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The patterns of particulate matter with diameters that are generally 2.5micrometers and smaller (PM2.5) are heterogeneous in China nationwide but canbe homogeneous region-wide. To reduce the adverse effects from PM2.5,policymakers need to develop location-specific regulations based on nationwideclustering analysis of PM2.5 concentrations. However, such an analysis ischallenging because the data have complex structures and are usually noisy. Inthis study, we propose a robust clustering framework using a novel concept ofdepth, which can handle both magnitude and shape outliers effectively andincorporate spatial information. We apply this framework to a PM2.5 dataset andreveal ten regions in China that have distinct PM2.5 patterns, and thehomogeneity within each cluster is also confirmed. The clusters have clearlyvisible boundaries and enable policymakers to develop local emission controlpolicies and establish regional collaborative systems to control air pollutionin China.

Regionalization of China's PM2.5 through Robust Spatio temporal Functional Clustering Method