Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms

Alessio Petrozziello; Guido Cervone; Pasquale Franzese; Sue Ellen Haupt; Raffaele Cerulli

doi:10.1080/08839514.2017.1300005

Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms

Alessio Petrozziello
, Guido Cervone
, Pasquale Franzese
, Sue Ellen Haupt
, Raffaele Cerulli

Pennsylvania State University
University of Portsmouth
National Center for Atmospheric Research
Ecology and the Environment
University of Salerno

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

A genetic algorithm is paired with a Lagrangian puff atmospheric model to reconstruct the source characteristics of an atmospheric release. Observed meteorological and ground concentration measurements from the real-world Dipole Pride controlled release experiment are used to test the methodology. A sensitivity study is performed to quantify the relative contribution of the number and location of sensor measurements by progressively removing them. Additionally, the importance of the meteorological measurements is tested by progressively removing surface observations and vertical profiles. It is shown that the source term reconstruction can occur also with limited meteorological observations. The proposed general methodology can be applied to reconstruct the characteristics of an unknown atmospheric release given limited ground and meteorological observations.

Original language	English
Pages (from-to)	119-133
Number of pages	15
Journal	Applied Artificial Intelligence
Volume	31
Issue number	2
DOIs	https://doi.org/10.1080/08839514.2017.1300005
State	Published - Feb 7 2017

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title = "Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms",

abstract = "A genetic algorithm is paired with a Lagrangian puff atmospheric model to reconstruct the source characteristics of an atmospheric release. Observed meteorological and ground concentration measurements from the real-world Dipole Pride controlled release experiment are used to test the methodology. A sensitivity study is performed to quantify the relative contribution of the number and location of sensor measurements by progressively removing them. Additionally, the importance of the meteorological measurements is tested by progressively removing surface observations and vertical profiles. It is shown that the source term reconstruction can occur also with limited meteorological observations. The proposed general methodology can be applied to reconstruct the characteristics of an unknown atmospheric release given limited ground and meteorological observations.",

author = "Alessio Petrozziello and Guido Cervone and Pasquale Franzese and Haupt, \{Sue Ellen\} and Raffaele Cerulli",

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AU - Petrozziello, Alessio

AU - Cervone, Guido

AU - Franzese, Pasquale

AU - Haupt, Sue Ellen

AU - Cerulli, Raffaele

PY - 2017/2/7

Y1 - 2017/2/7

N2 - A genetic algorithm is paired with a Lagrangian puff atmospheric model to reconstruct the source characteristics of an atmospheric release. Observed meteorological and ground concentration measurements from the real-world Dipole Pride controlled release experiment are used to test the methodology. A sensitivity study is performed to quantify the relative contribution of the number and location of sensor measurements by progressively removing them. Additionally, the importance of the meteorological measurements is tested by progressively removing surface observations and vertical profiles. It is shown that the source term reconstruction can occur also with limited meteorological observations. The proposed general methodology can be applied to reconstruct the characteristics of an unknown atmospheric release given limited ground and meteorological observations.

AB - A genetic algorithm is paired with a Lagrangian puff atmospheric model to reconstruct the source characteristics of an atmospheric release. Observed meteorological and ground concentration measurements from the real-world Dipole Pride controlled release experiment are used to test the methodology. A sensitivity study is performed to quantify the relative contribution of the number and location of sensor measurements by progressively removing them. Additionally, the importance of the meteorological measurements is tested by progressively removing surface observations and vertical profiles. It is shown that the source term reconstruction can occur also with limited meteorological observations. The proposed general methodology can be applied to reconstruct the characteristics of an unknown atmospheric release given limited ground and meteorological observations.

UR - https://www.scopus.com/pages/publications/85017438642

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SP - 119

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JO - Applied Artificial Intelligence

JF - Applied Artificial Intelligence

IS - 2

ER -

Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms

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