{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T02:43:01Z","timestamp":1776220981292,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2013Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["UIDB\/00319\/2020"],"award-info":[{"award-number":["UIDB\/00319\/2020"]}]},{"name":"FCT\u2013Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["UID\/04047\/2020"],"award-info":[{"award-number":["UID\/04047\/2020"]}]},{"name":"FCT\u2013Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the monitoring of carbon dioxide is an important proxy to estimate the air quality of indoor and outdoor environments, it is essential to obtain trustful data from CO2 sensors. However, the use of widely available low-cost sensors may imply lower data quality, especially regarding accuracy. This paper proposes a new approach for enhancing the accuracy of low-cost CO2 sensors using an extremely randomized trees algorithm. It also reports the results obtained from experimental data collected from sensors that were exposed to both indoor and outdoor environments. The indoor experimental set was composed of two metal oxide semiconductors (MOS) and two non-dispersive infrared (NDIR) sensors next to a reference sensor for carbon dioxide and independent sensors for air temperature and relative humidity. The outdoor experimental exposure analysis was performed using a third-party dataset which fit into our goals: the work consisted of fourteen stations using low-cost NDIR sensors geographically spread around reference stations. One calibration model was trained for each sensor unit separately, and, in the indoor experiment, it managed to reduce the mean absolute error (MAE) of NDIR sensors by up to 90%, reach very good linearity with MOS sensors in the indoor experiment (r2 value of 0.994), and reduce the MAE by up to 98% in the outdoor dataset. We have found in the outdoor dataset analysis that the exposure time of the sensor itself may be considered by the algorithm to achieve better accuracy. We also observed that even a relatively small amount of data may provide enough information to perform a useful calibration if they contain enough data variety. We conclude that the proper use of machine learning algorithms on sensor readings can be very effective to obtain higher data quality from low-cost gas sensors either indoors or outdoors, regardless of the sensor technology.<\/jats:p>","DOI":"10.3390\/s23136153","type":"journal-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:53:04Z","timestamp":1688518384000},"page":"6153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1766-5768","authenticated-orcid":false,"given":"Tiago","family":"Ara\u00fajo","sequence":"first","affiliation":[{"name":"Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Parnamirim 59124-455, Brazil"},{"name":"Algoritmi Research Centre, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0199-8664","authenticated-orcid":false,"given":"L\u00edgia","family":"Silva","sequence":"additional","affiliation":[{"name":"CTAC Research Centre, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6020-8087","authenticated-orcid":false,"given":"Ana","family":"Aguiar","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8967-118X","authenticated-orcid":false,"given":"Adriano","family":"Moreira","sequence":"additional","affiliation":[{"name":"Algoritmi Research Centre, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"ref_1","unstructured":"Goldman, J., Shilton, K., Burke, J., Estrin, D., Hansen, M., Ramanathan, N., Reddy, S., and Samanta, V. (2023, July 03). Participatory Sensing\u2014A Citizen-Powered Approach to Illuminating the Patterns That Shape Our World 2009. Available online: https:\/\/www.wilsoncenter.org\/sites\/default\/files\/media\/documents\/publication\/participatory_sensing.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.pmcj.2012.09.002","article-title":"Participatory Noise Mapping Works! An Evaluation of Participatory Sensing as an Alternative to Standard Techniques for Environmental Monitoring","volume":"9","author":"Stevens","year":"2013","journal-title":"Pervasive Mob. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7767","DOI":"10.1109\/JSEN.2017.2722819","article-title":"Environmental Monitoring for Smart Cities","volume":"17","author":"Manlio","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/JIOT.2018.2791522","article-title":"PortoLivingLab: An IoT-Based Sensing Platform for Smart Cities","volume":"5","author":"Santos","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rivas-S\u00e1nchez, Y.A., Moreno-P\u00e9rez, M.F., and Rold\u00e1n-Ca\u00f1as, J. (2019). Environment Control with Low-Cost Microcontrollers and Microprocessors: Application for Green Walls. Sustainability, 11.","DOI":"10.3390\/su11030782"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5656245","DOI":"10.1155\/2016\/5656245","article-title":"Citizen Sensing for Improved Urban Environmental Monitoring","volume":"2016","author":"Jiang","year":"2016","journal-title":"J. Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sun, C.Y., Kato, S., and Gou, Z. (2019). Application of Low-Cost Sensors for Urban Heat Island Assessment: A Case Study in Taiwan. Sustainability, 11.","DOI":"10.3390\/su11102759"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1109\/TMC.2015.2404786","article-title":"Urban Resolution: New Metric for Measuring the Quality of Urban Sensing","volume":"14","author":"Liu","year":"2015","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.envint.2014.11.019","article-title":"The Rise of Low-Cost Sensing for Managing Air Pollution in Cities","volume":"75","author":"Kumar","year":"2015","journal-title":"Environ. Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scs.2012.03.001","article-title":"City Noise-Air: An Environmental Quality Index for Cities","volume":"4","author":"Silva","year":"2012","journal-title":"Sustain. Cities Soc."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Spirjakin, D., Baranov, A., Karelin, A., and Somov, A. (2015, January 9\u201310). Wireless Multi-Sensor Gas Platform for Environmental Monitoring. Proceedings of the 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2015 Proceedings, Trento, Italy.","DOI":"10.1109\/EESMS.2015.7175883"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1109\/JSEN.2015.2499308","article-title":"Design and Evaluation of a Metropolitan Air Pollution Sensing System","volume":"16","author":"Hu","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gryech, I., Ben-Aboud, Y., Guermah, B., Sbihi, N., Ghogho, M., and Kobbane, A. (2020). Moreair: A Low-Cost Urban Air Pollution Monitoring System. Sensors, 20.","DOI":"10.3390\/s20040998"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., and Borowiak, A. (2019). Review of the Performance of Low-Cost Sensors for Air Quality Monitoring. Atmosphere, 10.","DOI":"10.3390\/atmos10090506"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Russell, H.S., Frederickson, L.B., Kwiatkowski, S., Emygdio, A.P.M., Kumar, P., Schmidt, J.A., Hertel, O., and Johnson, M.S. (2022). Enhanced Ambient Sensing Environment\u2014A New Method for Calibrating Low-Cost Gas Sensors. Sensors, 22.","DOI":"10.3390\/s22197238"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5281","DOI":"10.5194\/amt-9-5281-2016","article-title":"Community Air Sensor Network (CAIRSENSE) Project: Evaluation of Low-Cost Sensor Performance in a Suburban Environment in the Southeastern United States","volume":"9","author":"Jiao","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1007\/s10661-019-7231-8","article-title":"Analysing the Performance of Low-Cost Air Quality Sensors, Their Drivers, Relative Benefits and Calibration in Cities\u2014A Case Study in Sheffield","volume":"191","author":"Munir","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gaynullin, B., Hummelg\u00e5rd, C., Mattsson, C., Thungstr\u00f6m, G., and R\u00f6djeg\u00e5rd, H. (2023). Advanced Pressure Compensation in High Accuracy NDIR Sensors for Environmental Studies. Sensors, 23.","DOI":"10.3390\/s23052872"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Duvall, R., Long, R., Beaver, M., Kronmiller, K., Wheeler, M., and Szykman, J. (2016). Performance Evaluation and Community Application of Low-Cost Sensors for Ozone and Nitrogen Dioxide. Sensors, 16.","DOI":"10.3390\/s16101698"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"373","DOI":"10.5194\/jsss-7-373-2018","article-title":"Field Evaluation of a Low-Cost Indoor Air Quality Monitor to Quantify Exposure to Pollutants in Residential Environments","volume":"7","author":"Sharpe","year":"2018","journal-title":"J. Sens. Sens. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yamamoto, K., Togami, T., Yamaguchi, N., and Ninomiya, S. (2017). Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data. Sensors, 17.","DOI":"10.3390\/s17061290"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"291","DOI":"10.5194\/amt-11-291-2018","article-title":"A Machine Learning Calibration Model Using Random Forests to Improve Sensor Performance for Lower-Cost Air Quality Monitoring","volume":"11","author":"Zimmerman","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.icte.2020.06.004","article-title":"Improving Data Quality of Low-Cost IoT Sensors in Environmental Monitoring Networks Using Data Fusion and Machine Learning Approach","volume":"6","author":"Okafor","year":"2020","journal-title":"ICT Express"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.envint.2016.12.007","article-title":"Can Commercial Low-Cost Sensor Platforms Contribute to Air Quality Monitoring and Exposure Estimates?","volume":"99","author":"Castell","year":"2017","journal-title":"Environ. Int."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.buildenv.2014.12.011","article-title":"CO2 Based Occupancy Detection Algorithm: Experimental Analysis and Validation for Office and Residential Buildings","volume":"86","author":"Matthes","year":"2015","journal-title":"Build. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.enbuild.2016.09.002","article-title":"Indoor Occupancy Estimation from Carbon Dioxide Concentration","volume":"131","author":"Jiang","year":"2016","journal-title":"Energy Build."