An unrecognized source of PCB contamination in schools and other buildings

An investigation of 24 buildings in the Greater Boston Area revealed that one-third (8 of 24) contained caulking materials with polychlorinated biphenyl (PCB) content exceeding 50 ppm by weight, which is the U.S. Environmental Protection Agency (U.S. EPA) specified limit above which this material is considered to be PCB bulk product waste. These buildings included schools and other public buildings. In a university building where similar levels of PCB were found in caulking material, PCB levels in indoor air ranged from 111 to 393 ng/m3; and in dust taken from the building ventilation system, < 1 ppm to 81 ppm. In this building, the U.S. EPA mandated requirements for the removal and disposal of the PCB bulk product waste as well as for confirmatory sampling to ensure that the interior and exterior of the building were decontaminated. Although U.S. EPA regulations under the Toxic Substances Control Act stipulate procedures by which PCB-contaminated materials must be handled and disposed, the regulations apparently do not require that materials such as caulking be tested to determine its PCB content. This limited investigation strongly suggests that were this testing done, many buildings would be found to contain high levels of PCBs in the building materials and potentially in the building environment. The presence of PCBs in schools is of particular concern given evidence suggesting that PCBs are developmental toxins.

Processing of Synthetic Aperture Radar Images by a Multiscale Boundary Contour System and Feature Contour System

An improved Boundary Contour System (BCS) and Feature Contour System (FCS) neural network model of preattentive vision is applied to large images containing range data gathered by a synthetic aperture radar (SAR) sensor. The goal of processing is to make structures such as motor vehicles, roads, or buildings more salient and more interpretable to human observers than they are in the original imagery. Early processing by shunting center-surround networks compresses signal dynamic range and performs local contrast enhancement. Subsequent processing by filters sensitive to oriented contrast, including short-range competition and long-range cooperation, segments the image into regions. The segmentation is performed by three "copies" of the BCS and FCS, of small, medium, and large scales, wherein the "short-range" and "long-range" interactions within each scale occur over smaller or larger distances, corresponding to the size of the early filters of each scale. A diffusive filling-in operation within the segmented regions at each scale produces coherent surface representations. The combination of BCS and FCS helps to locate and enhance structure over regions of many pixels, without the resulting blur characteristic of approaches based on low spatial frequency filtering alone.

Processing of Synthetic Aperture Radar Images by the Boundary Contour System and Feature Contour System

An improved Boundary Contour System (BCS) and Feature Contour System (FCS) neural network model of preattentive vision is applied to two large images containing range data gathered by a synthetic aperture radar (SAR) sensor. The goal of processing is to make structures such as motor vehicles, roads, or buildings more salient and more interpretable to human observers than they are in the original imagery. Early processing by shunting center-surround networks compresses signal dynamic range and performs local contrast enhancement. Subsequent processing by filters sensitive to oriented contrast, including short-range competition and long-range cooperation, segments the image into regions. Finally, a diffusive filling-in operation within the segmented regions produces coherent visible structures. The combination of BCS and FCS helps to locate and enhance structure over regions of many pixels, without the resulting blur characteristic of approaches based on low spatial frequency filtering alone.