Development of a noise-assisted multivariate empirical mode decomposition framework for characterizing PM 2.5 air pollution in Taiwan and its relation to hydro-meteorological factors.
Environ Int
; 139: 105669, 2020 06.
Article
em En
| MEDLINE
| ID: mdl-32278196
ABSTRACT
To better understand air pollution problems, the relationships between PM2.5 and hydro-meteorological variables are studied using a state-of-the-art multivariate nonlinear and non-stationary filtering method, noise-assisted multivariate empirical mode decomposition (NAMEMD), and the time-dependent intrinsic correlation (TDIC) algorithm. Three characteristic scales (annual, diurnal and semi-diurnal) are shown to be significant to PM2.5 characterization, based on using NAMEMD filtering. Temporal fluctuations of local correlations among PM2.5 and hydro-meteorological variables are presented. On diurnal and semi-diurnal scales, seasonal variation of the local correlation between temperature and humidity is observed. A combined wind speed and direction analysis can be conducted using the NAMEMD-based algorithm. The pollutant roses that are generated from the reconstructed wind directions reveal the sources of PM2.5 on different scales. PM2.5 is found to be related to land breeze at the diurnal scale and to winter monsoons at the annual scale. The scale-dependent wind direction that contributes to the increase of PM2.5 can be identified.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Poluentes Atmosféricos
/
Poluição do Ar
Tipo de estudo:
Prognostic_studies
País como assunto:
Asia
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article