Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

Cavaliere, Alice; Carotenuto, Federico; Gennaro, Salvatore Filippo Di; Gioli, Beniamino; Gualtieri, Giovanni; Martelli, Francesca; Matese, Alessandro; Toscano, Piero; Vagnoli, Carolina; Zaldei, Alessandro

doi:10.3390/s18092843

Cited by 83 publications

(66 citation statements)

References 44 publications

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“…Similarly, high coefficients (>0.9) were also found in the laboratory when the sensor was tested in an aerosol chamber [38]. The AIRQino itself was tested in a similarly temperate urban environment in Florence (Italy) obtaining raw correlation coefficients >0.8 when compared with reference stations [23]. Nevertheless, in the Arctic deployment, while the SDS011 showed low absolute errors, the high negative relative bias (>−40%) demonstrated a consistent underestimation of the reference PM that wasn't seen, for example, in the deployment of the AIRQino in Florence (where the normalized mean bias was around 5% for both PM 2.5 and PM 10 , [23]).…”

Section: Discussionmentioning

confidence: 69%

“…The PM laser-scattering optical particle counter has a separate inlet and an internal fan able to provide continuous air recirculation at 0.75 m 3 h -1 and return densities in (µg m -3 ). The only but relevant practical difference with the standard AIRQino described in [23] is the addition of a 5 W ceramic heating element and a metallic cage for snow protection of the T and RH external sensor, aimed at increasing the performance in harsh environments. A small metallic net was added around the inlet for the PM sensor to provide protection from the snow to the particulate matter sensor as well.…”

Section: Airqino Low-cost Sensormentioning

confidence: 99%

“…The low-cost station AIRQino is a custom printed circuit board (PCB) developed by the Institute of Bioeconomy (IBE) of the National Research Council of Italy (CNR) that is able to integrate a set of low-cost sensors and transmit their data to a standard Arduino microcontroller by acting as an Arduino shield [23]. The board integrates sensors for air temperature (T) and relative humidity (RH) (AM2315 Adafruit, New York City, NY, USA), and a set of atmospheric components sensing elements including CO 2 (S8, SenseAir, Delsbo, Sweden) and particulate matter (PM 2.5 and PM 10 (SDS011, Nova Fitness, Jinan, China).…”

Section: Airqino Low-cost Sensormentioning

confidence: 99%

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Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Carotenuto

Brilli

Gioli

et al. 2020

Sensors

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The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m−3 for PM2.5 and ≈3 µg m−3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m−3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.

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Section: Discussionmentioning

confidence: 69%

Section: Airqino Low-cost Sensormentioning

confidence: 99%

Section: Airqino Low-cost Sensormentioning

confidence: 99%

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Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Carotenuto

Brilli

Gioli

et al. 2020

Sensors

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“…For the 24-hour PM measurements of SSys ( Figure A7), PA-II [19] and AirQUINO [76] by CNR (Firenze, IT) showed R 2 values close to 1 for PM 2.5 • For gaseous pollutants, high R 2 values ranging between 0.7 and 1.0 were found for the following multipollutant LCS: AirSensEUR [22] by LiberaIntentio, AirVeraCity, AQY and S-500 by Aeroqual, and SNAQ by the University of Cambridge (Cambridge, UK) ( Figure A3).…”

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confidence: 99%

Review of the Performance of Low-Cost Sensors for Air Quality Monitoring

et al. 2019

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A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R2 > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

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“…To the best of our knowledge, there no reports on the performance of the SDS011 sensor model in climates similar to Santiago, specifically, the Mediterranean type with dry summers. A few studies have assessed the performance of the SDS011 in humid subtropical climates, for example, in Thessaloniki, Greece (Genikomsakis et al 2018) and Florence, Italy (Cavaliere et al 2018). In both of these locations, R 2 above 0.85 was observed for the correlations between the PM 2.5 averages informed by the SDS011 and standard optical instruments.…”

Section: Accuracy Of Rh Sensor Measurements Compared To Reference Valuesmentioning

confidence: 99%

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile

Tagle

Rojas

Reyes

et al. 2020

Environ Monit Assess

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Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM 10 and PM 2.5 ) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM 10 -PM 2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/ Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filterbased sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM 2.5 (normalized root-mean-square error 9-24%) and PM 10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM 2.5 (R 2 0.47-0.86) than PM 10 (0.24-0.56). The correlations (R 2 ) between the 24-h PM 2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM 2.5 averages after correcting for RH.

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Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

Cited by 83 publications

References 44 publications

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Review of the Performance of Low-Cost Sensors for Air Quality Monitoring

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile

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