Environ Sci Pollut Res DOI 10.1007/s11356-017-9224-x

RESEARCH ARTICLE

Concentration estimation of heavy metal in soils from typical sewage irrigation area of Shandong Province, China using reflectance spectroscopy Fei Wang 1 & Chunfang Li 1 & Jining Wang 2 & Wentao Cao 1 & Quanyuan Wu 1

Received: 26 October 2016 / Accepted: 8 May 2017 # Springer-Verlag Berlin Heidelberg 2017

Abstract Since sewage irrigation can markedly disturb the status of heavy metals in soils, a convenient and accurate technique for heavy metal concentration estimation is of utmost importance in the cropland using wastewater for irrigation. This study therefore assessed the feasibility of visible and near infrared reflectance (VINR) spectroscopy for predicting heavy metal contents including Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg in the north plain of Longkou city, Shandong Province, China. A total of 70 topsoil samples were taken for in situ spectra measurement and chemical analysis. Stepwise multiple linear regression (SMLR) and principal component regression (PCR) algorithms were applied to establish the associations between heavy metals and reflectance spectral data pretreated by different transformation methods. Based on the criteria that minimal root mean square error (RMSE), maximal coefficient of determination (R2) for calibration, and greater ratio of standard error of performance to standard deviation (RPD) is related to the optimal model, SMLR model using first deviation data (RD1) provided the best prediction for the contents of Ni, Pb, As, Cd, and Hg, calibration using SNV data for Cr and continuum removal Responsible editor: Philippe Garrigues Electronic supplementary material The online version of this article (doi:10.1007/s11356-017-9224-x) contains supplementary material, which is available to authorized users. * Quanyuan Wu [email protected] 1

College of Geography and Environment, Shandong Normal University, 88 east of Wenhua Road, Jinan 250014, Shandong province, People’s Republic of China

2

General Station of Geological Environment Monitoring of Shandong province, 17 Jingshan Road, Jinan 250014, Shandong Province, People’s Republic of China

spectra for Zn, while PCR equation employed RD1 values was fit for prediction of the contents of Cu. The determination coefficients of all the reasonable models were beyond 0.6, and RPD indicated a fair or good result. In general, first deviation preprocessing tool outperformed other methods in this study, while raw spectra reflectance performed unsatisfactory in all models. Overall, VINR reflectance spectroscopy technique could be applicable to the rapid concentration assessment of heavy metals in soils of the study area. Keywords Sewage irrigation area . Heavy metal in soils . Quantitative estimation . Reflectance spectroscopy . Multiple linear regression

Introduction Heavy metal contamination in soils has been considered as a worldwide ecological issue since its potential and persistent threats to environment (Wu et al. 2015). Heavy metals are apt to accumulate in soils and hardly removed by natural degradation. They might disturb the edatope and enter living organisms through food chain, eventually posing a threat to human health (Chen et al. 2015). Most middle and upper latitude regions in the world suffered from water shortage, and as a consequence, sewage and other industrial effluents are regarded as a complementary source for agricultural irrigation particularly in the suburban area of developing countries (Piao et al. 2010). Large amount of nutrients such as nitrogen, phosphorus, and potassium occurring in the waste water can boost plant growth and reduce the consumption of fertilizer. However, heavy metals and other toxic materials with high concentrations in the sewage could generate soil pollution, which leads to the degradation of the quality of agricultural product. Rattan

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et al. (2005) discovered that soils in Delhi, India, receiving sewage irrigation for 10 years exhibited significant increase in Zn, Fe, Ni, and Pb. Jamali et al. (2009) stated that the contents of heavy metals were high in the grains of wheat grown in soil amended with domestic sewage sludge in Pakistan. And soil, vegetables, or cereal contaminations aroused by waste water irrigation also generally existed in China (Khan et al. 2008; Peng 2011; Xue et al. 2012; Lu et al. 2015; Wang et al. 2015); thus, it is imperative to assess the heavy metal concentrations in soils of sewage irrigation areas. According to Xie et al. (2012) and Gholizadeh et al. (2015), conventional methods for environmental soil assessment require numerous samples, followed by laboratory analyses that involve complex processes such as separation and preconcentration. Although such analysis yields a relatively high accuracy, it is time-consuming, costly, and only applicable in small-scale areas. Reflectance spectroscopy technique with little sample preparation provides a cost-effective alternative for soil property analysis in a relative large-scale area. It has shown to be a powerful tool in heavy metal concentration estimation, and many studies were conducted with respect to the quantitative estimation of different heavy metals in many regions (Pandit et al. 2010; Kooistra et al. 2001; Kooistra et al. 2003; Grzegorz et al. 2004; Wu et al. 2005; Choe et al. 2009; Tan et al. 2014; Wang et al. 2014). However, most spectra used for prediction of such information are currently measured in laboratories and cannot reflect the real spectral responses in the field, which will affect the evaluation accuracy. Longkou City is located in Yantai City of Shandong Province, China. The effective irrigated areas are about 8000 hm2, among which 56% are sewage irrigated cropland. The farmlands have been irrigated by waste water for more than 30 years and the effluent mainly comes from sewage disposal plants or industrial and domestic discharges. Heavy metal enrichment has occurred in some areas of Longkou City due to sewage irrigation, but little literatures are focused on the content assessment of heavy metals in soils using spectroscopy technique in this region. The objective of this study was therefore to evaluate the feasibility of rapid concentration estimation of heavy metals (chromium, copper, nickel, lead, zinc, arsenic, cadmium, mercury) based on VNIR technique. For this purpose, 70 soil samples were collected from the croplands of Longkou City to enable in situ spectral scanning and chemical analysis of heavy metals. Calibrations were then developed between heavy metal concentrations and reflectance spectra. Performances of different spectral transformation approaches for multivariable models using different mathematic algorithms were discussed. Some technical supports of rapid and inexpensive concentration estimation method were envisaged to provide in this study.

