Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance
Abstract
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
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