{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:25:30Z","timestamp":1775539530617,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T00:00:00Z","timestamp":1661126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Portuguese Science Foundation (\u201cFunda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia\u201d\u2014FCT)","award":["UIDB\/00313\/2020"],"award-info":[{"award-number":["UIDB\/00313\/2020"]}]},{"name":"Portuguese Science Foundation (\u201cFunda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia\u201d\u2014FCT)","award":["UIDP\/00313\/2020"],"award-info":[{"award-number":["UIDP\/00313\/2020"]}]},{"name":"Copernicus Academy\u2013European Union\u2019s Earth Observation Programme","award":["UIDB\/00313\/2020"],"award-info":[{"award-number":["UIDB\/00313\/2020"]}]},{"name":"Copernicus Academy\u2013European Union\u2019s Earth Observation Programme","award":["UIDP\/00313\/2020"],"award-info":[{"award-number":["UIDP\/00313\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Atmosphere"],"abstract":"<jats:p>The machine learning algorithm based on multiple-input multiple-output linear regression models has been developed to describe PM2.5 and PM10 concentrations over time. The algorithm is fact-acting and allows for speedy forecasts without requiring demanding computational power. It is also simple enough that it can self-update by introducing a recursive step that utilizes newly measured values and forecasts to continue to improve itself. Starting from raw data, pre-processing methods have been used to verify the stationary data by employing the Dickey\u2013Fuller test. For comparison, weekly and monthly decompositions have been achieved by using Savitzky\u2013Golay polynomial filters. The presented algorithm is shown to have accuracies of 30% for PM2.5 and 26% for PM10 for a forecasting horizon of 24 h with a quarter-hourly data acquisition resolution, matching other results obtained using more computationally demanding approaches, such as neural networks. We show the feasibility of using multivariate linear regression (together with the small real-time computational costs for the training and testing procedures) to forecast particulate matter air pollutants and avoid environmental threats in real conditions.<\/jats:p>","DOI":"10.3390\/atmos13081334","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T21:30:34Z","timestamp":1661203834000},"page":"1334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Modeling PM2.5 and PM10 Using a Robust Simplified Linear Regression Machine Learning Algorithm"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0976-1343","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Greg\u00f3rio","sequence":"first","affiliation":[{"name":"SpaceLayer Technologies, Uburu-IQ, Av. Em\u00eddio Navarro, 33, 3000-151 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5801-7601","authenticated-orcid":false,"given":"Carla","family":"Gouveia-Caridade","sequence":"additional","affiliation":[{"name":"SpaceLayer Technologies, Uburu-IQ, Av. Em\u00eddio Navarro, 33, 3000-151 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0947-5750","authenticated-orcid":false,"given":"Pedro J. S. B.","family":"Caridade","sequence":"additional","affiliation":[{"name":"CQC-ISM and Department of Chemistry, University of Coimbra Rua Larga, 3004-545 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,22]]},"reference":[{"key":"ref_1","unstructured":"(2022). World Health Statistics 2022: Monitoring Health for the SDGs, Sustainable Development Goals, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhong, B., Vardoulakis, S., Zhang, F., Pilot, E., Li, Y., Yang, L., Wang, W., and Krafft, T. (2016). Air Quality Strategies on Public Health and Health Equity in Europe\u2014A Systematic Review. Int. J. Environ. Res. Public Health, 13.","DOI":"10.3390\/ijerph13121196"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chu, Y., Liu, Y., Li, X., Liu, Z., Lu, H., Lu, Y., Mao, Z., Chen, X., Li, N., and Ren, M. (2016). A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth. Atmosphere, 7.","DOI":"10.3390\/atmos7100129"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Leung, D.Y. (2015). Outdoor-indoor air pollution in urban environment: Challenges and opportunity. Front. Environ. Sci., 2.","DOI":"10.3389\/fenvs.2014.00069"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1093\/bmb\/ldg028","article-title":"Ambient air pollution and health","volume":"68","author":"Katsouyanni","year":"2003","journal-title":"Br. Med. Bull."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.envint.2016.07.018","article-title":"Mortality and emergency hospitalizations associated with atmospheric particulate matter episodes across the UK in spring 2014","volume":"97","author":"Macintyre","year":"2016","journal-title":"Environ. Int."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.envint.2016.09.003","article-title":"Long-term trend and spatial pattern of PM 2.5 induced premature mortality in China","volume":"97","author":"Xie","year":"2016","journal-title":"Environ. Int."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"De Mattos Neto, P.S., Cavalcanti, G.D., Madeiro, F., and Ferreira, T.A. (2015). An Approach to Improve the Performance of PM Forecasters. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0138507"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21165","DOI":"10.1007\/s11356-016-7515-2","article-title":"A review on recent progress in observations, sources, classification and regulations of PM2.5 in Asian environments","volume":"23","author":"Gautam","year":"2016","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1038\/nrc3572","article-title":"Air pollution: A potentially modifiable risk factor for lung cancer","volume":"13","author":"Fajersztajn","year":"2013","journal-title":"Nat. Rev. Cancer"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Feng, J., and Yang, W. (2012). Effects of Particulate Air Pollution on Cardiovascular Health: A Population Health Risk Assessment. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0033385"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"921","DOI":"10.3389\/fpubh.2020.575330","article-title":"Air Pollution and Central Nervous System Disease: A Review of the Impact of Fine Particulate Matter on Neurological Disorders","volume":"8","author":"Kim","year":"2020","journal-title":"Front. Public Health"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"S\u00eerbu, C.A., Stefan, I., Dumitru, R., Mitrica, M., Manole, A.M., Vasile, T.M., Stefani, C., and Ranetti, A.E. (2022). Air Pollution and Its Devastating Effects on the Central Nervous System. Healthcare, 10.","DOI":"10.3390\/healthcare10071170"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.envint.2016.10.012","article-title":"Associations among plasma metabolite levels and short-term exposure to PM2.5 and ozone in a cardiac catheterization cohort","volume":"97","author":"Breitner","year":"2016","journal-title":"Environ. Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.apr.2015.09.007","article-title":"Mobile monitoring of particulate matter: State of art and perspectives","volume":"7","author":"Gozzi","year":"2016","journal-title":"Atmos. Poll. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4639","DOI":"10.1016\/S1352-2310(01)00124-8","article-title":"Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy)","volume":"35","author":"Marcazzan","year":"2001","journal-title":"Atmos. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wallace, L., Bi, J., Ott, W.R., Sarnat, J., and Liu, Y. (2021). Calibration of low-cost PurpleAir outdoor monitors using an improved method of calculating PM2.5. Atmos. Environ., 256.","DOI":"10.1016\/j.atmosenv.2021.118432"},{"key":"ref_18","unstructured":"Spurny, K.R. (1998). Aerosol Filstration and Sampling. Advances in Aerosol Filtration, CRC Press."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4103","DOI":"10.1016\/S1352-2310(97)00296-3","article-title":"Sources and processes affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (U.K.)","volume":"31","author":"Harrison","year":"1997","journal-title":"Atmos. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6547","DOI":"10.1016\/j.atmosenv.2004.08.037","article-title":"Speciation and origin of PM10 and PM2.5 in selected European cities","volume":"38","author":"Querol","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_21","unstructured":"Wilks, D.S. (2011). Statistics. Stat. Methods Atmos. Sci., 100."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3431","DOI":"10.1016\/j.jcp.2007.02.034","article-title":"The origins of computer weather prediction and climate modeling","volume":"227","author":"Lynch","year":"2008","journal-title":"J. Comp. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nazif, A., Mohammed, N.I., Malakahmad, A., and Abualqumboz, M.S. (2016). Application of Step Wise Regression Analysis in Predicting Future Particulate Matter Concentration Episode. Water Air Soil Pollut., 227.","DOI":"10.1007\/s11270-016-2823-1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1016\/j.eswa.2012.01.039","article-title":"A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition","volume":"39","author":"Bontempi","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/S1352-2310(99)00316-7","article-title":"Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile","volume":"34","author":"Trier","year":"2000","journal-title":"Atmos. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"374","DOI":"10.4236\/jsea.2010.34042","article-title":"Time Series Forecasting of Hourly PM10 Using Localized Linear Models","volume":"3","author":"Sfetsos","year":"2010","journal-title":"J. Soft. Eng. App."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.envsoft.2019.06.014","article-title":"A review of artificial neural network models for ambient air pollution prediction","volume":"119","author":"Cabaneros","year":"2019","journal-title":"Environ. Mod. Soft."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kalajdjieski, J., Zdravevski, E., Corizzo, R., Lameski, P., Kalajdziski, S., Pires, I.M., Garcia, N.M., and Trajkovik, V. (2020). Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12244142"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fan, J., Li, Q., Hou, J., Feng, X., Karimian, H., and Lin, S. (2017). A spatiotemporal prediction framework for air pollution based on deep RNN. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 4.","DOI":"10.5194\/isprs-annals-IV-4-W2-15-2017"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yi, X., Zhang, J., Wang, Z., Li, T., and Zheng, Y. (2018, January 19\u201323). Deep distributed fusion network for air quality prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219822"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"43331","DOI":"10.1109\/ACCESS.2019.2908081","article-title":"A sequence-to-sequence air quality predictor based on the n-step recurrent prediction","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"156053","DOI":"10.1109\/ACCESS.2020.3019095","article-title":"Echad: Embedding-based change detection from multivariate time series in smart grids","volume":"8","author":"Ceci","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"22408","DOI":"10.1007\/s11356-016-7812-9","article-title":"Deep learning architecture for air quality predictions","volume":"23","author":"Li","year":"2016","journal-title":"Environ. Sci. Poll Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"K\u00f6k, I., \u015eim\u015fek, M.U., and \u00d6zdemir, S. (2017, January 11\u201314). A deep learning model for air quality prediction in smart cities. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258144"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1109\/TKDE.2018.2823740","article-title":"Deep air learning: Interpolation, prediction, and feature analysis of fine-grained air quality","volume":"30","author":"Qi","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/2017GL075710","article-title":"Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yin, L., Wang, L., Huang, W., Tian, J., Liu, S., Yang, B., and Zheng, W. (2022). Haze Grading Using the Convolutional Neural Networks. Atmosphere, 13.","DOI":"10.3390\/atmos13040522"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kow, P.Y., Chang, L.C., Lin, C.Y., Chou, C.C., and Chang, F.J. (2022). Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data. Environ. Pollut., 306.","DOI":"10.1016\/j.envpol.2022.119348"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Justus, D., Brennan, J., Bonner, S., and McGough, A.S. (2018, January 10\u201313). Predicting the Computational Cost of Deep Learning Models. Proceedings of the 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622396"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.1021\/acs.est.1c01739","article-title":"Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework, 2022","volume":"56","author":"Boyd","year":"2022","journal-title":"Environ. Sci. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kaur, H., Pannu, H.S., and Malhi, A.K. (2020). A systematic review on imbalanced data challenges in machine learning: Applications and solutions, 2019. ACM Comput. Surv., 52.","DOI":"10.1145\/3343440"},{"key":"ref_42","unstructured":"Ramsundar, B., and Zadeh, R.B. (2018). TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning, O\u2019Reilly Media."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4370","DOI":"10.1016\/j.atmosenv.2011.05.045","article-title":"Factors influencing the variations of PM10 aerosol dust in Klang Valley, Malaysia during the summer","volume":"45","author":"Juneng","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ng, K.Y., and Awang, N. (2018). Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia. Environ. Monit. Assess., 190.","DOI":"10.1007\/s10661-017-6419-z"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shams, S.R., Jahani, A., Kalantary, S., Moeinaddini, M., and Khorasani, N. (2021). The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration. Urban Clim., 37.","DOI":"10.1016\/j.uclim.2021.100837"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Okkao\u011flu, Y., Akdi, Y., and \u00dcnl\u00fc, K.D. (2020). Daily PM10, periodicity and harmonic regression model: The case of London. Atmos. Environ., 238.","DOI":"10.1016\/j.atmosenv.2020.117755"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bai, L., Wang, J., Ma, X., and Lu, H. (2018). Air Pollution Forecasts: An Overview. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15040780"},{"key":"ref_48","unstructured":"(2020, January 20). Generated Using Copernicus Atmosphere Monitoring Service Information 2020. Available online: https:\/\/atmosphere.copernicus.eu\/data."