Material and method Study area The study was conducted in the north plain of Longkou City (37°34′35″N-37°44′49″N, 120°13′4″E-120°40′47″E) which extends for totally 527.85 km2. The study area is a typical temperate monsoon climate with average annual temperature of 12 °C and mean annual precipitation of 674 mm. Soilforming conditions are coherent in Longkou, and the main soil types are brown earth, cinnamon soil, moisture soil, and Shajiang black meadow soil. Rapid industrialization and urbanization occurred in Longkou during the last 10 years. The industry is relatively advanced, and the main domains are energy, machinery, chemical, textile, and building materials. However, negative effects also turned up due to the impropriate discharge of large amount of industrial and domestic waste. The effluent volume in 2014 was 3520 million tons, of which industrial sewage accounted for 38.3% and sanitary sewage for 61.7%. Partial sewage was drained into Yongwen and Huangshui River, leading to ecological damage and the reduction of living quality of residents along the rivers. But most sewage used for agriculture irrigation alleviated water stress to a certain degree. Data acquisition Soil sampling and spectroscopic measurement Sampling was conducted from June 25 to June 29, 2015. Permission to enter the studied areas was issued by General Station of Geological Environment Monitoring of Shandong Province, and no specific permits were required for the described field studies. According to the distribution of farmland and accessibility of roads, a total of 70 soil samples were collected and the specific sites are shown in Fig. 1. Soils were taken from the quadrat with size of 30 m × 30 m and multipoint mixing method was used in each quadrat. Sample points were selected along both diagonals and the center, giving a total of 5 points. Every point in each quadrat was obtained with surface soil at a depth of 0–20 cm and leaves; roots as well as stones were removed first. The soil weighing 1 kg was thoroughly mixed and then divided evenly into two subsamples for spectral measurement in situ and chemical analysis. Photos were taken for environmental records, and coordinates were measured using a Global Positioning System instrument. Soil reflectance was scanned using an ASD FieldSpec Handheld spectrometer produced by the American Analytical Spectral Devices Company (ASD Inc.). The detector uses PhotoDiode Array Detector with a low noise of 512 pixel arrays, angle of 25°, sampling interval of 1 nm, spectral resolution of 3 nm, and wavelength range of 325–1075 nm.

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Fig. 1 Study area with sampling points

The detector is handy for spectral measurement and convenient for field work. Meanwhile, heavy metals are usually affixed to iron-manganese oxide in soils of which the spectral response generally locates in the VNIR region. The wavelength range of the detector is adequate for predicting the contents of some heavy metals in soils using reflectance spectroscopy (Xie et al. 2007). Weather conditions were cloudless and windless. Using natural light as the illuminant, the spectrometer was warmed up for approximately 15–30 min, and then calibrated using a standard whiteboard. A probe was placed on the soil surface at a distance of 50 cm, and the level of the angle was kept normal within ±10°. Ten measurements were taken for each sample. The primitive spectra (R) are graphed in Fig. 2a.

Soil chemical analysis All soil samples were put into polyethylene plastic bags and taken back for chemical examination. Samples were air-dried, crushed with a wooden stick, and ground to pass through a 1 mm sieve. Heavy metals were digested using HNO3-HCLHF-HCLO4, and the contents of Cu, Zn, Pb, and Cd were determined by inductively coupled plasma atomic emission spectrometry, As was analyzed by inductively coupled plasma mass spectrometry, whlie Hg was determined using cold atomic absorption spectrometry. Guaranteed reagents were applied to chemical determination and GSS-4 soil; a standard reference material, obtained from Center for National Standard Reference Material of China, was inserted for quality controlling. The recoveries for all six heavy metals were within the range 100 ± 10% and all test results met accuracy requirement.

Spectral preprocessing Spectral preprocessing techniques include a variety of mathematical methods for light scattering correction during reflectance measurements and spectral feature enhancement before calibration (Ren et al. 2009). Apparent spectral curves that showed abnormal shape were firstly removed, and the mean value of remaining spectral curves of one sample was considered the actual reflectance spectrum. All spectra were subjected to fivepoint smoothing method, after which noise caused by measurement errors was reduced. Deviation treatment, continuum removal, and standard normal variate (SNV) transformation are widely used in the quantitative assessment between soil and surface parameters and confirmed by numbers of researches (Rossel et al. 2006b; Singh 2009; Song et al. 2015; Rinnan et al. 2009; Barthès et al. 2008; Senesi et al. 2009; Song et al. 2012). First deviation can handle baseline offset and make the outline of spectral data clearer through promotion of original spectra resolution and amplification of small peaks (Roberts et al. 2004). Continuum removal is aimed at quantifying the absorption of reflectance spectrum at a specific wavelength through normalizing a spectrum into a range of 0–1 (Gomez et al. 2008). And SNV is a mathematical transformation method of the Log (1/R) spectra used to remove slope variation and to correct for scatter effects (Cai et al. 2011). This transformation is done for each spectrum individually by subtracting the spectrum mean and then scaling with the standard deviation of spectrum (Park et al. 1997). The transformed spectra were graphed in Fig. 2b–e.

