{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:36:14Z","timestamp":1773801374571,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Sciences Program of the Ministry of Education of China","award":["23YJAZH223"],"award-info":[{"award-number":["23YJAZH223"]}]},{"name":"Humanities and Social Sciences Program of the Ministry of Education of China","award":["KFKT-2022-06"],"award-info":[{"award-number":["KFKT-2022-06"]}]},{"name":"Humanities and Social Sciences Program of the Ministry of Education of China","award":["41001234"],"award-info":[{"award-number":["41001234"]}]},{"name":"Key Laboratory of Spatial\u2013temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR","award":["23YJAZH223"],"award-info":[{"award-number":["23YJAZH223"]}]},{"name":"Key Laboratory of Spatial\u2013temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR","award":["KFKT-2022-06"],"award-info":[{"award-number":["KFKT-2022-06"]}]},{"name":"Key Laboratory of Spatial\u2013temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR","award":["41001234"],"award-info":[{"award-number":["41001234"]}]},{"name":"National Natural Science Foundation of China","award":["23YJAZH223"],"award-info":[{"award-number":["23YJAZH223"]}]},{"name":"National Natural Science Foundation of China","award":["KFKT-2022-06"],"award-info":[{"award-number":["KFKT-2022-06"]}]},{"name":"National Natural Science Foundation of China","award":["41001234"],"award-info":[{"award-number":["41001234"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the urgency of PM2.5 prediction becomes increasingly ingrained in public awareness, deep-learning methods have been widely used in forecasting concentration trends of PM2.5 and other atmospheric pollutants. Traditional time-series forecasting models, like long short-term memory (LSTM) and temporal convolutional network (TCN), were found to be efficient in atmospheric pollutant estimation, but either the model accuracy was not high enough or the models encountered certain challenges due to their own structure or some specific application scenarios. This study proposed a high-accuracy, hourly PM2.5 forecasting model, poly-dimensional local-LSTM Transformer, namely PD-LL-Transformer, by deep-learning methods, based on air pollutant data and meteorological data, and aerosol optical depth (AOD) data retrieved from the Himawari-8 satellite. This research was based on the Yangtze River Delta Urban Agglomeration (YRDUA), China for 2020\u20132022. The PD-LL-Transformer had three parts: a poly-dimensional embedding layer, which integrated the advantages of allocating and embedding multi-variate features in a more refined manner and combined the superiority of different temporal processing methods; a local-LSTM block, which combined the advantages of LSTM and TCN; and a Transformer encoder block. Over the test set (the whole year of 2022), the model\u2019s R2 was 0.8929, mean absolute error (MAE) was 4.4523 \u00b5g\/m3, and root mean squared error (RMSE) was 7.2683 \u00b5g\/m3, showing great accuracy for PM2.5 prediction. The model surpassed other existing models upon the same tasks and similar datasets, with the help of which a PM2.5 forecasting tool with better performance and applicability could be established.<\/jats:p>","DOI":"10.3390\/rs16111915","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T08:36:22Z","timestamp":1716798982000},"page":"1915","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["PD-LL-Transformer: An Hourly PM2.5 Forecasting Method over the Yangtze River Delta Urban Agglomeration, China"],"prefix":"10.3390","volume":"16","author":[{"given":"Rongkun","family":"Zou","sequence":"first","affiliation":[{"name":"Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China"},{"name":"Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, Shanghai 200063, China"}]},{"given":"Heyun","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China"}]},{"given":"Xiaoman","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China"}]},{"given":"Fanmei","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China"}]},{"given":"Chu","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China"}]},{"given":"Weiqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China"}]},{"given":"Liguo","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China"},{"name":"Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, Shanghai 200063, China"},{"name":"Institute of Eco-Chongming (IEC), No. 3663 Northern Zhongshan Road, Shanghai 200062, China"}]},{"given":"Xiaoyan","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.chemosphere.2014.01.032","article-title":"Fine particulate matter, temperature, and lung function in healthy adults: Findings from the HVNR study","volume":"108","author":"Wu","year":"2014","journal-title":"Chemosphere"},{"key":"ref_2","first-page":"E69","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":"1058","DOI":"10.1126\/science.276.5315.1058","article-title":"Heterogeneous and multiphase chemistry in the troposphere","volume":"276","author":"Ravishankara","year":"1997","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1289\/ehp.1409276","article-title":"Ambient PM2.5, O3, and NO2 Exposures and Associations with Mortality over 16 Years of Follow-Up in the Canadian Census Health and Environment Cohort (CanCHEC)","volume":"123","author":"Crouse","year":"2015","journal-title":"Environ. Health Perspect."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.envpol.2017.01.060","article-title":"Health burden attributable to ambient PM2.5 in China","volume":"223","author":"Song","year":"2017","journal-title":"Environ. Pollut."