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- Hybrid air quality monitor solutions are one of the best ways to extend air quality monitoring in environments as heterogeneous as urban areas, where various emission sources are concentrated.
- The use of sensor-based air quality measurement stations such as those offered by Kunak makes it possible to complement the readings of reference equipment by containing costs and collecting high-quality data.
- Kunak has developed several projects related to deploying this type of solution, including Atmo Hauts-de-France and SmartKalea in Donostia-San Sebastián.
The quality of the air we breathe in cities has improved in recent decades, but it is still not enough. There is no safe level of pollution. In fact, continuous exposure to any concentration of pollutants can lead to health problems, especially in the most vulnerable population groups.
But it is precisely in this context that hybrid air quality monitor ssystems become particularly important. They are an opportunity to take air quality measurement to the next level. They bring together the most accurate reference instruments to protect ourselves with the versatility of sensor-based air quality monitoring stations, at an unbeatable cost-efficiency ratio. And Kunak, with innovative proposals such as the Kunak AIR solutions, is a benchmark in the market.
Hybrid air quality monitoring networks: strength through unity
The introduction has already given you an idea of what hybrid air quality networks are.
However, if we had to define this solution in one sentence, it would be something like “a grid in which different sources and technologies are combined to get a detailed picture of what the air quality is like at a given place and time“.
Advantages of hybrid networks for air pollution monitoring
Why should any urban area, regardless of size, consider this type of solution? Current regulations in Europe require measurements (fixed continuous or mobile discontinuous) at previously selected sampling points that are considered representative for different factors (population, predominant pollutants, etc.).
The problem is that the equipment used in these measurements is expensive (several hundred thousand euros) and requires continuous maintenance. This circumstance makes it unfeasible to set up a dense monitoring grid, which is necessary to obtain hyperlocal data on a factor as changeable as pollution, in order to obtain an accurate picture of air quality.
Therefore, one of the great advantages of measurement systems based on Kunak AIR solutions is the ability to complement and expand the network while containing the cost. However, these compact stations cannot, at this stage, replace reference equipment, as standards and certification programmes have not yet been developed.
These air quality monitoring solutions also make it possible to:
- Support medical studies that analyse the health effects of air quality (a clear example, is the growing evidence of a correlation between COVID and air pollution).
- Create more detailed air quality maps and predictive models to trigger pollution alerts.
- Identify and characterise pollution hotspots. To measure, in short, where people live and are most exposed.
- Raise public awareness of the importance of enjoying high air quality.
- Define and delimit Low Emission Zones (LEZs) and assess their impact on improving air quality in cities.
Two examples of hybrid networks to monitor air quality
The hybrid monitoring initiatives that more and more cities are adopting are the perfect way to convey the benefits of these solutions. In this context, the C40 Cities Climate Leadership Group has published an interesting report compiling multiple examples.
However, for the purposes of this article, we are going to look at two specific projects in a little more detail:
- Atmo Hauts-de-France,
- a recent project we have developed in France, which reinforces Kunak’s experience in the deployment of networks of these characteristics (and which we have already demonstrated with our participation in the SmartKalea project in Donostia-San Sebastián), and
- Breathe London,
- a pioneering initiative in Europe as a whole, which is serving as an example to many cities for the development of similar initiatives.
Atmo Hauts-de-France: air quality monitoring, information and alerts at the service of the public
The network of air pollution monitoring stations that we have deployed for Atmo Hauts-de-France, one of the entities that make up Atmo France, is a clear example of the potential of our sensor solutions.
Configured as a system of hybrid air quality monitoring networks, it supplements the reference stations already in place on the territory. Its implementation is making it possible to collect data on a hyper-local scale, enabling, for example, the issuing of alerts in the event of episodes of high atmospheric pollution.
Breathe London, taking care of the health of visitors and residents of London
The City of London has been experiencing serious pollution problems for years. But in recent years it has taken numerous steps to minimise the problem, including the establishment of low and ultra-low emission zones (LEZs and ULEZs) and the deployment of a dense network of air quality measurement devices that transmit data in real-time.
The result is a city that, with hard work, is managing to reduce its pollution levels thanks to more and better information and a strategy that puts human health first.
A hybrid system can take advantage of all the strengths of each system by merging data from different sources and is proposed as the most suitable solution for an innovative, modern and efficient air quality monitoring station network.
Although different ways of calibrating sensors have been found, correlation of stations based on mobile sensors with nearby reference stations is still the best practice (1), and regular calibration of such sensors is of vital importance to maintain high measurement accuracy (2).
As low-cost sensors are not yet flawless in terms of accuracy, calibration and high-quality data collection must be a key aspect and should be taken into account in the design and deployment phases of a sensor network.
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