Water, Air, & Soil Pollution

, 229:375 | Cite as

Quantification of Oxidant Demand and Consumption for In Situ Chemical Oxidation Design: in the Case of Potassium Permanganate

  • You Li
  • Kun Yang
  • Xiaoyong LiaoEmail author
  • Hongying Cao
  • Daniel P. Cassidy


Accurate estimation of oxidant consumption during in situ chemical oxidation (ISCO) is the key to determining the treatment effectiveness in contaminated sites. We established the estimation model of soil oxidant demand (SOD) and simulation equations of potassium permanganate (KMnO4) dynamic consumption based on the reaction equation of KMnO4 with reductive minerals and the estimation model of SOD. Model validation, model application, and simulation assessment had been accomplished. Results indicated that the simulations are in good agreement with measured data. The confidence level of the SOD estimation model of KMnO4 was over 80%, with sensitivity in decreasing order as follows: organic matter content > initial KMnO4 concentration > reductive minerals (RMs). Particularly, the organic matter played a dominate role in the SOD model estimation. The coefficient of determination (R2) of the SOD dynamic consumption simulation equation was above 0.9. Among the various types of soils, the overall trend of SOD value and reaction period decreased as follows: clay > loam > sand. However, the consumption rate of KMnO4 decreased in the order of clay > sand > loam. In addition, SOD value, reaction period, and reaction rate all increased as the initial concentration of KMnO4 went up. This work can provide a methodology and reference for selecting and estimating of the optimal oxidant doses and reaction period during field application.


In situ chemical oxidation (ISCO) Oxidant dose Estimation model Dynamic simulation Potassium permanganate (KMnO4


Funding Information

This work was financially supported by the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2016-5-5) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19040302).

Supplementary material

11270_2018_3982_MOESM1_ESM.docx (21 kb)
ESM 1 (DOCX 21.4 kb)


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Key Laboratory of Land Surface Pattern and Simulation, Beijing Key Laboratory of Environmental Damage Assessment and Remediation, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Science (CAS)BeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of GeosciencesWestern Michigan UniversityKalamazooUSA

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