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An x-ray spectroscopic study of scleractinian coral skeletons indicated that, although some strontium substitutes for calcium in the aragonite structure, at concentrations of about 7500 parts per million, as much as 40 percent of the strontium resides in strontianite (SrCO3). A doublet peak in the Fourier transform of the extended x-ray absorption fine structure of the coral corresponded to six metal and 13 oxygen neighbors surrounding strontium at about 4.05 angstroms in strontium-substituted aragonite and at about 4.21 angstroms in strontianite. Thus, the mechanism of the temperature-sensitive partitioning of strontium between seawater and coral skeleton used for paleothermometry is unexpectedly complex.

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Optical, electron microprobe, and x-ray diffraction analysis of 88 samples of various compositions between Ag2S and Ag2Se synthesized at high temperature in sealed quartz tubing indicates the presence of two solid-solution series in this system at ambient (room) conditions. One series extends from Ag2S to approximately Ag2S0.4Se0.7 and has the Ag2S-III-type structure (monoclinic). The second series ranges from Ag2S0.3Se0.7 to Ag2Se and is characterized by the Ag2Se-II-type structure (orthorhombic). Members of both series, in appropriate proportions, characterize the apparent compositional gap between the two solid solutions. Gradual shifts in the locations of the x-ray diffraction peaks along the compositional gradient of each solid solution revealed an expansion of the d-spacing as the larger Se ion was substituted for S in the Ag2S-III-type structure and a contraction as S was substituted for Se in the Ag2Se-II-type structure. The reported discrete phase, Ag4SSe (aguilarite, orthorhombic), appears to be simply a member of the monoclinic Ag2S-III-type solid solution.

The use of land-use regression (LUR) techniques for modeling small-scale variations of intraurban air pollution has been increasing in the last decade. The most appealing feature of LUR techniques is the economical monitoring requirements. In this study, principal component analysis (PCA) was employed to optimize an LUR model for PM2.5. The PM2.5 monitoring network consisted of 13 sites, which constrained the regression model to a maximum of one independent variable. An optimized surrogate of vehicle emissions was produced by PCA and employed as the predictor variable in the model. The vehicle emissions surrogate consisted of a linear combination of several traffic variables (e.g., vehicle miles traveled, speed, traffic demand, road length, and time) obtained from a road network used for traffic modeling. The vehicle-emissions surrogate produced by the PCA had a predictive capacity greater (R2 = .458) than the traffic variable, Traffic Demand summarized for a 1 km buffer, with best predictive capacity (R2 = .341). The PCA-based method employed in this study was effective at increasing the fit of an ordinary LUR model by optimizing the utilization of a PM2.5 dataset from small-n monitoring network. In general, the method used can contribute to LUR techniques in two major ways: 1) by improving the predictive power of the input variable, by substituting a principal component for a single variable and 2) by creating an orthogonal set of predictor variables, and thus fulfilling the no colinearity assumption of the linear regression methods. The proposed PCA method, should be universally applicable to LUR methods and will expand their economical attractiveness.