Air Quality

Donated on 3/22/2016

Contains the responses of a gas multisensor device deployed on the field in an Italian city. Hourly responses averages are recorded along with gas concentrations references from a certified analyzer.

Dataset Characteristics

Multivariate, Time-Series

Subject Area

Computer Science

Associated Tasks

Regression

Feature Type

Real

# Instances

9358

# Features

15

Dataset Information

Additional Information

The dataset contains 9358 instances of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device. The 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. Evidences of cross-sensitivities as well as both concept and sensor drifts are present as described in De Vito et al., Sens. And Act. B, Vol. 129,2,2008 (citation required) eventually affecting sensors concentration estimation capabilities. Missing values are tagged with -200 value. This dataset can be used exclusively for research purposes. Commercial purposes are fully excluded.

Has Missing Values?

Yes

Introductory Paper

On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario

By S. D. Vito, E. Massera, M. Piga, L. Martinotto, G. Francia. 2008

Published in Sensors and Actuators B: Chemical

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
DateFeatureDateno
TimeFeatureCategoricalno
CO(GT)FeatureIntegerTrue hourly averaged concentration CO in mg/m^3 (reference analyzer)mg/m^3no
PT08.S1(CO)FeatureCategoricalhourly averaged sensor response (nominally CO targeted)no
NMHC(GT)FeatureIntegerTrue hourly averaged overall Non Metanic HydroCarbons concentration in microg/m^3 (reference analyzer)microg/m^3no
C6H6(GT)FeatureContinuousTrue hourly averaged Benzene concentration in microg/m^3 (reference analyzer)microg/m^3no
PT08.S2(NMHC)FeatureCategoricalhourly averaged sensor response (nominally NMHC targeted)no
NOx(GT)FeatureIntegerTrue hourly averaged NOx concentration in ppb (reference analyzer)ppbno
PT08.S3(NOx)FeatureCategoricalhourly averaged sensor response (nominally NOx targeted)no
NO2(GT)FeatureIntegerTrue hourly averaged NO2 concentration in microg/m^3 (reference analyzer)microg/m^3no

0 to 10 of 15

Additional Variable Information

0 Date (DD/MM/YYYY) 1 Time (HH.MM.SS) 2 True hourly averaged concentration CO in mg/m^3 (reference analyzer) 3 PT08.S1 (tin oxide) hourly averaged sensor response (nominally CO targeted) 4 True hourly averaged overall Non Metanic HydroCarbons concentration in microg/m^3 (reference analyzer) 5 True hourly averaged Benzene concentration in microg/m^3 (reference analyzer) 6 PT08.S2 (titania) hourly averaged sensor response (nominally NMHC targeted) 7 True hourly averaged NOx concentration in ppb (reference analyzer) 8 PT08.S3 (tungsten oxide) hourly averaged sensor response (nominally NOx targeted) 9 True hourly averaged NO2 concentration in microg/m^3 (reference analyzer) 10 PT08.S4 (tungsten oxide) hourly averaged sensor response (nominally NO2 targeted) 11 PT08.S5 (indium oxide) hourly averaged sensor response (nominally O3 targeted) 12 Temperature in °C 13 Relative Humidity (%) 14 AH Absolute Humidity

Dataset Files

FileSize
AirQualityUCI.xlsx1.2 MB
AirQualityUCI.csv766.7 KB

Papers Citing this Dataset

Boosting for Dynamical Systems

By Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu. 2019

Published in ArXiv.

Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range

By Jun-Gi Jang, Dongjin Choi, Jinhong Jung, U Kang. 2018

Published in CIKM '18.

Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine

By Martin Sundin, Saikat Chatterjee, Magnus Jansson. 2015

Published in

0 to 4 of 4

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4 citations
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Creators

Saverio Vito

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