{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T18:00:14Z","timestamp":1762624814153,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006541","name":"Comunidad de Madrid","doi-asserted-by":"publisher","award":["M2184"],"award-info":[{"award-number":["M2184"]}],"id":[{"id":"10.13039\/501100006541","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The high impact of air quality on environmental and human health justifies the increasing research activity regarding its measurement, modelling, forecasting and anomaly detection. Raw data offered by sensors usually makes the mentioned time series disciplines difficult. This is why the application of techniques to improve time series processing is a challenge. In this work, Singular Spectral Analysis (SSA) is applied to air quality analysis from real recorded data as part of the Help Responder research project. Authors evaluate the benefits of working with SSA processed data instead of raw data for modelling and estimation of the resulting time series. However, what is more relevant is the proposal to detect indoor air quality anomalies based on the analysis of the time derivative SSA signal when the time derivative of the noisy original data is useless. A dual methodology, evaluating level and dynamics of the SSA signal variation, contributes to identifying risk situations derived from air quality degradation.<\/jats:p>","DOI":"10.3390\/s22083054","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"3054","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Contribution of Singular Spectral Analysis to Forecasting and Anomalies Detection of Indoors Air Quality"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1588-0947","authenticated-orcid":false,"given":"Felipe","family":"Espinosa","sequence":"first","affiliation":[{"name":"Electronics Department, University of Alcala, E-28801 Alcal\u00e1 de Henares, Spain"}]},{"given":"Ana B.","family":"Bartolom\u00e9","sequence":"additional","affiliation":[{"name":"Electronics Department, University of Alcala, E-28801 Alcal\u00e1 de Henares, Spain"}]},{"given":"Pablo Villoria","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Electronics Technology Department, University Rey Juan Carlos, E-28933 M\u00f3stoles, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9243-2166","authenticated-orcid":false,"given":"M. C.","family":"Rodriguez-Sanchez","sequence":"additional","affiliation":[{"name":"Electronics Technology Department, University Rey Juan Carlos, E-28933 M\u00f3stoles, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2019). Monitoring Health for the SDGs: Sustainable Development Goals, World Health Organization (WHO)."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Molano, J.I., Obando-Bobadilla, L.M., and Ruiz-Nieto, M.P. (2018, January 13\u201316). Of cities traditional to smart cities. Proceedings of the 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), Caceres, Spain.","DOI":"10.23919\/CISTI.2018.8399337"},{"key":"ref_3","unstructured":"Sanchez, L., Galache, J.A., Gutierrez, V., Hernandez, J.M., Bernat, J., Gluhak, A., and Garcia, T. (2011, January 15\u201317). SmartSantander: The meeting point between Future Internet research and experimentation and the smart cities. Proceedings of the 2011 Future Network Mobile Summit, Warsaw, Poland. Available online: http:\/\/ieeexplore.ieee.org\/abstracl\/documenl\/6095264."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"132577","DOI":"10.1109\/ACCESS.2019.2941371","article-title":"Effect of event-based sensing on IoT node power efficiency. Case study: Air quality monitoring in smart cities","volume":"7","author":"Santos","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kalajdjieski, J., Stojkoska, B.R., and Trivodaliev, K. (2020, January 24\u201325). IoT Based Framework for Air Pollution Monitoring in Smart Cities. Proceedings of the 28th Telecommunications forum TELFOR 2020, Belgrade, Serbia.","DOI":"10.1109\/TELFOR51502.2020.9306531"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez, E., Casanova-Chafer, J., Romero, A., Vilanova, X., Mitrovics, J., and Llobet, E. (2020). LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events. Sensors, 20.","DOI":"10.3390\/s20216225"},{"key":"ref_7","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":"Atmos. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lasomsri, P., Yanbuaban, P., Kerdpoca, O.L., and Ouypornkochagorn, T. (2018, January 18\u201321). A Development of Low-Cost Devices for Monitoring Indoor Air Quality in a Large-Scale Hospital. Proceedings of the 2018 15th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Chiang Rai, Thailand.","DOI":"10.1109\/ECTICon.2018.8619934"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Polichetti, T., Miglietta, M.L., Alfano, B., Massera, E., de Vito, S., di Francia, G., Faucon, A., Saoutie, E., Boisseau, S., and Marchand, N. (2019). A Networked Wearable Device for Chemical Multisensing\u201d. Lecture Notes in Electrical Engineering, Springer International Publishing.","DOI":"10.1007\/978-3-030-04324-7_3"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Okigbo, C.A., Seeam, A., Guness, S.P., Bellekens, X., Bekaroo, G., and Ramsurrun, V. (2020). Low Cost Air Quality Monitoring: Comparing the Energy Consumption of an Arduino against a Raspberry Pi Based System, ACM. 2020 International Conference on Intelligent and Innovative Computing Applications (ICONIC).","DOI":"10.1145\/3415088.3415124"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4230","DOI":"10.1109\/JSEN.2014.2359832","article-title":"ISSAQ: An integrated sensing systems for real-time indoor air quality monitoring","volume":"14","author":"Kim","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sendra, S., Garcia-Navas, J.L., Romero-Diaz, P., and Lloret, J. (2019, January 10\u201313). Collaborative LoRa-Based Sensor Network for Pollution Monitoring in Smart Cities. Proceedings of the 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), Rome, Italy.","DOI":"10.1109\/FMEC.2019.8795321"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khalifeh, A., Darabkh, K.A., Khasawneh, A.M., Alqaisieh, I., Salameh, M., AlAbdala, A., Alrubaye, S., and Alassaf, A. (2021). Wireless Sensor Networks for Smart Cities: Network Design, Implementation and Performance Evaluation. Electronics, 10.","DOI":"10.3390\/electronics10020218"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"L\u00fctkepohl, H. (2005). New Introduction to Multiple Time Series Analysis, Springer.","DOI":"10.1007\/978-3-540-27752-1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Brockwell, P.J., and Davis, R.A. (2016). Intoduction to Time Series and Forecasting, Springer.","DOI":"10.1007\/978-3-319-29854-2"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101182","DOI":"10.1016\/j.phycom.2020.101182","article-title":"Application of fault detection using distributed sensors in smart cities","volume":"46","author":"Yongzhi","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Golyandina, N.E., and Zhigljavsky, A. (2013). Singular Spectrum Analysis for Time Series, Springer.","DOI":"10.1007\/978-3-642-34913-3"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"239","DOI":"10.6339\/JDS.2007.05(2).396","article-title":"Singular Spectrum Analysis: Methodology and Comparison","volume":"5","author":"Hassani","year":"2007","journal-title":"J. Data Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ali-Kazmi, S.N., Ulasyar, A., and Nadeem-Khan, M.F. (2020, January 16\u201317). IoT based Energy Efficient Smart Street Lighting Technique with Air Quality Monitoring. Proceedings of the 2020 14th International Conference on Open Source Systems and Technologies (ICOSST), Lahore, Pakistan.","DOI":"10.1109\/ICOSST51357.2020.9332982"},{"key":"ref_20","unstructured":"EPA (2021, November 25). Indoor Air Quality|EPA\u2019s Report on the Environment (ROE)|US EPA, Available online: https:\/\/www.epa.gov\/report-environment\/indoor-air-quality."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106957","DOI":"10.1016\/j.asoc.2020.106957","article-title":"Intelligent modeling strategies for forecasting air quality time series: A review","volume":"102","author":"Liu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105898","DOI":"10.1016\/j.asoc.2019.105898","article-title":"Air quality prediction by neuro fuzzy modeling approach","volume":"86","author":"Lin","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107850","DOI":"10.1016\/j.asoc.2021.107850","article-title":"A time series forecasting based multi-criteria methodology for air quality prediction","volume":"113","author":"Espinosa","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_24","unstructured":"Ljung, L. (2020). System Identification Toolbox. User\u2019s Guide, Matlab & Simulink, MathWorks."},{"key":"ref_25","unstructured":"Ljung, L. (2020). System Identification Toolbox. Getting Started Guide, Matlab & Simulink, MathWorks."},{"key":"ref_26","unstructured":"Ljung, L. (2020). System Identification Toolbox. Reference, Matlab & Simulink, MathWorks."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ljung, L. (1999). System Identification. Theory for the User, Prentice Hall. [2nd ed.].","DOI":"10.1002\/047134608X.W1046"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1016\/j.pce.2006.02.061","article-title":"Singular spectrum analysis and forecasting of hydrological time series","volume":"31","author":"Marques","year":"2006","journal-title":"Phys. Chem. 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