In the MASP, currently there are approximately 7.2 million passenger and commercial vehicles: 93.5% light-duty and 6.5% heavy-duty diesel vehicles. Of the light-duty vehicles, approximately 76.3% burn a mixture of 78-80% (v/v) gasoline and 22% ethanol (referred to as gasohol), and 17.2% use hydrated ethanol (95% ethanol + 5% water). In Brazil, light-duty vehicles run on a mixture of 78% gasoline and 22% anhydrous (neat) ethanol. Over the past several years, ambient ozone concentrations in the SPMA have reached levels of more than five times that considered protective of public health by the World Health Organization. In spring time, ozone levels routinely exceed the 160 micrograms/m3 hourly Brazilian National Ambient Air-Quality Standard. Approximately 90% of the ozone precursors in the SPMA atmosphere are emitted by the vehicle fleet (CETESB, 2006). According to the official state inventory of HC emissions from mobile sources, 22% are from gasohol-powered vehicles, 15% from diesel-powered vehicles, 6% from ethanol-powered vehicles, and 5% from motorcycles. In addition, a significant contribution to HC emissions comes from evaporative emissions, which constitute 48% of total HC emissions to the atmosphere. Hydrocarbons contribute to the formation of the photochemical smog, and are generally attributed to mobile sources. In the specific case of nitrogen oxides (NOx), 78% comes from diesel-powered vehicles, 13% from gasohol-powered vehicles, and 4% from ethanol-powered vehicles. Other important pollutant that has usually presented concentrations above the air quality standard is the PM10 (Particulate Matter with aerodynamic diameter less than 10 micra).
Many studies were performed using multivariate statistics for evaluating the particulate matter source in the MASP – Factor Analysis, Cluster Analysis and Principal Component Analysis, as reported in Castanho & Artaxo (2001). The results showed a significant participation of vehicular emission in the mass of fine particles, mainly related to the concentration of Black Carbon.
With new methodologies it was possible to determine with better resolution the elemental structure and the size distribution of particulate matter, many data is presented in the work of Miranda et al. (2002) and Sanchez-Ccoyllo et al. (2006). The estimation of ozone precursor emissions speciation is a rather complex task. Estimating spatial and temporal variation of vehicle emissions is the greatest source of uncertainty in modeling ozone. As in other places, data regarding motor vehicle emissions are scarce. To improve the vehicular emissions inventory for the light- and heavy-duty fleet in the metropolitan area of São Paulo (MASP), Brazil, measurements of vehicle emissions in road tunnels located in the MASP were performed. On March 22-26, 2004 and May 04-07, 2004, respectively, CO, CO2, NOx, SO2, and volatile organic compounds (VOCs) emissions were measured in two tunnels: the Janio Quadros, which carries light-duty vehicles; and the Maria Maluf, which carries light-duty vehicles and heavy-duty diesel trucks. Pollutant concentrations were measured inside the tunnels, and background pollutant concentrations were measured outside of the tunnels. The mean CO and NOx emission factors (in g km-1) were, respectively, 14.6 ± 2.3 and 1.6 ± 0.3 for light-duty vehicles, compared with 20.6 ± 4.7 and 22.3 ± 9.8 for heavy-duty vehicles. The total VOCs emission factor for the Maria Maluf tunnel was 1.4 ± 1.3 gkm-1. The main VOCs classes identified were aromatic, alkane, and aldehyde compounds. For the heavy-duty fleet, NOx emission factors were approximately 14 times higher than those found for the light-duty fleet. This was attributed to the high levels of NOx emissions from diesel vehicles. These results are presented in the work of Martins et al., 2006. In the same experiment were also calculated the emission factor of trace elements and particles. In the Jânio Quadros tunnel, the estimated light-duty vehicle emission factors were 16.4, 226.3, 133.6 and 93.2 mg/km, 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, 27, 3, 5 and 4.8 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 São Paulo tunnels are higher than those found in other cities of the world.
Comparisons between surface pollutants concentrations data, provided by automatic network stations for air quality monitoring and modeled values, show good agreement, with correlation coefficients greater than 0.7 for all stations considered. Mean values over all analyzed stations have an index of agreement higher than 0.8. The computational efficiency of the model shows that it can be considered for operational procedures with relatively small PC-based clusters and used as a tool for emission control strategies by environmental agencies concerned with the improvement of air quality.
The strategies to solve the bad air quality in MASP are related to the control of vehicular emission, the organization of the urban space stimulating, the use of clean fuels and the implementation of good quality public transportation.
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