized through a mixed loss function, and the loss function creatively fuses pixel loss, adversarial loss and physical constraint loss using ground truth value data; and finally, through cascade reasoning, applying the trained model to original satellite data, and directly outputting hectometer-level ultrahigh-resolution methane concentration data. According to the method, the authenticity and absolute precision of a reconstruction result are remarkably improved, the method does not depend on a prior emission list, the calculation efficiency is high, and an efficient and reliable technical means is provided for large-range and high-precision methane point source monitoring.

The invention discloses an industrial source carbon pollution discharge list dynamic accounting method and system, a storage medium and equipment, and belongs to the field of pollution monitoring, and the method comprises the steps: S1, obtaining the multi-source data of an industrial enterprise in a target region; s2, preprocessing the multi-source data; s3, constructing a first random forest fusion model based on the enterprise electricity consumption by using the preprocessed data, and predicting and verifying the industry pollutant discharge amount based on the first random forest fusion model; using the first random forest fusion model to predict the pollutant discharge amount of the enterprise in the industry passing the verification, and executing the step S4 for the industry not passing the verification; s4, constructing a second random forest fusion model based on the electricity consumption of the enterprise production equipment, and predicting the pollutant discharge amount of the enterprise in the industry based on the second random forest fusion model; and S5, completing construction of a day / hour scale industrial source carbon pollution dynamic emission list. According to the invention, the actual emission level of industrial source carbon pollution can be dynamically and comprehensively reflected in real time.

The application discloses an air quality prediction and source tracing method and system based on multi-scale model coupling, and belongs to the technical field of air quality prediction and evaluation. The method comprises the following steps: obtaining a pollution source emission inventory and meteorological field data, driving a regional scale model to perform regional air quality prediction, and obtaining a regional scale concentration field. The regional scale concentration field is taken as a background concentration field, and a local scale model and a small scale model are synchronously driven to perform pollution source contribution concentration prediction, and a local scale contribution concentration field and a small scale contribution concentration field are obtained respectively. Each scale concentration field is fused to generate a comprehensive concentration distribution map and perform source tracing analysis, and air quality influence is dynamically evaluated according to the source tracing analysis result. The application optimizes the accuracy of the regional scale model in small scale range simulation and solves the problem of the lack of a local scale concentration background field, and improves the accuracy of prediction and source tracing.

The invention belongs to the technical field of plastic chemical discharge data processing, and discloses a plastic chemical high spatial resolution discharge list compiling method and system.The method comprises the steps that a geographic information file of a target spatial resolution in a to-be-analyzed area is obtained, and total discharge amount data of plastic chemicals in all stages of the whole life cycle is obtained; based on the spatial distribution characteristics of chemicals in plastics in different life cycle stages, a multi-source spatial distribution weight factor is obtained, and the rasterized distribution of the discharge amount of the target spatial resolution is realized. The spatial distribution index method based on multi-source data fusion is adopted, factors such as social and economic activity intensity, land utilization types and pollution source characteristics are comprehensively considered, an accurate high-spatial-resolution emission list is obtained, spatial and temporal distribution of emission of chemicals in plastics can be helped to be clarified, and the accuracy of emission of the chemicals in the plastics is improved. And data support is provided for simulating fate distribution of chemicals in the environment, and a management decision department is helped to formulate more targeted resource environment strategies for different regions.

The application discloses a kind of black carbon emission inversion method, system, equipment and medium, it is related to carbon emission inventory inversion technical field, comprising: using empirical orthogonal function model to black carbon emission inventory is carried out spatial and temporal decomposition dimension reduction, obtain the multiple emission source regions of global scale indicating black carbon emission source;In each emission source region, consider the linear and nonlinear interaction between multiple influence factors, establish the nonlinear regression model between black carbon emission under the interaction of multiple influence factors;Satellite remote sensing observation true value and reanalysis meteorological field data of multiple influence factors are input nonlinear regression model to obtain the black carbon emission inventory in emission source region.The application can obtain accurate black carbon emission, and then obtain accurate black carbon emission inventory data.

The application discloses a kind of zero-carbon concrete based on shell and its mix proportion design method, belong to green building material technical field, the mix proportion design method of the zero-carbon concrete based on shell includes the following steps: calculating and trial-blending to determine the mix proportion of certain strength grade ordinary concrete, as reference mix proportion;Based on life cycle analysis method, determine the carbon emission inventory database of reference concrete, build carbon footprint accounting model;According to the principle of life cycle carbon footprint zero, calculate and determine shell replacement amount;Correct the actual amount of replaced sand aggregate, and then determine the mix proportion and strength grade of zero-carbon concrete.The application has the advantages of simple method, quantitative objective, low cost, easy to popularize and the like, fills the blank of zero-carbon building material design preparation method, and helps to promote the resource utilization of shell waste.