Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region

The use of images from the Sentinel-3B SYN satellite (surface reflectance and aerosol parameters over land) is currently one of the most advanced technologies utilized to identify atmospheric aerosol concentrations on a global scale. The general aim of this study is to analyze the evolution of aeros...

Full description

Autores:
Neckel, Alcindo
Santosh, M.
William Bodah, Brian
Stolfo Maculan, Laércio
Pinto, Diana
Korcelski, Cleiton
Carollo Toscan, Paloma
Pasa Cambrussi, Laura
Cezar Caino, Isadora
Dal Moro, Leila
Piccinato Junior, Dirceu
Tibério Cardoso, Grace
Oliveira de Almeida Silva, Caliane Christie
de Vargas Mores, Giana
Tipo de recurso:
Article of investigation
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/12891
Acceso en línea:
https://hdl.handle.net/11323/12891
https://repositorio.cuc.edu.co/
Palabra clave:
Remote sensing
Atmospheric pollution
Aerosols
Geospatial analyses
Global scale
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_d908c3f3db26d97014ef76e88a03a167
oai_identifier_str oai:repositorio.cuc.edu.co:11323/12891
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region
title Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region
spellingShingle Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region
Remote sensing
Atmospheric pollution
Aerosols
Geospatial analyses
Global scale
title_short Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region
title_full Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region
title_fullStr Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region
title_full_unstemmed Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region
title_sort Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine region
dc.creator.fl_str_mv Neckel, Alcindo
Santosh, M.
William Bodah, Brian
Stolfo Maculan, Laércio
Pinto, Diana
Korcelski, Cleiton
Carollo Toscan, Paloma
Pasa Cambrussi, Laura
Cezar Caino, Isadora
Dal Moro, Leila
Piccinato Junior, Dirceu
Tibério Cardoso, Grace
Oliveira de Almeida Silva, Caliane Christie
de Vargas Mores, Giana
dc.contributor.author.none.fl_str_mv Neckel, Alcindo
Santosh, M.
William Bodah, Brian
Stolfo Maculan, Laércio
Pinto, Diana
Korcelski, Cleiton
Carollo Toscan, Paloma
Pasa Cambrussi, Laura
Cezar Caino, Isadora
Dal Moro, Leila
Piccinato Junior, Dirceu
Tibério Cardoso, Grace
Oliveira de Almeida Silva, Caliane Christie
de Vargas Mores, Giana
dc.subject.proposal.eng.fl_str_mv Remote sensing
Atmospheric pollution
Aerosols
Geospatial analyses
Global scale
topic Remote sensing
Atmospheric pollution
Aerosols
Geospatial analyses
Global scale
description The use of images from the Sentinel-3B SYN satellite (surface reflectance and aerosol parameters over land) is currently one of the most advanced technologies utilized to identify atmospheric aerosol concentrations on a global scale. The general aim of this study is to analyze the evolution of aerosols in the atmosphere of the Kiev region in northern Ukraine during 2019, 2020, 2021 and 2022. Due to this study’s timing, both prior to and during the current military incursion into Ukraine, this study also evaluates the consequences of the invasion of the Russian army on the territory of Ukraine, in relation to the quantitative levels of aerosols present in the atmosphere. Satellite image data were modelled in SNAP software (Sentinel Application Platform). Using the JASP software (version 0.14.1.0), clusters with variations of T550 (Aerosol Optical Thickness) were generated. The Sentinel-3B SYN satellite images were made available by the European Space Agency (ESA), with moderate spatial resolution (>300 m), calibrated and normalized to an average standard of 0.83 µg/mg, with a maximum error of 6.62% in the 30 sampled points. Satellite image data were modelled in SNAP software. Using the JASP software (version 0.14.1.0), clusters with variations of T550 (Aerosol Optical Thickness) were generated. The results show variations in the concentration of T550 in different periods, revealing that the military conflict between Russia and Ukraine directly influenced the dynamics of aerosol concentration, attributed to factors incompatible with environmental sustainability.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022-12-07
dc.date.accessioned.none.fl_str_mv 2024-04-29T14:12:58Z
dc.date.available.none.fl_str_mv 2024-04-29T14:12:58Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Neckel, A.; Santosh, M.; Bodah, B.W.; Maculan, L.S.; Pinto, D.; Korcelski, C.; Toscan, P.C.; Cambrussi, L.P.; Caino, I.C.; Moro, L.D.; et al. Using the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region. Sustainability 2022, 14, 16357. https://doi.org/10.3390/su142416357
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/12891
dc.identifier.doi.none.fl_str_mv 10.3390/su142416357
dc.identifier.eissn.spa.fl_str_mv 2071-1050
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC – Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Neckel, A.; Santosh, M.; Bodah, B.W.; Maculan, L.S.; Pinto, D.; Korcelski, C.; Toscan, P.C.; Cambrussi, L.P.; Caino, I.C.; Moro, L.D.; et al. Using the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region. Sustainability 2022, 14, 16357. https://doi.org/10.3390/su142416357
10.3390/su142416357
2071-1050
Corporación Universidad de la Costa
REDICUC – Repositorio CUC
url https://hdl.handle.net/11323/12891
