Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site

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.

[1]  Addisson Salazar,et al.  Multiclass Alpha Integration of Scores from Multiple Classifiers , 2019, Neural Computation.

[2]  Sabina Licen,et al.  Odor control map: Self organizing map built from electronic nose signals and integrated by different instrumental and sensorial data to obtain an assessment tool for real environmental scenarios , 2018, Sensors and Actuators B: Chemical.

[3]  O. Simula,et al.  The Self-organizing map as a tool in knowledge engineering , 2000 .

[4]  Cesar H. Comin,et al.  Clustering algorithms: A comparative approach , 2016, PloS one.

[5]  Samson Damilola Fabiyi.  A review of unsupervised Artificial Neural Networks with applications , 2019 .

[6]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Magda Brattoli,et al.  Gas Chromatography Analysis with Olfactometric Detection (GC-O) as a Useful Methodology for Chemical Characterization of Odorous Compounds , 2013, Sensors.

[8]  T. Lacava,et al.  A satellite-based analysis of the Val d'Agri (South of Italy) Oil Center gas flaring emissions , 2014 .

[9]  Roberto Giua,et al.  Synergistic approaches for odor active compounds monitoring and identification: State of the art, integration, limits and potentialities of analytical and sensorial techniques , 2018, TrAC Trends in Analytical Chemistry.

[10]  Rajib Bandyopadhyay,et al.  Application of electronic nose for industrial odors and gaseous emissions measurement and monitoring--An overview. , 2015, Talanta.

[11]  Monica Angelucci,et al.  Self-organizing map algorithm as a tool for analysis, visualization and interpretation of electronic nose high dimensional raw data , 2018 .

[12]  Concha Bielza,et al.  A comparison of clustering quality indices using outliers and noise , 2012, Intell. Data Anal..

[13]  Pierluigi Barbieri,et al.  On odour tolerability criteria from odorant instrumental monitoring , 2018 .

[14]  Kenshi Hayashi,et al.  Recognition and sensing of organic compounds using analytical methods, chemical sensors, and pattern recognition approaches , 2019, Chemometrics and Intelligent Laboratory Systems.

[15]  Marcella Guarino,et al.  Measurements techniques and models to assess odor annoyance: A review. , 2019, Environment international.

[16]  J. H. Ruth.  Odor thresholds and irritation levels of several chemical substances: a review. , 1986, American Industrial Hygiene Association journal.

[17]  Martin Piringer,et al.  Conversion of the chemical concentration of odorous mixtures into odour concentration and odour intensity: A comparison of methods , 2015, 1512.06557.

[18]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[19]  Karl Ropkins,et al.  openair - An R package for air quality data analysis , 2012, Environ. Model. Softw..

[20]  Samson Damilola,et al.  A Review of Unsupervised Artificial Neural Networks with Applications , 2019, International Journal of Computer Applications.

[21]  Ron Wehrens,et al.  Flexible Self-Organizing Maps in kohonen 3.0 , 2018 .

[22]  Gianluigi de Gennaro,et al.  AN INTEGRATED HIGH TEMPORAL RESOLUTION APPROACH TO MONITOR VOCs CONCENTRATIONS AND ODOUR ANNOYANCE NEAR A PETROLEUM PLANT , 2018 .

[23]  P. Dalton,et al.  Evaluating the human response to chemicals: odor, irritation and non-sensory factors. , 2005, Environmental toxicology and pharmacology.

[24]  D. Shusterman,et al.  Critical review: the health significance of environmental odor pollution. , 1992, Archives of environmental health.

[25]  Selena Sironi,et al.  Monitoring odour emisssions from an oil & gas plant: Electronic nose performance testing in the field , 2017, 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN).

[26]  Laura Capelli,et al.  How Can Odors Be Measured? An Overview of Methods and Their Applications , 2020 .

[27]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[28]  Michele Penza,et al.  Odour Detection Methods: Olfactometry and Chemical Sensors , 2011, Sensors.

[29]  Marco Cervino,et al.  [Residential cohort study on mortality and hospitalization in Viggiano and Grumento Nova Municipalities in the framework of HIA in Val d'Agri (Basilicata Region, Southern Italy)]. , 2018, Epidemiologia e prevenzione.

[30]  P Barbieri,et al.  Multivariate classification and modeling in surface water pollution estimation , 2008, Analytical and bioanalytical chemistry.

[31]  Santiago Marco,et al.  The need for external validation in machine olfaction: emphasis on health-related applications , 2014, Analytical and Bioanalytical Chemistry.

[32]  Marco Cervino,et al.  [Recommendations from a health impact assessment in Viggiano and Grumento Nova (Basilicata Region, Southern Italy)]. , 2018, Epidemiologia e prevenzione.

[33]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[34]  I. Luginaah,et al.  Cumulative effects of noise and odour annoyances on environmental and health related quality of life. , 2015, Social science & medicine.

[35]  Gianpiero Cossarini,et al.  Understanding dynamic of biogeochemical properties in the northern Adriatic Sea by using self‐organizing maps and k‐means clustering , 2007 .

[36]  Lutgarde M. C. Buydens,et al.  Self- and Super-organizing Maps in R: The kohonen Package , 2007 .

[37]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[38]  Laura Capelli,et al.  Evolution of Electronic Noses from Research Objects to Engineered Environmental Odour Monitoring Systems: A Review of Standardization Approaches , 2019, Biosensors.

[39]  Davide Franco,et al.  A review of odour impact criteria in selected countries around the world. , 2017, Chemosphere.

[40]  Eylem Çetin,et al.  Ambient volatile organic compound (VOC) concentrations around a petrochemical complex and a petroleum refinery. , 2003, The Science of the total environment.

[41]  G. de Gennaro,et al.  An Innovative Methodological Approach for Monitoring and Chemical Characterization of Odors around Industrial Sites , 2018, Advances in Meteorology.

[42]  Selena Sironi,et al.  Electronic Noses for Environmental Monitoring Applications , 2014, Sensors.