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Fig. 2 Raw reflectance spectra and preprocessed spectra of soil samples

Model construction and validation In calibration stage, stepwise multiple linear regression and principal component regression approaches were applied to evaluate the quantitative association between the contents of heavy metals and various spectra data. For SMLR, the independent variable that is not included in the equation will be entered if the probability of F (a tolerance significance level) is sufficiently small at each step. Variables in the regression calibration are removed if their probability of F becomes enough large. When no more variables are qualified for the criteria of inclusion or removal, the method terminates. The parameter setting of F will affect the result of model construction. In this paper, the F value for inclusion was 0.1 and for removal was 0.15 (Vasques et al. 2008). PCR is the algorithm that combines principal component analysis and multiple linear regression (MLR). The independent variables are decomposed into orthogonal principal component (PC) firstly using nonlinear iterative partial least squares algorithm and full cross-validation of calibration set (Martens and Naes 1989). Based on the minimum of root mean square error of cross-validation, PC is chosen and then employed for calibrating MLR model. The

advantage which PCR prevails over MLR lies in that PCs are commonly unrelated and the noise is filtered (Shi et al. 2014). In modeling stage, all 70 samples were randomly divided into a calibration set (40 samples) and a validation set (30 samples). The accuracy of model construction was assessed by coefficient of determination (R2), root mean square error for calibration set (RMSEC), determination coefficient of the validation (r2), and root mean square error of the prediction (RMSEP). Generally, higher R2 values of the calibration equation are considered better and more accurate (Ko et al. 2004). R2 < 0.5 indicates unsuccessful models, which are not recommended. The equations with 0.5 ≤ R2 < 0.8 considered fair models which may be used for approximate quantitative estimation; R2 ≥ 0.8 indicates excellent models. And the smallest root mean square error (RMSE) value is related to the optimal calibration model (Gholizadeh et al. 2015). Additionally, ratio of standard error of performance to standard deviation (RPD) is also a useful indices of the practical utility of a calibration model to predict soil property considering the variation of the soil property (Zormoza et al. 2008). According to Albrecht et al. (2008) and Chang et al. (2001), the accuracy is classified as good when RPD > 2, acceptable when 1.4 < RPD < 2, and

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unreliable when RPD is less than 1.4. The calculation formula for RMSE and RPD were as follows: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi . n 0 2 RMSE ¼ ∑ ðεi −εi Þ n ð1Þ i¼1

RPD ¼

SD RMSE

ð2Þ

where εi represents the observed values, εi′ represents the predictive values, n represents the number of samples, and SD is the standard deviation. Spectral treatments were executed in ENVI 5.1 (Esri Inc.). The statistical analysis and modeling procedure were conducted in SPSS 20.0 (IBM Inc.).

Result and discussion Descriptive statistical analysis The descriptive statistical results of heavy metals in soils are summerized in Table 1, along with soil background values (BV) of the study area (Dai et al. 2011). The average concentrations of Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg were 61.38, 29.73, 29.44, 36.06, 62.74, 8.04, 0.33, and 0.03 mg kg−1, respectively. It can be seen that the mean values all exceeded the corresponding background values, indicating that sewage irrigation had a certain adverse influence on the concentration level of heavy meatls. They were 1.09 (Cr), 1.51 (Cu), 1.25 (Ni), 1.42 (Pb), 1.12 (Zn), 1.28 (As), 3.06 (Cd), and 1.03 (Hg) times higher than the BVs, and the order of enrichment levels in soils was Cd > Cu > Pb > As > Ni > Zn > Cr > Hg. The concentration ranges and standard deviations of Cu, Pb, and Zn showed greater fluctuant than other elements, suggesting that the obervious spatial variations and the outliers of Cu, Pb, and Zn may be attributed to the presence of point source contamination. Klomogorov-Smirnov testing results revealed that the contents of Cr, Cd, and As were normally distributed and the distributions of Cu, Ni, Pb, Zn, and Hg were normal after log-transfomation. Outliers that were commonly Table 1 Descriptive statistical analysis of heavy metal contents/ mg kg−1

