Chapter 4 CHARACTERIZING TEMPORALLY VARYING EMISSION
4.5 Discussion and Conclusion
In this chapter, we address the problem of estimating temporally and spatially varying emission sources using a single near-source air pollutant sensor. We use high frequency wind measurements to calculate backward particle trajectories and propose a Bayesian hierarchical model for the sensor measurements as a function of the backward trajectories. We show through a simulation study that our model is able to accurately estimate source strengths and locations given favorable meteorological conditions.
We analyze four days of measurements from two SPod sensors located on the perimeter of an industrial facility in Louisville, Kentucky. Multiple sources emitting over varying time spans were detected within the four days, with the strongest source occurring during the afternoon of June 19th, southwest of the sensor location. Due to the use of only one sensor the credible intervals of the source estimates are large.
A variety of extensions could be implemented to improve source estimates. The ad- dition of more sensors along the opposite edge of the facility would enable triangulation and reduce uncertainty. Estimates might also be improved by incorporating the output from a forward dispersion model, with source locations chosen based on the results from the backward trajectory model. Additionally, a forward dispersion model that accounts for the wind flow around physical structures in the domain could further refine our re- sults. Finally, modeling multiple sensors simultaneously using a multivariate framework would allow information to be shared between SPods and could lead to smaller credible intervals for our parameters.
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