Automatic siting for air quality monitoring stations

Patent metadata

Field	Value
Publication number	US-2018306762-A1
Application number	US-201715494759-A
Country	US
Kind code	A1
Filing date	Apr 24, 2017
Priority date	Apr 24, 2017
Publication date	Oct 25, 2018
Grant date	—

Abstract

A system, a computer readable storage medium, and a method for automatically siting for air quality monitoring stations includes collecting from air quality monitoring stations air pollution concentration data, collecting from meteorological stations meteorological data, and interpolating, by the at least one or more processors, the air pollution concentration data to provide interpolated air pollution concentration data. The method and system can further cluster the interpolated air pollution concentration data and the meteorological data to provide clustered data and select a candidate site for an air monitoring station using the clustered data. The method and system can further evaluate a relationship of the air pollution concentration data with the meteorological data by weighting the air pollution data with the meteorological data.

First claim

What is claimed is:

1 . A computer implemented method, comprising:
collecting from air quality monitoring stations, by at least one or more processors of at least one computing device, air pollution concentration data; collecting from meteorological stations, by the at least one or more processors, meteorological data; interpolating, by the at least one or more processors, at least the air pollution concentration data to provide interpolated air pollution concentration data; clustering, by the at least one or more processors, the interpolated air pollution concentration data and the meteorological data to provide clustered data; and selecting, by the at least one or more processors, a candidate site for an air monitoring station using the clustered data.

2 . The computer implemented method of claim 1 , wherein the interpolating comprises dividing the air pollution concentration data by time and applying time sliced data to different geographic areas.

3 . The computer implemented method of claim 1 , wherein the method further comprises evaluating a relationship of the air pollution concentration data with the meteorological data by weighting the air pollution concentration data with the meteorological data.

4 . The computer implemented method of claim 1 , wherein the method further comprises weighting the meteorological data by season of the year and the air pollution data by type of pollutant.

5 . The computer implemented method of claim 1 , wherein the method further comprises using meteorological data to find a center of meteorological fields of one time slice, combining a different time slice to find a trajectory of a meteorological condition in a certain region, matching the trajectory with the air pollution concentration data and trajectory clustering, and generating at least one of the candidate sites.

6 . The computer implemented method of claim 1 , wherein the method further comprises automatically generating a center of a field of pressure and a scope of the field of pressure.

7 . The computer implemented method of claim 1 , wherein the method further comprises automatically generating a center of a field of pressure and a scope of the field of pressure by matching isobars of pressure and extreme points of pressure, determining the Hausdoff distance of isobars and extreme points and determining the Sobel gradient of the extreme points and a nearest isobar.

8 . The computer implemented method of claim 1 , further comprising applying parameters and constraints to an automatic site finder after the automatic site finder receives all the candidate sites.

9 . The computer implemented method of claim 8 , wherein the parameters and constraints comprises calculating a weight of a candidate site based on a reshaping and clustering result, and iteratively calculating an object function to select a final output of the candidate site or candidate sites.

10 . The computer implemented method of claim 8 , wherein the parameters comprises one or more of a number of stations, a range of evaluation stations by percentage, range of reference stations by percentage, a range of highly polluting stations by percentage, a range of transform stations by percentage, or a minimum distance between every two stations.

11 . A system comprising:
at least one memory; and at least one processor of a computer system communicatively coupled to the at least one memory, the at least one processor, responsive to instructions stored in memory, being configured to perform a method comprising: collecting from an air quality monitoring stations, air pollution concentration data; collecting from meteorological stations, meteorological data; interpolating, by the at least one processor, at least the air pollution concentration data to provide interpolated air pollution concentration data; clustering, by the at least one processor, the interpolated air pollution concentration data and the meteorological data to provide clustered data; and selecting, by the at least one or more processors, a candidate site for an air monitoring station using the clustered data.

12 . The system of claim 11 , further comprising instructions stored in memory which when executed by the at least one processor causes the at least one processor to perform the operation of evaluating a relationship of the air pollution concentration data with the meteorological data by weighting the air pollution concentration data with the meteorological data.

13 . The system of claim 11 , wherein the instructions stored in memory which when executed by the at least one processor causes the at least one processor to perform the operation of weighting the meteorological data by time of the year and the air pollution data by type of pollutant.

14 . The system of claim 11 , wherein the instructions stored in memory which when executed by the at least one processor causes the at least one processor to perform the operation of using meteorological data to find a center of meteorological fields of one time slice, combining a different time slice to find a trajectory of a meteorological condition in a certain region, matching the trajectory with the air pollution concentration data and trajectory clustering, and generating at least one of the candidate sites.

15 . The system of claim 11 , wherein the instructions stored in memory which when executed by the at least one processor causes the at least one processor to perform the operation of automatically generating a center of a field of pressure and a scope of the field of pressure by matching isobars of pressure and extreme points of pressure, determining the Hausdoff distance of isobars and extreme points and determining the Sobel gradient of the extreme points and a nearest isobar.

16 . The system of claim 15 , wherein the instructions stored in memory which when executed by the at least one processor causes the at least one processor to further perform applying parameters and constraints to an automatic site finder after the automatic site finder receives all the candidate sites.

17 . A non-transitory computer-readable storage medium having stored therein instructions which, when executed by at least one or more processors of at least one computing device, cause a computer system to perform a method comprising:
collecting from air quality monitoring stations, by at least one or more processors of at least one computing device, air pollution concentration data; collecting from meteorological stations, by the at least one or more processors, meteorological data; interpolating, by the at least one or more processors, at least the air pollution concentration data to provide interpolated air pollution concentration data; clustering, by the at least one or more processors, the interpolated air pollution concentration data and the meteorological data to provide clustered data; and selecting, by the at least one or more processors, a candidate site for an air monitoring station using the clustered data.

18 . The non-transitory computer-readable storage medium of claim 17 , further comprising weighting, by the at least one or more processors, the meteorological data by time of the year and the air pollution data by type of pollutant.

19 . The non-transitory computer-readable storage medium of claim 17 , further comprising applying parameters and constraints to an automatic site finder after the automatic site finder receives all the candidate sites.

20 . The non-transitory computer-readable storage medium of

Assignees

• IBM

Inventors

• Dong Jin
• Rui Xiao Guang
• Yao Jian
• Yin Wen Jun
• Zhang Chao
• Zhang Xin
• Zhao yu xin

Classifications

• G01N33/0022

  using a number of analysing channels · CPC title

• G01N1/2273

  Atmospheric sampling · CPC title

• G01N35/00871

  Communications between instruments or with remote terminals · CPC title

• G01N2015/0046

  in gas, e.g. smoke · CPC title

• G01N33/0075 Primary

  for multiple spatially distributed sensors, e.g. for environmental monitoring · CPC title