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Sustainability
dc.relation.references.spa.fl_str_mv 1. Mao, S.; Lang, J.; Chen, T.; Cheng, S. Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme through sensitivity analysis combined with optimization model. Environ. Pollut. 2021, 284, 117186. [CrossRef] [PubMed]
2. Leng, S.; Li, S.W.; Hu, Z.Z.; Wu, H.Y.; Li, B.B. Development of a micro-in-meso-scale framework for simulating pollutant dispersion and wind environment in building groups. J. Clean. Prod. 2022, 364, 132661. [CrossRef]
3. Zhang, X.; Wang, J. Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety. JSSR 2022, 3, 372–397. [CrossRef]
4. Félix, O.I.; Csavina, J.; Field, J.; Rine, K.P.; Sáez, A.E.; Betterton, E.A. Use of lead isotopes to identify sources of metal and metalloid contaminants in atmospheric aerosol from mining operations. Chemosphere 2015, 122, 219–226. [CrossRef]
5. Bodah, B.W.; Neckel, A.; Stolfo Maculan, L.; Milanes, C.B.; Korcelski, C.; Ramírez, O.; Mendez-Espinosa, J.F.; Bodah, E.T.; Oliveira, M.L. Sentinel-5P TROPOMI satellite application for NO2 and CO studies aiming at environmental valuation. J. Clean. Prod. 2022, 357, 131960. [CrossRef]
6. Jiao, X.; Zeng, R.; Lan, G.; Zuo, S.; He, J.; Wang, C. Mechanistic study on photochemical generation of I•/I2•− radicals in coastal atmospheric aqueous aerosol. Sci. Total Environ. 2022, 825, 154080. [CrossRef]
7. Wang, H.; He, C.; Modini, R.L.; Wang, W.; Lu, H.; Morawska, L. Mixing state of printer generated ultrafine particles: Implications for the complexity of indoor aerosols. Atmos. Environ. 2021, 259, 118550. [CrossRef]
8. Chen, S.; Zhang, R.; Mao, R.; Zhang, Y.; Chen, Y.; Ji, Z.; Gong, Y.; Guan, Y. Sources, characteristics and climate impact of light-absorbing aerosols over the Tibetan Plateau. Earth-Sci. Rev. 2022, 232, 104111. [CrossRef]
9. Yang, J.; Zhao, C.; Sun, Y.; Chi, Y.; Yang, Y. Aerosol first indirect effect over narrow longitude regions of North Pacific and same-latitude lands. Atmos. Environ. 2022, 277, 119081. [CrossRef]
10. Li, N.; Georas, S.; Alexis, N.; Fritz, P.; Xia, T.; Williams, M.A.; Horner, E.; Nel, A. A work group report on ultrafine particles (American Academy of Allergy, Asthma & Immunology): Why ambient ultrafine and engineered nanoparticles should receive special attention for possible adverse health outcomes in human subjects. J. Allergy Clin. Immunol. 2016, 138, 386–396. [CrossRef]
11. Moreno-Ríos, A.L.; Tejeda-Benítez, L.P.; Bustillo-Lecompte, C.F. Sources, characteristics, toxicity, and control of ultrafine particles: An overview. Geosci. Front. 2020, 13, 101147. [CrossRef]
12. Fan, M.Y.; Zhang, Y.L.; Lin, Y.C.; Cao, F.; Sun, Y.; Qiu, Y.; Xing, G.; Dao, X.; Fu, P. Specific sources of health risks induced by metallic elements in PM2.5 during the wintertime in Beijing, China. Atmos. Environ. 2021, 246, 118112. [CrossRef]
13. Lee, M.H.; Yang, W.; Chae, N.; Choi, S. High resolution size characterization of particulate contaminants for radioactive metal waste treatment. Nucl. Eng. Technol. 2021, 53, 2277–2288. [CrossRef]
14. Middya, A.I.; Roy, S. Pollutant specific optimal deep learning and statistical model building for air quality forecasting. Environ. Pollut. 2020, 301, 118–972. [CrossRef]
15. Mohammadshirazi, A.; Kalkhorani, V.A.; Humes, J.; Speno, B.; Rike, J.; Ramnath, R.; Clark, J.D. Predicting airborne pollutant concentrations and events in a commercial building using low-cost pollutant sensors and machine learning: A case study. Build. Environ. 2022, 213, 108833. [CrossRef]
16. Fernandez-Moran, R.; Gómez-Chova, L.; Alonso, L.; Mateo-García, G.; López-Puigdollers, D. Towards a novel approach for Sentinel-3 synergistic OLCI/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation. ISPRS J. Photogramm. Remote Sens. 2021, 181, 238–253. [CrossRef]
17. Neckel, A.; Oliveira, M.L.; Castro Bolaño, L.J.; Maculan, L.S.; Moro, L.D.; Bodah, E.T.; Moreno-Ríos, A.L.; Bodah, B.W.; Silva, L.F. Biophysical matter in a marine estuary identified by the Sentinel-3B OLCI satellite and the presence of terrestrial iron (Fe) nanoparticles. Mar. Pollut. Bull. 2021, 173, 112925. [CrossRef]
18. ESA. European Space Agency. Sentinel-5P Pre-Operations Data Hub–European, 2022. Available online: https://s5phub. copernicus.eu/dhus/ (accessed on 1 August 2022).
19. Sanusi, M.; Ramli, A.; Hassan, W.; Lee, M.; Izham, A.; Said, M.; Wagiran, H.; Heryanshah, A. Assessment of impact of urbanisation on background radiation exposure and human health risk estimation in Kuala Lumpur, Malaysia. Environ. Int. 2017, 104, 91–101. [CrossRef]
20. Xu, C.; Zhang, Z.; Ling, G.; Wang, G.; Wang, M. Air pollutant spatiotemporal evolution characteristics and effects on human health in North China. Chemosphere 2022, 294, 133814. [CrossRef]
21. Pereira, P.; Baši´c, F.; Bogunovic, I.; Barcelo, D. Russian-Ukrainian war impacts the total environment. Sci. Total Environ. 2022, 837, 155865. [CrossRef]
22. UKRCENSUS. State Statistics Service of Ukraine. All-Ukrainian Population Censos, 2021. Available online: https://www. ukrcensus.gov.ua/eng/ (accessed on 10 August 2022).
23. Climate Change Knowledge Portal. Ukraine, 2022. Available online: https://climateknowledgeportal.worldbank.org/country/ ukraine/climate-data-historical (accessed on 10 August 2022).
24. Simplemaps. Ukraine Cities Database, 2022. Available online: https://simplemaps.com/data/ua-cities (accessed on 18 August 2022).
25. Racioppi, F.; Rutter, H.; Nitzan, D.; Borojevic, A.; Carr, Z.; Grygaski, T.J.; Jarosi ´nska, D.; Netanyahu, S.; Schmoll, O.; Stuetzle, K.; et al. The impact of war on the environment and health: Implications for readiness, response, and recovery in Ukraine. Lancet 2022, 400, 871–873. [CrossRef]
26. Fletcher, K. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services; ESA Communications: Oakville, ON, Canada, 2012; ISBN 978-92-9221-419-7.
27. Moro, L.D.; Maculan, L.S.; Pivoto, D.; Cardoso, G.T.; Pinto, D.; Adelodun, B.; Bodah, B.W.; Santosh, M.; Bortoluzzi, M.G.; Branco, E.; et al. Geospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil. Sustainability 2022, 14, 9733. [CrossRef]
28. Clevis, Q.; Tucker, G.E.; Lancaster, S.T.; Desitter, A.; Gasparini, N.; Lock, G. A simple algorithm for the mapping of TIN data onto a static grid: Applied to the stratigraphic simulation of river meander deposits. Comput. Geosci. 2006, 32, 749–766. [CrossRef]
29. Refice, A.; Giachetta, E.; Capolongo, D. SIGNUM: A Matlab, TIN-based landscape evolution model. Comput. Geosci. 2012, 45, 293–303. [CrossRef]
30. Goellner, E.; Neckel, A.; Bodah, B.W.; Maculan, L.S.; Almeida Silva, C.C.O.D.; Piccinato, D.; Grub, J.; Cambrussi, L.P.; Korcelski, C.; Oliveira, M.L. Geospatial analysis of Ae. aegypti foci in southern Brazil. J. Environ. Chem. Eng. 2021, 9, 106645. [CrossRef]
31. Ialongo, I.; Stepanova, N.; Hakkarainen, J.; Virta, H.; Gritsenko, D. Satellite-based estimates of nitrogen oxide and methane emissions from gas flaring and oil production activities in Sakha Republic, Russia. Atmos. Environ. X. 2021, 11, 100114. [CrossRef]
32. Sarkar, T.; Anand, S.; Bhattacharya, A.; Sharma, A.; Venkataraman, C.; Sharma, A.; Ganguly, D.; Bhawar, R. Evaluation of the simulated aerosol optical properties over India: COALESCE model inter-comparison of three GCMs with ground and satellite observations. Sci. Total Environ. 2022, 852, 158442. [CrossRef]
33. Trujillo-Acatitla, R.; Tuxpan-Vargas, J.; Ovando-Vázquez, C. Oil spills: Detection and concentration estimation in satellite imagery, a machine learning approach. Mar. Pollut. Bull. 2022, 184, 114132. [CrossRef]
34. Alandihallaj, M.A.; Emami, M.R. Satellite replacement and task reallocation for multiple-payload fractionated Earth observation mission. Acta Astronaut. 2022, 196, 157–175. [CrossRef]
35. Naghizadeh, A.; Metaxas, D.N. Condensed Silhouette: An Optimized Filtering Process for Cluster Selection in K-Means. Procedia Comput. Sci. 2020, 176, 205–214. [CrossRef]
36. Maroni, D.; Cardoso, G.T.; Neckel, A.; Maculan, L.S.; Oliveira, M.L.; Bodah, E.T.; Bodah, B.W.; Santosh, M. Land surface temperature and vegetation index as a proxy to microclimate. J. Environ. Chem. Eng. 2021, 9, 105796. [CrossRef]
37. Niu, G.; Ji, Y.; Zhang, Z.; Wang, W.; Chen, J.; Yu, P. Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method. Energy Rep. 2021, 7, 250–256. [CrossRef]
38. Borlea, I.D.; Precup, R.E.; Borlea, A.B. Improvement of K-means Cluster Quality by Post Processing Resulted Clusters. Procedia Comput. Sci. 2022, 199, 63–70. [CrossRef]
39. Ahmad, A.; Khan, S.S. initKmix-A novel initial partition generation algorithm for clustering mixed data using k-means-based clustering. Expert Syst. Appl. 2021, 167, 114149. [CrossRef]
40. Zhou, X.Y.; Lu, G.; Xu, Z.; Yan, X.; Khu, S.T.; Yang, J.; Zhao, J. Influence of Russia-Ukraine War on the Global Energy and Food Security. Resour. Conserv. Recycl. 2023, 188, 106657. [CrossRef]
41. Rawtani, D.; Gupta, G.; Khatri, N.; Rao, P.K.; Hussain, C.M. Environmental damages due to war in Ukraine: A perspective. Sci. Total Environ. 2022, 850, 157932. [CrossRef]
42. Khalfaoui, R.; Gozgor, G.; Goodell, J.W. Impact of Russia-Ukraine war attention on cryptocurrency: Evidence from quantile dependence analysis. Financ. Res. Lett. 2022, 49, 103365. [CrossRef]
43. Bougias, A.; Episcopos, A.; Leledakis, G.N. Valuation of European firms during the Russia–Ukraine war. Econ. Lett. 2022, 218, 110750. [CrossRef]
44. Umar, M.; Riaz, Y.; Yousaf, I. Impact of Russian-Ukraine war on clean energy, conventional energy, and metal markets: Evidence from event study approach. Resour. Policy 2022, 79, 102966. [CrossRef]
45. Lo, G.D.; Marcelin, I.; Bassène, T.; Sène, B. The Russo-Ukrainian war and financial markets: The role of dependence on Russian commodities. Financ. Res. Lett. 2022, 50, 103194. [CrossRef]
46. Adekoya, O.B.; Oliyide, J.A.; Yaya, O.S.; Al-Faryan, M.A.S. Does oil connect differently with prominent assets during war? Analysis of intra-day data during the Russia-Ukraine saga. Resour. Policy 2022, 77, 102728. [CrossRef]
47. Silva, L.F.; Oliveira, M.L.; Milanes, C.B.; Bodah, B.W.; Cambrussi, L.P.; Dotto, G. Effects of atmospheric pollutants on human health and deterioration of medieval historical architecture (North Africa, Tunisia). Urban Clim. 2022, 41, 101046. [CrossRef]
48. Silva, L.F.O.; Pinto, D.; Neckel, A.; Oliveira, M.L.S.; Sampaio, C.H. Atmospheric nanocompounds on Lanzarote Island: Vehicular exhaust and igneous geologic formation interactions. Chemosphere 2020, 254, 1–14. [CrossRef]
49. Oliveira, M.L.; Pinto, D.; Zanchett, M.R.D.; Silva, L.F. Air pollutants and their degradation of a historic building in the largest metropolitan area in Latin America. Chemosphere 2021, 277, 130286. [CrossRef] 50. Rovira, J.; Nadal, M.; Schuhmacher, M.; Domingo, J.L. Environmental impact and human health risks of air pollutants near a large chemical/petrochemical complex: Case study in Tarragona, Spain. Sci. Total Environ. 2021, 787, 1–12. [CrossRef]
51. Ly, A.; Cornelisse, J. How to Train a Machine Learning Model in JASP: Clustering, 2019. Available online: https://jasp-stats.org/ 2019/11/19/how-to-train-a-machine-learning-model-in-jasp-clustering/ (accessed on 28 August 2022).
