prediction of lead concentration in soil using reflectance spectroscopy

Accepted Manuscript

Prediction of lead concentration in soil using reflectance spectroscopy

Ali Al Maliki, David Bruce, Gary Owens

PII: S2352-1864(14)00003-0DOI: http://dx.doi.org/10.1016/j.eti.2014.08.002Reference: ETI 2

To appear in: Environmental Technology & Innovation

Received date: 9 April 2014Revised date: 13 July 2014Accepted date: 8 August 2014

Please cite this article as: Al Maliki A, Bruce D, Owens G. Prediction of lead concentration insoil using reflectance spectroscopy. Environmental Technology & Innovation (2014),http://dx.doi.org/10.1016/j.eti.2014.08.002

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http://dx.doi.org/10.1016/j.eti.2014.08.002

1

ETI 2 1

Title page and abstract 2

Manuscript Number: ETI-D-14-00004R1 3 Title: Prediction of Lead Concentration in Soil Using Reflectance Spectroscopy 4 Article Type: Research Paper 5 Keywords: Spectroscopy, Partial Least Squares Regression, lead contamination, Regression analysis, 6 HyLogger™ analysis 7 Corresponding Author: Mr. Ali Al MAliki, 8 Affiliations: 9

1- Ali Al Maliki*: corresponding author 10

* Centre for Environmental Remediation and Risk Assessment (CERAR), University of South Australia, 11

Mawson Lakes Campus GPO Box 2471, Adelaide SA 5001 Australia, mob:+61430143684, 12

[email protected]. 13

2- David Bruce: 14

School of Natural and Built Environments, Barbara Hardy Institute, University of South Australia, 15

Mawson Lakes Campus GPO Box 2471, Adelaide SA 5001 Australia [email protected] 16

3- Gary Owens: 17

Division Office Research IEE, Mawson Institute, University of South Australia, Mawson Lakes Campus 18

GPO Box 2471, Adelaide SA 5001 Australia [email protected] 19

First Author: Ali Al Maliki, PhD candidate 20 Order of Authors: Ali Al Maliki, PhD candidate 21 Affiliation: 22 Abstract: Visible-Near and short-wave infrared reflectance spectroscopy has the potential to become 23 an important additional technique in the geosciences areas for soil classification, mapping and remote 24 determination of soil properties and mineral composition. It is also becoming increasingly important to 25 improve the spatial resolution of soil maps to better tackle localized issues such as soil contamination. 26 Long-term spiked soils having a range of lead (Pb) concentrations from five different locations across 27 Australia were analysed for a range of physio-chemical properties as well as their spectral reflectance 28 between 400-2500nm. Spectral and chemical analyses were correlated using partial least squares 29 regression (PLSR), to predict soil Pb concentration. While across all soils studied (n=31), the Pb 30 content was weakly predicted from spectra, reliable correlations with the major spectrally active 31 components were found in models of total carbon and iron, which were predicted much better than 32 most other soil constituents. However, a good prediction of Pb concentration was found in two of the 33 suitable soil subsets which indicated that spectral reflectance analysis may require soils to be of the 34 same type in order to be effective. For a long-term atmospheric smelter emission Pb contaminated soil, 35 the correlations between Pb measurements and spectral reflectance in both calibration R2C and 36 validation (R2V) modes reached 0.99 and 0.75 respectively with a calibration root mean square error 37 (RMSEc) of 19 and validation root mean square error (RMSEV) of 345 and acceptable in performance 38 of deviation RPD 1.7. For a long-term spiked LTS soil, both R2C and R2V reached 0.99 and 0.96 39 respectively with a RMSEc of 58 and a RMSEV of 396 with excellent RPD of 12.15. These results 40 indicated that reflectance spectroscopy has the potential to rapidly determine Pb contamination in a 41 homogeneous soil. 42

2

1. Introduction

Metal emissions from mining and smelters are believed to be a significant global problem for human health [1-3]. 43

Despite several reforms of emission processes through regulatory policy over the last 30 years particularly in the 44

petroleum industry, smelters and the associated industrial processes undertaken at such sites represent the major 45

cause for contamination and accumulation of metals in soils in the vicinity of industrial operations. 46

