New Insights into Mycobacterium bovis Prevalence in Wild Mammals in Portugal

A survey to determine the prevalence of Mycobacterium bovis in wild mammals in Portugal was conducted by testing samples from hunted animals and those found dead between 2009 and 2013. In this study, we investigated 2116 wild mammals. Post-mortem examinations were performed, and tissues were collected from wild mammals representing 8 families and 11 different species, with a total of 393 animals analysed. Cultures were performed, and acid-fast isolates were identified by PCR. Tissues were also screened for Mycobacterium bovis by directly extracting DNA and testing for the Mycobacterium bovis-specific sequences. Mycobacterium bovis prevalence was 26.9% (95% CI: 22.8-31.5%). Mycobacterium bovis was recorded in 106 of the 393 studied species: prevalence by species were 26.9% (95% CI: 16.8-40.2%) in red foxes, 20.0% (95% CI: 7.0-45.2%) in Egyptian mongooses, 21.4% (95% CI: 16.2-27.7%) in wild boar and 38.3% (95% CI: 29.9-47.4%) in red deer. Mycobacterium bovis infection was detected in six of eight taxonomic families. For some species, the small sample sizes obtained were a reflection of their restricted range and low abundance, making estimates of infection prevalence very difficult (1 beech marten of 4; 1 Eurasian otter of 3; 2 common genet of 3). Infection was not detected in European badgers, hedgehog, wild rabbits and hare. The results of this study confirm the presence of Mycobacterium bovis infection in wild carnivores in Portugal.

Study of atmospheric ozone formation by means of a neural network-based model

The occurrence of high ozone levels in the atmosphere of urban areas has become a serious pollution problem in a number of large cities in the world. Although mathematical models have been proposed for predicting ozone concentrations as a function of a number of gas components, sometimes there are uncertainties due to lack of the combined effects of meteorological factors and the complex chemical reaction system involved. The application of neural network models, based on measured values of air pollutants and meteorological factors at different locations within the São Paulo Metropolitan Area, combine chemical and meteorological information. This has shown to be a promising tool for predicting ozone concentration. Simulations carried out with the model indicate the sensitivity of ozone in relation to different air pollution and weather conditions. Predictions using this model have shown good agreement with measured values of ozone concentrations.

São Paulo aerosol characterization study

The São Paulo Metropolitan area (SPMA) is characterized as having one of the worst air pollution problems in Brazil, with frequent violations of air quality standards for particulate matter. This paper presents the results of a receptor model source apportionment study carried out to develop a quantitative database on which a control strategy could be developed. The study was conducted in four sites with distinct land uses. Fine, coarse (CP), and total suspended particles (TSP) samples were collected on Teflon and glass filters and analyzed by x-ray fluorescence (XRF), ion chromatography, and thermal evolution. The sources were characterized by similar methodology. Chemical mass balance (CMB) receptor modeling indicated that carbonaceous material plays an important role in the aerosol composition; that the three major source categories contributing to the fine particles are vehicles, secondary carbon, and sulfates; and that the main contributors to CP and TSP are road dust and vehicles. All sampling sites presented the same general pattern in terms of source contribution, although this contribution varied from site to site.