Uncertainty characterization in the retrieval of an atmospheric point release. (March 2017)

• Record Type: Journal Article
• Authors: Singh, Sarvesh Kumar; Kumar, Pramod; Turbelin, Grégory; Rani, Raj
• Is Part Of: Atmospheric environment. Volume 152(2017)
• Page Start: 34
• Page End: 50
• Publication Date: 2017-03
• Subjects: Confidence bounds -- FFT07 field experiment -- MUST field experiment -- Renormalization -- Source reconstruction -- Uncertainty; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2016.12.016
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: The study proposes a methodology in a recent inversion technique, called as Renormalization, to characterize the uncertainties in the reconstruction of a point source. The estimates are derived for measuring the inversion error, the degree of model fit towards measurements (model determination coefficient) and the confidence intervals for the retrieved point source parameters (mainly, location and strength). The inversion error is reflected through an angular estimate which measures the deviation between the measured and predicted concentrations. The uncertainty estimation methodology is evaluated for point source reconstruction studies, using real measurements from two field experiments, known as Fusion Field Trials 2007 (FFT07) in flat terrain and Mock Urban Setting Test (MUST) in urban like terrain. In FFT07 and MUST experiments, the point source location is retrieved with an average Euclidean distance of 22 m and 15 m respectively. The source strength is retrieved, on average, within a factor of 1.5 in both the datasets. The inversion error is observed as 24 o and 21 o in FFT07 and MUST experiment, respectively. The 95% confidence interval estimates show that the uncertainty in the retrieved parameters is relatively large in approximately 50% FFT07 and 30% MUST trials in spite of their closeness towards true source parameters. For a comparative analysis, the interval estimates are also compared with a more general method of uncertainty estimation, Residual Bootstrap Sampling. In most of the trials, we observed that the intervals estimates with the present method are comparable (within 10–20% variations) to bootstrap estimates. The proposed methodology provides near accurate and computationally efficient uncertainty estimates in comparison to the methods based on Hessian and sampling procedures. Highlights: Uncertainty estimation is proposed in renormalization inversion technique. Evaluation with Fusion Field and MUST field datasets. The present confidence estimates are comparable with bootstrap estimates.