Details

Autor(en) / Beteiligte

• Andrade, Maria de Fatima
• de Miranda, Regina Maura
• Fornaro, Adalgiza
• Kerr, Americo
• Oyama, Beatriz
• de Andre, Paulo Afonso
• Saldiva, Paulo

Titel

Vehicle emissions and PM(2.5) mass concentrations in six Brazilian cities

Ist Teil von

• Air quality, atmosphere and health, 2012-03, Vol.5 (1), p.79

Ort / Verlag

Netherlands

Erscheinungsjahr

2012

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 (PM(2.5)) 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 PM(2.5) samples, employing gravimetry to determine PM(2.5) 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 PM(2.5) 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/m(3), respectively. In São Paulo and Rio de Janeiro, black carbon explained approximately 30% of the PM(2.5) mass. We used receptor models to identify distinct source-related PM(2.5) 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 PM(2.5) 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.