{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T21:02:45Z","timestamp":1771102965013,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61872325"],"award-info":[{"award-number":["61872325"]}]},{"name":"National Natural Science Foundation of China","award":["62172373"],"award-info":[{"award-number":["62172373"]}]},{"name":"National Natural Science Foundation of China","award":["2652019028"],"award-info":[{"award-number":["2652019028"]}]},{"name":"National Natural Science Foundation of China","award":["2652018082"],"award-info":[{"award-number":["2652018082"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["61872325"],"award-info":[{"award-number":["61872325"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["62172373"],"award-info":[{"award-number":["62172373"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["2652019028"],"award-info":[{"award-number":["2652019028"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["2652018082"],"award-info":[{"award-number":["2652018082"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aerosol optical depth (AOD) observations have been widely used to generate wide-coverage PM2.5 retrievals due to the adverse effects of long-term exposure to PM2.5 and the sparsity and unevenness of monitoring sites. However, due to non-random missing and nighttime gaps in AOD products, obtaining spatiotemporally continuous hourly data with high accuracy has been a great challenge. Therefore, this study developed an automatic geo-intelligent stacking (autogeoi-stacking) model, which contained seven sub-models of machine learning and was stacked through a Catboost model. The autogeoi-stacking model used the automated feature engineering (autofeat) method to identify spatiotemporal characteristics of multi-source datasets and generate extra features through automatic non-linear changes of multiple original features. The 10-fold cross-validation (CV) evaluation was employed to evaluate the 24-hour and continuous ground-level PM2.5 estimations in the Beijing-Tianjin-Hebei (BTH) region during 2018. The results showed that the autogeoi-stacking model performed well in the study area with the coefficient of determination (R2) of 0.88, the root mean squared error (RMSE) of 17.38 \u00b5g\/m3, and the mean absolute error (MAE) of 10.71 \u00b5g\/m3. The estimated PM2.5 concentrations had an excellent performance during the day (8:00\u201318:00, local time) and night (19:00\u201307:00) (the cross-validation coefficient of determination (CV-R2): 0.90, 0.88), and captured hourly PM2.5 variations well, even in the severe ambient air pollution event. On the seasonal scale, the R2 values from high to low were winter, autumn, spring, and summer, respectively. Compared with the original stacking model, the improvement of R2 with the autofeat and hyperparameter optimization approaches was up to 5.33%. In addition, the annual mean values indicated that the southern areas, such as Shijiazhuang, Xingtai, and Handan, suffered higher PM2.5 concentrations. The northern regions (e.g., Zhangjiakou and Chengde) experienced low PM2.5. In summary, the proposed method in this paper performed well and could provide ideas for constructing geoi-features and spatiotemporally continuous inversion products of PM2.5.<\/jats:p>","DOI":"10.3390\/rs14184432","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Wenhao","family":"Chu","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9320-4218","authenticated-orcid":false,"given":"Chunxiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China"},{"name":"Observation and Research Station of Beijing Fangshan Comprehensive Exploration, Ministry of Natural Resources, Beijing 100083, China"}]},{"given":"Yuwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China"}]},{"given":"Rongrong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5498-1949","authenticated-orcid":false,"given":"Pengda","family":"Wu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1161\/HYPERTENSIONAHA.109.130237","article-title":"Insights Into the Mechanisms and Mediators of the Effects of Air Pollution Exposure on Blood Pressure and Vascular Function in Healthy Humans","volume":"54","author":"Brook","year":"2009","journal-title":"Hypertension"},{"key":"ref_2","first-page":"6","article-title":"The Impact of PM2.5 on the Human Respiratory System","volume":"8","author":"Xing","year":"2016","journal-title":"J. Thorac. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1016\/j.envint.2018.10.019","article-title":"Underlying Causes of PM2.5-Induced Premature Mortality and Potential Health Benefits of Air Pollution Control in South and Southeast Asia from 1999 to 2014","volume":"121","author":"Shi","year":"2018","journal-title":"Environ. Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1007\/s00477-020-01941-7","article-title":"Influence of AOD Remotely Sensed Products, Meteorological Parameters, and AOD\u2013PM2.5 Models on the PM2.5 Estimation","volume":"35","author":"Xu","year":"2021","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"117410","DOI":"10.1016\/j.atmosenv.2020.117410","article-title":"Observation of PM2.5 Using a Combination of Satellite Remote Sensing and Low-Cost Sensor Network in Siberian Urban Areas with Limited Reference Monitoring","volume":"227","author":"Lin","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"117293","DOI":"10.1016\/j.atmosenv.2020.117293","article-title":"Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5","volume":"223","author":"Li","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1029\/2003GL018174","article-title":"Intercomparison between Satellite-Derived Aerosol Optical Thickness and PM2.5 Mass: Implications for Air Quality Studies","volume":"30","author":"Wang","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12280","DOI":"10.1021\/acs.est.5b01413","article-title":"Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 Km Resolution MODIS AOD","volume":"49","author":"Xie","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2017.06.001","article-title":"Estimating Ground-Level PM2.5 Concentrations in Beijing Using a Satellite-Based Geographically and Temporally Weighted Regression Model","volume":"198","author":"Guo","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7436","DOI":"10.1021\/es5009399","article-title":"Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing","volume":"48","author":"Ma","year":"2014","journal-title":"Environ. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s13143-020-00215-0","article-title":"A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges","volume":"57","author":"Ranjan","year":"2021","journal-title":"Asia-Pac. J. Atmos. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.fmre.2021.04.007","article-title":"Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives","volume":"1","author":"Zhang","year":"2021","journal-title":"Fundam. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, C., Lee, K., Kim, S., Yu, J., Jeong, S., and Yeom, J. (2021). Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13112121"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.apr.2020.10.020","article-title":"Estimating Hourly PM2.5 Concentrations Using Himawari-8 AOD and a DBSCAN-Modified Deep Learning Model over the YRDUA, China","volume":"12","author":"Lu","year":"2021","journal-title":"Atmos. Pollut. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7863","DOI":"10.5194\/acp-21-7863-2021","article-title":"Himawari-8-Derived Diurnal Variations in Ground-Level PM2.5 Pollution across China Using the Fast Space-Time Light Gradient Boosting Machine (LightGBM)","volume":"21","author":"Wei","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"134021","DOI":"10.1016\/j.scitotenv.2019.134021","article-title":"Stacking Machine Learning Model for Estimating Hourly PM2.5 in China Based on Himawari 8 Aerosol Optical Depth Data","volume":"697","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.atmosenv.2019.04.020","article-title":"MODIS AOD Sampling Rate and Its Effect on PM2.5 Estimation in North China","volume":"209","author":"Song","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1080\/15481603.2019.1703288","article-title":"Estimating Ground-Level Particulate Matter Concentrations Using Satellite-Based Data: A Review","volume":"57","author":"Shin","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.atmosenv.2019.01.027","article-title":"Extreme Gradient Boosting Model to Estimate PM2.5 Concentrations with Missing-Filled Satellite Data in China","volume":"202","author":"Chen","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105146","DOI":"10.1016\/j.atmosres.2020.105146","article-title":"Estimation of Hourly Full-Coverage PM2.5 Concentrations at 1-Km Resolution in China Using a Two-Stage Random Forest Model","volume":"248","author":"Jiang","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"117921","DOI":"10.1016\/j.atmosenv.2020.117921","article-title":"Evaluation of Gap-Filling Approaches in Satellite-Based Daily PM2.5 Prediction Models","volume":"244","author":"Xiao","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.atmosenv.2017.02.023","article-title":"Spatiotemporal Prediction of Continuous Daily PM2.5 Concentrations across China Using a Spatially Explicit Machine Learning Algorithm","volume":"155","author":"Zhan","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1021\/acs.est.7b05381","article-title":"Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model","volume":"52","author":"Brokamp","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"143","DOI":"10.5194\/isprs-annals-IV-3-143-2018","article-title":"Real-time and seamless monitoring of ground-level pm2.5 using satellite remote sensing","volume":"IV-3","author":"Li","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8019","DOI":"10.1109\/JSTARS.2021.3103020","article-title":"Hourly PM2.5 Concentration Monitoring With Spatiotemporal Continuity by the Fusion of Satellite and Station Observations","volume":"14","author":"Wu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11985","DOI":"10.1002\/2017GL075710","article-title":"Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach: Deep Learning for PM2.5 Estimation","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.5194\/acp-20-3273-2020","article-title":"Improved 1 Km Resolution PM2.5 Estimates across China Using Enhanced Space\u2013Time Extremely Randomized