Dataset Open Access

iSCAPE Outdoor Sensor Deployment Data

González, Óscar; Barberán, Víctor; Camprodon, Guillem; Gharbia, Salem; Pilla, Francesco; Ottosen, Thor-Bjørn; Kumar, Prashant; Kalaiarasan, Gopinath; Barwise, Yendle; Di Nicola, Francesca; Di Sabatino, Francesca; Brattich, Erika

Dataset Description

This dataset contains all the deployment data using low cost sensors during the iSCAPE project. The dataset is divided in a series of deployments, each of them described on a yaml file with the test name. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each test. The tests are described in the comment of the yaml file, and are meant to be self explanatory. Two different types of tests are herein presented:

  1. Intervention monitoring tests (Guildford and Surrey). Two intervention monitoring results were conducted in the sites of Surrey (UoS) Living Lab and Hasselt (UH) Living Lab. The intervention in Surrey aimed at characterising the behaviour of green infrastructure and the effect on the pollutants dispersion next to traffic conditions. Two different sets of two stations were delivered and deployed, one set in the vicinity of Stoke Park, and the other in the vicinity of Sutherland Memorial Park (both in Guildford - UK). In the case of Hasselt, two Living Lab Stations were deployed. The first one was used to assess pollutant concentrations in the Bassischool Kuringen in Hasselt. The other station was deployed near the University of Hasselt.
  2. Sensor Calibration tests (CSIC, Dublin and Bologna). The tests conducted in Bologna (by UNIBO, 2018), Dublin (by UCD, 2019) and Barcelona (by IAAC, 2019) were intended as an assessment of the sensor technology in an outdoor environment scenario, by co-locating the iSCAPE LLSs with reference instrumentation.

A complete description of these datasets and the result of their analysis is shown in D7.8 of iSCAPE which can be found in this url: https://www.iscapeproject.eu/results/.

Sensors

The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and the Living Lab Station or Smart Citizen Station. These are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The Smart Citizen Station is meant to expand the capabilities of the Smart Citizen Kit, aiming to measure pollutants with more advanced sensors. The hardware is licensed under CERN Open Hardware License V1.2 and is fully described in the HardwareX Open Access publication: https://doi.org/10.1016/j.ohx.2019.e00070. The sensor documentation can be found at https://docs.smartcitizen.me and with this DOI at Zenodo: https://doi.org/10.5281/zenodo.2555029.

In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.

  • Air temperature (ºC): Sensirion SHT-31 [TEMP]
  • Relative Humidity (%rh): Sensirion SHT-31 [HUM]
  • Noise level (dBA): Invensense ICS-434342 [NOISE_A]
  • Ambient light (lux): Rohm BH1721FVC [LIGHT]
  • Barometric pressure (kPa): NXP MPL3115A26 [PRESS]
  • Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]

In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.

