title: A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition.
abstract: BACKGROUND
  Heterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies.
  
  
  OBJECTIVE
  Use cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative.
  
  
  METHODS
  109 monitoring sites with 75% reported speciation data during the period 2003-2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources.
  
  
  RESULTS
  Overall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location.
  
  
  CONCLUSIONS
  The framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research.
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA. elena.austin@mail.harvard.edu
authors.forename: Elena
authors.initials: E
authors.lastname: Austin
authors.forename: Brent A
authors.initials: BA
authors.lastname: Coull
authors.forename: Antonella
authors.initials: A
authors.lastname: Zanobetti
authors.forename: Petros
authors.initials: P
authors.lastname: Koutrakis
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D000818:Animals; D016000:Cluster Analysis; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D052638:Particulate Matter / Q000032:analysis*; D014481:United States
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: AQS; Air Quality Standards; Air pollution profiles; CPC; Cluster analysis; Condensation Particle Counter; EC; EPA; Effect modification; Elemental Carbon; Environmental Protection Agency; Multi-pollutant mixtures; NAS; National Academies of Science; OC; Organic Carbon; PM(2.5); Particle matter with a diameter of 2.5 μm or less; k-Means