Spatial distribution of particulate matter, collected using low cost...
- zenodo.org
binUpdated Apr 24, 2025
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EmailClick to copy linkLink copiedCiteJanani Venkatraman Jagatha; Janani Venkatraman Jagatha; Christoph Schneider; Christoph Schneider; Sebastian Schubert; Luxi Jin; Sebastian Schubert; Luxi Jin (2025). Spatial distribution of particulate matter, collected using low cost sensors, in Downtown-Singapore [Dataset]. http://doi.org/10.5281/zenodo.14280847binAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.14280847Dataset updatedApr 24, 2025AuthorsJanani Venkatraman Jagatha; Janani Venkatraman Jagatha; Christoph Schneider; Christoph Schneider; Sebastian Schubert; Luxi Jin; Sebastian Schubert; Luxi JinLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredSingaporeDescriptionThe dataset consists of particulate matter concentration and meteorology data, measured in Singapore, Chinatown, and Central business district from March 13, 2018, to March 16, 2018. The data collectors walked from the Outram district - Chinatown to the Central Business District in Singapore. The measurements were carried out using a hand-held air quality sensor ensemble (URBMOBI 3.0).
The dataset contains information from two URBMOBI 3.0 devices and one reference-grade device (Grimm 1.109). The data from the sensors and Grimm are denoted by the subscript, 's1', 's2', and 'gr', respectively.
singapore_all_pm_25.geojson : The observed PM concentration and meteorology, aggregated using a 25 m buffer around the measurement points.
Information on working with geojson file can be found under GeoJSON .
Units:
PM : µg/m³
Scaled_PM_MM : Dimensionless entity scaled using Min-Max-Scaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)
Scaled_PM_SS : Dimensionless entity scaled using Standard-Scaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)
Air temperature: °C
Relative humidity: %The measurements are part of the "Effects of heavy precipitation events on near-surface climate and particulate matter concentrations in Singapore". It is funded by the support from Humboldt-Universität zu Berlin for seed funding for collaborative projects between National University of Singapore and Humboldt-Universität zu Berlin.
Data Set for Probabilistic Indoor Temperature Forecasting
- zenodo.org
binUpdated Oct 16, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteRoman Kempf; Marcel Arpogaus; Tim Baur; Gunnar Schubert; Roman Kempf; Marcel Arpogaus; Tim Baur; Gunnar Schubert (2024). Data Set for Probabilistic Indoor Temperature Forecasting [Dataset]. http://doi.org/10.5281/zenodo.11911791binAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.11911791Dataset updatedOct 16, 2024AuthorsRoman Kempf; Marcel Arpogaus; Tim Baur; Gunnar Schubert; Roman Kempf; Marcel Arpogaus; Tim Baur; Gunnar SchubertLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescription1. Dataset Manifest
This text provides a description of the dataset used for model training and evaluation in our study "A Tutorial on Deep Learning for Probabilistic Indoor Temperature Forecasting". The dataset consists of various simulated thermal and environmental parameters for different room configurations. Below, you will find a table detailing each column in the dataset along with its description and unit of measurement.
1.1. Columns Description
Column Name Description Unit timeTime stamp of the measurement - ZweiPersonenBuero.TAirAir temperature inside a two-person office °C heatStat.Heat.Q_flowHeating rate in the room W weaDat.AirPressureAtmospheric pressure Pa weaDat.AirTempOutside air temperature °C weaDat.SkyRadiationLongwave sky radiation W/m² weaDat.TerrestrialRadiationTerrestrial radiation W/m² weaDat.WaterInAirAbsolute humidity g/kg VAirAir volume in the room m³ AExt0Exterior wall area facing the south m² AExt1Exterior wall area facing the north m² AIntTotal interior wall area m² AFloorFloor area of the room m² AWin0Window area facing the south m² AWin1Window area facing the north m² azi0Azimuth (direction) of the first exterior wall rad azi1Azimuth (direction) of the second exterior wall rad idUnique identifier for the room configuration - is_holidayIndicator whether the day is a holiday (1 for yes, 0 for no) - 1.2. Note on Multi-Value Columns
For rooms with multiple exterior walls (rooms 15-30):
- AExt: {Exterior wall 1 area, Exterior wall 2 area}
- AWin: {Window area on exterior wall 1, Window area on exterior wall 2}
- azi: {Azimuth of exterior wall 1, Azimuth of exterior wall 2}
Example:
- AExt = {10, 15}
- AWin = {2, 0}
- azi = {0, 3.1415}
This indicates two exterior walls with areas of 10 m² and 15 m² facing south (0 rad) and north (3.1415 rad), respectively. The south-facing wall has a window of 2 m², while the north-facing wall has no window.
1.3. Data Sources
- Room Model: Simulated using the reduced-order package of the Modelica Buildings Library.
- Weather Data: Provided by the German Meteorological Service (DWD) in Test Reference Year (TRY) format.
This comprehensive dataset provides crucial parameters required to train and evaluate thermal models for different room configurations. The simulation data ensures a diverse range of environmental and occupancy conditions, enhancing the robustness of the models.
1.4. Data scaling
The data set contains the raw data as well as the scaled data used for training and testing the model. The scaling was carried out using the StandardScaler package.
1.5. Weather data license
This data set contains weather data recorded by the DWD under license „Datenlizenz Deutschland – Namensnennung – Version 2.0" (URL). The data is provided by "Bundesinstitut für Bau-, Stadt- und Raumforschung". The data can be downloaded from here. We use data from the year 2015 from Heilbronn. We have added the weather data to the data set unchanged.
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FacebookTwitterEmailSpatial distribution of particulate matter, collected using low cost sensors, in Downtown-Singapore
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The dataset consists of particulate matter concentration and meteorology data, measured in Singapore, Chinatown, and Central business district from March 13, 2018, to March 16, 2018. The data collectors walked from the Outram district - Chinatown to the Central Business District in Singapore. The measurements were carried out using a hand-held air quality sensor ensemble (URBMOBI 3.0).
The dataset contains information from two URBMOBI 3.0 devices and one reference-grade device (Grimm 1.109). The data from the sensors and Grimm are denoted by the subscript, 's1', 's2', and 'gr', respectively.
singapore_all_pm_25.geojson : The observed PM concentration and meteorology, aggregated using a 25 m buffer around the measurement points.
Information on working with geojson file can be found under GeoJSON .
Units:
PM : µg/m³
Scaled_PM_MM : Dimensionless entity scaled using Min-Max-Scaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)
Scaled_PM_SS : Dimensionless entity scaled using Standard-Scaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)
Air temperature: °C
Relative humidity: %
The measurements are part of the "Effects of heavy precipitation events on near-surface climate and particulate matter concentrations in Singapore". It is funded by the support from Humboldt-Universität zu Berlin for seed funding for collaborative projects between National University of Singapore and Humboldt-Universität zu Berlin.