introduced by anthropogenic activities were the main reason that led to the non-normal of concentrations (Lv et al. 2014). Besides, all elements were positively skewed, following the sequence of Pb > Cu > Zn > As > Ni > Hg > Cd > Cr, which also implied that the contents of Pb, Cu, and Zn were apt to be influenced by human inputs. Despite the contributions of human activities, the content levels of Cu, Pb, Zn, and Cd in Longkou city did not present high values compared to other researches. The average content of Cu was lower than the mean level in the sewage irrigation areas of Henan and Shanxi province. The mean concentration of Zn in Liangshui typical sewage area of Beijing was 85.59 mg kg−1 which was 1.36 times than the average level in Longkou (Hu et al. 2008). For Pb, the mean in the soils along the Musi River in India was 512 mg kg−1(Chary et al. 2008), but the concentration levels of Pb in Taiyuan City (Xie et al. 2011), Tianjin City, China (Sun et al. 2015) and the target place were much alike. The concentrations of Cd in northwest China were characterized with the range of 1.27– 2.51 mg kg−1, and the reference values in Alicante were 0.7 mg kg−1. It can produce adverse effects on agricultural soils when Cd level exceeds 3 mg kg−1(Lv et al. 2013), and therefore, the perniciousness of Cd in soils of Longkou was much lower. In general, although heavy metal accumulations existed in the soils of Longkou city, no significant contamination occurred in this area. Spectral feature analysis As can be seen in Fig. 2a, the spectra curves of the 70 samples showed almost the same shape in the wavebands that varied from 325 to 1025 nm, with reflectance in near-infrared interval little higher than in visible band. Generally, positive peaks named as reflection characteristic bands represent the component of interest, and negative peaks that considered absorption features correspond to interfering components (Rossel et al. 2006a), and therefore, both the positive and negative peaks constitute the characteristic bands of a spectrum. In Fig. 2a, it illustrated that the characteristic wavebands of all spectra curves were much alike as well, but different in the reflectance

Items

Cr mg kg−1

Cu

Ni

Pb

Zn

As

Cd

Hg

Maximum Minimum Mean Standard deviation (SD) Skewness Kurtosis K-S testing Asymp. Sig. Background values

108.1 31.6 61.38 14.9 0.63 1.19 0.83 56.2

193.4 14.6 29.73 24.55 4.85 29.32 0 19.6

74.6 12.3 29.44 8.81 2.42 10.28 0.04 23.5

195.7 11.4 36.06 23.47 5.43 33.58 0 25.4

187.7 29.4 62.74 24.17 3.55 15.85 0.01 56.1

20.48 4.55 8.04 2.29 2.52 11.66 0.39 6.3

0.87 0.14 0.33 0.12 1.47 5.37 0.83 0.108

0.08 0.01 0.03 0.01 2.23 8.11 0 0.029

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values. However, only tiny positive peaks existed around 590, 760, and 935 nm, and the characteristic wavebands of raw spectra were not apparent. But spectral preprocessed technique protruded the soil spectral features in a certain degree. More characteristic bands were detected in the first deviation data including spectra around 576–596, 600–625, 630–650, 679, 709, and 746–769 nm as well as the second deviation (Fig. 2b). Continuum removal spectra mainly revealed the absorption features of the soil samples. The absorption bands, as shown in Fig. 2c, occurred at the wavelength that ranged between 380 and 420 nm which were primarily located in visible band and peaks that turned up around 400 nm. Little positive peaks were also found around 450, 590, 760, and 935 nm in the CR spectra. However, the pretreatment result of SNV was not satisfactory as expected. Characteristic wavebands were inconspicuous demonstrated in Fig. 2d, and only 400 and 590 nm was detected as the reflection feature band. From Fig. 2, it was clear that the major characteristic bands of reflectance spectra lied around 590, 660, 760, and 935 nm, and the essential absorption features were shown in 370– 540 nm. According to Xie et al. (2007), absorption characteristics of soil reflectance spectra have some specific associations with soil properties. In visible region (400–700 nm), absorption features of soil spectra are due to electron transitions of some metal irons such as Fe2+, Fe3+, and Mn3+. Additionally, the absorption around 410 and 476 nm is caused by Fe-Mn oxide absorptive action. Although intensive bands may not be associated with the presence of heavy metals studied in this research, it is clear that these metals can interact with the main spectrally active components of soil (Kooistra et al. 2001). Therefore, chemometrics models can be created to screen the concentrations of heavy metals in soils. Model calibration and validation Outliers are defined as the observation deviated significantly from normal values. In order to decrease the inaccuracy caused by outliers during modeling process, some observations of each heavy metal were dashed-out before calibrating based on the criterion that if the predicted versus actual difference for a sample was 3 SD or more from the mean difference (Yang et al. 2006). The statistical results for calibration and validation are summarized in Table 2, and the effect of each model was evaluated according to R2, RMSE, and RPD values. As can be seen in Table 2, four spectral indices yielded different prediction accuracy, and results using two modeling strategy are quite different. Spectral transformation can promote the relationship between the contents of heavy metals and the spectra to a certain degree, since all the R2 of calibrations that based on spectral treatment were higher than those with raw reflectance, and RMSEC reduced after

