An investigation of the effect of operational conditions on a sequential extraction procedure for arsenic in soil in Thailand

Chemosphere 242 (2020) 125230

Contents lists available at ScienceDirect

Chemosphere journal homepage: www.elsevier.com/locate/chemosphere

An investigation of the effect of operational conditions on a sequential extraction procedure for arsenic in soil in Thailand Chatchai Srithongkul a, Chanida Krongchai a, Choochad Santasup b, Sila Kittiwachana a, c, d, * a

Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand Department of Plant and Soil Sciences, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand Environmental Science Research Center (ESRC), Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand d Center of Excellence for Innovation in Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand b c

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


Factors affecting the extraction efficiency of sequential extraction procedure were studied.
A total of 19 extraction variables was screened using Plackett-Burman design.
The screened variables were further investigated using central composite design.
The case study soil needed less operation time with lower extraction concentrations.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 August 2019 Received in revised form 6 October 2019 Accepted 25 October 2019 Available online xxx

Sequential Extraction Procedure (SEP) can be used to evaluate the toxicity characteristics of heavy metals in soil, including arsenic (As), by separating the metals into several different fractions using selective extraction solvents in sequence. To accomplish this separation task, various factors that are known to affect the extraction process should be carefully considered. This research aimed to investigate the effect of the operational conditions on the SEP for As in soil using experimental designs. In the first step, a Plackett-Burman design was used twice to screen the important extraction variables from a total of 19 studied variables. As a result, SSR, extraction time of the first fraction (F1), and concentrations of sodium acetate (NaOAc) in F2 and ammonium oxalate (NH4Ox) in F6 were identified as significant to the amount of the extracted As. The selected variables were further investigated using a central composite design with response surface methodology. The optimized SEP characterized by 1:75 g:mL of SSR, an extraction time of 7 h 20 min of F1, 0.16 M of NaOAc and 0.11 M of NH4OAc were applied to extract a sample from contaminated agricultural soil obtained from the north of Thailand. The fractionation result was compared with the result obtained from a previously reported SEP method. It was found that similar extraction results could be achieved (91e97% As recovery). However, the optimized method revealed certain advantages in that it required dramatically less operation time (from 68 h to 32 h) and lower concentrations of the extraction solvents. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Lena Q. Ma Keywords: Arsenic contamination Sequential extraction procedure Arsenic fractionation Plackett-burman design Central composite design Arsenic remediation

* Corresponding author. Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand. E-mail address: [email protected] (S. Kittiwachana). https://doi.org/10.1016/j.chemosphere.2019.125230 0045-6535/© 2019 Elsevier Ltd. All rights reserved.

