Higher-order Network Analysis of Fine Particulate Matter (PM 2.5) Transport in China at City Level

Abstract

Specification of PM _2.5 transmission characteristics is important for pollution control and policymaking. We apply higher-order organization of complex networks to identify major potential PM _2.5 contributors and PM _2.5 transport pathways of a network of 189 cities in China. The network we create in this paper consists of major cities in China and contains information on meteorological conditions of wind speed and wind direction, data on geographic distance, mountains, and PM _2.5 concentrations. We aim to reveal PM _2.5 mobility between cities in China. Two major conclusions are revealed through motif analysis of complex networks. First, major potential PM _2.5 pollution contributors are identified for each cluster by one motif, which reflects movements from source to target. Second, transport pathways of PM _2.5 are revealed by another motif, which reflects transmission routes. To our knowledge, this is the first work to apply higher-order network analysis to study PM _2.5 transport.

Figure 1

Triangular motifs.

Figure 3

Nine clusters obtained by m ₈-motif spectral clustering algorithm. Tableau Public 10.3 (https://public.tableau.com/) was used to create the map.

Figure 5

20 clusters obtained by m ₉-motif spectral clustering algorithm. Tableau Public 10.3 (https://public.tableau.com/) was used to create the map.

Figure 2

SSE varies with the number of clusters (K).

Figure 4

Spy plot of two representative clusters of Fig. 3 in January of 2016. The major potential PM _2.5 contributors in each cluster are marked in the plot. The number order in the spy plot is the ID in Supplementary Table S1.

Figure 6

m ₉-motif analysis for three representative clusters obtained by motif spectral clustering algorithm based on January of 2016. Tableau Public 10.3 (https://public.tableau.com/) was used to create these maps.