ISSN 1006-9585

CN 11-3693/P

+Advanced Search 中文版
An Objective Identification Method for Dust Weather Based on the K-means Clustering Algorithm

1.Beijing Information Science and Technology University;2.International Center for Climate and Environment Sciences

Fund Project:

National Natural Science Foundation of China (Grant 41975119, 42075166, 41830966), Beijing Information Science and Technology University (S2226080)

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials

    Time series analysis methods have been developed before to identify dust weather based on pollutant concentrations, but the criteria used are subject to considerable uncertainty. Therefore, in this study, we propose an objective identification method for dust weather based on the K-means clustering method by using the hourly concentration of PM2.5 and PM10 from the environmental monitoring stations. The core idea of this method is as follows: first select the optimal number of classifications K for cluster analysis; then classify the cluster groups that show large scattering in the distribution of PM2.5 and PM10 concentrations until no further classification is needed. This method is applied to identify dust weather in Xi"an from February to April 2018. The results show that this method can effectively identify the main dust weather events. Based on this method, typical characteristics of dust weather can be obtained: the ratio of PM2.5 to PM10 concentration is less than 43.5%, and the PM10 concentration is greater than 228μg/m3, which is consistent with our knowledge that the PM10 concentration is high and mainly consists of coarse particles during the dust event. Overall, this method has a clear physical basis, and it is easy to operate, suitable for massive data processing, and promising for applications in relevant areas.

    Cited by
Get Citation
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
  • Received:May 04,2023
  • Revised:October 12,2023
  • Adopted:November 28,2023
  • Online: January 05,2024
  • Published: