Unveiling air pollution patterns in Yemen: a spatial-temporal functional data analysis.
Autor: | Hael MA; School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, China. 2014mohanned@gmail.com.; Department of Data Science and Information Technology, Taiz University, 9674, Taiz, Yemen. 2014mohanned@gmail.com. |
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Jazyk: | angličtina |
Zdroj: | Environmental science and pollution research international [Environ Sci Pollut Res Int] 2023 Apr; Vol. 30 (17), pp. 50067-50095. Date of Electronic Publication: 2023 Feb 15. |
DOI: | 10.1007/s11356-023-25790-3 |
Abstrakt: | The application of spatiotemporal functional analysis techniques in environmental pollution research remains limited. As a result, this paper suggests spatiotemporal functional data clustering and visualization tools for identifying temporal dynamic patterns and spatial dependence of multiple air pollutants. The study uses concentrations of four major pollutants, named particulate matter (PM2.5), ground-level ozone (O (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.) |
Databáze: | MEDLINE |
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