Dataset Open Access

Benzene Concentration Dataset

Chang Wei Tan; Christoph Bergmeir; Francois Petitjean; Geoffrey I Webb


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  <dc:creator>Chang Wei Tan</dc:creator>
  <dc:creator>Christoph Bergmeir</dc:creator>
  <dc:creator>Francois Petitjean</dc:creator>
  <dc:creator>Geoffrey I Webb</dc:creator>
  <dc:date>2020-06-21</dc:date>
  <dc:description>This dataset is part of the Monash, UEA &amp; UCR time series regression repository. http://tseregression.org/

This goal of this dataset is to predict benzene concentration in an Italian city. This dataset contains 8878 time series obtained from the Air Quality dataset from the UCI repository. The time series has 8 dimensions which consists of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device, as well as temperature, relative humidity and absolute humidity. The Air Quality Chemical Multisensor device was located on the field in a significantly polluted area, at road level, within an Italian city. Data were recorded from March 2004 to February 2005 (one year) representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses. Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer.

Please refer to https://archive.ics.uci.edu/ml/datasets/Air+Quality for more details.

Relevant papers
S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005.
Saverio De Vito, Marco Piga, Luca Martinotto, Girolamo Di Francia, CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization, Sensors and Actuators B: Chemical, Volume 143, Issue 1, 4 December 2009, Pages 182-191, ISSN 0925-4005.
S. De Vito, G. Fattoruso, M. Pardo, F. Tortorella and G. Di Francia, Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction, in IEEE Sensors Journal, vol. 12, no. 11, pp. 3215-3224, Nov. 2012. doi: 10.1109/JSEN.2012.2192425


Citation request
S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005</dc:description>
  <dc:identifier>https://zenodo.org/record/3902673</dc:identifier>
  <dc:identifier>10.5281/zenodo.3902673</dc:identifier>
  <dc:identifier>oai:zenodo.org:3902673</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.3902672</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/ts_regression</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>time series</dc:subject>
  <dc:subject>regression</dc:subject>
  <dc:title>Benzene Concentration Dataset</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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