Összesen 1 találat.
#/oldal:
Részletezés:
Rendezés:

1.

001-es BibID:BIBFORM083792
035-os BibID:(cikkazonosító)117080 (WoS)000510946800004 (Scopus)85075889215
Első szerző:Machado, Milena
Cím:Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance / Milena Machado, Valdério Anselmo Reisen, Jane Meri Santos, Neyval Costa Reis, Severine Frère, Pascal Bondon, Márton Ispány, Higor Henrique Aranda Cotta
Dátum:2020
ISSN:1352-2310
Megjegyzések: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 modeled 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.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Annoyance
principal component analysis
logistic regression
relative risk
Megjelenés:Atmospheric Environment. - 222 (2020), p. 1-24. -
További szerzők:Reisen, Valdério Anselmo Santos, Jane Meri Reis, Neyval Costa Frère, Severine Bondon, Pascal Ispány Márton (1966-) (informatikus, matematikus) Cotta, Higor Henrique Aranda
Pályázati támogatás:EFOP-3.6.1-16-2016-00022
EFOP
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Rekordok letöltése1