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.enbuild.2017.04.080","article-title":"Occupancy Determination Based on Time Series of CO2 Concentration, Temperature and Relative Humidity","volume":"147","author":"Szczurek","year":"2017","journal-title":"Energy Build."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1289\/ehp.1510037","article-title":"Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation, and Volatile Organic Compound Exposures in Office Workers: A Controlled Exposure Study of Green and Conventional Office Environments","volume":"124","author":"Allen","year":"2016","journal-title":"Environ. Health Perspect."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1289\/ehp.1104789","article-title":"Is CO2 an Indoor Pollutant? Direct Effects of Low-to-Moderate CO2 Concentrations on Human Decision-Making Performance","volume":"120","author":"Satish","year":"2012","journal-title":"Environ. Health Perspect."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1111\/j.1600-0668.1999.00003.x","article-title":"Association of Ventilation Rates and CO2 Concentrations with Health and Other Responses in Commercial and Institutional Buildings","volume":"9","author":"Fisk","year":"1999","journal-title":"Indoor Air"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e2019GH000237","DOI":"10.1029\/2019GH000237","article-title":"Fossil Fuel Combustion Is Driving Indoor CO2 Toward Levels Harmful to Human Cognition","volume":"4","author":"Karnauskas","year":"2020","journal-title":"Geohealth"},{"key":"ref_32","first-page":"107","article-title":"The Effect of Low Ventilation Rates on the Cognitive Function of a Primary School Class","volume":"6","author":"Coley","year":"2007","journal-title":"Int. J. Vent."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.3390\/s100302088","article-title":"Metal Oxide Gas Sensors: Sensitivity and Influencing Factors","volume":"10","author":"Wang","year":"2010","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"286","DOI":"10.3390\/iot1020017","article-title":"Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context","volume":"1","author":"Silva","year":"2020","journal-title":"IoT"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_36","unstructured":"(2023, July 03). Hanwei Electronics MG811 Datasheet 2016. Available online: https:\/\/www.alldatasheet.com\/datasheet-pdf\/pdf\/221116\/SUMIDA\/MG811.html."},{"key":"ref_37","unstructured":"Zhengzhou Winsen Electronics Technology (2023, July 03). MH-Z16 Datasheet. Available online: https:\/\/www.winsen-sensor.com\/d\/files\/MH-Z16.pdf."},{"key":"ref_38","unstructured":"Vaisala (2023, July 03). GM70 Hand-Held Carbon Dioxide Meter for Spot-Checking Applications. Available online: https:\/\/docs.vaisala.com\/v\/u\/B210824EN-G\/en-US."},{"key":"ref_39","unstructured":"(2016, June 28). Lascar Electronics Certificate of Calibration. Available online: http:\/\/www.lascarelectronics.com\/pdf-usb-datalogging\/data-logger0800188001331301358.pdf."},{"key":"ref_40","unstructured":"(2023, June 28). Scikit-Learn Scikit-Learn. Available online: https:\/\/scikit-learn.org\/stable\/."},{"key":"ref_41","unstructured":"Johnson, M., and Nguyen, D.Q. (2020, December 12). How Much Data Is Enough?. Available online: http:\/\/web.science.mq.edu.au\/~mjohnson\/papers\/Johnson17Power-talk.pdf."},{"key":"ref_42","first-page":"12","article-title":"An Evaluation of Training Size Impact on Validation Accuracy for Optimized Convolutional Neural Networks","volume":"1","author":"Tschannen","year":"2018","journal-title":"SMU Data Sci. Rev."},{"key":"ref_43","unstructured":"(2021, March 24). Scikit-Learn ExtraTrees Regressor Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.ExtraTreesRegressor.html."},{"key":"ref_44","unstructured":"Portugal Minist\u00e9rio do Ambiente, do O. do T. e E., Minist\u00e9rio da Sa\u00fade e da Solidariedade, and Minist\u00e9rio do Emprego e Seguran\u00e7a (2013). Portaria No 323-A\/2013: Regulamento de Desempenho Energ\u00e9tico dos Edif\u00edcios de Com\u00e9rcio e Servi\u00e7os (Recs) Requisitos de Ventila\u00e7\u00e3o e Qualidade do ar Interior. Di\u00e1rio Da Rep\u00fablica, 1, 6644(2)\u20136644(9)."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Eisa, S., and Moreira, A. (2017). A Behaviour Monitoring System (BMS) for Ambient Assisted Living. Sensors, 17.","DOI":"10.3390\/s17091946"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3896\/IBRA.1.48.1.13","article-title":"The Effect of Different Concentrations of Carbon Dioxide (CO2) in a Mixture with Air or Nitrogen upon the Survival of the Honey Bee (Apis mellifera)","volume":"48","year":"2009","journal-title":"J. Apic. Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cecchi, S., Spinsante, S., Terenzi, A., and Orcioni, S. (2020). A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring. Sensors, 20.","DOI":"10.3390\/s20092726"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6153\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:06:14Z","timestamp":1760126774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,4]]},"references-count":47,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136153"],"URL":"https:\/\/doi.org\/10.3390\/s23136153","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,4]]}}}