Spatial Variability Characteristics of Soil Available Mn and Zn in the Middle Reaches of Tuojiang River Basin

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Abstract:

The available states of Manganese (Mn) and zinc (Zn) in soil exert important roles in same biochemical reactions. Their deficiency can result in plant micronutrient disorders, but the excess contents may contribute to several environmental issues. Their spatial distribution and influential factors in the middle reaches of Tuojiang River Basin, a typical region with the broken terrain and densely populated hill, were studied with the combination of statistics, geostatistics, global positioning system (GPS) and geographic information system (GIS). According to the data of the contents in topsoil (0~20 cm) from the 296 random sampling points,the contents of soil available Mn and Zn were 5.95 and 1.05 mg kg-1, respectively. The highest value regions (>11 mg kg-1) of Mn contents were mainly distributed in the central parts of study area, and first decreased to the both sides from the central and then increased towards the northwest. The highest value regions (>1.8 mg kg-1) of Zn contents were mainly distributed in the northwest parts, and then reduced gradually towards to the southeast and southwest presenting zonal shapes. Almost 30% of the area had higher than 50% probability to exceed the threshold value (7.00 mg kg-1) of the soil available Mn based on probability kringing, Similarly, more than 70% part of the area with the probability more than 50% exceed the threshold value (0.50 mg kg-1) of soil available Zn. The content and spatial distribution of soil Mn and Zn were affected by parent materials, landform types, slopes, landuse patterns, textures, pH and organic matters (OM). While the above factors except for OM and texture (P>0.05) had significant influence on soil available Mn (P<0.05). On the contrary, only OM was the significant influent factor to soil available Zn (P<0.05).

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Advanced Materials Research (Volumes 518-523)

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2204-2212

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May 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Römheld V, Marschner H. Function of micronutrients in plants. In: Mortvedt JJ et al. (Ed.). Micronutrients in Agriculture 1991; 297–328.

DOI: 10.2136/sssabookser4.2ed.c9

Google Scholar

[2] MacFarlane GR, Burchett MD. Cellular distribution of Cu, Pb and Zn in the Grey Mangrove Avicennia marina (Forsk.). Vierh Aquat Bo 2000; 68: 45–59.

DOI: 10.1016/s0304-3770(00)00105-4

Google Scholar

[3] Loska K, Wiechula D. Application of principal component analysis for the estimation of source heavy metal contamination in surface sediments from Rybnik Reservoir. Chemosphere 2003; 51: 723–733.

DOI: 10.1016/s0045-6535(03)00187-5

Google Scholar

[4] Ghrefat H, Yusuf N. Assessing Mn, Fe, Cu, Zn, and Cd pollution in bottom sediments of Wadi Al-Arab Dam, Jordan. Chemosphere 2006; 65: 2114–2121.

DOI: 10.1016/j.chemosphere.2006.06.043

Google Scholar

[5] Fang F, Wang Q, Li J. Urban environmental mercury in Changchun, a metropolitan city in Northeastern China: source, cycle, and fate. Sci Total Environ 2004; 330: 159–70.

DOI: 10.1016/j.scitotenv.2004.04.006

Google Scholar

[6] Bacon JR, Hewitt IJ, Cooper P. Reproducibility of the BCR sequential extraction procedure in a long-term study of the association of heavy metals with soil components in an upland catchment in Scotland. Sci Total Environ 2005; 337: 191–205.

DOI: 10.1016/j.scitotenv.2004.06.010

Google Scholar

[7] Goovaerts P. Geostatistical modeling of uncertainty in soil science. Geoderma 2001; 103: 3–26.

Google Scholar

[8] Franco C, Soares A, Delgado J. Geostatistical modelling of heavy metal contamination in the topsoil of Guadiamar river margins (Spain) using a stochastic simulation technique. Geoderma 2006; 136: 852–864.

DOI: 10.1016/j.geoderma.2006.06.012

Google Scholar

[9] Carlon C, Critto A, Marcomini A, Nathanail P. Risk based characterization of contaminated industrial site using multivariate and geostatistical tools. Environmental Pollution 2001; 111: 417–427.

