{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:08:46Z","timestamp":1776071326642,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Program of the National Natural Science Foundation of China","award":["42430304"],"award-info":[{"award-number":["42430304"]}]},{"name":"Key Program of the National Natural Science Foundation of China","award":["U2139201"],"award-info":[{"award-number":["U2139201"]}]},{"name":"Joint Program of the National Natural Science Foundation of China","award":["42430304"],"award-info":[{"award-number":["42430304"]}]},{"name":"Joint Program of the National Natural Science Foundation of China","award":["U2139201"],"award-info":[{"award-number":["U2139201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The 2015 Tianjin Port chemical explosion highlighted the severe environmental and structural impacts of industrial disasters. This study presents an Adaptive Weighted Coherence Ratio technique, a novel approach for assessing such damage using synthetic aperture radar (SAR) data. Our method overcomes limitations in traditional techniques by incorporating temporal and spatial weighting factors\u2014such as distance from the explosion epicenter, pre- and post-event intervals, and coherence quality\u2014into a robust framework for precise damage classification. This approach effectively captures extreme damage scenarios, including crater formation in inner blast zones, which are challenging for conventional coherence scaling. Through a detailed analysis of the Tianjin explosion, we reveal asymmetric damage patterns influenced by high-rise buildings and demonstrate the method\u2019s applicability to other industrial disasters, such as the 2020 Beirut explosion. Additionally, we introduce a technique for estimating crater dimensions from coherence profiles, enhancing assessment in severely damaged areas. To support structural analysis, we model air pollutant dispersal using HYSPLIT simulations. This integrated approach advances SAR-based damage assessment techniques, providing rapid reliable classifications applicable to various industrial explosions, aiding disaster response and recovery planning.<\/jats:p>","DOI":"10.3390\/rs16224241","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T08:06:32Z","timestamp":1731571592000},"page":"4241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Weighted Coherence Ratio Approach for Industrial Explosion Damage Mapping: Application to the 2015 Tianjin Port Incident"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhe","family":"Su","sequence":"first","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Energy Resources, China University of Geosciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105101","DOI":"10.1016\/j.ssci.2020.105101","article-title":"Hazardous chemical leakage accidents and emergency evacuation response from 2009 to 2018 in China: A review","volume":"135","author":"Hou","year":"2021","journal-title":"Saf. Sci."},{"key":"ref_2","first-page":"17","article-title":"Failures, repeated\u2014The Tianjin explosion","volume":"286","author":"Bloor","year":"2022","journal-title":"Loss Prev. Bull."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1002\/prs.11789","article-title":"Facts related to August 12, 2015 explosion accident in Tianjin, China","volume":"34","author":"Huang","year":"2015","journal-title":"Process Saf. Prog."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.psep.2023.09.057","article-title":"Hazard evaluation framework for large yield explosions in urban environments: A case study of Beirut explosion","volume":"179","author":"Hu","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1002\/prs.11837","article-title":"Anatomy of Tianjin Port fire and explosion: Process and causes","volume":"35","author":"Fu","year":"2016","journal-title":"Process Saf. Prog."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jenvman.2017.05.021","article-title":"Characterization of post-disaster environmental management for hazardous materials incidents: Lessons learnt from the Tianjin warehouse explosion, China","volume":"199","author":"Zhang","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1080\/10807039.2018.1480352","article-title":"Nitrate contamination in a coastal soil and water system: A case study after the Tianjin Port 8\/12 explosion, China","volume":"25","author":"Liu","year":"2019","journal-title":"Hum. Ecol. Risk Assess."