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Eliminating redundant spatial variation to better understand the variance of interest of soil potentially toxic elements at different sampling scales in different soil types south of Nanjing, China

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Abstract

Sampling scale and prediction of spatial distribution are essential in surveys of soil metal pollution. Sufficient sampling density encompassing the principal spatial sources of variance and prediction of polluted areas with the help of soil maps makes pollution evaluation more reliable and subsequent soil remediation assessment more efficient. Two soil sampling schemes, using 232 points at 2-km intervals in 2002 for sampling at county scale and 109 points at 200–1000-m intervals in 2012 at town scale, were used to study the potentially toxic metals Cu, Cd, Cr, Hg, Ni, Pb, Zn, and the metalloid As in an urban-rural hinge area. We focused on finding characteristics of the explanatory power of soil type toward different sampling scales from 200 to 2000 m, a routine sampling scale in practice for remediation of soil potentially toxic elements (PTEs). We also attempted to eliminate the redundant spatial variation to better understand the variance of soil PTEs. Spatial variation of PTEs at different scales was compared and estimated using soil map units based on geostatistical methods. The explanatory power of the soil map units selected at different scales was significantly different at P < 0.01 and the smaller scales better explained the spatial variance. Anthropic activities profoundly affected the contents of PTEs in soils and the amounts of anthropogenic pollutants released often exceed the contribution from natural sources. Variances of interest of Cr and Cu were underestimated by 72.4 and 32.8%, respectively, due to soil type as a factor but were overestimated for other elements by percentages following the sequence Zn (45.4%) > Hg (28.6%) > Pb (28.8%) > Ni (26.73%) > As (13.7%) > Cd (10.5%). Eliminating variances of zero interest would be helpful in increasing the effectiveness of remediation of metal-contaminated soils.

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References

  • Bui EN (2004) Soil survey as a knowledge system. Geoderma 120:17–26

    Article  Google Scholar 

  • Buttafuoco G, Tarvainen T, Jarva J, Guagliardi I (2016) Spatial variability and trigger values of arsenic in the surface urban soils of the cities of Tampere and Lahti, Finland. Environ Earth Sci 75:896

    Article  Google Scholar 

  • Buttafuoco G, Guagliardi I, Tarvainen T, Jarva J (2017) A multivariate approach to study the geochemistry of urban topsoil in the city of Tampere, Finland. J Geochem Explor 181:191–204

    Article  CAS  Google Scholar 

  • Cai LM, Xu ZC, Ren MZ, Guo Q, Hu X, Hu G, Wan H, Peng P (2012) Source identification of eight hazardous heavy metals in agricultural soils of Huizhou, Guangdong Province China. Ecotoxicol Environ Saf 78:2–8

    Article  CAS  Google Scholar 

  • Chen J (2007) Rapid urbanization in China: a real challenge to soil protection and food security. Catena 69:1–15

    Article  Google Scholar 

  • Chen TB, Zheng YM, Lei M, Huang ZC, Wu HT, Chen H, Fan KK, Yu K, Wu X, Tian QZ (2005) Assessment of heavy metal pollution in surface soils of urban parks in Beijing, China. Chemosphere 60:542–551

    Article  CAS  Google Scholar 

  • Chen T, Liu XM, Zhu MZ, Zhao K, Wu J, Xu J, Huang P (2008) Identification of trace element sources and associated risk assessment in vegetable soils of the urban-rural transitional area of Hangzhou, China. Environ Pollut 151:67–78

    Article  CAS  Google Scholar 

  • Chen T, Liu XM, Li X et al (2009) Heavy metal sources identification and sampling uncertainty analysis in a field-scale vegetable soil of Hangzhou, China. Environ Pollut 157:1003–1010

    Article  CAS  Google Scholar 

  • Dai JR, Pang XG, Yu C (2011) Geochemical baselines and background values and element enrichment characteristics in soils in eastern Shandong Province. Geochimica 40:577–587

    CAS  Google Scholar 

  • de la Cueva AV, Marchant B, Quintana JR et al (2014) Spatial variation of trace elements in the peri-urban soil of Madrid. J Soils Sediments 14:78–88

    Article  Google Scholar 

  • Facchinelli A, Sacchi E, Mallen L (2001) Multivariate statistical and GIS-based approach to identify heavy metal sources in soils. Environ Pollut 114:313–324

    Article  CAS  Google Scholar 

  • FAO/ISRIC/ISSS (1998) World reference base for soil resources. In: World Soil Resources Reports, Rome, 1–87

  • Gong ZT, Zhang GL, Luo GB (1999) Diversity of anthrosols in China. Pedosphere 9:193–204

    Google Scholar 

  • Guagliardi I, Cicchella D, De Rose R (2012) A geostatistical approach to assess concentration and spatial distribution of heavy metals in urban soils. Water Air Soil Pollut 223:5983–5998

    Article  CAS  Google Scholar 

  • Guagliardi I, Cicchella D, De Rosa R et al (2018) Geochemical sources of vanadium in soils: evidences in a southern Italy area. J Geochem Explor 184:358–364

    Article  CAS  Google Scholar 

  • Hsiao CSK, Juang KW, Lee DY (2000) Estimating the second-stage sample size and the most probable number of hot spots from a first-stage sample of heavy-metal contaminated soil. Geoderma 95:73–88

    Article  CAS  Google Scholar 

  • Lagacherie P, Voltz M (1997) Predicting soil properties over a region using sample information from a mapped reference area. Eur J Soil Sci 48:19–30

    Article  Google Scholar 

  • Li YQ, Ma CQ, Robinson PT (2016) Petrology and geochemistry of Cenozoic intra-plate basalts in east-Central China: constraints on recycling of an oceanic slab in the source region. Lithos 262:27–43

    Article  CAS  Google Scholar 

  • Liu R, Wang ME, Chen WP, Peng C (2016) Spatial pattern of heavy metals accumulation risk in urban soils of Beijing and its influencing factors. Environ Pollut 210:174–181

    Article  CAS  Google Scholar 

  • Luo XS, Xue Y, Wang YL, Cang L, Xu B, Ding J (2015) Source identification and apportionment of heavy metals in urban soil profiles. Chemosphere 127:152–157

    Article  CAS  Google Scholar 

  • Lv J, Liu Y, Zhang Z, Dai B (2014) Multivariate geostatistical analyses of heavy metals in soils: spatial multi-scale variations in Wulian, eastern China. Ecotoxicol Environ Saf 107:140–147

    Article  CAS  Google Scholar 

  • Manta DS, Angelone M, Bellanca A, Neri R, Sprovieri M (2002) Heavy metals in urban soils: a case study from the city of Palermo (Sicily), Italy. Sci Total Environ 300:229–243

    Article  CAS  Google Scholar 

  • Mico C, Recatala L, Peris A et al (2006) Assessing heavy metal sources in agricultural soils of an European Mediterranean area by multivariate analysis. Chemosphere 65:863–872

    Article  CAS  Google Scholar 

  • National key scientific research project of China 75–60–01-01, 1990

  • Navas A, Machin J (2002) Spatial distribution of heavy metals and arsenic in soils of Aragon (Northeast Spain): controlling factors and environmental implications. Appl Geochem 17:961–973

    Article  CAS  Google Scholar 

  • Oliver MA, Webster R (1987) The elucidation of soil pattern in the Wyre Forest of the West Midlands, England. 2. spatial-distribution. J Soil Sci 38:293–307

    Article  Google Scholar 

  • Oliver MA, Webster R (2014) A tutorial guide to geostatistics: computing and modeling variograms and kriging. Catena 113:56–69

    Article  Google Scholar 

  • Reimann C, de Caritat P (2017) Establishing geochemical background variation and threshold values for 59 elements in Australian surface soil. Sci Total Environ 578:633–648

    Article  CAS  Google Scholar 

  • Rodriguez JA, Nanos N, Grau JM et al (2008) Multiscale analysis of heavy metal contents in Spanish agricultural topsoils. Chemosphere 70:1085–1096

    Article  CAS  Google Scholar 

  • Sacristan D, Recatala L, Viscarra Rossel RA (2015) Toxicity and bioaccumulation of cu in an accumulator crop (Lactuca sativa L.) in different Australian agricultural soils. Sci Hortic 193:346–352

    Article  CAS  Google Scholar 

  • SEPAC (1995) Environmental Quality Standard for Soils (GB 15618-1995)

  • Shi XZ, Yu DS, Sun WX et al (2004) Reference benchmarks relating to great groups of genetic soil classification of China with soil taxonomy. Chin Sci Bull 49:1507–1511

    Article  CAS  Google Scholar 

  • Shi WJ, Yue TX, Fan ZM (2012) An overview of high accuracy surface modeling of soil properties. Procedia Environ Sci 13:2304–2309

    Article  Google Scholar 

  • Sun CY, Liu JS, Wang Y, Sun L, Yu H (2013) Multivariate and geostatistical analyses of the spatial distribution and sources of heavy metals in agricultural soil in Dehui, Northeast China. Chemosphere 92:517–523

    Article  CAS  Google Scholar 

  • Wang JF, Jiang CS, Hu MG, Cao ZD, Guo YS, Li LF, Liu TJ, Meng B (2013) Design-based spatial sampling: theory and implementation. Environ Model Softw 40:280–288

    Article  CAS  Google Scholar 

  • Webster R (2000) Is soil variation random? Geoderma 97:149–163

    Article  Google Scholar 

  • Webster R, Welham SJ, Potts JM, Oliver MA (2006) Estimating the spatial scales of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood. Comput Geosci 32:1320–1333

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Wu J, Norverll WA, Hopkins DG et al (2003) Improved prediction and mapping of soil copper by kriging with auxiliary data for cation-exchange capacity. Soil Sci Soc Am J 67:919–927

    Article  CAS  Google Scholar 

  • Wu CF, Wu JP, Luo YM, Zhang H, Teng Y (2008) Statistical and geostatistial characterization of heavy metal concentrations in a contaminated area taking into account soil map units. Geoderma 144:171–179

    Article  CAS  Google Scholar 

  • Xie YF, Chen TB, Lei M, Yang J, Guo QJ, Song B, Zhou XY (2011) Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. Chemosphere 82:468–476

    Article  CAS  Google Scholar 

  • Yang Y, Christakos G (2015) Uncertainty assessment of heavy metal soil contamination mapping using spatiotemporal sequential indicator simulation with multi-temporal sampling points. Environ Monit Assess 187:571. https://doi.org/10.1007/s10661-015-4785-y

  • Zhang ZY, Li JY, Mamat Z et al (2016) Sources identification and pollution evaluation of heavy metals in the surface sediments of Bortala River, Northwest China. Ecotoxicol Environ Saf 126:94–101

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported by the National Plan for Science, Technology and Innovation: the comprehensive evaluation technology research on regional development of villages and towns (2012BAJ22B02-03).

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Authors and Affiliations

  1. College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China

    Wenyong Wang, Jiajia Li, Zhaofu Li & Jianjun Pan

  2. Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China

    Yabing Zhang

Authors
  1. Wenyong Wang
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  2. Jiajia Li
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  3. Zhaofu Li
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  4. Jianjun Pan
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Correspondence to Jianjun Pan.

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Responsible editor: Zhihong Xu

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Wang, W., Li, J., Li, Z. et al. Eliminating redundant spatial variation to better understand the variance of interest of soil potentially toxic elements at different sampling scales in different soil types south of Nanjing, China. Environ Sci Pollut Res 25, 29038–29053 (2018). https://doi.org/10.1007/s11356-018-2872-7

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  • DOI: https://doi.org/10.1007/s11356-018-2872-7

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