Northeast Region

Background: Inorganic arsenic is a potent carcinogen and toxicant associated with numerous adverse health outcomes. The contribution of drinking water from private wells and regulated community water systems (CWSs) to total inorganic arsenic exposure is not clear. Objectives: To determine the association between drinking water arsenic estimates and urinary arsenic concentrations in the 2003-2014

Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil

Introduction The Chesapeake Bay Program (CBP) has operated a geographic information system (GIS) program since the early 1990s to address the established and growing need for and use of geospatial data, maps, and analysis within the CBP Partnership. This report is intended to detail the standard operating procedures of the CBP GIS program and address the quality assurance, quality control, and oth