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CN-116087044-B - PM based on CMB model2.5On-line source analysis method and equipment

CN116087044BCN 116087044 BCN116087044 BCN 116087044BCN-116087044-B

Abstract

The invention discloses a PM 2.5 online source analysis method and equipment based on a CMB model, which can automatically identify pollution sources without inputting a large amount of receptor data, and the method comprises the following steps of acquiring historical observation data of particulate matters and chemical components thereof to be monitored at a point to be monitored; the method comprises the steps of screening historical component concentration data for constructing a PMF model based on historical observation data, using mass spectrum information m/z 44 as an SOA source identification component, calculating uncertainty corresponding to each concentration data based on a preset uncertainty calculation method, inputting the PMF model, calculating to obtain component spectrum data of each pollution source of a point to be monitored and corresponding uncertainty, obtaining real-time observation data of chemical components of particles in the point to be monitored, screening the real-time observation data, finishing calculation based on the preset uncertainty calculation method to obtain real-time component concentration data and corresponding uncertainty, inputting the CMB model, and calculating to obtain contribution results of each pollution source to PM 2.5 .

Inventors

  • HUANG XIAOFENG
  • PENG XING
  • HE LINGYAN
  • ZENG LIWU
  • Yao Peiting

Assignees

  • 北京大学深圳研究生院

Dates

Publication Date
20260505
Application Date
20221118

Claims (7)

  1. 1. The PM 2.5 online source analysis method based on the CMB model is characterized by comprising the following steps of: Step one, acquiring historical observation data of particulate matters to be monitored and chemical components of the particulate matters to be monitored at a point to be monitored; Screening historical component concentration data for constructing PMF input data based on the historical observation data, and calculating uncertainty corresponding to each concentration data based on a preset uncertainty calculation method; Step three, PMF input data are input into the PMF model, and calculation is carried out to obtain the component spectrum data of each pollution source of the point location to be monitored and the corresponding uncertainty thereof, The PMF input data includes the historical constituent concentration data and the uncertainty; Step four, acquiring real-time observation data of chemical components of the particulate matters in the point to be monitored; Step five, screening the real-time observation data and completing calculation based on the predetermined uncertainty calculation method to obtain real-time component concentration data and corresponding uncertainty thereof; step six, inputting the component spectrum data and the corresponding uncertainty thereof, the real-time component concentration data and the corresponding uncertainty thereof into a CMB model, and carrying out operation to obtain the contribution result of each pollution source to PM 2.5 , Wherein said historical component concentration further comprises an identification component for each of said sources of contamination, When the historical component concentration data for constructing the PMF model is screened out based on the historical observation data, organic matter fragments with the mass-to-charge ratio of 44 are also screened out as SOA source identification components, The identification component comprises the SOA source identification component, When the PMF model is operated, a factor component spectrum matrix is obtained firstly, then the pollution source type represented by each factor is identified based on the identification component of each factor to obtain the component spectrum data, the uncertainty of the component spectrum data is further evaluated based on the bootstrap function of the PMF model to obtain the corresponding uncertainty, The predetermined uncertainty calculation method is based on a formula The degree of uncertainty is calculated and, In the formula, For concentration data, k j is the relative uncertainty of the jth chemical component, Is that Is not determined by the degree of uncertainty of (2).
  2. 2. The CMB model-based PM 2.5 online source resolution method of claim 1, wherein: The number of the factors is set to 8-12 based on the actual situation of the pollution source.
  3. 3. The CMB model-based PM 2.5 online source resolution method of claim 2, wherein: wherein the number of the factors is 9, and the factors comprise biomass combustion, secondary sulfate, fire coal, secondary nitrate, industrial emission, SOA, ship emission, motor vehicle emission and dust emission.
  4. 4. The CMB model-based PM 2.5 online source resolution method of claim 1, wherein: Wherein the number of the chemical components is 13, including ：OM、BC、Cl - 、NO 3 - 、SO 4 2- 、NH 4 + 、K、Si、Ca、Fe、Zn、V、m/z 44.
  5. 5. The CMB model-based PM 2.5 online source resolution method of claim 1, wherein: Wherein the number of the chemical components is 16, including ：OM、NO 3 - 、SO 4 2- 、NH 4 + 、BC、Si、K、Ca、V、Cr、Mn、Fe、Ni、Zn、As、m/z 44.
  6. 6. The CMB model-based PM 2.5 online source resolution method of claim 1, wherein: The real-time observation data are data of PM 2.5 , water-soluble ions, organic matters, black carbon and atmospheric metal elements.
  7. 7. A computer device comprising a memory having executable code stored therein and a processor which, when executing the executable code, implements the method of any of claims 1-5.