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4607","DOI":"10.1016\/j.atmosenv.2004.05.030","article-title":"Ensemble dispersion forecasting\u2014Part I: Concept, approach and indicators","volume":"38","author":"Galmarini","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4619","DOI":"10.1016\/j.atmosenv.2004.05.031","article-title":"Ensemble dispersion forecasting\u2014Part II: Application and evaluation","volume":"38","author":"Galmarini","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.5194\/gmd-8-2777-2015","article-title":"A regional air quality forecasting system over Europe: The MACC-II daily ensemble production","volume":"8","author":"Peuch","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1016\/0004-6981(86)90335-5","article-title":"Time series analysis in acid rain modeling: Evaluation of filling missing values by linear interpolation","volume":"20","author":"Terry","year":"1986","journal-title":"Atmos. Environ."},{"key":"ref_53","first-page":"427","article-title":"Distribution of the Estimators for Autoregressive Time Series with a Unit Root","volume":"74","author":"Dickey","year":"1979","journal-title":"J. Amer. Stat. Ass."},{"key":"ref_54","unstructured":"Spiegel, M.R., and Stephens, L.J. (2008). Schaum\u2019s Outline of Theory and Problems of Probability and Statistics, McGraw-Hill."},{"key":"ref_55","unstructured":"Bontempi, G. (2008, January 17\u201319). Long term time series prediction with multi-input multi-output local learninge. Proceedings of the 2nd ESTSP 2008, Porvoo, Finland."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.1016\/j.neucom.2009.11.030","article-title":"Multiple-output modeling for multi-step-ahead time series forecasting","volume":"73","author":"Sorjamaa","year":"2010","journal-title":"Neurocomputing"},{"key":"ref_57","first-page":"62","article-title":"Machine learning strategies for time series forecasting","volume":"138","author":"Bontempi","year":"2013","journal-title":"Lect. Notes Bus. Infor. Proc."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Qin, J., Guo, J., Xu, X., Kong, T., Wang, X., Ma, L., and Wurm, M. (2021). A universal and fast method to solve linear systems with correlated coefficients using weighted total least squares. Meas. Sci. Technol., 33.","DOI":"10.1088\/1361-6501\/ac32ec"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1108\/eb024706","article-title":"The pareto principle: Its use and abuse","volume":"1","author":"Sanders","year":"1987","journal-title":"J. Serv. Mark."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/01621459.1993.10476299","article-title":"Linear model selection by cross-validation","volume":"88","author":"Shao","year":"1993","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","article-title":"Estimation of prediction error by using K-fold cross-validation","volume":"21","author":"Fushiki","year":"2009","journal-title":"Stat. Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/0169-2070(93)90079-3","article-title":"Accuracy measures: Theoretical and practical concerns","volume":"9","author":"Makridakis","year":"1993","journal-title":"Int. J. Forecast."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_64","unstructured":"(2022). Air Quality in Europe 2021, Technical Report."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1016\/S1352-2310(03)00221-8","article-title":"Characterising seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long-term Swiss monitoring data, 2003","volume":"37","author":"Gehrig","year":"2003","journal-title":"Atmos. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1021\/ef0101715","article-title":"Review of PM2.5 and PM10 apportionment for fossil fuel combustion and other sources by the Chemical Mass Balance receptor model","volume":"16","author":"Chow","year":"2002","journal-title":"Energy Fuels"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","article-title":"A novel hybridization of artificial neural networks and ARIMA models for time series forecasting","volume":"11","author":"Khashei","year":"2011","journal-title":"Appl. Soft. Comput."},{"key":"ref_68","first-page":"359","article-title":"Multi-step time series analysis and forecasting strategy using ARIMA and evolutionary algorithms","volume":"14","author":"Kumar","year":"2022","journal-title":"Int. J. Inf. Technol."}],"container-title":["Atmosphere"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4433\/13\/8\/1334\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:13:29Z","timestamp":1760141609000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4433\/13\/8\/1334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,22]]},"references-count":68,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["atmos13081334"],"URL":"https:\/\/doi.org\/10.3390\/atmos13081334","relation":{},"ISSN":["2073-4433"],"issn-type":[{"value":"2073-4433","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,22]]}}}