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Investigation of Air Quality beside a Municipal Landfill: The Fate of Malodour Compounds as a Model VOC

机译:市政垃圾填埋场旁的空气质量调查:恶臭化合物作为VOC模型的命运

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This paper presents the results of an investigation on ambient air odour quality in the vicinity of a municipal landfill. The investigations were carried out during the spring–winter and the spring seasons using two types of the electronic nose instrument. The field olfactometers were employed to determine the mean odour concentration, which was from 2.1 to 32.2 ou/m 3 depending on the measurement site and season of the year. In the case of the investigation performed with two types of the electronic nose, a classification of the ambient air samples with respect to the collection site was carried out using the k-nearest neighbours (kNN) algorithm supported with the cross-validation method. Correct classification of the ambient air samples collected during the spring–winter season was at the level from 71.9% to 87.5% and from 84.4% to 94.8% for the samples collected during the spring season depending on the electronic nose type utilized in the studies. It was also revealed that the kNN algorithm applied for classification of the samples exhibited better discrimination abilities than the algorithms of the linear discriminant analysis (LDA) and quadratic discriminant function (QDA) type. Performed seasonal investigations proved the ability of the electronic nose to discriminate the ambient air samples differing in odorants’ concentration and collection site.
机译:本文介绍了对一个城市垃圾填埋场附近的环境空气气味质量进行调查的结果。调查是在春季,冬季和春季使用两种类型的电子鼻部仪器进行的。使用现场嗅觉计确定平均气味浓度,该浓度为2.1至32.2 ou / m 3,具体取决于测量地点和一年中的季节。在使用两种类型的电子鼻进行调查的情况下,使用交叉验证方法支持的k最近邻(kNN)算法对收集地点的周围空气样本进行分类。根据研究中所使用的电子鼻类型,对春冬季收集的环境空气样本的正确分类为从春季的71.9%到87.5%,从春季的84.4%到94.8%。还发现,与线性判别分析(LDA)和二次判别函数(QDA)类型的算法相比,用于样本分类的kNN算法具有更好的判别能力。进行的季节性调查证明,电子鼻可以区分气味剂浓度和收集地点不同的环境空气样本。

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