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. 2012 Sep 15:45:112-21.
doi: 10.1016/j.envint.2012.04.003. Epub 2012 May 14.

A framework for identifying distinct multipollutant profiles in air pollution data

Elena Austin  1 , Brent Coull, Dylan Thomas, Petros Koutrakis
Affiliations

A framework for identifying distinct multipollutant profiles in air pollution data

Elena Austin et al. Environ Int. .

Abstract

Background: The importance of describing, understanding and regulating multi-pollutant mixtures has been highlighted by the US National Academy of Science and the Environmental Protection Agency. Furthering our understanding of the health effects associated with exposure to mixtures of pollutants will lead to the development of new multi-pollutant National Air Quality Standards.

Objectives: Introduce a framework within which diagnostic methods that are based on our understanding of air pollution mixtures are used to validate the distinct air pollutant mixtures identified using cluster analysis.

Methods: Six years of daily gaseous and particulate air pollution data collected in Boston, MA were classified solely on their concentration profiles. Classification was performed using k-means partitioning and hierarchical clustering. Diagnostic strategies were developed to identify the most optimal clustering.

Results: The optimal solution used k-means analysis and contained five distinct groups of days. Pollutant concentrations and elemental ratios were computed in order to characterize the differences between clusters. Time-series regression confirmed that the groups differed in their chemical compositions. The mean values of meteorological parameters were estimated for each group and air mass origin between clusters was examined using back-trajectory analysis. This allowed us to link the distinct physico-chemical characteristics of each cluster to characteristic weather patterns and show that different clusters were associated with distinct air mass origins.

Conclusions: This analysis yielded a solution that was robust to outlier points and interpretable based on chemical, physical and meteorological characteristics. This novel method provides an exciting tool with which to identify and further investigate multi-pollutant mixtures and link them directly to health effects studies.

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Conflict of interest statement

The authors declare they have no actual or potential competing financial interests.

Figures

Figure 1
Figure 1
SSB/SSW criteria for selecting clustering method
Figure 2
Figure 2
Overall Deviation in Weather Parameters. For each value of k, the overall deviation is described as the percent change in deviation as compared to the total variability in the dataset.
Figure 3
Figure 3
DB index for values of k between 2–8
Figure 4
Figure 4
Heatmaps of Temporal Distributions. Darker colors represent higher frequencies.
Figure 5
Figure 5. Back trajectory visualization
The legend bar in these figures corresponds to the distance (m) from the Boston sampling location that the back trajectories were located. The direction of the bars on the figure corresponds to the direction of the back-trajectories. The length of the bars on the figure correspond fraction of the total trajectories that came from a given direction. Only trajectories that were between 0 and 500 meters from ground level are presented in this figure.
Figure 6
Figure 6
Radial distances from Boston
See this image and copyright information in PMC

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