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		<doi>1 0.5923/j. ajee .20130301.08</doi>
		<issn>2166-4633</issn>
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		<label>lattes: 5142426481528206 3 RobertiAnfoCampDegr:2013:EnApEm</label>
		<citationkey>RobertiAnfoCampDegr:2013:EnApEm</citationkey>
		<title>Entropic approach for emission rate estimation of area pollutant sources</title>
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		<year>2013</year>
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		<author>Roberti, Débora R,</author>
		<author>Anfossi, Domenico,</author>
		<author>Campos Velho, Haroldo Fraga de,</author>
		<author>Degrazia, Gervásio Annes,</author>
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		<affiliation>Department of Physics, Federal University of Santa Maria (UFSM), Santa Maria, 97105-900, Brazil</affiliation>
		<affiliation></affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Department of Physics, Federal University of Santa Maria (UFSM), Santa Maria, 97105-900, Brazil</affiliation>
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		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress>haroldo@lac.inpe.br</electronicmailaddress>
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		<journal>American Journal of Environmental Engineering</journal>
		<volume>3</volume>
		<number>1</number>
		<pages>56-62</pages>
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		<keywords>atmospheric pollutant, source identification, inverse problems, entropic regularization.</keywords>
		<abstract>The estimation of the area source pollutant strength is a relevant issue for atmospheric environment. This characterizes an inverse problem in the atmospheric pollution dispersion. In the inverse analysis, an area source domain is considered, where the strength of such area source term is assumed unknown. The inverse problem is formulated as a non-linear optimization approach, whose objective function is given by the square difference between the measured pollutant concentration and the mathematical models, associated with a regularization operator. The forward problem is addressed by a source-receptor scheme, where a regressive Lagrangian model is applied to compute the transition matrix. A quasi-Newton method is employed for minimizing the objective function. The second order maximum entropy regularization is used, and the regularization parameter is calculated by the L-curve technique. This inverse problem methodology is tested with synthetic observational data, producing good inverse solutions.</abstract>
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		<language>en</language>
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