# Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area and Soil Sampling

^{3}of mine waste rocks and tailings piled around the pit heads have not undergone proper environmental treatment (Figure 1); high concentrations of copper and lead were found near the waste rock pile and pit heads [23]. Furthermore, it is estimated that the soil contamination due to mine waste rocks and tailings has been dispersed downslope by surface erosion.

#### 2.2. Geostatistical Methods

_{i}), at location x

_{i}(x is the location coordinates vector and i = 1,…, N indicates the sampling points) is interpreted as a particular realization of a random variable Z(x

_{i}). The set of dependent random variables {Z(x

_{i}), i = 1,…, N} constitutes a random function Z(x) [14,16].

_{i}), z(x

_{i}+ h)] of the same variable z(x

_{i}) separated by a lag vector h. It is a function of the lag h, a vector in distance and direction, of the two data pair values, and can be calculated as follows.

_{i}is a data value of the vicinity, for which the location and value are already known, λ

_{i}is the weight of each data, and N is the total number of data used for the kriging prediction. According to a method determining weight, several types of kriging can be classified: simple kriging, ordinary kriging, universal kriging and kriging with external drift [26].

_{i}is a primary variable, N is the number of data for the primary variable, λ

_{i}is the weight of the primary variable, u

_{j}is a secondary variable, M is the number of data for the secondary variable, and k

_{j}is the weight of the secondary variable. When the number of primary variables is small and the number of secondary variables with relatively low accuracy is large, co-kriging is usually used [18]. A spatial correlation should exist between the two variables, and using the secondary variable can reduce the uncertainty of the predicted value for the primary variable. Thus, co-kriging is known to be suitable for geostatistical integration of two data with complementary characteristics. To apply co-kriging, there should be a variogram for each variable, and a cross variogram between the primary and secondary variables is necessary.

#### 2.3. Four Different Approaches for Mapping Copper and Lead Concentrations

## 3. Results and Discussion

^{2}, was 0.99 for copper and lead, indicating a very strong correlation. However, the PXRF analysis data, with relatively low accuracy, tended to be higher than that of ICP–AES. Therefore, the trend equations of these two data were calculated as shown in the figure and used in approach 3 to transform the overestimated PXRF analysis data (n = 100) into approximate values close to those determined by ICP–AES analysis.

