CN-122021379-A - Atmospheric pollutant-greenhouse gas collaborative tracing method and system based on receptor model

CN122021379ACN 122021379 ACN122021379 ACN 122021379ACN-122021379-A

Abstract

The invention relates to the technical field of atmospheric environment monitoring, and particularly discloses an atmospheric pollutant-greenhouse gas collaborative tracing method and system based on a receptor model, wherein the physical behavior difference of atmospheric pollutants, greenhouse gas concentration and meteorological data in the diffusion process is quantified by synchronously monitoring the atmospheric pollutants, the greenhouse gas concentration and the meteorological data, and a multidimensional dynamic behavior difference index is generated; the invention solves the defects of independent analysis paths and preliminary collaborative paths in the traditional tracing technology, improves tracing accuracy and physical reality of results by combining chemical constraint and physical verification, is particularly suitable for the identification of unsteady and unregistered emission sources, realizes the intelligent crossing of the system, and has self-learning and evolution capability.

Inventors

LIU CHUNLEI
XIE FANGJIAN
TIAN FENG
ZHENG XINMEI
Xie Diesong
Dou Daodao
WANG YAN
SUN YUEYIN

Assignees

南京市生态环境保护科学研究院

Dates

Publication Date: 20260512
Application Date: 20251216

Claims (10)

