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[1] The meteorological mechanisms for lofting trace gases and aerosols out of the planetary boundary layer (PBL) into the free troposphere are key to understanding local air pollution problems as well as regional and global atmospheric chemistry and climate issues. Over the North American continent, convective storms and lifting in warm conveyor belts transport pollutants into the free troposphere. Little is known about the vertical distribution of pollutants and dust over east Asia, and the processes leading to transport, transformation, and removal of these species remain uncertain. To provide insight into these mechanisms, we report on eight flights based out of Shenyang in NE China as part of the U.S./China EAST-AIRE project conducted in April 2005. We evaluate profiles of trace species, along with back trajectories and satellite data, in the meteorological context of cyclonic systems. The warm-sector PBL air ahead of a cold front was highly polluted, while in the free troposphere concentrations of trace gases and aerosols were lower, but well above background; we measured $300 ppb CO, $2 ppb SO 2 , $70 ppb O 3 , and $ 8 Â 10 À5 m À1 aerosol scattering between $1000 and 4000 m altitude. Satellite observations indicate that the entire plume contained almost 10 5 tons of SO 2 and that the gas decayed with a lifetime of 3-5 d. Roughly the same mass of aerosol was transported into the free troposphere. Over the east Asian continent, dry convection appears to dominate with warm conveyor belts first coming into play as the cyclonic systems move off the coast.

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Intersection delays are the major contributing factor to arterial delays. Methods to estimate intersection delay patterns by using measured travel times are studied. The delay patterns provide a way to estimate the delay for any vehicle arriving at the intersection at any time, which is useful for providing time-dependent intersection delay information to the driving public. The model requires sampled travel times between two consecutive locations on arterial streets, one upstream and the other downstream of a signalized intersection, without the need to know signal timing or traffic flow information. Signal phases can actually be estimated from the delay patterns, which is a unique feature of the proposed method in this paper. The proposed model is based on two observations regarding delays for signalized intersections: ( a) delay can be approximately represented by piecewise linear curves due to the characteristics of queue forming and discharging and ( b) there is a nontrivial increase in delay after the start of the red time that enables detection of the start of a cycle. A least-squares–based algorithm is developed to match measured delays in each cycle by using piecewise linear curves. The proposed model and algorithm are tested by using field experiment data with reasonable results.

Several findings call into question current practices. The chief conclusion is that the accuracy of freight generation (FG) and freight trip generation (FTG) models depends on the consistency between the model's structure and actual FG-FTG patterns, the degree of internal heterogeneity of the economic and land use aggregation used to estimate the model, and the appropriateness of the spatial aggregation procedure used to obtain the desired FG-FTG estimates. Relative to model structure, the paper establishes strong reasons to treat FG and FTG as separate concepts, because the latter is the output of logistic decisions, whereas the former is determined by the economics of production and consumption. The connection between business size variables–for example, employment–and FG is relatively strong because they are economic input factors, whereas the one with FTG is weaker because inventory and transportation costs come into play. Thus it is generally not correct to assume proportionality between FTG and business size or to assume that using constant FTG rates could be problematic. For instance, only 18% of the industry sectors in New York City exhibit constant FTG rates per employee. For economic and land use aggregation, the finer the level of detail the better, as independent variables have a better chance to explain FG-FTG. In the case of spatial aggregation, the correct aggregation procedure depends on the underlying disaggregate model. For a FG-FTG model to work well, both economic and land use and spatial aggregations must be appropriate.

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