Volatile organic compounds and particulate matter in child care facilities in the District of Columbia: Results from a pilot study.

BACKGROUND: Many young children in the U.S. spend a significant portion of their day in child care facilities where they may be exposed to contaminants linked to adverse health effects. Exposure data on volatile organic compounds (VOCs) and particulate matter (PM) in these settings is scarce. OBJECTIVE: To guide the design of a larger exposure assessment study in urban child care facilities, we conducted a pilot study in which we characterized indoor concentrations of select VOCs and PM. METHODS: We recruited 14 child care facilities in the District of Columbia (Washington, DC) and measured indoor concentrations of seven VOCs (n=35 total samples; 2-5 samples per facility): benzene, carbon tetrachloride, chloroform, ethylbenzene, o-xylene, p-xylene, and toluene in all facilities; and collected real-time PM measurements in seven facilities. We calculated descriptive statistics for contaminant concentrations and computed intraclass correlation coefficients (ICC) to evaluate the variability of VOC levels indoors. We also administered a survey to collect general health information on the children attending these facilities, and information on general housekeeping practices and proximity of facilities to potential sources of target contaminants. RESULTS: We detected six of the seven VOCs in the majority of child care facilities with detection frequencies ranging from 71% to 100%. Chloroform and toluene were detected in all samples. Median (range) concentrations for toluene, chloroform, benzene, o-xylene, ethylbenzene, and carbon tetrachloride were: 5.6µg/m(3) (0.6-16.5µg/m(3)), 2.8µg/m(3) (0.4-53.0µg/m(3)), 1.4µg/m(3) (below the limit of detection or

Multiwavelength output from a Nd:YAG /Cr:LiSAF hybrid laser.

A prototype solid-state, multispectral hybrid laser has been designed and tested. The laser provides simultaneous outputs at several wavelengths. The hybrid-laser concept is based on the efficient use of flash-lamp-pump energy distributed between two complementary lasing materials, Nd:YAG and Cr:LiSAF, that share the same pump cavity. The prototype Q-switched hybrid laser provides dual-fundamental-wavelength output at 850 and 1064 nm as well as frequency-doubled output at 532 nm. The laser achieved 3.6% slope efficiency (combined) in free-running operation and 2.4% when Q switched. Higher efficiencies can be obtained with improvements in laser crystal quality and pump cavity configuration.

Detection of mines and minelike targets using principal component and neural-network methods.

This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signal-to-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.