Description

PM 2.5 online source analysis method and equipment based on CMB model Technical Field The invention belongs to the field of air pollution control, and particularly relates to a PM 2.5 online source analysis method and equipment based on a CMB model. Background PM 2.5 has important influences on climate change, human health, visibility and the like, and is a primary pollutant affecting the air quality of China. PM 2.5 pollution is a rapid-change dynamic process, and the main source of PM 2.5 can be rapidly identified, so that scientific basis can be provided for the establishment of PM 2.5 pollution control scheme, and the method is a necessary condition for fine treatment of PM 2.5. Based on an online monitoring means, PM 2.5 is comprehensively and chemically monitored in real time, and an online PM 2.5 source analysis technology is established by combining a receptor model, so that the contribution of each pollution source to particulate matters can be rapidly identified and quantified, and technical support is provided for the accurate treatment of PM 2.5 pollution. The Chemical Mass Balance (CMB) model and the positive definite matrix factorization (PMF) model are two dominant receptor models. The CMB model quantitatively evaluates the contribution of each pollution source to pollutants based on the component spectrum information of each pollution source and the receptor data, and has the advantages of less input receptor data and definite pollution source. The PMF model does not need to input pollution source component spectrum data, utilizes a weighted least square method to decompose an observation receptor data matrix into a factor spectrum matrix and a factor contribution matrix, identifies pollution sources based on the factor spectrum matrix and quantifies the contribution of each pollution source to pollutants based on the factor contribution. Because of numerous and complex sources of particulate matter pollution sources, representative pollution source component spectrum information is difficult to obtain, and the PM 2.5 on-line source analysis technology based on the receptor model mainly selects to use a PMF model for analysis. However, the PMF model also has its limitations. First, the PMF model needs to identify the source of each factor based on research experience, and cannot automatically identify the pollution source of particulate matter. Secondly, because the pollution sources have the problem of collinearity, namely the component spectrums of the pollution sources are similar, the PMF model cannot distinguish the collinearity sources, and the analyzed factors have the problem of mixing, in particular, the Secondary Organic Aerosol (SOA) is easy to mix with the primary and secondary sources and cannot be independently and accurately evaluated by the PMF model. Thirdly, a large amount of acceptor data are required to be input into the PMF model to identify a quantitative pollution source, a large amount of historical observation data are required to be input for acquiring a source analysis result of particulate matters at a certain moment, and the source analysis result at the moment is influenced by the input acceptor data, so that the analysis result is larger in uncertainty. Disclosure of Invention In order to solve the problems, the invention provides a PM 2.5 online source analysis method and equipment based on a CMB model, which can automatically identify pollution sources without inputting a large amount of receptor data, and adopts the following technical scheme: the invention provides a PM 2.5 on-line source analysis method based on a CMB model, which comprises the following steps of firstly obtaining historical observation data of particulate matters and chemical components of the particulate matters to be monitored in a point to be monitored, secondly screening historical component concentration data used for constructing the PMF model based on the historical observation data, calculating uncertainty corresponding to each concentration data based on a preset uncertainty calculation method, thirdly inputting PMF input data into the PMF model, calculating to obtain component spectrum data of each pollution source of the point to be monitored and corresponding uncertainty thereof, wherein the PMF input data comprises the historical component concentration data and the uncertainty, fourthly obtaining real-time observation data of the chemical components of the particulate matters in the point to be monitored, fifthly screening the real-time observation data, finishing calculation based on the preset uncertainty calculation method to obtain real-time component concentration data and corresponding uncertainty thereof, sixthly inputting the component spectrum data and the corresponding uncertainty, the real-time component concentration data and the corresponding uncertainty thereof into the PM model, and calculating to obtain contribution results of each pollution source to the PM