Particulate matter sensor network
Develop an app or platform to crowd-source information for comparing changes in environmental factors, such as temperature, relative humidity, air pollution, with occurrence of symptoms of allergies and respiratory diseases. Create tools for public entry and grading of symptoms, including but not limited to cough, shortness of breath, wheezing, sneezing, nasal obstruction, itchy eyes; and geographic mapping of symptom frequency and intensity. Create a platform for comparison of symptom map with NASA provided data, with visualization options for web and/or smart phone.
Air pollution is a problem in cities all around the globe. Emissions from traffic, buildings, industry and various other sources can have adverse health effects. Nowadays, many countries are actively trying to counter such problems with regulations. An important step for counter-measures is the measurement of the actual air quality. For example, the European Union developed measurement protocols and thresholds, which should not be exceeded and where actions have to be taken by the governments.
One of the monitored pollutants is particulate matter: small particles that are taken up while breathing and have the ability to permeate into the bodies. The measurement and monitoring of such particles is usually done only at a few sites within the city, and there are significant delays until the measurement data is available publicly (implied by the official measurement protocol).
In Stuttgart, the station Neckartor exhibits the highest measured concentrations within Germany. Therefore a citizen science project was created to build up a sensor network for PM measurements. The sensors are maintained by citizens at locations within and around the city. Hardware for the sensors has been selected and software was written to distribute and monitor the measurements within the network. Currently the sensors are mounted at fixed locations and the data is gathered in a central database.
Goals and ideas
Within the challenge, two new components will be developed:
- Mobile sensor: A GPS sensor will be added for measurements on-the-go
- Visualisation: A map based visualisation of the most recent measurements
Additionally, the following topics could be of interest:
- identify and add existing air quality data from external sources
- visualize the track data on a map, too
Implementation
Mobile sensor
- add GPS to existing sensor setup, see commit
- enable push GPS data to Django API (changes for GPS measurements see commit) and/or via MQTT
Visualisation
The map visualisation was hacked during the NASA Space-Apps challenge 2016 https://2016.spaceappschallenge.org/locations/stuttgart-germany
- adapt the django API: request to get the latest measurement values
- create a map visualisation based on the API input
Repository is located at https://github.com/opendata-stuttgart/feinstaub-map
- mfa provided the changes to the API (see commit)
Map application
The map background is based on OpenStreetMap provided via mapbox. The application itself was created in JavaScript on top of a leaflet layer. The implemetation makes use of various frameworks and is on ECMA6 language level.