52. Lee, Y.L.; Makam, S.; McKelvey, S.; Lu, M.W. Durability Reliability Demonstration Test Methods. Procedia Eng. 2015, 133, 31–59. [CrossRef]
53. Moustafa, K.; Hu, Z.; Mourelatos, Z.P.; Baseski, I.; Majcher, M. System reliability analysis using component-level and system-level accelerated life testing. Reliab. Eng. Syst. 2021, 214, 107755. [CrossRef]
54. Sánchez-Piñero, J.; Novo-Quiza, N.; Moreda-Piñeiro, J.; Turnes-Carou, I.; Muniategui-Lorenzo, S.; López-Mahía, P. Multi-class organic pollutants in atmospheric particulate matter (PM2.5) from a Southwestern Europe industrial area: Levels, sources and human health risk. Environ. Res. 2022, 214, 114195. [CrossRef] [PubMed]
55. Tong, Y.; Zhao, X.; Li, H.; Pei, Y.; Ma, P.; You, J. Using homing pigeons to monitor atmospheric organic pollutants in a city heavily involving in coal mining industry. Chemosphere 2022, 307, 135679. [CrossRef]
56. Cui, Y.; Zhang, G.; Wang, W.; Shen, Y.; Zhai, X.; Wu, X.; Li, R.; Wu, B.; Xue, Y. Ten-year emission characteristics of atmospheric pollutants from incineration of sacrificial offerings in China. Res. J. Environ. Sci. 2022, 114, 391–400. [CrossRef] [PubMed]
57. Guo, X.; Li, S.; Zhang, Y.; Wu, B.; Guo, W. Applications of dynamic simulation for source analysis of soil pollutants based on atmospheric diffusion and deposition model. Sci. Total Environ. 2022, 839, 156057. [CrossRef]
58. Dong, J.; Wang, X.; Li, J.; Hao, C.; Jiao, L. The Spatial-Temporal Differentiation of Aerosol Optical Properties and Types in the Beijing–Tianjin–Hebei Region Based on the Ecological Functional Zones. Sustainability 2022, 14, 12656. [CrossRef]
59. Yan, C.; Wang, L.; Zhang, Q. Study on Coupled Relationship between Urban Air Quality and Land Use in Lanzhou, China. Sustainability 2021, 13, 7724. [CrossRef]
60. Pilarczyk, B.; Tomza-Marciniak, A.; Pilarczyk, R.; Udała, J.; Kruzhel, B.; Ligocki, M. Content of essential and non-essential elements in wild animals from western Ukraine and the health risks associated with meat and liver consumption. Chemosphere 2020, 244, 125506. [CrossRef] [PubMed]
61. Vystavna, Y.; Huneau, F.; Schäfer, J.; Motelica-Heino, M.; Blanc, G.; Larrose, A.; Vergeles, Y.; Diadin, D.; Le Coustumer, P. Distribution of trace elements in waters and sediments of the Seversky Donets transboundary watershed (Kharkiv region, Eastern Ukraine). Appl. Geochem. 2012, 27, 2077–2087. [CrossRef]
62. Labunska, I.; Levchuk, S.; Kashparov, V.; Holiaka, D.; Yoschenko, L.; Santillo, D.; Johnston, P. Current radiological situation in areas of Ukraine contaminated by the Chornobyl accident: Part 2. Strontium-90 transfer to culinary grains and forest woods from soils of Ivankiv district. Environ. Int. 2021, 146, 106282. [CrossRef]
63. Maloshtan, I.; Polishchuk, S.; Kashparov, V.; Yoschenko, V. Assessment of radiological efficiency of countermeasures on peat-bog soils of Ukrainian Polissya. J. Environ. Radioact. 2017, 175–176, 52–59. [CrossRef]
64. Poursanidis, D.; Traganos, D.; Reinartz, P.; Chrysoulakis, N. On the use of Sentinel-2 for coastal habitat mapping and satellitederived bathymetry estimation using downscaled coastal aerosol band. Int. J. Appl. Earth Obs. Geoinf. 2019, 80, 58–70. [CrossRef]
65. Butz, A.; Galli, A.; Hasekamp, O.; Landgraf, J.; Tol, P.; Aben, I. TROPOMI aboard Sentinel-5 Precursor: Prospective performance of CH4 retrievals for aerosol and cirrus loaded atmospheres. Remote Sens. Environ. 2012, 120, 267–276. [CrossRef]
66. Yang, Y.; Chen, Y.; Yang, K.; Cermak, J.; Chen, Y. High-resolution aerosol retrieval over urban areas using sentinel-2 data. Atmos. Res. 2021, 264, 105829. [CrossRef]
dc.relation.citationendpage.spa.fl_str_mv 14
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationissue.spa.fl_str_mv 24
dc.relation.citationvolume.spa.fl_str_mv 14
dc.rights.eng.fl_str_mv © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights.license.spa.fl_str_mv Atribución 4.0 Internacional (CC BY 4.0)
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Atribución 4.0 Internacional (CC BY 4.0)
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
https://creativecommons.org/licenses/by/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv 14 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.region.none.fl_str_mv Ukraine
dc.publisher.spa.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.publisher.place.spa.fl_str_mv Switzerland
dc.source.spa.fl_str_mv https://www.mdpi.com/2071-1050/14/24/16357
institution Corporación Universidad de la Costa
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/24ad9f1d-a272-4188-b812-ee0baf183dc4/download
https://repositorio.cuc.edu.co/bitstreams/7351632a-a49c-4040-85a0-c416bf4deec6/download
https://repositorio.cuc.edu.co/bitstreams/78b25d5b-7c2d-4929-9844-4f8759920753/download
https://repositorio.cuc.edu.co/bitstreams/bb6faa09-0dc9-45f4-8805-a4f4c4239fd0/download
bitstream.checksum.fl_str_mv 4f4159c8df3d16e1e1a53f4f89d48f14
2f9959eaf5b71fae44bbf9ec84150c7a
cb80bf513413c1ebfebe3d70158ebd37
3881f056a3e57cc9c9b17bc63bd2ca22
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1828166841009176576
spelling Atribución 4.0 Internacional (CC BY 4.0)© 2022 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Neckel, AlcindoSantosh, M.William Bodah, BrianStolfo Maculan, LaércioPinto, DianaKorcelski, CleitonCarollo Toscan, PalomaPasa Cambrussi, LauraCezar Caino, IsadoraDal Moro, LeilaPiccinato Junior, DirceuTibério Cardoso, GraceOliveira de Almeida Silva, Caliane Christiede Vargas Mores, Giana2024-04-29T14:12:58Z2024-04-29T14:12:58Z2022-12-07Neckel, A.; Santosh, M.; Bodah, B.W.; Maculan, L.S.; Pinto, D.; Korcelski, C.; Toscan, P.C.; Cambrussi, L.P.; Caino, I.C.; Moro, L.D.; et al. Using the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region. Sustainability 2022, 14, 16357. https://doi.org/10.3390/su142416357https://hdl.handle.net/11323/1289110.3390/su1424163572071-1050Corporación Universidad de la CostaREDICUC – Repositorio CUChttps://repositorio.cuc.edu.co/The use of images from the Sentinel-3B SYN satellite (surface reflectance and aerosol parameters over land) is currently one of the most advanced technologies utilized to identify atmospheric aerosol concentrations on a global scale. The general aim of this study is to analyze the evolution of aerosols in the atmosphere of the Kiev region in northern Ukraine during 2019, 2020, 2021 and 2022. Due to this study’s timing, both prior to and during the current military incursion into Ukraine, this study also evaluates the consequences of the invasion of the Russian army on the territory of Ukraine, in relation to the quantitative levels of aerosols present in the atmosphere. Satellite image data were modelled in SNAP software (Sentinel Application Platform). Using the JASP software (version 0.14.1.0), clusters with variations of T550 (Aerosol Optical Thickness) were generated. The Sentinel-3B SYN satellite images were made available by the European Space Agency (ESA), with moderate spatial resolution (>300 m), calibrated and normalized to an average standard of 0.83 µg/mg, with a maximum error of 6.62% in the 30 sampled points. Satellite image data were modelled in SNAP software. Using the JASP software (version 0.14.1.0), clusters with variations of T550 (Aerosol Optical Thickness) were generated. The results show variations in the concentration of T550 in different periods, revealing that the military conflict between Russia and Ukraine directly influenced the dynamics of aerosol concentration, attributed to factors incompatible with environmental sustainability.14 páginasapplication/pdfengMultidisciplinary Digital Publishing Institute (MDPI)Switzerlandhttps://www.mdpi.com/2071-1050/14/24/16357Using the sentinel-3B satellite in geospatial analysis of suspended aerosols in the kiev, Ukraine regionArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85UkraineSustainability1. Mao, S.; Lang, J.; Chen, T.; Cheng, S. Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme through sensitivity analysis combined with optimization model. Environ. Pollut. 2021, 284, 117186. [CrossRef] [PubMed]2. Leng, S.; Li, S.W.; Hu, Z.Z.; Wu, H.Y.; Li, B.B. Development of a micro-in-meso-scale framework for simulating pollutant dispersion and wind environment in building groups. J. Clean. Prod. 2022, 364, 132661. [CrossRef]3. Zhang, X.; Wang, J. Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety. JSSR 2022, 3, 372–397. [CrossRef]4. Félix, O.I.; Csavina, J.; Field, J.; Rine, K.P.; Sáez, A.E.; Betterton, E.A. Use of lead isotopes to identify sources of metal and metalloid contaminants in atmospheric aerosol from mining operations. Chemosphere 2015, 122, 219–226. [CrossRef]5. Bodah, B.W.; Neckel, A.; Stolfo Maculan, L.; Milanes, C.B.; Korcelski, C.; Ramírez, O.; Mendez-Espinosa, J.F.; Bodah, E.T.; Oliveira, M.L. Sentinel-5P TROPOMI satellite application for NO2 and CO studies aiming at environmental valuation. J. Clean. Prod. 2022, 357, 131960. [CrossRef]6. Jiao, X.; Zeng, R.; Lan, G.; Zuo, S.; He, J.; Wang, C. Mechanistic study on photochemical generation of I•/I2•− radicals in coastal atmospheric aqueous aerosol. Sci. Total Environ. 2022, 825, 154080. [CrossRef]7. Wang, H.; He, C.; Modini, R.L.; Wang, W.; Lu, H.; Morawska, L. Mixing state of printer generated ultrafine particles: Implications for the complexity of indoor aerosols. Atmos. Environ. 2021, 259, 118550. [CrossRef]8. Chen, S.; Zhang, R.; Mao, R.; Zhang, Y.; Chen, Y.; Ji, Z.; Gong, Y.; Guan, Y. Sources, characteristics and climate impact of light-absorbing aerosols over the Tibetan Plateau. Earth-Sci. Rev. 2022, 232, 104111. [CrossRef]9. Yang, J.; Zhao, C.; Sun, Y.; Chi, Y.; Yang, Y. Aerosol first indirect effect over narrow longitude regions of North Pacific and same-latitude lands. Atmos. Environ. 2022, 277, 119081. [CrossRef]10. Li, N.; Georas, S.; Alexis, N.; Fritz, P.; Xia, T.; Williams, M.A.; Horner, E.; Nel, A. A work group report on ultrafine particles (American Academy of Allergy, Asthma & Immunology): Why ambient ultrafine and engineered nanoparticles should receive special attention for possible adverse health outcomes in human subjects. J. Allergy Clin. Immunol. 2016, 138, 386–396. [CrossRef]11. Moreno-Ríos, A.L.; Tejeda-Benítez, L.P.; Bustillo-Lecompte, C.F. Sources, characteristics, toxicity, and control of ultrafine particles: An overview. Geosci. Front. 2020, 13, 101147. [CrossRef]12. Fan, M.Y.; Zhang, Y.L.; Lin, Y.C.; Cao, F.; Sun, Y.; Qiu, Y.; Xing, G.; Dao, X.; Fu, P. Specific sources of health risks induced by metallic elements in PM2.5 during the wintertime in Beijing, China. Atmos. Environ. 2021, 246, 118112. [CrossRef]13. Lee, M.H.; Yang, W.; Chae, N.; Choi, S. High resolution size characterization of particulate contaminants for radioactive metal waste treatment. Nucl. Eng. Technol. 2021, 53, 2277–2288. [CrossRef]14. Middya, A.I.; Roy, S. Pollutant specific optimal deep learning and statistical model building for air quality forecasting. Environ. Pollut. 2020, 301, 118–972. [CrossRef]15. Mohammadshirazi, A.; Kalkhorani, V.A.; Humes, J.; Speno, B.; Rike, J.; Ramnath, R.; Clark, J.D. Predicting airborne pollutant concentrations and events in a commercial building using low-cost pollutant sensors and machine learning: A case study. Build. Environ. 2022, 213, 108833. [CrossRef]16. Fernandez-Moran, R.; Gómez-Chova, L.; Alonso, L.; Mateo-García, G.; López-Puigdollers, D. Towards a novel approach for Sentinel-3 synergistic OLCI/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation. ISPRS J. Photogramm. Remote Sens. 2021, 181, 238–253. [CrossRef]17. Neckel, A.; Oliveira, M.L.; Castro Bolaño, L.J.; Maculan, L.S.; Moro, L.D.; Bodah, E.T.; Moreno-Ríos, A.L.; Bodah, B.W.; Silva, L.F. Biophysical matter in a marine estuary identified by the Sentinel-3B OLCI satellite and the presence of terrestrial iron (Fe) nanoparticles. Mar. Pollut. Bull. 2021, 173, 112925. [CrossRef]18. ESA. European Space Agency. Sentinel-5P Pre-Operations Data Hub–European, 2022. Available online: https://s5phub. copernicus.eu/dhus/ (accessed on 1 August 2022).19. Sanusi, M.; Ramli, A.; Hassan, W.; Lee, M.; Izham, A.; Said, M.; Wagiran, H.; Heryanshah, A. Assessment of impact of urbanisation on background radiation exposure and human health risk estimation in Kuala Lumpur, Malaysia. Environ. Int. 2017, 104, 91–101. [CrossRef]20. Xu, C.; Zhang, Z.; Ling, G.; Wang, G.; Wang, M. Air pollutant spatiotemporal evolution characteristics and effects on human health in North China. Chemosphere 2022, 294, 133814. [CrossRef]21. Pereira, P.; Baši´c, F.; Bogunovic, I.; Barcelo, D. Russian-Ukrainian war impacts the total environment. Sci. Total Environ. 2022, 837, 155865. [CrossRef]22. UKRCENSUS. State Statistics Service of Ukraine. All-Ukrainian Population Censos, 2021. Available online: https://www. ukrcensus.gov.ua/eng/ (accessed on 10 August 2022).23. Climate Change Knowledge Portal. Ukraine, 2022. Available online: https://climateknowledgeportal.worldbank.org/country/ ukraine/climate-data-historical (accessed on 10 August 2022).24. Simplemaps. Ukraine Cities Database, 2022. Available online: https://simplemaps.com/data/ua-cities (accessed on 18 August 2022).25. Racioppi, F.; Rutter, H.; Nitzan, D.; Borojevic, A.; Carr, Z.; Grygaski, T.J.; Jarosi ´nska, D.; Netanyahu, S.; Schmoll, O.; Stuetzle, K.; et al. The impact of war on the environment and health: Implications for readiness, response, and recovery in Ukraine. Lancet 2022, 400, 871–873. [CrossRef]26. Fletcher, K. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services; ESA Communications: Oakville, ON, Canada, 2012; ISBN 978-92-9221-419-7.27. Moro, L.D.; Maculan, L.S.; Pivoto, D.; Cardoso, G.T.; Pinto, D.; Adelodun, B.; Bodah, B.W.; Santosh, M.; Bortoluzzi, M.G.; Branco, E.; et al. Geospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil. Sustainability 2022, 14, 9733. [CrossRef]28. Clevis, Q.; Tucker, G.E.; Lancaster, S.T.; Desitter, A.; Gasparini, N.; Lock, G. A simple algorithm for the mapping of TIN data onto a static grid: Applied to the stratigraphic simulation of river meander deposits. Comput. Geosci. 2006, 32, 749–766. [CrossRef]29. Refice, A.; Giachetta, E.; Capolongo, D. SIGNUM: A Matlab, TIN-based landscape evolution model. Comput. Geosci. 