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Empirical investigation of the Junge variability-lifetime relationship using long-term monitoring data on PCB concentrations in air.

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>15/04/2009
<mark>Journal</mark>Environmental Science and Technology
Issue number8
Number of pages7
Pages (from-to)2746-2752
<mark>Original language</mark>English


In 1974, Junge derived an empirical relationship between the variability of concentrations of volatile trace gases in air at remote locations and their atmospheric residence time. Here, the Junge relationship is adapted to incorporate the deposition and revolatilization of semivolatile chemicals and applied to interpret nearly a decade of data on polychlorinated biphenyl (PCB) concentrations in air. A multimedia fate model, which accounts for deposition and revolatilization, is used to estimate the characteristic travel distance (CTD) for PCBs, where CTD serves as a measure of the effective atmospheric lifetime for semivolatile organic chemicals. Data are taken from sites of the Integrated Atmospheric Deposition Network in the North American Great Lakes and the Alert monitoring station in the Arctic, which is operated by the Canadian Northern Contaminants Program. Five factors that may introduce variability into measured concentrations are defined. By suppressing the effect of three of these factors in the data analysis, we identified variability consistent with the Junge relationship in many of the annual data sets (62%), with the relationship showing statistical significance (p < 0.05) in 23% of these annual data sets. The more remote monitoring sites from the Great Lakes region display the highest number of statistically significant Junge-type relationships between the variability in concentrations in air and estimated long-range transport potential in air. At sites in close proximity to areas of high population density, variability in PCB concentrations in air displays patterns that are consistent with primary or secondary temperature-driven volatilization sources. Analysis of variability in long-term monitoring data, using the techniques developed and illustrated here, provides useful insights into the factors that control the behavior of persistent semivolatile chemicals in the environment.