1. The atmospheric pollutant-greenhouse gas collaborative tracing method based on the receptor model is characterized by comprising the following steps of: S1, acquiring a collaborative monitoring data set of a target area, wherein the collaborative monitoring data set comprises atmospheric pollutant concentration data, greenhouse gas concentration data and meteorological data obtained through synchronous monitoring; s2, based on a collaborative monitoring data set, quantifying physical behavior difference of atmospheric pollutants and greenhouse gases in a diffusion process, and generating a multidimensional dynamic behavior difference index; s3, inputting a collaborative monitoring data set and a multidimensional dynamic behavior difference index to a self-adaptive inversion system for collaborative tracing, and driving the self-adaptive inversion system to execute iterative inversion and verification to generate a collaborative tracing result; s4, generating a monitoring strategy optimization suggestion based on the iterative inversion and verification process, and outputting a collaborative traceability result and the monitoring strategy optimization suggestion.
2. The method for collaborative tracing of atmospheric pollutants and greenhouse gases based on a receptor model according to claim 1, wherein the specific step of quantifying the physical behavior difference of the atmospheric pollutants and the greenhouse gases in the diffusion process to generate a multidimensional dynamic behavior difference index comprises the following steps: identifying and tracking the same transmitted air mass in the target area based on meteorological data in the collaborative monitoring dataset; Extracting the atmospheric pollutant concentration sequence and the greenhouse gas concentration sequence corresponding to the same transmission air mass at different spatial positions according to the collaborative monitoring data set; And calculating attenuation synchronous deviation, spatial distribution deformation vector and time profile asymmetry of the atmospheric pollutant concentration sequence and the greenhouse gas concentration sequence, and forming a multidimensional dynamic behavior difference index by the attenuation synchronous deviation, the spatial distribution deformation vector and the time profile asymmetry.
3. The atmospheric pollutant-greenhouse gas collaborative tracing method based on a receptor model according to claim 1, wherein the specific steps of driving the adaptive inversion system to perform iterative inversion and verification and generating a collaborative tracing result comprise: acquiring an initial traceability hypothesis set containing various source emission characteristics, and establishing an emission proportion cooperative constraint condition based on source type physicochemical properties; Combining emission proportion cooperative constraint conditions, and carrying out chemical component screening on the initial tracing hypothesis set to obtain candidate tracing hypotheses; Performing atmospheric diffusion process simulation on each candidate traceability hypothesis to obtain a corresponding simulated concentration field, and calculating a corresponding simulated multidimensional behavioral difference index according to the simulated concentration field; And comparing the physical behaviors of each simulated multidimensional behavior difference index with the multidimensional dynamic behavior difference index, and selecting a candidate traceability hypothesis with the highest matching degree as a collaborative traceability result.
4. The atmospheric pollutant-greenhouse gas collaborative traceability method based on a receptor model according to claim 3, wherein after the physical behavior comparison of each simulated multidimensional behavioral difference index and the multidimensional dynamic behavioral difference index, further comprising: Defining a credibility threshold for judging the matching degree, and judging whether the matching degree of all candidate traceability hypotheses is lower than the credibility threshold; if yes, inputting the deviation characteristics of the multidimensional dynamic behavior difference indexes and the simulated multidimensional behavior difference indexes into a source parameter intelligent reasoning engine to generate probability distribution about unknown source parameters; generating new traceability hypothesis constraint conditions based on probability distribution about unknown source parameters; And updating the initial tracing hypothesis set by using the new tracing hypothesis constraint condition, and returning to execute the step of screening chemical components of the initial tracing hypothesis set.
5. The atmospheric pollutant-greenhouse gas collaborative tracing method based on a receptor model according to claim 3, wherein the method for performing chemical component screening on an initial tracing hypothesis set by combining emission proportion collaborative constraint conditions to obtain candidate tracing hypotheses comprises the following steps: acquiring source component spectrum information corresponding to each traceable hypothesis, and calculating a theoretical ratio range corresponding to each traceable hypothesis according to the emission proportion cooperative constraint condition; extracting an actual concentration increment ratio from the collaborative monitoring data set; And comparing each theoretical ratio range with the actual concentration increment ratio, and eliminating traceability hypotheses of which the theoretical ratio range does not cover the actual concentration increment ratio to obtain candidate traceability hypotheses.
6. The method for collaborative tracing of atmospheric pollutants and greenhouse gases based on a receptor model according to claim 3, wherein the performing the atmospheric diffusion process simulation on each candidate tracing hypothesis to obtain a corresponding simulated concentration field comprises: constructing a virtual source parameter set corresponding to each candidate traceability hypothesis, wherein the virtual source parameter set comprises source position, source intensity and emission time characteristics; Acquiring a hybrid Lagrange-Euler coupling model for simulating atmospheric diffusion, and taking real-time three-dimensional wind field data and a virtual source parameter set in the collaborative monitoring data set as input of the hybrid Lagrange-Euler coupling model; And driving the hybrid Lagrange-Euler coupling model to operate so as to simulate the diffusion process of atmospheric pollutants and greenhouse gases emitted by the virtual source under the action of the real-time three-dimensional wind field and output a simulated concentration field.
7. The atmospheric pollutant-greenhouse gas collaborative traceability method based on the acceptor model according to claim 1, wherein the specific step of generating monitoring strategy optimization suggestions based on iterative inversion and verification comprises the following steps: Extracting hypothesis matching degree data in the iterative inversion and verification process and model correction records possibly occurring in the iteration; calculating uncertainty quantization scores of the collaborative traceability result according to the assumed matching degree data and the model correction record; and generating a monitoring strategy optimization suggestion by combining the spatial distribution information of the collaborative traceability result and the uncertainty quantization score.
8. The method for collaborative tracing of atmospheric pollutants and greenhouse gases based on a receptor model according to claim 1, further comprising, prior to the acquiring the collaborative monitoring dataset of the target area: Receiving and analyzing a tracing task instruction input by a user, wherein the tracing task instruction comprises a target area range and a target time window; Configuring acquisition parameters of a collaborative monitoring data set according to the tracing task instruction; And starting a monitoring network according to the acquired parameters to complete the acquisition of the collaborative monitoring data set.
9. The atmospheric pollutant-greenhouse gas collaborative traceability method based on a receptor model according to claim 1, further comprising: acquiring a historical emission list or a historical traceability result as reference data; performing space-time comparison analysis on the collaborative traceability result and the reference data, and identifying emission hot spot change or newly-increased potential emission sources from the space-time comparison analysis result; and generating an early warning report containing emission hot spot change or newly-added potential emission source information, and outputting the early warning report and the collaborative traceability result together.
10. Atmospheric pollutant-greenhouse gas collaborative traceability system based on receptor model, characterized by comprising: the collaborative monitoring data set acquisition module is used for acquiring a collaborative monitoring data set of a target area, wherein the collaborative monitoring data set comprises atmospheric pollutant concentration data, greenhouse gas concentration data and meteorological data obtained through synchronous monitoring; The dynamic behavior difference index generation module is used for quantifying physical behavior differences of atmospheric pollutants and greenhouse gases in the diffusion process based on the collaborative monitoring data set to generate a multidimensional dynamic behavior difference index; The collaborative tracing result generation module is used for inputting a collaborative monitoring data set and a multidimensional dynamic behavior difference index into the adaptive inversion system for collaborative tracing, driving the adaptive inversion system to execute iterative inversion and verification, and generating a collaborative tracing result; And the optimization suggestion generation and output module is used for generating a monitoring strategy optimization suggestion based on the iterative inversion and verification process and outputting a collaborative traceability result and the monitoring strategy optimization suggestion.

