Objective Identification Method for Dust Weather Based on the K-means Clustering Algorithm

Time-series analysis methods have been developed previously to identify dust weather based on pollutant concentrations; however, the criteria used are subject to considerable uncertainties. Therefore, herein, we propose an objective identification method for dust weather. This method is based on the K-means clustering algorithm and involves using hourly concentrations of PM2.5 and PM10 obtained from environmental monitoring stations. The flow path of this method involves first selecting the optimal number of classifications K for cluster analysis, followed by classifying the cluster groups that show substantial scattering in the distribution of PM2.5 and PM10 concentrations until no further classification is needed. Further, this method is applied to identify the 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 being less than 43.5%, and the PM10 concentration being greater than 228 μg/m³, which is consistent with physical characteristics that the PM10 concentration is high and mainly consists of coarse particles during the dust event. Overall, this method has a clear physical basis; it is also easy to operate, suitable for massive data processing, and promising for applications in relevant areas.