Batch tests of cross-linked lithium and manganese imprinted polymers of variable composition to assess their ability to extract lithium and manganese from synthetic brines at T=45 deg C .

Chemical data from 43,334 wells were used to examine the role of land surface-soil-aquifer connections in producing elevated manganese concentrations (>300 µg/L) in United States (U.S.) groundwater. Elevated manganese and dissolved organic carbon (DOC) concentrations were associated with shallow water tables and organic-carbon rich soils, suggesting soil-derived DOC supported manganese reduction. Manganese and DOC concentrations were higher near rivers than farther from rivers, suggesting river-derived DOC also supported manganese reduction. Anthropogenic nitrogen may also affect manganese concentrations in groundwater. In parts of the northeastern U.S. containing poorly buffered soils, ~40% of the samples with elevated manganese concentrations had pH values <6 and elevated concentrations of dissolved oxygen and nitrate relative to samples with pH ≥6, suggesting acidic recharge produced by the oxidation of ammonium in fertilizer helped mobilize manganese. An estimated 2.6 million people potentially consume groundwater with elevated manganese concentrations, the highest densities of which occur near rivers and in areas with organic-carbon rich soil. Results from this study indicate land surface-soil-aquifer connections play an important role in producing elevated manganese concentrations in groundwater used for human consumption.

Data used in the figures and tables presented in the manuscript. This dataset is associated with the following publication: Rue, K., K. Rusevova, C. Biles, and S. Huling. Abiotic hydroxylamine nitrification involving manganese- and iron-bearing minerals. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, issue}: 567-575, (2018).

Data used in the figures and tables presented in the manuscript. This dataset is associated with the following publication: Rue, K., K. Rusevova, C. Biles, and S. Huling. Abiotic hydroxylamine nitrification involving manganese- and iron-bearing minerals. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, issue}: 567-575, (2018).

Measurements of Manganese collected at American River At Rainbow Bridge. Currently collected twice a year, previously collected quarterly. Access further information for this data set by contacting Bureau of Reclamation, California-Great Basin Region, Environmental Affairs Division (CGB-157). See ResultAttributes for STAFF_GAUGE, SMPL_DEPTH, SMPL_CATEGORY_NAME, METHOD_CODE, RESULT_RL, RESULT_RL-UNIT_STD_NAME, RESULT_MDL, RESULT_MDL-UNIT_STD_NAME, USBR_QA_SUBTYPE_NAME, USBR_QULFR_DESCRIPTION. STAFF_GAUGE is the water height in decimal feet measured by gauge (e.g., 15.2). SMPL_DEPTH is the vertical depth at which sample is collected (e.g., 0 - 15 cm). For water samples: depth below water/air interface. For sediment and soil samples: depth below water/solid or air/solid interface. SMPL_CATEGORY_NAME is the category type of sample (e.g., Composite). METHOD_CODE is the name of method used to obtain result (e.g., EPA 200.8). RESULT_RL is the result reporting limit (accounting for dilution) (e.g., 0.02). RESULT_RL-UNIT_STD_NAME is the unit associated with RESULT_RL (e.g., mg/L). RESULT_MDL is the result method detection limit (e.g., 0.007). RESULT_MDL-UNIT_STD_NAME is the unit associated with RESULT_MDL (e.g., mg/L). USBR_QA_SUBTYPE_NAME is the quality control type of the sample (e.g., USBR_BLANK_SPIKE). USBR_QULFR_DESCRIPTION is the quality assurance description (if any) (e.g., Result may have a high bias.).

This data release contains the U.S. salient statistics and world production data extracted from the MANGANESE data sheet of the USGS Mineral Commodity Summaries 2023.

This data release contains the U.S. salient statistics and world production data extracted from the MANGANESE data sheet of the USGS Mineral Commodity Summaries 2025.

This data release contains the U.S. salient statistics and world production data extracted from the MANGANESE data sheet of the USGS Mineral Commodity Summaries 2024.

