Articles | Volume 16, issue 20
https://doi.org/10.5194/amt-16-4723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-4723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Oct 2023
Research article |
| 20 Oct 2023
| 20 Oct 2023
Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors
Alice Cavaliere
CORRESPONDING AUTHOR
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Lorenzo Brilli
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Bianca Patrizia Andreini
ARPAT, Tuscany Region Environmental Protection Agency, Via Porpora, 22, 50144 Florence, Italy
Federico Carotenuto
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Beniamino Gioli
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Tommaso Giordano
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Marco Stefanelli
ARPAT, Tuscany Region Environmental Protection Agency, Via Porpora, 22, 50144 Florence, Italy
Carolina Vagnoli
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Alessandro Zaldei
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Giovanni Gualtieri
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Related authors
Elena Barbaro, Francisco Ardini, Silvia Becagli, Stefano Bertinetti, Federica Bruschi, Giulia Calzolai, Alice Cavaliere, David Cappelletti, Stefano Crocchianti, Matteo Feltracco, Andrea Gambaro, Fabio Giardi, Stefania Gilardoni, Marco Grotti, Dominic Heslin-Rees, Radovan Krejci, Mery Malandrino, Matilde Mataloni, Simonetta Montaguti, Silvia Nava, Marco Paglione, Matteo Rinaldi, Chiara Ripa, Mirko Severi, Andrea Spolaor, Rita Traversi, Hilde Uggerud, Giulio Verazzo, Tessa Viglezio, Karl Espen Yttri, Wenche Aas, and Mauro Mazzola
EGUsphere, https://doi.org/10.5194/egusphere-2026-1467, https://doi.org/10.5194/egusphere-2026-1467, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study analyzes how Arctic atmospheric layering affects aerosol distribution by comparing data from the Gruvebadet (61 m) and Zeppelin (472 m) observatories in Svalbard. Results show consistent levels of human-made pollutants like sulfate at both heights, confirming long-range transport from Eurasia. Conversely, natural biogenic tracers were more concentrated at lower altitudes. By integrating chemical and meteorological data, the research highlights the need for multi-level monitoring.
Elena Barbaro, Francisco Ardini, Silvia Becagli, Stefano Bertinetti, Federica Bruschi, Giulia Calzolai, Alice Cavaliere, David Cappelletti, Stefano Crocchianti, Matteo Feltracco, Andrea Gambaro, Fabio Giardi, Stefania Gilardoni, Marco Grotti, Dominic Heslin-Rees, Radovan Krejci, Mery Malandrino, Matilde Mataloni, Simonetta Montaguti, Silvia Nava, Marco Paglione, Matteo Rinaldi, Chiara Ripa, Mirko Severi, Andrea Spolaor, Rita Traversi, Hilde Uggerud, Giulio Verazzo, Tessa Viglezio, Karl Espen Yttri, Wenche Aas, and Mauro Mazzola
EGUsphere, https://doi.org/10.5194/egusphere-2026-1467, https://doi.org/10.5194/egusphere-2026-1467, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study analyzes how Arctic atmospheric layering affects aerosol distribution by comparing data from the Gruvebadet (61 m) and Zeppelin (472 m) observatories in Svalbard. Results show consistent levels of human-made pollutants like sulfate at both heights, confirming long-range transport from Eurasia. Conversely, natural biogenic tracers were more concentrated at lower altitudes. By integrating chemical and meteorological data, the research highlights the need for multi-level monitoring.
Fabio Giardi, Silvia Nava, Giulia Calzolai, Giulia Pazzi, Massimo Chiari, Andrea Faggi, Bianca Patrizia Andreini, Chiara Collaveri, Elena Franchi, Guido Nincheri, Alessandra Amore, Silvia Becagli, Mirko Severi, Rita Traversi, and Franco Lucarelli
Atmos. Chem. Phys., 22, 9987–10005, https://doi.org/10.5194/acp-22-9987-2022, https://doi.org/10.5194/acp-22-9987-2022, 2022
Short summary
Short summary
The restriction measures adopted to contain the COVID-19 virus offered a unique opportunity to study urban particulate emissions in the near absence of traffic, which is one of the main emission sources in the urban environment. However, the drastic decrease in this source of particulate matter during the months of national lockdown did not lead to an equal decrease in the total particulate load. This is due to the inverse behavior shown by different sources, especially secondary sources.
Cited articles
Aleixandre, M., Gerboles, M., and Spinelle, L.: Report of the laboratory and in-situ validation of micro-sensors and evaluation of suitability of model equations NO9: CairClipNO2 of CAIRPOL (F), Publications Office of the European Union, Luxembourg, oCLC: 1111194588, 2013. a
Asair: Datasheet AM2305C, https://asairsensors.com/wp-content/uploads/2021/09/Data-Sheet-AM2315C-Humidity-and-Temperature-Module-ASAIR-V1.0.02.pdf, 29 September 2021. a
Aula, K., Lagerspetz, E., Nurmi, P., and Tarkoma, S.: Evaluation of Low-Cost Air Quality Sensor Calibration Models, ACM Transactions on Sensor Networks, 3512889, https://doi.org/10.1145/3512889, 2022. a
Azen, R. and Budescu, D. V.: The Dominance Analysis Approach for Comparing Predictors in Multiple Regression, Psychol. Meth., 8, 129–148, https://doi.org/10.1037/1082-989X.8.2.129, 2003. a
Barcelo-Ordinas, J. M., Ferrer-Cid, P., Garcia-Vidal, J., Ripoll, A., and Viana, M.: Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks, Sensors, 19, 2503, https://doi.org/10.3390/s19112503, 2019. a
Bart, M., Williams, D. E., Ainslie, B., McKendry, I., Salmond, J., Grange, S. K., Alavi-Shoshtari, M., Steyn, D., and Henshaw, G. S.: High Density Ozone Monitoring Using Gas Sensitive Semi-Conductor Sensors in the Lower Fraser Valley, British Columbia, Environ. Sci. Technol., 48, 3970–3977, https://doi.org/10.1021/es404610t, 2014. a
Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018. a, b
Bisignano, A., Carotenuto, F., Zaldei, A., and Giovannini, L.: Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway, Atmos. Environ., 275, 119008, https://doi.org/10.1016/j.atmosenv.2022.119008, 2022. a, b
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Brilli, L., Carotenuto, F., Andreini, B. P., Cavaliere, A., Esposito, A., Gioli, B., Martelli, F., Stefanelli, M., Vagnoli, C., Venturi, S., Zaldei, A., and Gualtieri, G.: Low-Cost Air Quality Stations' Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment, Atmosphere, 12, 1065, https://doi.org/10.3390/atmos12081065, 2021. a
Burgués, J. and Marco, S.: Low Power Operation of Temperature-Modulated Metal Oxide Semiconductor Gas Sensors, Sensors, 18, 339, https://doi.org/10.3390/s18020339, 2018. a
Camalier, L., Cox, W., and Dolwick, P.: The effects of meteorology on ozone in urban areas and their use in assessing ozone trends, Atmos. Environ., 41, 7127–7137, https://doi.org/10.1016/j.atmosenv.2007.04.061, 2007. a
Cavaliere, A.: Code and dataset for: Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of NO2 and O3 Sensors, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7826791, 2023. a
Cavaliere, A., Carotenuto, F., Di Gennaro, F., Gioli, B., Gualtieri, G., Martelli, F., Matese, A., Toscano, P., Vagnoli, C., and Zaldei, A.: Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors, Sensors, 18, 2843, https://doi.org/10.3390/s18092843, 2018. a
Chakraborty, S., Mittermaier, S., Carbonelli, C., and Servadei, L.: Explainable AI for Gas Sensors, in: 2022 IEEE Sensors, 1–4, IEEE, https://doi.org/10.1109/SENSORS52175.2022.9967180, 2022. a
Claveau, C., Giraudon, M., Coville, B., Saussac, A., Eymard, L., Turcati, L., and Payan, S.: Performance comparison between electrochemical and semiconductors sensors for the monitoring of O3, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-75, 2022. a
Concas, F., Mineraud, J., Lagerspetz, E., Varjonen, S., Liu, X., Puolamäki, K., Nurmi, P., and Tarkoma, S.: Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis, ACM Transactions on Sensor Networks, 17, 1–44, https://doi.org/10.1145/3446005, 2021. a, b, c, d, e
Cook, R. D.: Detection of Influential Observation in Linear Regression, Technometrics, 19, 15–18, https://doi.org/10.1080/00401706.1977.10489493, 1977. a
