Retrieving pollution emission source using CFD and satellite data

Patent metadata

Field	Value
Publication number	US-10372846-B2
Application number	US-201514938963-A
Country	US
Kind code	B2
Filing date	Nov 12, 2015
Priority date	Nov 12, 2015
Publication date	Aug 6, 2019
Grant date	Aug 6, 2019

Abstract

A method for modeling air pollution includes receiving a weather model for a particular geographic region. Satellite-observed pollution observation data over the geographic region is received. A physical dispersion model for pollution over the geographic region is generated using the received weather model. The received satellite-observed pollution observation data is interpolated to the generated physical model. The interpolated satellite-observed pollution observation data and the generated physical model are combined using weighted coefficients for both the interpolated satellite-observed pollution observation data and the generated physical model. The weighted coefficients are calculated in accordance with a relative error in both the physical dispersion model and the satellite-observed pollution observation data.

First claim

What is claimed is:

1. A method for modeling air pollution, comprising:
receiving a weather model for a particular geographic region;
receiving satellite-observed pollution observation data over the geographic region;
generating a physical dispersion model for pollution over the geographic region using the received weather model;
interpolating the received satellite-observed pollution observation data to the generated physical model; and
combining the interpolated satellite-observed pollution observation data and the generated physical model using weighted coefficients for both the interpolated satellite-observed pollution observation data and the generated physical model to produce a hybrid weather model,
wherein the weighted coefficients are calculated in accordance with a relative error in both the physical dispersion model and the satellite-observed pollution observation data, and
wherein the hybrid weather model has a greater resolution than the received satellite-observed pollution observation data alone.

2. The method of claim 1 , wherein the satellite-observed pollution observation data has a courser resolution than the physical dispersion model.

3. The method of claim 1 , wherein the generated physical dispersion model is a computed fluid dynamics (CFD) model.

4. The method of claim 1 , wherein the physical dispersion model is generated from the received weather model using one or more observed emissions levels taken at one or more observation stations.

5. The method of claim 1 , wherein the physical dispersion model is generated from the received weather model using one or more observed emissions levels taken from the satellite-observed pollution observation data.

6. The method of claim 1 , wherein the physical dispersion model is optimized by minimizing a difference between one or more observed emissions levels taken at one or more observation stations and calculated emissions levels at locations of the one or more observation stations that are calculated from the physical dispersion model.

7. The method of claim 1 , wherein the physical dispersion model is optimized by minimizing a difference between one or more observed emissions levels taken from the satellite-observed pollution observation data at one or more locations and calculated emissions levels at the one or more locations that are calculated from the physical dispersion model.

8. The method of claim 1 , wherein the relative error in both the physical dispersion model and the satellite-observed pollution observation data is calculate based on a calculated inversing model error of the physical dispersion model and a predetermined error of the satellite-observed pollution observation data.

9. The method of claim 1 , further comprising displaying the combined interpolated satellite-observed pollution observation data and the generated physical dispersion model as a visual representation of air pollution levels over the particular geographic region.

10. A method for modeling air pollution, comprising:
receiving a weather model for a particular geographic region;
receiving satellite-observed pollution observation data over the geographic region;
generating a physical dispersion model for pollution over the geographic region using the received weather model;
interpolating the received satellite-observed pollution observation data to the generated physical model to produce a hybrid weather model; and
optimizing the generated physical dispersion model by minimizing a difference between one or more observed emissions levels taken from the satellite-observed pollution observation data at one or more locations and calculated emissions levels at the one or more locations that are calculated from the physical dispersion model,
wherein the hybrid weather model has a greater resolution than the received satellite-observed pollution observation data alone.

11. The method of claim 10 , wherein the satellite-observed pollution observation data has a courser resolution than the physical dispersion model.

12. The method of claim 10 , wherein the generated physical dispersion model is a computed fluid dynamics (CFD) model.

13. The method of claim 10 , further comprising displaying the optimized physical dispersion model as a visual representation of air pollution levels over the particular geographic region.

14. A computer system comprising:
a processor; and
a non-transitory, tangible, program storage medium, readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for modeling air pollution, the method comprising:
receiving a weather model for a particular geographic region;
receiving satellite-observed pollution observation data over the geographic region;
generating a physical dispersion model for pollution over the geographic region using the received weather model;
interpolating the received satellite-observed pollution observation data to the generated physical model; and
combining the interpolated satellite-observed pollution observation data and the generated physical model using weighted coefficients for both the interpolated satellite-observed pollution observation data and the generated physical model to produce a hybrid weather model,
wherein the weighted coefficients are calculated in accordance with a relative error in both the physical dispersion model and the satellite-observed pollution observation data, and
wherein the hybrid weather model has a greater resolution than the received satellite-observed pollution observation data alone.

15. The computer system of claim 14 , wherein the satellite-observed pollution observation data has a courser resolution than the physical dispersion model.

16. The computer system of claim 14 , wherein the generated physical dispersion model is a computed fluid dynamics (CFD) model.

17. The computer system of claim 14 , wherein the physical dispersion model is generated from the received weather model using one or more observed emissions levels taken at one or more observation stations.

18. The computer system of claim 14 , wherein the physical dispersion model is generated from the received weather model using one or more observed emissions levels taken from the satellite-observed pollution observation data.

19. The computer system of claim 14 , wherein the physical dispersion model is optimized by minimizing a difference between one or more observed emissions levels taken at one or more observation stations and calculated emissions levels at locations of the one or more observation stations that are calculated from the physical dispersion model.

20. The computer system of claim 14 , further comprising displaying the combined interpolated satellite-observed pollution observation data and the generated physical dispersion model as a visual representation of air pollution levels over the particular geographic region.

Assignees

• IBM

Inventors

• Bai Xin Xin
• Dong Jin
• Du Hui Bj
• Rui Xiao Guang
• Wang Hai Feng Hf
• Xie Bao Guo Bg
• Yin Wen Jun
• Zhang Meng Mm

Classifications

• B01D53/56

  Nitrogen oxides (B01D53/60 takes precedence) · CPC title

• G06F30/23

  using finite element methods [FEM] or finite difference methods [FDM] · CPC title

• H04W4/021

  Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences · CPC title

• G01W1/10

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title