Abstract
Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.
Highlights
People are aware of the risk correlated to pollution exposition
In this paper we present an assessment of the evolution of air quality by means of a two stage odor control map approach, applied to patterns of sensor signals of a commercial electronic nose implementing ten MOS sensors, in proximity of a gas and oil pretreatment plant, characterized by fugitive emissions [27,28] hardly described by dispersion modeling, and object of attention for health impacts assessment on population of the surrounding area [29,30]
We firstly ran a model using all the data recorded during the whole period: 80017 min recordings for the ten sensors of PEN 3 e-nose were collected from 7th April 2017 to 28th June 2017 at the monitoring site close to COVA
Summary
People are aware of the risk correlated to pollution exposition. Odor annoyances generate immediate alarm among citizens because malodor is considered a warning about the possible presence of toxic volatile compounds. Annoyances present can cause stress in the population reducing the quality of life and leading to health side-effects [1,2,3,4]. From the point of view of environmental and industrial olfactory nuisances, impacts come from factors as frequency, intensity, duration, offensiveness, and location of the odor events, on the whole known as FIDOL [9,10]. The time dimension of olfactory pollution is relevant since contacts between odorants in the environment and exposed population can be discontinuous, repeated at irregular intervals, of variable duration, making sampling of transient events impacting on the complaining citizens a relevant and not a trivial task. A continuous monitoring is highly preferable for studying malodor phenomena and
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