Description

Atmospheric pollutant-greenhouse gas collaborative tracing method and system based on receptor model Technical Field The invention belongs to the technical field of atmospheric environment monitoring, and relates to an atmospheric pollutant-greenhouse gas collaborative tracing method and system based on a receptor model. Background Improvement of atmospheric environmental quality and emission reduction of greenhouse gases are currently important global issues. In order to realize the cooperative control of the atmospheric pollutants and the greenhouse gases, the emission sources thereof are accurately identified and quantified, namely source analysis is carried out, and the method is a precondition for formulating an effective emission reduction strategy. The receptor model is a key technical method in the field of atmospheric analysis, and the contribution of various pollution sources to the atmospheric environment sampling point, namely the chemical composition and concentration change of a receptor, is reversely deduced by analyzing the point. With the proposal of the synergistic demand of pollution and carbon reduction, a synergistic traceability technology aiming at atmospheric pollutants and greenhouse gases is developed. Currently, two technical paths are generally adopted for the traceable analysis of atmospheric pollutants and greenhouse gases. One is an independent analysis path, namely, a receptor model for atmospheric pollutants such as particulate matters and a source list or model for greenhouse gases are respectively established, the two types of substances are traced separately, and then the results are manually compared. The other is a preliminary cooperative path, namely, synchronous observation of pollutants and greenhouse gases is realized at a monitoring end, and then, in a single acceptor model framework, the greenhouse gases are tried to be used as a tracer or a component to carry out source analysis calculation together with other pollutants. However, the above prior art has significant drawbacks in practical applications. For independent analysis paths, because of mutual fracture of monitoring data, model mechanisms and hypothesis conditions, the respective tracing results often have contradictions in space orientation and contribution estimation, and unified and self-consistent conclusions are difficult to form. For the preliminary synergistic path, the adopted traditional acceptor model is essentially a static model based on conservation of chemical components, and the model treats greenhouse gas and atmospheric pollutants equally, and cannot fully consider inherent differences of physical diffusion behaviors of sedimentation, chemical reaction, remote transmission and the like, so that the model lacks enough physical constraint in the inversion process, and the accuracy of a source analysis result is affected. Disclosure of Invention In view of this, in order to solve the problems set forth in the background art, a method and a system for collaborative tracing of atmospheric pollutants and greenhouse gases based on a receptor model are provided. The invention aims at providing an atmospheric pollutant-greenhouse gas collaborative tracing method based on a receptor model according to the first aspect of the invention, which comprises the following steps of S1, acquiring a collaborative monitoring dataset of a target area, wherein the collaborative monitoring dataset comprises atmospheric pollutant concentration data, greenhouse gas concentration data and meteorological data obtained by synchronous monitoring. S2, based on the collaborative monitoring data set, quantifying physical behavior difference of atmospheric pollutants and greenhouse gases in the diffusion process, and generating a multidimensional dynamic behavior difference index. S3, inputting the collaborative monitoring data set and the multidimensional dynamic behavior difference index into a self-adaptive inversion system for collaborative tracing, and driving the self-adaptive inversion system to execute iterative inversion and verification to generate a collaborative tracing result. S4, generating a monitoring strategy optimization suggestion based on the iterative inversion and verification process, and outputting a collaborative traceability result and the monitoring strategy optimization suggestion. The invention provides an atmospheric pollutant-greenhouse gas collaborative traceability system based on a receptor model, which comprises a collaborative monitoring data set acquisition module, a collaborative monitoring data set acquisition module and a collaborative monitoring data set acquisition module, wherein the collaborative monitoring data set comprises atmospheric pollutant concentration data, greenhouse gas concentration data and meteorological data obtained through synchronous monitoring. And the dynamic behavior difference index generation module is used for quantifying the physical behavior difference