Low cost sensor in field calibrations (training and test data) - Beijing 2017

Dataset

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Access to this dataset is Open

Description

DESCRIPTION
Sensor box containing a multi-sensor ensemble was located at in Beijing alongside other reference instruments which were part of the AIRPOLL/AIRPRO campaign. There were 50 low-cost sensors inside the sensor instrument:
• 6 x NO2 electrochemical
• 6 x OX electrochemical
• 6 x CO electrochemical
• 32 x Total VOC Metal oxide sensors
• 2 x Humidity and temperature probes

The data from the sensor instrument was recorded every two seconds, with collection and storage on a Latte Panda micro-computer.
The file contains the data used for training machine learning algorithms. Both the sensor data and the reference measurements are included.

COLUMN NAMES
TheTime: 1 minute averaged date and time, local time for Beijing, China
Temp : Temperature of the air that comes into contact with the sensors (oC)
RH: Relative humidity of the air that comes into contact with the sensors (%)
##_ref_ppb: The reference measurements for NO2, OX and CO (all in units of ppb). A NO2 Teledyne CAPS instrument was used for the NO2 reference, ozone was measured with a TEI 49 UV absorption monitor and [OX] was calculated by adding the [NO2] and [O3]. CO was measured with a CO Aerolaser VUV fluorescence and sample inlet located 100m above ground.
Orig_typ_ECname: The median of the six EC sensors (ECname can be NO2, OX or CO), after the standard conversion factors were applied to each one (ppb).
Orig_ind_ECname: The median of the six EC sensors (ECname can be NO2, OX or CO), after each sensor had its unique factory conversion factors applied (ppb). These conversion factors were supplied by the sensor company after calibration at the company’s factory.
ECname_gbtree_pred: The prediction made using the XGBoost boosted regression trees ML algorithm for the different EC sensors. Each prediction was made using all of the sensor instrument data to make the predictions. During training the respective reference measurements were used as a target for the algorithms. CO starts a day late as there was less CO data.
ECname_gblinear_pred: The prediction made using the XGBoost boosted linear regression ML algorithm for the different EC sensors. Each prediction was made using all of the sensor instrument data to make the predictions. During training the respective reference measurements were used as a target for the algorithms. CO starts a day late as there was less CO data.
NO2_GP_pred: The Gaussian Process prediction for the NO2 sensor. Each prediction was made using all of the sensor instrument data to make the predictions. During training the NO2 reference measurements were used as a target for the algorithms.
NO2_GP_std: One standard deviation from the NO2 Gaussian Process prediction.

PEOPLE RESPONSIBLE FOR DATA COLLECTION
Sensor data : Kate Smith and Pete Edwards
NO2, O3 and CO reference data: James Lee and Freya Squires.

Data embargoed until June 25 2019 due to funder requirements.
Date made available28 Aug 2018
PublisherUniversity of York
Date of data production2 Jun 2017 - 25 Jun 2017
Geographical coverageInstitute of Atmospheric Physics, Chinese Academy of Sciences, Chao Yang District, Beijing 100029, China
Geospatial point (coordinates in decimal format)39.978,116.387

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