A total of 625 buildings and outdoor locations in the San Diego, California, area were monitored using the Allergenco Sampl-Air MK-3 impaction sampler or the Zefon Air-O-Cell slit bioaerosol cassette. Locations were classified by rigid criteria as clean commercial, commercial with mold growth, clean residential, residential with water staining, and residential with mold growth. In addition, coastal and inland outdoor locations were measured. Seven categories (total spores, Ascospores/Basidiospores, Cladosporium, Smut/Myxomycetes-like, Aspergillus/Penicillium (AS/PE), Alternaria, and Unidentified/Other) were detected frequently enough that maximum likelihood estimate techniques could be used to determine distribution parameters and, thus, treat these as continuous variables. For total counts (no nondetectables) an analysis of variance was used to examine differences in location means. For the other categories Land's confidence limits were generated and visually compared for differences among locations. For 12 other categories (Curvularia, Dreschlera, Epicoccum, Fusarium, Mildew-like, Pithomyces, Rusts, Stachybotrys, Stemphyllium, Torula, Ulocladium, and Zygomycetes-like), detection generally occurred in less than 10% of samples. These genera were treated as dichotomous (detect/nondetect) data, and Chi-square analyses differentiated between locations. For total counts, values were significantly different on the order of clean < outdoor < moldy. There was a large difference between the moldy and other location classes. For AS/PE, moldy location means were clearly higher than those for clean buildings and outdoors, although the clean and outdoor means could not be differentiated. For all other genera the results tend to indicate little or no ability to discriminate location. For example, there were no differences in the probabilities of detecting Stachybotrys among the various locations. In our study only total counts, usually driven by AS/PE concentrations, had value in determining whether a building is mold contaminated employing our set of rigorous location classification criteria.

A key consideration for responsible development of mineral and energy resources is the well-being of workers. Respirable dust in mining environments represents a serious concern for occupational health. In particular, coal miners can be exposed to a variety of dust characteristics depending on their work activities, and some exposures may pose risk for lung diseases like CWP and silicosis. As underscored by common regulatory frameworks, respirable dust exposures are generally characterized on the basis of total mass concentration, and also the silica mass fraction. However, relatively little emphasis has been placed on other dust characteristics that may be important in terms of identifying health risks. Comprehensive particle-level analysis to estimate chemistry, size, and shape distributions of particles is possible. This paper describes a standard methodology for characterization of respirable coal mine dust using scanning electron microscopy (SEM) with energy dispersive X-ray (EDX). Preliminary verification of the method is shown based several dust samples collected from an underground mine in Central Appalachia.

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