Recent industry changes in swine-management practices have resulted in a growing controversy surrounding the environmental and public health effects of modern swine production. The numerous wastes produced by intensive swine production not only pose a significant challenge to effective environmental management but also are associated with decreased air quality in confinement houses, potentially transferable antimicrobial resistance patterns, and several infectious agents that can be pathogenic to humans. Published studies have documented a variety of contaminants, microbial agents, and health effects in those occupationally exposed to swine, and these have provided the groundwork for an increasing body of research to evaluate possible community health effects. Nonetheless, several factors limit our ability to define and quantify the potential role of intensive swine-rearing facilities in occupational and community health. Our incomplete understanding and ability to detect specific exposures; the complicated nature of disease etiology; pathogenesis; and surveillance; and the inherent difficulties associated with study design all contribute to the inadequate level of knowledge that currently prevails. However; an evaluation of the published literature; and a recognition of the elements that may be compromising these studies; provides the foundation from which future studies may develop.

Recent industry change in swine-management practices have resulted in a growing controversy surrounding the environmental and public health effects of modern swine production. The numerous wastes produced by intensive swine production not only pose a significant chalienge to effective environmental management but also are associated with decreased air quality in confinement houses, potentialy transferable antimicrobial resistance patterns, and several infectious agents that can be pathogenic to humans. Published studies have documented a variety of contaminants, microbial agents, and health efFects in those occupationally exposed to swine, and these have provided the groundwork for an increasing body of research to evaluate possible community health effects. Nonetheless, several factors limit our ability to define and quantify the potential role of intensive swine-rearing facilities in occupational and community health. Our incomplete understanding and ability to detect specific exposures; the complicated nature of disease etiology, pathogenesis, and surveillance; and the inherent difficulties assodated with study design all contribute to the inadequate level ofknowledge that currendy prevails. However, an evaluation ofthe published literature, and a recognition of the elements that may be compromising these studies, provides the foundation from which future studies may develop.

Numerical studies were performed to evaluate and compare four different algorithms for tomographically reconstructing pollutant concentrations in indoor air measured with an optical remote sensing system. With a remote sensing/computed tomography system, two-dimensional maps of air concentrations can be created for an entire room with good spatial and temporal resolution. The success of such a system for characterizing the flow of contaminants in air, exposure assessment, and leak detection depends on the choice of tomographic reconstruction algorithm. A systematic method was developed to evaluate the performance of four algorithms: ART, ART3, SIRT, and SART. One hundred and twenty test maps were reconstructed by each algorithm under ideal and nonideal sampling conditions, and image quality was evaluated using four criteria. The nonideal sampling conditions included simulation of measurement noise and reduction in the number density of rays. Performance of the algorithms was found to be intimately related to the number of peaks in the test maps. The importance of using multiple measures of image quality was underscored by the fact that for some sampling conditions simulated, performance of the algorithms was judged differently depending on the evaluation criteria. Results indicated that using numerical studies is successful for evaluating such algorithms.

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