Large cities in developed countries are usually dotted with a network of reference monitoring stations. However, it means a high cost of installation and maintenance, providing accurate data but only in a few locations. On the other hand, this network is not possible in smaller cities or underdeveloped regions.
Hence, air quality monitoring using another type of equipment is necessary. One solution is to integrate air pollution sensors in a device, that takes the raw data and converts them into useful information for the end-user. Those air quality devices are lower in cost, easier to use and less bulky than traditional equipment. They provide the possibility for citizens and communities to monitor in real-time their local air quality that may affect their health.
However, nowadays, there is not enough information on how to test these types of devices to ensure their adequate performance. As it is explained by EPA in the Peer Review and Supporting Literature Review of Air Sensors Technology Performance Targets document, “The lack of accepted performance specifications for air sensors is limiting the understanding of the quality of the data produced with this technology and is leading to confusion in the marketplace, as the buyers are uncertain of how well air sensors currently perform, how to operate them, and how well sensors need to perform to be suitable for a given purpose”.
There is not enough information on how to test these types of devices to ensure their adequate performance.
Moreover, two different air quality sensor performance standards are identified. In the case of Europe, the CEN Technical Committee 264, Working Group 42 is currently developing Technical Specifications for the performance requirements and test methods for low-cost sensors under prescribed laboratory and field conditions. On the other hand, China’s Ministry of Environmental Protection Department of Hebei province has developed a generic performance standard for sensors.
Hereinafter, we present some real study cases results as well as three external evaluation processes carried out by different organizations:
Real Case Studies and Evaluation processes
In the study cases, R2 value is included for each pollutant. It is a partial measure of how much air quality data agree with reference measurements according to a regression model. We decided to use the R2 since it is the most common metric used in the evaluations. However, this metric has some limitations. The data need to be homogeneous, following a normal distribution, which is not always the case in real studies. It could improve when the range of the reference measurements increases or depending on the seasonality of sampling regarding the different studies.
Finally, R2 is insensitive to bias between the air quality device and the reference data. Thus, we have included the Mean Absolute Error (MAE) in our studies, to show the accuracy of the results.
Co-location field study in Sabadell and Barcelona (Spain)
It is recommended to carry out evaluations using co-locations alongside reference instruments as a means to evaluate performance (Figure 1).
The device is placed in the field near a reference instrument for a period to provide a direct comparison of the device outputs to a calibrated reference instrument. Unfortunately, it is a challenge to observe the entire dynamic target gases, cross-sensitive pollutants, and environmental parameters.
World Athletics started in 2018 to create a real-time air quality network in stadiums to monitor air pollutant concentrations and help athletes choose the best times to train and compete and help organizers to protect the health of athletes. In the long-term, working with United Nations (UN), NGOs and governments, the project aims to raise awareness of how air quality impacts people’s quality of life.
Additionally, the World Athletics Health and Science Department is evaluating the correlation between air quality and athletic performance. As part of the World Athletics program, the athletics track at the Mexican Olympic Sports Center (CDOM) had an air quality monitor installed in January 2019.
Before Mexico City, the majority of the pilot project had been conducted in stadiums in Monaco, Addis Ababa, Sydney, and Yokohama.
Kunak Air A-10obtained high R2 values regarding the different gas pollutants and PMs in the three real case studies, R2> 0.85 for gases and R2> 0.75 for particles. The exception is NO2 in Mexico City study, due to the large humidity transients (from 80% to 40%) at high temperatures (>20°C) (Figure 2).
This humidity transient issue is more significant in lower concentrations of NO2, as it occurs during summer.
Nevertheless, the daily, weekly and monthly trends are perfectly tracked, with similar average concentrations.
In 2017, EPA and five other federal agencies issued a Wildland Fire Sensors Challenge to improve smoke monitoring and provide data to protect public health.
Sensor developers and researchers were encouraged to develop new and innovative air sensor monitoring technologies to measure air pollutants from smoke during wildland fires. Smoke from fires is harmful to health that can irritate the eyes, nose, and throat, cause persistent coughing, wheezing and difficulty breathing and worsen heart and lung disease.
Comparative results of the field studies and evaluation processes:
The results at the laboratory reached high R2 values, proving that in controlled laboratory conditions, Kunak Air A10 performance is optimal. This is observed in Figure 3, in the case of the CO at different temperatures and humidity values.
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