Particulate matter monitoring

Patent metadata

Field	Value
Publication number	US-11714074-B2
Application number	US-202017133885-A
Country	US
Kind code	B2
Filing date	Dec 24, 2020
Priority date	Nov 28, 2016
Publication date	Aug 1, 2023
Grant date	Aug 1, 2023

Abstract

Techniques for monitoring particulate matter (PM) mass concentration using relatively low cost devices are described. A computer-implemented method comprises determining, by a device operatively coupled to a processor, relationships between: first PM mass data determined by a monitor station device for a first atmospheric area over a period of time; first PM count data determined by a reference PM count device for the first atmospheric area over the period of time; and first conditional information comprising first values for defined conditional parameters, wherein the first values are associated with the first atmospheric area over the period of time. The method further includes generating an initial conversion model based on the relationships, wherein the conversion model converts a PM count to a PM mass based on one or more conditional parameters of the defined conditional parameters and features for updating the conversion model.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
determining, by one or more components operatively coupled to a processor, relationships between first particulate matter mass data and first particulate matter count data;
determining, by the one or more components, a first conversion model based on relationships between a particulate matter count and a particulate matter mass, wherein the first conversion model converts the particulate matter count to the particulate matter mass based on one or more conditional parameters of defined conditional parameters; and
evaluating, by the one or more components, second particulate matter mass data for a first atmospheric area, wherein the second particulate matter mass data is associated with second particulate matter count data determined by a reference particulate matter count device for the first atmospheric area over a second period of time, wherein the first conversion model or a second conversion model is selected based on a value of a relative direct current offset being above or below a threshold value of the relative direct current offset.

2. The computer-implemented method of claim 1 , further comprising:
receiving, by the one or more components, conditional information; and
updating, by the one or more components, the first conversion model, in response to the receiving the conditional information.

3. The computer-implemented method of claim 1 , wherein the defined conditional parameters are selected from a group consisting of accumulated pulse height, accumulated pulse area and the relative direct current offset.

4. The computer-implemented method of claim 1 , wherein the defined conditional parameters are selected from a group consisting of: particle composition, size distribution, temperature, and humidity.

5. The computer-implemented method of claim 1 , wherein the determining the first conversion model comprises employing machine learning.

6. The computer-implemented method of claim 1 , further comprising:
employing, by the one or more components, the first conversion model to determine a current particulate matter mass for the first atmospheric area based on second particulate matter count data determined for the first atmospheric area and second conditional information comprising second values for the one or more conditional parameters.

7. The computer-implemented method of claim 6 , wherein the second values are associated with the first atmospheric area.

8. The computer-implemented method of claim 7 , wherein the second particulate matter count data was determined by a particulate matter count device.

9. The computer-implemented method of claim 1 , further comprising:
receiving, by the one or more components, the second particulate matter mass data; and
determining, by the one or more components, conditional information comprising values for the defined conditional parameters, wherein the values are associated with the first atmospheric area over the second period of time, and wherein the second particulate matter mass data is associated with second particulate matter count data determined by the reference particulate matter count device for the first atmospheric area over the second period of time.

10. The computer-implemented method of claim 9 , further comprising:
updating, by the one or more components, the first conversion model based on the second particulate matter count data, the second particulate matter mass data, and the conditional information.

11. The computer-implemented method of claim 1 , wherein the reference particulate matter count device comprises a light scattering device, and wherein the first particulate matter mass data is determined by a monitor station device selected from a group consisting of: a tapered element oscillating microbalance device and a beta ray attenuation device.

12. A system, comprising:
a memory;
a processor operably coupled to the memory, wherein the processor:
receives first particulate matter mass data and first particulate matter count data; and
determines a first conversion model based on relationships between a particulate matter count and a particulate matter mass, wherein the first conversion model converts the particulate matter count to the particulate matter mass based on one or more conditional parameters of defined conditional parameter, wherein the system also evaluates second particulate matter mass data for a first atmospheric area, wherein the second particulate matter mass data is associated with second particulate matter count data determined by a reference particulate matter count device for the first atmospheric area over a second period of time wherein selected values comprise relative direct current offset values associated with a high pollution state based on a subset of relative direct current offset values respectively associated with a subset of particulate matter mass concentration levels within a threshold mass concentration level range.

13. The system of claim 12 , wherein the defined conditional parameter comprises at least one of an accumulated pulse height or an accumulated pulse area.

14. The system of claim 12 , wherein the defined conditional parameters comprise the relative direct current offset.

15. The system of claim 12 , wherein the processor also:
selects the first conversion model or a second conversion model to employ in association with determining a current particulate matter mass for a first atmospheric area or a second atmospheric area based on second particulate matter count data for the first atmospheric area or the second atmospheric area and second conditional information for the first atmospheric area or the second atmospheric area.

16. The system of claim 15 , wherein the second conditional information comprises second values for the defined conditional parameters associated with the first atmospheric area or the second atmospheric area at a current time, including a current relative direct current offset value, and wherein the model selection component selects the first conversion model or the second conversion model based on the current relative direct current offset value being above or below a threshold relative direct current offset value.

17. A computer program product for monitoring particulate matter mass concentration, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to:
determine relationships between first particulate matter mass data and first particulate matter count data; and
create at least one conversion model based on the relationships, wherein the conversion model converts a particulate matter count to a particulate matter mass based on one or more conditional parameters of defined conditional parameters, wherein the at least one conversion model comprises a first conversion model associated with low pollution states based on a first subset of first conditional information associated with a first subset of particulate mass concentration levels below a threshold level and a second conversion model associated with high pollution states based on a second subset of the first conditional information associated with a second subset of the particulate mass concentration levels above the threshold level.

18. The computer program product of claim 17 , wherein the program instructions are further executable by the processor to cause the processor to:
receive second particulate data, and second conditional information; and
update the convers

Assignees

• IBM

Inventors

• Wang Junsong
• Wang Lingyun

Classifications

• G01N33/0027 Primary

  concerning the detector · CPC title

• G01N15/0205

  by optical means · CPC title

• G01N2015/0046

  in gas, e.g. smoke · CPC title

• G06N20/00 Primary

  Machine learning · CPC title