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Apportionment of primary and secondary pollutants during the summer 2001 Pittsburgh Air Quality Study (PAQS) is reported. Several sites were included in PAQS, with the main site (the supersite) adjacent to the Carnegie Mellon University campus in Schenley Park. One of the additional sampling sites was located at the National Energy Technology Laboratory, located ϳ18 km southeast of downtown Pittsburgh. Fine particulate matter (PM 2.5 ) mass, gas-phase volatile organic material (VOM), particulate semivolatile and nonvolatile organic material (NVOM), and ammonium sulfate were apportioned at the two sites into their primary and secondary contributions using the U.S. Environmental Protection Agency UNMIX 2.3 multivariate receptor modeling and analysis software. A portion of each of these species was identified as originating from gasoline and diesel primary mobile sources. Some of the organic material was formed from local secondary transformation processes, whereas the great majority of the secondary sulfate was associated with regional transformation contributions. The results indicated that the diurnal patterns of secondary gas-phase VOM and particulate semivolatile and NVOM were not correlated with secondary ammonium sulfate contributions but were associated with separate formation pathways. These findings are consistent with the bulk of the secondary ammonium sulfate in the Pittsburgh area being the result of contributions from distant transport and, thus, decoupled from local activity involving organic pollutants in the metropolitan area.

We present an optimal control methodology, which we refer to as concentration-ofmeasure optimal control (COMOC), that seeks to minimize a concentration-of-measure upper bound on the probability of failure of a system. The systems under consideration are characterized by a single performance measure that depends on random inputs through a known response function. For these systems, concentration-of-measure upper bound on the probability of failure of a system can be formulated in terms of the mean performance measure and a system diameter that measures the uncertainty in the operation of the system. COMOC then seeks to determine the optimal controls that maximize the confidence in the safe operation of the system, defined as the ratio of the design margin, which is measured by the difference between the mean performance and the design threshold, to the system uncertainty, which is measured by the system diameter. This strategy has been assessed in the case of a robot-arm maneuver for which the performance measure of interest is assumed to be the placement accuracy of the arm tip. The ability of COMOC to significantly increase the design confidence in that particular example of application is demonstrated.