DOI: 10.1016/j.atmosenv.2019.117080
论文题名: Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance
作者: Machado M. ; Reisen V.A. ; Santos J.M. ; Reis Junior N.C. ; Frère S. ; Bondon P. ; Ispány M. ; Aranda Cotta H.H.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 222 语种: 英语
英文关键词: Air pollution
; Houses
; Industrial emissions
; Particles (particulate matter)
; Regression analysis
; Risk assessment
; Risk perception
; Time series
; Time series analysis
; Value engineering
; Agricultural operations
; Air pollutant concentrations
; Annoyance
; Logistic regressions
; Metropolitan regions
; Multivariate time series techniques
; Relative risks
; Residential environment
; Principal component analysis
; atmospheric pollution
; metropolitan area
; multivariate analysis
; particulate matter
; principal component analysis
; probability
; regression analysis
; risk assessment
; time series analysis
; wind erosion
; air pollutant
; annoyance
; article
; Brazil
; human
; particulate matter
; principal component analysis
; probability
; risk assessment
; risk factor
; time series analysis
; Brazil
; Espirito Santo
; Vitoria [Espirito Santo]
; Animalia
学科: Annoyance
; Logistic regression
; Principal component analysis
; Relative risk
中文摘要: As well known, Particulate matter (PM) is an air pollutant that causes damage to the health of humans, other animals, plants, affects the climate and is a potential cause of annoyance through deposition on various surfaces. The perceived annoyance caused by particulate matter is related mainly to the increase of settled dust in urban and residential environments. PM can originate from many sources, i.e., paved and unpaved roads, buildings, agricultural operations and wind erosion represent the largest contributions beyond the relatively minor vehicular and industrial sources emissions. The aim of this paper is to quantify the relationship between perceived annoyance and particulate matter concentration and to estimate the relative risk (RR). The data was collected in the Metropolitan Region of Vitoria (MRV), Brazil. For this purpose, the variables of interest were modelled using vector time series model (VAR), principal component analysis (PCA), and logistic regression (LOG). The combination of these techniques resulted in a hybrid model denoted as LOG-PCA-VAR which allows to estimate RR by handling multipollutant effects. This study shows that there is a strong association between the perceived annoyance and different sizes of PM. The estimates of RR indicate that an increase in air pollutant concentrations significantly contributes in increasing the probability of being annoyed. © 2019 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/161065
Appears in Collections: 气候变化与战略
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作者单位: Instituto Federal de Ciência e Tecnologia Do Espírito Santo, Guarapari, E.S, Brazil; Department of Statistics, Universidade Federal Do Espírito Santo, Vitoria, Brazil; Department of Environmental Engineering, Universidade Federal Do Espírito Santo, Vitoria, Brazil; Université Du Littoral Côte D'Opale, Maison de La Recherche en Science de L'homme, Dunkerque, France; Laboratoire des Signaux et Systems (L2S), CNRS-CentraleSupélec-Université Paris-Sud, Gif-sur-Yvette, France; University of Debrecen, Debrecen, Hungary
Recommended Citation:
Machado M.,Reisen V.A.,Santos J.M.,et al. Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance[J]. Atmospheric Environment,2020-01-01,222