Trees","volume":"20","author":"Wei","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111221","DOI":"10.1016\/j.rse.2019.111221","article-title":"Estimating 1-Km-Resolution PM2.5 Concentrations across China Using the Space-Time Random Forest Approach","volume":"231","author":"Wei","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"144263","DOI":"10.1016\/j.scitotenv.2020.144263","article-title":"Constructing a Spatiotemporally Coherent Long-Term PM2.5 Concentration Dataset over China during 1980\u20132019 Using a Machine Learning Approach","volume":"765","author":"Li","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, J., Fogelman-Souli\u00e9, F., and Largeron, C. (2018, January 12\u201315). Towards Automatic Complex Feature Engineering. Proceedings of the International Conference on Web Information Systems Engineering, Dubai, United Arab Emirates.","DOI":"10.1007\/978-3-030-02925-8_22"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2347736.2347755","article-title":"A Few Useful Things to Know about Machine Learning","volume":"55","author":"Domingos","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105536","DOI":"10.1016\/j.envint.2020.105536","article-title":"Spatiotemporal Trends of PM2.5 Concentrations in Central China from 2003 to 2018 Based on MAIAC-Derived High-Resolution Data","volume":"137","author":"He","year":"2020","journal-title":"Environ. Int."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106726","DOI":"10.1016\/j.envint.2021.106726","article-title":"Satellite-Derived 1-Km Estimates and Long-Term Trends of PM2.5 Concentrations in China from 2000 to 2018","volume":"156","author":"He","year":"2021","journal-title":"Environ. Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"154363","DOI":"10.1016\/j.scitotenv.2022.154363","article-title":"MERRA-2 PM2.5 Mass Concentration Reconstruction in China Mainland Based on LightGBM Machine Learning","volume":"827","author":"Ma","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"529","DOI":"10.5194\/essd-13-529-2021","article-title":"A 6-Year-Long (2013\u20132018) High-Resolution Air Quality Reanalysis Dataset in China Based on the Assimilation of Surface Observations from CNEMC","volume":"13","author":"Kong","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.apr.2020.11.016","article-title":"Climatology and Calibration of MERRA-2 PM2.5 Components over China","volume":"12","author":"Zhao","year":"2021","journal-title":"Atmos. Pollut. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"117666","DOI":"10.1016\/j.atmosenv.2020.117666","article-title":"Evaluation on the Surface PM2.5 Concentration over China Mainland from NASA\u2019s MERRA-2","volume":"237","author":"Ma","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","article-title":"Random Forest","volume":"47","author":"Rigatti","year":"2017","journal-title":"J. Insur. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"112136","DOI":"10.1016\/j.rse.2020.112136","article-title":"Reconstructing 1-Km-Resolution High-Quality PM2.5 Data Records from 2000 to 2018 in China: Spatiotemporal Variations and Policy Implications","volume":"252","author":"Wei","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhan, Q., Fan, Z., Yan, S., Yang, S., and Yang, C. (2019, January 5\u20137). New MAIAC AOD Product Based High Resolution PM2.5 Spatial-Temporal Distribution Change at Urban Scale\u2014Case Study of Wuhan. Proceedings of the 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Shanghai, China.","DOI":"10.1109\/Multi-Temp.2019.8866902"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105801","DOI":"10.1016\/j.envint.2020.105801","article-title":"Construction of a Virtual PM2.5 Observation Network in China Based on High-Density Surface Meteorological Observations Using the Extreme Gradient Boosting Model","volume":"141","author":"Gui","year":"2020","journal-title":"Environ. Int."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"nwaa307","DOI":"10.1093\/nsr\/nwaa307","article-title":"Robust Prediction of Hourly PM2.5 from Meteorological Data Using LightGBM","volume":"8","author":"Zhong","year":"2021","journal-title":"Natl. Sci. Rev."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Guryanov, A. (2019, January 17\u201319). Histogram-Based Algorithm for Building Gradient Boosting Ensembles of Piecewise Linear Decision Trees. Proceedings of the International Conference on Analysis of Images, Social Networks and Texts, Kazan, Russia.","DOI":"10.1007\/978-3-030-37334-4_4"},{"key":"ref_44","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018, January 3\u20138). CatBoost: Unbiased Boosting with Categorical Features. Proceedings of the Advances in Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_45","unstructured":"Dorogush, A.V., Ershov, V., and Gulin, A. (2018). CatBoost: Gradient Boosting with Categorical Features Support. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Horn, F., Pack, R., and Rieger, M. (2019, January 16\u201320). The Autofeat Python Library for Automated Feature Engineering and Selection. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, W\u00fcrzburg, Germany.","DOI":"10.1007\/978-3-030-43823-4_10"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Selvam, S.K., and Rajendran, C. (2021). Tofee-Tree: Automatic Feature Engineering Framework for Modeling Trend-Cycle in Time Series Forecasting. Neural Comput. Appl., 1\u201320.","DOI":"10.1007\/s00521-021-06438-0"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, M., Ding, Z., and Pan, M. (2020, January 17\u201320). LbR: A New Regression Architecture for Automated Feature Engineering. Proceedings of the 2020 International Conference on Data Mining Workshops (ICDMW), Sorrento, Italy.","DOI":"10.1109\/ICDMW51313.2020.00066"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shi, Q., Zhang, Y.-L., Li, L., Yang, X., Li, M., and Zhou, J. (2020, January 20\u201324). SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks. Proceedings of the 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA.","DOI":"10.1109\/ICDE48307.2020.00146"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Khurana, U., Samulowitz, H., and Turaga, D. (2018, January 2\u20137). Feature Engineering for Predictive Modeling Using Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11678"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2022.01.005","article-title":"Soil Moisture Content Retrieval from Landsat 8 Data Using Ensemble Learning","volume":"185","author":"Zhang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Shukla, S. (2016, January 27\u201328). Analysis of K-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. Proceedings of the 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, India.","DOI":"10.1109\/IACC.2016.25"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity Analysis of K-Fold Cross Validation in Prediction Error Estimation","volume":"32","author":"Rodriguez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_54","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1038\/s41592-018-0019-x","article-title":"The Curse(s) of Dimensionality","volume":"15","author":"Altman","year":"2018","journal-title":"Nat. Methods"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1093\/bioinformatics\/bty943","article-title":"Identify Origin of Replication in Saccharomyces Cerevisiae Using Two-Step Feature Selection Technique","volume":"35","author":"Dao","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"12106","DOI":"10.1021\/acs.est.1c01863","article-title":"Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion","volume":"55","author":"Geng","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_58","first-page":"2876","article-title":"Impact of Residential Coal Combustion Control in Beijing-Tianjin-Hebei and Surrounding Region on PM2.5 in Beijing","volume":"34","author":"Xu","year":"2021","journal-title":"Res. Environ. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"114276","DOI":"10.1016\/j.envpol.2020.114276","article-title":"Spatiotemporal Variations and Influencing Factors of PM2.5 Concentrations in Beijing, China","volume":"262","author":"Zhang","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zheng, Y., and Li, C. (2018). Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China. Sustainability, 10.","DOI":"10.3390\/su10124519"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1021\/acs.estlett.8b00573","article-title":"Diurnal Patterns in Global Fine Particulate Matter Concentration","volume":"5","author":"Manning","year":"2018","journal-title":"Environ. Sci. Technol. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, L., Xiong, Q., Wu, G., Gautam, A., Jiang, J., Liu, S., Zhao, W., and Guan, H. (2019). Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing\u2013Tianjin\u2013Hebei Region, China, from 2013 to 2018. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16214276"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"118212","DOI":"10.1016\/j.atmosenv.2021.118212","article-title":"A CatBoost Approach with Wavelet Decomposition to Improve Satellite-Derived High-Resolution PM2.5 Estimates in Beijing-Tianjin-Hebei","volume":"249","author":"Ding","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.5194\/acp-15-2969-2015","article-title":"Exploring the Severe Winter Haze in Beijing: The Impact of Synoptic Weather, Regional Transport and Heterogeneous Reactions","volume":"15","author":"Zheng","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Blagus, R., and Lusa, L. (2013). SMOTE for High-Dimensional Class-Imbalanced Data. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-106"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yu, Z., Qu, Y., Wang, Y., Ma, J., and Cao, Y. (2021). Application of Machine-Learning-Based Fusion Model in Visibility Forecast: A Case Study of Shanghai, China. Remote Sens., 13.","DOI":"10.3390\/rs13112096"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"112890","DOI":"10.1016\/j.rse.2022.112890","article-title":"Application of Geostationary Satellite and High-Resolution Meteorology Data in Estimating Hourly PM2.5 Levels during the Camp Fire Episode in California","volume":"271","author":"Vu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.scitotenv.2019.03.480","article-title":"Satellite-Based High-Resolution Mapping of Ground-Level PM2.5 Concentrations over East China Using a Spatiotemporal Regression Kriging Model","volume":"672","author":"Hu","year":"2019","journal-title":"Sci. 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