{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:00:27Z","timestamp":1775026827657,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004564","name":"Ministarstvo Prosvete, Nauke i Tehnolo\u0161kog Razvoja","doi-asserted-by":"publisher","award":["\/"],"award-info":[{"award-number":["\/"]}],"id":[{"id":"10.13039\/501100004564","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93\u20130.97, 0.82\u20130.94 and 0.73\u20130.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.<\/jats:p>","DOI":"10.3390\/s21103338","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T22:53:40Z","timestamp":1620773620000},"page":"3338","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Ivan","family":"Vajs","sequence":"first","affiliation":[{"name":"Innovation Center, School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"},{"name":"School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-1314-6191","authenticated-orcid":false,"given":"Dejan","family":"Drajic","sequence":"additional","affiliation":[{"name":"Innovation Center, School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"},{"name":"School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"},{"name":"DunavNET, DNET Labs, Trg Oslobodjenja 127, 21000 Novi Sad, Serbia"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-9054-2799","authenticated-orcid":false,"given":"Nenad","family":"Gligoric","sequence":"additional","affiliation":[{"name":"DunavNET, DNET Labs, Trg Oslobodjenja 127, 21000 Novi Sad, Serbia"},{"name":"Faculty of Information Technology, Alfa BK University, Palmira Toljatija 3, 11070 Novi Beograd, Serbia"}]},{"given":"Ilija","family":"Radovanovic","sequence":"additional","affiliation":[{"name":"Innovation Center, School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"},{"name":"School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-1398-8452","authenticated-orcid":false,"given":"Ivan","family":"Popovic","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"ref_1","unstructured":"(2021, March 14). State of World Population 2007. Unleashing the Potential of Urban Growth, United Nations Population Fund (UNFPA), Online Report. Available online: https:\/\/linproxy.fan.workers.dev:443\/http\/www.unfpa.org\/public\/publications\/pid\/408."},{"key":"ref_2","unstructured":"World Health Organization (WHO) (2021, March 14). Global Health Observatory (GHO) data. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.who.int\/gho\/urban_health\/situation_trends\/urban_population_growth_text\/en\/."},{"key":"ref_3","unstructured":"(2021, March 14). The World\u2019s Cities in 2016. Data Booklet. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.un.org\/en\/development\/desa\/population\/publications\/pdf\/urbanization\/the_worlds_cities_in_2016_data_booklet.pdf."},{"key":"ref_4","unstructured":"WHO (2016). Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease, WHO Document Production Services."},{"key":"ref_5","unstructured":"(2021, March 14). Air Quality Guidelines for Europe, 2nd ed; WHO Regional Publications, European Series, No 91. Available online: www.euro.who.int\/document\/e71922.pdf."},{"key":"ref_6","unstructured":"(2021, March 14). Directive 2008\/50\/EC of the European Parliament and of the Council of 21 May 2008 on Ambient Air Quality and Cleaner Air for Europe OJ L 152, 11.6.2008, p. 1\u201344. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/eur-lex.europa.eu\/legal-content\/en\/ALL\/?uri=CELEX%3A32008L0050."},{"key":"ref_7","unstructured":"(2021, March 14). Directive 2015\/1480\/EC of the European Parliament and of the Council of 28 August 2015 on Ambient Air Quality and Cleaner air for Europe. O. J. Eur. Union, 2015, O. J. L 226, 29.8.2015, p. 4\u201311. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=celex:32015L1480."},{"key":"ref_8","unstructured":"(2021, April 21). Air Quality Monitoring System. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.horiba.com\/uk\/process-environmental\/products\/system-engineering\/air-quality-monitoring-system\/."},{"key":"ref_9","unstructured":"(2021, March 14). ekoNET Air Quality Device. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/ekonet.solutions\/air-monitoring\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/JSEN.2008.917477","article-title":"An environmental air pollution monitoring system based on the IEEE 1451 standard for low cost requirements","volume":"8","author":"Kularatna","year":"2008","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32","DOI":"10.5755\/j01.eie.26.2.25734","article-title":"Reliable Low-Cost Air Quality Monitoring Using Off-The-Shelf Sensors and Statistical Calibration","volume":"26","author":"Drajic","year":"2020","journal-title":"Elektronika Elektrotechnika"},{"key":"ref_12","unstructured":"(2021, March 14). CiteairII: Common Information to European Air. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.airqualitynow.eu\/download\/CITEAIRComparing_Urban_Air_Quality_across_Borders.pdf."},{"key":"ref_13","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":"Atmosph. Meas. Tech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.5194\/amt-11-3717-2018","article-title":"Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application","volume":"11","author":"Bigi","year":"2018","journal-title":"Atmosph. Meas. Tech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.snb.2017.07.155","article-title":"Calibrating chemical multi-sensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches","volume":"255","author":"Esposito","year":"2018","journal-title":"Sens. Actuat. B Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.atmosenv.2018.04.019","article-title":"Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment","volume":"184","author":"Johnson","year":"2018","journal-title":"Atmosp. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.snb.2015.03.031","article-title":"Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide","volume":"215","author":"Spinelle","year":"2015","journal-title":"Sens. Actuat. B Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.snb.2016.07.036","article-title":"Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part B: NO, CO and CO2","volume":"238","author":"Spinelle","year":"2017","journal-title":"Sens. Actuat. B Chem."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.atmosenv.2019.06.028","article-title":"In search of an optimal calibration method of low-cost gas sensors for ambient air pollutants: Comparison of linear, multilinear and artificial neural network approaches","volume":"213","author":"Topalovic","year":"2019","journal-title":"Atmosp. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.snb.2018.04.021","article-title":"Using statistical methods to carry out in field calibrations of low cost air quality sensors","volume":"267","author":"Cordero","year":"2018","journal-title":"Sens. Actuat. B Chem."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Di Antonio, A., Popoola, O.A.M., Ouyang, B., Saffell, J., and Jones, R.L. (2018). Developing a relative humidity correction for low-cost sensors measuring ambient particulate matter. Sensors, 18.","DOI":"10.3390\/s18092790"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MCOM.001.1900515","article-title":"Toward Massive Scale Air Quality Monitoring","volume":"58","author":"Motlagh","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MPRV.2019.2925600","article-title":"Pervasive Data Science on the Edge","volume":"18","author":"Lagerspetz","year":"2019","journal-title":"IEEE Perv. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alhasa, K.M., Mohd Nadzir, M.S., Olalekan, P., Latif, M.T., Yusup, Y., Iqbal Faruque, M.R., Ahamad, F., Aiyub, K., Md Ali, S.H., and Khan, M.F. (2018). Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System. Sensors, 18.","DOI":"10.3390\/s18124380"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4883","DOI":"10.5194\/amt-11-4883-2018","article-title":"The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog","volume":"11","author":"Jayaratne","year":"2018","journal-title":"Atmosp. Meas. Tech."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Samad, A., Obando Nu\u00f1ez, D.R., Solis Castillo, G.C., Laquai, B., and Vogt, U. (2020). Effect of Relative Humidity and Air Temperature on the Results Obtained from Low-Cost Gas Sensors for Ambient Air Quality Measurements. Sensors, 20.","DOI":"10.3390\/s20185175"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.atmosenv.2014.11.002","article-title":"Evaluation and calibration of Aeroqual series 500 portable gas sensors for accurate measurement of ambient ozone and nitrogen dioxide","volume":"100","author":"Lin","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gao, Y., Dong, W., Guo, K., Liu, X., Chen, Y., Liu, X., Bu, J., and Chen, C. (2016, January 10\u201314). Mosaic: A low-cost mobile sensing system for urban air quality monitoring. Proceedings of the 35th Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2016), San Francisco, CA, USA.","DOI":"10.1109\/INFOCOM.2016.7524478"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.atmosenv.2018.08.028","article-title":"Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise\u2014Part II","volume":"193","author":"Borrego","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1016\/j.snb.2016.03.038","article-title":"Dynamic neural network architectures for on field stochastic calibration of indicative low cost air quality sensing systems","volume":"231","author":"Esposito","year":"2016","journal-title":"Sens. Actuat. B Chem."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Li, X., Li, Z., Jiang, S., Li, Y., Jia, J., and Jiang, X. (2014, January 3). AirCloud: A cloud-based air-quality monitoring system for everyone. Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems (SenSys 14), New York, NY, USA.","DOI":"10.1145\/2668332.2668346"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, C.-C., Kuo, C.-T., Chen, S.-Y., Lin, C.-H., Chue, J.-J., Hsieh, Y.-J., Cheng, C.-W., Wu, C.-M., and Huang, C.-M. (2018, January 26\u201330). Calibration of low-cost particle sensors by using machine-learning method. Proceedings of the 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS 2018), Chengdu, China.","DOI":"10.1109\/APCCAS.2018.8605619"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, W.-C.V., Lung, S.-C.C., and Liu, C.-H. (2020). Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network. Sensors, 20.","DOI":"10.3390\/s20175002"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Piedrahita, R., Dick, R.P., Hannigan, M., Lv, Q., and Shang, L. (2013, January 20\u201323). A hybrid sensor system for indoor air quality monitoring. Proceedings of the IEEE International Conference on Distributed Computing in Sensor Systems (DCoSS 2013), Cambridge, MA, USA.","DOI":"10.1109\/DCOSS.2013.48"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Johnston, S.J., Basford, P.J., Bulot, F.M.J., Apetroaie-Cristea, M., Easton, N.H.C., Davenport, C., Foster, G.L., Loxham, M., Morris, A.K.R., and Cox, S.J. (2019). City scale particulate matter monitoring using LoRaWAN based air quality IoT devices. Sensors, 19.","DOI":"10.3390\/s19010209"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.envint.2017.05.005","article-title":"Mapping urban air quality in near real-time using observations from low-cost sensors and model information","volume":"106","author":"Schneider","year":"2017","journal-title":"Environ. Int."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Penza, M., Suriano, D., Pfister, V., Prato, M., and Cassano, G. (2017, January 3\u20136). Urban Air Quality Monitoring with Networked Low-Cost Sensor-Systems. Proceedings of the Eurosensors, Paris, France.","DOI":"10.3390\/proceedings1040573"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"550147720951334","DOI":"10.1177\/1550147720951334","article-title":"Method for rapid deployment of low-cost sensors for a nationwide project in the Internet of things era: Air quality monitoring in Taiwan","volume":"16","author":"Chen","year":"2020","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.atmosenv.2018.09.030","article-title":"Use of networks of low cost air quality sensors to quantify air quality in urban settings","volume":"194","author":"Popoola","year":"2018","journal-title":"Atmosp. Environ."},{"key":"ref_43","unstructured":"Engelhardt, M., and Bain, L.J. (2000). Introduction to Probability and Mathematical Statistics, Duxbury Press."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/2.485891","article-title":"Artificial neural networks: A tutorial","volume":"29","author":"Jain","year":"1996","journal-title":"Computer"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","unstructured":"Department of Ecology, State of Washington (2021, March 14). Air Monitoring Site Selection and Installation Procedure, Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/fortress.wa.gov\/ecy\/publications\/documents\/1602021.pdf."},{"key":"ref_47","unstructured":"(2021, March 14). Greater London Authority, Guide for Monitoring Air Quality in London, Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.london.gov.uk\/sites\/default\/files\/air_quality_monitoring_guidance_january_2018.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/21\/10\/3338\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:59:27Z","timestamp":1760162367000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/21\/10\/3338"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,11]]},"references-count":47,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21103338"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/s21103338","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,11]]}}}