transformation. For Cr, the model using SNV data combined with SMLR method was the best prediction with R2 of 0.71 and RMSEC of 6.85 mg kg−1, and the corresponding RPD was 1.62, suggesting acceptable level. Besides, the other two equations using the same mathematic strategy were considerably satisfying because of the high R2 values above 0.5, while model cannot be constructed using original spectra for the given F value. However, R2 values of calibrations for Cr constructed by principal component regression were relatively lower than previous equations, since the highest coefficient of determination was 0.47 from the model using RD1 data, which implied unsuccessful model fitting. In addition, RMSEC was higher meaning less accurate. The calibration equation of Cu element using first deviation spectra and PCR yielded a fair result, as the RMSEC was relatively lower than most models and determination coefficient (R2 = 0.84) was the greatest. The calibration was classified as good accuracy according to its RPD (2.14). Meanwhile, the SMLR model using first deviation spectra was quite satisfactory as well of which the R2 was 0.66 and RPD was up to 2.61, while models based on R, CR, and SNV data when using the two modeling methods were not recommended for quantitative estimation of Cu. There were two appropriate equations that can be used for approximate quantitative estimation of Ni, namely the one using RD1 data and SMLR method, and the equation using RD1 data and PCR method. R2 value of the former calibration was 0.74, RPD reached 1.78, and the RMSEC was the lowest. Although the R2 of the later model is beyond 0.5, the corresponding RPD suggested an unreliable accuracy. The rest were hard for estimation of the contents of Ni. For Pb, the R2 of the four models when using stepwise multiple linear regression was 0.62, 0.76, 0.21, and 0.38, respectively, indicating that the former two were much more prominent. Besides, the PCR model based on first deviation transformed spectra performed as outstanding as the SMLR calibration using the same spectral indices in terms of R2 value, but the RMSEC of the SMLR model was much lower and RPD, with value of 2.03, indicated good accuracy. The equation using RD1 data as well as SMLR strategy was therefore the eligible predictive model for Pb. When using the first modeling algorithm, there were two satisfying calibrations of Zn constructed by first deviation data and continuum removal spectra. The p a r a m e t er s o f R D 1 d a t a m o d el w er e R 2 = 0 .6 8 , RMSEC = 5.99 mg kg−1 and RPD = 1.62, and R2 = 0.77, RMSEC = 5.62 mg kg−1 and RPD = 1.73 of the CR data model. By contrast, the performance of the calibration using continuum removal spectral source was much outstanding, while the results of raw data and SNV were poor. However, RMSEs of all equations of Zn using principal component regression were higher than the corresponding values of SMLR results. The construction consequence of arsenic was similar with Ni, as models using SNV data and the R2 of equation based on primitive reflectance was fairly low under

Environ Sci Pollut Res Table 2 Elements

Statistical description of the performances of calibration models and validation results associated with different heavy metals and spectral data Spectral data

