Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil

In the metropolitan area of Sao Paulo, Brazil, ozone and particulate matter ( PM) are the air pollutants that pose the greatest threat to air quality, since the PM and the ozone precursors ( nitrogen oxides and volatile organic compounds) are the main source of air pollution from vehicular emissions. Vehicular emissions can be measured inside road tunnels, and those measurements can provide information about emission factors of in-use vehicles. Emission factors are used to estimate vehicular emissions and are described as the amount of species emitted per vehicle distance driven or per volume of fuel consumed. This study presents emission factor data for fine particles, coarse particles, inhalable particulate matter and black carbon, as well as size distribution data for inhalable particulate matter, as measured in March and May of 2004, respectively, in the Janio Quadros and Maria Maluf road tunnels, both located in Sao Paulo. The Janio Quadros tunnel carries mainly light-duty vehicles, whereas the Maria Maluf tunnel carries light-duty and heavy-duty vehicles. In the Janio Quadros tunnel, the estimated light-duty vehicle emission factors for the trace elements copper and bromine were 261 and 220 mu g km(-1), respectively, and 16, 197, 127 and 92 mg km(-1), respectively, for black carbon, inhalable particulate matter, coarse particles and fine particles. The mean contribution of heavy-duty vehicles to the emissions of black carbon, inhalable particulate matter, coarse particles and fine particles was, respectively 29, 4, 6 and 6 times higher than that of light-duty vehicles. The inhalable particulate matter emission factor for heavy-duty vehicles was 1.2 times higher than that found during dynamometer testing. In general, the particle emissions in Sao Paulo tunnels are higher than those found in other cities of the world.

Flood Risk Assessment in Coastal Drainage Basins through a Multivariate Analysis within a GIS-Based Model

This paper presents a GIS-based multicriteria flood risk assessment and mapping approach applied to coastal drainage basins where hydrological data are not available. It involves risk to different types of possible processes: coastal inundation (storm surge), river, estuarine and flash flood, either at urban or natural areas, and fords. Based on the causes of these processes, several environmental indicators were taken to build-up the risk assessment. Geoindicators include geological-geomorphologic proprieties of Quaternary sedimentary units, water table, drainage basin morphometry, coastal dynamics, beach morphodynamics and microclimatic characteristics. Bioindicators involve coastal plain and low slope native vegetation categories and two alteration states. Anthropogenic indicators encompass land use categories properties such as: type, occupation density, urban structure type and occupation consolidation degree. The selected indicators were stored within an expert Geoenvironmental Information System developed for the State of Sao Paulo Coastal Zone (SIIGAL), which attributes were mathematically classified through deterministic approaches, in order to estimate natural susceptibilities (Sn), human-induced susceptibilities (Sa), return period of rain events (Ri), potential damages (Dp) and the risk classification (R), according to the equation R=(Sn.Sa.Ri).Dp. Thematic maps were automatically processed within the SIIGAL, in which automata cells (""geoenvironmental management units"") aggregating geological-geomorphologic and land use/native vegetation categories were the units of classification. The method has been applied to the Northern Littoral of the State of Sao Paulo (Brazil) in 32 small drainage basins, demonstrating to be very useful for coastal zone public politics, civil defense programs and flood management.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

SCALING LIMIT FOR A DRAINAGE NETWORK MODEL

We consider the two-dimensional version of a drainage network model introduced ill Gangopadhyay, Roy and Sarkar (2004), and show that the appropriately rescaled family of its paths converges in distribution to the Brownian web. We do so by verifying the convergence criteria proposed in Fontes, Isopi, Newman and Ravishankar (2002).