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Northeast Region

We conduct impartial, multi- and interdisciplinary research and monitoring on a large range of natural-resource issues that impact the quality of life of citizens and wildlife throughout Connecticut, Delaware, Kentucky, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia, and Washington D.C.

News

USGS Celebrates New Office with Official Ribbon Cutting Ceremony

USGS Celebrates New Office with Official Ribbon Cutting Ceremony

WaterMarks Newsletter - Fall 2024

WaterMarks Newsletter - Fall 2024

Meet our New Staff at the New England WSC - October 2024

Meet our New Staff at the New England WSC - October 2024

Publications

Geospatial PDF map of the compilation of GIS data for the mineral industries of select countries in the Indo-Pacific region

Introduction In 2024, the U.S. Geological Survey's (USGS) National Minerals Information Center (NMIC) completed the project titled "Compilation of geospatial data for the mineral industries of select countries in the Indo-Pacific." This project aimed to leverage the expertise and capabilities of the NMIC to collect, synthesize, and interpret geospatial data to inform on the extractive resources of
Authors
Elizabeth R. Neustaedter, Erica R. Wolfe

Quantifying potential effects of China’s gallium and germanium export restrictions on the U.S. economy

China’s export controls on gallium and germanium exemplify concerns regarding the reliability of supplies of mineral commodities that are essential to economic development, national security, and transition to renewable energy. This report presents a new model that quantifies the potential effects of mineral commodity supply disruptions on the U.S. economy. After calculating postdisruption equilib
Authors
Nedal T. Nassar, Ensieh Shojaeddini, Elisa Alonso, Brian Jaskula, Amy Tolcin

Predictive understanding of stream salinization in a developed watershed using machine learning

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed. We comp
Authors
Jared David Smith, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling, Jeffrey M Sadler, Vincent T. DePaul, Zoltan Szabo
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