DOI: 10.1016/s0269-7491(00)00089-0

Google Scholar

[10] Komnitsas K, Modis K. Geostatistical risk estimation at waste disposal sites in the presence of hot spots. Journal of Hazardous Materials 2009; 164: 1185–1190.

DOI: 10.1016/j.jhazmat.2008.09.027

Google Scholar

[11] Liu XM, Wu JJ, Xu JM. Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS. Environmental Pollution 2006; 141(2): 257–264.

DOI: 10.1016/j.envpol.2005.08.048

Google Scholar

[12] Zhao YF, Shi XZ, Huang B. Spatial Distribution of Heavy Metals in Agricultural Soils of an Industry-Based Peri-Urban Area in Wuxi, China. Pedosphere 2007; 17(1): 44-51.

DOI: 10.1016/s1002-0160(07)60006-x

Google Scholar

[13] Joshi AK, Crossa J, Arun B. Genotype environment interaction for zinc and iron concentration of wheat. Field Crops Research 2010; 116: 268–277.

DOI: 10.1016/j.fcr.2010.01.004

Google Scholar

[14] Hu YF, Deng LJ, Zhang SR. Study on spatial variability and its influential factors of soils nitrogen in typical small watershed in the hilly region of the middle Sichuan. Journal of Soil and Water Conservation,In Chinese 2008; 22(3): 70-75.

Google Scholar

[15] Zhang X, Qi Y, Walling DE. A preliminary assessment of the potential for using 210Pbex measurement. Catena 2006; 68: 1–9.

Google Scholar

[16] Gee GW, Bauder JW. Particle-size analysis. In: Klute A, editor. Methods of Soil Analysis: Part 1. Physical and Mineralogical Methods, vol. 9. Agronomy Series. Madison, WI7 American Society of Agronomy 1986; 383–411.

DOI: 10.2136/sssabookser5.1.2ed.c15

Google Scholar

[17] Western AW, Zhou SL, Grayson RB, McMahon TA, Bloschl G, Wilson DJ. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. J Hydrol 2004; 286: 113–134.

DOI: 10.1016/j.jhydrol.2003.09.014

Google Scholar

[18] Saby N, Arrouays D, Boulonne L, Jolivet C, Pochot A. Geostatistical assessment of Pb in soil around Paris, France. Sci Total Environ 2006; 367: 212–221.

DOI: 10.1016/j.scitotenv.2005.11.028

Google Scholar

[19] Korre A, Durucan S, Koutroumani A. Quantitative – spatial assessment of the risks associated with high Pb loads in soils around Lavrio, Greece. Appl Geochem 2002; 17: 1029–45.

DOI: 10.1016/s0883-2927(02)00058-6

Google Scholar

[20] Wong SC, Li XD, Zhang G, Qi SH, Min YS. Heavy metals in agricultural soils of the Pearl River Delta, South China. Environ Pollut 2002; 119: 33–44.

DOI: 10.1016/s0269-7491(01)00325-6

Google Scholar

[21] Nicholson FA, Smith SR, Alloway BJ, Carlton-Smith C, Chambers BJ. An inventory of heavy metals inputs to agricultural soils in England and Wales. Sci Total Environ 2003; 311: 205–19.

DOI: 10.1016/s0048-9697(03)00139-6

Google Scholar

[22] Kabata-Pendias A. Soil–plant transfer of trace elements—an environmental issue. Geoderma 2004; 122: 143–149.

DOI: 10.1016/j.geoderma.2004.01.004

Google Scholar

[23] Jahiruddin M, Chambers BJ, Cresser MS, Livesey NT. Effects of soil properties on the extraction of zinc. Geoderma 1992; 52: 199–208.

DOI: 10.1016/0016-7061(92)90036-7

Google Scholar

[24] Boruvka L, Mladkova L, Drabek O. Factors controlling spatial distribution of soil acidification and Al forms in forest soils. Journal of Inorganic Biochemistry 2005; 99: 1796–1806.

DOI: 10.1016/j.jinorgbio.2005.06.028

Google Scholar