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. 2013 Sep:59:244-54.
doi: 10.1016/j.envint.2013.06.003. Epub 2013 Jul 9.

A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition

Elena Austin  1 , Brent A Coull, Antonella Zanobetti, Petros Koutrakis
Affiliations

A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition

Elena Austin et al. Environ Int. 2013 Sep.

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.

Objectives: 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.

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.

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Figures

Fig. 1
Fig. 1
Chemical speciation sites (n = 109).
Fig. 2
Fig. 2
Selecting the number of clusters (k).
Fig. 3
Fig. 3
Variability of the adjusted Rand Index as a function of the number of clusters selected. (The vertical line represents the number of clusters, k = 31, selected for this study).
Fig. 4
Fig. 4
Heatmap of the log of the species enrichment factors by cluster.
Fig. 5
Fig. 5
Species ratios by cluster.
Fig. 6
Fig. 6
Group 1, Eastern and Central clusters.
Fig. 7
Fig. 7
Group 2, Industrial clusters.
Fig. 8
Fig. 8
Group 3, Western clusters.
Fig. 9
Fig. 9
Group 4, Coastal clusters.
Fig. 10
Fig. 10
Heatmaps of single city clusters.
Fig. 11
Fig. 11
Sensitivity analysis; removing 20% of the observations before calculating global mean.
Fig. 12
Fig. 12
Sensitivity analysis; removing 10% of the sites prior to clustering.
Eq. 1
Eq. 1
Species fraction.
Eq. 2
Eq. 2
Modified Z-score.
Eq. 3
Eq. 3
Change in overall deviation.
Eq. 4
Eq. 4
Enrichment factors.
See this image and copyright information in PMC

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