SMLR

PCR

Calibration

Validation

Calibration

Validation

R2

RMSEC

RPD

r2

RMSEP

R2

RMSEC

RPD

r2

RMSEP

– 0.56

– 9.36

– 1.62

– 0.49

– 10.16

0.06 0.47

12.44 10.19

1.22 1.49

0.04 0.44

12.69 10.52

0.51 0.71

8.26 6.85

1.84 2.21

0.42 0.6

9.06 7.05

0.43 0.24

10.61 11.48

1.43 1.32

0.41 0.19

10.71 11.83

0.23 0.66

4.96 2.3

1.09 2.37

0.18 0.61

5.08 2.61

0.08 0.84

5.46 2.54

1 2.14

0.07 0.79

5.59 2.74

0.41 0.36

2.94 3.09

1.85 1.76

0.37 0.31

3.12 3.51

0.38 0.34

3.18 3.29

1.71 1.65

0.35 0.33

3.26 3.36

0.1 0.74

5.41 3.13

1.03 1.78

0.07 0.69

5.65 3.44

0.06 0.52

5.67 4.71

0.98 1.18

0.04 0.5

5.93 4.75

0.2

5.11

1.09

0.17

5.23

0.11

5.48

1.01

0.09

5.51

RD1 CR SNV

– 0.62 0.76 0.21 0.38

– 3.58 2.58 4.83 4.4

– 1.47 2.03 1.09 1.19

– 0.54 0.71 0.17 0.31

– 3.72 2.5 4.99 4.75

0.05 0.12 0.76 0.12 0.33

5.86 5.14 3.93 5.37 4.69

0.95 1.02 1.34 0.98 1.12

0.02 0.09 0.71 0.1 0.31

6.07 5.44 4.37 5.39 4.82

Zn

R RD1 CR SNV

– 0.68 0.77 0.31

– 5.99 5.62 8.54

– 1.62 1.73 1.14

– 0.63 0.67 0.24

– 6.12 5.78 8.69

0.03 0.68 0.18 0.12

10.01 8.33 9.57 9.89

0.97 1.17 1.01 0.98

0.01 0.64 0.17 0.08

10.45 8.79 9.63 10.17

As

R RD1 CR SNV

0.07 0.76 0.2 –

1.52 0.88 1.41 –

1.01 1.75 1.09 –

0.04 0.68 0.17 –

1.66 0.97 1.47 –

0.06 0.49 0.2 0.12

1.99 1.03 1.49 1.56

0.77 1.5 1.03 0.99

0.03 0.44 0.19 0.1

2.14 1.27 1.52 1.69

Cd

R RD1 CR SNV

0.32 0.73 0.33 0.49

0.08 0.06 0.08 0.07

1.19 1.73 1.17 1.28

0.23 0.66 0.27 0.39

0.11 0.006 0.086 0.077

0.08 0.63 0.21 0.23

0.094 0.085 0.09 0.089

1.01 1.12 1.06 1.07

0.05 0.62 0.18 0.2

0.11 0.088 0.094 0.091

Hg

R

0.08

0.0087

0.93

0.05

0.009

0.09

0.0084

0.96

0.007

0.0085

RD1 CR SNV

0.76 0.08 0.15

0.0042 0.0088 0.0081

1.92 0.92 1

0.71 0.07 0.12

0.004 0.008 0.008

0.55 0.18 0.15

0.0061 0.0079 0.0083

1.32 1.02 0.97

0.48 0.16 0.11

0.0068 0.008 0.0091

Cr

R RD1 CR SNV

Cu

Ni

Pb

R RD1 CR SNV R RD1 CR SNV R

Note: B–^ represents no data

the two methods. The optimal calibration was generated by SMLR and RD1 spectra (R2 = 0.76, RMSEC = 0.88 mg kg−1, RPD = 1.75), and the suboptimal model was created by PCR and first deviation transformed data (R 2 = 0.49, RMSEC = 1.03 mg kg−1, RPD = 1.5). For Cd, when the first mathematic algorithm was considered, the accuracies of models using original spectra and continuum removal data were almost consistent, of which the R2 values were approximately 0.3, RMSECs were 0.08 mg kg−1, and SNV transformation outperformed the primitive reflectance and CR treatment. Hence, RD1 data showed an adequate prediction in

SMLR (R2 = 0.73, RPD = 1.73), while there existed no reliable models for Cd using principal component regression strategy. The rules of modeling results of Hg were also similar with Cd. Calibration based on first deviation spectra, with R2 of 0.76, RMSEC of 0.004 mg kg−1, and RPD of 1.92, was the optimal model for prediction. The performance of different spectral data and modeling algorithms that validation results delineated was alike to the calibration set. Good models were still robust after verification. But the correlation between measured contents of heavy metals and predictive values in validation set was relatively

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lower, for the r2 in validation results were less than the corresponding R 2 values. And RMSEPs were greater than RMSECs as well. Every heavy metal in this experiment had an acceptable model (1.4 < RPD < 2) which can be used for concentration assessment. Some even had the predictive calibration that yielded good accuracy. On the basis of the amount of calibrations that R2 is above 0.5 and R2 values, the modeling consequence of Zn prevailed over other heavy metals and followed by the order of Pb > Hg > As > Ni > Cd > Cr > Cu. From the results of calibration and validation of models, we can state that the first deviation data was the most suitable indice in this study. Similar results were reported by Wu et al. (2007), Shi et al. (2016), and Galvez-Sola et al. (2013). Stenberg et al. (2010) indicated that the RD was by far the most popular spectral preprocessing techniques for soil property prediction using VNIR spectroscopy. Generally, SMLR outperformed PCR except Cu in this study. Though the principal components extracted from different spectral data represent the primary information, they had a weak link with the contents of heavy metals. In other word, the principal components may contain relatively few information about the spectral response of the heavy metals, since the signals of heavy metals are usually feeble due to their content level (Rathod et al. 2013) and may be eliminated during the analysis procedure. However, in stepwise multiple linear regression process, those significant reflectance or spectral values which had remarkable correlation with the concentrations of heavy metals were selected. As a consequence, SMLR is more suitable for model construction in this study. It can consider that SMLR is a powerful tool for predicting the contents of heavy metals, as Choe et al. (2008) found a fair relationship between spectral variations and heavy metals in soils using the modeling strategy, and Kemper and Sommer (2002) also deemed that calibration could be constructed with a high accuracy. Nonetheless, models can always be outputted during PCR process though some results were considerably unsuccessful, but many spectral indices are hard to predict the contents of heavy metals under the given F value during SMLR analysis. Besides, it is probably that linear regression is not fit for depicting the relation between spectral data and concentration values, and therefore, some non-linear modeling strategy should be further studied.

Conclusion This study demonstrated the application of VINR spectroscopy coupled with SMLR as well as PCR algorithms to offer an alternative for rapid concentration estimation of heavy metals, including Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg, using 70 soil samples collected from the sewage irrigation farmland of Longkou city. Average contents of 8 heavy metals were higher

than the respective background values, and the accumulation of Cd was more severe. In spite of that, no significant contamination occurred in this region. Model construction results showed that for Ni, Pb, As, Cd, and Hg, the optimal predictability was obtained by the RD1 data combined with SMLR approach with the coefficients of determination all above 0.6, while SNV was the most suitable treatment for the estimation of Cr and continuum removal data performed well in evaluating the contents of Zn under the same modeling strategy. First deviation transformed values as well as PCR can develop the most satisfactory prediction for Cu. Consequently, the overall calibration effect followed the order of RD1 > CR > SNV > R since feature bands were significantly protruded after deviation preprocessing and ultimately introduced in stepwise multiple linear regression analysis. The results of RPD revealed that all elements had an acceptable model that can be used for approximate assessment of heavy metals. As a result, we can suggest that VINR spectroscopy, first deviation transformation technique, as well as stepwise multiple linear regression method can be the economical tool used for rapid and accurate content prediction of heavy metals in soils of the study area. Additionally, further research should focus on the improvement of prediction accuracy. Acknowledgement This research was supported by National Natural Science Foundation of China (Grant No.41371395).

