Unveiling air pollution patterns in Yemen: a spatial-temporal functional data analysis.

ABSTRACT: 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 (O₃), carbon monoxide (CO), and sulfur oxides (SO₂), measured over 37 cities in Yemen from 1980 to 2022. The proposed tools include Fourier transformation, B-spline functions, and generalized-cross validation for data smoothing, as well as static and dynamic visualization methods. Innovatively, a functional mixture model was used to capture/identify the und

SUBMITTER: Hael MA 

PROVIDER: S-EPMC9930045 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature