Effects of Air Pollution Control on Climate

Urban air pollution and climate are closely connected due to shared generating processes (e.g., combustion) for emissions of the driving gases and aerosols. They are also connected because the atmospheric lifecycles of common air pollutants such as CO, NOx and VOCs, and of the climatically important methane gas (CH4) and sulfate aerosols, both involve the fast photochemistry of the hydroxyl free radical (OH). Thus policies designed to address air pollution may impact climate and vice versa. We present calculations using a model coupling economics, atmospheric chemistry, climate and ecosystems to illustrate some effects of air pollution policy alone on global warming. We consider caps on emissions of NOx, CO, volatile organic carbon, and SOx both individually and combined in two ways. These caps can lower ozone causing less warming, lower sulfate aerosols yielding more warming, lower OH and thus increase CH4 giving more warming, and finally, allow more carbon uptake by ecosystems leading to less warming. Overall, these effects significantly offset each other suggesting that air pollution policy has a relatively small net effect on the global mean surface temperature and sea level rise. However, our study does not account for the effects of air pollution policies on overall demand for fossil fuels and on the choice of fuels (coal, oil, gas), nor have we considered the effects of caps on black carbon or organic carbon aerosols on climate. These effects, if included, could lead to more substantial impacts of capping pollutant emissions on global temperature and sea level than concluded here. Caps on aerosols in general could also yield impacts on other important aspects of climate beyond those addressed here, such as the regional patterns of cloudiness and precipitation.

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

Lean Transition of Emerging Industrial Capability

This volume of the final report documents the technical work performed from December 1998 through December 2002 under Cooperative Agreement F33615-97-2-5153 executed between the U.S. Air Force, Air Force Research Laboratory, Materials and Manufacturing Directorate, Manufacturing Technology Division (AFRL/MLM) and the McDonnell Douglas Corporation, a wholly-owned subsidiary of The Boeing Company. The work was accomplished by The Boeing Company, Phantom Works, Huntington Beach, St. Louis, and Seattle; Ford Motor Company; Integral Inc.; Sloan School of Management in the Massachusetts Institute of Technology; Pratt & Whitney; and Central State University in Xenia, Ohio and in association with Raytheon Corporation. The LeanTEC program manager for AFRL is John Crabill of AFRL / MLMP and The Boeing Company program manager is Ed Shroyer of Boeing Phantom Works in Huntington Beach, CA. Financial performance under this contract is documented in the Financial Volume of the final report.