A time series analysis of multiple ambient pollutants to investigate the underlying air pollution dynamics and interactions

NCUIR > Engineering College > Department of Civil Engineering > journal & Dissertation > Item 987654321/100347

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/100347

Title:	A time series analysis of multiple ambient pollutants to investigate the underlying air pollution dynamics and interactions
Authors:	林遠見;Yu, Hwa-Lung;Lin, Yuan-Chien;Kuo, Yi-Ming
Contributors:	工學院土木工程學系
Keywords:	aerosols;Aerosols - analysis;Air Pollutants - analysis;Air pollution;air quality;Concentration (composition);diurnal variation;Diurnal variations;dust storms;Dynamic factor analysis;Dynamic tests;Dynamics;emissions;Environmental Pollutants - analysis;factor analysis;Factor Analysis, Statistical;Meteorological Concepts;nitrogen oxides;Particulate emissions;Particulate matter;Particulate Matter - analysis;particulates;Photochemical reactions;pollutants;quality control;rain;Secondary pollutants;Taiwan;Temporal dynamics;Temporal logic;Time series;time series analysis;traffic;uncertainty
Date:	2015-09-01
Issue Date:	2026-04-21 13:58:27 (UTC+8)
Publisher:	Elsevier Ltd.;England: Elsevier Ltd
Abstract:	摘要： •DFA investigated the associations among aerosols and meteorological factors.•The common trends of the yearlong and diurnal variations were identified.•Common trends can reveal the secondary pollution including NOx processes.•Interactions between aerosols were identified in different scenarios. Understanding the temporal dynamics and interactions of particulate matter (PM) concentration and composition is important for air quality control. This paper applied a dynamic factor analysis method (DFA) to reveal the underlying mechanisms of nonstationary variations in twelve ambient concentrations of aerosols and gaseous pollutants, and the associations with meteorological factors. This approach can consider the uncertainties and temporal dependences of time series data. The common trends of the yearlong and three selected diurnal variations were obtained to characterize the dominant processes occurring in general and specific scenarios in Taipei during 2009 (i.e., during Asian dust storm (ADS) events, rainfall, and under normal conditions). The results revealed the two distinct yearlong NOx transformation processes, and demonstrated that traffic emissions and photochemical reactions both critically influence diurnal variation, depending upon meteorological conditions. During an ADS event, transboundary transport and distinct weather conditions both influenced the temporal pattern of identified common trends. This study shows the DFA method can effectively extract meaningful latent processes of time series data and provide insights of the dominant associations and interactions in the complex air pollution processes. 其他題名： Chemosphere 出版者： England: Elsevier Ltd 出版日期： 2015-09-01 出處： Chemosphere (Oxford), 2015-09, Vol.134, p.571-580 版權： 2014 Elsevier Ltd 版權： Copyright © 2014 Elsevier Ltd. All rights reserved. 識別號： ISSN: 0045-6535 識別號： ISSN: 1879-1298 識別號： EISSN: 1879-1298 識別號： DOI: 10.1016/j.chemosphere.2014.12.007 識別號： PMID: 25600321
Appears in Collections:	[Department of Civil Engineering] journal & Dissertation

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