Data and preliminary machine-learning models used to predict manganese and 1,4-dioxane in groundwater on Long Island are documented in this data release. Concentration data used to develop the models were from 910 wells for manganese and 553 wells for 1,4-dioxane, primarily public supply wells, from U.S. Geological Survey, U.S. Environmental Protection Agency (USEPA), and Suffolk County Water Authority sources. Thirty-two explanatory variables describe depth, groundwater flow, land use, soil properties, and other features of the aquifer system. The models use XGBoost, an ensemble tree machine learning method. Four models are documented for manganese, predicting the probability of concentrations relative to four thresholds: 10 micrograms per liter (detection), 50 micrograms per liter (the USEPA Secondary Maximum Contaminant Level), 150 micrograms per liter, and 300 micrograms per liter (the USEPA lifetime health advisory). One model is documented for 1,4-dioxane, predicting the probability of concentrations relative to 0.07 micrograms per liter (detection). The models were used to predict concentrations in two layers of the upper glacial aquifer and three layers of the Magothy aquifer. Predictions were made at a 500-square-foot resolution across the entire island for manganese and across Suffolk County, which occupies the eastern two-thirds of Long Island, for 1,4-dioxane. The data are provided in data tables, raster files, and model files. One data table describes the 32 explanatory variables (LI_mn_14dx_exp_vars.txt). One data table describes the well data and includes the manganese and 1,4-dioxane concentrations, explanatory variables, and predictions for the wells (LI_mn_14dx_well_data.txt). There is a compressed group (zip file) of five files providing the explanatory variable data used to make predictions for the five aquifer layers (LI_mn_14dx_predinput_griddata.zip) and a zip file of 25 files providing model predictions for each model and aquifer layer (LI_mn_14dx_predoutput_rasters.zip). The data release also contains a tif-format raster file of the prediction grid (LI_mn_14dx_prediction_grid.tif). The models are documented in a zip file (LI_mn_14dx_models.zip) that contains the model object files (R data format) and scripts that can be used to run the models to produce the predictions provided in this data release. Filenames for prediction input and for model output are distinguished by names and numbers as follows: 1_upper_glacial, top layer of the upper glacial aquifer; 3_upper_glacial, bottom layer of the upper glacial aquifer; 5_Magothy, top layer of the Magothy aquifer; 14_Magothy, middle layer of the Magothy aquifer; and 23_Magothy, bottom layer of the Magothy aquifer.

Data used to model and map manganese concentrations in groundwater in the Northern Atlantic Coastal Plain (NACP) aquifer system, eastern USA, are documented in this data release. The model predicts manganese concentration within four classes and is based on concentration data from 4492 wells. The well data were compiled from U.S. Geological Survey, U.S. Environmental Protection Agency, Suffolk County Water Authority (Suffolk County, New York), and state agency sources. The four concentration classes are based on guidelines for drinking water quality: below detection (class 1, less than 10 micrograms per liter (ug/L)); detected but less than the aesthetic guideline of 50 ug/L (class 2); greater than the aesthetic guideline but less than the health guideline of 300 ug/L (class 3); and greater than the health guideline of 300 ug/L (class 4). The thresholds of 50 ug/L and 300 ug/L are a Secondary Maximum Contaminant Level and a lifetime health advisory, respectively, from the U.S. Environmental Protection Agency for public water supplies. The model is built with the XGboost machine learning method. Explanatory variables (predictors) include well depth, soil characteristics, hydrologic variables, groundwater residence time, and predicted values of pH and of the probability of low dissolved oxygen from previous machine learning models of the aquifer system. The data are provided in data tables, raster files, and model files, organized as follows. One data table describes the 27 explanatory variables used in the model (NACP_Mn_explanatory_variables.csv). There is a data table for the well data used to develop the models, which includes the manganese concentrations, concentration classes, regional aquifer, explanatory variables, and predicted concentration class for the wells (NACP_Mn_well_data.csv). There is a compressed group (zip file) of 10 files (one for each regional aquifer) for explanatory variable data used to make predictions for the regional aquifers (NACP_Mn_prediction_input_aquifers.zip). There are two zip files providing model output, one for predictions made for each aquifer in text format and one for tif-format rasters of predictions for each aquifer. The data release also contains a tif-format raster file of the prediction grid and a zip file with the model object file (R data format) and a script that can be used to run the model to produce the predictions provided in this data release. Filenames for prediction input and for model output are distinguished by codes abbreviating the aquifer name and position in the vertical stack of 19 regional aquifers and confining units, as follows: Surficial aquifer, 1surf; Upper Chesapeake aquifer, 3upch; Lower Chesapeake aquifer, 5loch; Piney Point aquifer, 7pipt; Aquia aquifer, 9aqia; Monmouth - Mt. Laurel Aquifer, 11moml; Matawan aquifer, 13mtwn; Magothy Aquifer, 15mgty; Potomac-Patapsco aquifer, 17popt; Potomac-Patuxent aquifer, 19popx. The nine confining units are not represented in the model or predictions.