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The paper analyses the intensity changes of three pollution parameter vectors in space and time. The RGB raster pollution data of the Lithuanian territory used for the research were prepared according to the digital images of the Sentinel-2 Earth satellites. The numerical vectors of environmental pollution parameters CH4 (methane), NO2 (nitrogen dioxide) and for direct comparison O2 (oxygen gas) were used for the calculations. The covariance function theory was used to perform the analysis of intensity changes in digital vectors. Estimates of the covariance functions of the numerical vectors of pollution parameters and O2 or the auto-covariance functions of single vectors are calculated from random functions consisting of arrays of measurement parameters of all parameters vectors. Correlation between parameters vectors depends on the density of parameters and their structure. Estimates of covariance functions were calculated by changing the quantization interval on a time scale and using a compiled computer program using the Matlab procedure package. The probability dependence between the environmental pollution parameter vectors and trace gas of the territory in Lithuania and their change in time scale was determined.
Czasopismo
Rocznik
Tom
Strony
555--565
Opis fizyczny
Bibliogr. 24 poz., rys., wykr., tab.
Twórcy
autor
- Department Geodesy and Cadastre, Vilnius Gediminas Technical University, Sauletekio av. 11, LT-10223, Vilnius, Lithuania
- Department Geodesy and Cadastre, Vilnius Gediminas Technical University, Sauletekio av. 11, LT-10223, Vilnius, Lithuania
autor
- Department Geodesy and Cadastre, Vilnius Gediminas Technical University, Sauletekio av. 11, LT-10223, Vilnius, Lithuania
Bibliografia
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- [2] Gac JM, Petelczyc M. Reconstruction of dynamics of SO2 concentration in troposphere based on results of direct measurements. Ecol Chem Eng S. 2019;26(1):59-68. DOI: 10.1515/eces-2019-0002.
- [3] Janas M, Zawadzka A. Assessment of the monitoring of an industrial waste landfill. Ecol Chem Eng S. 2018:25(4):659-69. DOI: 10.1515/eces-2018-0044.
- [4] Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, et al. Sentinel-2: ESA's optical high-resolution mission for gmes operational services. RS of Enviro. 2012;120(15):25-36. DOI: 10.1016/j.rse.2011.11.026.
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- [6] Meygret A, Baillarin S, Gascon F, Hillairet E, Dechoz C, Lacherade S, et al. SENTINEL-2 Image Quality and Level 1 Processing. Int Soc Opt Eng. 2009;7452. DOI: 10.1117/12.826184.
- [7] Copernicus Open Access Hub. Available from: https://scihub.copernicus.eu/
- [8] Dufour DG, Drummond JR, McElroy CT, Midwinter C. Simultaneous measurements of visible (400-700 nm) and infrared (3.4 µm) NO2 absorption. Phys Chem. 2006;110:12414-8. DOI: 10.1021/jp0634306.
- [9] Stachowiak D, Jaworski P, Krzaczek P, Grzegorz M. Laser-based monitoring of CH4, CO2, NH3, and H2S in animal farming-system characterization and initial demonstration. Sensors. 2018;18(2):529. DOI: 10.3390/s18020529.
- [10] Wang W, Zhang L, Zhang W. Analysis of optical fiber methane gas detection system. Proc Engin. 2013;52:401-7. DOI: 10.1016/j.proeng.2013.02.160.
- [11] Lithuania’s Greenhouse Gas Inventory Report. Available from: http://klimatas.gamta.lt/files/NIR_2019_04_15_FINAL.pdf, June 2019.
- [12] SENTINEL-2 Radiometric Resolutions. Available from: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric.
- [13] SENTINEL online. Available from: https://sentinel.esa.int, September 2019.
- [14] Emran BJ, Tenant DD, Najjaran H. Low-altitude aerial methane concentration mapping. RS. 2017;9:823. DOI: 10.3390/rs9080823.
- [15] Daugėla I, Sužiedelytė-Visockienė J, Aksamitauskas VČ. RPAS and GIS for landfill analysis. 10th Conference EKO-DOK 2018. E3S Web Conf, 44, 2018:2267-1242. DOI: 10.1051/e3sconf/20184400025.
- [16] Zhu Z, Xu Y, Jiang B. A one ppm NDIR methane gas sensor with single frequency filter denoising algorithm. Sensing. 2012;12:12729-40. DOI: 10.3390/s120912729.
- [17] Koch KR. Introduction to Bayesian Statistics. Berlin-Heidelberg: Springer Verlag; 2007. DOI: 10.1007/978-3-540-72726-2_1.
- [18] Dematteis N., Giordan D., Allasia P. Image classification for automated image cross-correlation applications in the geosciences. Appl Sci. 2019;9(11):2357. DOI: 10.3390/app9112357.
- [19] Skeivalas J, Obuchovski R, Kilikevičius A. The analysis of gravimeter performance by applying the theory of covariance functions. Indian J Phys. 2019;93:1377-84. DOI: 10.1007/s12648-019.
- [20] Jia Y, Guo Y, Yan Ch, Sheng H, Cui G, Zhong X. Detection and localization for multiple stationary human targets based on cross-correlation of dual-station SFCW radars. RS. 2019;11:1428. DOI: 10.3390/rs11121428.
- [21] Dematteis N, Giordan D, Allasia P. Image classification for automated image cross-correlation applications in the geosciences. App Sci. 2019;9(11):2357. DOI: 10.3390/app9112357.
- [22] Skeivalas J, Obuchovski R. An analysis of variation of geomagnetic field parameters upon applying the theory of covariance functions. Metr Meas Syst. 2019;26(2):363-76. DOI: 10.3846/1392-1541.2008.34.88-91.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0b5cd6f6-87c3-46ac-9743-0f5cf12cbe7e