Page 1

January–February 2012 • Volume 41 • Issue No. 1 Pages 1–52 • ISSN 0049-8246

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Spotlight on: European Conference on X-Ray Spectrometry, 20–25 June 2010, Figueira da Foz, Coimbra, Portugal. Part V Guest edited by Maria Luisa de Carvalho and Joaquim dos Santos


Research article Received: 3 March 2011

Revised: 27 March 2012

Accepted: 11 April 2012

Published online in Wiley Online Library: 30 May 2012

(wileyonlinelibrary.com) DOI 10.1002/xrs.2397

Correction for the effect of soil moisture on in situ XRF analysis using low-energy background R. O. Bastos,* F. L. Melquiades and G. E. V. Biasi X-ray fluorescence (XRF) analyses are affected by many matrix and geometrical factors that, generally, are possible to handle in laboratory conditions. However, when in situ analyses are considered, constraints in the measurement conditions make more difficult to handle some factors, such as moisture, affecting the measurement accuracy. Efforts have been made to correct some of the effects by inserting some steps in the sample preparation process. The problem is that each step added in this process, aiming a better precision and accuracy, makes the in situ measurement harder and longer to accomplish, influencing negatively the intrinsic advantages of the in situ measurement. In this work, we propose a method to correct the effect of soil moisture on in situ XRF analysis using low-energy background. The method demands a simple calibration, after which a long drying procedure is not necessary before measuring the samples. Copyright © 2012 John Wiley & Sons, Ltd. Keywords: in situ XRF; soil; moisture; background

Introduction

304

In recent years, the pollution of the environment by hazardous metals has become a great concern, as various pollutants have been released in the environment owing to industrialization and urbanization and accumulated in the surface soil. This has led to an increasing use of various analytical techniques, in the laboratory or in the field, for investigation or control of cleaning operations at polluted sites. Among the various analytical techniques, fieldportable X-ray fluorescence (XRF) has gained widespread acceptance as a viable analytical approach for field application and has been reported as practical and economical, when screening for the high concentration of metal during a remedial investigation, offering some distinct advantages compared with conventional methods that have been applied in the analysis of environmental samples.[1–3] It is well known that matrix effects, sample heterogeneity and water content should be considered in XRF quantification methodologies for soil analysis.[4–7] Moisture content may affect the accuracy of soil and sediment sample analyses, especially for elements with an atomic number smaller than 30, in a factor up to 20% for nonsaturated soils. Indeed, moisture content may be a major source of error when analyzing samples of surface soil or sediment that are saturated with water.[8] The influence of water content in rocks and soils on field-portable XRF analysis and the method to correct for it have not been extensively treated in the literature.[7] Ge et al.[7] used X-ray scattering in the sample as a parameter to correct moisture influence in the concentration analysis. In spite of that, it is a common practice in energy-dispersive X-ray fluorescence (EDXRF) measurements to omit the scattering peaks from the acquisition spectra, because for inorganic elements quantification, they are not considered, allowing a higher number of channels in an energy interval, favoring the deconvolution procedure used to evaluate net areas.

X-Ray Spectrom. 2012, 41, 304–307

The objective of this work was to propose a method for correcting the moisture influence on wet soil analysis, measured by portable EDXRF equipment, without the use of the scattering peaks.

The influence of moisture Material and method Moisture tests were performed by sampling the soil and leaving the samples on aluminum recipients in open air, under the sun, for distinct times, from 0 to 2 h, with an interval of 30 min between each measurement. A sample, dried for 24 h at 60  C, was measured as well to compare the results. Soil samples were collected at the university campus, in an area near the laboratory, by using plastic accessories. Aliquots of 20 g were disposed in aluminum recipients under the sun. Two grinding procedures were performed: samples were grinded for 1 min using a porcelain mortar and pestle set, and samples were grinded for 1 min using a porcelain mortar and pestle set and sieved (particle size smaller than 125 mm). As a result of previous tests,[9] both grinding procedures demonstrated to be equivalent. Three grams of soil were placed in XRF cells covered with Mylar film for irradiation. The PXRF-LFNA02 equipment, consisting of an Ag mini X-ray tube (40 kV, 100 mA, 0.7 mm spot size, 50 mm Ag filter) and a Si-PIN detector (221 eV resolution for 5.9 keV and 25 mm Be window), was employed to accomplish the measurements. Acquisition time was 500 s. Calibration curves, calculated using five standard reference materials of soil and sediment (IPT42, IPT51, IPT57, IAEA 375 and PTXRF-IAEA04), were used for the system sensitivity determination.

* Correspondence to: Rodrigo Bastos, Physics Department, State University of Center West, Rua Presidente Zacarias, 875, 85015-430, Guarapuava, PR, Brazil. E-mail: bastosrodrigoo@yahoo.com.br Physics Department, State University of Center West, Rua Presidente Zacarias, 875, 85015-430, Guarapuava, PR, Brazil

Copyright © 2012 John Wiley & Sons, Ltd.


Correction for moisture on XRF analysis using low-energy background Results It was possible to identify Mn, Ni, Zn, Br, Y, Nb and Pb in the soil samples. Ti, Fe and Zr were quantified using the calibration curves. The detection limits obtained were, in mg kg1: (286  24) Ti, (44.8  1.9) Fe and (11.6  0.2) Zr. Concentration values for Ti, Fe and Zr measured in the same sample dried during different times are shown in Fig. 1. Results do not show significant changes in the concentration values for samples grinded for 1 min and for samples grinded and sieved (<125 mm). When the drying procedure is considered, after drying for 30 min, no significant improvements were encountered for samples dried until 2 h. The values measured for the soil dried for 24 h at 60  C show that moisture influence in the concentration values may reach 20%, which cannot be neglected if measurements with some accuracy are aimed. This variation of the estimated concentrations is mostly because the water attenuates the radiation from the source and the fluorescence radiation generated in the sample.

element’s characteristic X-ray intensity (dIx) is directly proportional to the increment of water content in the sample (do). Eqn 1 shows the following relation: dIx ¼ mm Ix do

(1)

where mm is the correlation coefficient (in %1 units) and Ix is the intensity of the referred element’s characteristic X-ray. After the integration of Eqn 1, by doing Ix = I0 for o = 0, we find that Ix ¼ I0 emm o

(2)

If the intensity of the scattered radiation (Is) is directly proportional to the water content in the wet samples, it is possible to write Eqn 3: o ¼ a þ bIs

(3)

where a and b are constants experimentally determined. With the use of Eqns 2 and 3, it is possible to write Eqn 4, which corrects the measured characteristic X-ray Ix: I0 ¼ Ix emm ðaþbIs Þ

Correcting the effect of moisture

(4)

Method proposed by Ge et al.[7] The method proposed by Ge et al.[7] considers that if the matrix components do not vary significantly, the reduction in a certain 115000 110000

100000 95000

85000

Zr

Concentrations (mg . kg-1)

90000

1450 1400 1350 1300 1250 1200 1150 1100

Concentrations without corrections

Figure 2 shows the effect caused in spectra acquired for the same sample with distinct contents of waters. It shows that the characteristic X-ray peak areas diminish with the growing content of water. In the region of the spectrum where the scattering dominates, it is interesting to observe that the scattering (coherent and incoherent) grows with the water content. The total scattering was the parameter used by Ge et al.[7] to correct the error due to attenuation of characteristic X-ray peaks. Another effect worth observing in Fig. 2 is that the background radiation for lower energies is attenuated similar to the peaks. This happens because the coherent and incoherent scatterings of the X-ray tube spectrum are less expressive for energies lower than 16 keV due to its attenuation. These observations point that the attenuated background, for low energies, may be an independent parameter for the correction of the moisture effect in the analysis. 4000

48000

3500

60000

46000

50000

44000

Ti

3000

wet dried 2 h dried 24 h

42000

Counts

Fe

105000

New method for the moisture effect correction

40000

2500 2000 1500

40000

Counts

1000

38000

Se c1 m Se c2 m Se c2 Se p c3 m Se c3 Se p c4 m Se c4 Se p c5 m Se c5 Se p c6 m Se c6 p

36000

X-Ray Spectrom. 2012, 41, 304–307

500 0 3.5

3000 2500 2000 1500 1000 500 0

4.0

4.5

5.0

5.5

Energy (keV)

0

2

4

6

8 10 Energy (keV)

12

14

16

18

Figure 2. Spectra acquired for the same granulometric and chemical soil composition, one in natura (relatively wet), one dried for 2 h in open air and the other dried for 24 h in an oven.

Copyright © 2012 John Wiley & Sons, Ltd.

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305

Figure 1. Moisture influence for in situ soil analysis of Fe, Zr and Ti. Sec1m: not dried, grinded for 1 min; Sec2m: dried under the sun for 30 min, grinded for 1 min; Sec2p: dried under the sun for 30 min, grinded and sieved (<125 mm); Sec3m: dried under the sun for 1 h, grinded for 1 min; Sec3p: dried under the sun for 1 h, grinded and sieved (<125 mm); Sec4m: dried under the sun for 1.5 h, grinded for 1 min; Sec4p: dried under the sun for 1.5 h, grinded and sieved (<125 mm); Sec5m: dried under the sun for 2 h, grinded for 1 min; Sec5: dried under the sun for 2 h, grinded and sieved (<125 mm); Sec6m: dried in an oven for 24 h, grinded for 1 min; Sec6p: dried in an oven for 24 h, grinded and sieved (<125 mm).

30000


R. O. Bastos, F. L. Melquiades and G. E. V. Biasi The only alteration in the method proposed by Ge et al.[7] is the use the background (F), under a low-energy peak, in place of the scattered radiation intensity (Is). Equation 3 may be written as Eqn 5: o ¼ a þ bF

(5)

Parameter b should assume negative values in this case. Application of the new method

CZr ¼ C’Zr e0:00498o

(8)

o ¼ 144:6  0:01316F

(9)

12000 9000

5

10

15

20

25

30

0.110 0.102 0.094 0.087 0.079 0.071

Experimental Data Linear Fit 0

5

10

15

20

25

30

6000 3000 24000

0

5

10

15

20

25

30

22000

Zr

0

Background under peaks (counts)

20000 18000 Experimental Data Linear Fit

16000 14000 12000

0

5

0

5

10

15

20

25

30

10

15

20

25

30

10000

Ti

Zr

(7)

15000

0.131

Ti

Ln (Element Concentration(%))

CFe ¼ C’Fe e0:00666o

18000

7.131

5.6 5.1 4.7 4.3 3.9

(6)

21000

13.989 12.846 11.703 10.560 9.417 8.274

5.987 5.567 5.146 4.726 0.193 0.172 0.152

CTi ¼ C’Ti e0:00782o

where C′X is the concentration to be corrected and CX is the corrected concentration, both in arbitrary units for element X.

Fe

Fe

First, it is interesting to verify if Eqn 2 is valid, considering the intensities measured for samples of varying contents of water. Figure 3 presents the Fe, Zr and Ti estimated concentrations as a function of the weight loss percentage in the samples during the drying procedure. Because the values of concentration are proportional to the intensities, these graphs should respect the linearity of Eqn 2. Note in Fig. 3 that Fe and Ti concentrations tend to obey Eqn 2. The concentrations of Zr show a similar behavior in a disperser graph. It happens because its characteristic Ka X-ray energy is higher than the Fe and Ti energies. The scattering of the source radiation in the sample begins to interfere more as the energy increases. The second verification that must be carried out to make possible the method to apply is the verification of Eqn 5. To accomplish this, the backgrounds of the three peaks of interest are shown in Fig. 4 as a function of the weight loss percentage during the sample drying process. Figure 4 shows the significant correlation (p < 0.0001) encountered between the background counts under the Ka peak of Ti

and the mass percentage of water in the sample. The Fe and Zr backgrounds do not present the same behavior (R = 0.36 and p = 0.1 for Fe; R = 0.21 and p = 0.35 for Zr). This conforms to the expectations because Ti has a lower Ka energy. Figure 5 presents the variation of the mass attenuation coefficient related to the coherent and incoherent scatterings and to the photoelectric effect, for dry and wet soils, in the energy range that includes Ti Ka (4.509 keV), Fe Ka (6.400 keV) and Zr Ka (15.748 keV). The curves plotted in the graphs were calculated using the WinXCom software (Technical University of Denmark, Denmark).[10–12] We observe that the differences between dry and wet soils are greater for lower energies. The results simulated by WinXCom emphasize, as is well known, that the effect of the attenuation by the water is greater for lower energies. Equations 6–9 are Eqn 4 written for the correction of each element concentration; mm was obtained from Fig. 3, and parameters a and b were obtained from Ti in Fig. 4:

3.5

8000

3.0 6000

2.6

4000 0

5

10

15

20

25

30

Mass percentage of water

306

Figure 3. Element concentration as a function of the mass percentage of water. Correlation coefficients and p-values for statistical significance: R = 0.80 and p < 0.0001 for Fe; R = 0.89 and p < 0.0001 for Ti; and R = 0.49 and p = 0.02 for Zr.