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Abbreviations

PTEs | Potentially toxic trace elements |

ICP–AES | Inductively coupled plasma atomic emission spectrometry |

PXRF | Portable X-ray fluorescence |

DEM | Digital elevation model |

RMSE | Root-mean-square error |

## References

- Kim, S.M.; Choi, Y.; Suh, J.; Oh, S.; Park, H.D.; Yoon, S.H.; Go, W.R. ArcMine: A GIS extension to support mine reclamation planning. Comput. Geosci.
**2012**, 46, 84–95. [Google Scholar] [CrossRef] - Krishna, A.K.; Mohan, K.R.; Murthy, N.N.; Periasamy, V.; Bipinkumar, G.; Manohar, K.; Rao, S.S. Assessment of heavy metal contamination in soils around chromite mining areas, Nuggihalli, Karnataka, India. Environ. Earth. Sci.
**2013**, 70, 699–708. [Google Scholar] [CrossRef] - Song, J.; Choi, Y.; Yoon, S.H. Analysis of photovoltaic potential at abandoned mine promotion districts in Korea. Geosyst. Eng.
**2015**, 18, 168–172. [Google Scholar] [CrossRef] - Gholizadeh, A.; Borůvka, L.; Vašát, R.; Saberioon, M.; Klement, A.; Kratina, J.; Tejnecký, V.; Drábek, O. Estimation of potentially toxic elements contamination in anthropogenic soils on a brown coal mining dumpsite by reflectance spectroscopy: A case study. PLoS ONE
**2015**, 10. [Google Scholar] [CrossRef] [PubMed] - Kim, S.M.; Choi, Y.; Suh, J.; Oh, S.; Park, H.D.; Yoon, S.H. Estimation of soil erosion and sediment yield from mine tailing dumps using GIS: A case study at the Samgwang mine, Korea. Geosyst. Eng.
**2012**, 15, 2–9. [Google Scholar] [CrossRef] - Lee, H.; Choi, Y. A Study on the Soil Contamination Maps Using the handheld XRF and GIS in abandoned mining areas. J. Korean Assoc. Geogr. Inf. Stud.
**2014**, 17, 195–206. [Google Scholar] [CrossRef] - Radu, T.; Diamond, D. Comparison of soil pollution concentrations determined using AAS and portable XRF techniques. J. Hazard. Mater.
**2009**, 171, 1168–1171. [Google Scholar] [CrossRef] [PubMed] - Hou, X.; He, Y.; Jones, B.T. Recent advances in portable X-ray fluorescence spectrometry. Appl. Spectrosc. Rev.
**2004**, 39, 1–25. [Google Scholar] [CrossRef] - Tolner, M.; Vaszita, E.; Gruiz, K. On-site screening and monitoring of pollution by a field-portable X-ray fluorescence measuring device. In Proceedings of the Consoil 2010 Conference, Salzburg, Austria, 22–24 September 2010.
- Higueras, P.; Oyarzun, R.; Iraizoz, J.M.; Lorenzo, S.; Esbri, J.M.; Martinez-Coronado, A. Low-cost geochemical surveys for environmental studies in developing countries: Testing a field portable XRF instrument under quasi-realistic conditions. J. Geochem. Explor.
**2012**, 113, 3–12. [Google Scholar] [CrossRef] - Kalnicky, D.J.; Singhvi, R. Field portable XRF analysis of environmental samples. J. Hazard. Mater.
**2001**, 83, 93–122. [Google Scholar] [CrossRef] - Carr, R.; Zhang, C.; Moles, N.; Harder, M. Identification and mapping of heavy metal pollution in soils of a sports ground in Galway City, Ireland, using a portable XRF analyser and GIS. Environ. Geochem. Health
**2008**, 30, 45–52. [Google Scholar] [CrossRef] [PubMed] - Isaaks, E.H.; Srivastava, R.M. Applied Geostatistics; Oxford University Press: New York, NY, USA, 1989. [Google Scholar]
- Buttafuoco, G.; Tallarico, A.; Falcone, G. Mapping soil gas radon concentration: A comparative study of geostatistical methods. Environ. Monit. Assess.
**2007**, 131, 135–151. [Google Scholar] [CrossRef] [PubMed] - Webster, R.; Oliver, M.A. Geostatistics for Environmental Scientists, 2nd ed.; Wiley: Chichester, UK, 2007. [Google Scholar]
- Buttafuoco, G.; Tallarico, A.; Falcone, G.; Guagliardi, I. A geostatistical approach for mapping and uncertainty assessment of geogenic radon gas in soil in an area of southern Italy. Environ. Earth. Sci.
**2010**, 61, 491–505. [Google Scholar] [CrossRef] - Steiger, B.; Webster, R.; Schulin, R.; Lehmann, R. Mapping heavy metals in polluted soil by disjunctive kriging. Environ. Pollut.
**1996**, 94, 205–215. [Google Scholar] [CrossRef] - Goovaerts, P. Geostatistics for Natural Resources Evaluation; Oxford University Press: New York, NY, USA, 1997. [Google Scholar]
- Li, X.; Lee, S.; Wong, S.; Shi, W.; Thornton, I. The study of metal contamination in urban soils of Hong Kong using a GIS-based approach. Environ. Pollut.
**2004**, 129, 113–124. [Google Scholar] [CrossRef] [PubMed] - Lee, C.S.; Li, X.; Shi, W.; Cheung, S.C.; Thornton, I. Metal contamination in urban, suburban, and country park soils of Hong Kong: A study based on GIS and multivariate statistics. Sci. Total Environ.
**2006**, 356, 45–61. [Google Scholar] [CrossRef] [PubMed] - Cheng, J.; Shi, Z.; Zhu, Y. Assessment and mapping of environmental quality in agricultural soils of Zhejiang Province, China. J. Environ. Sci.
**2007**, 19, 50–54. [Google Scholar] [CrossRef] - Mahmoudabadi, E.; Sarmadian, F.; Savaghebi, G.H.; Alijani, Z. Accuracy assessment of geostatistical methods for zoning of heavy metals in soils of urban-industrial areas. Int. Res. J. Appl. Basic. Sci.
**2012**, 3, 991–999. [Google Scholar] - MOE. Soil Contamination Assessment Report in Abandoned Metallic Mines; Ministry of Environment: Kyunggi, Gwachun, Korea, 2007; pp. 973–987. [Google Scholar]
- Buttafuoco, G.; Conforti, M.; Aucelli, P.P.C.; Robustelli, G.; Scarciglia, F. Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation. Environ. Earth. Sci.
**2012**, 66, 1111–1125. [Google Scholar] [CrossRef] - Goovaerts, P.; Webster, R.; Dubois, J.P. Assessing the risk of soil contamination in the Swiss Jura using indicator geostatistics. Environ. Ecol. Stat.
**1997**, 4, 49–64. [Google Scholar] [CrossRef] - Chile’s, J.P.; Delfiner, P. Geostatistics: Modelling Spatial Uncertainty, 2nd ed.; Wiley: New York, NY, USA, 2012. [Google Scholar]
- Roth, C. Is lognormal kriging suitable for local estimation? Math. Geol.
**1998**, 30, 999–1009. [Google Scholar] [CrossRef] - Choi, Y.; Park, H.D.; Sunwoo, C. Flood and gully erosion problems at the Pasir open pit coal mine, Indonesia: A case study of the hydrology using GIS. Bull. Eng. Geol. Environ.
**2008**, 67, 251–258. [Google Scholar] [CrossRef] - Choi, Y.; Yi, H.; Park, H.D. A new algorithm for grid-based hydrologic analysis by incorporating stormwater infrastructure. Comput. Geosci.
**2011**, 37, 1035–1044. [Google Scholar] [CrossRef] - Choi, Y. A new algorithm to calculate weighted flow-accumulation from a DEM by considering surface and underground stormwater infrastructure. Environ. Modell. Softw.
**2012**, 30, 81–91. [Google Scholar] [CrossRef]

