Air pollution is very difficult to be measured. City administrators normally deploy accurate but very expensive and quite scattered stations to control air quality and related human health. Some important gases and organic volatile compounds that may affect human health are commonly identified and accepted as main air quality indicators. To monitor them, stations use to incorporate sophisticated systems mainly based on ultraviolet fluorescence, quimioluminiscence, ultraviolet photometry and non-dispersive infrared spectrometry among others. As alternatives for building-up low cost monitoring networks, different groups and companies have been working in other technologies and sensor devices. However, because of the many scattered initiatives, information about the usefulness of these low-cost sensors is difficult to be found and a step forward for their evaluation is necessary. In the following table preliminary information on manufacturers and the current state of technology is reported in terms of the gases and parameters that can be targeted with them. To provide adequate information on air quality spatial distribution, the directive also states that those fixed measurements may be supplemented by indicative measurements. Thus, the legislative importance of indicative measurements is to be stressed, as “the results of indicative measurement shall be taken into account for the assessment of air quality with respect to the limit values” (EC, 2008). European Commision (EC), 2008. Directive 2008/50/EC on ambient air quality and cleaner air for Europe, 21/05/2008
In the following Table the Pollutant Reference Table:
CNR developed a circuit board "AirQuino", Arduino Shield compatible, integrated with low cost and high resolution sensors, dedicated to the monitoring of environmental parameters and air quality pollutants (Noise, Humidity, Temperature, CO, CO2, O3, NO2, CH4), road pavement quality (accelerometer) and the indices of well-being (globethermometer to calculate the index of thermal comfort) in an urban environment. The board integrates a microprocessor unit that acquires all the sensors installed and analyses fast data from accelerometer and noise sensor.
AirQuino circuit board
- MOS-type gas sensors
- In clean air, donor electrons in tin dioxide are attracted toward oxygen which is adsorbed on the surface of the sensing material, preventing electric current flow.
- In the presence of reducing gases, the surface density of adsorbed oxygen decreases as it reacts with the reducing gases. Electrons are released into the tin dioxide, allowing current to flow freely through the sensor.
- CO2, is a NDIR, non dispersive infrared, sensor and the analysis chamber is protected by a membrane which avoid dust contamination of the detectors, the maintenance needed is a recalibration every year.
- Air temperature (°C) and humidity (%); this sensor is an integrated sensor, encapsulated in a plastic shed with a porous filter, the maintenance required a cleaning procedure due with compressed air to be done every year, in case of damage the sensor coul be easily replaced removing the solar shield;
- O3 ozone, (counts/index); MOS-type gas sensors, the air pollutants sensors are installed on a sensor board, connected to the motherboard with a red connector.
- NO2 (counts/index); MOS-type gas sensors, the air pollutants sensors are installed on a sensor board, connected to the motherboard with a red connector, we suggest a sensor board replacement every 1-2 years depending on the environmental conditions
- CO (counts/index); MOS-type gas sensors, the air pollutants sensors are installed on a sensor board, connected to the motherboard with a red connector, we suggest a sensor board replacement every 1-2 years depending on the environmental conditions
- VOC (counts/index); MOS-type gas sensors, the air pollutants sensors are installed on a sensor board, connected to the motherboard with a red connector, we suggest a sensor board replacement every 1-2 years depending on the environmental conditions
- Particles PM2.5 and PM10 (µg/m3); this device is based on laser scattering principle. Light scattering can be induced when particles go through the detecting area. The scattered light is transformed into electrical signals and these signals will be amplified and processed. The number and diameter of particles can be obtained by analysis because the signal waveform has certain relations with the particles diameter, the maintenance suggested is a sensor replacement every 2-3 years depending on the environmental conditions, in particular the internal contaminations caused by high level of dust in the atmosphere.
The system is provided with an internal DC-DC converter unit that accept a wide range of voltage input, from 10Vdc to 30Vdc.
The power consumption is 200mA@12Vdc , 2,5W.
Through General Packet Radio Service (GPRS) technology, the sensor transmits geolocated measured data to the server connected to the applications and web server allowing to visualize real time observations on a web browser.
A comprehensive spatial data infrastructure has been developed. It is composed by: a central Geo Database for data storage and management; a GIS engine; a web application for viewing, querying and performing analysis. The interoperability of this system is guaranteed because all its components are based on the Open Data approach and open source services and follow the INSPIRE, OGC (Open Geospatial Consortium) directives and standards.
AIRQino framework components
AIRQino air quality monitoring station
- The SensorWebBike for air quality monitoring in a smart city, IET Conference on Future Intelligent Cities, London, UK, 4–6 Dec. 2014. Vagnoli, C., Martelli, F., De Filippis, T., Di Lonardo, S., Gioli, B., Gualtieri, G., Matese, A., Rocchi, L., Toscano, P., Zaldei A.
- AIRQino, a low-cost air quality mobile platform, EGU General Assembly Conference Abstracts, Vienna, Austria, 12–17 April 2015. Zaldei, A., 2014. Zaldei, A., Vagnoli, C., Di Lonardo, S., Gioli, B., Gualtieri, G., Toscano, P., Martelli, F., Matese, A., 2015.
- An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories Zaldei A., Camilli F., De Filippis T., Di Gennaro F., Di Lonardo S., Dini F., Gioli B., Gualtieri G., , Matese A., Nunziati W., Rocchi L., Toscano P., Vagnoli C. Transportation Research Procedia 00 (2016) 000–000