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s11869-015-0371-2","article-title":"On the August 12, 2015 occurrence of explosions and fires in Tianjin, China, and the atmospheric impact observed in central Korea","volume":"8","author":"Chung","year":"2015","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4433","DOI":"10.1080\/01431160600675895","article-title":"Satellite radar and optical remote sensing for earthquake damage detection: Results from different case studies","volume":"27","author":"Stramondo","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1109\/TGRS.2006.883149","article-title":"Coherence- and amplitude-based analysis of seismogenic damage in Bam, Iran, using ENVISAT ASAR data","volume":"45","author":"Arciniegas","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1038\/nature07373","article-title":"Medieval forewarning of the 2004 Indian Ocean tsunami in Thailand","volume":"455","author":"Jankaew","year":"2008","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, M., Park, S.E., and Lee, S.J. (2023). Detection of damaged buildings using temporal SAR data with different observation modes. Remote Sens., 15.","DOI":"10.3390\/rs15020308"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10095020.2022.2128902","article-title":"A review of multi-class change detection for satellite remote sensing imagery","volume":"27","author":"Zhu","year":"2024","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cheng, G., Huang, Y., Li, X., Lyu, S., Xu, Z., Zhao, H., Zhao, Q., and Xiang, S. (2024). Change detection methods for remote sensing in the last decade: A comprehensive review. Remote Sens., 16.","DOI":"10.3390\/rs16132355"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, X., Feng, G., He, L., An, Q., Xiong, Z., Lu, H., Wang, W., Li, N., Zhao, Y., and Wang, Y. (2023). Evaluating urban building damage of 2023 Kahramanmaras, Turkey earthquake sequence using SAR change detection. Sensors, 23.","DOI":"10.3390\/s23146342"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4011205","DOI":"10.1109\/LGRS.2024.3406966","article-title":"QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick Damaged-building Detection","volume":"21","author":"Sun","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Macchiarulo, V., Giardina, G., Milillo, P., Aktas, Y.D., and Whitworth, M.R. (2024). Integrating post-event very high resolution SAR imagery and machine learning for building-level earthquake damage assessment. Bull. Earthq. Eng.","DOI":"10.1007\/s10518-024-01877-1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lang, F., Zhu, Y., Zhao, J., Hu, X., Shi, H., Zheng, N., and Zha, J. (2024). Flood Mapping of Synthetic Aperture Radar (SAR) Imagery Based on Semi-Automatic Thresholding and Change Detection. Remote Sens., 16.","DOI":"10.3390\/rs16152763"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Amitrano, D., Di Martino, G., Di Simone, A., and Imperatore, P. (2024). Flood detection with SAR: A review of techniques and datasets. Remote Sens., 16.","DOI":"10.3390\/rs16040656"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"114373","DOI":"10.1016\/j.rse.2024.114373","article-title":"Flood inundation monitoring using multi-source satellite imagery: A knowledge transfer strategy for heterogeneous image change detection","volume":"314","author":"Zhao","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104371","DOI":"10.1016\/j.ijdrr.2024.104371","article-title":"Scalable and rapid building damage detection after hurricane Ian using causal Bayesian networks and InSAR imagery","volume":"104","author":"Wang","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1002\/rse2.257","article-title":"Radar and optical remote sensing for near real-time assessments of cyclone impacts on coastal ecosystems","volume":"8","author":"Mondal","year":"2022","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Adriano, B., Xia, J., Baier, G., Yokoya, N., and Koshimura, S. (2019). Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 Sulawesi earthquake and tsunami in Palu, Indonesia. Remote Sens., 11.","DOI":"10.3390\/rs11070886"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Koshimura, S., Moya, L., Mas, E., and Bai, Y. (2020). Tsunami damage detection with remote sensing: A review. Geosciences, 10.","DOI":"10.3390\/geosciences10050177"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2021.02.016","article-title":"Learning from multimodal and multitemporal earth observation data for building damage mapping","volume":"175","author":"Adriano","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Boloorani, A.D., Darvishi, M., Weng, Q., and Liu, X. (2021). Post-war urban damage mapping using InSAR: The case of Mosul City in Iraq. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030140"},{"key":"ref_27","first-page":"100505","article-title":"Integration of Sentinel-1 and Sentinel-2 data for change detection: A case study in a war conflict area of Mosul city","volume":"22","author":"Fakhri","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2021.01.001","article-title":"Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon","volume":"173","author":"ElGharbawi","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aimaiti, Y., Sanon, C., Koch, M., Baise, L.G., and Moaveni, B. (2022). War-related building damage assessment in Kyiv, Ukraine, using Sentinel-1 radar and Sentinel-2 optical images. Remote Sens., 14.","DOI":"10.3390\/rs14246239"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, Q., Jin, G., Xiong, X., Ye, H., and Xie, Y. (2023). Monitoring urban change in conflict from the perspective of optical and SAR satellites: The