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.envint.2015.10.016","article-title":"Review on recent progress in observations, source identifications and countermeasures of PM2.5","volume":"86","author":"Liang","year":"2016","journal-title":"Environ. Int."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atmosenv.2007.09.003","article-title":"Air pollution in mega cities in China","volume":"42","author":"Chan","year":"2008","journal-title":"Atmos. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1289\/ehp.1408646","article-title":"Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter","volume":"123","author":"Martin","year":"2015","journal-title":"Environ. Health Perspect."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.atmosenv.2014.12.010","article-title":"How well do satellite AOD observations represent the spatial and temporal variability of PM2.5 concentration for the United States?","volume":"102","author":"Li","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.apr.2019.11.020","article-title":"Mitigating MODIS AOD non-random sampling error on surface PM2.5 estimates by a combined use of Bayesian Maximum Entropy method and linear mixed-effects model","volume":"11","author":"Fu","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_11","first-page":"100396","article-title":"Evaluation of Aerosol Optical Depth (AOD) and PM2.5 associations for air quality assessment","volume":"20","author":"Yang","year":"2020","journal-title":"Remote Sens. Appl.-Soc. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101543","DOI":"10.1016\/j.apr.2022.101543","article-title":"Air quality prediction using spatio-temporal deep learning","volume":"13","author":"Hu","year":"2022","journal-title":"Atmos. Pollut. Res."},{"key":"ref_13","unstructured":"Li, Y., Xue, Y., Guang, J., She, L., Chen, G.L., and Fan, C. (August, January 28). Hourly Ground Level PM2.5 Estimation for the Southeast of China Based on Himawari-8 Observation Data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118826","DOI":"10.1016\/j.envpol.2022.118826","article-title":"Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China","volume":"297","author":"Song","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xue, Y., Li, Y., Guang, J., Tugui, A., She, L., Qin, K., Fan, C., Che, Y.H., Xie, Y.Q., and Wen, Y.N. (2020). Hourly PM2.5 Estimation over Central and Eastern China Based on Himawari-8 Data. Remote Sens., 12.","DOI":"10.3390\/rs12050855"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1016\/j.procs.2020.04.221","article-title":"Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models","volume":"171","author":"Gad","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.scitotenv.2018.04.251","article-title":"A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information","volume":"636","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"44416","DOI":"10.1109\/ACCESS.2019.2908975","article-title":"High Spatial Resolution PM2.5 Retrieval Using MODIS and Ground Observation Station Data Based on Ensemble Random Forest","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","unstructured":"Li, H.Q., and Shi, X.H. (2016, January 9\u201311). Data Driven based PM2.5 Concentration Forecasting. Proceedings of the International Conference on Biological Engineering and Pharmacy (BEP), Shanghai, China."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.scitotenv.2018.09.111","article-title":"Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting","volume":"651","author":"Zhou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"131898","DOI":"10.1016\/j.jclepro.2022.131898","article-title":"PM2.5 volatility prediction by XGBoost-MLP based on GARCH models","volume":"356","author":"Dai","year":"2022","journal-title":"J Clean Prod."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2243743","DOI":"10.1080\/23311916.2023.2243743","article-title":"Air quality analysis and PM2.5 modelling using machine learning techniques: A study of Hyderabad city in India","volume":"10","author":"Mathew","year":"2023","journal-title":"Cogent Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e2023EA002911","DOI":"10.1029\/2023EA002911","article-title":"Predicting PM2.5 Concentrations Across USA Using Machine Learning","volume":"10","author":"Vignesh","year":"2023","journal-title":"Earth Space Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114465","DOI":"10.1016\/j.envres.2022.114465","article-title":"Evaluation of different machine learning approaches and aerosol optical depth in PM2.5 prediction","volume":"216","author":"Karimian","year":"2023","journal-title":"Environ. Res."},{"key":"ref_25","first-page":"D14205","article-title":"Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach","volume":"114","author":"Gupta","year":"2009","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gupta, P., and Christopher, S.A. (2009). Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD011497"},{"key":"ref_27","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_28","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","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1109\/LGRS.2019.2900270","article-title":"Deep Learning Architecture for Estimating Hourly Ground-Level PM2.5 Using Satellite Remote Sensing","volume":"16","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2022.05.011","article-title":"The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)","volume":"190","author":"Wang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., and Schmidhuber, J. (1996, January 6\u20138). Bridging long time lags by weight guessing and \u201clong short term memory\u201d. Proceedings of the Sintra Workshop on Spatiotemporal Models in Biological and Artificial Systems, Sintra, Portugal.