气候变化领域集成服务门户(CCPortal): Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance

CCPortal

DOI	10.1016/j.atmosenv.2019.117080
	Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance
	Machado M.; Reisen V.A.; Santos J.M.; Reis Junior N.C.; Frère S.; Bondon P.; Ispány M.; Aranda Cotta H.H.
发表日期	2020
ISSN	13522310
卷号	222
英文摘要	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
英文关键词	Annoyance; Logistic regression; Principal component analysis; Relative risk
学科领域	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
语种	英语
scopus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
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120810
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
推荐引用方式 GB/T 7714	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],2020,222.
APA	Machado M..,Reisen V.A..,Santos J.M..,Reis Junior N.C..,Frère S..,...&Aranda Cotta H.H..(2020).Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance.Atmospheric Environment,222.
MLA	Machado M.,et al."Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance".Atmospheric Environment 222(2020).