Spectral techniques provide spectrally rich, and generally spatially continuous information, that can be used to 47

determine soil properties and mineralogy, which can in turn be applied for mapping and monitoring of soil 48

contamination. Depending on the material’s spectral response, reflectance spectroscopy is also relatively less 49

expensive and faster than traditional wet chemical measurements [4-7]. Using spectral remote sensing methods, 50

several direct and indirect soil contamination characteristics, can be extracted simply by correlation spectra and 51

PLSR [8]. Previous studies have shown that many soil properties are associated with distinct spectral signatures [6, 52

9, 10] including cation exchange capacity CEC [11], soil organic matter content [6, 11, 12], iron Fe content [11, 13] 53

and soil electrical conductivity [14]. 54

Heavy metals exhibit absorption features in soil spectra throughout the range of 350-2500nm, However, it was 55

difficult to identify these metals because they may represent much weaker large-order overtones of the soil 56

constituents, which can also overlap [15]. The absorption feature for Pb prediction could be attributed to absorption 57

features of some soil constituents (i.e. iron, organic carbon and clay mineral) [8] For example, a significant 58

correlation between total carbon and Pb (R=0.415) was indicator for predicted Pb through diffuse reflectance 59

spectroscopy, due to a specific absorption feature of carbon in the VNIR [16]. Also, Vohland et al. (2009) showed 60

that the good correlation between organic carbon and a set of heavy metals Cu, Pb and Zn with R=0.67, 0.66 and 61

0.65 respectively was helpful to reveal coherence between these components with spectral data. They identified 62

(predicted) these metals in similar spectral bands that attributed to the absorption features of organic carbon. 63

A number of multivariate regression for assessing heavy metal contamination have been developed to indirectly 64

identify metal contents via their association with other spectrally active materials. For example Zhang et al., (2010) 65

compared between three prediction methods to find appropriate spectral bands to determinate soil Pb content; they 66

maintained that the spectral absorption at 838, 1930 and 2148 nm was appropriate to detect soil lead content. Pandit 67

3

et al. (2010) observed that the PLS was suitable for evaluating urban atmospheric deposition of Pb contaminants. 68

Kemper and Sommer (2002) found reliable correlations between Fe and heavy metal concentrations using multiple 69

linear regressions (MLR) analysis and an artificial neural network (ANN). Organic matter (OM) was primarily 70

responsible for predicting a range of heavy metals based on multivariate regression of spectral reflectance [17] 71

While many of the above studies have focussed on detection of Pb from reflectance, it is likely both direct and 72

indirect methods will be needed to remotely detect and quantify Pb concentrations. If a combination of spectral 73

techniques can be found to detect Pb then time and costs will be minimized compared to traditional methods, thus 74

providing the opportunity to significantly analyse more samples over a larger spatial extent, allowing the spatial 75

distribution patterns of contamination to be more reliably determined [8]. Therefore, this research investigated 1) 76

whether soil Pb contamination or any related soil properties can be discriminated using reflectance spectroscopy and 77

2) whether any relationship existed between the reflectance spectra of a soil and geochemical measures of pollution. 78

79

2. Materials and Methods 80

2.1 Soil sample preparation 81

Five different sites across Australia were examined in this study. Soils samples from these sites were organised 82

into subsets classified according to the Australian Soil Classification System [18]. The locations and classifications 83

were 1) Victoria (V) - a red ferrosol 2) Birdwood (B) - a brown chromasol 3) Halbury (H) - a red chromasol 4) Port 84

Pirie (PP) –a loamy sand collected from four different location in the vicinity of a Pb and Zn smelter and 5) a long 85

term spiked soil (LTS) which was a natural garden loam. With the exception of the soils collected from Port Pirie, 86

each other subset included a number of soils of differing Pb concentration obtained by spiking the original sampled 87

soil with increasing concentrations of Pb to give artificially contaminated soils. All spiked process was conducted 88

based on Sanderson (2008), more details about the soil samples collection and the spiked process in [19]. Lead 89

acetate [Pb(CH3COO)2).3H2O] solution was used to spike the LTS soil to give 8 nominal Pb concentrations, (150, 90

300, 1000, 1500, 3000, 5000, 7000, and 10000 mg kg-1), while lead nitrate [Pb(NO3)2] solution was used to dose the 91

other three soil samples to give final concentrations of approximately 0, 30, 300, 1500, 10,000 mg kg-1. Final of 31 92

soil samples after collection and/or spiking, were sieved < 2mm to avoid roughness effects that may otherwise affect 93

the overall reflectance of the soil, and air dried to constant weight at 60οC prior to any further analysis. 94

95

4

96

97

2.2 Soil physiochemical properties 98

Heavy metal content of the soils was determined using Inductively Coupled Plasma – Mass Spectrometry (ICP-MS) 99

following microwave assisted digestion with aqua regia[20]. Total carbon and nitrogen contents were determined 100

via dry oxidation (automated combustion) on a LECO CNS analyser. Soil pH and electrical conductivity (EC) were 101

measured in 1:5 soils: water suspension using a pH/Conductivity meter (Smart CHEM-LAB, TPS, Australia). Soli 102

texture was determined using a micro-pipette method[21]. 103

2.3 Reflectance spectroscopy 104

Visible-Near infrared VNIR and short-wave infrared SWIR reflectance spectra were obtained between 400 – 2500 105

nm using a HyLogger™ system (model VSTR-1) with a high intensity halogen lamp as a light source [22] available 106

at the Minerals Drill Core Library(DMITRE Minerals). The HyLogger™ system was developed by CSIRO to 107

rapidly measure reflectance spectra and extract mineralogy from soil cores [22]. Reflectance spectra were 108

determined on sub-samples (80 g) of each sieved soil placed in a plastic Petri-dish (90 x 15 mm) for HyLogger™ 109

analysis. The HyLogger system is specifically designed to continuously examine intact soil cores, thus some 110

redesign of the sampling program was required to sample the petri dish contained samples so that many sections of 111

each Petri-dish contained soil could be examined within a few minutes. In a single reflectance run ten Petri-dishes 112

were placed in one line for every processing event and at least 20 individual points were sampled across the surface 113

of each Petri dish. This run produced a set of raw data files for each Petri dish soil sample and the Spectral Geologist 114

(TSG) software was used to process the data streams from these raw files. For each soil sample, after removing any 115

poor spectra (due to sampling reflectance close the petri dish edge or work bench), twenty individual reflectance 116

spectra were taken across the surface of the soil within the Petri-dish (Figure1 a, b) and export them into a suitable 117

form. Subsequently, the individual spectra were averaged to obtain single representative reflectance spectra for 118

transformation and processing. Spectral reflectances were measured at a 4 nm bandwidth from 400 to 2500 nm so 119

that a total of 531 transformations spectra per spectrum were examined to describe geochemical variables from soil 120

properties including: Pb and Fe in (mg/kg), C (%) and N (%) in addition to proportion of soil texture. Figure 1c was 121

5

exposed the average reflectance of all samples for each soil group and the typical soil reflectance shape with specific 122

absorbance bands around 1400, 1900 and 2200 nm, which are related to water and hydroxyl absorption [23-26]. 123

124

125

126

127

128

129

130

131

132

133

134

135

136

Fig 1 (a) a HyLogger™ system, (b) twenty individual spectral reflectance obtained from points across the surface of 137

the soil within the Petri-dish using and TSG program, (c) measured soil reflectance between 350 – 2500 nm with 138

average spectra for each soil group and specific absorbance bands. 139

2.4 Regression analysis and statistics 140

HyLogger data were subjected to multivariate analysis using Unscrambler X software package version 10.2. 141

Many pre-processing techniques have previously been successfully applied to spectral reflectance raw data in order 142

to assist in the interpretation of overlapping weak spectral overtones, to remove spectral noise originating from the 143

effects of non-homogeneous distributions of particle sizes and to provide optimum predictions. These techniques 144

a b

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

400 700 1000 1300 1600 1900 2200 2500

Ref

lect

an

ce

Wavelength (nm)

H

B

PP

LTS

V

6

included averaging, smoothing, absorption (log 1/R, where R is reflectance), transformations, normalization and 145

derivative processing [23,27,28]. Although some of the transformation techniques were successful in improving the 146

detection of absorption features, preliminary results indicated that normalisation performed slightly better than other. 147

The effect of two of these transformations on the reflectance spectra of the LTS soil is shown in Figure 2. 148

149

150

151

152

153

154

155

156

157

Fig 2 Raw reflectance (a) the K-M pre-process (b) and Normalize transformation feature (c) for soil loam (LTS) 158

Principal Components Analysis (PCA) of the spectral data was used to determine the number of mathematical 159

components which represented the data set. Sample groups, patterns, similarities and differences can be described in 160

score plots [29, 30]. The first few PCs are usually sufficient for a robust model and over-fitting issues can be 161

eliminated. Thereafter, a PLSR model was used to estimate the known geochemical parameters of the soils, such as 162

Pb content. PLSR is a common regression method for relating x spectral parameter (predictors) with y measured soil 163

variables (dependants) [7, 23, 29, 31, 32], resulting in the smallest potential number of components depending on 164

the relative significance criteria of the models. This allows for the simplification of the relationship between 165

matrices x and y that contain HyLogger data and soil characteristics respectively. PLSR was applied to the 166

reflectance spectra of the twenty six soils to establish an optimum number of latent variables (relevant factors). 167

The regression process aimed to:1) identify the most significant spectral information associated with each soil 168

constituent and 2) correlate reflectance spectra with Pb concentrations in soils. A full cross validation (CV) was 169

carried out based on “leaving-one out”, as an internal validation. The relative quality of the PLSR models was 170

evaluated via comparing the estimated values with the measured values through many factors including, the 171

0

0.1

0.2

0.3

0.4

0.5

0.6

400 800 1200 1600 2000 2400

Re

fle

ctan

ce

Wavelenth (nm)

0

1

2

3

4

5

6

7

8

400 800 1200 1600 2000 2400

Re

fle

ctan

ce

Wavelength(nm)

S1

S2

S3

S4

S5

S6

S7

S8

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

400 800 1200 1600 2000 2400

Re

fle

ctan

ce

Wavelength

7

coefficient of determination (R2), the root mean square error (RMSE) for both calibration (RMSEC) and validation 172

based on the cross validation method (leaving one out) RMSEV, coefficient of regression or the slope of the linear 173

regression, the absolute estimation error or standard errors (SE) for both calibration (SEC) and cross validation (SEV) 174

, as well as intercept of regression and the bias, which were all taken as relative predictors of the best model [33]. 175

Other statistical parameter of prediction accuracy considered was the ratio of performance to deviation (RPD) which 176

is the ratio of the standard deviation SD of the reference values to the RMSECV (Eq 1) 177

........................................1 178

In general, the lowest RMSE, SE, bias, offset (intercept) with higher RPD also R2 coefficient of regression close to 179

1.0 were used as indicators of the most accurate regressions [28, 33-36]. 180

3. Results and discussion 181

The average physiochemical characteristics of the soils used in this study are summarized in Table 1. 182

183

The soils varied slightly in soil texture with three soils (LTS, PP and H) being respectively a loamy sand, 184

sandy(except pp8) and a fine sand, while the other two soils (B and V) were sandy loams and sand clay loam. 185

The independent data matrix (spectral data) transformed by PCA, and the first two principal components PC1, PC2 186

or factors explained most of the independent data variance in the soil and showed that the spectral differentiation 187

was able to distinguish the five groups and the soil samples within a cluster were similar in type or texture of soil 188

Table 1: Selected average physio-chemical characteristics of soils studied

Sample ID Fe (ppm) pH EC

(ds m-1)

C

(%)

N

(%)

Sand

(%)

Clay

(%)

Silt

(%)

Texture

Bulk Victoria (V) n=4 100275 4.4 0.914 5.00 0.41 59.8 20.0 20 sandy-clay

loam

Bulk Halbury (H) n=5 8773 7.0 0.348 1.34 0.10 81 9 10 fine sand

Bulk Birdwood (B) n=5 5224 4.6 0.615 2.52 0.14 58 19 23 sandy loam

Port Pirie 01 (PP) n=1 52769 7.8 3.65 0.74 0.03 78 8 14 sandy







Port Pirie 08 (PP) n=1 33189 8.4 0.55 5.25 0.23 26 26 48 loam


Long term spiked (LTS)

n=8

2447 6.5 2.20 1.25 0.06 92.5 1.18 6 loam sand

8

samples (Fig. 3). Using the first two PC, a scatter plot assisted in explaining variance in the spectra data and enabled 189

a decision, on the most significant spectral parameters. The closer together the samples appeared in the score plot 190

the more similar with respect to X (PC1) and Y (PC2) they were. The result of PCA was in agreement with previous 191

study by Chang et al [37] who studied a diverse range of soil properties from four major areas and found that soil 192

from same area had similar soil properties and also a similar response to the incident light. 193

