| 000 | 02362nam a22002537a 4500 | ||
|---|---|---|---|
| 003 | ZW-GwMSU | ||
| 005 | 20230403204518.0 | ||
| 008 | 230403b |||||||| |||| 00| 0 eng d | ||
| 040 |
_aMSU _cMSU _erda |
||
| 100 | _aAHN, Joo Sung | ||
| 245 | _aPredicting natural arsenic contamination of bedrock groundwater for a local region in Korea and its application | ||
| 264 |
_aVerlag _bSpringer _c2013 |
||
| 336 |
_2rdacontent _atext _btxt |
||
| 337 |
_2rdamedia _aunmediated _bn |
||
| 338 |
_2rdacarrier _avolume _bnc |
||
| 440 |
_aEnvironmental earth sciences _vVolume , number , |
||
| 520 | _aA logistic regression model for the probability of arsenic exceeding the drinking water guidelines (10 μg/L) in bedrock groundwater was developed for a selected county in Korea, where arsenic occurrence and release reactions have been investigated. Arsenic was enriched naturally by the oxidation of sulfide minerals in metasedimentary rocks and mineralized zones, and due to high mobility in alkaline pH conditions, concentrations were high in groundwater of the county. When considering these reactions of arsenic release and water quality characteristics, several geological and geochemical factors were selected as influencing variables in the model. In the final logistic regression model, geological units of limestone and metasedimentary rocks, the concentrations of nitrate and sulfate, and distances to closed mines and adjacent granite were retained as statistically significant variables. Predicted areas of high probability agreed well with known spatial contamination patterns in the county. The model was also applied to an adjacent county, where the groundwater has not previously been tested for the presence of arsenic, and a probability map for arsenic contamination was then produced. Through the analysis of arsenic concentrations at the wells of high probability, it was determined that the applied model accurately indicated the arsenic contamination of groundwater. The logistic regression approach of this study can be applied to predict arsenic contamination in areas of similar geological and geochemical conditions to the county used in this model. | ||
| 650 | _aarsenic | ||
| 650 | _agroundwater | ||
| 650 | _alogistic regression | ||
| 700 | _aCHO, Yong-Chan | ||
| 856 | _uhttps://doi.org/10.1007/s12665-012-2179-9 | ||
| 942 |
_2lcc _cJA |
||
| 999 |
_c161589 _d161589 |
||