Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1429
Title: Odour pollution assessment through indirect methods based on the monitoring of technological parameters - case study
Authors: Bucur, Elena
Danciulescu, Valeriu
Tanase, Gheorghita
Lehr, Carol Blaziu
Vasile, Andrei
Keywords: Odour;Dynamic olfactometry;Multiple regression method;Intensive livestock farming
Issue Date: Sep-2019
Publisher: National Research and Development Institute for Industrial Ecology, INCD-ECOIND
Abstract: 
The odours in the ambient air, through the discomfort that they induce, represent an actual problem for the communities located nearby companies with technological processes that emit in the air different strong and unpleasant odorous substances.The standard method for odour assessment involves measuring the concentration through dynamic olfactometry according with SR EN 13725:2003, a very complex method that requires specialized working staff and expensive equipment. The paper presents an indirect method for odour assessment in the ambient air, based on monitoring the process and meteorological data - Predictive Emission Monitoring Systems (PEMS) and it is applied to a livestock farm. Using the multiple regression analysis of the monitoring data for the most important specific technological and meteorological parameters it can be developed a mathematical model that could be used for the calculation of odour concentration in air, without
the necessity of direct measurement, after the initial step. For the case study presented in the paper, the distance between the slurry lagoon was identified as a significant statistical parameter that can determine in a proportion of 72% the concentration of odour in the ambient air nearby the farm; the margin of error for odour concentration assessment, according to the model validation tests, is ± 8%, acceptable value for an estimation method by mathematical modelling.
Description: 
Romanian Journal of Ecology & Environmental Chemistry, Vol. 1, 2019, pp. 4-10
URI: http://hdl.handle.net/123456789/1429
ISSN: ISSN 2668-5418, ISSN-L 2668-5418
Appears in Collections:RJEEC, Volume 1, no.1, 2019

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