Urban air pollution: a representative survey of PM2.5 mass concentrations in six Brazilian cities

In urban areas of Brazil, vehicle emissions are the principal source of fine particulate matter (PM2.5). The World Health Organization air quality guidelines state that the annual mean concentration of PM2.5 should be below 10 μg m−3. In a collaboration of Brazilian institutions, coordinated by the University of São Paulo School of Medicine and conducted from June 2007 to August 2008, PM2.5 mass was monitored at sites with high traffic volumes in six Brazilian state capitals. We employed gravimetry to determine PM2.5 mass concentrations, reflectance to quantify black carbon concentrations, X-ray fluorescence to characterize elemental composition, and ion chromatography to determine the composition and concentrations of anions and cations. Mean PM2.5 concentrations and proportions of black carbon (BC) in the cities of São Paulo, Rio de Janeiro, Belo Horizonte, Curitiba, Recife, and Porto Alegre were 28.1 ± 13.6 μg m−3 (38% BC), 17.2 ± 11.2 μg m−3 (20% BC), 14.7 ± 7.7 μg m−3 (31% BC), 14.4 ± 9.5 μg m−3 (30% BC), 7.3 ± 3.1 μg m−3 (26% BC), and 13.4 ± 9.9 μg m−3 (26% BC), respectively. Sulfur and minerals (Al, Si, Ca, and Fe), derived from fuel combustion and soil resuspension, respectively, were the principal elements of the PM2.5 mass. We discuss the long-term health effects for each metropolitan region in terms of excess mortality risk, which translates to greater health care expenditures. This information could prove useful to decision makers at local environmental agencies

Reliability of reflectance measures in passive filters

Measurements of optical reflectance in passive filters impregnated with a reactive chemical solution may be transformed to ozone concentrations via a calibration curve and constitute a low cost alternative for environmental monitoring, mainly to estimate human exposure. Given the possibility of errors caused by exposure bias, it is common to consider sets of m filters exposed during a certain period to estimate the latent reflectance on n different sample occasions at a certain location. Mixed models with sample occasions as random effects are useful to analyze data obtained under such setups. the intra-class correlation coefficient of the mean of the m measurements is an indicator of the reliability of the latent reflectance estimates. Our objective is to determine m in order to obtain a pre-specified reliability of the estimates, taking possible outliers into account. To illustrate the procedure, we consider an experiment conducted at the Laboratory of Experimental Air Pollution, University of São Paulo, Brazil (LPAE/FMUSP), where sets of m = 3 filters were exposed during 7 days on n = 9 different occasions at a certain location. the results show that the reliability of the latent reflectance estimates for each occasion obtained under homoskedasticity is k(m) = 0.74. A residual analysis suggests that the within-occasion variance for two of the occasions should be different from the others. A refined model with two within-occasion variance components was considered, yielding k(m) = 0.56 for these occasions and k(m) = 0.87 for the remaining ones. To guarantee that all estimates have a reliability of at least 80% we require measurements on m = 10 filters on each occasion. (C) 2014 the Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).INAIRA - Instituto Nacional de Avaliacao Integrada de Risco AmbientalConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ São Paulo, Inst Math & Stat, BR-05508 São Paulo, BrazilUniv São Paulo, Sch Med, BR-05508 São Paulo, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilCNPq: 15/2008FAPESP: 2008/57717-6CNPq: 308613/2011-2Web of Scienc

Vehicle emissions and PM2.5 mass concentrations in six Brazilian cities

In Brazil, the principal source of air pollution is the combustion of fuels (ethanol, gasohol, and diesel). In this study, we quantify the contributions that vehicle emissions make to the urban fine particulate matter (PM2.5) mass in six state capitals in Brazil, collecting data for use in a larger project evaluating the impact of air pollution on human health. From winter 2007 to winter 2008, we collected 24-h PM2.5 samples, employing gravimetry to determine PM2.5 mass concentrations; reflectance to quantify black carbon concentrations; X-ray fluorescence to characterize elemental composition; and ion chromatography to determine the composition and concentrations of anions and cations. Mean PM2.5 concentrations in the cities of São Paulo, Rio de Janeiro, Belo Horizonte, Curitiba, Porto Alegre, and Recife were 28, 17.2, 14.7, 14.4, 13.4, and 7.3 μg/m3, respectively. In São Paulo and Rio de Janeiro, black carbon explained approximately 30% of the PM2.5 mass. We used receptor models to identify distinct source-related PM2.5 fractions and correlate those fractions with daily mortality rates. Using specific rotation factor analysis, we identified the following principal contributing factors: soil and crustal material; vehicle emissions and biomass burning (black carbon factor); and fuel oil combustion in industries (sulfur factor). In all six cities, vehicle emissions explained at least 40% of the PM2.5 mass. Elemental composition determination with receptor modeling proved an adequate strategy to identify air pollution sources and to evaluate their short- and long-term effects on human health. Our data could inform decisions regarding environmental policies vis-à-vis health care costs

The development and evaluation of a self-questioning study technique

Includes bibliographical reference

Consumer Choice Among Durables: A Random Utility Model and an Application to Brazilian Households

204 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1983.Consumer choice among durables cannot be satisfactorily accommodated by models directly derived from the conventional, deterministic model. This study proposes an axiomatic and probabilistic framework for modelling consumer choice preferences among durables in the market and discusses its theoretical properties. The related qualitative econometric model is specified, the empirically testable hypotheses implied by the proposed framework are discussed, as are the empirical properties of the measures relevant to behavior in the market for durables.For the empirical test of the model, Brazilian (state of Sao Paulo) cross-sectional household data (ENDEF survey) is used for the set of four durables: refrigerator, blender, sewing machine and television. The estimation results strongly support the theory. The implications to Brazilian (Sao Paulo) consumer behavior in the market for durables are derived and discussed. Among others, the results for Sao Paulo consumers sharply contradicts the notion that preferences for durables revealed in the market merely reproduce those of the high income consumer.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD