The Application of Expectation and Standard Deviation Calculations in the Evaluation of Dissolved Arsenic in the Pu River, Liaoning Province, Northeastern China

  • Xue Feng
  • Haozhen Zhang
  • Liangliang Li
  • Kan ZhangEmail author
  • Tieliang Wang


Water samples were collected from the Pu River in 2017 to research the distribution and accumulation characteristics of dissolved arsenic. We mainly built three types of expectation and standard deviation calculations corresponding to discrete, weighted and continuous random variables. The continuous expectation and standard deviation calculations are defined based on the concentration function and average formula, and the weighted expectation and standard deviation calculations are defined based on the relationship between the concentration and distance. The results indicate that the discrete expectation (1.8351 \({\upmu }\text{g}\)/L) and standard deviation (0.6410 \({\upmu }\text{g}\)/L) describe the average level and the deviation degree, respectively, of dissolved arsenic, and the continuous expectation (1.8684 \({\upmu }\text{g}\)/L) and standard deviation (0.5375 \({\upmu }\text{g}\)/L) mainly describe the average level and the dispersion degree, respectively, of dissolved arsenic after its accumulation. The weighted expectation (1.2997 \({\upmu }\text{g}\)/L) and standard deviation (0.2816 \({\upmu }\text{g}\)/L) reflect the average level and the dispersion degree, respectively, of dissolved arsenic and reveal the quantitative relationship between the concentration of dissolved arsenic and distance. The combination of the three types of expectation and standard deviation calculations and the concentration function may comprehensively describe the distribution and accumulation characteristics of dissolved arsenic, which can provide a theoretical foundation for guiding the reduction of arsenic pollution in the Pu River.


Arsenic Expectation Standard deviation Concentration function Pu River 



The research was supported by National Natural Science Foundation of China (Grant No. 31570706), Natural Science Foundation of Liaoning Province (Grant No. 20180550973) and Science and Technology Project of Liaoning Provincial Department of Education (Grant No. LSNYB201609). The authors would like to thank the referees for their invaluable suggestions.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xue Feng
    • 1
  • Haozhen Zhang
    • 2
  • Liangliang Li
    • 3
  • Kan Zhang
    • 1
    Email author
  • Tieliang Wang
    • 4
  1. 1.College of SciencesShenyang Agricultural UniversityShenyangChina
  2. 2.Environmental ScienceLiaoning Normal UniversityDalianChina
  3. 3.Analysis and Testing CenterShenyang Agricultural UniversityShenyangChina
  4. 4.College of Water ConservancyShenyang Agricultural UniversityShenyangChina

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