References Albrecht R, Joffre R, Petit JL et al (2008) Calibration of chemical and biological changes in cocomposting of biowastes using nearinfrared spectroscopy. Environ Sci Technol 43(3):804–811 Barthès BG, Brunet D, Hien E et al (2008) Determining the distributions of soil carbon and nitrogen in particle size fractions using nearinfrared reflectance spectrum of bulk soil samples. Soil Biol Biochem 40(6):1533–1537 Cai J, Chen Q, Wan X et al (2011) Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy. Food Chem 126(3):1354–1360 Chang C-W, Laird DA, Mausbach MJ et al (2001) Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Sci Soc Am J 65(2):480–490 Chary NS, Kamala CT, Raj DSS (2008) Assessing risk of heavy metals from consuming food grown on sewage irrigated soils and food chain transfer. Ecotoxicol Environ Saf 69(3):513–524 Chen H, An J, Wei S, Gu J (2015) Spatial patterns and risk assessment of heavy metals in soils in a resource-exhausted City, Northeast China. PLoS One 10(9):e0137694. doi:10.1371/journal. pone. 0137694 Choe E, van der Meer F, van Ruitenbeek F et al (2008) Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: a case study of the Rodalquilar mining area, SE Spain. Remote Sens Environ 112(7):3222–3233 Choe E, Kim KW, Bang S et al (2009) Qualitative analysis and mapping of heavy metals in an abandoned au–Ag mine area using NIR spectroscopy. Environ Geol 58(3):477–482

Environ Sci Pollut Res Dai J-R, Pang X-G, Yu C et al (2011) Geochemical features and contamination assessment of soil elements in east Shandong Province. Geol China 38(5):1387–1395 (in chinese) Galvez-Sola L, Morales J, Mayoral AM et al (2013) Estimation of parameters in sewage sludge by near-infrared reflectance spectroscopy (NIRS) using several regression tools. Talanta 110:81–88 Gholizadeh A, Borůvka L, Vašát R, Saberioon M, Klement A, Kratina J et al (2015) Estimation of potentially toxic elements contamination in anthropogenic soils on a BrownCoal mining dumpsite by reflectance spectroscopy: a case study. PLoS One 10(2):e0117457. doi:10. 1371/journal.pone.0117457 Gomez C, Lagacherie P, Coulouma G (2008) Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma 148(2):141–148 Grzegorz S, Gregory WM, Tomasz IS et al (2004) Near-and-midinfrared diffuse reflectance spectroscopy for measuring soil metal content. J Environ Qual 33:2056–2069 Hu W, Wang H-Y, Zha T-G et al (2008) Soil heavy metal accumulation and speciation in a sewage-irrigated area along the Liangshui River, Beijing. Ecol Environ 17(4):1491–1497 (in chinese) Jamali MK, Kazi TG, Arain MB et al (2009) Heavy metal accumulation in different varieties of wheat (Triticum aestivum L.) grown in soil amended with domestic sewage sludge. J Hazard Mater 164(2): 1386–1391 Kemper T, Sommer S (2002) Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ Sci Technol 36(12):2742–2747 Khan S, Cao Q, Zheng YM et al (2008) Health risks of heavy metals in contaminated soils and food crops irrigated with wastewater in Beijing, China. Environ Pollut 152(3):686–692 Ko HJ, Choi HL, Park HS et al (2004) Prediction of heavy metal content in compost using near-infrared reflectance spectroscopy. Asian Australas J Anim Sci 17(12):1736–1740 Kooistra L, Wehrens R, Leuven R et al (2001) Possibilities of visible– near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Anal Chim Acta 446(1):97–105 Kooistra L, Leuven RSEW, Wehrens R et al (2003) A Comparision of methods to relate grass reflectance to soil metal contamination. Int J Remote Sens 24(24):4995–5010 Lu Y, Song S, Wang R et al (2015) Impacts of soil and water pollution on food safety and health risks in China. Environ Int 77:5–15 Lv JS, Liu Y, Zhang Z-L et al (2013) Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils. J Hazard Mater 261:387–397 Lv J, Liu Y, Zhang Z-L et al (2014) Multiple geostatistical analyses of heavy metals in soils: spatial multi-scale variations in Wulian, eastern China. Ecotoxicol Environ Saf 107:140–147 Martens H, Naes T (1989) Multivariate calibration. John Wiley & Sons, Chichester Pandit CM, Filippelli GM, Li L (2010) Estimation of heavy-metal contamination in soil using reflectance spectroscopy and partial leastsquares regression. Int J Remote Sens 31(15):4111–4123 Park RS, Gordon FJ, Agnew RE et al (1997) The use of near infrared reflectance spectroscopy on dried samples to predict biological parameters of grass silage. Anim Feed Sci Technol 68(3):235–246 Peng S (2011) Water resources strategy and agricultural development in China. J Exp Bot 62(6):1709–1713 Piao S, Ciais P, Huang Y et al (2010) The impacts of climate change on water resources and agriculture in China[J]. Nature 467(7311):43– 51 Rathod PH, Rossiter DG, Noomen MF et al (2013) Proximal spectral sensing to monitor phytoremediation of metal-contaminated soils. Int J Phytoremediation 15(5):405–426

