Advertisement

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 Zhang
  • Tieliang Wang
Article
  • 67 Downloads

Abstract

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.

Keywords

Arsenic Expectation Standard deviation Concentration function Pu River 

Notes

Acknowledgements

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.

References

  1. Anttila S, Kairesalo T (2010) Mean and variance estimations with different pixel sizes: case study in a small water quality monitoring area in southern Finland. Boreal Environ Res 15(3):335–346Google Scholar
  2. Baig JA, Kazi TG, Shah AQ (2011) Evaluation of arsenic levels in grain crops samples, irrigated by tube well and canal water. Food Chem Toxicol 49(8):265–270CrossRefGoogle Scholar
  3. Benzer S (2017) Concentrations of arsenic and boron in water, sediment and the tissues of fish in emet stream (Turkey). Bull Environ Contam Toxicol 98(12):1–6Google Scholar
  4. Chen H, Jilili A, Liu W, Chen J (2016) Correlation between heavy metals organic matter, pH value in the soils along the Bortala River. Res Soil Water Conserv 23(12):210–213Google Scholar
  5. Cott PA, Zajdlik BA, Palmer MJ, Mcpherson MD (2016) Arsenic and mercury in lake whitefish and burbot near the abandoned giant mine on Great Slave lake. J Gt Lakes Res 42(9):223–232CrossRefGoogle Scholar
  6. Feng MM, Zhang Y, Li M, X Y, Li YP, Quan QM (2017) Characteristics of heavy metals in Jialing River main stream in Guangyan City. Sichuan Environ 36(9):40–45Google Scholar
  7. Francesconi KA (2010) Arsenic species in seafood: origin and human health implications. Pure Appl Chem 82(9):373–381CrossRefGoogle Scholar
  8. Fu LL, Yao CQ, Li XB, Jiang BH (2013) Evaluation of heavy metal pollution in soils of agricultural lands in Shenyang and source analysis. Guangdong Agric Sci 40(16):178–181Google Scholar
  9. Gao JH, Ma YQ, Qin YW, Xu XL (2013) Speciation and distribution characteristics of arsenic in overylying water, pore water and sediments of Dahuofang reservoir. Acta Sci Circum 33(9):2573–2578Google Scholar
  10. Garg N, Singla P (2011) Arsenic toxicity in crop plants: physiological effects and tolerance mechanisms. Environ Chem Lett 9(10):303–321CrossRefGoogle Scholar
  11. He JS, Han YZ, Zhang L, Wang WS, Li C (2010) Research on ecological scheduling of reservoir in Puhe basin. Water Resour Power 28(9):34–36Google Scholar
  12. Huang QC, Wei YH, Wu YZ (2009) Study on the effects of arsenic pollution on human health. Stud Trace Elem Health 26(11):65–67Google Scholar
  13. Jing Y (2011) Pollution status of Puhe River and eco-restoration technological countermeasures. Environ Prot Sci 37(12):25–28Google Scholar
  14. Jing Y (2012) Study on pollution characteristics and improvement approach of Puhe River water quality in Shenyang. Environ Prot Sci 38(11):29–32Google Scholar
  15. Jing Y, Cui DC (2017) Puhe River water ecological restoration projects and efficacy evaluation. Environ Prot Sci 43(11):85–89Google Scholar
  16. Kato K, Sakawa M (2011) An interactive fuzzy satisficing method based on variance minimization under expectation constraints for multiobjective stochastic linear programming problems. Soft Comput 15(8):131–138CrossRefGoogle Scholar
  17. Li XM, Wang ZW, Tang XQ, Huang S, Zhao QX (2007) Determining weights of heavy metal contaminations and its application to soil environmental quality assessment. J Agro-Environ Sci 26(6):2281–2286Google Scholar
  18. Li RZ, Tong F, Zhou AJ, Wu YD, Zhang P, Yu J (2011a) Fuzzy assessment model for the health risk of heavy metals in urban dusts based on trapezoidal fuzzy numbers. Acta Sci Circum 31(8):1790–1798Google Scholar
  19. Li YJ, Song ZG, Li RD, Feng L, Yang TH, Wei LH, Han H (2011b) Investigation and fuzzy comprehensive evaluation on pollution status of heavy metal of Pu River. Res Soil Water Conserv 18(9):121–125Google Scholar
  20. Liang R (2016) Analysis of the eco-compensation of enterprise in atmospheric pollution control-based on expectancy theory. J Fuqing Branch Fujian Norm Univ 138(12):95–99Google Scholar
  21. Ma L, Yang XB, Tong CZ, Wu AJ, Liu MH (2008) The geochemical characteristics of heavy metal elements in sediments of Hunhe drainage area in Liaoning province. Rock Miner Anal 27(10):184–188Google Scholar
  22. Pan ZW, Jin JL, Wu KY, Ding K (2014) Research on the indexes and policy-decision-making models of regional water environmental system vulnerability. Resour Environ Yangtza Basin 23(4):518–525Google Scholar
  23. Pasias IN, Thomaidis NS, Bakeas EB, Piperaki EA (2013) Application of zirconium–iridium permanent modifier for the simultaneous determination of lead, cadmium, arsenic, and nickel in atmospheric particulate matter by multi-element electrothermal atomic absorption spectrometry. Environ Monit Assess 185(8):6867–6879CrossRefGoogle Scholar
  24. Rees F, Simonnot MO, Morel JL (2014) Short-term effects of biochar on soil heavy metal mobility are controlled by intra-particle diffusion and soil pH increase. Eur J Soil Sci 65(8):149–161CrossRefGoogle Scholar
  25. Sarin SC, Nagarajan B, Jain S, Liao L (2009) Analytic evaluation of the expectation and variance of different performance measures of a schedule on a single machine under processing time variability. J Combin Optim 17(11):400–416CrossRefGoogle Scholar
  26. Shi XJ (2016) An expectation-variance model for uncertain travelling salesman problem. J Liaocheng Univ (Nat Sci) 29(9):55–59Google Scholar
  27. Tian HZ, Qu YP (2018) Emission and control of atmospheric arsenic from coal combustion in China. Electr Power 41(12):82–86Google Scholar
  28. Tokar EJ, Diwan BA, Waalkes MP (2010) Arsenic exposure transforms human epithelial stem/progenitor cells into a cancer stem-like phenotype. Environ Health Perspect 118(8):108–115CrossRefGoogle Scholar
  29. Torre MLDL, Sánchezrodas D, Grande JA, Gomez T (2010) Relationships between pH, colour and heavy metal concentrations in the Tinto and Odiel Rivers (southwest Spain). Hydrol Res 41(12):406–413CrossRefGoogle Scholar
  30. Wang XY (2012) Pollution assessment and bioavailability assessment of heavy metal in soils of a vegetable production base in the Xinxiang City based on its chemical fractionation. J Henan Norm Univ Nat (Sci Ed) 40(11):183–186Google Scholar
  31. Wang H (2016) Researches on pollution characteristics of water quality in Liao River basin. Environ Sci Manag 41(12):51–54CrossRefGoogle Scholar
  32. Wang R, Wei YS (2013) Pollution and control of tetracyclines and heavy metals residues in animal manure. J Agro-Environ Sci 32(9):1705–1719Google Scholar
  33. Wang XJ, Diao FY, Zhang J, Guo DM, Cui X (2011) Determination and evaluation of lead, cad mium and arsenic in edible fish in the markets of Jinan. J Shandong Univ (Health Sci) 49(12):153–155Google Scholar
  34. Wang Q, Fan ZP, Li FY, Ju WP (2015) River habitat quality assessment, water quality analysis and their response relation of Pu River basin. Chin J Ecol 34(9):516–523Google Scholar
  35. Wu AJ, Chen YY, Ling S, Wu HH (2008) Geochemical characteristics of heavy metal elements in paddy and corn from Tieling, Liaoning province. Geol Resour 17(4):302–306Google Scholar
  36. Wu CX, Wu J, Yang G, Qi H, Li Y, Zhang Q (2010) Determination of weighted of heavy metal contaminations by improved AHP model and its application to safety evaluation on heavy metal concentration in crops. J Sichuan Agric Univ 28(10):345–350Google Scholar
  37. Wu XL, Yang YL, Tang QF, Xu Q, Liu X, Huang D, Y Y, Yin JC (2011a) Ecological risk assessment and source analysis of heavy metals in river waters, groundwater along river banks and river sediments in Shenyang. Chin J Ecol 30(10):438–447Google Scholar
  38. Wu XL, Yang YL, Xu Q, Huang YY, Lu GH, He J, Liu XD (2011b) Evaluations of heavy metal pollution status in surface soils adjacent to the rivers and irrigation channel in Shenyang, China. J Agro-Environ Sci 30(9):282–288Google Scholar
  39. Xu HZ, Chang J (2008) Evaluation and methods of heavy metal pollution in main soils of Anhui province. Chin J Soil Sci 39(9):411–415Google Scholar
  40. Yan XL, Liao XY, Yu BB, Zhang WB (2011) Accumulation of soil arsenic by Panax notoginseng and its associated health risk. Environ Sci 32(10):880–885Google Scholar
  41. Yan X, Dan G, Fan Z, Chen Z, Wang X, Man Z (2013) Relationships between heavy metal concentrations in roadside topsoil and distance to road edge based on field observations in the Qinghai-Tibet plateau, china. Int J Environ Res Public Health 10(3):762–775CrossRefGoogle Scholar
  42. Yang WT, Wang YJ, Zhou H, Yi KX, Zeng M, Peng PQ, Liao BH (2015) Transformation and mobility of arsenic in the rhizosphere and non-rhizosphere soil at different growth stages of rice. Environ Sci 36(9):694–699Google Scholar
  43. You B, Xilifuhan J, Song X (2017) Analysis of the arsenic, mercury contents with Pike and Carp meat in northern lake of Xinjiang. Arid Environ Monit 31(10):110–112 (118)Google Scholar
  44. Yu YJ, Wang FF, Fang JD, Sun P (2007) Advance in research on environmental arsenic pollution to human health. J Environ Health 24(10):181–183Google Scholar
  45. Zhang SC, Ye HX, Zhang M (2008) Adsorption and release of heavy metal pollution in sediments of Beiershili pool wetland in Daqing, northeastern China. J Beijing For Univ 30(1) 287–291Google Scholar
  46. Zhang ZB, Wang K, Pu RF (2009) Evaluation for the heavy metal pollution in soils of Jinchang City based on double weighing factors of fuzzy mathematical model. J Northwest Norm Univ (Nat Sci) 45(2):89–92, 98Google Scholar
  47. Zhang K, Su FL, Liu XM, Song Z, Feng X (2017a) Heavy metal concentrations in water and soil along the Hun River, Liaoning, China. Bull Environ Contam Toxicol 99(3):391–398CrossRefGoogle Scholar
  48. Zhang K, Su FL, Liu XM, Song Z, Feng X (2017b) The average concentration function of dissolved copper in Hun River, Liaoning province, northeastern China. Environ Sci Pollut Res 24(35):27225–27234CrossRefGoogle Scholar
  49. Zhao YL, He TT, Li JH, Fu X, Wang YY, Zeng JY, Hou ZD (2012) The weight analysis of attribute interval recognition model of soil heavy metal pollution. Ecol Environ Sci 21(9):1624–1629Google Scholar
  50. Zheng J, Wang H, Li Y, Wang DM (2016) Pu River water quality assessment based on the single factor water quality identification index method. J Shenyang Ligong Univ 35(8):93–96 (101)Google Scholar

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
  • 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

Personalised recommendations