Cordero, J. M., Borge, R., and Narros, A.: Using statistical methods to carry out in field calibrations of low cost air quality sensors, Sensor. Actuator. B, 267, 245–254, https://doi.org/10.1016/j.snb.2018.04.021, 2018. a
De Vito, S., Piga, M., Martinotto, L., and Di Francia, G.: CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization, Sensor. Actuator. B, 143, 182–191, https://doi.org/10.1016/j.snb.2009.08.041, 2009. a
De Vito, S., Di Francia, G., Esposito, E., Ferlito, S., Formisano, F., and Massera, E.: Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices, Pattern Recogn. Lett., 136, 264–271, https://doi.org/10.1016/j.patrec.2020.04.032, 2020. a
Dekking, F. M., Kraaikamp, C., Lopuhaä, H. P., and Meester, L. E.: A Modern Introduction to Probability and Statistics, Springer Texts in Statistics, Springer London, London, https://doi.org/10.1007/1-84628-168-7, 2005. a
Di Lonardo, S., Zaldei, A., Toscano, P., Matese, A., Gioli, B., Rocchi, L., Vagnoli, C., De Filippis, T., Gualtieri, G., and Martelli, F.: The SensorWebBike for air quality monitoring in a smart city, in: IET Conference on Future Intelligent Cities, 2, Institution of Engineering and Technology, London, UK, https://doi.org/10.1049/ic.2014.0043, 2014. a
Draper, N. R. and Smith, H.: Applied regression analysis, Wiley series in probability and statistics, Wiley, New York, 3rd edn., ISBN 978-0-471-17082-2, 1998. a
Esposito, E., Salvato, M., Vito, S. D., Fattoruso, G., Castell, N., Karatzas, K., and Francia, G. D.: Assessing the Relocation Robustness of on Field Calibrations for Air Quality Monitoring Devices, in: Sensors and Microsystems, Lecture Notes in Electrical Engineering, edited by: Leone, A., Forleo, A., Francioso, L., Capone, S., Siciliano, P., and Di Natale, C., Springer International Publishing, Cham, 457, 303–312, https://doi.org/10.1007/978-3-319-66802-4_38, 2018. a
Fine, G. F., Cavanagh, L. M., Afonja, A., and Binions, R.: Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring, Sensors, 10, 5469–5502, https://doi.org/10.3390/s100605469, 2010. a
Gäbel, P., Koller, C., and Hertig, E.: Development of Air Quality Boxes Based on Low-Cost Sensor Technology for Ambient Air Quality Monitoring, Sensors, 22, 3830, https://doi.org/10.3390/s22103830, 2022. a, b
Gu, K., Qiao, J., and Lin, W.: Recurrent Air Quality Predictor Based on Meteorology- and Pollution-Related Factors, IEEE T. Ind. Inform., 14, 3946–3955, https://doi.org/10.1109/TII.2018.2793950, 2018. a
Han, P., Mei, H., Liu, D., Zeng, N., Tang, X., Wang, Y., and Pan, Y.: Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2, Sensors, 21, 256, https://doi.org/10.3390/s21010256, 2021. a
Han, S., Bian, H., Feng, Y., Liu, A., Li, X., Zeng, F., and Zhang, X.: Analysis of the Relationship between O3, NO and NO2 in Tianjin, China, Aerosol Air Qual. Res., 11, 128–139, https://doi.org/10.4209/aaqr.2010.07.0055, 2011. a, b
Holstius, D. M., Pillarisetti, A., Smith, K. R., and Seto, E.: Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California, Atmos. Meas. Tech., 7, 1121–1131, https://doi.org/10.5194/amt-7-1121-2014, 2014. a
Idrees, Z. and Zheng, L.: Low cost air pollution monitoring systems: A review of protocols and enabling technologies, Journal of Industrial Information Integration, 17, 100123, https://doi.org/10.1016/j.jii.2019.100123, 2020. a
Johnson, N. E., Bonczak, B., and Kontokosta, C. E.: Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment, Atmos. Environ., 184, 9–16, https://doi.org/10.1016/j.atmosenv.2018.04.019, 2018. a, b
Karagulian, F.: New Challenges in Air Quality Measurements, in: Air Quality Networks, Environmental Informatics and Modeling, edited by: De Vito, S., Karatzas, K., Bartonova, A., and Fattoruso, G., Springer International Publishing, Cham, https://doi.org/10.1007/978-3-031-08476-8_1, 1–18, 2023. a
Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., and Borowiak, A.: Review of the Performance of Low-Cost Sensors for Air Quality Monitoring, Atmosphere, 10, 506, https://doi.org/10.3390/atmos10090506, 2019. a, b
Lin, Y., Dong, W., and Chen, Y.: Calibrating Low-Cost Sensors by a Two-Phase Learning Approach for Urban Air Quality Measurement, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2, 1–18, https://doi.org/10.1145/3191750, 2018. a, b
Lundberg, S. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, Advances in neural information processing systems, arXiv, 2, https://doi.org/10.48550/ARXIV.1705.07874, 2017. a, b
Lundberg, S. and Lee, S.-I.: SHAP, https://shap.readthedocs.io/en/latest/ (last access: 16 June 2022), 2022. a
Maag, B., Saukh, O., Hasenfratz, D., and Thiele, L.: Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors., in: EWSN, 169–180, 2016. a
Maag, B., Zhou, Z., and Thiele, L.: A Survey on Sensor Calibration in Air Pollution Monitoring Deployments, IEEE Internet Things, 5, 4857–4870, https://doi.org/10.1109/JIOT.2018.2853660, 2018. a
Malings, C., Tanzer, R., Hauryliuk, A., Kumar, S. P. N., Zimmerman, N., Kara, L. B., Presto, A. A., and R. Subramanian: Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring, Atmos. Meas. Tech., 12, 903–920, https://doi.org/10.5194/amt-12-903-2019, 2019. a
Masson, N., Piedrahita, R., and Hannigan, M.: Approach for quantification of metal oxide type semiconductor gas sensors used for ambient air quality monitoring, Sensor. Actuator. B, 208, 339–345, https://doi.org/10.1016/j.snb.2014.11.032, 2015. a, b
Maxim Integrated: Datasheet DS18B20, https://datasheets.maximintegrated.com/en/ds/DS18B20.pdf, last access: 29 September 2021. a
Mead, M., Popoola, O., Stewart, G., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J., McLeod, M., Hodgson, T., Dicks, J., Lewis, A., Cohen, J., Baron, R., Saffell, J., and Jones, R.: The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks, Atmos. Environ., 70, 186–203, https://doi.org/10.1016/j.atmosenv.2012.11.060, 2013. a
Meng, X., Liu, C., Chen, R., Sera, F., Vicedo-Cabrera, A. M., Milojevic, A., Guo, Y., Tong, S., Coelho, M. D. S. Z. S., and Saldiva, P. H. N.: Short Term Associations of Ambient Nitrogen Dioxide with Daily Total, Cardiovascular, and Respiratory Mortality: Multilocation Analysis in 398 Cities, BMJ, n534, https://doi.org/10.1136/bmj.n534, 2021. a
Miech, J., Stanton, L., Gao, M., Micalizzi, P., Uebelherr, J., Herckes, P., and Fraser, M.: Calibration of Low-Cost NO2 Sensors through Environmental Factor Correction, Toxics, 9, 281, https://doi.org/10.3390/toxics9110281, 2021. a
Mijling, B., Jiang, Q., de Jonge, D., and Bocconi, S.: Field calibration of electrochemical NO2 sensors in a citizen science context, Atmos. Meas. Tech., 11, 1297–1312, https://doi.org/10.5194/amt-11-1297-2018, 2018. a
Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S., Jayaratne, R., Kumar, P., Lau, A. K., Louie, P. K., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., Rahman, M. M., Ristovski, Z., Shafiei, M., Tjondronegoro, D., Westerdahl, D., and Williams, R.: Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?, Environ. Int., 116, 286–299, https://doi.org/10.1016/j.envint.2018.04.018, 2018. a
Mueller, M., Meyer, J., and Hueglin, C.: Design of an ozone and nitrogen dioxide sensor unit and its long-term operation within a sensor network in the city of Zurich, Atmos. Meas. Tech., 10, 3783–3799, https://doi.org/10.5194/amt-10-3783-2017, 2017. a
Narayana, M. V., Jalihal, D., and Nagendra, S. M. S.: Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art, Sensors, 22, 394, https://doi.org/10.3390/s22010394, 2022. a, b, c, d
Nowack, P., Konstantinovskiy, L., Gardiner, H., and Cant, J.: Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability, Atmos. Meas. Tech., 14, 5637–5655, https://doi.org/10.5194/amt-14-5637-2021, 2021. a, b, c
Nuvolone, D., Petri, D., and Voller, F.: The Effects of Ozone on Human Health, Environ. Sci. Pollut. Res., 25, 8074–8088, https://doi.org/10.1007/s11356-017-9239-3, 2018. a
Pancholi, P., Kumar, A., Bikundia, D. S., and Chourasiya, S.: An observation of seasonal and diurnal behavior of O3–NOx relationships and local/regional oxidant (OX = O3 + NO2) levels at a semi-arid urban site of western India, Sustain. Environ. Res., 28, 79–89, https://doi.org/10.1016/j.serj.2017.11.001, 2018. a
Peterson, P., Aujla, A., Grant, K., Brundle, A., Thompson, M., Vande Hey, J., and Leigh, R.: Practical Use of Metal Oxide Semiconductor Gas Sensors for Measuring Nitrogen Dioxide and Ozone in Urban Environments, Sensors, 17, 1653, https://doi.org/10.3390/s17071653, 2017. a
Piedrahita, R., Xiang, Y., Masson, N., Ortega, J., Collier, A., Jiang, Y., Li, K., Dick, R. P., Lv, Q., Hannigan, M., and Shang, L.: The next generation of low-cost personal air quality sensors for quantitative exposure monitoring, Atmos. Meas. Tech., 7, 3325–3336, https://doi.org/10.5194/amt-7-3325-2014, 2014. a
Rai, A. C., Kumar, P., Pilla, F., Skouloudis, A. N., Di Sabatino, S., Ratti, C., Yasar, A., and Rickerby, D.: End-user perspective of low-cost sensors for outdoor air pollution monitoring, Sci. Total Environ., 607-608, 691–705, https://doi.org/10.1016/j.scitotenv.2017.06.266, 2017. a
Sahu, R., Nagal, A., Dixit, K. K., Unnibhavi, H., Mantravadi, S., Nair, S., Simmhan, Y., Mishra, B., Zele, R., Sutaria, R., Motghare, V. M., Kar, P., and Tripathi, S. N.: Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O3 and NO2 concentrations in diverse environments, Atmos. Meas. Tech., 14, 37–52, https://doi.org/10.5194/amt-14-37-2021, 2021. a
Sales-Lérida, D., Bello, A. J., Sánchez-Alzola, A., and Martínez-Jiménez, P. M.: An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis, Sensors, 21, 4781, https://doi.org/10.3390/s21144781, 2021. a
Sayahi, T., Garff, A., Quah, T., Lê, K., Becnel, T., Powell, K. M., Gaillardon, P.-E., Butterfield, A. E., and Kelly, K. E.: Long-term calibration models to estimate ozone concentrations with a metal oxide sensor, Environ. Pollut., 267, 115363, https://doi.org/10.1016/j.envpol.2020.115363, 2020. a
Schmitz, S., Towers, S., Villena, G., Caseiro, A., Wegener, R., Klemp, D., Langer, I., Meier, F., and von Schneidemesser, E.: Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors, Atmos. Meas. Tech., 14, 7221–7241, https://doi.org/10.5194/amt-14-7221-2021, 2021. a
Seabold, S. and Perktold, J.: statsmodels: Econometric and statistical modeling with python, in: 9th Python in Science Conference, Austin, Texas, 28–30 June 2021, https://doi.org/10.25080/Majora-92bf1922-011, 2010. a
Sensortech, S. G. X.: Datasheet MiCS-2714, https://www.sgxsensortech.com/content/uploads/2014/08/1107_Datasheet-MiCS-2714.pdf (last access: 29 September 2021), a. a
Sensortech, S. G. X.: Datasheet MiCS-2614, https://sensorsandpower.angst-pfister.com/fileadmin/products/datasheets/188/MOS-Ozone-MiCS-2614_1620-21530-0006-E-0714.pdf (last access: 29 September 2021), b. a
Shekhar, S., Bhagat, S., Kunjithapatham, S., and Kolluri, B. K.: Dominance-Analysis, https://github.com/dominance-analysis/dominance-analysis, (last access: 29 September 2022), 2019. a
Smets, K., Verdonk, B., and Jordaan, E. M.: Evaluation of performance measures for SVR hyperparameter selection, in: 2007 International Joint Conference on Neural Networks, IEEE, 637–642, 2007. a
Spiess, A.-N. and Neumeyer, N.: An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach, BMC Pharmacology, 10, 6, https://doi.org/10.1186/1471-2210-10-6, 2010. a
