Analysis of fuel emissions is crucial for understanding the pathogenesis of mortality because of air pollution. The objective of this study is to assess cardiovascular and inflammatory toxicity of diesel and biodiesel particles. Mice were exposed to fuels for 1 h. Heart rate (HR), heart rate variability, and blood pressure were obtained before exposure, as well as 30 and 60 min after exposure. After 24 h, bronchoalveolar lavage, blood, and bone marrow were collected to evaluate inflammation. B100 decreased the following emission parameters: mass, black carbon, metals, CO, polycyclic aromatic hydrocarbons, and volatile organic compounds compared with B50 and diesel; root mean square of successive differences in the heart beat interval increased with diesel (p < 0.05) compared with control; low frequency increased with diesel (p < 0.01) and B100 (p < 0.05) compared with control; HR increased with B100 (p < 0.05) compared with control; mean corpuscular volume increased with B100 compared with diesel (p < 0.01), B50, and control (p < 0.001); mean corpuscular hemoglobin concentration decreased with B100 compared with B50 (p < 0.001) and control (p < 0.05); leucocytes increased with B50 compared with diesel (p < 0.05); platelets increased with B100 compared with diesel and control (p < 0.05); reticulocytes increased with B50 compared with diesel, control (p < 0.01), and B100 (p < 0.05); metamyelocytes increased with B50 and B100 compared with diesel (p < 0.05); neutrophils increased with diesel and B50 compared with control (p < 0.05); and macrophages increased with diesel (p < 0.01), B50, and B100 (p < 0.05) compared with control. Biodiesel was more toxic than diesel because it promoted cardiovascular alterations as well as pulmonary and systemic inflammation.

This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.

Air pollution is an important public health issue. High levels of carbon monoxide in the atmosphere are hazardous to human health. Studies regarding the concentration of this and other gases in the atmosphere allow political actions to manage and reduce the emission of pollutants. In this context, this paper studied the annual, seasonal, weekly and daily variations of carbon monoxide (CO) concentration for the Metropolitan Region of São Paulo (MRSP). We studied three sites in the MRSP, two of them are located in areas under the influence of heavy vehicle traffic (Osasco and Congonhas) and the third one in a city park (Ibirapuera Park). The results showed high influence of gasoline vehicles on CO emission. In the annual scale, CO concentration decreased due to improvements in emission technology, despite the increasing number of vehicles. CO emission showed a seasonal, weekly and diurnal cycle associated to meteorological conditions and emission patterns. The highest values of mean concentration were observed in June/July for Osasco (2.20 ppm), Congonhas (2.04 ppm) and Ibirapuera (1.04 ppm), during the morning, due to weak dispersion of the polluting gases and higher emission from the rush hours.

An index for frost prediction is proposed and calibrated against observations. It takes into account: (1) the main meteorological variables that favour or oppose to frost; (2) weights attributed to these variables; and (3) means and standard deviations of these variables, only for cases in which frost occurs, as defined by observation of temperatures that are ≤ 6°C. The meteorological variables used for the frost index IG (from the Portuguese, Índice de Geada) are numerically predicted by a regional weather forecast model. An outcome of the calibration processes results is that temperature has the largest contribution, followed by pressure and winds, while the other variables were adjusted to obey the restriction that the sum of weights are equal to 1. After index calibration and threshold determination, the method was applied for the 2017 winter season, and a case study for May 2018 was also considered. In order to verify whether the new index can satisfactorily contribute to the weather forecasting, the results using the IG were compared with the temperature outputs of the numerical regional model. It was found that for three selected areas, and for all the forecasted hours, the IG produces better results than the model's direct temperature forecasts. Thus, it was concluded that the use of the IG in an operational environment potentially provides considerable improvement in the predictive skill of frost events.