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Mass percentage of water Figure 4. Background counts under the Ka peaks as a function of the mass percentage of water. Correlation coefficients and p-values for statistical significance: R = 0.83 and p < 0.0001 for Ti; R = 0.36 and p = 0.1 for Fe; and R = 0.21 and p = 0.35 for Zr.

Copyright © 2012 John Wiley & Sons, Ltd.

X-Ray Spectrom. 2012, 41, 304–307


Correction for moisture on XRF analysis using low-energy background 1.6

115000

1.4

110000 105000

Fe

1.2

cm2.g-1

1.0

95000

0.8

90000

0.6

400 Wet soil (20% weight of water) Wet soil (10% weight of water) Dry soil

1450 1400 1350 1300 1250 1200 1150 1100

Corrected concentrations Concentrations without corrections

48000 46000 44000

200

Ti

(cm2.g-1)

300

Zr

Incoherent Scatering

0.0 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.018

Concentrations (mg . kg-1)

85000 Coherent Scattering

0.4 0.2

100000

42000 40000

100

38000

Photoelectric effect

36000

Se

c1 m Se c2 m Se c2 Se p c3 m Se c3 Se p c4 m Se c4 Se p c5 m Se c5 Se p c6 m Se c6 p

0 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.018

Energy (MeV) Figure 5. Mass attenuation coefficient related to the coherent and incoherent scatterings and to the photoelectric effect in the energy range, which includes the Ti Ka (4.509 keV), the Fe Ka (6.400 keV) and the Zr Ka (15.748 keV).

Figure 6 shows the results obtained for the corrected concentration values together with the non-corrected values. The average corrected concentrations and the respective standard deviations obtained for the analyzed elements are (4.61  0.11)% for Ti, (11.00  0.15)% for Fe and (1428  54) mg kg 1 for Zr.

Conclusions This work focused on the development of a procedure for correcting the moisture interference in EDXRF measurements made in soils by using portable equipment. On the basis of the proposal of Ge et al.[7], we verified the possibility to use an independent parameter other than the source radiation scattered by the sample. The parameter used was the background radiation for low energies (more specifically the background under the Ti Ka peak). The results obtained after the correction are very satisfactory, being better applicable for energies where the source scattered radiation is not predominant over attenuation. Acknowledgements We would like to thank the Nuclear Applied Physics Laboratory of State University of Londrina for the technical support. We are also

Figure 6. Corrected element concentrations together with the noncorrected ones.

grateful to Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Paraná (013/2009-17546) and to the Conselho Nacional de Desenvolvimento Tecnológico, CNPq (476024/2009-9) for the financial support.

References [1] E. Makinen, M. Korhonen, E. L. Viskari, S. Haapamaki, M. Jarvinen, L. Lu, Water Air Soil Pollut. 2006, 171, 95–110. [2] X. Hou, Y. He, B. T. Jones, Appl. Spectr. Reviews 2004, 39(1), 1–25. [3] F. L. Melquiades, C. R. Appoloni, Radiat. Phys. Chem. 2004, 262(2), 533–541. [4] R. M. Rousseau, Spectrochim. Acta, Part B 2006, 61, 759–777. [5] R. Al-Merey, J. Karajou, H. Issa, Appl. Radiat. Isot. 2005, 62, 501–508. [6] M. Bosa, J. A. M. Vrielinkb, Anal. Chim. Acta 2005, 545, 92–98. [7] L. Ge, W. Lai, Y. Lin, X-Ray Spectrom. 2005, 34, 28–34. [8] EPA Method 6200, Field portable X-ray fluorescence spectrometry for the determination of elemental concentrations in soil and sediment. United States Environmental Protection Agency (US-EPA), 2007. URL http://www.epa.gov/osw/hazard/testmethods/sw846/pdfs/6200.pdf [accessed on 22 February 2011]. [9] F. L. Melquiades, R. O. Bastos, G. E. V. Biasi, P. S. Parreira, C. R. Appoloni, AIP Conference Proceedings 2011, 1352, 317–320. [10] L. Gerward, N. Guilbert, K. Bjorn Jensen, H. Levring, Radiat. Phys. Chem. 2001, 60, 23–24. [11] L. Gerward, N. Guilbert, K. B. Jensen, H. Levring, WinXCom - a program for calculating X-ray attenuation coefficients. Radiat. Phys. Chem. 2004, 71, 653–654. [12] M. J. Berger, J. H. Hubbell, XCOM: Photon Cross Sections Database, Web Version 1.2. National Institute of Standards and Technology, Gaithersburg, MD 20899, USA, 1987/99. Available at http://physics. nist.gov/xcom Originally published as NBSIR 87-3597 “XCOM: Photon Cross Sections on a Personal Computer”.

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X-Ray Spectrom. 2012, 41, 304–307

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Research article Received: 16 May 2010

Revised: 10 November 2011

Accepted: 23 November 2011

Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI 10.1002/xrs.2364

Determination of Pb, As, Cd and trace elements in polluted soils near a lead–zinc mine using polarized X-ray fluorescence spectrometry and the characteristics of the elemental distribution in the area Liqiang Luo,* Binbin Chu, Yingchun Li, Tao Xu, Xiaofang Wang, Jing Yuan, Jianlin Sun, Ying Liu, Ying Bo, Xiuchun Zhan, Shuxian Wang and Lijun Tang Polarization energy dispersive X-ray fluorescence spectrometry was used in the determination of Pb, As and Cd, as well as Cr, Cu, Zn, Ni and other minor and trace elements in the soil samples taken from a polluted area by lead mine exploitation. Two difficulties have to be overcome. One is strong overlap of Pb La over As Ka and another is lack of suitable certified reference materials. The different excitation conditions and analytical lines were tried to reduce the impact of overlap of Pb La over As Ka. When KBr was used as the second target, compared with Zr, the proportion from Pb La was reduced about six times. Even so, however, the overlap was not reduced enough to be ignored. The inductively coupled plasma atomic emission spectrometry and mass spectrometry methods were used to analyze parts of soil samples and provide data for compensating lack of reference materials. By this method, the analytical concentration range of Pb, As and Cd were significantly extended. The analytical range of Pb, As and Cd were 1.4 mg/g~4.2%, 0.6 mg/g~9.3% and 0.5 mg/g~1500 mg/g, respectively. The high concentrations of Pb, As and Cd were found in the samples in the vicinity of the Pb-Zn mine. The concentrations of Pb, As, Cd, Zn and Cu were higher than the Class III in the Chinese environmental quality standard for soils. The highest concentrations of Pb, As, Cd and Zn in the soil samples were 14 960, 2726, 65 and 9439 mg/g, respectively. Copyright © 2012 John Wiley & Sons, Ltd. Keywords: Toxic Elements; X-Ray Fluorescence; Soil; Ecology and Environment Pollution

Introduction

X-Ray Spectrom. 2012, 41, 133–143

* Correspondence to: Liqiang Luo, National Research Center of Geoanalysis, Beijing, 100037, China. E-mail: luolq_xrs@hotmail.com National Research Center of Geoanalysis, Beijing100037, China

Copyright © 2012 John Wiley & Sons, Ltd.

133

Study of ecology and biogeochemistry is helpful for understanding ecological toxicology of As, Cd, Hg, Pb and Cr. Anthropogenic activities, such as mine exploitations, contaminate usually a large amount of water, soil and farmlands. Toxic elements from them eventually will enter plants and food chains and cause damages to human health. Dietary intake of the toxic metals has also a great impact on human health. Natural pollution is another possible pollution origin. As the toxic elements may cause such great healthy risk, there is a strong need to get details about bioavailability and toxicity of these metals. For this, people are conducting research into determining the relationship between bioavailability and toxicity of the metals, such as Cd in soil,[1] estimating the environmental impact of As,[2,3] and evaluating Pb, Cd and As bioavailability in the abandoned mine site.[4] Soil is important media to transport toxic elements into plants and environments. X-ray fluorescence (XRF) spectrometry plays a key role in the determination of toxic elements, especially Pb and As in soil samples. In the determination of toxic elements in the soils contaminated with Pb and As, there are two problems for one to have to face. A severe problem arises from the overlap of Pb La over As Ka. Another is no suitable certified reference materials to contain wide concentrations of

Pb and As enough to cover the whole analytical range in the samples. The determination of 31 elements of soils and environmental samples was reported by H. Matsunami[5] with polarized energy dispersive XRF (PEDXRF) spectrometry using pressed powder pellets. Among them, the results for 12 compounds obtained using the proposed EDXRF spectrometry compared favorably with those by conventional wet chemical methods. The results for eight compounds obtained using the method exhibited poor agreements with those by chemical methods as incomplete dissolution and/or volatilization losses, such as Cr, Zr and Sn. However, the results of other 11 components, including Pb, were not as good as the first group of 12 elements. The determination of 17 elements in soils, including Pb, which were analyzed by Kodirov and Shukurov[6] with XRF spectrometry, and their distribution was studied in 21 sampling locations. However, no data of As and Cd were given. Heavy metal analysis around Iskenderun Bay in Turkey was carried out by Cevik and Makarovska[7] with EDXRF spectrometry. Pb and As were determined using Pb La


L. Luo et al. and As Ka. However, it is not able to ďŹ nd any details on how to determine Pb and As using Pb La and As Ka simultaneously without considering the overlap problem of Pb La over As Ka. Based on the sample analysis using inductively coupled plasma mass spectrometry (ICP-MS), in addition, M. Tuomela[8] reported inďŹ&#x201A;uence of Pb contamination in boreal forest soil on the growth and ligninolytic activity of litter-decomposing fungi. Y. Shibata[9] reported that XRF analysis of Cr, As, Se, Cd, Hg and Pb in soil and analytical lines were Cr Ka, As Ka, Se Ka, Cd Ka, Hg La and Pb Lb with accompanying corrections for overlapping of Se Kb to Pb Lb and Pb La to As Ka. Pb, Zn and As in soil were determined using EDXRF, and Pb Lb and As Ka were chosen as analytical lines by Ostachowicz et al.[10] The polarized-beam EDXRF was also used for trace metal analysis of vegetation samples, and the overlap between Pb and As was considered using selective excitation conditions by Margui et al.[11] However, both of the aforementioned papers deal with rather low concentrations of lead in the analyzed samples. In general, it is still very difďŹ cult to solve the overlap of Pb La over As Ka if the concentration of lead is high, or even very high compared with the concentration of arsenic. At the same time, the high concentrations of Pb available in the certiďŹ ed materials are usually below several hundreds of mg/g. The concentrations of Pb in the contaminated soil samples taken from the polluted areas rages from mg/g to several per cent. Available certiďŹ ed reference materials could not cover the whole range of Pb. Thus, one who wants to analyze As and Pb in the soil samples with high lead concentration has to face the difďŹ culty of the lack of the certiďŹ ed materials with suitable concentration ranges of As and Pb. In this study, we tried to ďŹ nd a suitable and robust method to analyze the soils samples with high concentrations of As and Pb in the case of no adequate number of certiďŹ ed reference materials. The landscape area near a Pb-Zn mine in southeast of China has been chosen as the ecology and environmental geochemical investigation ďŹ eld. Water, soil, plants, animals and human tissues were sampled in the past 5 years. The toxic elements in soil were determined using XRF spectrometry. Experimental conditions, especially for Pb and As, were examined and chosen. Soil samples taken from the ďŹ eld were analyzed using ICP atomic emission spectrometry (ICP-AES) and mass spectrometry (ICP-MS) to compensate the absence of certiďŹ ed materials for Pb, As and Cd. The characteristics of Pb, Cd, As, Cr, Cu, Zn and Ni in soils were studied and reported.

