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DOI10.1016/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
ISSN1352-2310
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
关键词AnnoyanceLogistic regressionPrincipal component analysisRelative risk
语种英语
scopus关键词Agricultural robots; Air pollution; Houses; Industrial emissions; Logistic regression; Particles (particulate matter); Principal component analysis; Risk assessment; Risk perception; Time series; Value engineering; Agricultural operations; Air pollutant concentrations; Annoyance; Metropolitan regions; Multivariate time series techniques; Relative risks; Residential environment; Time series modeling; Time series 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/249451
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
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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).
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