Valdério Anselmo Reisen

Subjective assessments of air pollution annoyance reveal that individuals’ focus on specific risks is influenced by their attachment to place, beliefs, values, and behavior rather than the composition or toxic effects of air pollutants. Additionally, the social context plays a role in shaping how communities react to and perceive air pollution impacts. This study examines residents’ environmental perceptions regarding the effects of settleable particles before and after the interruption of a large industrial source in the southern region of Espírito Santo, Brazil (South America). A second objective was to model the relationship between air pollution annoyance and other perceived variables under both scenarios. Data were collected through surveys conducted before and after the interruption of the industrial plant. The Pearson chi-square test and ordinal logistic regression model analyzed the data. Results indicate a shift in residents’ concerns with a focus on social and well-being issues. We also found a small number of items relating to dust annoyance and home ownership that can be used to predict the air pollution impact for individual community members. The findings show that settleable particles are directly perceived by exposed populations and significantly affect community health and quality of life.

The generalized linear autoregressive moving-average model (GLARMA) has been used in epidemiology to evaluate the impact of pollutants on health. These effects are quantified through the relative risk (RR) measure, which inference can be based on the asymptotic properties of the maximum likelihood estimator. However, for small series, this can be troublesome. This work studies different types of bootstrap confidence intervals (CIs) for the RR. The simulation study revealed that the model parameter related to the data’s autocorrelation could influence the intervals’ coverage. Problems could arise when covariates present an autocorrelation structure. To solve this, using the vector autoregressive (VAR) filter in the covariates is suggested.

A spectral criterion involving the model parameters is given for the existence and uniqueness of a periodically correlated and seasonal non‐negative integer‐valued autoregressive process. The structure of the mean and covariance functions of the periodically stationary distribution of the model is derived using its implicit state‐space representation. Two infinite series representations for the process, the moving average, and the immigrant generation, are established. Based on the latter representation, a novel and parallelizable simulation method is proposed to generate the process.

The aim of this paper is to propose a generalization of the local bootstrap for periodogram statistics to the case when weakly stationary time series are contaminated by additive outliers. In order to achieve robustness, we suggest to replace the classical version of the periodogram with the M-periodogram in the local bootstrap procedure. The robust bootstrap periodogram is implemented in the Whittle estimator to obtain confidence intervals for the parameters of a time series model. A finite sample size investigation was conducted to compare the performance of the classical local bootstrap with the one proposed in this paper, to estimate 95% confidence intervals for the parameters of autoregressive and of seasonal autoregressive time series. The results have shown that the robust estimator is resistant to additive outlier contamination and produces confidence intervals with coverage percentage closer to 95\% and with lower amplitudes than the ones obtained with the classical estimator, even for small percentages and magnitudes of outliers. It was also empirically demonstrated that when the expected number of outliers is kept constant, the coverage percentages of the confidence intervals of the robust estimators tend to 95\% as the sample size increases. An application to the daily mean concentration of the particulate matter with diameter smaller than \SI{10}{\micro\meter} (PM$_{10}$) was considered to illustrate the methodologies in a real data context. All the results presented here give strong motivation to use the proposed robust methodology in practical situations in which weakly stationary time series are contaminated by additive outliers.