case of Mariupol, a city in the conflict between RUS and UKR. Remote Sens., 15.","DOI":"10.3390\/rs15123096"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kopiika, N., Ninic, J., and Mitoulis, S. (2024, January 10\u201314). Damage characterisation using Sentinel-1 images: Case study of bridges in Ukraine. Proceedings of the IABSE Symposium, Manchester 2024: Construction\u2019s Role for a World in Emergency, Manchester, UK.","DOI":"10.2749\/manchester.2024.0367"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1177\/87552930241262761","article-title":"Large-scale building damage assessment based on recurrent neural networks using SAR coherence time series: A case study of the 2023 Turkey\u2013Syria earthquake","volume":"40","author":"Yang","year":"2024","journal-title":"Earthq. Spectra"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.atmosenv.2016.08.086","article-title":"Estimating ship emissions based on AIS data for port of Tianjin, China","volume":"145","author":"Chen","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.psep.2019.08.028","article-title":"Case study of the Tianjin accident: Application of barrier and systems analysis to understand challenges to industry loss prevention in emerging economies","volume":"131","author":"Chen","year":"2019","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/16742834.2019.1682926","article-title":"Impact of an accidental explosion in Tianjin Port on enhanced atmospheric nitrogen deposition over the Bohai Sea inferred from aerosol nitrate dual isotopes","volume":"13","author":"Zong","year":"2020","journal-title":"Atmos. Ocean. Sci. Lett."},{"key":"ref_36","unstructured":"Tianjin Port Explosion Accident Investigation Team of the State Council (2016). Tianjin Port \u201c8.12\u201d Ruihai Company Dangerous Goods Warehouse Investigation Report on Extraordinarily Serious Fire and Explosion Accidents, Tianjin Port Explosion Accident Investigation Team of the State Council. (In Chinese)."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2671","DOI":"10.30638\/eemj.2015.284","article-title":"Hazardous properties of ammonium nitrate and modeling of explosions using TNT equivalency","volume":"14","author":"Ozunu","year":"2015","journal-title":"Environ. Eng. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"328","DOI":"10.14429\/dsj.69.13637","article-title":"Blast wave characteristics and TNT equivalent of improvised explosive device at small-scaled distances","volume":"69","author":"Chiquito","year":"2019","journal-title":"Def. Sci. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mandal, D., Vaka, D.S., Bhogapurapu, N.R., Vanama, V.S.K., Kumar, V., Rao, Y.S., and Bhattacharya, A. (2019). Sentinel-1 SLC Preprocessing Workflow for Polarimetric Applications: A Generic Practice for Generating Dual-pol Covariance Matrix Elements in SNAP S-1 Toolbox. Preprints, 2019110393.","DOI":"10.20944\/preprints201911.0393.v1"},{"key":"ref_40","unstructured":"Hogenson, K., Kristenson, H., Kennedy, J., Johnston, A., Rine, J., Logan, T.A., Zhu, J., Williams, F., Herrmann, J., and Smale, J. (2016, January 12\u201316). Hybrid Pluggable Processing Pipeline (HyP3): A Cloud-Native Infrastructure for Generic Processing of SAR Data. Proceedings of the 2016 AGU Fall Meeting, San Francisco, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2859","DOI":"10.1109\/IGARSS.2002.1026802","article-title":"Damage estimation model using temporal coherence ratio","volume":"Volume 5","author":"Ito","year":"2002","journal-title":"Proceedings of the IEEE International Geoscience and Remote Sensing Symposium"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hall, G.B., and Leahy, M.G. (2008). The geospatial data abstraction library. Open Source Approaches in Spatial Data Handling, Springer.","DOI":"10.1007\/978-3-540-74831-1"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1029\/98EO00426","article-title":"New, improved version of Generic Mapping Tools released","volume":"79","author":"Wessel","year":"1998","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/36.175330","article-title":"Decorrelation in interferometric radar echoes","volume":"30","author":"Zebker","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","first-page":"1","article-title":"Surface ruptures and building damage of the 2003 Bam, Iran, earthquake mapped by satellite synthetic aperture radar interferometric correlation","volume":"110","author":"Fielding","year":"2005","journal-title":"J. Geophys. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1016\/j.procs.2024.06.425","article-title":"Damage Proxy Mapping with SAR interferometric coherence change","volume":"239","author":"Fielding","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/5.838084","article-title":"Synthetic aperture radar interferometry","volume":"88","author":"Rosen","year":"2000","journal-title":"Proc. IEEE"},{"key":"ref_48","unstructured":"Takeuchi, S., Suga, Y., Yonezawa, C., and Chen, A.J. (2000, January 24\u201328). Detection of Urban Disaster Using InSAR: A Case Study for the 1999 Great Taiwan Earthquake. Proceedings of the IEEE IGARSS, Honolulu, HI, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1080\/01431160118187","article-title":"Decorrelation of SAR data by urban damages caused by the 1995 Hyogoken-nanbu earthquake","volume":"22","author":"Yonezawa","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0273-1177(01)00334-9","article-title":"Application of ERS-2\/SAR data for the 1999 Taiwan earthquake","volume":"28","author":"Suga","year":"2001","journal-title":"Adv. Space Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"439","DOI":"10.5194\/isprs-annals-IV-2-W4-439-2017","article-title":"A SAR intensity images change detection method based on fusion difference detector and statistical properties","volume":"4","author":"Cui","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mastro, P., Masiello, G., Serio, C., and Pepe, A. (2022). Change detection techniques with synthetic aperture radar images: Experiments with random forests and Sentinel-1 observations. Remote Sens., 14.","DOI":"10.3390\/rs14143323"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1109\/TGRS.2005.857987","article-title":"A detail-preserving scale-driven approach to change detection in multitemporal SAR images","volume":"43","author":"Bovolo","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TGRS.2004.842441","article-title":"An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images","volume":"43","author":"Bazi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1109\/LGRS.2012.2191387","article-title":"Wavelet fusion on ratio images for change detection in SAR images","volume":"9","author":"Ma","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/36.905230","article-title":"Analysis of topographic decorrelation in SAR interferometry using ratio coherence imagery","volume":"39","author":"Lee","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1080\/01431160600928567","article-title":"Mapping damage during the Bam (Iran) earthquake using interferometric coherence","volume":"28","author":"Hoffmann","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111693","DOI":"10.1016\/j.rse.2020.111693","article-title":"A review on synthetic aperture radar-based building damage assessment in disasters","volume":"240","author":"Ge","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/36.239913","article-title":"Change detection techniques for ERS-1 SAR data","volume":"31","author":"Rignot","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","first-page":"1386","article-title":"Application of log-cumulants to change detection on multi-temporal SAR images","volume":"Volume 2","author":"Bujor","year":"2003","journal-title":"Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.1109\/JSTARS.2014.2328344","article-title":"Unsupervised change detection in SAR image based on gauss-log ratio image fusion and compressed projection","volume":"7","author":"Hou","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Agapiou, A. (2020). Damage proxy map of the Beirut explosion on 4th of August 2020 as observed from the Copernicus sensors. Sensors, 20.","DOI":"10.3390\/s20216382"},{"key":"ref_63","unstructured":"Gr\u00fcnthal, G. (1998). European Macroseismic Scale 1998 (EMS-98), European Seismological Commission."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Pilger, C., Gaebler, P., Hupe, P., Kalia, A.C., Schneider, F.M., Steinberg, A., Sudhaus, H., and Ceranna, L. (2021). Yield estimation of the 2020 Beirut explosion using open access waveform and remote sensing data. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-93690-y"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"04022008","DOI":"10.1061\/(ASCE)NH.1527-6996.0000550","article-title":"Impacts of 2020 Beirut explosion on port infrastructure and nearby buildings","volume":"23","author":"Sadek","year":"2022","journal-title":"Nat. Hazards Rev."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yu, G., Duh, Y.S., Yang, X., Li, Y., Chen, Y., Li, Y., Li, J., Chen, R., Gong, L., and Yang, B. (2022). Holistic case study on the explosion of ammonium nitrate in Tianjin Port. Sustainability, 14.","DOI":"10.3390\/su14063429"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Xu, R., Chen, L., Zheng, Y., Li, Z., Cao, M., and Fang, Q. (2021). Study of crater in the Gobi desert induced by ground explosion of large amounts of TNT explosive up to 10 tons. Shock Vib., 7357877.","DOI":"10.1155\/2021\/7357877"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1175\/BAMS-D-14-00110.1","article-title":"NOAA\u2019s HYSPLIT atmospheric transport and dispersion modeling system","volume":"96","author":"Stein","year":"2015","journal-title":"Bull. Amer. Meteor. Soc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:32:15Z","timestamp":1760113935000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":68,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224241"],"URL":"https:\/\/doi.org\/10.3390\/rs16224241","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]}}}