194

195

196

197

198

199

200

201

Fig 3 Score plot showing five distinct clusters using correlation loadings (ellipse), the soil samples within a cluster 202 are similar in texture 203

Overall, when using the entire set of soils (n=31), Pb concentrations predicted from spectral reflectance 204

measurements have a good correlation with the known concentrations of Pb in the soils obtained from acidic 205

digestion using PLSR regression model. Moreover, better predictive results for the soil Pb concentrations were 206

found on individual subsets, being good for Port Pirie (n=9) and LTS (n=8) in (Fig. 4) indicating that spectral 207

reflectance analysis may require soils to be of the same type in order to be effective. Prediction of Pb concentrations 208

from soil reflectance was fair for H (n=5), indicating that poor prediction may be due to the soil samples were 209

relative less or as a result of solution the soils were spiked with, which mean that lead acetate was more suitable to 210

use to spike soil than lead nitrate for giving artificially contaminated soils. 211

9

Correlation coefficients between soil measurements and spectra data were applied to evaluate the spectral response 212

for soil properties. The relationship between the normalised spectral data and measured values for some soil 213

properties (total C, iron Fe content) that have distinct spectral signatures in the VNIR region are shown in Table 2. A 214

significant Person correlations R between 0.70-0.79 were obtained for LTS in range of spectra between1928-215

1992nm, also a good Person correlation value of 0.60 was found between 578-708 for PP samples. In contrast, a 216

weak correlation was found in total set of samples between Pb measurements and spectral reflectance. Heavy 217

metals exhibit absorption features in soil spectra throughout the range of 350-2500nm, However, it was difficult to 218

identify these metals because they may represent much weaker large-order overtones of the soil constituents, which 219

can also overlap [15]. 220

221

Table 2 Correlation between Pb concentration, total carbon and iron content with

spectral reflectance variables Range of spectral nm with

highest correlation

Range of best correlation coefficient

between spectra and soil Pb

concentrations

All set of (data n=31)

Pb ppm 1404-1420 0.22-0.24

C% 1200-1248 0.31-0.31

Fe ppm 744-796 0.70-0.71

LTS(n=8)

Pb ppm 1928-1992 0.70- 0.79

C% 2096-2280 0.75-0.80

Fe ppm 876-984 0.75-0.80

PP(n=9)

Pb ppm 578-708 0.60

C% 756-772 0.29

Fe ppm 880-888 0.60

222

Since Pb is not spectrally active in the reflectance spectra for the total set of soils considered here (n =31) a poor 223

prediction of Pb concentration, exhibiting low R2 values and RMSE > 50%, was obtained using direct reflectance 224

spectra. However, removal of calibration outliers was applied in this study to obtain a better fit with slightly lower 225

error. The algorithms within the Unscrambler software were used to identify and remove calibration outliers based 226

on content and new calibrations were then developed. 227

In spite of, the soil Pb concentrations predicted from regression models for total set of samples had some acceptable 228

accuracy parameters in validation mode, such as R2= 0.46 and PDR=1.59, the RMSEv values were relatively high 229

(2.5573), which reflected a weak prediction as shown in Table 3. 230

10

Although, better predictive results were obtained from spectrally active soil constituents such as carbon C and iron 231

Fe (Fig 4), the correlations between the Pb contaminant and these constituents “in this study” were not helpful to 232

predict Pb concentration indirectly, because the amount of Pb was increased directly proportionally to the amount of 233

Pb acetate or Pb nitrate added, while the other soil contents were almost invariant through soil spiked process. 234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

Fig 4 Relationship between predicted and measure concentration for the PLSR models for soil carbon (upper plots) 250 and iron content (lower plots) in calibration model (left) and Validation (right). 251

11

252

For the individual subset result, a good prediction of Pb via reflectance was found in only two of the soil subsets (PP 253

and LTS) (Fig 5). For the PP subset, the correlation between Pb measurements and predicted Pb value from spectral 254

reflectance in both calibration (R2c) and validation (R2

v) models was good (0.99 and 0.75 respectively), with suitable 255

RMSEc of 19 and RMSEV of 345, as well as acceptable RPD of 1.7 (Table 3). For the LTS subset, the correlation 256

was also good with R2 reaching 0.99 and 0.96 for both R2cand R2

v respectively, with a RMSEc of 58 and RMSEV of 257

396, where the accuracy for PLSR regression model evaluated using RPD of 12.15 indicated that the model was 258

excellent [28, 33, 38]. Far less accurate models were established for the red chromasol soil from Halbury which had 259

high RMSE for the validation mode (Table 3). The poor accuracy for the Halbury Pb prediction model might be 260

related to the method of spiking. Soils from Port Pirie were natural unspiked soils, whereas soils H, B and V were 261

spiked with Pb nitrate solution rather than Pb acetate solution which was used to spike the LTS soil. 262

In addition to the method of spiking, a very poor prediction was found for Pb in both the red ferrosol (V) and the 263

brown chromasol (B) which may be related to the soil texture as both of these soils were sandy loams as shown in 264

Table1, So, the prediction of Pb concentration could be decrease when the clay particles in soil is increase. It is very 265

clear, that the calibration set in PLS model was more response to subsets of (LTS, PP and H) that have sand particles 266

more than clay Table1. The result of indicated sand fraction using near infrared reflectance spectroscopy NIRS was 267

applicable with other study to predict diverse soil properties by Chang et al[37], the authors found that the prediction 268

accuracy for sand texture model was more than accuracy for model of clay [37]. Moreover, strong and positive 269

correlation spectral bands with sand texture were found in visible spectral range of 380-700[13]. 270

Alternatively the poor prediction may be related to the way these two soils were prepare as both were artificially 271

contaminated by spiking with Pb nitrate. The relative better prediction for the other three soils (LTS, PP and H) had 272

a very of similar soil texture (loamy sand, sandy, fine sand). 273

Table 3 Summary of statistics related to soil Pb regression models which used to predict Pb concentration in

spiked soils samples, the confidence level of P=0.005 was used in all prediction models

PLSR

with normalised treatment

No. of

samples

PLS

Maximum

components

Calibration Validation

R2 RMSEC R2 RMSEV SD RPDVa

Spectrally active model for all soil set(n=31)

soil Pb 31 7 0.89 1.083 0.46 2.553 4066 1.59

soil Felog 31 7 0.94 0.12 0.87 0.205 1 2.7

12

274

RPDV a - ratio of the standard deviation of the reference value to the RMSE of validation. RPD > 2 are considered 275 excellent models while 1.4-2 indicate acceptable models 276 277

278

279

280

281

282

283

284

285

286

287

288

289

290

soil C% 31 7 0.98 0.18 0.88 0.48 1.40 2.90

Model of Pb concentrations in each sub set

long term atmospheric PP 9 7 0.99 19 0.75 345 599 1.7

long term spiked soil LTS 8 6 0.99 58 0.96 396 3629 12.15

red chromasol spiked H 5 3 0.90 350 0.84 1700 4041 2.38

red ferrosol V 4 2 0.05 474 NA 1237 5625 4.54

brown chromasol B 5 3 0.54 235 NA 492 4303 7.91

13

291

Fig 5 Relationship between predicted and measure concentration for the PLSR models. Construction model (blue) 292

and Validation (red) for a long-term atmospheric smelter emission Pb contaminated soil PP (upper plots) and a 293

long term spiked LTS garden loam (lower plots) 294

4. Conclusion 295

Near infrared reflectance spectroscopy coupled with PLSR shows some potential as a method for determining soil 296

Pb contamination. The present study has shown that it is possible to remotely detect Pb contamination using direct 297

technique across a range of soil types from different locations. However, while the, Pb content was weakly 298

correlated with spectral data over the entire samples range considered, reliable correlations between Pb 299

measurements and spectral reflectance were found in both long term spiked (LTS) and long term atmospheric 300

emission Pb contaminated soil (PP) in range of spectra between 1928-1992nm and 578-708 respectively. According 301

to this correlation, the homogeneous soil subsets studied of LTS (garden loam) and Pb contaminated in PP (sandy 302

soil) were establish a good direct prediction models for Pb concentration using PLSR with excellent accuracy of R2, 303

RMSE, and RPD. This indicated that Pb content prediction was possible, but only when soils were sourced from the 304

same location or are of the same soil type (i.e. texture). This study also found that the number of components 305

required for establishing an optimum number of latent variables should be relatively high for successful prediction. 306

Consequently, small subsets of soils, with only a few samples had limited predictive capability for Pb using the 307

PLSR techniques developed here. The results of this research added another evidence for potential using reflectance 308

spectroscopy to rapidly determine Pb contamination in the VNIR region. Future research will evaluate these 309

techniques for a larger number of soil samples from across Port Pirie to detect Pb contamination based on regression 310

model prediction. 311

5. Acknowledgements 312

The Centre for Environmental Risk Assessment and Remediation (CERAR) at the University of South Australia is 313

acknowledge for providing access to laboratory facilities. The assistance of Dr. Alan Mauger from the Department 314

for Manufacturing, Innovation, Trade, Resources and Energy (DMITRE) where the reflectance spectra were 315

recorded is also gratefully acknowledged by all authors. . The first author gratefully acknowledges the ministry of 316

14

science and technology for provided him the opportunity to complete a PhD as well as the financial support of the 317

Iraq government, Ministry of Higher Education and Scientific Research. Dr Gary Owens gratefully acknowledges 318

the financial support of the Australian government under the Australian Research Council (ARC) Future Fellowship 319

Program, Grant Number FT120100799. 320

6. References 321

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3. Martley, E., B. Gulson, and H. Pfeifer Metal concentrations in soils around the copper smelter and 326 surrounding industrial complex of Port Kembla, NSW, Australia. Science of the Total Environment, 2004. 327 325: p. 113-127. 328

4. Summers, D., Discriminating and mapping soil variability with hyperspectral reflectance data, in Faculty 329 of science, School of Earth and Environmental Science. 2009, Adelaide University Adelaide 330

5. Choe, E., et al., Mapping of heavy metal pollution in stream sediments using combined geochemistry, field 331 spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain. 332 Remote Sensing of Environment 2008. 112: p. 3222–3233. 333

6. Ben-Dor, E., et al., Mapping of several soil properties using DAIS-7915 hyperspectral scanner data—a 334 case study over clayey soils in Israel. International Journal of Remote Sensing, 2002. 23(6): p. 1043-1062. 335

7. Wu, Y., et al., Possibilities of reflectance spectroscopy for the assessment of contaminant elements in 336 suburban soils Applied Geochemistry, 2005. 20(6): p. 1051-1059 337

8. Al Maliki, A., D. Bruce, and G. Owens, Capabilities of remote sensing hyperspectral images for the 338 detection of lead contamination: a review, in ISPRS Annals of the Photogrammetry, Remote Sensing and 339 Spatial Information Sciences. 2012: Melbourne. p. 55. 340

9. Kemper, T. and S. Sommer, Estimate of Heavy Metal Contamination in Soils after a Mining Accident 341 Using Reflectance Spectroscopy Environmental Science & Technology 2002 36 p. 2742–2747 342

10. Bray, J., R. Rossel, and A. McBratney, Diagnostic screening of urban soil contaminants using diffuse 343 reflectance spectroscopy. Australian Journal of Soil Research, 2009. 47 p. 433-442. 344

11. Fox, A. and R. Metla, Soil property analysis using principal components analysis, soil line, and regression 345 models. Soil Science Society of America Journal, 2005. 69(6): p. 1782–1788. 346

12. Galvão, S. and I. Vitorello, Role of organic matter in obliterating the effects of iron on mspectral 347 reflectance and colour of Brazilian tropical soils. . International Journal of Remote Sensing of 348 Environment, 1998. 19(10): p. 1969-1979. 349

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1- Spectral reflectance using PLSR showed potential for determining Pb contamination

2- It is possible to remotely detect Pb using both direct and indirect methods from different locations

3- This study constitutes the first attempt to estimate Pb contamination from spectral data

prediction of lead concentration in soil using reflectance spectroscopy

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