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Steven R. Hanna, Robert Paine, David Heinold, Elizabeth Kintigh, and Dan Baker

analysis. The result is a linear regression equation where the perturbations in predicted concentrations are expressed as linear combinations of the perturbations in input variables or model parameters. The fraction of the variance explained by each parameter is also estimated. Despite the fact that model simulations at three ambient air monitor locations (see Fig. 1 ) are included in the outputs, no attempt is made in this paper to compare model simulations with observations. The current paper is

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Edith Gégo, P. Steven Porter, Alice Gilliland, and S. Trivikrama Rao

impact of any regulatory program ( Cox and Chu 1993 ; Flaum et al. 1996 ; Kuebler et al. 2001 ). Accordingly, the concentration changes we report here were obtained after moderating the influence of meteorological conditions on ambient ozone concentrations. Following Brankov et al. (1998) , we use back-trajectory analysis in an attempt to specifically assess the changes that occurred throughout the eastern United States following the reduction in emissions from sources in the ORV, the source

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C. Hogrefe, W. Hao, K. Civerolo, J.-Y. Ku, G. Sistla, R. S. Gaza, L. Sedefian, K. Schere, A. Gilliland, and R. Mathur

1. Introduction Many U.S. air quality forecasting programs for ozone (O 3 ) and fine particulate matter (PM 2.5 ) operated by federal, state, and local agencies are based on a combination of weather prediction, statistical analysis, and expert judgment ( Gaza 1998 ; Ryan et al. 2000 ; Dye et al. 2000 ; U.S. EPA 2003a ). The application of grid-based photochemical modeling systems to provide real-time air quality forecasts has been a fairly recent development and has been mostly restricted to

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M. Talat Odman, Yongtao Hu, Alper Unal, Armistead G. Russell, and James W. Boylan

–5 simulated days that represent the 20% best/worst visibility days at each site considered here ( Table 1 ). Classification and Regression Tree (CART) analysis was used to group the meteorological conditions that lead to good/bad visibility at each class I area and to determine the representativeness of simulated days ( Douglas et al. 2006 ). Each day was assigned a weight that characterizes the frequency of occurrence of its meteorological conditions among the 20% best or 20% worst visibility days. Note

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George Kallos, Marina Astitha, Petros Katsafados, and Chris Spyrou

) , and Kallos et al. (2006) . There are also indications of the existence of transport patterns on larger scales toward/from the Mediterranean region ( Ramanathan et al. 2001 ; Lelieveld et al. 2002 ; Carmichael et al. 2002 ). The current status of knowledge on the above aspects is discussed in this paper, providing some summary remarks on the paths and scales of transport and transformation of PM in the greater Mediterranean region (GMR). The tools used for such analysis are atmospheric and air

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