Gail P. Thelin

Regression models for explaining and predicting concentrations of organochlorine pesticides in fish from streams in the United States

Empirical regression models were developed for estimating concentrations of dieldrin, total chlordane, and total DDT in whole fish from U.S. streams. Models were based on pesticide concentrations measured in whole fish at 648 stream sites nationwide (1992-2001) as part of the U.S. Geological Surveys National Water Quality Assessment Program. Explanatory variables included fish lipid content, estimates (or surrogates) representing historical agricultural and urban sources, watershed characteristics, and geographic location. Models were developed using Tobit regression methods appropriate for data with censoring. Typically, the models explain approximately 50 to 70% of the variability in pesticide concentrations measured in whole fish. The models were used to predict pesticide concentrations in whole fish for streams nationwide using the U.S. Environmental Protection Agencys River Reach File 1 and to estimate the probability that whole-fish concentrations exceed benchmarks for protection of fish-eating wildlife. Predicted concentrations were highest for dieldrin in the Corn Belt, Texas, and scattered urban areas; for total chlordane in the Corn Belt, Texas, the Southeast, and urbanized Northeast; and for total DDT in the Southeast, Texas, California, and urban areas nationwide. The probability of exceeding wildlife benchmarks for dieldrin and chlordane was predicted to be low for most U.S. streams. The probability of exceeding wildlife benchmarks for total DDT is higher but varies depending on the fish taxon and on the benchmark used. Because the models in the present study are based on fish data collected during the 1990s and organochlorine pesticide residues in the environment continue to decline decades after their uses were discontinued, these models may overestimate present-day pesticide concentrations in fish.

Shaded relief map of U.S. topography from digital elevations

Much geologic and geophysical information that lies encoded within land surface form can be revealed by image processing large files of digitized elevations in fast machines and mapping the results. This convergence of computers, analytic software, data, and output devices has created exciting opportunities for automating the numerical and spatial study of topography. One recent result is the accompanying shaded relief map of the conterminous 48 states. A shaded relief image of topography mimics a cloud-free monochrome aerial photograph taken at low Sun. Gray tones represent different terrain slopes and azimuths calculated from adjacent elevations stored in a uniform grid, or digital elevation model (DEM). Sun elevation and direction can be varied to generate complementary views of the same area. The synoptic coverage of these computer images is a major advantage; unlike a photograph, image extent is limited only by size of the elevation array. Shaded relief maps also are free of the distortion found in radar images and of the vegetation and cultural features that mask topographic form on Landsat and SPOT images.