Artificial intelligence helps to obtain increasingly accurate information on air quality

Researchers have developed a way of collecting more and increasingly accurate information on air quality in urban areas. The findings can benefit, for example, urban planning and reduce health hazards associated with air pollution.

Researchers at the University of Helsinki have developed a technique for obtaining accurate information on air quality using simple and inexpensive measuring equipment. Thanks to the new technique, low-cost sensors could be utilised with a considerably broader scope.

The World Health Organization (WHO) estimates that air pollution kills seven million people worldwide every year, which is why air quality is monitored using a range of means. Fixed measuring sites, such as the Stations Measuring Earth Surfaces and Atmosphere Relations" (SMEAR) stations of the University of Helsinki, provide reliable results, but the technologies used are expensive.

Attempts have been made to develop less expensive sensors to complement the fixed measuring sites, which could be installed in great numbers all over the world. With inexpensive sensors, the extent of information on air quality could be considerably increased, both temporally and geographically.

“Low-cost sensors could be installed, for example, in offices or public transport. Individuals could also purchase such sensors to measure the air quality of their immediate surroundings. The masses of data accumulated through the sensors would benefit research focused on population health, urban planning and environmental research,” says Postdoctoral Researcher Martha Arbayani Zaidan from the Institute for Atmospheric and Earth System Research (INAR), University of Helsinki.

The problem so far with low-cost air quality sensors has been that the data produced has not always been of sufficiently high quality, resulting in the necessity of comparing and calibrating the figures with those of the SMEAR stations and other fixed measuring sites. That takes time and money.

Advanced technology for simple sensors

The University of Helsinki researchers propose a new, integrated model for air quality measurements where low-cost sensors are combined with technological solutions, thanks to which the sensors’ measurements begin to match those of fixed measuring devices.

Such solutions include automated adjustment of measuring accuracy through Artificial Intelligence (AI) techniques and specific mathematical models, known as virtual sensors.

“The mathematical models we employ make it possible to, for example, estimate black carbon concentrations in the environment. Black carbon is an air pollutant which simple sensors alone are usually unable to measure. However, its concentration could be estimated using AI techniques,” Zaidan says.

Comparison sites on Mäkelänkatu and in Kumpula

The researchers tested their technique by comparing the measurements of four low-cost sensors with the results of fixed measuring equipment at two sites in Helsinki: the SMEAR station in Kumpula, operated by the University of Helsinki and Finnish Meteorological Institute (FMI), and a measuring site operated by the Helsinki Region Environmental Services Authority on Mäkelänkatu.

The results have made the independent use of low-cost measuring devices easier without having to continuously compare and calibrate their measurements to those of fixed measuring sites.

The technique proposed by the researchers has only been in the prototype form so far, and there are still some remaining challenges before deploying them to the field, such as in internet communication and the energy-harvesting methods of low-cost sensors. However, Zaidan sees a lot of potential in them.

“With the technique, we are proposing, sufficiently accurate data can be gained from low-cost sensors. Our technique could be used to significantly extend the network for measuring air quality, with the data accumulated used for the benefit of humanity.”

The study was published in the IEEE Sensors Journal in October.


Martha Arbayani Zaidan, Naser Hossein Motlagh, Pak L. Fung, David Lu, Hilkka Timonen, Joel Kuula, Jarkko V. Niemi, Sasu Tarkoma, Tuukka Petäjä, Markku Kulmala, and Tareq Hussein, "Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors," in IEEE Sensors Journal, doi: 10.1109/JSEN.2020.3010316.

Further information:

Martha Arbayani Zaidan

Postdoctoral Researcher, Institute for Atmospheric and Earth System Research (INAR)

+358 50 311 9543

Read more:

Further information about the SMEAR research stations

Further information about MegaSense

Institute for Atmospheric and Earth System Research (INAR)