Filters: Tags: Clay County (X) > partyWithName: Melinda L Erickson (X)
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This dataset provides aqueous nitrate+nitrite, aqueous manganese, aqueous iron, and total sulfate measurements in groundwater samples from 254 newly constructed private residential wells between 2014 and 2016. The study focuses on three geologically distinct regions of Minnesota: central, northwest, and northeast. These study regions were chosen due to their prevalent elevated As concentrations in drinking water. Each of the 254 wells were sampled in three rounds by the Minnesota Department of Health (MDH). The timing of the three sampling rounds was (1) immediately or shortly after well construction (round 1); (2) 3-6 months after initial sample collection (round 2); and (3) 12 months after initial sample collection...
Categories: Data;
Tags: Anoka County,
Becker County,
Beltrami County,
Carlton County,
Carver County,
This data release provides total and aqueous arsenic (As) determinations and associated field readings collected from groundwater sampled from 254 newly constructed private residential wells between 2014 and 2016. The study focuses on three regions of Minnesota that differ geologically: south-central (herein called central), northwest, and northeast. These study regions were chosen due to their prevalent elevated As concentrations in drinking water. Each of the 254 wells were sampled in three rounds by the Minnesota Department of Health (MDH). The timing of the three sampling rounds was (1) immediately or shortly after well construction (round 1); (2) 3-6 months after initial sample collection (round 2); and (3)...
Categories: Data;
Types: Citation;
Tags: Anoka County,
Becker County,
Beltrami County,
Carlton County,
Carver County,
This data release contains: (1) ASCII grids of predicted probability of elevated arsenic in groundwater for the Northwest and Central Minnesota regions, (2) input arsenic and predictive variable data used in model development and calculation of predictions, and (3) ASCII files used to predict the probability of elevated arsenic across the two study regions. The probability of elevated arsenic was predicted using Boosted Regression Tree (BRT) modeling methods using the gbm package in R Studio version 3.4.2. The response variable was the presence or absence of arsenic >10 µg/L, the U.S. Environmental Protection Agency’s maximum contaminant level for arsenic, in 3,283 wells located throughout both study regions (1,363...
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