2012, 45, 293–303. [CrossRef]30. Goellner, E.; Neckel, A.; Bodah, B.W.; Maculan, L.S.; Almeida Silva, C.C.O.D.; Piccinato, D.; Grub, J.; Cambrussi, L.P.; Korcelski, C.; Oliveira, M.L. Geospatial analysis of Ae. aegypti foci in southern Brazil. J. Environ. Chem. Eng. 2021, 9, 106645. [CrossRef]31. Ialongo, I.; Stepanova, N.; Hakkarainen, J.; Virta, H.; Gritsenko, D. Satellite-based estimates of nitrogen oxide and methane emissions from gas flaring and oil production activities in Sakha Republic, Russia. Atmos. Environ. X. 2021, 11, 100114. [CrossRef]32. Sarkar, T.; Anand, S.; Bhattacharya, A.; Sharma, A.; Venkataraman, C.; Sharma, A.; Ganguly, D.; Bhawar, R. Evaluation of the simulated aerosol optical properties over India: COALESCE model inter-comparison of three GCMs with ground and satellite observations. Sci. Total Environ. 2022, 852, 158442. [CrossRef]33. Trujillo-Acatitla, R.; Tuxpan-Vargas, J.; Ovando-Vázquez, C. Oil spills: Detection and concentration estimation in satellite imagery, a machine learning approach. Mar. Pollut. Bull. 2022, 184, 114132. [CrossRef]34. Alandihallaj, M.A.; Emami, M.R. Satellite replacement and task reallocation for multiple-payload fractionated Earth observation mission. Acta Astronaut. 2022, 196, 157–175. [CrossRef]35. Naghizadeh, A.; Metaxas, D.N. Condensed Silhouette: An Optimized Filtering Process for Cluster Selection in K-Means. Procedia Comput. Sci. 2020, 176, 205–214. [CrossRef]36. Maroni, D.; Cardoso, G.T.; Neckel, A.; Maculan, L.S.; Oliveira, M.L.; Bodah, E.T.; Bodah, B.W.; Santosh, M. Land surface temperature and vegetation index as a proxy to microclimate. J. Environ. Chem. Eng. 2021, 9, 105796. [CrossRef]37. Niu, G.; Ji, Y.; Zhang, Z.; Wang, W.; Chen, J.; Yu, P. Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method. Energy Rep. 2021, 7, 250–256. [CrossRef]38. Borlea, I.D.; Precup, R.E.; Borlea, A.B. Improvement of K-means Cluster Quality by Post Processing Resulted Clusters. Procedia Comput. Sci. 2022, 199, 63–70. [CrossRef]39. Ahmad, A.; Khan, S.S. initKmix-A novel initial partition generation algorithm for clustering mixed data using k-means-based clustering. Expert Syst. Appl. 2021, 167, 114149. [CrossRef]40. Zhou, X.Y.; Lu, G.; Xu, Z.; Yan, X.; Khu, S.T.; Yang, J.; Zhao, J. Influence of Russia-Ukraine War on the Global Energy and Food Security. Resour. Conserv. Recycl. 2023, 188, 106657. [CrossRef]41. Rawtani, D.; Gupta, G.; Khatri, N.; Rao, P.K.; Hussain, C.M. Environmental damages due to war in Ukraine: A perspective. Sci. Total Environ. 2022, 850, 157932. [CrossRef]42. Khalfaoui, R.; Gozgor, G.; Goodell, J.W. Impact of Russia-Ukraine war attention on cryptocurrency: Evidence from quantile dependence analysis. Financ. Res. Lett. 2022, 49, 103365. [CrossRef]43. Bougias, A.; Episcopos, A.; Leledakis, G.N. Valuation of European firms during the Russia–Ukraine war. Econ. Lett. 2022, 218, 110750. [CrossRef]44. Umar, M.; Riaz, Y.; Yousaf, I. Impact of Russian-Ukraine war on clean energy, conventional energy, and metal markets: Evidence from event study approach. Resour. Policy 2022, 79, 102966. [CrossRef]45. Lo, G.D.; Marcelin, I.; Bassène, T.; Sène, B. The Russo-Ukrainian war and financial markets: The role of dependence on Russian commodities. Financ. Res. Lett. 2022, 50, 103194. [CrossRef]46. Adekoya, O.B.; Oliyide, J.A.; Yaya, O.S.; Al-Faryan, M.A.S. Does oil connect differently with prominent assets during war? Analysis of intra-day data during the Russia-Ukraine saga. Resour. Policy 2022, 77, 102728. [CrossRef]47. Silva, L.F.; Oliveira, M.L.; Milanes, C.B.; Bodah, B.W.; Cambrussi, L.P.; Dotto, G. Effects of atmospheric pollutants on human health and deterioration of medieval historical architecture (North Africa, Tunisia). Urban Clim. 2022, 41, 101046. [CrossRef]48. Silva, L.F.O.; Pinto, D.; Neckel, A.; Oliveira, M.L.S.; Sampaio, C.H. Atmospheric nanocompounds on Lanzarote Island: Vehicular exhaust and igneous geologic formation interactions. Chemosphere 2020, 254, 1–14. [CrossRef]49. Oliveira, M.L.; Pinto, D.; Zanchett, M.R.D.; Silva, L.F. Air pollutants and their degradation of a historic building in the largest metropolitan area in Latin America. Chemosphere 2021, 277, 130286. [CrossRef] 50. Rovira, J.; Nadal, M.; Schuhmacher, M.; Domingo, J.L. Environmental impact and human health risks of air pollutants near a large chemical/petrochemical complex: Case study in Tarragona, Spain. Sci. Total Environ. 