Characterization of spatially homogeneous regions based on temporal patterns of fine particulate matter in the continental United States.
Statistical analyses of time-series or spatial data have been widely used to investigate the behavior of ambient air pollutants. Because air pollution data are generally collected in a wide area of interest over a relatively long period, such analyses should take into account both spatial and temporal characteristics. The objective of this study is 2-fold: (1) to identify an efficient way to characterize the spatial variations of fine particulate matter (PM2.5) concentrations based solely upon their temporal patterns, and (2) to analyze the temporal and seasonal patterns of PM2.5 concentrations in spatially homogenous regions. This study used 24-hr average PM2.5 concentrations measured every third day during a period between 2001 and 2005 at 522 monitoring sites in the continental United States. A k-means clustering algorithm using the correlation distance was used to investigate the similarity in patterns between temporal profiles observed at the monitoring sites. A k-means clustering analysis produced six clusters of sites with distinct temporal patterns that were able to identify and characterize spatially homogeneous regions of the United States. The study also presents a rotated principal component analysis (RPCA) that has been used for characterizing spatial patterns of air pollution and discusses the difference between the clustering algorithm and RPCA.
Kim
Seoung Bum
SB
Department of Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, Arlington, TX, USA. sbkim@uta.edu
Temiyasathit
Chivalai
C
Chen
Victoria C P
VC
Park
Sun-Kyoung
SK
Sattler
Melanie
M
Russell
Armistead G
AG
D000392:Air Movements; D004784:Environmental Monitoring; D008962:Models, Theoretical; D052638:Particulate Matter / Q000737:chemistry*; D013997:Time Factors; D014481:United States
D052638:Particulate Matter