CN-121999898-A - TVOC emission remote sensing monitoring method based on multi-source data driving

Abstract

The invention discloses a remote sensing monitoring method of TVOC emission based on multi-source data driving, which comprises the following steps of analyzing relevant environmental factor data affecting TVOC emission according to total TVOC concentration measured on the ground in combination with various environmental factors, estimating and fitting an atmospheric methane vertical profile, calculating the ratio of the surface methane concentration to the whole column methane concentration, establishing a TVOC emission model by acquiring and matching various parameters through a Gaussian process regression algorithm, and calculating the TVOC emission of each grid in a monitoring range through the model to obtain the TVOC emission data of the full coverage of a monitoring area. The method has the advantages that the method can acquire higher daily space-time resolution, greatly improves the space coverage of VOC emission data, meets the requirement of area fine monitoring, reduces the consumption of computing resources, improves the computing efficiency, can quickly acquire the VOC emission data, greatly reduces the cost and improves the production efficiency.

Inventors

• HE HU
• ZHAO JINGANG
• SUN JIANCHENG
• LI HAORAN
• ZHANG GUOQING
• ZHANG YANBO
• ZHANG WEIDONG
• GU ZHENQI
• Ma Ruojun

Assignees

• 中国石油化工股份有限公司
• 中国石油化工股份有限公司胜利油田分公司

Dates

Publication Date

20260508

Application Date

20241101

Claims (9)

1. 1. The TVOC emission remote sensing monitoring method based on the multi-source data driving is characterized by comprising the following steps of: step S1, analyzing relevant environmental factor data influencing TVOC emission according to the total concentration of the TVOC measured on the ground and combining various environmental factors; s2, acquiring and processing average dry air mixing ratio concentration data and methane layering concentration data of a methane column, estimating and fitting an atmospheric methane vertical profile, calculating the ratio of the surface methane concentration to the whole column methane concentration through the atmospheric methane vertical profile, and converting the average dry air mixing ratio concentration data of the methane column into surface methane concentration data; Step S3, a TVOC emission model is established by utilizing a Gaussian process regression algorithm through acquiring and matching the ground measured TVOC overall concentration data, the surface methane concentration data and the related environmental factor data affecting the TVOC emission, and ten-fold cross validation is carried out; and step S4, calculating the TVOC emission quantity of each grid in the monitoring range based on the TVOC emission model established in the step S3, so as to obtain the TVOC emission data of the full coverage of the monitoring area.
2. 2. The monitoring method according to claim 1, wherein the relevant environmental factor data analyzed in step S1 to affect TVOC emissions includes a temperature factor, a wind speed factor, a moisture factor, a topography factor, and an atmospheric methane concentration factor.
3. 3. The method of monitoring according to claim 1, wherein the ground-measured TVOC data is obtained by fixing a surveying instrument to a moving vehicle for a navigation measurement.
4. 4. The method of monitoring as set forth in claim 2, wherein the methane column average dry air mixture ratio concentration data is from a second-level off-line data product of TROPOMI satellite methane column concentrations, and the methane stratification concentration data is from ECMWF atmospheric pressure mode simulated methane stratification concentration data CAMS.
5. 5. The method of monitoring as claimed in claim 2, wherein, The temperature factor is from the surface temperature data of MODIS; The wind speed factor is from horizontal and vertical wind speed data of ERA; the water vapor factor is from the atmospheric water vapor content data of the MODIS; the terrain factors come from Digital Elevation Model (DEM) data of the USGS; The atmospheric methane stratified concentration data are from CAMS EGG4 stratified methane concentration data; The atmospheric methane vertical column concentration factor is from the XCH4 data product of TROPOMI.
6. 6. The monitoring method according to claim 1, wherein the TROPOMI methane column average dry air mix concentration data is an integrated average value from the surface to the top of the atmosphere, and the ratio of the surface methane concentration to the whole column methane concentration is calculated by the atmospheric methane vertical profile, and the TROPOMI methane column average dry air mix concentration data is converted into the surface methane concentration data.
7. 7. The method of monitoring of claim 1, wherein the atmospheric methane vertical profile is obtained based on a methane concentration data fit from the earth's surface to the top of the atmosphere.
8. 8. The method according to claim 1, wherein the step S3 is specifically: Step 301, obtaining a modeling data set by matching TVOC data actually measured by ground navigation with surface methane concentration data and related environmental factor data; Step S302, modeling TVOC emissions using a gaussian process regression model, the response function of which is defined according to the following formula, Where y i is the corresponding objective function of x i , σ 2 is the error variance, and β is the coefficient, all calculated from the sample data.
9. 9. The monitoring method according to claim 1, wherein the grid in the step S4 is corresponding grid data obtained by performing spatial rasterization on temperature, wind speed, water vapor, topography factors, atmospheric methane concentration factors, TROPOMI methane column concentration data and cam methane stratified concentration data.

Description

TVOC emission remote sensing monitoring method based on multi-source data driving Technical Field The invention relates to the technical field of environmental monitoring, in particular to a TVOC emission remote sensing monitoring method based on multi-source data driving. Background Volatile organic compounds (Total Volatile Organic Compounds, TVOC) refer to organic compounds that have a relatively high saturated vapor pressure, low boiling point, and are volatile at standard conditions, and typically include non-methane hydrocarbons, oxygen-containing, chlorine-containing, nitrogen-containing, and sulfur-containing organic compounds. TVOC is an important precursor for forming fine particulate matter (PM 2.5) and ozone (O 3) in the atmosphere, and has adverse effects on the environment and health. The source of TVOC is wide and can be divided into two major categories, namely natural source and artificial source. Natural sources mainly comprise natural processes such as plant metabolism, volcanic eruption and the like, while artificial sources are concentrated on aspects such as industrial activities, traffic emission, building decoration and the like. For example, solvents and paints often used in chemical, printing and surface coating processes can release significant amounts of TVOC. Currently, methods for estimating TVOC emissions mainly include an emission factor method and a physical model simulation method. Both methods can be used to generate emissions listings of different scales covering countries, regions, cities and industrial parks. However, existing emissions lists suffer from the following disadvantages: 1. The spatial resolution is low-the existing emission list is mostly based on statistics of administrative divisions or 0.05 ° or even lower spatial resolution. This means that they can only give overall TVOC emissions over a large spatial area, and it is difficult to accurately reflect the emissions differences in fine areas. 2. The existing model results are mostly estimated data, and the effective verification of actual emission is lacking. For example, conventional emissions factor methods rely on emissions source activity data and corresponding emissions factors, while capable of giving a wide range of statistical results, are difficult to refine regional emissions monitoring. The emission model based on physical mechanism of Megan et al is capable of simulating the natural source VOC emission process, but its spatial resolution is also limited and lacks support for actual observation data. Therefore, the current TVOC emission monitoring and estimating method still has a large improvement space in terms of accuracy and verifiability, and particularly, obtaining accurate TVOC emission data in a higher resolution area range becomes a technical problem to be solved. Disclosure of Invention In order to solve the problems of low spatial resolution, complex calculation, long time consumption and lack of reliable verification in TVOC emission monitoring in the prior art, the invention provides a TVOC emission remote sensing monitoring method based on multi-source data driving. By combining satellite remote sensing observation data, ground actual measurement data and atmospheric chemical pattern data, a Gaussian process regression model is established, the VOC emission data with high precision and full coverage of the area can be quickly and efficiently obtained, the refined TVOC emission data is satisfied, and data support is provided for quantifying the TVOC emission and monitoring the TVOC emission. In order to achieve the above object, the present invention provides a TVOC emission remote sensing monitoring method based on multi-source data driving, the monitoring method comprising the steps of: step S1, analyzing relevant environmental factor data influencing TVOC emission according to the total concentration of the TVOC measured on the ground and combining various environmental factors; S2, acquiring and processing average dry air mixing ratio concentration data and methane layering concentration data of a methane column, estimating and fitting an atmospheric methane vertical profile, calculating the ratio of the surface methane concentration to the whole column methane concentration through the atmospheric methane vertical profile, and converting the average dry air mixing ratio concentration data of the methane column into the surface methane concentration data; Step S3, a TVOC emission model is established by utilizing a Gaussian process regression algorithm through acquiring and matching the ground measured TVOC overall concentration data, the surface methane concentration data and the related environmental factor data affecting the TVOC emission, and ten-fold cross validation is carried out; and step S4, calculating the TVOC emission quantity of each grid in the monitoring range based on the TVOC emission model established in the step S3, so as to obtain the TVOC emission data of th