Spinelle, L., Aleixandre, M., and Gerboles, M.: Protocol of evaluation and calibration of low-cost gas sensors for the monitoring of air pollution, Publications Office of the European Union, Luxembourg, https://doi.org/10.2788/9916, 2013. a
Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and Bonavitacola, F.: Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide, Sensor. Actuator. B, 215, 249–257, https://doi.org/10.1016/j.snb.2015.03.031, 2015. a, b, c
Spinelle, L., Gerboles, M., Aleixandre, M., and Bonavitacola, F.: Evaluation of metal oxides sensors for the monitoring of O3 in ambient air at ppb level, Chem. Engineer. Trans., 54, 319–324, https://doi.org/10.3303/CET1654054, 2016. a
Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and Bonavitacola, F.: Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Part B: NO, CO and CO2, Sensor. Actuator. B, 238, 706–715, https://doi.org/10.1016/j.snb.2016.07.036, 2017. a, b
TeamHG-Memex: Eli5, https://eli5.readthedocs.io/en/latest/ (last access: 25 October 2022), 2022. a
Vega García, M. and Aznarte, J. L.: Shapley additive explanations for NO2 forecasting, Ecol. Inform., 56, 101039, https://doi.org/10.1016/j.ecoinf.2019.101039, 2020. a
Wang, A., Machida, Y., deSouza, P., Mora, S., Duhl, T., Hudda, N., Durant, J. L., Duarte, F., and Ratti, C.: Leveraging Machine Learning Algorithms to Advance Low-Cost Air Sensor Calibration in Stationary and Mobile Settings, Atmos. Environ., 301, 119692, https://doi.org/10.1016/j.atmosenv.2023.119692, 2023. a
Wang, C., Yin, L., Zhang, L., Xiang, D., and Gao, R.: Metal Oxide Gas Sensors: Sensitivity and Influencing Factors, Sensors, 10, 2088–2106, https://doi.org/10.3390/s100302088, 2010. a
Wei, P., Ning, Z., Ye, S., Sun, L., Yang, F., Wong, K., Westerdahl, D., and Louie, P.: Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring, Sensors, 18, 59, https://doi.org/10.3390/s18020059, 2018. a
Williams, D. E., Henshaw, G. S., Bart, M., Laing, G., Wagner, J., Naisbitt, S., and Salmond, J. A.: Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network, Meas. Sci. Technol., 24, 065803, https://doi.org/10.1088/0957-0233/24/6/065803, 2013. a
World Health Organization: WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, World Health Organization, Geneva, https://apps.who.int/iris/handle/10665/345329 (last access: 26 September 2022), section: xxi, 273 pp., 2021. a
Yang, J.: Fast TreeSHAP: Accelerating SHAP Value Computation for Trees, arXiv, 3, https://doi.org/10.48550/ARXIV.2109.09847, 2021. a
Yang, J.: FastTreeSHAP, https://github.com/linkedin/FastTreeSHAP (last access: 29 November 2022), 2022. a
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., and Vagnoli, C.: An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories, Transport. Res. Proced., 24, 531–538, https://doi.org/10.1016/j.trpro.2017.06.002, 2017. a
Zauli-Sajani, S., Marchesi, S., Pironi, C., Barbieri, C., Poluzzi, V., and Colacci, A.: Assessment of air quality sensor system performance after relocation, Atmos. Pollut. Res., 12, 282–291, https://doi.org/10.1016/j.apr.2020.11.010, 2021. a
Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., and Subramanian, R.: A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring, Atmos. Meas. Tech., 11, 291–313, https://doi.org/10.5194/amt-11-291-2018, 2018. a, b
Short summary
We assessed calibration models for two low-cost stations equipped with O3 and NO2 metal oxide sensors. Environmental parameters had improved accuracy in linear and black box models. Moreover, interpretability methods like SHapley Additive exPlanations helped identify the physical patterns and potential problems of these models in a field validation. Results showed both sensors performed well with the same linear model form, but unique coefficients were required for intersensor variability.
We assessed calibration models for two low-cost stations equipped with O3 and NO2 metal oxide...
