[1] During the summer of 2004, five altitude-controlled tracking balloons were flown as part of the International Consortium for Atmospheric Research on Transport and Transformations (ICARTT) campaign. These Controlled Meteorological (CMET) balloons, newly developed at the University of Massachusetts, are notable for their light weight ($1 kg mass), efficient altitude control, ease of launch, long-duration flight capability, and ability to perform repeated quasi-Lagrangian soundings. The balloons were embedded in urban plumes from New York and Boston which they tracked over New England, eastern Canada, and the Atlantic Ocean while maintaining a nearly constant altitude. The flights ranged from 10 to 111 hours and covered a maximum distance of 3000 km. Balloon flight tracks are used here to assess the accuracy of trajectory models during intensive aircraft sampling periods. A new method is presented for increasing the number of available reference trajectories by dividing the balloon flights into shorter segments for statistical analysis. For trajectory durations between 2 and 12 hours, mean trajectory errors are found to be approximately 26% and 34% of the flight distance for ECMWF-based and GFS-based trajectories, respectively. Anomalously large model errors observed during three of the flights are found to be the result of a narrow low-level jet (15 July) and synoptic-scale flow patterns (9 and 10 August). The results from this study should be useful to researchers evaluating the performance of trajectory models and chemical transport models during the ICARTT campaign. Complete CMET balloon and model trajectory data sets are available as a supplement to this paper.

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Design cognition includes the formulation of problems, the generation of solutions, and the utilization of design process strategies. Here, we measure the cognitive load to generate solutions to engineering challenges for sustainability using functional near-infrared spectroscopy (fNIRS). fNIRS can be used to study brain activity in more natural environments, while also providing better spatial resolution than EEG and better temporal resolution than fMRI. It therefore offers new opportunities for exploring how brain activity relates to engineering design. While there is literature describing which brain regions support particular cognitive functions, far less is known about how these are developed through learning and how they support design thinking. By measuring hemodynamic responses during brainstorming tasks with freshmen (n=14) and senior (n=9) engineering students we find a significant difference (p<0.001) in the cognitive activation required to generate solutions. Freshmen engineering students show 5 times greater activation in the dorsolateral prefrontal cortex (known to involve working memory, cognitive flexibility, planning, inhibition, and abstract reasoning) compared to seniors. While seniors show an average of 10 times increase in activation in the premotor cortex (known to be involved in the management of uncertainty, control of behavior, and self-reflection in decision making). The number of solutions generated was also significant (p=0.032). Freshmen generated 5.6 solutions on average during the brainstorming activity while seniors developed 4.1. In many ways, this initial work serves as a proof of concept in using neuroimaging to study the processes involved in engineering design. Through a better understanding of these processes, we can begin to explore specific elements of the engineering curriculum that may contribute to student ability to manage complexity inherent in engineering design problems. We hope this interdisciplinary study integrating engineering education and neuroscience generates conversation about other engineering design tasks and settings, in which, fNIRS can be effectively used as a new tool.