ARL Weekly News – August 26, 2022
National Ambient Air Monitoring Conference
Phil Stratton, Winston Luke and Xinrong Ren attended the National Ambient Air Monitoring Conference (NAAMC) in Pittsburgh, PA from Monday-Thursday, August 22-25. The conference was primarily for federal, state, local and tribal air pollution organizations involved with operating, planning, or managing air monitoring networks and reporting data to EPA Air Quality System (AQS) and AIRNOW.
Besides plenary sessions and technical breakouts, Phil, Winston and Xinrong also attended trainings on air monitoring topics that are important to address future challenges of air monitoring. During the meeting, they met with some colleagues from federal and state environmental agencies as well as academia to discuss potential collaboration opportunities. They also visited exhibits set up by instrument vendors to discuss technical issues encountered in the field along with instrument improvements and requested features.
Oak Ridge (TN) Chamber of Commerce
LaToya Myles gave an invited presentation to the Oak Ridge (TN) Chamber of Commerce on August 23 during their virtual networking monthly event called The Bridge.
Fang, S., Dong, X., Zhuang, S., Tian, Z., Chai, T., Xu, Y., Zhao, Y., Sheng, Li., Ye, X., and Xiong, W., Oscillation-free Source Term Inversion of Atmospheric Radionuclide Releases With Joint Model Bias Corrections and Non-smooth Competing Priors, Journal of Hazardous Materials, (2022) doi: 10.1016/j.jhazmat.2022.129806
Abstract: The source term of atmospheric radionuclide releases is essential for the hazardous consequence assessment and emergency response. However, the artificial release oscillations in the source term estimate remain a fundamental challenge and may deliver misleading information, because of the unavoidable model biases and observation uncertainties. We propose a new method that removes oscillations while recovering the release details. This method explicitly corrects the model biases using the joint correction model and compensates the observation uncertainties through non-smooth competing priors that involve two rival functions. The new priors better model the unsteady feature of the radionuclide releases and distinguish the true releases from oscillations, enabling release-preserving oscillation removal. We extend the projected alternating minimization algorithm for an efficient solution. The method achieves oscillation-free and nearly perfect profiles for real releases of the Perfluoro-Methyl-Cyclo-Hexane on continental and regional scales, and the radionuclide 41Ar on a local scale, outperforming state-of-the-art and very recent methods. The sensitivities to model inputs and key parameters are also investigated. Robust performance is exhibited under emissions of both radioactive and non-radioactive substances, different meteorological inputs and numbers of observations, paving the way for identifying dynamic atmospheric radionuclide releases at multiple scales, especially when the release status is unknown.