2021, 787, 1–12. [CrossRef]51. Ly, A.; Cornelisse, J. How to Train a Machine Learning Model in JASP: Clustering, 2019. Available online: https://jasp-stats.org/ 2019/11/19/how-to-train-a-machine-learning-model-in-jasp-clustering/ (accessed on 28 August 2022).52. Lee, Y.L.; Makam, S.; McKelvey, S.; Lu, M.W. Durability Reliability Demonstration Test Methods. Procedia Eng. 2015, 133, 31–59. [CrossRef]53. Moustafa, K.; Hu, Z.; Mourelatos, Z.P.; Baseski, I.; Majcher, M. System reliability analysis using component-level and system-level accelerated life testing. Reliab. Eng. Syst. 2021, 214, 107755. [CrossRef]54. Sánchez-Piñero, J.; Novo-Quiza, N.; Moreda-Piñeiro, J.; Turnes-Carou, I.; Muniategui-Lorenzo, S.; López-Mahía, P. Multi-class organic pollutants in atmospheric particulate matter (PM2.5) from a Southwestern Europe industrial area: Levels, sources and human health risk. Environ. Res. 2022, 214, 114195. [CrossRef] [PubMed]55. Tong, Y.; Zhao, X.; Li, H.; Pei, Y.; Ma, P.; You, J. Using homing pigeons to monitor atmospheric organic pollutants in a city heavily involving in coal mining industry. Chemosphere 2022, 307, 135679. [CrossRef]56. Cui, Y.; Zhang, G.; Wang, W.; Shen, Y.; Zhai, X.; Wu, X.; Li, R.; Wu, B.; Xue, Y. Ten-year emission characteristics of atmospheric pollutants from incineration of sacrificial offerings in China. Res. J. Environ. Sci. 2022, 114, 391–400. [CrossRef] [PubMed]57. Guo, X.; Li, S.; Zhang, Y.; Wu, B.; Guo, W. Applications of dynamic simulation for source analysis of soil pollutants based on atmospheric diffusion and deposition model. Sci. Total Environ. 2022, 839, 156057. [CrossRef]58. Dong, J.; Wang, X.; Li, J.; Hao, C.; Jiao, L. The Spatial-Temporal Differentiation of Aerosol Optical Properties and Types in the Beijing–Tianjin–Hebei Region Based on the Ecological Functional Zones. Sustainability 2022, 14, 12656. [CrossRef]59. Yan, C.; Wang, L.; Zhang, Q. Study on Coupled Relationship between Urban Air Quality and Land Use in Lanzhou, China. Sustainability 2021, 13, 7724. [CrossRef]60. Pilarczyk, B.; Tomza-Marciniak, A.; Pilarczyk, R.; Udała, J.; Kruzhel, B.; Ligocki, M. Content of essential and non-essential elements in wild animals from western Ukraine and the health risks associated with meat and liver consumption. Chemosphere 2020, 244, 125506. [CrossRef] [PubMed]61. Vystavna, Y.; Huneau, F.; Schäfer, J.; Motelica-Heino, M.; Blanc, G.; Larrose, A.; Vergeles, Y.; Diadin, D.; Le Coustumer, P. Distribution of trace elements in waters and sediments of the Seversky Donets transboundary watershed (Kharkiv region, Eastern Ukraine). Appl. Geochem. 2012, 27, 2077–2087. [CrossRef]62. Labunska, I.; Levchuk, S.; Kashparov, V.; Holiaka, D.; Yoschenko, L.; Santillo, D.; Johnston, P. Current radiological situation in areas of Ukraine contaminated by the Chornobyl accident: Part 2. Strontium-90 transfer to culinary grains and forest woods from soils of Ivankiv district. Environ. Int. 2021, 146, 106282. [CrossRef]63. Maloshtan, I.; Polishchuk, S.; Kashparov, V.; Yoschenko, V. Assessment of radiological efficiency of countermeasures on peat-bog soils of Ukrainian Polissya. J. Environ. Radioact. 2017, 175–176, 52–59. [CrossRef]64. Poursanidis, D.; Traganos, D.; Reinartz, P.; Chrysoulakis, N. On the use of Sentinel-2 for coastal habitat mapping and satellitederived bathymetry estimation using downscaled coastal aerosol band. Int. J. Appl. Earth Obs. Geoinf. 2019, 80, 58–70. [CrossRef]65. Butz, A.; Galli, A.; Hasekamp, O.; Landgraf, J.; Tol, P.; Aben, I. TROPOMI aboard Sentinel-5 Precursor: Prospective performance of CH4 retrievals for aerosol and cirrus loaded atmospheres. Remote Sens. Environ. 2012, 120, 267–276. [CrossRef]66. Yang, Y.; Chen, Y.; Yang, K.; Cermak, J.; Chen, Y. High-resolution aerosol retrieval over urban areas using sentinel-2 data. Atmos. Res. 2021, 264, 105829. [CrossRef]1412414Remote sensingAtmospheric pollutionAerosolsGeospatial analysesGlobal scalePublicationORIGINALUsing the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region.pdfUsing the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region.pdfArtículoapplication/pdf2613169https://repositorio.cuc.edu.co/bitstreams/24ad9f1d-a272-4188-b812-ee0baf183dc4/download4f4159c8df3d16e1e1a53f4f89d48f14MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/7351632a-a49c-4040-85a0-c416bf4deec6/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTUsing the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region.pdf.txtUsing the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region.pdf.txtExtracted texttext/plain54025https://repositorio.cuc.edu.co/bitstreams/78b25d5b-7c2d-4929-9844-4f8759920753/downloadcb80bf513413c1ebfebe3d70158ebd37MD53THUMBNAILUsing the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region.pdf.jpgUsing the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region.pdf.jpgGenerated Thumbnailimage/jpeg16318https://repositorio.cuc.edu.co/bitstreams/bb6faa09-0dc9-45f4-8805-a4f4c4239fd0/download3881f056a3e57cc9c9b17bc63bd2ca22MD5411323/12891oai:repositorio.cuc.edu.co:11323/128912024-09-17 14:16:48.139https://creativecommons.org/licenses/by/4.0/© 2022 by the authors. Licensee MDPI, Basel, Switzerland.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares