Search USGS⌕ Search

USGS · ofr95773

Chemical analyses of ground-water samples from the Rio Grande Valley in the vicinity of Albuquerque, New Mexico, October 1993 through January 1994

Abstract

A study was conducted to investigate general ground-water- quality conditions and contaminant locations in the Rio Grande Valley in the vicinity of Albuquerque, New Mexico. Water samples from 36 observation wells in 12 well nests were analyzed. The well nests are located along three roads near the Rio Grande--two well nests near Paseo del Norte, five well nests near Monta?o Road, and five well nests near Rio Bravo Boulevard. The water samples were collected from October 19, 1993, through January 18, 1994. Water-quality types by major-ion composition were calcium bicarbonate (found in most samples), sodium sulfate, calcium sulfate, and calcium sulfate chloride. Nutrients were detected in all but one sample. Ammonia was detected in 34 samples, nitrite in 4 samples, and nitrate in 17 samples. Orthophosphate was detected in 31 samples. Organic carbon was detected in all samples collected. The trace elements arsenic and barium were detected in all samples and zinc in 31 samples. Fourteen samples contained detectable copper. Cadmium was detected in one sample, chromium in two samples, lead in four samples, and selenium in two samples. Mercury and silver were not detected.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D. W. Wilkins, J.L. Schlottmann, D. M. Ferree. 1996. Chemical analyses of ground-water samples from the Rio Grande Valley in the vicinity of Albuquerque, New Mexico, October 1993 through January 1994. https://doi.org/10.3133/ofr95773

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Special Contributing Area Loading Program user’s manual

Information on the Special Contributing Area Loading Program execution and functions are presented in this user’s manual. An appendix presents a potential improvement for the user to consider. The hydrologic routing simulation method to model flow through multiple reservoirs, or sewer system components, is described. The use of Special Contributing Areas is described to run a successful simulation, which includes user input of hydrologic time series of flow components and the necessary formats. Upon completion of a successful Special Contributing Area Loading Program simulation, the program outputs hydrologic time series and a descriptive text file containing the model results for each defined sub-unit, or Special Contributing Area. The output time series contain flows through, and overflows from, the three reservoirs in the series, and the text file contains input and output path locations.

Open-File Report↗

Estimating aftershock risk for entry into earthquake-damaged buildings

We present a simple method to estimate the risk of experiencing strong shaking from aftershocks during entry into earthquake-damaged buildings. We compute wait times until the probability of strong ground shaking from aftershocks reaches a predefined risk threshold; for example, a 0.4 percent probability of experiencing Modified Mercalli Intensity 7 or greater shaking during the planned building entry. We also develop a relation between aftershock probability and the probability of strong shaking, so that users can reference the U.S. Geological Survey aftershock forecast during an ongoing aftershock sequence to determine if the risk threshold has been met. We apply our method to active continental regions (for example, the Western United States), stable continental regions (for example, the Central and Eastern United States), and subduction zones (for example, Cascadia or Alaska).

Open-File Report↗

End-user needs for remote sensing wetlands of the Prairie Pothole Region of North America

The Prairie Pothole Region (PPR) of North America comprises globally important grassland and wetland ecosystems critical for numerous populations of migratory birds. Due to the importance of this region for migratory birds, and particularly waterfowl, and the threats of habitat loss due to intensifying agriculture, there is a mature and diverse system of conservation organizations, agencies, and partnerships that spends hundreds of millions of dollars annually on habitat conservation to support migratory bird populations. Remote sensing can be a powerful tool for observing and evaluating global change at large scales as well as expanding inferences from field studies to the broader landscape with statistical models. However, development and utilization of these tools has lagged behind their demand for several reasons, including concerns over spatial and temporal resolution and accuracy of products; perception of a misalignment with decision-maker needs; technological barriers such as skill sets of conservation professionals, computing resources, data access, and usability. In this report, we summarize the needs of conservation professionals and scientists who use or want to use remote sensing data products to inform science about wetland change and conservation of wetlands in the PPR. We assembled this information through several methods leading up to, during, and following a January 2026 PPR Wetland Remote Sensing Workshop. The workshop included United States and Canadian scientists, conservation professionals, and policy experts. Our goal was to bring together end-users and remote sensing product developers jointly to explore reducing the lag between product development and utilization of products to inform science and conservation. Specifically, we aimed to identify gaps in wetland remote sensing that limit effective monitoring, management, and conservation in the PPR, and to develop a framework that outlines pathways to address these gaps by fostering collaboration, improving communication networks, encouraging discussion, and building on existing and ongoing efforts. This report summarizes our participants’ descriptions of end-user needs and the outcomes of the workshop.

Prairie Pothole region↗