German researchers are developing a measurement and analysis system that can better predict air quality in cities by combining existing meteorological data sources with data from road traffic air quality sensors, drone flights and citizens’ smartphones. The Smart Air Quality Network (SmartAQnet) being developed by the Karlsruhe Institute of Technology (KIT) uses a closely meshed network for its data sources with a high temporal and spatial resolution. The €2.9m (US$3.4m) project, which started in spring 2017 and will last for three years, is also aiming to better understand the distribution of fine dust in cities. The city of Augsburg, Germany, is the first place to be studied.
Matthias Budde, computer scientist at the Chair for Pervasive Computing Systems at Karlsruhe Institute of Technology, has developed a plug-in sensor and an app for the project, which turns the camera of a smartphone into a fine dust detector. The flash of the phone emits light into the measurement area. This light is scattered by the fine dust. The camera captures the measurement result in the form of a picture. From its brightness, the dust concentration can be calculated.
Although precise measurement stations for air quality can be found at a few critical points, usually where air pollution is very high, they are expensive. So far, detailed local information on air pollution has not been available to the individual citizen. “We try to pool all of the available data and complement it by a multi-level network of sensors to improve the dataset,” Budde said. “Except for personal data, all data collected by researchers and citizens will be made available to the interested public as open data.”
The data can also be used by scientists, urban planners, authorities and citizens. As well as data from the low-cost citizen smartphone sensors, SmartAQnet collects air and weather data from classical measurement stations and measurements by drones for information on the fine dust distribution in higher air layers. One of the project’s major challenges is therefore to evaluate the data measured by the variety of instruments, from stationary, scientific, high-precision devices to low-cost systems that can be operated easily by non-experts. “For this purpose, smart algorithms and big data analyses are required,” Budde said.
Findings obtained from the SmartAQnet project will be used by climate researchers for numerical simulation under the Urban Climate under Change programme funded by the German Federal Ministry of Education and Research (BMBF).
- October 2017