2

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

1. Introduction Arsenic (As) is among the most hazardous chemical elements as it is known to have adverse effects on the environment and living organisms. In soil, the As element mainly exists in four oxidation states including arsine (As(-III)), element arsenic (As(0)), arsenite (As(III)) and arsenate (As(V)). Each of the As compounds possesses different characteristics and, more importantly, presents differing levels of toxicity. For example, As(III) is considered more toxic than As(V) with greater mobility (Jain and Ali, 2000; Larios et al., 2012). Organic As compounds, such as dimethylarsenate (DMA) and methylarsonate (MMA), are generally known to be less hazardous than inorganic As compounds such as As(III) and As(V) (Hughes et al., 2011). Some As compounds present in iron-As and calciumAs compounds, such as FeAsO4, Ca3(AsO4)2 and CaHAsO3, have less bioactivity and are more stable in the soil (Naseri et al., 2014; Yoon et al., 2010). For this reason, individual concentrations of As should be evaluated rather than a total concentration of the element in order to determine the degree of As contamination in the soil. However, due to the substantial complexity of the soil structure, it is not easy to evaluate individual As compounds present in the soil. Since As compounds are bound to soil with different strength, one possible way to evaluate As compounds is to separate the different varieties of the As compounds into several forms or fractions based on their solubility accounting for the most labile to the most stable forms. Subsequently, the extracted As in each of the As fractions can be related to certain specific characteristics. For example, most labile fractions are more toxic and display greater levels of bioavailability. On the other hand, more stable As fractions can be located in the next group and can be classified as As with less mobility (Bagherifam et al., 2014; Larios et al., 2013). To accomplish this separation task, Sequential Extraction Procedure (SEP) was employed, wherein various extraction solutions with different leaching strengths were sequentially used to extract the As fractions that were differently bound with the relevant soil particles. Presently, a number of SEPs have been reported (Javed et al., 2013; Keon et al., 2001; Tessier et al., 1979; Wenzel et al., 2001). However, each of the proposed SEPs have adopted different extraction conditions, such as different amounts of soil sample per volume of extractant or Soil Solution Ratio (SSR), as well as different extraction times, washing times, and solvent types along with the relevant concentrations (Chunguo and Zihui, 1988; Goh and Lim, 2005; Huang and Kretzschmar, 2010; Javed et al., 2013; Keon et al., 2001; Kreidie et al., 2011; Larios et al., 2013; Tessier et al., 1979; Wenzel et al., 2001). For example, the first step of the Javed method (Javed et al., 2013) began with the most labile arsenic that was extracted with water after 0.5 h, while the Wenzel method (Wenzel et al., 2001) was initiated with loosely adsorbed arsenic where the As was extracted using ammonium sulfate over 4 h. In some cases, the first As fraction was initiated with the same extractant, yet the extraction times were still different. For example, the extraction times of the first fraction were 0.5 h and 24 h for the Javed and Larios methods (Larios et al., 2013), respectively. Variations in extraction conditions can result in different extraction outcomes and may significantly influence the degree of extraction efficiency, and can also lead to a diverse range of conclusions from the experiments. Design of Experiment (DoE) is a set of multivariate analysis techniques that aim to acquire the maximum amount of information from an experimental system, whereas the number of experimental runs is small or minimum (Brereton, 2003). Based on the established multivariate mathematical relationship, the importance of the studied variables can be evaluated in accordance with the interactions that take place alongside them. Plackett-Burman

Design (PBD) is a design that is often used to evaluate a large number of variables that influence the quality of the model (Dejaegher and Vander Heyden, 2011). This design reduces the number of experimental runs by ensuring that the degree of orthogonality among the studied variables is remained. Thus, PBD can be used for the screening of important or significant variables. On the other hand, Central Composite Design (CCD) does not anticipate the degree of orthogonality that exists between the studied factors in the design. Instead, this design systematically assigns the samples to be placed across the studied space in the greatest amount possible. This is to ensure that each of the experimental runs effectively account for variations in the studied system. Since interactions and quadratic terms can be included, interactions that take place between the studied factors can be evaluated. In addition, the established mathematical model obtained from the CCD observation could be used to provide quantitative predictions of the studied system, thus allowing for the possibility of identifying the optimal conditions that result, along with the best yields of the predicted parameters or responses (Dejaegher and Vander Heyden, 2011). For example, the optimi~ eiro zation of sample preparation for As determination (Moreda-Pin et al., 2002) and the extractions of heavy metals in the refinery catalyst (Gerayeli et al., 2013) were successfully achieved by using the CCD models. Recently, an extraction method used for investigating As in paddy rice was optimized using a CCD model based on the significant extraction variables screened by a PBD model (Ma et al., 2016). This research study aimed to investigate the effect of various operational conditions on a SEP for arsenic in soil based on the use of experimental design methods. The SEP used in this study has been previously reported (Srithongkul et al., 2019). This procedure was adapted from the 5-step Wenzel method (Wenzel et al., 2001) and the 10-step Javed method (Javed et al., 2013). The modified SEP was performed under common conditions and some hazardous and unstable chemicals, such as concentrated hydrofluoric acid (HF) and Ti-citrate-EDTA-bicarbonate, were avoided. Still, the extracted fractions that resulted from the modified SEP could have some relation to other important information associated with the As that was present in the contaminated soil. In the first step, all of the potentially important extraction variables, including SSR, concentrations of the extraction solvents, extraction times and washing times of each extraction step, were screened using PBD. After that, the variables identified as being significant were evaluated and finely evaluated using CCD to obtain the optimal extraction condition. To check the applicability of the optimized method, the developed protocol was compared with the previously reported SEP using As contaminated soil collected from an agricultural area in the north of Thailand. The research study provided a simplified extraction protocol for As fractionation in contaminated soil, which clearly reduced operational time and costs as well as the amount of chemical waste.

2. Materials and methods 2.1. Sample collection and soil chemical analysis Soil samples were collected from an agricultural area in Samoeng District of Chiang Mai Province, located in the north of Thailand. The samples were collected from the surface layer of the soil (0e15 cm) using a stainless-steel blade. To ensure that the collected soil was a representative sample, the study area of 7  30 square m was divided into 10 sub-grid cells at 6  3.5 m per grid. For each cell, 10 soil samples were randomly collected resulting in a total of 10  10 ¼ 100 samples. After being air-dried, the soil

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

samples were sieved through a 2-mm sieve and mixed uniformly with each other in a polyethylene bag. Soil physico-chemical characterization was determined as follows. Soil textural analysis was performed using the pipette method (Miller and Miller, 1987). Soil pH was determined using 1:1 soil:H2O (Eckert and Sims, 1995). Potassium and calcium were determined using ammonium acetate pH 7 via the extraction method (Wolf and Beegle, 2011). Soil organic matter (%OM) was analyzed using the Walkley-Black method (Schulte, 1995). Micronutrients (Fe, Cu, Mn and Mg) were determined using the DTPA extraction method (Lindsay and Norvell, 1978). The total As content and residual As present in the soil were determined using di-acid (nitric acid (HNO3) and perchloric acid (HClO4)) digestion methods (Estefan et al., 2013). After the digestion process, the soil samples were analyzed with Hydride Generation Atomic Adsorption Spectroscopy (HGAAS) (Analytik Jena model ZEEnit 700) that was connected to the batch system (HS55 batch modular, Analytik Jena). The samples were measured in triplicate. Certified Reference Material (CRM) (CP-1, AgroMAT Compost) was employed to evaluate quality control and assurance.

3

glassware was cleaned by being soaked in the cleaner reagent (Decon Labs. Decon 90) for at least 12 h and then rinsed with deionized water before being used. 2.3. Obtaining optimized protocol

2.2. Sequential extraction procedure experiment

2.3.1. Variable screening using Plackett-Burman design Plackett-Burman Design (PBD) is usually performed if there are a large number of variables that might be relevant to the modeling. This design could be regarded as a compact or highly fractional factorial design since the number of experiments is intensely reduced (Vanaja and Shobha Rani, 2007). PBD aims to examine a mathematical relationship between a set of predictive variables and dependent variables or responses. Consequently, this relationship information can be used to evaluate whether the predictive variables are important or are significant in relation to the changes in the studied responses. In this research study, the extraction factors, including Soil Solution Ratio (SSR labeled as A, g:mL), extraction time (TE, h), concentration of extraction solution (C, M) and washing time (TW, min) resulting in a total of 19 extraction variables, were initially used in the screening process. The amount of extracted As was used as the response. The protocol of SEP was as follows:

The studied SEP consists of 8 As fractions. Fraction 1 (F1) is comprised of soluble arsenic. It is the first step of extraction that is aimed at extracting the greatest amount of labile arsenic. This fraction is understood to be readily bioavailable for plants because it can be easily taken up by plants (Larios et al., 2013). F2 is made up of loosely adsorbed arsenic. Often, the first two fractions are defined as being loosely adsorbed, exchangeable or non-specifically sorbed arsenic (Javed et al., 2013; Wenzel et al., 2001). This As is usually weakly bound to the solid particles that are found throughout the outer-sphere complexes and is equilibrated with the aqueous phase, therefore, becoming bioavailable (Javed et al., 2013; Sarkar et al., 2005). F3 is comprised of strongly adsorbed arsenic. This fraction can be recognized as As with less mobility because the As in this fraction is adsorbed on metal oxide surfaces such as iron (Fe), aluminium (Al) and manganese (Mn), forming inner-sphere complexes in soil constituents (Adriano, 2001; Javed et al., 2013; Larios et al., 2013). F4 is made up of carbonate bound As. These As compounds are considered less mobile due to the precipitation that occurs with calcium forming in calcium arsenate nez et al., 2012). F5 is comprised of arsenic that is co(Moreno-Jime precipitated with amorphous Fe, Al and Mn oxyhydroxides. Various types of reagents, such as dithionite-citrate-bicarbonate, hydrochloric acid, oxalic acid, hydroxylamine hydrochloric with acetic acid, and ammonium oxalate (NH4Ox) with oxalic acid called Tamm’s reagent (Chao and Zhou, 1983; Javed et al., 2013; Pawluk, 1972; Tessier et al., 1979), were used to extract As in this fraction. F6 is made up of arsenic co-precipitated with crystalline Fe and Al oxyhydroxide which can be extracted using NH4Ox with ascorbic acid (Wenzel et al., 2001). F7 is made up of organic matter and secondary sulfides. This fraction is characterized by the complexation and peptization properties of natural organic matter, such as the humic and fulvic acids that likely influence arsenic retention in the soil (Sarkar et al., 2007). Lastly, F8 is made up of residual As where the concentration of As in this fraction can be determined using the acid digestion method (Javed et al., 2013; Keon et al., 2001; Larios et al., 2013; Wenzel et al., 2001). Details of the arsenic fractions, target phases and extractants are summarized in Table 1. All extraction solutions were prepared using analytical grade materials. The pH values of the extractants, if required, were adjusted with sodium hydroxide (NaOH) or appropriate acid (HOAc or HNO3) to bring the pH to the desired value. All

1. Soluble arsenic (F1); the soil sample was weighed in a centrifuge tube (Corning, high density polyethylene) A g:mL and then shaken on a platform shaker (GFL 3006) with a reciprocal movement at 250 rpm in deionized water (Milli-Q, resistivity 15 MU) for TE1 h at room temperature. After that, the sample was centrifuged (benchtop centrifuge, MSE) and the supernatant was separated. The centrifugation time of 30 min at 3500 rpm was used to ensure that the colloidal materials in the soil sample were separated (Gimbert et al., 2005). The supernatant was then decanted without removing any sediment particles and filtered through 2.5 mm cellulose filters (Whatman’s filter paper grade 5). The decanted extractant was acidified at 1% of the final concentration of HNO3 to prevent any precipitation before HGAAS analysis. 2. Loosely adsorbed arsenic (F2); the residual soil sample from the previous step was combined with A g:mL of C2 M NaOAc (pH 8.2). The sample was shaken at 250 rpm for TE2 h. After completion of extraction, the sample was centrifuged, separated and filtered. The soil sample was then washed with deionized water for T W2 min. The washing solution was combined with the decanted solution of F2 and then acidified as has been described in the previous step. 3. Strongly adsorbed arsenic (F3); A g:mL of C3 M NaH2PO4 (pH 5) was added to the residual soil sample brought forward from the previous step. The soil sample was shaken at 250 rpm for TE3 h. After that, the sample was centrifuged, separated and filtered. The filtered soil was washed for T W3 min. The washing solution was combined with the decanted solution of F3 and then acidified as has been described previously. 4. Carbonate bound arsenic (F4); A g:mL of C4 M NaOAc (pH 5) was added into the residual soil sample from the previous step. The soil sample was shaken at 250 rpm for TE4 h. After that, the sample was centrifuged, separated and filtered. The soil was then washed for T W4 min. The washing solution was combined with the decanted solution of F4 and then acidified as has been explained in the previous step. 5. Arsenic co-precipitated with amorphous Fe, Al and Mn oxyhydroxides (F5); A g:mL of Tamm’s reagent (C5 M NH4Ox, pH 3) was added into the residual soil sample brought forward from the previous step. The soil sample was shaken at 250 rpm under dark conditions for TE5 h. Subsequently, the sample was

4

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

Table 1 Sequential extraction procedure of arsenic fractionation. Fractions

Names

Extractantsa

Studied Variablesb

F1 F2 F3 F4 F5 F6 F7 F8

Soluble arsenic Loosely adsorbed arsenic Strongly adsorbed arsenic Carbonate bound with arsenic Arsenic co-precipitated with amorphous Fe, Al and Mn oxyhydroxide Arsenic co-precipitated with crystalline Fe and Al oxyhydroxide Organic matter and secondary sulfides Residual arsenic

Deionized water NaOAc, pH 8.2 NaH2PO4, pH 5 NaOAc, pH 5 with HOAc Tamm’s reagent (ammonium oxalate/oxalic acid, pH 3) NH4Ox with 0.1 M ascorbic acid, pH 3.25 30% H2O2 and NH4OAc (1:2), pH 2 HNO3 þ HClO4 digestion

TE1 TE2, TE3, TE4, TE5, TE6, TE7, e

C2, C3, C4, C5, C6, C7,

TW2 TW3 TW4 TW5 TW6 TW7

a NaOAc ¼ sodium acetate, NaH2PO4 ¼ sodium phosphate, HOAC ¼ acetic acid, NH4Ox ¼ ammonium oxalate, NH4OAc ¼ ammonium acetate, HNO3 ¼ nitric acid and HClO4 ¼ perchloric acid. b TE ¼ extraction time, C ¼ concentration and Tw ¼ washing time.

centrifuged, separated and filtered. The residual was washed for T W5 min. The washing solution was combined with the decanted solution of F5 and then acidified as has been explained in the previous step. 6. Arsenic co-precipitated with crystalline Fe and Al oxyhydroxides (F6); A g:mL of C6 M NH4Ox with 0.1 M ascorbic acid (pH 3.25) was added into the residual soil sample brought forward from the previous step. The soil sample was immersed in water in a basin at 95  C for TE6 h. After being allowed to cool down, the sample was centrifuged, separated and filtered. The residual was washed for T W6 min. The washing solution was combined with the decanted solution of F6 and then acidified as described in the previous step. 7. Organic matter and secondary sulfide bound arsenic (F7); A g:mL of 30% H2O2 þ C7 M NH4OAc at a ratio of 1:2 (pH 2) was added to the residual soil sample. The soil sample was shaken at 250 rpm for TE7 h. Subsequently, the soil sample was centrifuged, separated and filtered. The sample was acidified according to the previously described step. The operational conditions reported in previous SEP studies (Chunguo and Zihui, 1988; Goh and Lim, 2005; Huang and Kretzschmar, 2010; Javed et al., 2013; Keon et al., 2001; Kreidie et al., 2011; Larios et al., 2013; Tessier et al., 1979; Wenzel et al., 2001) have been summarized in supplementary Table S1. The selections of the low and high values of the variables for the PBD study are summarized in Table 2. The chosen range was decided based on a criterion that the difference between the low and high levels for each variable should allow the investigation of the effect on the studied response and the range was adequate to examine the interaction among the other variables on the constructed PBD model. It is noted that the levels of the studied variable were given by e (minus) for the low level and þ (plus) for the high level. The zero-level where the variable was set at the middle value was also included to avoid the risk of missing non-linear relationship in the middle of the model (Lundstedt et al., 1998). Notably, the SSR ratio was kept constant for all the extraction steps in each experimental run. 2.3.2. Model optimization using central composite design In contrast to PBD, which aims to determine the influence of a number of factors on a response, CCD, sometimes called a response surface design, aims to define the operational conditions that result in a maximum yield of the response (Vanaja and Shobha Rani, 2007). CCD incorporates additional experiments into the model to provide an adequate number of degrees of freedom so that the calculations of the interactions and quadratic terms can be enabled. As a result, a mathematical estimation for the optimal conditions of the model is achieved. The total number of experiments (N) was calculated by applying the following equation (Sarabia and Ortiz,

2009):

N ¼ 2k þ 2k þ nc

(1)

where k is the number of factors, 2k represents the number of factorial points, 2k is the number of axial points, and nc is the number of replicates at the central point. The experimental data and the response were then fitted to a second-order polynomial model. If the number of the studied variables is 2 (k ¼ 2), the estimation of the regression coefficients can be as follows:

y ¼ b0 þ b1 x1 þ b2 x2 þ b12 x1 x2 þ b11 x21 þ b22 x22

(2)