Rattan RK, Datta SP, Chhonkar PK et al (2005) Long-term impact of irrigation with sewage effluents on heavy metal content in soils, crops and groundwater—a case study. Agric Ecosyst Environ 109(3):310–322 Ren H-Y, Zhuang D-F, Singh A-N et al (2009) Estimation of as and cu contamination in agricultural soils around a mining area by reflectance spectroscopy: a case study. Pedosphere 19(6):719–726 Rinnan Å, van den Berg F, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal Chem 28(10):1201–1222 Roberts CA, Workman J, Reeves JB (2004) Near-infrared spectroscopy in agriculture. American Society of Agronomy, Madison Rossel RAV, McGlynn RN, McBratney AB (2006a) Determining the composition of mineral-organic mixes using UV–Vis–NIR diffuse reflectance spectroscopy. Geoderma 137(1):70–82 Rossel RAV, Walvoort DJJ, McBratney AB et al (2006b) Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1):59–75 Senesi GS, Dell’Aglio M, Gaudiuso R et al (2009) Heavy metal concentrations in soils as determined by laser-induced breakdown spectroscopy (LIBS), with special emphasis on chromium. Environ Res 109(4):413–420 Shi T, Chen Y, Liu Y et al (2014) Visible and near-infrared reflectance spectroscopy—an alternative for monitoring soil contamination by heavy metals. J Hazard Mater 265:166–176 Shi T, Wang J, Chen Y et al (2016) Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants. Int J Appl Earth Obs Geoinf 52:95–103 Singh AN (2009) Estimation of as and cu contamination in agricultural soils around a mining area by reflectance spectroscopy: a case study. Pedosphere 6:006 Song Y, Li F, Yang Z et al (2012) Diffuse reflectance spectroscopy for monitoring potentially toxic elements in the agricultural soils of Changjiang River Delta, China. Appl Clay Sci 64:75–83 Song L, Jian J, Tan DJ et al (2015) Estimate of heavy metals in soil and streams using combined geochemistry and field spectroscopy in wan-sheng mining area, Chongqing, China. Int J Appl Earth Obs Geoinf 34:1–9 Stenberg B, Rossel RAV, Mouazen AM et al (2010) Chapter five-visible and near infrared spectroscopy in soil science. Adv Agron 107:163– 215 Sun Y-F, Wang Z-W, Meng W-Q et al (2015) Contents and health risk assessment of heavy metals in wheat and Rice grown in Tianjin sewage irrigation area, China. J Agro-Environ Sci 34(4):679–685 (in Chinese) Tan K, Ye Y-Y, Du P-J et al (2014) Estimation of heavy metal concentrations in reclaimed mining soil using reflectance spectroscopy. Spectrosc Spectr Anal 34(12):3317–3322 Vasques GM, Grunwald S, Sickman JO (2008) Comparison of multiple methods for inferential modeling of soil carbon using visible/nearinfrared spectra. Geoderma 146(1):14–25 Wang J, Cui L, Gao W et al (2014) Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy. Geoderma 216:1–9 Wang Q, Liu J, Cheng S (2015) Heavy metals in apple orchard soils and fruits and their health risks in Liaodong peninsula, Northeast China. Environ Monit Assess 187(1):1–8 Wu Y-Z, Chen J, Wu X-M et al (2005) Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils. Appl Geochem 20(6):1051–1059 Wu Y-Z, Chen J, Ji J et al (2007) A mechanism study of reflectance spectroscopy for investigating heavy metals in soils. Soil Sci Soc Am J 71(3):918–926 Wu Q, Leung JYS, Geng X et al (2015) Heavy metal contamination of soil and water in the vicinity of an abandoned e-waste recycling site:

Environ Sci Pollut Res implications for dissemination of heavy metals. Sci Total Environ 506:217–225 Xie X-L, Sun B, Hao H-T (2007) Relationship between visible-near infrared reflectance spectroscopy and heavy metal of soil concentration. Spectrosc Spectr Anal 44(6):982–992 (in Chinese) Xie W-Y, Fan G-S, Zhou H-P et al (2011) Access of heavy metals pollution of the sewage irrigation region in Taiyuan, China. J AgroEnviron Sci 30(8):1553–1560 (in Chinese) Xie X-L, Zhang X-P, Sun B (2012) Visible and near-infrared diffuse reflectance spectroscopy for prediction of soil properties near a copper smelter. Pedosphere 22(3):351–366

Xue Z-J, Liu S-Q, Liu Y-L et al (2012) Health risk assessment of heavy metals for edible parts of vegetables grown in sewage-irrigated soils in suburbs of Baoding City, China. Environ Monit Assess 184(6): 3503–3513 Yang Z-L, Han L-J, Fan X (2006) Rapidly estimating nutrient contents of fattening pig manure from floor scrapings by near infrared reflectance spectroscopy. J Near Infrared Spectrosc 14(4):261–268 Zormoza R, Guerrero C, Mataix-Solera J et al (2008) Near infrared spectroscopy for determination of various physical, chemical and biochemical properties in Mediterranean soils. Soil Biol Biochem 40(7):1923–1930