Luftdatenpumpe Save

Acquire and process live and historical air quality data without efforts. Filter by station-id, sensor-id and sensor-type, apply reverse geocoding, store into time-series and RDBMS databases, publish to MQTT, output as JSON, or visualize in Grafana. Data sources: Sensor.Community (, IRCELINE, and OpenAQ.

Project README

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*Acquire and process live and historical air quality data without efforts.*

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Acquire and process live and historical air quality data without efforts.

Filter by station-id, sensor-id and sensor-type, apply reverse geocoding, store into time-series_ and RDBMS_ databases (InfluxDB_ and PostGIS_), publish to MQTT_, output as JSON, or visualize in Grafana_.

Data sources: Sensor.Community_ (, IRCELINE, and OpenAQ_.


  1. Luftdatenpumpe_ acquires the measurement readings either from the livedata API of luftdaten.info_ or from its archived CSV files published to To minimize impact on the upstream servers, all data gets reasonably cached.

  2. While iterating the readings, it optionally filters on station-id, sensor-id or sensor-type and restrains information processing to the corresponding stations and sensors.

  3. Then, each station's location information gets enhanced by

    • attaching its geospatial position as a Geohash_.
    • attaching a synthetic real-world address resolved using the reverse geocoding service Nominatim_ by OpenStreetMap_.
  4. Information about stations can be

    • displayed on STDOUT or STDERR in JSON format.
    • filtered and transformed interactively through jq_, the swiss army knife of JSON manipulation.
    • stored into RDBMS_ databases like PostgreSQL_ using the fine dataset_ package. Being built on top of SQLAlchemy_, this supports all major databases.
    • queried using advanced geospatial features when running PostGIS_, please follow up reading the Luftdatenpumpe PostGIS tutorial_.
  5. Measurement readings can be

    • displayed on STDOUT or STDERR in JSON format, which allows for piping into jq_ again.
    • forwarded to MQTT_.
    • stored to InfluxDB_ and then
    • displayed in Grafana_.



# List networks
luftdatenpumpe networks

# List LDI stations
luftdatenpumpe stations --network=ldi --station=49,1033 --reverse-geocode

# Store list of LDI stations and metadata into RDBMS database (PostgreSQL), also display on STDERR
luftdatenpumpe stations --network=ldi --station=49,1033 --reverse-geocode --target=postgresql://luftdatenpumpe@localhost/weatherbase

# Store LDI readings into InfluxDB
luftdatenpumpe readings --network=ldi --station=49,1033 --target=influxdb://luftdatenpumpe@localhost/luftdaten_info

# Forward LDI readings to MQTT
luftdatenpumpe readings --network=ldi --station=49,1033 --target=mqtt://

For a full overview about all program options including meaningful examples, you might just want to run luftdatenpumpe --help on your command line, or visit the Luftdatenpumpe usage_ documentation section.


Luftdaten-Viewer displays stations and measurements from (LDI) in Grafana.

Map display and filtering

  • Filter by different synthesized address components and sensor type.
  • Display measurements from filtered stations on Panodata Map Panel_.
  • Display filtered list of stations with corresponding information in tabular form.
  • Measurement values are held against configured thresholds so points are colored appropriately.

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Map popup labels

  • Humanized label computed from synthesized OpenStreetMap address.
  • Numeric station identifier.
  • Measurement value, unit and field name.

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If you are running Python 3 already, you can installing the program using pip. We recommend to use a Python virtualenv_.


pip install luftdatenpumpe --upgrade

At this point, you should be able to conduct simple tests like luftdatenpumpe stations as seen in the synopsis section above. At least, you should verify the installation succeeded by running::

luftdatenpumpe --version

At install Luftdatenpumpe_, you will find more detailed installation instructions about how to install and configure auxiliary services, and eventually resolve some prerequisites.



Using Luftdatenpumpe, you can build user-friendly interactive GIS systems on top of PostGIS, InfluxDB and Grafana. This setup is called "Luftdaten-Viewer", and some example scenarios can be inspected at Luftdatenpumpe gallery_.


These installation instructions outline how to setup the whole system to build similar interactive data visualization compositions of map-, graph- and other panel-widgets like outlined in the "Testimonials" section.

  • Luftdaten-Viewer Applications_
  • Luftdaten-Viewer Databases_
  • Luftdaten-Viewer Grafana_

Other projects

Sensor.Community public data aggregator

Visualize recent sensor data on a world map for Sensor.Community and for different other official networks, like EEA, Luchtmeetnet, Atmo AURA/Sud/Occitanie, and Umweltbundesamt.

Project information


Any kind of contribution, feedback, or patch, is much welcome. Create an issue_ or submit a patch if you think we should include a new feature, or to report or fix a bug.


  • Source code_
  • Documentation_
  • Community Forum_
  • Python Package Index (PyPI)_


The project is licensed under the terms of the GNU AGPL license, see LICENSE_.

Content attributions

The copyright of particular images and pictograms are held by their respective owners, unless otherwise noted.

  • Water Pump Free Icon <>_ from Icon Fonts <>_ is licensed by CC BY 3.0.

.. _Community Forum: .. _Create an issue: .. _dataset: .. _Documentation: .. _Erneuerung der Luftdatenpumpe: .. _Geohash: .. _Grafana: .. _InfluxDB: .. _IRCELINE: .. _jq: .. _LICENSE: .. .. _Luftdatenpumpe: .. _MQTT: .. _Nominatim: .. _OpenAQ: .. _OpenStreetMap: .. _Panodata Map Panel: .. _PostgreSQL: .. _PostGIS: .. _Python Package Index (PyPI): .. _RDBMS: .. _Sensor.Community: .. _Source code: .. _SQLAlchemy: .. _The Hiveeyes Project: .. _time-series:

.. _install Luftdatenpumpe: .. _Luftdaten-Viewer Applications: .. _Luftdaten-Viewer Cron Job: .. _Luftdaten-Viewer Databases: .. _Luftdaten-Viewer Grafana: .. _Luftdatenpumpe gallery: .. _Luftdatenpumpe PostGIS tutorial: .. _Luftdatenpumpe usage: .. _Python virtualenv:

Open Source Agenda is not affiliated with "Luftdatenpumpe" Project. README Source: earthobservations/luftdatenpumpe