Modeling simultaneous exceedance of drinking-water standards of arsenic and nitrate in the Southern Ogallala aquifer using multinomial logistic regression

Kartik Venkataraman, Venkatesh Uddameri

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Abstract

The occurrence of elevated levels of arsenic and nitrate in aquifers impacted by agricultural activities is common and can result in adverse health effects in rural areas. Numerous wells located in the Ogallala aquifer in the Southern High Plains of Texas have tested positive for both arsenic and nitrate MCL exceedance. To model the simultaneous exceedance of both chemicals, two types of Logistic Regression (LR) models were developed by (a) treating arsenic and nitrate independently and combining the marginal probabilities of their exceedance, and (b) treating the two exceedances together by using a multinomial model. Influencing variables representative of both soil and aquifer properties and data for which was readily available were identified. The predictive capacities of the two models were evaluated using Received Operating Characteristics (ROCs) and spatial trends in predictions were studied. The LR model constructed from the marginal probabilities had lower overall accuracy (59% correct classifications) and was extremely conservative by over-predicting outcomes. In contrast, the multinomial model showed good overall accuracy (79% correct classifications), made the correct predictions 90% of the time when both arsenic and nitrate MCL exceedances were observed, and was a good fit for wells located in agricultural areas. The results of the multinomial model also confirm previous studies that attributed shallow subsurface arsenic to anthropogenic activities. Based on the insights provided by the model it is recommended that where agricultural areas are concerned, the occurrence of arsenic and nitrate are better evaluated together.

Original languageEnglish
Pages (from-to)16-27
Number of pages12
JournalJournal of Hydrology
Volume458-459
DOIs
StatePublished - Aug 21 2012

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Keywords

  • Arsenic
  • Land use
  • Logistic regression
  • Nitrate
  • Ogallala aquifer
  • Receiver operating characteristics

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