A longitudinal study of the influence of air pollutants on children: a robust multivariate approach.

This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM).

Parameters influencing population annoyance pertaining to air pollution.

Annoyance caused by particulate matter qualifies as a public health problem, as it can be seen as an ambient stressor causing stress, diseases and affecting the quality of life. Previous studies have shown a complex relationship between annoyance and perception of air pollution, health problems occurrences, air pollutants concentration levels and location within the urban area, which should be better investigated. The objective of this work is to identify the parameters that influences perceived annoyance caused by air pollution.

Deconstruction of annoyance due to air pollution by multiple correspondence analyses.

Annoyance caused by air pollution is a matter of public health as it can cause stress and ill-health and affect quality of life, among other burdens. The aim of this study is to apply the multiple correspondence analyses (MCA) technique as a differential tooling to explore relationships between variables that can influence peoples' behaviour concerning annoyance caused by air pollution. Data were collected through a survey on air pollution, environmental issues and quality of life.