Materials and methods Sampling site

134

The investigation area is located in Mount Qixia, the suburb of Nanjing city, Jiangsu province, the southeast of China. Qixia Pb-Zn-Mn mine is just below Mount Qixia and Jiuxiang River. It is part of Mountain Lingzheng, approximately 440 m high and 22 km from Nanjing city. Yangtze River ďŹ&#x201A;ows along the north of Mount Qixia, just 1.5 km away. Exploiting the Pb-Zn-Mn mine has lasted for over 50 years. Pb, Zn, Mn, Ag, Au and S are major products. The depth of exploitation is about 700 m. Major minerals include zinc blende, galenite, pyrite, dialogite, chalcopyrite and marcasite. Mount Qixia is also a tour resort as the Buddhistical temples within its valley attract people for pilgrimage. Around it are residential houses

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and farmlands. Human habitation is inďŹ&#x201A;uenced by the sewage sludge, dust and slag. Two hundred and ďŹ ve soils samples were taken from landscape, farmlands, residential yards, trafďŹ c and industrial areas near the leadâ&#x20AC;&#x201C;zinc mine at Mount Qixia, in order to investigate ecological and environmental situations around the mine. CertiďŹ ed materials and calibration samples Twelve sediment certiďŹ ed materials GBW07301~07312 (GSD1~12), 8 soil certiďŹ ed materials GBW07401~07408 (GSS-1~GSS-8) and 13 rock certiďŹ ed reference materials GBW07103~07114 and GBW07120~07125 (GSR-1~18) were used as calibration samples when the analytical method was built and the regression lines were calculated. In order to compensate the lack of standards in calculating the regression coefďŹ cients and extend the calibration ranges of elements, 12 practical soil samples taken from the contamination area were chosen, and Pb, As and Cd in them were determined using ICP-MS. Because the soil samples contained high concentrations of Pb, it seems difďŹ cult to get a robust result. Thus, some of them were analyzed twice or even triple by using different approaches of dissolving samples to improve analytical accuracy with the ICP-AES and ICP-MS methods. Then, the 12 soil samples were added into the standard set for calculating regression coefďŹ cients. Minor and trace elements in the soil samples were determined using XRF spectrometry, combined with the ICP-AES and ICP-MS. In addition, eight mineral certiďŹ ed reference materials were chosen as the calibration according to the needs for the analyzed elements, which included GBW07235 (lead ore), GBW07236 (lead ore), GBW07240 (tungsten ore), GBW07269 (galena), GBW07270 (sphalerite), GBW07277 (arsenic ore), GBW07282 (tin ore) and GBW07287 (leadâ&#x20AC;&#x201C;zinc ore). Analytical methods and instrumentation Pb, As, Cd, Cr, Cu, Zn and Ni were major toxic elements in evaluation of pollution on environmental effects, and thus, those elements in soils were determined using a polarized EDXRF spectrometer. After being taken from ďŹ elds, soil samples were put in the laboratory and dried naturally. Stone, plants and other impurity materials were removed from the samples, and the remaining soil samples were powdered into 47 mm. This grain size might not be ďŹ ne enough to give high precise and accurate results for the elements Mg, Al and Si, as shown in Table 5a. The reasons might be from the grain size, mineral and matrix effects. Before weighting, the soil was dried at 105  C for 2 h. Then the soil sample was pressed into powder pellet by adding 1.575 g wax into 7.000 g soil sample. Soil samples were analyzed mainly using Spectro Lab 2000 with a 400-W X-ray tube with Pd primary target and a Si(Li) detector. Its energy resolution is about 150 eV (@5.9 keV). The seriously overlapped line of Pb La over As Ka was considered. Analytical data using Pb Lb, As Kb, as well as As Ka were compared. Based on the compared results, Pb (Lb) and As (Ka) were chosen as analytical lines of Pb and As. For the other elements, such as Cr, Mn, Fe, Cu, Zn and Cd, the Ka lines were used. The measuring conditions for major and minor elements in soils were listed in Table 1. Each sample was measured triple times using XRF. Uncertainty and correlation coefďŹ cients were

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X-Ray Spectrom. 2012, 41, 133â&#x20AC;&#x201C;143


Determination of toxic elements in polluted soils by EDXRF a volatile element, it is not dissolved using the aforementioned method. Thus, 0.2000 g of sample was weighted into beaker, and 10 ml of new mixed aqua regia [concentrated nitric and hydrochloric acids (1:1)] was put into the beaker. It was put into water heater for 2 h with being agitated about each half hour. After its being taken down and cooled, it was diluted to 25 ml. It was diluted 10 times again and used for the determination of As using ICP-MS. The samples for calibrations were analyzed twice, and parts of them were carried out three times using ICP-MS and ICP-AES independently.

Table 1. Measurement conditions for elements in soil samples Element

Tube voltage (kV)

Tube current (mA)

Target

Time/s

40 53 44 35 15

1.5 5 3 2 5

Mo Al2O3 Pd Co HOPG

550 550 200 550 200

Fe~Sr, Hf~W, Hg~Bi Ag~La, Ce~Nd Y~Mo, Th, U K, Ca, Ti~Mn Na~Cl

calculated. Twenty per cent of the soil samples were sampled twice as quality-controlled samples. The limits of detection of elements were calculated by the program with Spectro Lab 2000 according to each sample and were averaged based on nine samples, as given in Table 2. Because of the overlap of Pb La over As Ka with the high concentrations of Pb in the contaminated soil samples, the limit of detection of As was rather high compared with nearby elements, such as Zn, Rh, Sr. The limit of detection of As was from 0.6 to 9 mg/g generally, which depended on the concentrations of the related elements to be determined. The calculation of the limit of detection was made by the software in Spectro Lab 2000 as reported by J. Heckel et al.[12] Considering the possibility of overcoming the overlap problem of Pb La over As Ka by using KBr as a secondary excitation target, PANalytical Epsilon 5 with Gd as the primary target was also used to study the resolution degree of overlapped lines of Pb La and As Ka in our practical application of soil analysis. Pb, Cu and Zn in soil samples were determined using ICP-AES (IRIS Advantage) and As and Cd were determined using ICP-MS (X-series). After 0.1000 g of sample for the determination of Pb, Cu, Zn and Cd was weighted into polytetraďŹ&#x201A;uoroethene container, 3 ml HCl, 2 ml HNO3, 3 ml HF and 1 ml HClO4 were put into the container. After being capped, the container was put on the heater plate for 2 h at 130  C. After about half an hour, it was heated again at 150  C for 4 h. Then the cover of the polytetraďŹ&#x201A;uoroethene container was moved out and heated up to 180  C until the acid smog vanished. 1.5 ml HCl (1 : 1) was put into it and heated for 2 min. The container was taken down from the plate, 0.5 ml HNO3 (1 : 1) was put into it along its wall. The solution was put into container and diluted quantitatively into 10 ml. The solution was ready for the determination of Pb, Cu and Zn using ICP-AES. The solution was diluted again one time for the determination of Cd using ICP-MS. Because arsenic is

Results and discussion In this part, we discuss three issues. One is how to analyze As when Pb is very high in the contaminated soil and there is strong overlap of Pb La over As Ka. The second issue is how to analyze high concentrations of Pb, As and Cd in such soil samples without a set of suitable certiďŹ ed materials with the concentration range wide enough to cover the contaminated soil samples. The third part is discussing the application of the developed method to the environmental and ecological evaluation of Pb, As and Cd in the area. Overlap of Pb La over As Ka Overlap between As Ka and Pb La exists when soil samples are determined. Generally, the energy resolution of a conventional energy XRF spectrometer with Si(Li) detector is about 150 eV at 5.9 keV, which is not high enough to resolve the overlapped lines between As Ka and Pb La. When Pb concentration was low in soil samples, selective excitation by using the different secondary targets might be a good choice to reduce the overlap of Pb La over As Ka, such as the selection of KBr as the second target.[11] When Pb concentration was high in soils, however, the validity of such approach seems not strong enough to overcome the severe overlap problem. Thus, two polarized EDXRF spectrometers with the different secondary targets, a PANalytical Epsilon 5, and a Spectro Lab2000, were used to evaluate the peak overlap between As Ka and Pb La lines in the soil samples. The Pb La lines can be excited using both of Zr Ka and Kb lines as the fact of Zr Ka1 = 15.774 keV, Kb1 = 17.666 keV and Pb LIIIabs = 13.0352 keV. The intensities at the position 10.5 keV of As Ka and Pb La peaks in GSS-4 and GBW07236 certiďŹ ed

Table 2. Limit of detection and concentration range in calibration samples (mg/g) Element

LOD

MgO Al2O3 SiO2 P 2O 5 K 2O CaO TiO2 MnO Fe2O3 â&#x20AC;&#x201D;

146 30 30 28 10 9.4 5.0 1.2 5.0 â&#x20AC;&#x201D;

Maximum range (%) 21.8 16.3 76.40 0.18 7.48 51.1 3.36 1.53 14.8 â&#x20AC;&#x201D;

Element

LOD

V Cr Ni Cu Zn Ga As Rb Sr Y

4.9 1.2 0.8 1.0 0.9 1.3 3.6 0.5 0.5 0.5

Maximum range 300 795 516 3 200 62 000 39.3 7 800 470 1 198 67

Element Zr Nb Mo Cd Sn Ba Ce Pb Th â&#x20AC;&#x201D;

LOD 0.5 0.6 0.9 0.5 0.7 3.4 5.8 1.4 1.5 â&#x20AC;&#x201D;

Maximum range 1 540 95 330 1 500 370 1 899 400 33 800 79 â&#x20AC;&#x201D;

135

LOD, limit of detection.

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L. Luo et al. reference materials, determined using Epsilon 5, were very high, and the roughly counts at 10.5 keV was about 13 (cps/ mA/ch) for GBW07236. The concentrations of Pb and As in them are listed in Table 3. On the contrary, Epsilon 5 was equipped with KBr secondary target, and Br Ka lines (Ka1 = 11.923 and Ka2 = 11.877 keV) cannot excite the Pb L series of lines. In this case, thus, less intensity, as shown in Fig. 1, was observed at 10.5 keV, as compared with Zr secondary target. The content of As in GBW07236 was lower than that in GSS-04. However, the intensity at 10.5 keV from GBW07236 was 2.4 times higher than that from GSS-04, as shown in Table 3 and Fig. 1. The concentration of Pb in samples GBW07236 was 104 times higher than GSS-04 and that of Pb in GBW07235 was 119 times higher than that in GSD-07. That means that the overlap case of Pb La over As Ka occurs still even if KBr is chosen as the secondary target. This fact was confirmed again by another couple of certified reference materials of GBW07235 and GSD-07. The concentrations of As in both of the samples were nearly the same as each other with extremely difference concentrations of Pb in them. Because of the heavy overlapped intensity of Pb La over As Ka, the

intensity at 10.5 keV from the sample GBW07235 was 12.4 (cps/mA/ch), much higher than that (1.5 cps/mA/ch) from GSD-07. That means that the overlap of Pb La over As Ka cannot be eliminated using KBr and sometimes it is still very heavy. Such situation happened because of the Br Kb line. The energies of Kb1,2 (13.290 and 13.465) of Br lines, in fact, are higher than the absorption edge of Pb LIIIabs (13.035 keV). Thus, La1(L3–M5) and La2(L3–M4) of Pb will occur as the LIII is excited using Br Kb1,2. In addition, the second target may not produce a pure monochromatic X-ray and some tube continuum spectrum may still exist. Thus, Pb La and Lb lines can still be observed even if KBr is used as the second target. Spectro Lab 2000 is available in our laboratory and thus has been used in the practical application to determine Pb, As, Cd and other elements in the soil samples taken from the contaminated area. Because As Kb and Pb Lb were separated rather widely compared with As Ka and Pb La, they were especially suitable to be chosen as the analytical lines for the quantitative analysis of the soil sample in the area highly contaminated with Pb and As and the accurate analytical results were obtained in our practical applications. On the

Table 3. Concentrations of Pb and As in two certified reference materials and their measured intensities with the different secondary targets Sample

As (mg/g)

Pb (mg/g)

Different secondary target with Gd X-ray tube in PANalytical Epsilon 5 Second target — — GBW07236 43 6 100 GSS-04 58 58.5 GBW07235 85 41 700 GSD-07 84 350 Mo secondary target with Pd X-ray the in Spectro Lab 2000 GSR-12 0.23 (4.44) GSR-14 0.25 7.7 GBW-07282 7 800 28 200

Cps @10.5 keV

Cps @10.5 keV

KBr 2.3 0.97 12.4 1.5

Zr 13.0 1.1 82.5 2.7

— — —

— — —

136

Figure 1. Comparison of spectra of two certified reference materials with different concentrations of As (58 mg/g in GSS-04 (light color) and 43 mg/g in GBW07236 (dark color)) and Pb excited using KBr as the second target.

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Determination of toxic elements in polluted soils by EDXRF contrary, the arsenic content in most of common soil samples usually was not so high to be detected to produce the robust and accurate data when As Kb was chosen as the analytical line. Thus, As Ka and Pb Lb were used as the analytical lines to determine As and Pb in the soils samples in the work with the overlap correction of Pb La over As Ka. In this way, the rather accurate results were obtained and no significant deviation between using As Kb and Ka were observed from the experimental data. Such overlap calibration was indeed necessary for getting an accurate prediction results in the determination of As while Pb existed. Otherwise, huge error will be observed. Matrix effects and lack of standards Matrix effects play a key role in the determinations of the toxic elements in the polluted soils and sediments. A great challenge to determine Pb, As and Cd in such soil and sediment samples taken from the polluted area was first from the very absence of the certified reference materials with a wide range of the concentrations of the elements. The maximum concentrations of available certified reference materials on soils, sediments and rocks were up to 412 mg/g for As, 636 mg/g for Pb and 4.3 mg/g for Cd. The maximum concentrations in the practical pollution soil samples, however, were up to 2379 mg/g for As, 20 325 mg/g for Pb and 222 mg/g for Cd. The ranges of the concentrations of three elements in the soil samples were much greater than that in the available certified reference material. Without a set of suitable standards, it would be very difficult to build a robust prediction model. Besides 12 certified sediment reference materials, 8 certified soil materials and 13 certified rock materials that covered low range of the elemental concentrations, as mentioned in the part of Materials and Methods, eight certified mineral reference materials were chosen to compensate the lack of the certified

reference materials with the very high range of the elemental concentrations. However, the matrixes of the certified mineral reference materials were quite different from the soil and sediment samples taken from the polluted areas. Among eight certified mineral reference materials, only parts of them were able to be used in the training set according to different element to be calibrated. The eight certified mineral reference materials could not compensate the lack of standards in building matrix correction models. On the basis of initial EDXRF data, 25 samples of the practical soil and sediment samples taken from the contaminated site, with middle and high concentrations of Pb, As and Cd, were selected, and Pb, As and Cd in them were determined using ICP-MS combined with ICP-AES and then their concentrations were used as the standard values in the regression calculations. It is noted that the sample analysis by ICP-MS and ICP-AES must be repeated two or three times to get rather accuracy and robust results and single analytical data did not promise the believable results. The certified reference materials and standards were divided into two groups, one being used as a training set and another as a test set for examining the accuracy and robust of analytical results and correction models. Fifteen from 25 practical soil samples were chosen and added into the training set by considering their capability to compensate the shortage in the medium and high concentrations of Pb, As and Cd in the training sample set in a concentration ranges from hundreds to over thousands of mg/g. Another ten samples were chosen as prediction samples in the test set that were listed in the first ten samples in the Table 4a. Totally, 51 samples were used to make calibration model. Besides the choice of the standard samples, the choice of matrix correction approaches was a key in the accurate and robust prediction of element concentrations. Although a fundamental parameter approach, SPECTRO procedure, an extended

Table 4a. Comparison of calibration and prediction results of As, Cd and Pb using polarization EDXRF, ICP-AES/MS and certified values As (mg/g) Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

KX-4 KX-6B QT-12 QT-14 QT-45 QT-47 QT-57 Tu-1 Tu-2 Z-24-T GSD-02 GSR-07 GSR-15 KX-6A-1 QT-11 Z-31-T XPM-5-12

ECptn 62.9 1 359 679.7 1 787 139.1 226 298.5 442.2 2 517 157.4 6.6 7.6 21.4 1 085 1 496 60.3 817.6

Cd (mg/g)

Pb (mg/g)

FPCptn

Ref/Chm

ECptn

FPCptn

Ref/Chm

75.4 1 411 718.8 1 913 163.8 259.3 332.6 482.5 2 556 182.2 8.1 9.9 21.0 1 135 1 681 71 855.3

63.7 1 275 703 1 601 142 204 316 459 2 379 166 6.2 6.3 25.0 1 076 1 506 54.8 689.9

10.1 235.7 42.6 111.6 18.6 48.2 26.5 4.0 67.5 4.4 0.3 0.5 0.4 63.9 75 5.2 1.1

10.3 246.3 42.9 115.1 19.3 50.2 27.2 3.9 66.3 4.5 <0.5 <0.5 <0.5 64.6 76 5.3 0.9

10.1 222 44.3 120 20.3 52.9 29.6 5.13 67.5 4.72 0.1 0.1 0.1 63.0 69 5.49 1.92

ECptn

FPCptn

875.9 19 380 8 404 16 620 1 517 6 656 3 180 1 046 6 593 1 000 53.3 245.3 8 9 213 7 121 525.3 9 286

770.2 20 180 7 519 17 640 1 370 5 929 2 836 903.2 6 034 858.5 43 219.8 3.5 9 203 7 751 467.5 7 699

Ref/Chm 774 20 325 7 070 14 200 1 555 5 502 2 736 983 5 746 909 32 196 9.0 6 898 5 630 455 7 940

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137

ECptn, extended Compton scatter matrix correction methods; FPCptn, combination of fundamental parameters and the extended Compton scatter method; Ref/Chm, certified values for certified materials and the analytical results by ICP-MS or ICP-AES.


L. Luo et al. Table 4b. Comparison of calibration and prediction results of As, Cd and Pb in certified reference materials of mineral and ore by polarization EDXRF, ICP-AES/MS and Certified Values Element

As (mg/g)

Sample

ECptn a

GBW07235 (As) GBW07236 (As, Cd, Pb) GBW07240 (Cd, Pb) GBW07270 (Cd) GBW07277 (Cd) GBW07282 (As, Pb) GBW07287 (As, Cd, Pb)

FPCptn

80.6 28.9 1161 <28 11 5700 7 590 827

86.0 39.7 1263 <16 88 760 7 837 792

Cd (mg/g) Ref/Chm 85.1 43.2 1800 3.3 93 300 7 800 860

ECptn 4.1 2.5 24.2 1 503 3.4 <2.8 151

Pb (mg/g)

FPCptn 4.0 2.2 23.8 1 419 3.0 1.8 160

Ref/Chm 3.2 2.6 26.1 1 500 3.9 32.4 160

ECptn

FPCptn

Ref/Chm

37 140 4 753 2 051 577 144 26 980 32 390

31 600 4 527 2 053 576 97 27 250 31 700

41 700 6 100 2 600 990 160 28 200 33 800

The notes for ECptn, FPCptn and Ref/Chm were as shown in Table 4a. The element or elements in the parentheses mean element or they are used in calibration standard set.

a

Table 4c. Correlation relation between calibration (AsRefChem) and prediction data (ECptn) Intercept As Cd Pb

ECptn FPCptn ECptn FPCptn ECptn FPCptn

5.4357 1.4748 0.3338 1.1399 108.2587 2.2513

Standard error 5.5283 5.9773 0.3450 0.6356 70.3979 72.4422

Slope 1.0418 1.0897 1.0270 1.0692 1.0759 1.0876

Standard error 0.0097 0.0107 0.0087 0.0129 0.01803 0.01855

R2 0.9954 0.9947 0.9962 0.9951 0.9834 0.9828

Notes: Refer to Table 4a for explanation of ECptn, FPCptn and Ref/Chem.

138

Compton scattering model, Lucas-Tooth Price and several others were available in the Lab 2000 XRF spectrometer, a single model was not good enough to make an accurate and robust prediction for wide range of the concentrations of lead and arsenic. Thus, the fundamental parameter approach and the extended Compton scatter model in SPECTRO procedure were combined to make matrix corrections for the determination of Pb, As and Cd, as well as the other minor and trace elements in the contaminated soils. In addition, a single process of Compton method was used to build an analysis procedure for comparison in the work. Both of As Ka and Kb were first tried to be used as analytical lines in the research, and the results were compared. The calibration and prediction results did not show big differences between the data from both methods after the overlapping of Pb over As was calibrated. The results of As obtained using the extended Compton model were based on As Kb. The others were calculated using the combination matrix correction approach of the fundamental parameters with the extended Compton model and based on As Ka. The comparison of XRF data with the certified or ICP-AES/MS values were shown in Tables 4a and 4b. When the extended Compton calibration model in Lab 2000 was used to make correction of matrix effects, in about 72% cases, the prediction deviations of As by the extended Compton calibration model was lower than that by the combination approach of the fundamental parameters and the extended Compton calibration because most of samples contained rather low concentrations of As. For most of mineral and ore samples, the prediction deviations of As using the extended Compton calibration model were higher than the combination approach. For Cd, the predictability of both

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the extended Compton calibration model and the combined method was similar with each other. The prediction deviation of Cd using the extended Compton model was a little higher than the combined methods only in 54% cases. However, the combined method with the fundamental parameters produced more accurate results than the extended Compton model in about 80% cases for Pb. In general, the combined approach produced higher accuracy data and was more suitable for the complex system with a wide range of concentrations and the samples containing high concentrations of elements. By comparison of analytical data in the training and test data sets of the certified reference materials, no significant system error was found and the prediction deviation from the test set was not higher than that from the training set, and generally even lower than the training set for Pb, especially in the case of the high concentration of Pb, as shown in Table 4a. For example, the relative analytical deviations of Pb in KX-6A-1 and QT-11 in the training set were even higher than the relative prediction deviation of Pb in QT-14 that was on the top of the prediction errors in the test set. The prediction errors depend on the predicted concentration range of the elements as follows: (1) For the samples with the concentrations of three elements between detection limit and 10 mg/g, most of the relative deviations (RDs) of Pb from the certified values or ICPMS data were between 15% and 30%, the maximum RD was about 61% (sample GSR-15). For As, the RDs were between 20% and 30% and the maximum was 58% (GSR-7). The concentrations of Cd in all of the certified reference materials (GSD, GSR and GSS series) were below 5 mg/g. Among

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Determination of toxic elements in polluted soils by EDXRF them, most were below detection limit. For the samples with the concentrations of Cd between 1 and 10 mg/g, most of the RDs were between 5% and 25%. The maximum was 53% (XXPM-5-12). (2) For the samples with the concentrations between 10 and 100 mg/g, most RDs of Pb were from 5% to 15% and the maximum was 34% (GSD-2). For As, they were between 5% and 20% and the maximum was 30% (Z-31-T). For the samples with the Cd concentrations between 10 and 222 mg/g, RDs were below 10.9% and most from 2% to 10%. (3) For the sample with the concentrations between 100 and 1000 mg/g, most RDs of Pb were from 1% to 10%, and the maximum was 12% (GSR-07). Most RDs of As were from 5% to 20%, and the maximum was 27% (QT-47). (4) For the sample with Pb concentrations above 1000 mg/g (until 20 325 mg/g), most RDs of samples (10 in 16 samples) were below 10% with one about 15% and another with 24%. The RDs of EDXRF from three samples determined using ICP-AES/MS were between 33% and 38%. For the concentrations of As were above 1000 mg/g (until about 2400 mg/g), RDs of As were below 12%, except one with 19.4% (QT-14).

ECptn FPCptn

3000

Prediction Data

2500 2000 1500 1000 500 0 0

500

1000

1500

2000

2500

AsRefChem Figure 2. Correlation relation between calibration (AsRefChem) and prediction data for As concentrations.

The correlation relationship between calibration data and prediction results for As, Cd and Pb were shown in Table 4c and their R2 values are above 0.98. An example of such correlation between them for As element was shown in Fig. 2. That means their correlations between the calibration standards and prediction data are acceptable. Mineral effects Not all of certified materials of ore and minerals were used in the training set, but only parts of them were chosen as the calibration samples as shown by the elements in parentheses in Table 4b. The certified material GBW07269 was a galena mineral and was excluded in the training/calibration set because of its typical mineral characteristics and the values were quite outside the concentration range of elements in most samples. The analytical RDs of Pb and Cd for the sample were about 10% and 110%, respectively. For the other lead, arsenic and tungsten ores, the RDs were in the range of 20%–39%. The RDs of Pb in tin ore and lead–zinc ore were from 3% to 6%. Except GBW07240, the RD of As was below 10% for the mineral and ore materials. Except GBW07282, the RD of Cd was below 25%. The big errors were from mineral effects, as well as the lack of certified mineral materials. The tails from the ores GBW07240 (Cd: 26.1 mg/g) and GBW 07282 (Cd: 32.4 mg/g) were very different from the sediment and soil samples A-QT-12 (Cd: 44.3 mg/g) and GSD-12 (Cd: 4.0 mg/g), even if the concentrations of Cd were not very different with each other. The fitting of tails and Compton ratio was not able to make a compensative correction for the mineral effects to give the accurate results. The significant mineral effects resulted in the prediction error of Cd. The concentrations of Pb in QT-12 (soil sample), GBW07326 (lead ore) and KX-6A-1 (soil) were 7070, 6100 and 6898 mg/g, respectively. The big differences of the peak intensities were observed among three samples, and their peak intensities were separately 83 035, 60 016 and 59 238 at the energy of Pb Lb (12.62 keV). The intensities of GBW07326 and KX-6A-1 at the energy position were nearly as the same as each other, but their Compton peaks displayed quite difference with 110 152 and 66 127 counts, respectively. On the contrary, the intensities of QT-12 and KX-6A-1 represented a similar change trends in respect of Pb La, Pb Lb and Mo Compton scatter peak. An

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139

Figure 3. Comparison of the maximum, minimum and mean of concentrations of the elements in the soil samples with soil background values.


L. Luo et al. Table 5a. Comparison of analytical data of soil of PEDXRF and certified values (mg/g) for major, minor and trace elements Component Sample GSD-09 GSD-11 GSR-01 GSR-03 GSR-04 GSR-05 GSR-06 GSS-1 GSS-3 GSS-6 Component Sample GSD-09 GSD-11 GSR-01 GSR-03 GSR-04 GSR-05 GSR-06 GSS-1 GSS-3 GSS-6 Component Sample GSD-09 GSD-11 GSR-01 GSR-03 GSR-04 GSR-05 GSR-06 GSS-1 GSS-3 GSS-6 Component Sample GSD-09 GSD-11 GSR-01 GSR-03 GSR-04 GSR-05 GSR-06 GSS-1 GSS-3 GSS-6 Component Sample GSD-09 GSD-11 GSR-01 GSR-03 GSR-04 GSR-05 GSR-06 GSS-1

MgO XRF (%)

Al2O3 Certified (%)

XRF (%)

Certified (%)

SiO2 XRF (%)

2.83  0.29 0.61  0.27 0.42  0.25 6.92  0.20 0.06  0.08 2.08  0.28 5.80  0.39 1.91  0.29 0.71  0.17 0.09  0.08

P Certified (%)

2.39  0.03 11.87  0.37 10.58  0.05 65.44  1.01 64.89  0.06 0.62  0.03 10.93  0.31 10.37  0.05 76.81  1.65 76.3  0.1 0.420  0.016 13.75  0.11 13.40  0.04 79.26  0.39 72.83  0.06 7.77  0.08 15.58  0.21 13.83  0.07 46.88  0.35 44.64  0.06 0.082  0.011 3.49  0.18 3.52  0.05 95.16  0.54 90.4  0.1 2.01  0.03 18.97  0.35 18.82  0.08 61.29  0.80 59.2  0.1 5.19  0.06 5.66  0.24 5.03  0.05 17.47  0.44 15.60  0.04 1.81  0.04 15.47  0.38 14.18  0.08 63.81  0.74 62.60  0.09 0.580  0.016 12.98  0.21 12.24  0.05 78.34  0.43 74.72  0.11 0.34  0.02 25.10  0.27 21.23  0.09 49.43  0.43 56.93  0.11 K2O CaO Ti XRF (%) Certified (%) XRF (%) Certified (%) XRF Certified 2.07  0.07 1.99  0.03 5.51  0.08 5.35  0.04 5 201.8  162.3 5 514  70 3.11  0.07 3.28  0.03 0.45  0.02 0.47  0.01 1 882.2  88.4 2 098  40 5.03  0.06 5.01  0.03 1.55  0.05 1.55  0.02 1 506.2  81.6 1 738  30 2.26  0.07 2.32  0.03 8.17  0.14 8.81  0.05 13 688.0  498.9 14 144  200 0.71  0.03 0.650  0.014 0.32  0.04 0.30  0.02 1 544.4  80.4 1 558  40 4.03  0.11 4.16  0.05 0.59  0.04 0.60  0.02 3 813.0  217.0 3 956  60 0.72  0.05 0.78  0.02 34.94  0.40 35.67  0.14 1 790.4  84.8 1 978  40 2.53  0.05 2.59  0.02 1.67  0.06 1.72  0.03 4 692.8  152.2 4 855  70 3.08  0.05 3.04  0.02 1.26  0.04 1.27  0.02 2 025.4  80.3 2 218  40 1.68  0.06 1.70  0.03 0.22  0.02 0.22  0.01 4 449.2  114.0 4 375  50 TFe2O3 V Cr XRF (%) Certified (%) XRF Certified XRF Certified 4.98  0.09 4.86  0.04 85.8  6.5 97  2 90.7  10.1 85.0  2.7 4.31  0.08 4.39  0.04 36.4  5.5 46.8  1.2 35.8  3.9 40  1 2.08  0.06 2.14  0.02 22.0  2.9 24  1 4.4  2.5 5.0  0.9 13.33  0.20 13.40  0.09 160.4  15.5 167  5 119.6  18.6 134  4 3.28  0.06 3.22  0.03 27.2  4.2 33.0  1.2 16.1  2.6 20  1 7.73  0.10 7.60  0.04 79.3  5.4 87  2 98.7  16.3 99  2 2.54  0.08 2.52  0.03 27.8  10.6 36  3 30.9  6.9 32  2 5.33  0.08 5.19  0.04 75.3  5.1 86  2 62.9  3.9 62.0  1.6 2.06  0.04 2.00  0.02 29.8  3.9 36.5  1.1 34.7  1.5 32.0  0.3 8.31  0.13 8.09  0.06 115.9  8.8 130  3 73.2  4.8 75  2 Cu Zn Ga XRF Certified XRF Certified XRF Certified 35.0  2.1 32.1  0.6 74.4  3.2 78.0  1.4 14.4  1.8 14.0  0.4 72.6  3.4 78.6  1.1 379.7  13.9 373  6 18.7  1.7 18.5  0.6 2.8  2.4 3.2  0.4 23.7  2.4 28  1 20.0  2.7 19  1 55.9  5.6 48.6  1.1 138.2  8.7 150  4 24.6  1.9 24.8  0.6 17.7  1.6 19.0  0.6 15.6  2.5 20  1 5.6  1.3 5.3  0.5 45.1  3.0 42  1 52.0  4.3 55  2 25.1  3.2 25.6  1.6 23.4  2.9 23.4  0.8 43.2  6.6 52  2 7.4  1.6 7.1  0.5 20.7  1.6 21  0.6 696.6  23.4 680  11 19.8  2.1 19.3  0.8 11.7  1.7 11.4 27.1  2.8 31.4  1.1 14.6  1.6 13.7  0.6 419.9  13.5 390  6 95.6  5.5 96.6  2.4 31.8  3.9 29.5  1.8 Rb Sr Y XRF Certified XRF Certified XRF Certified 81.2  3.5 80.0  1.5 163.5  8.3 166  4 24.0  2.2 27  1 400.4  12.7 408  6 26.7  2.9 29.0  1.4 37.3  4.5 42.7  2.1 478.5  20.2 466  10 106.1  6.1 106  3 60.5  4.1 62  2 37.6  4.0 37  2 1 104.6  60.1 1 100  30 19.0  2.5 22  1 28.1  2.7 29.0  1.3 56.6  4.0 58  2 19.1  2.5 21.5  1.1 206.4  10.1 205  5 89.1  8.1 90  4 23.3  2.2 26  1 30.9  4.1 32  2 898.8  56.1 913  28 7.8  2.0 9.1  0.9 140.5  6.1 140  3 155.2  6.0 155  3 22.2  2.7 25  1

XRF 781.7  57.7 262.2  132.7 485.3  33.9 4 160.2  173.2 986.6  68.9 640.8  97.7 243.6  79.3 887.4  55.7 368.2  51.4 375.1  79.4 Mn XRF 609.5  18.7 2 234.2  66.7 426.6  17.2 1 289.2  52.3 143.9  16.5 174.3  11.5 416.6  26.4 1 727.2  49.1 304.2  10.5 1 516.0  65.9 Ni XRF 31.9  2.0 16.2  3.4 4.7  1.7 131.5  6.5 15.9  1.5 39.3  4.9 12.4  2.3 21.2  3.4 12.2  1.9 53.0  2.3 As XRF 8.2  1.0 190.3  12.5 2.5  0.8 — 7.8  1.6 0.4  0.5 2.3  1.0 36.2  3.7 4.7  0.8 221.8  14.1 Zr XRF 341.5  30.0 137.6  17.0 146.1  10.2 264.1  20.4 195.0  12.0 93.2  10.2 54.0  16.3 240.4  13.0

Certified 655  11 258  13 406  9 4 149  60 961  21 699  18 227  17 742  13 319  9 306  15 Certified 620  8 2 478  33 465  7 1 317  24 155  8 155  5 465  12 1 781  24 310  5 1 471  32 Certified 32.3  0.8 14.4  0.4 2.3  0.3 140  3 16.6  0.4 37  1 17.8  0.8 20.4  0.6 12.2  0.4 53  1 Certified 8.4  0.4 188  6 2.10  0.16 0.79  0.23 9.1  0.7 1.4  0.2 4.7  0.4 33.5  1.7 4.4  0.3 220  7 Certified 370  10 153  6 167  5 277  10 214  5 96  5 62  8 245  6

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Determination of toxic elements in polluted soils by EDXRF

Table 5a. (Continued) Component

MgO

Sample GSS-3 GSS-6 Component Sample GSD-09 GSD-11 GSR-01 GSR-03 GSR-04 GSR-05 GSR-06 GSS-1 GSS-3 GSS-6 Component Sample GSD-09 GSD-11 GSR-01 GSR-03 GSR-04 GSR-05 GSR-06 GSS-1 GSS-3 GSS-6

Al2O3

XRF (%)

Certified (%)

86.2  4.1 239.1  8.1

85  2 237  4 Nb

XRF 14.1  1.8 22.4  2.2 38.5  2.9 66.3  8.2 4.0  1.1 11.6  1.9 5.8  2.2 14.0  1.7 7.0  1.5 23.4  2.0

Certified 17.7  0.8 25  1 40.0  1.4 68  4 5.9  0.5 14.3  0.9 6.6  1.1 16.6  0.7 9.3  0.7 27  1 Ce

XRF 67.0  10.5 51.4  5.3 95.1  9.4 97.5  13.0 44.4  8.2 100.0  12.4 24.7  7.5 61.3  4.9 34.7  28.3 63.4  7.0

Certified 78  4 58  2 108  4 105  5 48  2 109  5 25.4  1.8 70  2 39  13 66  3

XRF (%)

SiO2

Certified (%)

380.7  16.1 37.7  4.1 Mo XRF 0.6  0.4 5.2  0.9 2.8  0.6 2.0  0.7 0.5  0.2 0.5  0.1 — 0.8  0.8 — 16.0  1.7 Pb XRF 20.8  2.4 600.8  21.2 31.6  3.0 1.7  2.6 4.7  0.9 5.3  2.0 14.5  4.1 92.7  6.3 25.1  2.7 328.8  13.4

XRF (%)

380  8 39  2

12.7  1.5 16.6  1.9

P Certified (%)

XRF

Certified

15.0  0.6 18.8  0.8

223.7  14.7 198.7  15.6

246  7 220  7

Sn Certified XRF 0.64  0.05 2.7  0.8 5.9  0.3 354.9  61.7 3.5  0.1 12.1  2.2 2.6  0.1 2.3  0.7 0.76  0.08 1.2  0.3 0.35  0.05 2.1  1.2 0.38  0.03 1.5  0.7 1.40  0.06 6.6  1.6 0.30  0.04 2.5  1.4 18.0  0.8 72.1  8.7

Ba Certified 2.6  0.2 370  29 13  1 2.0  0.3 1.1  0.1 2.0  0.2 0.98  0.28 6.1  0.4 2.5  0.2 72.4  4.1

XRF 437.3  20.3 243.6  19.2 321.2  27.3 561.7  25.7 128.9  16.0 434.5  34.1 133.6  12.9 599.5  31.9 1 206.4  67.7 107.7  13.6

Th Certified 23  1 636  10 31.0  1.3 7.2  1.2 7.6  0.4 8.7  0.9 18.3  1.4 98  3 26.0  1.3 314  6

XRF 10.0  1.0 18.5  2.5 50.3  3.0 4.0  2.6 4.3  1.3 10.4  1.9 4.2  2.8 10.2  1.5 4.6  2.5 19.5  2.7

Certified 430  8 260  8 343  13 526  12 143  7 450  16 120  6 590  15 1 210  30 118  6 Cd

Certified 12.4  0.4 23.3  0.7 54.0  1.3 6.0  0.5 7.0  0.2 12.8  0.6 4.1  0.3 11.6  0.4 6.0  0.3 23  1

XRF 0.4  0.0 2.8  1.0 — 0.5  0.0 — — — 4.7  0.7 — —

Certified 0.26  0.02 2.30  0.07 0.032  0.009 0.07  0.01 0.060  0.011 0.03  0.01 0.069  0.014 4.3  0.2 02 0.130  0.016

Table 5b. Root mean square and relative RMS obtained using the PEDXRF for the certified materials MgO RMS Average (mg/g) Relative RMS (%) RMS Average (mg/g) Relative RMS (%)

Al2O3

SiO2

0.52 1.31 4.14 2.12(%) 12.32(%) 61.81(%) 24.6 10.7 6.7 Ga As Rb 0.87 2.50 5.17 18 47 172 4.9 5.3 3.0

P

K2O

CaO

96.83 0.10 0.41 872 2.55(%) 5.60(%) 11.1 4.0 7.3 Sr Y Zr 5.29 2.95 17.75 304 1.7

27 11.0

205 8.7

Ti 376.80 4243 8.9 Nb 2.51 23 10.9

Mn

TFe2O3

90.04 0.16 922 5.34(%) 9.8 3.0 Mo Sn 0.82 5.05 3 24.2

47 10.7

V

Cr

Ni

10.70 74 14.4 Ba 28.34

7.19 58 12.3 Ce 7.97

3.45 35 10.0 Pb 13.72

419 6.8

71 117 11.3 11.7

Cu

Zn

10.54 8.45 67 156 15.7 5.4 Th Cd 2.78 0.23 16 17.3

1 31.5

RRMS = (RMS/average concentration)  100.

X-Ray Spectrom. 2012, 41, 133–143

were 7519, 4527 and 9203, still showing huge variation. The matrix effects with the significant mineral effects were not corrected very well using the hybrid correction model because the mineral effects were not able to be corrected by any models yet. In addition, it was observed that the ratio of Pb Lb and the corresponding Compton peak were 0.95 0.54 and 1.04. If using the extended Compton scatter model to compensate matrix and particle changes, then the predictive values were 8404, 4753 and 9213. The mineral effects in those samples still had great impacts on the analytical accuracy and could not be removed simply by the matrix correction. In general, the significant deviations of Pb from the standard values arose from the mineral effects. Another reason of the

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exception and interesting fact was that the intensity at Pb Lb of the sample GBW07326, a certified material of lead ore, was higher than that at Pb La, being a reverse situation. Among three samples, its intensity at Pb La was lowest, but its Compton scatter peak was highest. The result was that the intensity ratio of Pb La to the Compton scatter peak was much lower than an expecting ratio with such level of concentration of Pb. Thus, the prediction concentration of Pb in the sample was much lower than the certified value. The reason might have been from an interference line. However, no such interference line from known elements in the certified material could have been found. By the SPECTRO procedure with fundamental parameters and Compton model, the prediction concentrations


L. Luo et al. big analytical error was that the lack of certified reference materials with high concentrations of Pb, which is still difficult to be overcome at this stage. The deviation of Cd was mainly from the low concentration in the samples, which was close to the limit of the detection of Cd.

Comparison of analytical data of major and other minor elements As an evaluation tool in ecological and environmental investigation, the polarized EDXRF was not only used in the determination of As, Cd and Pb but also applied in analyzing other major, minor and trace elements in the contaminated soil, which was the advantage of such EDXRF. The analytical data were the prediction results by the method, as shown in Tables 5a and 5b. The samples in the table were not included in the training set but just as the test set. The absolute differences between mean measured values and certified values of Al2O3 and SiO2 in the samples, in most cases, were not less than the expanded uncertainty of differences between result and certified value. That means that there were significant difference between the measurement results and the certified values of Al2O3 and SiO2 in the samples. The data of V, Y, Nb and Cd show that there was significant difference between the measurement results and the certified values of those elements. The result of Cd was because the concentrations of Cd in the reference materials were lower than the detection limits and not detectable using the technique. In fact, there were only two of samples in 10 certified reference materials in which the concentrations of Cd were above detection limit. However, for the contaminated soils, the concentrations of Cd were above the detection limit, as shown in Table 4a. An interesting fact is that only Ga, Rb, Sr and Sn show the perfect results of

no significant difference between the measurement results and the certified values for the whole 10 certified reference materials. For most other elements in those samples, parts of samples show significant differences; other parts did not. Their root mean square (RMS) and relative RMS were shown in Table 5b. In general, the analytical results show that these data were able to be used in making an evaluation in ecological and environmental investigation.

Distribution and characteristics of toxic elements in landscape soils The goal of the determination of toxic elements is to investigate the impacts of the toxic pollution on ecology, environment and human health. Obviously, a major pollution source is anthropogenic activity of mining. In order to investigate the ecological and environmental situations around the Pb-Zn mine, over 500 samples of water, soil, plants, aero particles, animals and human blood and hair were taken from landscape, farmlands, residential yards, traffic and industrial areas near the lead–zinc mine at Mount Qixia. Among them were 205 soil samples. Total of the soil samples taken from the areas were analyzed using the polarization energy XRF spectrometry. As the first step, the pollution of the toxic elements in the landscape and the hillsides on the top of the Pb-Zn mine was investigated. Twenty-one samples were taken from the surface soils in the landscape and the hillsides. The high concentrations of Pb, As, Cd and Zn were found, as shown in Table 6. The data represented four groups of situations. Samples from Tu-1 to 5 were taken from Qixia Park. The soil samples at the pools on the hillside within the Park (Tu-1, 2, 4, 5) displayed the significant high concentrations of Pb, As, Cd and Zn. The concentration of Cu was high in the sample Tu-4. When

Table 6. Average concentrations of soil samples taken from different sites Element

Pb (mg/g)

As (mg/g)

Cd (mg/g)

Cr (mg/g)

Cu (mg/g)

Zn (mg/g)

81.5  6.5 99.5  11.8 60.5  3.9 76.4  2.5 64.3  2.5 80.9  3.9 81.3  2.0 94.9  2.7 76.3  4.0 112.6  2.0 59.4  8.3 82.7  4.2 147.1  5.2 28.9  2.8 78.9  1.7 64.8  6.0 68.7  3.9 78.3  0.6 70.2  12.5 80.4  4.8 55.9  0.8

84.1  1.9 359.9  12.3 18.8  0.9 23.1  23.1 288.8  12.1 32.1  2.8 38.5  3.2 36.1  1.4 38.0  1.6 32.6  1.1 35.7  2.5 42.3  1.2 50.3  3.1 14.6  1.3 29.5  1.7 29.8  1.0 29.9  3.5 32.3  2.1 27.2  2.1 42.4  2.7 32.7  1.7

735.4  21.7 9 530.7  140.8 90.6  1.7 2 340.7  79.2 731.8  5.9 76.4  4.2 95.9  4.1 100.8  4.3 85.2  0.3 452.8  6.1 121.7  5.8 100.8  4.2 2 323.3  14.6 51.2  5.5 75.6  3.2 115.7  7.1 102.2  4.4 135.5  4.6 84.5  1.6 177.2  5.0 73.6  2.8

Sample

142

T-1 T-2 T-3 T-4 T-5 L-49 L-50 L-51 L-52 L-53 L-54 L-55 L-56 CW-S-1 DXCPT-1 L-04 L-05 L-06A L-06B L-09 L-48

905.3  14.9 6 088.7  53.9 40.7  1.2 15 003.0  122.5 4 499.0  212.6 32.2  2.5 58.7  4.0 45.2  1.0 197.1  3.6 43.2  0.8 116.5  2.2 45.1  1.7 1 240.0  31.0 26.7  2.6 24.0  0.8 38.5  0.1 36.0  0.4 39.6  2.2 31.5  1.1 75.2  1.5 186.2  4.5

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486.2  10.4 2 586.3  26.8 93.1  1.5 2 388.3  42.6 513.9  17.8 18.7  1.5 20.9  3.0 27.6  0.7 29.0  1.7 131.1  2.6 33.8  1.6 24.0  1.4 400.7  10.5 9.3  1.4 12.2  1.5 10.1  0.9 9.5  0.1 15.1  0.7 12.0  0.7 20.5  0.9 33.4  0.4

4.8  0.6 69.7  1.0 1.2  0.9 16.7  0.9 5.3  1.4 0.7  0.6 1.1  0.3 0.6 0.6  0.4 1.8  1.2 2.0  0.6 0.5  0.4 15.0  1.1 — 0.4 0.5  0.2 — 0.5  0.1 — 1.0  0.4 0.7  0.2

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X-Ray Spectrom. 2012, 41, 133–143


Determination of toxic elements in polluted soils by EDXRF being taken, the soil samples were moist because they were near the pools with water. High concentrations of such toxic elements may arise from the weathering exposure of original mineral and rocks on the surface or near the surface, slag and waste water. In contrast, only high contents of As and Cd were found in the soil samples taken from the opposite area without pools, as shown by the sample Tu-3 in Table 6. In both situation, all of the soils (Tu-1~5) on the hillsides show very high concentrations of As and Cd. They were beyond the Grade III in the Chinese environmental quality standard for soils GB15618-1995 (the critical values of Pb, As, Cd and Zn in the standard are 500, 40, 1.0 and 500 mg/g, respectively). In this standard, there are five grades in total. Grade I represents Natural Background, Grade II means no pollution and no harm to plants in farmland, and Grade III indicates that the soil can only be used for forest or plants except vegetables. The concentrations of Pb, As, Cd and Zn in the soil in the landscape area near the mine were beyond the limited values. The same situation occurred in the sample L-53, which is located within the Qixia Temple area at the same side as the sample Tu-3. The concentration of As was high with a little higher concentration of Cd, but the concentrations of Pb and Zn were not so significantly high. A very significant and impacting fact was that very high concentrations of Pb, As, Cd and Zn were found in the soil sample taken near an air uptake from the mine, just located on the hillsides in the park and above the pools, which was similar to the soils at the pools on the hillside and higher than the Class III. The reason should be from the aerosol pollution from the mine vent. As the landscape is just located on the hills, valleys and lakes with the lead–zinc mine, in general, the pollution should arise out of the surface weathering of original mineral and rocks, as well as the sediments and waste water soaked out from slag by fountains, rains and mining water. The other soil samples were taken from the areas far away from the mine and used for comparison purpose with the area contaminated with the Pb-Zn mine. The concentrations of Pb, As, Cd and Zn were not high in the reference area. After excluding eight soil samples with the significant high concentrations of Pb, As, Cd and Zn from Table 6, the maximum, minimum and mean values of the element concentrations in 13 soil samples were calculated and compared with the soil background values.[13,14] Except As, as shown in Fig. 3, the maxima and minima of Pb, Cd, Cr, Ni, Cu and Zn were within the soil background ranges. The maximum of As was higher than maximum of the soil backgrounds, which may imply that the potential high content of As exists in this area. Most of the elements were in normal range of background values. The background values based on our investigation were compared with the published data. And the significant high concentrations of Pb, As and Cd were found near the Pb-Zn mine area. Both data and facts reveal that the developed analytical method and data are reliable to be used in the evaluation of ecological and environmental investigation.

Conclusions

X-Ray Spectrom. 2012, 41, 133–143

Acknowledgements This work was supported by National Natural Science Foundation of China (20775018) and National High Tech R&D Program (2007AA06Z124). Authors gratefully acknowledge the experimental and instrumental supports by Professor A. Ji and the PANalytical in Shanghai, China.

References [1] K. Vig, M. Megharaj, N. Sethunathan, R. Naidu. Adv. Environ. Res. 2003, 8, 121–135. [2] D. Caussy. Ecotoxicol. Environ. Saf. 2003, 56, 164–173. [3] H. Fakih, M. Davranche, A. Dia, B. Nowack, G. Norin, P. Petitjean, X. Chatellier, G. Gruau. Chem. Geol. 2009, 259, 290–303. [4] M. C. Navarro, C. Pérez-Sirvent, M. J. Martínez-Sánchez, J. Vidal, J. Marimón. Chemosphere 2006, 63, 484–489. [5] H. Matsunami, K. Matsuda, S.- Yamasaki, K. Kimura, Y. Ogawa, Y. Miura, I. Yamaji, N. Tsuchiya. Soil Sci. Plant Nutr. 2010, 56, 530–540. [6] O. Kodirov, N. Shukurov. Acta Geol. Sin. English Edition 2009, 83, 985–990. [7] U. Cevik, B. Koz, Y. Makarovska. X-Ray Spectrom. 2010, 39, 202–207. [8] M. Tuomela, K. T. Steffen, E. Kerko, H. Hartikainen, M. Hofrichter, A. Hatakka. FEMS Microbiol. Ecol. 2005, 53, 179–186. [9] Y. Shibata, J. Suyama, M. Kitano, T. Nakamura. X-Ray Spectrom. 2009, 38, 410–416. [10] J. Ostachowicz, B. Ostachowicz, B. Holynska, W. Baran. X-Ray Spectrom. 1995, 24, 81–83. [11] E. Marguí, R. Padilla, M. Hidalgo, I. Queralt, R. V. Grieken. X-Ray Spectrom. 2006, 35(3), 169–177. [12] J. Heckel, M. Brumme, A. Weinert, K. Irmer. X-Ray Spectrom. 1991, 20, 287–292. [13] F. Wei, J. Chen, Y. Wu, C. Zheng. Chin. J. Environ Sci. 1991, 12(4), 12–19. [14] Chinese Environmental Agency, Background Data of Elements in Chinese Soils, Chinese Environemnt Sciences Press, Beijing, 1990, pp 330–378.

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Polarization energy XRF spectrometry is a useful tool for determining high concentrations of toxic elements in soils and has been successfully applied in the determination of the toxic elements and other major, minor and trace elements in the

polluted area near a Pb-Zn mine. The analytical data produced by the PEDXRF method is accurate and precious enough to be used in evaluating the effects of Pb, As and Cd elements in the contaminated soils on ecology and environments. The second target KBr in such an EDXRF spectrometer can be used to reduce the overlap effect of Pb La over As Ka. However, such overlap is too close and too heavy to be neglected, especially in the case of high concentrations of lead, such as in the vicinity of soils contaminated with Pb. The lack of certified reference materials with high concentrations of Pb, As and Cd and mineral effect have great impacts on improving prediction accuracy and precision. One choice of compensating the shortages was to use chemical methods and ICP-AES/MS to provide the compared data of elemental concentrations in the training set of data. However, more certified reference materials with wider ranges of concentrations of such toxic elements are demanded strongly when XRF spectrometry is used in the determination of toxic elements in pollution areas. On the basis of the geochemical investigations near the lead– zinc mine in the south of China and the data of toxic elements in the soils obtained using the developed XRF method, it was confirmed that the landscape soils in the vicinity of the mine has been polluted seriously by lead, arsenic and cadmium. The contaminated soil, as well as lead ore itself, would become a source of new pollution when dust or aerosols are produced and spread around the area by winds. Such pollution would have harmful effects on the residents and the farm activities in the area. Our biogeochemistry investigation has revealed more details and will be reported in the succeeding papers.


Research article Received: 14 July 2010

Revised: 11 December 2011

Accepted: 20 December 2011

Published online in Wiley Online Library: 25 January 2012

(wileyonlinelibrary.com) DOI 10.1002/xrs.2374

Levels and sources of heavy metal contamination in road dust in selected major highways of Accra, Ghana Sampson Manukure Atiemo, Francis Gorman Ofosu,* Innocent Joy Kwame Aboh and Osborne Cruickshank Oppon Environmental studies have revealed significant contributions of vehicular exhaust emissions to high pollution levels in urban dwellings. The levels and sources of heavy metal contaminations of some major roads in Accra have been investigated in this work. Street dust samples collected from four major roads in Accra (Mallam Junction-Weija road, John Teye-Pokuase road, Tema Motorway and Tetteh Quarshie Interchange in Accra) were analysed for their elemental concentrations using energy-dispersive X-ray fluorescence. Twenty elements were identified: K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Zr and Pb. Significant concentration levels were obtained for K, Ca, Ti, V, Cr, Mn, Fe, Cu, Zn, Br, Rb, Sr, Y, Zr and Pb in all the samples and were used for the source identification. Enrichment factors and principal component analysis were used to verify the anthropogenic contribution to road dust. Results obtained for the enrichment factors showed moderate enrichment for V, Cr and Cu, while Zn, Br, Zr and Pb were significantly enriched. Principal component analysis identified four sources and their contributions to the elemental contents in the road dust. Natural crust, brake wear, tyre wear and vehicle exhaust emission were the four sources identified. The contribution of vehicular non-exhaust emissions to heavy metal contamination in the road dust was found to be greater than that of exhaust emissions. Copyright © 2012 John Wiley & Sons, Ltd.

Introduction

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* Correspondence to: Francis Gorman Ofosu, National Nuclear Research Institute, Ghana Atomic Energy Commission, P.O. Box LG80, Legon, Accra, Ghana.E-mail: fgofosu@gmail.com National Nuclear Research Institute, Ghana Atomic Energy Commission, Legon, Accra, Ghana

Copyright © 2012 John Wiley & Sons, Ltd.

105

Particulate emissions from paved road dust are a complex mixture derived from different sources. Airborne particles originating from road dust consist of vehicle exhaust (fossil fuel combustion) and non-exhaust emissions created by means of tyre, brake and general vehicle wear processes.[1,2] In addition, particles that settle on the road from both nearby and distant sources become part of the mixture and can be subsequently resuspended by vehicles or wind. A wide variety of human activities in the urban environment generate particulate matter. These may include power stations, water-transported material, dry and wet atmospheric deposition, biological inputs, road surface wear and road paint degradation, among others.[3] Metals, even at trace levels, are an important factor affecting human health.[4] Vehicular traffic, industry and weathered materials are the main factors that influence the levels of trace elements in dust samples in most cities of the world.[5] Contaminated dust may threaten health since it can easily be remobilized by wind from the road into air, vehicles and residences.[1] They easily get into the human system by means of ingestion, inhalation and skin contact. Road dust can easily be carried by storm water runoff, which results in massive transport of contaminants to environmentally critical media such as water bodies (water reservoirs, rivers and seas). Lead (Pb) is one of the hazardous metals that affect biota in the ecosystem.[6] The main source of Pb pollution was leaded gasoline in the form of tetraethyl lead used as an octane enhancer until it was phased out by the Government of Ghana in

2004. Road dust has been shown to contain high amounts of Pb, which mainly originate from petroleum combustion. Tyre abrasions contribute to most of the zinc (Zn) pollution present in the road dust. Wearing of the brake linings and other leakages, abrasions and spills from the vehicles are the major sources of pollution of heavy metals, especially copper.[7] In recent years, many environmental studies have revealed that atmospheric pollution due to automobile exhaust emission is a significant source of heavy metal contamination in urbanized areas. The multi-element source profile for road dust for an area can be of help to assess the contribution from road dust to air pollutants with the use of factor analysis. Principal component analysis (PCA) was the factor analysis technique used in this work for the source identification. This technique is by far the most frequently used in source apportionment modelling. It is estimated that from 2006 to 2007, up to about 50% of the new publications used PCA.[8] Such statistical methods are commonly used for identification of the relative importance of different sources.[9–11] Input data for source assignments are usually chemical species analysed by any of the well-known analytical methods. The model assumes that the amount of a given element is a linear sum of a well-defined number of independent sources


S. M. Atiemo et al.

xij ¼

n X

aik skj

k¼1

where xij is the concentration of the ith element in the jth sample, aik is the average concentration of the ith element in the particles from the kth source, and skj is the mass of particles from the kth source contributing to the jth sample. This equation can be expressed in matrix form to include all of the m elements in the n samples X ¼AF where X is a matrix of sample vectors; A is the matrix of loading vectors related to the source compositions; and F is the matrix of scores that are related to the contribution of that source type to the variance of that particular measured variable. Enrichment factor (EF) can be used to differentiate between metals originating from human activities and those of natural provenances, and also to assess the degree of anthropogenic influence. The EF of an element X(EFX) in the sample with respect to natural abundance is calculated according to the following formula:     EFX ¼ XS =ESðrefÞ = XC =ECðrefÞ where EFX is the enrichment factor for the element X; XS is the concentration of element of interest in sample; ES(ref) is the concentration of the reference element used for normalization in the sample; XC is the concentration of the element in the crust; EC(ref) is the concentration of the reference element used for normalization in the crust; and E(ref) is the reference element for normalization.[12,13] The crustal elemental concentration used in this study is the average continental crust data.[14,15] Studies by Kylander et al.[16] on street dust in Ghana only looked at Pt and Pd to ascertain the contributions of catalytic converters and leaded fuel to environmental pollution. Platinum is widely used in catalytic converters as the most active catalyst and is used with Pd and Rh as oxidation and reduction catalysts, respectively. This study was carried out in the era when Pb was used as an octane enhancer in gasoline. The phasing out of Pb in Ghana and the introduction of Mn-based additives have necessitated a comprehensive work on heavy metal pollution from road dust to ascertain the levels of Pb and Mn build-up in road dust. The objective of the study was therefore to quantify the heavy metal concentration in road dust of grain sizes less than 100 mm (PM100) and to use factor analysis to apportion source contributions to heavy metals in road dust.

Materials and methods Site selection and sample collection

Sample preparation and analysis Samples collected from each spot (on each sampling day) were homogenously mixed to form a composite sample. The samples were sieved using a mesh (metric test sieve Bs 410, WS Tyler) with a geometric diameter of 100 mm. The analyses were restricted to the size fractions below 100 mm because particles of such sizes[19] are easily resuspended and are classified as aerodynamic. Also, particulate sizes of vehicular emissions mostly fall below the 100-mm aerodynamic diameter. Sieving was done on a mechanical shaker (Retsch AS 200) for 30 min at an amplitude of 10 mm/g. Ten grams of the samples was made into thick sample pellets of 2.5-cm diameter, using a hydraulic press (Specac) with an applied load of 10 metric tons. The elemental concentrations were determined using energy-dispersive X-ray fluorescence with a secondary target excitation arrangement. Energy-dispersive X-ray fluorescence was preferred because it is a rapid and non-destructive method for the analysis of trace and major elements in street dust samples. A 3000-W maximum power ITAL Compact 3 K5 X-ray generator and a Canberra Si(Li) detector of 165-eV resolution for 5.9-KeV X-ray energy were used. A Mo tube with a Mo secondary target at 45 to both the incident and emergent beams was used for the irradiation of the soil samples. The primary and secondary beams were also at 45 to the sample target. Sample pellets were irradiated with a tube X-ray of 40 kV and 20-mA power. The data acquisition and analysis were done using Maestro-32 (MCA) software and the fundamental parameters approach in the QXAS (Quantitative X-Ray Analysis Software)[20] package, respectively. Standards were irradiated to determine instrumental constant for fluorescence and scatter to establish the calibration of the system for the quantitative program. The IAEA Soil-7 was the standard reference material used for the validation of the analytical results.

Results and discussion

106

Four sites (Fig. 1) were selected because of their high vehicular density throughout the day as they are all major roads leading to Accra. The John Teye-Pokuase is the main link road between Accra and Kumasi (the second largest city of Ghana). This road is characterized by high vehicular density with few human activities except for few artisanal workshops situated along the road. The Mallam Junction-Weija road serves as the link between Accra and the Western corridor of the country. This road is also characterized by both light and heavy-duty vehicles for most part of the day. The Tema Motorway is noted for high-speed vehicular

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movements with toll booths at both ends, which serve as the only means of slowing down vehicular movements. The Tetteh Quarshie Interchange, which is situated at the Accra end of the Tema Motorway, is noted for heavy vehicular traffic during morning and evening rush hours. The samples were collected with soft-touch brushes and plastic dust pans and kept in pre-cleaned self-sealed polythene bags. At least 50 g of road dust samples was collected from each site at the opposite ends of the road. New brushes and dust pans were used for each sampling site to avoid cross-contamination.[17,18] The samples were collected between October 2008 and March 2009 at 6-day intervals. A minimum of 15 samples were collected from each site for the period of sampling. Sampling was not done on rainy days. The samples were collected between 8.30 pm and 10.30 pm on each sampling day because of heavy traffic and high-speed vehicles on the highways during daytime.

Elemental concentrations The concentrations of elements obtained from the elemental analysis are shown in Table 1. The results show high concentrations of heavy metals in road dust from the selected sites. As seen from Table 1, the highest concentration of Pb was found on the Tema Motorway followed by Tetteh Quarshie Interchange, which is the motorway extension. The results also show that the highest concentration of manganese (Mn), 549 mg/kg, was recorded at the Tetteh Quarshie Interchange. The interchange is characterized by

Copyright © 2012 John Wiley & Sons, Ltd.

X-Ray Spectrom. 2012, 41, 105–110


Heavy metal contamination in road dust in Accra, Ghana ROAD MAP OF THE STUDY AREA

Figure 1. A map showing the selected roads for sampling.

Table 1. Mean concentration of elements in road dust from the sampling site (mg/kg) Element JT Mean SD Min Max MJ Mean SD Min Max TM Mean SD Min Max TQ Mean SD Min Max

K

Ca

Ti

10 900 2 710 5 220 15 400

9 570 2 350 6 530 1 340

2 340 548 1 320 3 190

10 900 12 100 3 630 4 990 4 250 5 000 1 560 2 280

2 430 1 440 894 6 030

13 500 2 360 5 900 10 900 4 080 7 010 2 380 4 990 11 300 25 600 2 820 9 520 5 890 1 380 1 590 4 480

V

Cr

Mn

Fe

Cu

Zn

Br

Rb

Sr

Y

Zr

Pb

120 34.0 83.0 180

262 71.8 170 384

22 400 2 810 1 790 2 580

34.5 9.4 24.8 55.9

132.5 55.9 81.1 260.4

BDL — — BDL

21.4 2.0 18.3 25.5

102.3 16.2 81.7 121.5

10.9 1.6 7.0 13.2

1 040 359 391 16 400

37.1 6.0 26.7 45.7

281 120 201 4 948

159 95.4 85.8 344

304 191 131 735

28 900 19 800 2 920 6 680

71.7 32.5 20.9 188.7

255 93.0 70.0 994

2.7 1.4 1.6 12.8

44.2 298 15.9 94.1 17.5 101.4 141 1 060

17.7 8.0 6.30 51.2

1 700 747 405 5 270

94.1 39.6 25.1 355

4 150 1 730 2 120 9 560

392 154 223 673

175 97.8 97.3 440

397 118 259 657

40 900 21 600 4 020 11 200

51.9 263.8 24.0 114.7 25.8 136.2 174.6 1 100

13.0 5.1 1.7 48.3

34.7 15.0 10.0 194

220.8 120.7 86.7 880.2

12.3 6.5 60.0 70.2

1 020 696 356 6 550

167 70.0 30.1 815

4 140 2 350 1.0 9 590

292 119 176 455

210 98.8 92.5 396

549 345 186 1 270

52 500 30 000 2 340 113 000

124.6 656 89.8 344 40.8 205 268.4 1 840

6.7 3.5 1.8 22.3

56.4 358.2 28.5 198.7 17.8 84.4 160.3 1 200

30.8 13.1 9.5 74.4

1 930 687 563 5 620

150 33.4 43.2 385

196 35.5 171 221

X-Ray Spectrom. 2012, 41, 105–110

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107

JT, John Teye-Pokuase road; MJ, Mallam Junction Weija road; TQ, Tetteh Quarshie Interchange; TM, Tema Motorway; SD, standard deviation; BDL, below detection limit; n = 15 (n, number of replicate samples).


S. M. Atiemo et al. intense traffic congestion during the morning and evening rush hours. The high frequency of intermittent braking of the vehicles plying that stretch of the road at these times of the day could lead to high levels of emission from gasoline-based vehicles.[21] It can also be for this same reason of high frequency of intermittent braking that high levels of Cu and Zn, which are products of brake and tyre wear, were obtained at the interchange. The results shown in Table 1 further reveal considerably high levels of Zr at all the sites. This can be due to zirconium oxide and zirconium silicate, which are used in brake lining materials,[22] and the granitic rocks used for the road construction.[23] A profile of the elemental concentrations of the street dust for the selected sites in Accra was calculated by taking the average values for these four sites. The mean concentration levels of the 15 elements of the road dust for all the sampling locations are shown in Table 2, in addition to the source profiles of road dust obtained from other studies for comparison. The values obtained in this study are quite commensurate with that of the other studies with slight variations. The mean Cr concentration level obtained in this work was 166 mg/kg, which is higher than those obtained in other studies but showing little significant difference. This can possibly be due to rapid tyre wear resulting from the high patronage of used lorry tyres imported into the country. The result obtained for Cu was quite comparable to those of other studies. The Zn concentration was very close to that in Luanda; higher than those in London, Palermo, Dhaka City and Ottawa; and less than that in Hong Kong. Pb concentration was greater than those in Dhaka City and Ottawa but less than those in the other cities. The V levels were higher than those reported in the works done in Hong Kong and Ottawa. Mn levels were lower than those in Hong Kong and Ottawa, as seen in Table 2. Enrichment factors The values of the EFs obtained for the element at the sampling locations are presented in Table 3. The values of the EFs for K, Ca, Ti, Mn, Sr, Rb and Y were all below 2. According to this result, they could be classified as crustal in origin. V, Cr and Cu showed moderate enrichment, while Zn, Br, Zr and Pb were significantly enriched, which gives an indication that there are contributions from anthropogenic sources of these elements in the road dust.

The highest EF for Pb was recorded on the Tema Motorway, indicating a strong anthropogenic influence (Fig. 2). Source assignment The results of the studies were also subjected to PCA (Table 4) to determine the source profiles and the percentage contribution of each source. It was assumed that the pollution sources are independent of each other. Varimax rotation, which yielded the most consistent results, was used in the factor analysis. The results of the PCA analysis of PM100 grain sizes gave four components, which accounted for 88.5% of the cumulative variance as shown in Table 4. Component 1, which gave the highest percentage variance of 44. 8%, has high factor loadings for Ti, Cr, Mn, Fe, Cu, Zn, Br, Rb, Sr, Y and Zr. These elements are abundant in the natural soil and are therefore associated with the natural crust.[17] The

Table 3. Enrichment Factors of the elements at the sampling locations Species K Ca Ti V Cr Mn Fea Cu Zn Br Sr Rb Y Zr Pb

JT

MJ

TM

TQ

1.3 0.6 1.0 3.3 3.0 0.7 1.0 1.7 4.8 — 0.7 0.6 0.8 16.1 7.5

1.1 0.6 0.8 3.3 2.9 0.6 1.0 2.2 5.3 3.1 0.8 1.5 0.8 16.2 10.8

1.6 1.1 1.5 4.7 2.1 0.8 1.0 1.7 7.5 16.1 0.7 0.8 0.6 9.7 26.3

0.7 0.8 0.9 2.6 2.4 0.6 1.0 2.7 9.3 3.0 0.6 1.0 0.9 11.4 11.1

JT, John Teye-Pokuase road; MJ, Mallam Junction Weija road; TQ, Tetteh Quarshie Interchange; TM, Tema Motorway. a Reference element.

Table 2. Source profiles of road dust (mg/kg) obtained in the present work with those of other investigations. Element

108

K Ca Ti V Cr Mn Fe Cu Zn Br Rb Sr Y Zr Pb

Present work (2009) 11 700 17 800 3 271 292 166 378 36 200 70.7 327 7.5 39.2 244.8 17.9 1 430 120

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London[24]

Palermo[25]

Luanda[1]

Dhaka City[18]

Hong Kong[26]

Ottawa[27]

12 700

103

2 370 36.6 124 594

34.0 43.3 432

46.0 154

110 2 840

65.8 113

74.0

378 120

39.1

25.5

73.0 183

98.0 207

41.8 317

294

544

351

Copyright © 2012 John Wiley & Sons, Ltd.

X-Ray Spectrom. 2012, 41, 105–110


Heavy metal contamination in road dust in Accra, Ghana

Enrichment Factor

30 25 20

JT MJ TM TQ

15 10 5 0 K Ca Ti

V Cr Mn Fe Cu Zn Br Sr Rb Y Zr Pb

Element NB - Fe is the reference element; Enrichment factor = 1

Figure 2. Enrichment factors of the elements at the sampling locations.

Table 4. Results of the principal component analysis Elements

Component 1

K Ca Ti V Cr Mn Fe Cu Zn Br Rb Sr Y Zr Pb % of Variance

0.1 0.1 0.5 0.2 0.6 0.6 0.7 0.8 0.8 0.5 0.9 0.8 0.9 0.9 0.4 44.8

2 0.1 0.9 0.3 0.2 0.5 0.7 0.6 0.4 0.5 0.2 0.1 0.4 0.2 0.1 0.7 20.4

3 0.9 0.1 0.5 0 0.4 0.1 0.2 0.4 0.1 0.3 0.3 0 0.2 0.2 0.5 14.2

4 0.1 0.1 0.3 0.9 0.1 0.2 0.3 0.3 0.3 0.6 0.2 0.3 0.2 0.1 0.2 9.1

X-Ray Spectrom. 2012, 41, 105–110

Conclusion This work investigated the elemental contents of road dust on four selected major roads in Accra. The dust particles of size fractions less than 100 mm were considered in this study for the reason that they are more aerodynamic and can therefore be carried far when resuspended. The result of the elemental concentrations obtained in this work compared well with those of similar works done in other cities with slight variations. EF calculations and PCA were used to ascertain the contribution levels of natural and anthropogenic sources. The results of the PCA showed sources of crustal origin and vehicular exhaust and non-exhaust emissions. Contribution from crustal origin, as expected, gave the highest contribution of 44.8%. For the non-exhaust emissions, tyre wear contributed more than brake dust, with contributions of 20.5% and 14.2%, respectively. Exhaust emissions contributed the least with 9.1%. Industrial contributions were not observed because the sampling locations were not within areas of heavy industrial settings. Acknowledgements We thank the National Nuclear Research Institute of the Ghana Atomic Energy Commission for providing the facilities for this study. We also thank Mr James Adeti (Technologist XRF lab) and Mr Nash Owusu Bentil (Principal Technologist) and his team for their assistance during the analysis.

References [1] L. Ferreira-Baptista, E. D. Miguel. Atmospheric Environment 2005, 9, 4501. [2] L. Han, G. Zhuang, S. Cheng, Y. Wang, J. Li. Atmospheric Environment 2007, 41, 7485. [3] K. N. Yu, Z. L. L. Yeung, L. Y. L. Lee, M. J. Stokes, R. C. W. Kwok. Applied Radiation and Isotopes 2002, 57, 279.

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109

second component that accounted for 20.4% has high loadings of Ca, Cr, Mn, Fe, Zn and Pb. Zinc (Zn) is added to tyres in the form of ZnO to activate the vulcanization process and is present in the brass coating that is applied to the steel wires that reinforce the tyre structure. The release of Zn through tyre wear is considered as one of the main sources of anthropogenic input to the environment.[28] Other elements such as Cr, Sr and Pb are present in tyres in trace quantities. Component 2 could therefore be attributed to tyre wear emissions. The third component, which accounts for 14.2% of the variance, has high loadings of K, Ti and Pb. Potassium titanate fibres are used in brake lining for tyre reinforcement. The presence of these elements in high concentrations in road dust is a significant source of anthropogenic input to the environment.[28,29] The elements Rb, Cu and Pb are present in trace quantities in brake lining. Copper (Cu) is used as a high-temperature lubricant to control heat transfer in brake lining. The third component can thus be attributed to brake wear emissions. The fourth component accounts for 9.1% of the sample variance and has high loadings of V and Br. Vehicular exhaust emissions are mostly associated with sulphur and other trace elements such as V, Ti, Cr, Fe, Cu, Zn and Br, which are present in natural crude oil. These components are likely to be attributed to vehicular exhaust emissions.

The EF values obtained for K, Ca and Mn showed that they are of crustal origin, but the PCA rather showed high anthropogenic contributions for these elements. The discrepancy may be due to the fact that their concentrations in the natural soil are less than their crustal averages.[15] Mn had high factor loadings in components 1 and 2, which are natural soil and tyre contributions, respectively. The PCA analysis did not show any significant contributions of Mn from exhaust emissions. This shows that the build-up of Mn from vehicular emissions as a result of the use of methylcyclopentadienyl manganese tricarbonyl as a gasoline octane enhancer in Ghana since its inception in 2004 has not yet had any effect on the natural background level. There was no significant contribution of Pb from exhaust emissions, which shows that the build-up of Pb due to vehicular emissions is no longer prevalent in the environment since its use as an octane enhancer in gasoline was phased out in 2004, although Pb is a very stable isotope in soil.[29] The high enrichment of Pb is found to be mainly due to brake and tyre wear. Zr was also seen to be mostly of anthropogenic origin from the EF results, but the PCA showed that it was rather mostly of crustal origin. Zirconium oxide and zirconium silicate are used in brake lining materials,[22] and their emissions from brake wear could have been a contributing factor to the significant enrichment of Zr in road dust, but this did not show up in the PCA. The contents of Zr in the granitic rocks (crustal origin) used for the road reconstruction may have rendered the minor contributions from the other sources insignificant.


S. M. Atiemo et al. [4] Y. Wang, G. Zhuang, C. Xu, Z. An. Atmospheric Environment 2007, 41, 417. [5] M. Abu-Allaban, J. A. Gillies, W. Alan, A. W. Gertler, R. Clayton, D. Proffitt. Atmospheric Environment 2003, 37, 5283. [6] E. K. Yetimoglu, O. Ercan. Journal of the Brazil Chemical Society 2008, 19, 1399. [7] I. F. Al-Momani. Jordan Journal of Chemistry. 2009, 4, 77. [8] M. Viana, M. Pandolfi, M. C. Minguillón, X. Querol, A. Alastuey, E. Monfort, I. Celades. Atmospheric Environment 2008, 42(16), 3820. [9] R. Wilson, J. Spengler. Particles in Our Air: Concentrations and Health Effects 1996, 15. [10] P. Johnson, C. Bennet, I. Eliasson, S. E. Lindgreen. Atmospheric Environment 2004, 38, 4075. [11] I. J. K. Aboh. Henriksson D, Laursen J, Lundin M, Pind N. Lindgren SE and Wahnström T. X-Ray Spectrometry. 2007, 36, 104. [12] J. M. Lopez, M. S. Callen, R. Murillo, T. Garcıa, M. V. Navarro, M. T. de la Cruz, A. M. Mastral. Environmental Research 2005, 99, 58. [13] D. Meza-Figueroa, M. De la O-Villanueva, M. De la Parra. Atmospheric Environment 2007, 41, 276. [14] H. J. M. Bowen, Environmental chemistry of the elements, Academic Press, London. Evolution. Blackwell Science Publishers, Oxford, 1979. [15] S. R. Taylor, S. M. McLennan, Blackwell Science Publishers, Oxford, 1985.

[16] M. E. Kylander, S. Rauch, G. M. Morrison, K. Andam. Journal of Environmental Monitoring 2003, 5, 91. [17] M. L. Bosco, D. Varrica, G. Dongarra. Environmental Research 2005, 99, 18. [18] F. Ahmed, H. Ishiga. Atmospheric Environment 2006, 40, 3835. [19] X. Lu, L. Wang, K. Lei, J. Huang, Y. Zhai. Journal of Hazardous Materials 2009, 161, 1058. [20] G. Bernasconi, A. Tajani, P. Kregsamer. Quantitative X-Ray Analysis Software (QXAS) manual. IAEA software package for nuclear analysis 1996, version 1.2, 63. [21] R. B. Voegborlo, M. B. Chirgawi. Journal of Science and Technology 2007, 27, 86. [22] M. Eriksson, F. Bergman, S. Jacobson. Wear 1999, 232, 163. [23] Huzita K and Kasama T. Geological Survey of Japan 1983; 115. [24] Thornton I. Soils in the Urban Environment Blackwell, Oxford, 1991, 47. [25] D. Varrica, G. Dongarra, G. Sabatino, F. Monna. Environmental Geology 2003, 44, 222. [26] Z. L. L. Yeung, R. C. W. Kwok, K. N. Yua. Applied Radiation and Isotopes 2003, 58, 339. [27] P. E. Rasmussen. Subramanian KS and Jessiman BJ. Science of the Total Environment 2001, 267, 125. [28] K. Adachi, Y. Tainosho. Environment International 2004, 30, 1009. [29] A. Thorpe, R. M. Harrison. Science of the Total Environment 2008, 400, 279.

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