**Figure 1.**Study area: (

**a**) Boundary of target mapping area (128°59ʹ56.433ʹʹ–129°0’4.726” E, 35°6’42.377”–35°6’48.218” N) and the locations of contamination sources and soil sampling for the ICP–AES and PXRF analyses. The extent of the soil sampling area is larger than that of the target mapping; (

**b**,

**c**) Photographs of closed pit heads and mine waste rocks on the slope, respectively.

**Figure 2.**Results of element analysis: (

**a**,

**c**) Copper and lead contents in soil analyzed by ICP–AES, respectively; and (

**b**,

**d**) copper and lead contents in soil analyzed by PXRF, respectively. The ICP–AES analysis results for the 11 validation samples are not illustrated.

**Figure 3.**Histograms of copper and lead contents analyzed by the ICP–AES and PXRF instruments: (

**a**,

**b**) copper and lead by ICP–AES, respectively; (

**c**,

**d**) copper and lead by PXRF, respectively; (

**e**,

**f**) natural logarithms of copper and lead by ICP–AES, respectively; and (

**g**,

**h**) natural logarithms of copper and lead by PXRF, respectively.

**Figure 4.**Graphs showing the correlation of ICP–AES and PXRF analysis data for: (

**a**) copper; and (

**b**) lead.

**Figure 5.**Results of variogram modeling: (

**a**,

**b**) copper and lead for approach 1, respectively; (

**c**,

**d**) copper and lead for approach 2, respectively; (

**e**,

**f**) copper and lead for approach 3, respectively; (

**g**,

**h**) copper and lead for approach 4 (primary), respectively; (

**i**,

**j**) copper and lead for approach 4 (secondary), respectively; and (

**k**,

**l**) copper and lead for approach 4 (cross variogram), respectively.

**Figure 6.**Soil contamination maps for copper generated by approaches: (

**a**) 1; (

**b**) 2; (

**c**) 3; and (

**d**) 4.

**Figure 9.**Plot showing the correlation of ICP–AES analysis data and estimated values of copper content in soil contamination maps generated by approaches: (

**a**) 1; (

**b**) 2; (

**c**) 3; and (

**d**) 4.

**Figure 10.**Plot showing the correlation of ICP–AES analysis data and estimated values of lead content in soil contamination maps generated by approaches: (

**a**) 1; (

**b**) 2; (

**c**) 3; and (

**d**) 4.

**Table 1.**Four different approaches designed in this study to generate soil contamination maps for copper and lead by using the inductively coupled plasma atomic emission spectrometry (ICP–AES) and portable X-ray fluorescence (PXRF) analysis data.

ID | Input Data | Geostatistical Method |
---|---|---|

1 | ICP–AES analysis data | Ordinary kriging |

2 | PXRF analysis data | Ordinary kriging |

3 | ICP–AES and PXRF analysis data transformed by considering the correlation between them | Ordinary kriging |

4 | ICP–AES (primary) and PXRF (secondary) analysis data | Co-kriging |

**Table 2.**Results of element analysis by using the ICP–AES instrument for 23 soil samples. Electrical conductivity (EC) and pH of the soil samples are also provided. The sample locations are shown in Figure 1.

ID | Cu (mg/kg) | Pb (mg/kg) | EC (µS·cm) | pH | Remark |
---|---|---|---|---|---|

89 | 636 | 188 | 44.0 | 5.76 | 12 samples in which the PXRF and ICP–AES analyses were performed together |

90 | 4437 | 811 | 17.8 | 5.69 | |

91 | 1080 | 223 | 26.3 | 5.04 | |

92 | 17 | 26 | 27.1 | 4.72 | |

93 | 57 | 81 | 32.4 | 4.56 | |

94 | 1338 | 402 | 36.4 | 5.01 | |

95 | 33 | 59 | 51.3 | 4.80 | |

96 | 29 | 46 | 30.1 | 5.57 | |

97 | 344 | 94 | 27.6 | 4.68 | |

98 | 1535 | 872 | 13.7 | 5.32 | |

99 | 4041 | 1924 | 22.4 | 4.93 | |

100 | 48 | 29 | 48.3 | 5.09 | |

V1 | 1517 | 602 | 29.7 | 4.95 | 11 samples collected at random points for validating the soil contamination maps |

V2 | 2364 | 562 | 26.7 | 5.01 | |

V3 | 1176 | 262 | 35.4 | 5.20 | |

V4 | 3814 | 1545 | 30.3 | 4.95 | |

V5 | 334 | 114 | 38.2 | 4.89 | |

V6 | 48 | 29 | 59.6 | 4.88 | |

V7 | 2454 | 695 | 65.0 | 4.41 | |

V8 | 1368 | 536 | 31.7 | 5.10 | |

V9 | 69 | 32 | 14.5 | 5.47 | |

V10 | 589 | 132 | 37.1 | 5.21 | |

V11 | 567 | 187 | 50.9 | 5.38 |

**Table 3.**Variogram modeling parameters used for the generation of soil contamination maps and results of cross-validation test.

ID of Approach | Element | Model | Type | Nugget | Sill | Range (Major/Minor) | Major Direction ^{1} | Cross-Validation | |
---|---|---|---|---|---|---|---|---|---|

ME ^{2} | RMSE ^{3} | ||||||||

1 | Cu | Gaussian model | Isotropic model | 0.6 | 3.2 | 90 | - | 475 | 2392 |

Pb | 0.3 | 2.2 | 80 | 88 | 611 | ||||

2 | Cu | Geometric anisotropic model | 0.4 | 1.7 | 70/35 | 115 | 232 | 588 | |

Pb | 0.2 | 0.8 | 75/40 | 115 | 35 | 170 | |||

3 | Cu | Geometric anisotropic model | 0.25 | 1.4 | 65/40 | 115 | 86 | 330 | |

Pb | 0.2 | 0.8 | 75/40 | 115 | 29 | 140 | |||

4 (primary) | Cu | Isotropic model | 0.6 | 3.2 | 90 | - | 572 | 1044 | |

Pb | 0.3 | 2.2 | 80 | 56 | 248 | ||||

4 (secondary) | Cu | 0.5 | 1.6 | 90 | - | - | |||

Pb | 0.5 | 0.7 | 80 | - | - | ||||

4 (cross variogram) | Cu | N/A | 2.2 | 90 | - | - | |||

Pb | N/A | 1.0 | 80 | - | - |

^{1}Parameters required for geometric anisotropic models;

^{2}Mean error (mg/kg);

^{3}Root-mean-square error (mg/kg).

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Lee, H.; Choi, Y.; Suh, J.; Lee, S.-H.
Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study. *Int. J. Environ. Res. Public Health* **2016**, *13*, 384.
https://doi.org/10.3390/ijerph13040384

**AMA Style**

Lee H, Choi Y, Suh J, Lee S-H.
Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study. *International Journal of Environmental Research and Public Health*. 2016; 13(4):384.
https://doi.org/10.3390/ijerph13040384

**Chicago/Turabian Style**

Lee, Hyeongyu, Yosoon Choi, Jangwon Suh, and Seung-Ho Lee.
2016. "Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study" *International Journal of Environmental Research and Public Health* 13, no. 4: 384.
https://doi.org/10.3390/ijerph13040384