{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:56:36Z","timestamp":1771635396289,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91644216"],"award-info":[{"award-number":["91644216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771391"],"award-info":[{"award-number":["41771391"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2018YFC0213903"],"award-info":[{"award-number":["2018YFC0213903"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFC0213904"],"award-info":[{"award-number":["2018YFC0213904"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19010403, XDA19040201"],"award-info":[{"award-number":["XDA19010403, XDA19040201"]}]},{"name":"Guangxi Key Research and Development Project","award":["Guike AB20238015"],"award-info":[{"award-number":["Guike AB20238015"]}]},{"name":"TUOHAI special project","award":["HBHZX202002"],"award-info":[{"award-number":["HBHZX202002"]}]},{"name":"Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University","award":["KYCXTD201903"],"award-info":[{"award-number":["KYCXTD201903"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Climate change and air pollution are emerging topics due to their possible enormous implications for health and social perspectives. In recent years, tropospheric ozone has been recognized as an important greenhouse gas and pollutant that is detrimental to human health, agriculture, and natural ecosystems, and has shown a trend of increasing interest. Machine-learning-based approaches have been widely applied to the estimation of tropospheric ozone concentrations, but few studies have included tropospheric ozone profiles. This study aimed to predict the Northern Hemisphere distribution of Lower-Stratosphere-to-Troposphere (LST) ozone at a pressure of 100 hPa to the near surface by employing a deep learning Long Short-Term Memory (LSTM) model. We referred to a history of all the observed parameters (meteorological data of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), satellite data, and the ozone profiles of the World Ozone and Ultraviolet Data Center (WOUDC)) between 2014 and 2018 for training the predictive models. Model\u2013measurement comparisons for the monitoring sites of WOUDC for the period 2019\u20132020 show that the mean correlation coefficients (R2) in the Northern Hemisphere at high latitude (NH), Northern Hemisphere at middle latitude (NM), and Northern Hemisphere at low latitude (NL) are 0.928, 0.885, and 0.590, respectively, indicating reasonable performance for the LSTM forecasting model. To improve the performance of the model, we applied the LSTM migration models to the Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flights in the Northern Hemisphere from 2018 to 2019 and three urban agglomerations (the Sichuan Basin (SCB), North China Plain (NCP), and Yangtze River Delta region (YRD)) between 2018 and 2019. The results show that our models performed well on the CARIBIC data set, with a high R2 equal to 0.754. The daily and monthly surface ozone concentrations for 2018\u20132019 in the three urban agglomerations were estimated from meteorological and ancillary variables. Our results suggest that the LSTM models can accurately estimate the monthly surface ozone concentrations in the three clusters, with relatively high coefficients of 0.815\u20130.889, root mean square errors (RMSEs) of 7.769\u20138.729 ppb, and mean absolute errors (MAEs) of 6.111\u20136.930 ppb. The daily scale performance was not as high as the monthly scale performance, with the accuracy of R2 = 0.636~0.737, RMSE = 14.543\u201316.916 ppb, MAE = 11.130\u201312.687 ppb. In general, the trained module based on LSTM is robust and can capture the variation of the atmospheric ozone distribution. Moreover, it also contributes to our understanding of the mechanism of air pollution, especially increasing our comprehension of pollutant areas.<\/jats:p>","DOI":"10.3390\/rs13071374","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T10:34:09Z","timestamp":1617359649000},"page":"1374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM)"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4412-5003","authenticated-orcid":false,"given":"Xinxin","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"}]},{"given":"Xiaoyan","family":"Lu","sequence":"additional","affiliation":[{"name":"Guangxi Eco-Environmental Monitoring Center, Nanning 530028, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1242-5412","authenticated-orcid":false,"given":"Lu","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Belfast BT37 0QB, UK"}]},{"given":"Liangfu","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jinhua","family":"Tao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"}]},{"given":"Zhibao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China"}]},{"given":"Lili","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3531","DOI":"10.1029\/1999JD901011","article-title":"What controls tropospheric ozone?","volume":"105","author":"Lelieveld","year":"2000","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4437","DOI":"10.1002\/grl.50835","article-title":"Are recent Arctic ozone losses caused by increasing greenhouse gases?","volume":"40","author":"Rieder","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2434","DOI":"10.1021\/jp312781c","article-title":"O(D-1) Kinetic Study of Key Ozone Depleting Substances and Greenhouse Gases","volume":"117","author":"Baasandorj","year":"2013","journal-title":"J. Phys. Chem. A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3985","DOI":"10.1109\/TGRS.2016.2532353","article-title":"Ozone Profile Retrievals from the Cross-Track Infrared Sounder","volume":"54","author":"Ma","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Young, P.J., Naik, V., Fiore, A.M., Gaudel, A., Guo, J., Lin, M.Y., Neu, J.L., Parrish, D.D., Rieder, H.E., and Schnell, J.L. (2018). Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends. Elem. Sci. Anth., 6.","DOI":"10.1525\/elementa.265"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2630","DOI":"10.1016\/j.atmosenv.2006.11.016","article-title":"A synthesis of AOT40-based response functions and critical levels of ozone for agricultural and horticultural crops","volume":"41","author":"Mills","year":"2007","journal-title":"Atmos. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.atmosenv.2016.11.030","article-title":"Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality","volume":"150","author":"Taylan","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ayres, J.G., Ayres, J., Maynard, R.L., and Richards, R. (2006). Air Pollution and Health, Imperial College Press.","DOI":"10.1142\/9781860949234"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"14535","DOI":"10.5194\/acp-19-14535-2019","article-title":"Source attribution of European surface O-3 using a tagged O-3 mechanism","volume":"19","author":"Butler","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.5194\/acp-18-2341-2018","article-title":"Decadal changes in summertime reactive oxidized nitrogen and surface ozone over the Southeast United States","volume":"18","author":"Li","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.atmosenv.2017.05.008","article-title":"Spatial and temporal variation of particulate matter and gaseous pollutants in China during 2014\u20132016","volume":"161","author":"Li","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.scib.2017.06.005","article-title":"Attribution of PM2.5 exposure in Beijing\u2013Tianjin\u2013Hebei region to emissions: Implication to control strategies","volume":"62","author":"Li","year":"2017","journal-title":"Sci. Bull."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1016\/j.scib.2018.07.001","article-title":"Explicit diagnosis of the local ozone production rate and the ozone-NOx-VOC sensitivities","volume":"63","author":"Tan","year":"2018","journal-title":"Sci. Bull."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, X., Chance, K., Sioris, C.E., Spurr, R.J.D., Kurosu, T.P., Martin, R.V., and Newchurch, M.J. (2005). Ozone profile and tropospheric ozone retrievals from the Global Ozone Monitoring Experiment: Algorithm description and validation. J. Geophys. Res. Atmos., 110.","DOI":"10.1029\/2005JD006240"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5587","DOI":"10.5194\/amt-11-5587-2018","article-title":"Retrievals of tropospheric ozone profiles from the synergism of AIRS and OMI: Methodology and validation","volume":"11","author":"Fu","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5163","DOI":"10.5194\/acp-6-5163-2006","article-title":"Differences between ground Dobson, Brewer and satellite TOMS-8, GOME-WFDOAS total ozone observations at Hradec Kralove, Czech","volume":"6","author":"Vanicek","year":"2006","journal-title":"Atmos. Chem. Phys."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6159","DOI":"10.5194\/acp-20-6159-2020","article-title":"Developing a novel hybrid model for the estimation of surface 8 h ozone (O-3) across the remote Tibetan Plateau during 2005\u20132018","volume":"20","author":"Li","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Susskind, J., Barnet, C., Blaisdell, J., Iredell, L., Keita, F., Kouvaris, L., Molnar, G., and Chahine, M. (2006). Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder\/Advanced Microwave Sounding Unit as a function of fractional cloud cover. J. Geophys. Res. Atmos., 111.","DOI":"10.1029\/2005JD006272"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nassar, R., Logan, J.A., Worden, H.M., Megretskaia, I.A., Bowman, K.W., Osterman, G.B., Thompson, A.M., Tarasick, D.W., Austin, S., and Claude, H. (2008). Validation of Tropospheric Emission Spectrometer (TES) nadir ozone profiles using ozonesonde measurements. J. Geophys. Res. Atmos., 113.","DOI":"10.1029\/2007JD008819"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.crte.2015.06.001","article-title":"Tracking pollutants from space: Eight years of IASI satellite observation","volume":"347","author":"Clerbaux","year":"2015","journal-title":"Comptes Rendus Geosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.5194\/acp-10-2521-2010","article-title":"Ozone profile retrievals from the Ozone Monitoring Instrument","volume":"10","author":"Liu","year":"2010","journal-title":"Atmos. Chem. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5263","DOI":"10.5194\/amt-12-5263-2019","article-title":"TROPOMI\/S5P total ozone column data: Global ground-based validation and consistency with other satellite missions","volume":"12","author":"Garane","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s11707-014-0480-5","article-title":"Comparison of Suomi-NPP OMPS total column ozone with Brewer and Dobson spectrophotometers measurements","volume":"9","author":"Bai","year":"2015","journal-title":"Front. Earth Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ghoneim, O.A., and Manjunatha, B.R. (2017, January 13\u201316). Forecasting of Ozone Concentration in Smart City using Deep Learning. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (Icacci), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126024"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.envpol.2019.05.101","article-title":"Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China","volume":"252","author":"Feng","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.envpol.2017.10.029","article-title":"Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment","volume":"233","author":"Zhan","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Danielsen, E.F. (1968). Stratospheric-Tropospheric Exchange Based on Radioactivity Ozone and Potential Vorticity. J. Atmos. Sci., 25.","DOI":"10.1175\/1520-0469(1968)025<0502:STEBOR>2.0.CO;2"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13341","DOI":"10.5194\/acp-16-13341-2016","article-title":"Analysis of the latitudinal variability of tropospheric ozone in the Arctic using the large number of aircraft and ozonesonde observations in early summer 2008","volume":"16","author":"Ancellet","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pittman, J.V., Pan, L.L., Wei, J.C., Irion, F.W., Liu, X., Maddy, E.S., Barnet, C.D., Chance, K., and Gao, R.-S. (2009). Evaluation of AIRS, IASI, and OMI ozone profile retrievals in the extratropical tropopause region using in situ aircraft measurements. J. Geophys. Res., 114.","DOI":"10.1029\/2009JD012493"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Watson, G.L., Telesca, D., Reid, C.E., Pfister, G.G., and Jerrett, M. (2019). Machine learning models accurately predict ozone exposure during wildfire events. Environ. Pollut., 254.","DOI":"10.1016\/j.envpol.2019.06.088"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.neunet.2019.09.033","article-title":"Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance","volume":"121","author":"Sayeed","year":"2020","journal-title":"Neural Networks"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M.D., Kaifel, A.K., Weber, M., Tellmann, S., Burrows, J.P., and Loyola, D. (2003). Ozone profile retrieval from Global Ozone Monitoring Experiment (GOME) data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY)). J. Geophys. Res. Atmos., 108.","DOI":"10.1029\/2002JD002784"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, R.Y., Ma, Z.W., Liu, Y., Shao, Y.C., Zhao, W., and Bi, J. (2020). Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach. Environ. Int., 142.","DOI":"10.1016\/j.envint.2020.105823"},{"key":"ref_34","first-page":"713","article-title":"Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction","volume":"14","author":"Jumin","year":"2020","journal-title":"Eng. Appl. Comput. Fluid"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Stubi, R., Levrat, G., Hoegger, B., Viatte, P., Staehelin, J., and Schmidlin, F.J. (2008). In-flight comparison of Brewer-Mast and electrochemical concentration cell ozonesondes. J. Geophys. Res. Atmos., 113.","DOI":"10.1029\/2007JD009091"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Logan, J.A., Staehelin, J., Megretskaia, I.A., Cammas, J.P., Thouret, V., Claude, H., De Backer, H., Steinbacher, M., Scheel, H.E., and St\u00fcbi, R. (2012). Changes in ozone over Europe: Analysis of ozone measurements from sondes, regular aircraft (MOZAIC) and alpine surface sites. J. Geophys. Res. Atmos., 117.","DOI":"10.1029\/2011JD016952"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8575","DOI":"10.5194\/acp-10-8575-2010","article-title":"Analysis of C-13 and O-18 isotope data of CO2 in CARIBIC aircraft samples as tracers of upper troposphere\/lower stratosphere mixing and the global carbon cycle","volume":"10","author":"Assonov","year":"2010","journal-title":"Atmos. Chem. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.5194\/acp-10-3965-2010","article-title":"Greenhouse gas relationships in the Indian summer monsoon plume measured by the CARIBIC passenger aircraft","volume":"10","author":"Schuck","year":"2010","journal-title":"Atmos. Chem. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1016\/j.scitotenv.2016.10.081","article-title":"Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects","volume":"575","author":"Wang","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1175\/WAF-D-17-0175.1","article-title":"Impact of Adaptively Thinned AIRS Cloud-Cleared Radiances on Tropical Cyclone Representation in a Global Data Assimilation and Forecast System","volume":"33","author":"Reale","year":"2018","journal-title":"Weather Forecast."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3097","DOI":"10.5194\/acp-19-3097-2019","article-title":"From ERA-Interim to ERA5: The considerable impact of ECMWF\u2019s next-generation reanalysis on Lagrangian transport simulations","volume":"19","author":"Hoffmann","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/4053718","article-title":"Temporal and Spatial Change Monitoring of Drought Grade Based on ERA5 Analysis Data and BFAST Method in the Belt and Road Area during 1989\u20132017","volume":"2019","author":"Xue","year":"2019","journal-title":"Adv. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.scitotenv.2015.01.105","article-title":"Exploring the interaction between O-3 and NOx pollution patterns in the atmosphere of Barcelona, Spain using the MCR\u2013ALS method","volume":"517","author":"Malik","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.atmosenv.2014.11.005","article-title":"Surface ozone concentration trends and its relationship with weather types in Spain (2001\u20132010)","volume":"101","author":"Zarrabeitia","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"10503","DOI":"10.1029\/2019JD030572","article-title":"Disentangling the Drivers of the Summertime Ozone-Temperature Relationship Over the United States","volume":"124","author":"Kerr","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.atmosenv.2017.06.049","article-title":"Influence of relative humidity on heterogeneous reactions of O-3 and O-3\/SO2 with soot particles: Potential for environmental and health effects","volume":"165","author":"He","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.saa.2019.04.046","article-title":"Influence of relative humidity on SO2 oxidation by O3 and NO2 on the surface of TiO2 particles: Potential for formation of secondary sulfate aerosol","volume":"219","author":"He","year":"2019","journal-title":"Spectrochim. Acta A"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"10839","DOI":"10.5194\/acp-15-10839-2015","article-title":"Springtime daily variations in lower-tropospheric ozone over east Asia: The role of cyclonic activity and pollution as observed from space with IASI","volume":"15","author":"Dufour","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical characterization of random forest variable importance measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s11869-008-0008-9","article-title":"Update of WHO air quality guidelines","volume":"1","author":"Krzyzanowski","year":"2008","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3589","DOI":"10.5194\/acp-19-3589-2019","article-title":"Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes","volume":"19","author":"Williams","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_54","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hara, K., Saito, D., and Shouno, H. (2015, January 12\u201317). Analysis of Function of Rectified Linear Unit Used in Deep learning. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280578"},{"key":"ref_56","unstructured":"Chollet, F.O. (2018). Deep Learning with Python, Manning Publications, Co."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"152766","DOI":"10.1109\/ACCESS.2019.2948658","article-title":"Assessing Optimizer Impact on DNN Model Sensitivity to Adversarial Examples","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liao, Z.H., Ling, Z.H., Gao, M., Sun, J.R., Zhao, W., Ma, P.K., Quan, J.N., and Fan, S.J. (2021). Tropospheric Ozone Variability Over Hong Kong Based on Recent 20 years (2000\u20132019) Ozonesonde Observation. J. Geophys. Res. Atmos., 126.","DOI":"10.1029\/2020JD033054"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Xiao, X.L., Mudiyanselage, T.B., Ji, C.Y., Hu, J., and Pan, Y. (2019, January 14\u201317). Fast Deep Learning Training Through Intelligently Freezing Layers. Proceedings of the 2019 International Conference on Internet of Things (Ithings) and IEEE Green Computing and Communications (Greencom) and Ieee Cyber, Physical and Social Computing (Cpscom) and Ieee Smart Data (Smartdata), Atlanta, GA, USA.","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00205"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, X.Y., Wu, K., Wang, H.L., Liu, Y.M., Gu, S., Lu, Y.Q., Zhang, X.L., Hu, Y.S., Ou, Y.H., and Wang, S.G. (2020). Summertime ozone pollution in Sichuan Basin, China: Meteorological conditions, sources and process analysis. Atmos. Environ., 226.","DOI":"10.1016\/j.atmosenv.2020.117392"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:26:09Z","timestamp":1760361969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,2]]},"references-count":60,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071374"],"URL":"https:\/\/doi.org\/10.3390\/rs13071374","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,2]]}}}