  • Air Temperature (ºC) Sensirion SHT-31 [TEMP]
  • Relative Humidity (% REL) Sensirion SHT-31 [HUM]
  • Noise Level (dBA) Invensense ICS-434342 [NOISE_A]
  • Ambient Light (Lux) Rohm BH1721FVC [LIGHT]
  • Barometric pressure and AMSL (Pa and Meters) NXP MPL3115A26 [PRESS]
  • Carbon Monoxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]
  • Nitrogen Dioxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]
  • Carbon Monoxide (ppm) Alphasense CO-B4 [GB_1W, GB_1A, final calculated valueCO_DELTAS_OVL_X-XX-XX - all the same]
  • Nitrogen Dioxide (ppb) Alphasense NO2-B43F [GB_2W, GB_2A, final calculated value NO2_DELTAS_OVL_0-30-50 or NO2_DELTAS_OVL_0-5-50]     
  • Ozone (ppb) Alphasense OX-B431 [GB_3W, GB_3A, final value O3_DELTAS_OVL_0-30-50 or O3_DELTAS_OVL_0-5-50]
  • Gases Board Temperature (ºC) Sensirion SHT-31 [GB_TEMP] or [EXT_TEMP]
  • Gases Board Rel. Humidity (% REL) Sensirion SHT-31 [GB_HUM]  or [EXT_HUM]
  • PM 1 (µg/m3) Plantower PMS5003 [EXT_PM_1] or [EXT_PM_A_1], [EXT_PM_B_1] for each PM sensor in the case of the Living Lab Station
  • PM 2.5 (µg/m3) Plantower PMS5003 [EXT_PM_25] or [EXT_PM_A_25], [EXT_PM_B_25] for each PM sensor in the case of the Living Lab Station
  • PM 10 (µg/m3) Plantower PMS5003 [EXT_PM_10] or [EXT_PM_A_10], [EXT_PM_B_10] for each PM sensor in the case of the Living Lab Station
  • PN between 0.3um<0.5um particle size (#/l) Plantower PMS5003 [EXT_PN_03] or [EXT_PN_A_03], [EXT_PN_B_03] for each PM sensor in the case of the Living Lab Station
  • PN between 0.5um<1um particle size (#/l) Plantower PMS5003 [EXT_PN_05] or [EXT_PN_A_05], [EXT_PN_B_05] for each PM sensor in the case of the Living Lab Station
  • PN between 1m<2.5um particle size (#/l) Plantower PMS5003 [EXT_PN_1] or [EXT_PN_A_1], [EXT_PN_B_1] for each PM sensor in the case of the Living Lab Station
  • PN between 2.5m<5um particle size (#/l) Plantower PMS5003 [EXT_PN_25] or [EXT_PN_A_25], [EXT_PN_B_25] for each PM sensor in the case of the Living Lab Station
  • PN between 5m<10um particle size (#/l) Plantower PMS5003 [EXT_PN_5] or [EXT_PN_A_5], [EXT_PN_B_5] for each PM sensor in the case of the Living Lab Station
  • PN between >10um particle size (#/l) Plantower PMS5003 [EXT_PN_10] or [EXT_PN_A_10], [EXT_PN_B_10] for each PM sensor in the case of the Living Lab Station

The files with the _processed suffix, are processed files which:

1. Resample the data using pandas resampling with mean() - reference here

2. Clean NaN and wrong readings.

3. Add calculations for electrochemical sensors based on this methodology

How to find the data

Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:

  • author: who has been in charge of performing the test (internal reference - not relevant)
  • comment: describing in general terms what was done in the test, and with what purpose
  • commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only
  • devices: a descriptor containing different fields for traceability (below)
  • id: the test name
  • project: within the test was performed, in this case it is always iscape
  • report: if there is any report analysing the test
  • type_test: indoor, oudoor test or other.

Description of devices entry

For each device that was used in the test, two generic types are used:

  • low cost sensors (type: STATION or KIT)
  • high end sensors (type: REFERENCE)

For low cost Smart Citizen sensors, the fields are:

  • alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed
  • device_id: device id in Smartcitizen API
  • fileNameInfo: not used
  • fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv
  • fileNameRaw: (only if source = csv is used) raw file name
  • frequency: original recording frequency
  • location: for timezone correction only, not accurate
  • max_date: last recording date
  • min_date: first recording date
  • name: self-explanatory
  • pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)
  • source: api or csv
  • type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT
  • version: smartcitizen hardware version

For high end sensors, the fields are:

  • channels: which channels the device was recording for internal convertion
    • names: which are the columns in the csv file
    • pollutants: which pollutants do they respectively refer to
    • units: the units of these pollutants
  • equipment: the brand of the analyser
  • fileNameProc: same as above
  • fileNameRaw: same as above
  • index: format in which the timeindex is done, for parsing purposes
    • format: (example '%Y-%m-%d %H:%M:%S')
    • frequency: frequency at which the device was recorded
    • name: column name
  • location: same as above
  • name: name of the device
  • type: REFERENCE (always for these devices)
  • source: csv

iSCAPE Dataset Reference Numbers:

The datasets here presented are related to the following iSCAPE dataset reference numbers:

  • DS_TS_049
  • DS_TS_050
  • DS_TS_051
  • DS_TS_052
  • DS_TS_053
  • DS_TS_055
  • DS_TS_056
  • DS_TS_057
  • DS_TS_058

