Alessandro José Queiroz Sarnaglia

A baixa qualidade do ar em interiores tem impactos negativos a saúde humana, sendo responsável pelo surgimento e agravamento de inúmeros sintomas respiratórios. Em escolas, diversos contaminantes têm alcançado concentrações acima das permitidas, principalmente devido a sua localização em áreas poluídas. Por outro lado, a percepção humana da qualidade do ar tem sido relacionada especialmente a questões térmicas, olfativas e visuais, sendo necessário mais estudos que associem a cognição humana com a presença de poluentes aéreos no ambiente. O objetivo deste estudo foi analisar os níveis de MP₁₀, MP_2.5 e CO₂, com a percepção da qualidade do ar por estudantes de diferentes salas de aula, naturalmente ventiladas, em um Instituto de Educação localizado em uma zona urbana. A metodologia envolveu monitoramento dos poluentes e parâmetros ambientais, além da aplicação de questionários padronizados de percepção da qualidade do ar. Ao todo, foram respondidos 132 questionários por estudantes de diferentes turnos. Os resultados mostram que o nível de satisfação com a qualidade do ar foi considerado normal e bom, embora encontradas concentrações elevadas de material particulado nas salas de aula. Destaca-se que os alunos foram mais sensíveis ao MP₁₀, registrando em sua percepção a piora da qualidade do ar interior com o aumento da concentração desse poluente. No processo projetual, examinar a localização de janelas em relação a fontes de poluição do ar pode contribuir para garantir a qualidade do ar interior. O estudo reforça a importância do planejamento projetual com estratégias eficientes em busca de um ambiente educacional mais saudável.  

Recently, segmented regression has been utilized as a “working” model for a bootstrap test to detect true oxygen uptake plateau. This approach employs an iterative procedure based on least squares to fit the model. However, it is widely acknowledged that least squares is highly sensitive to outliers, often yielding inefficient estimates. This paper proposes an alternative iterative method by substituting the least squares step with a M-estimator step. Leveraging the robust features of M-estimators, the proposed method is expected to exhibit resilience against outliers. To empirically investigate the performance of the proposed method, a Monte Carlo simulation study is conducted. For comparison purposes, the classical iterative method is also considered. The results indicate that, in uncontaminated scenarios, both methods exhibit similar behavior, with the classical method presenting a slightly superior performance. However, in contaminated cases, the classical method is highly deteriorated, with significant bias and root mean squared error, while the proposed method demonstrates much better performance. Both methods are employed to fit the “working” 3-segment regression model and perform a plateau test on apparently contaminated oxygen uptake data. The results reveal that, due to the presence of outliers, the classical method produces inflated estimates for the error variance and larger standard errors for all parameters, in comparison to the proposed method. Moreover, the plateau test conducted using the classical (robust) method leads to the rejection (confirmation) of a plateau of oxygen uptake for the same data. Overall, these findings highlight the superiority of the proposed method in handling outliers.

Objective: Some proposals for oxygen uptake plateau identification are based on linear regression adaptations. However, linear regression does not adequately explain the oxygen uptake nonlinear dynamics. Recently, segmented regression was considered as an alternative to fit this dynamics, by performing an approximation by straight line segments, which provided a satisfactory fit. In this context, the non-plateau and plateau hypotheses were verified by means of a Wald-type test. This work aims to extend these proposals to scenarios with autocorrelated data. Methods: We propose an algorithm to estimate the segmented regression model under autocorrelation using generalized least squares and suggest a bootstrap method to resample from the null distribution of Wald’s statistic. The performance of the estimate and methods of the plateau diagnosis were evaluated via Monte Carlo experiments. Results: The empirical results show that, under autocorrelation, the proposed estimator performs better when compared to the classic method, mainly in scenarios with small sample sizes and moderate/strong autocorrelation structure. The simulations also showed that the plateau diagnosis test has a coherent empirical Type 1 Error probability and good power. Conclusion: We proposed an alternative to estimate the parameters of a segmented regression model for autocorrelated data and an oxygen consumption plateau bootstrap test, and concluded the methods present good performance under simulated and applied case studies. Significance: The proposed method was used to model real oxygen consumption data. Empirical evidence shows that the methods can be used to objectively identify the plateau in oxygen consumption only by specifying a tolerable significance level.

Objective: The classic criterion for VO2máx plateau identification was proposed by Taylor et al. (1955), however, there are many critiques of this method. In this paper, we propose a new statistic-based VO2max plateau identification methodology. In addition, we aim to test for eventual differences between characteristics of the individuals who presented and those who did not present the maximum VO2max plateau. Method: Forty-one (n = 41) physically active men participated. The subjects underwent a cardiopulmonary exercise test using ramp protocol to measure the VO2max and other physiological variables. The identification of VO2max plateau was performed by segmented linear regression with unknown breakpoints for each individual. Results: Although 58.54% (24) of the sample presented VO2max plateau, no significant differences were observed in metabolic, ventilatory and velocity variables between the groups. Conclusion: The methodology presents advantages, since it is adequate to analyze VO2 variations individually and because it is based on statistical techniques, which considers the continuous records of the maximal effort test, not segmenting it in sections for analysis. The comparison between groups, according to the occurrence of the plateau, showed no differences between them.

This paper compares three estimators for periodic autoregressive (PAR) models. The first is the classical periodic Yule-Walker estimator (YWE). The second is a robust version of YWE (RYWE) which uses the robust autocovariance function in the periodic Yule-Walker equations, and the third is the robust least squares estimator (RLSE) based on iterative least squares with robust versions of the original time series. The daily mean particulate matter concentration (PM10) data is used to illustrate the methodologies in a real application, that is, in the Air Quality area.