Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, 34127 Trieste, Italy.
Department of Biology, University of Bari "Aldo Moro", Via Orabona 4, 70126 Bari, Italy.
Sensors (Basel). 2020 Mar 29;20(7):1887. doi: 10.3390/s20071887.
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.
目前人们已经意识到暴露于污染环境中的风险。因此，异味扰民被认为是存在有毒挥发性化合物的警告。恶臭常常会引起市民的即刻警觉，而电子鼻是一种方便的仪器，可以检测具有高监测频率的混合气味化合物。本文通过对一家油气预处理厂附近环境空气中成分的模式识别研究，展示了电子鼻的应用。该电子鼻采用 10 个金属氧化物半导体（MOS）传感器，连续户外放置三个月，每天 24 小时监测。通过自组织映射（SOM）算法对总共 80017 个电子鼻向量进行了详细分析，然后对整个数据集的 SOM 输出进行 k-均值聚类，结果表明存在一个异常数据聚类。保留了多传感器系统动态响应特征的数据，我们确定了一个具有 264 个与在现场采集的空气混合物相关的循环传感器响应的 SOM，以及四个主要的空气类型分布（聚类）。其中一个传感器分布与工厂的挥发性有机化合物（VOC）逸散排放的气味有关，这是通过使用总挥发性有机化合物（VOC）检测器以及风速和风向数据来实现的。评估了整体和每日的聚类频率，使我们能够确定在监测点存在与工业排放相关的空气的持续时间。经过改进的模型使我们能够确认传感器响应的异常检测。
