Relationships between Passive Sampler and Continuous Ozone (O(3)) Measurement Data in Ecological Effects Research

In ecological effects research, there is a rapid increase in the application of passive sampling techniques for measuring ambient ozone (O(3)) concentrations. Passive samplers provide data on cumulative exposures of a plant to a pollutant. However, O(3) is not an accumulative contaminant within the plant tissue, and use of prolonged passive sampling durations cannot account for the dynamics of the occurrences of O(3) that have a significant influence on the plant response. Therefore, a stochastic Weibull probability model was previously developed and applied to a site in Washington State (1650 m MSL) to simulate the cumulative exposure data from a passive sampler, to mimic the corresponding frequency distributions of hourly O(3) concentrations that would otherwise have been obtained by continuous monitoring. At that site the correlation between the actual passive sampler and the continuous monitor data was R2 = 0.74. The simulation of the hourly O(3) data was based on and compared with the results obtained from a colocated continuous monitor. In this paper we report the results of the model application to data from an unrelated monitoring site (New Hampshire, 476 m MSL) with poor correlation between the passive sampling and continuous monitoring (R2 = 0.24). In addition, as opposed to the previous work, we provide comparisons of the frequency distributions of the hourly O(3) concentrations obtained by the simulation and the actual continuous monitoring. In spite of the major difference in the R2 values, at both sites the simulation provided very satisfactory results within the 95% confidence interval, suggesting its broad applicability. The final objective of this overall approach is to develop a generic model that can simulate reasonably well the occurrences of ambient O(3) concentrations that are dependent upon the elevation of the measurement site and the synoptic and local meteorology. Such an effort would extend the relative utility of the passive sampling data in explaining stochastic plant responses.