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Framework for stochastic identification of atmospheric contamination source in an urban area

Author:
Kopka, Piotr, Wawrzynczak, Anna
Source:
Atmospheric environment 2018 v.195 pp. 63-77
ISSN:
1352-2310
Subject:
Bayesian theory, atmospheric chemistry, cities, field experimentation, models, toxic substances, urban areas, urbanization
Abstract:
Locating and quantifying the hazardous material source plays a significant role in the emergency action plans related to the intentional or accidental release. This issue is the most important in the cities where the threat to peoples health is the highest. In this paper, we present a complete framework to identify in real-time the airborne toxics source in an urban environment. Proposed framework utilizes the adjusted sequential version of the Approximate Bayesian Computation (ABC) methodology. Dedicated modifications enhanced the estimation of the posterior distributions of contamination source parameters significantly. The source parameters assessments are dynamically updated with the use of on-line arriving concentrations of released substance reported by sensors network. In reconstruction we utilize the advanced Quick Urban & Industrial Complex Dispersion Modeling system (QUIC), developed by Los Alamos National Laboratory, to predict the concentrations at the sensors locations. We validate the proposed methodology on real data coming from a full-scale field experiment DAPPLE conducted in central London in 2007. We confirm the effectivity of the proposed framework by successful estimation of six parameters characterizing the contamination source, i.e., source position (x,y,z) in a city environment, the mass of release (q), the release start time (s) and its duration (l). The presented results prove the utility of the proposed approach for event reconstruction problem in the highly urbanized environment. The presented framework can be adopted quickly during the emergency response planning in any complex urban environment with the use of suitable dispersion model.
Agid:
6157748