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y.Y., Sun, Q.S., Liu, J., and Petrosian, O. (2024). Long-Term Forecasting of Air Pollution Particulate Matter (PM2.5) and Analysis of Influencing Factors. Sustainability, 16.","DOI":"10.3390\/su16010019"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s10651-021-00501-8","article-title":"A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation","volume":"28","author":"Ding","year":"2021","journal-title":"Environ. Ecol. Stat."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kim, H.S., Han, K.M., Yu, J., Kim, J., Kim, K., and Kim, H. (2022). Development of a CNN plus LSTM Hybrid Neural Network for Daily PM2.5 Prediction. Atmosphere, 13.","DOI":"10.3390\/atmos13122124"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, D., Liu, J.P., and Zhao, Y.Y. (2022). Prediction of Multi-Site PM2.5 Concentrations in Beijing Using CNN-Bi LSTM with CBAM. Atmosphere, 13.","DOI":"10.3390\/atmos13101719"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"26933","DOI":"10.1109\/ACCESS.2020.2971348","article-title":"A Hybrid CNN-LSTM Model for Forecasting Particulate Matter PM2.5","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"118707","DOI":"10.1016\/j.eswa.2022.118707","article-title":"Investigation of nearby monitoring station for hourly PM2.5 forecasting using parallel multi-input 1D-CNN-biLSTM","volume":"211","author":"Zhu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30, Available online: https:\/\/arxiv.org\/abs\/1706.03762."},{"key":"ref_41","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_42","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2024, April 14). Improving Language Understanding by Generative Pre-Training. Available online: https:\/\/www.mikecaptain.com\/resources\/pdf\/GPT-1.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tong, W.T., Limperis, J., Hamza-Lup, F., Xu, Y., and Li, L.X. (2023). Robust Transformer-based model for spatiotemporal PM2.5 prediction in California. Earth Sci. Inform.","DOI":"10.1007\/s12145-023-01138-w"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1007\/s10661-023-12020-z","article-title":"A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST","volume":"195","author":"Verma","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"101839","DOI":"10.1016\/j.apr.2023.101839","article-title":"Long-term PM2.5 concentrations forecasting using CEEMDAN and deep Transformer neural network","volume":"14","author":"Zeng","year":"2023","journal-title":"Atmos. Pollut. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13535","DOI":"10.1007\/s13762-023-04900-1","article-title":"Modeling air quality PM2.5 forecasting using deep sparse attention-based transformer networks","volume":"20","author":"Zhang","year":"2023","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"160446","DOI":"10.1016\/j.scitotenv.2022.160446","article-title":"Predicting hourly PM2.5 concentrations in wildfire-prone areas using a SpatioTemporal Transformer model","volume":"860","author":"Yu","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, H.Q., Zhang, L.F., and Wu, R. (2023). MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas. Atmosphere, 14.","DOI":"10.3390\/atmos14081294"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1007\/s12145-023-01002-x","article-title":"PM2.5 forecasting based on transformer neural network and data embedding","volume":"16","author":"Limperis","year":"2023","journal-title":"Earth Sci. Inform."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.5194\/acp-11-1621-2011","article-title":"Air quality and emissions in the Yangtze River Delta, China","volume":"11","author":"Li","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"108623","DOI":"10.1016\/j.ecolind.2022.108623","article-title":"Spatial-temporal characteristics of carbon emissions from land use change in Yellow River Delta region, China","volume":"136","author":"Zhang","year":"2022","journal-title":"Ecol. Indicators"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1162\/106454602320184202","article-title":"Ant colony optimization and stochastic gradient descent","volume":"8","author":"Meuleau","year":"2002","journal-title":"Artif Life"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Saeed, U., Ahmad, S., Alsadi, J., Ross, D., and Rizvi, G. (2013, January 15\u201319). Implementation Of Neural Network For Color Properties Of Polycarbonates. Proceedings of the 29th International Conference of the Polymer-Processing-Society (PPS), Nuremberg, Germany.","DOI":"10.1063\/1.4873733"},{"key":"ref_54","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_55","unstructured":"(2012). Ambient Air Quality Standards (Standard No. GB 3095-2012). Available online: https:\/\/www.chinesestandard.net\/PDF.aspx\/GB3095-2012."},{"key":"ref_56","unstructured":"(2012). Technical Regulation on Ambient Air Quality Index (on trial) (Standard No. HJ 633-2012). Available online: https:\/\/www.chinesestandard.net\/PDF.aspx\/HJ633-2012."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1915\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:49:19Z","timestamp":1760107759000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1915"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111915"],"URL":"https:\/\/doi.org\/10.3390\/rs16111915","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,27]]}}}