Files (588.9 MB)
Name Size
2018-03_EXT_DUBLIN_TESTS_KIT0000.csv
md5:aeb2fc574692b785eb905758c86376a5
465.1 kB Download
2018-03_EXT_DUBLIN_TESTS_KIT0001.csv
md5:37ac0244239470109cb962d24bb53676
479.3 kB Download
2018-03_EXT_DUBLIN_TESTS_STATION0002.csv
md5:c9ac74d5798e0d076dae5df66d725cd0
1.7 MB Download
2018-09_EXT_BOLOGNA_TEST_WALL_MO_STATION_ARPAE.csv
md5:e6c966fd69b0908d040ed542c8dfef6a
1.7 MB Download
2018-09_EXT_BOLOGNA_TEST_WALL_MO_STATION_SCK2.csv
md5:7ba423524a7e5f7e8aeebf3f066532b5
12.1 MB Download
2018-09_EXT_BOLOGNA_TEST_WALL_RE_STATION_ARPAE.csv
md5:0654acd8a8b610bbc1ed21d0f8af05be
2.0 MB Download
2018-09_EXT_BOLOGNA_TEST_WALL_STATIONSCK3.csv
md5:579890921176524578aaf1125083c8fb
12.6 MB Download
2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_DAY_REF.csv
md5:23736ec9c9c7b6ffdf424e2d24cf7be4
1.7 kB Download
2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv
md5:4ad73dbf609cf520bcbd12d96d8f1750
540.8 kB Download
5262.csv
md5:06fb0752a7f65beb0f6b4a7a9b6ab2db
16.8 MB Download
5262_PROCESSED.csv
md5:8186510966fc8b2f5e28c76db6fb588c
47.2 MB Download
5527.csv
md5:fc670114566b6b8020d46a2e10cb8417
37.0 MB Download
5527_PROCESSED.csv
md5:966017469c295788164517894e92edf5
53.6 MB Download
5528.csv
md5:9204c1a4afcc8bf71390e28a8a1c1367
33.8 MB Download
5528_PROCESSED.csv
md5:2527a1313b4e7ea0c92a011a57ee9a69
52.6 MB Download
5565.csv
md5:6bb9ffb9f1259258820facb00514162d
21.8 MB Download
5565_PROCESSED.csv
md5:7cb27a52b4798ba1ff10cbf28fe57a5e
53.1 MB Download
8739.csv
md5:1aca68ff651f428ea6ca78087fc9c0aa
46.2 MB Download
8739_PROCESSED.csv
md5:53859a2aae47232f311f3ac5addc5e8b
34.3 MB Download
9397.csv
md5:d784ee84bd17379da46ea3a9bc85097e
46.9 MB Download
9397_PROCESSED.csv
md5:92e318a09a6f9e8e040443ebe2dc6304
34.2 MB Download
9530.csv
md5:e2ba6fd2719d3bb66fdb894a4062e071
7.2 MB Download
9530_PROCESSED.csv
md5:784a8e7ac173be848d727cb294771568
10.0 MB Download
9694.csv
md5:19c16e35b03a82a8c94517749d7dda57
32.1 MB Download
9694_PROCESSED.csv
md5:345010a25351d447c374cbd22d6f36ff
30.2 MB Download
test_description_2018-03_EXT_DUBLIN_TESTS.yaml
md5:f6ed89d11f935cb580b600c0ad88a17d
5.0 kB Download
test_description_2018-09_EXT_BOLOGNA_TEST_WALL_MO.yaml
md5:1bd37f9379a54456cc970ae06b86565b
2.9 kB Download
test_description_2018-09_EXT_BOLOGNA_TEST_WALL_RE.yaml
md5:a925a37e5187e27d8afbadcf5c3dbdc2
2.8 kB Download
test_description_2019-03_EXT_UCD_URBAN_BACKGROUND_API.yaml
md5:e8612e4436cca9a48c6944c9beae713b
2.0 kB Download
test_description_2019-03_EXT_UOS_URBAN_BACKGROUND_API.yaml
md5:7ba0cf4094ce7affb67cf4a96510dc6f
1.0 kB Download
test_description_2019-06_EXT_HASSELT_SCHOOL.yaml
md5:2114d087813db0ead864b645d49de213
1.2 kB Download
test_description_2019-06_EXT_UOS_HEDGES_API_SECOND_BATCH.yaml
md5:82bc49d0ff467bfd4de57f0dd885df12
1.0 kB Download
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