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Nitrogen concentrations and loads and seasonal nitrogen loads in selected Long Island Sound tributaries, water years 1995-2021 (ver. 1.1, February 2024)

Published by U.S. Geological Survey | Department of the Interior | Metadata Last Checked: January 27, 2026 | Last Modified: 2024-02-29T00:00:00Z
This U.S. Geological Survey (USGS) data release presents tabular data on nitrogen concentrations and loads for multiple nitrogen species, and river discharge data used in the analysis of data collected from October 1994 to September 2021. Data on flow and nitrogen concentrations were analyzed using the USGS Exploration and Graphics for RivEr Trends (EGRET) R package, and the method of Weighted Regression on Time Discharge and Season (WRTDS). Data and outputs summarized are for water-quality data collected from 17 water-quality monitoring stations in the Long Island Sound watershed. Specific data in tabular format for this release include: calculated annual nitrogen concentration and loads, calculated annual flow-normalized nitrogen concentrations and loads by water year (for sites with 20 years of data or more), and calculated annual seasonal loads of each nitrogen constituent by calendar year. Measured daily river discharge data and sampled nitrogen concentration data for each water-quality monitoring site are available as outputs accessible within R statistical software and as comma-separated values (CSV) files. This data release provides an update of data originally generated for use in nitrogen budget models for Long Island Sound (Vlahos and others, 2020), and the associated data release (Mullaney, 2020). Mullaney, J.R., 2020, Nitrogen concentrations and loads and seasonal nitrogen loads in selected Long Island Sound tributaries, water years 1995-2016: U.S. Geological Survey data release, https://doi.org/10.5066/P9AVXGBB. Vlahos, P., Whitney, M.M., Menniti, C., Mullaney, J.R., Morrison, J., Jia, Y., 2020, Nitrogen budgets of the Long Island Sound estuary: Estuarine, Coastal, and Shelf Science, v. 232, no 104693, https://doi.org/10.1016/j.ecss.2019.106493.

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