where y is the response variable, in this case, it is the amount of the As extracted in F1 to F7. The parameters of x1 to x2 refer to the independent variables used in the investigation. Notably, b0, b1, b11 and b12 represent the regression coefficients of the intercept, linear, quadratic and interaction terms, respectively, in the polynomial equation. In this research study, the As content in F1 to F7 was used in the screening and optimization processes (PBD and CCD) because the residual amount of As present in F8 was generally determined by the standard acid digestion method (Javed et al., 2013; Keon et al., 2001; Larios et al., 2013; Wenzel et al., 2001). The optimized protocol was applied to the soil sample collected from a contaminated area in an agricultural field located in the north of Thailand. In order to make a valid comparison, the previously reported SEP method (Srithongkul et al., 2019) was also performed. 2.4. Statistical analysis Statistical analysis was performed with Statistix, version 8. Data were compared by variance analysis (ANOVA). The significant differences observed among the different protocols were compared by Least Significant Difference (LSD). The design of the experiment for PBD and CCD was implemented using Minitab 17. The calculation of optimized protocol for each variable in the CCD model was achieved using in-house MATLAB scripts (MATLAB V10.0, The Math Works Inc., Natick). 3. Results and discussion 3.1. Characteristics of the studied soil The characteristics of the soil samples are presented in supplementary material, Table S2. A soil pH value of 7.53 ± 0.07 indicated that the soil was slightly alkaline, which was appropriate for plant growth. According to the soil texture analysis, the soil was classified as clay. Additionally, the %OM was relatively low (1.51 ± 0.17%). The total concentration of As in the soil was

Table 2 Plackett-Burman design with 19 independent variables (first screening) and response (amount of As extracted from F1 to F7). Runs A: SSR (g:mL)

F1: soluble F2: loosely TE1: Time

TE2: Time

F3: strongly

TE3: C2: NaOAc TW2: (M) Washing Time time (min)

1:10 ()

2 3 4

1:10 () 24 h (þ) 5 h (þ) 0.005 () 10 () 1:100 (þ) 24 h (þ) 1 h () 1 (þ) 30 (þ) 1:10 () 30 min () 5 h (þ) 1 (þ) 10 ()

5 6

1:100 (þ) 30 min () 1 h () 1 (þ) 30 (þ) 1:100 (þ) 24 h (þ) 5 h (þ) 0.005 () 10 ()

7

1:10 ()

8

1:100 (þ) 24 h (þ)

9

1:100 (þ) 30 min () 5 h (þ) 1 (þ)

10

1:100 (þ) 30 min () 5 h (þ) 0.005 () 30 (þ)

11

1:10 ()

24 h (þ)

1 h () 1 (þ)

10 ()

12

1:100 (þ) 24 h (þ)

5 h (þ) 1 (þ)

10 ()

13 14 15

1:100 (þ) 30 min () 5 h (þ) 1 (þ) 10 () 1:10 () 30 min () 1 h () 0.005 () 30 (þ) 1:100 (þ) 24 h (þ) 1 h () 0.005 () 10 ()

16

1:10 ()

24 h (þ)

1 h () 1 (þ)

30 (þ)

17

1:10 ()

24 h (þ)

5 h (þ) 1 (þ)

30 (þ)

18

1:100 (þ) 30 min () 1 h () 0.005 () 10 ()

19

1:10 ()

30 min () 1 h () 0.005 () 10 ()

20

1:10 ()

24 h (þ)

21e 1:55 (0) 25

30 min () 5 h (þ) 0.005 () 30 (þ)

30 min () 1 h () 1 (þ)

10 ()

1 h () 0.005 () 30 (þ) 30 (þ)

5 h (þ) 0.005 () 30 (þ)

12 h 3 h (0) 0.50 (0) 15 min (0)

20 (0)

TE4: TW3: Washing Time time (min)

F5: amorphous

TE5: Time C5: NH4Ox C4: NaOAc TW4: (M) Washing (M) time (min)

F6: crystalline

F7: organic

As extracted (mg kg1)

TE6: Time C6: NH4Ox TW5: Washing (M) time (min)

TE7: Time C7: TW6: Washing NH4OAc time (M) (min)

0.50 (þ)

30 (þ)

1 h ()

0.1 ()

30.26

8 h () 1 (þ)

30 (þ)

5 h (þ) 1 (þ)

10 ()

1 h ()

0.50 (þ)

30 (þ)

0.05 () 0.50 (þ) 0.50 (þ)

10 () 30 (þ) 30 (þ)

16 h (þ) 1 h () 16 h (þ)

3.2 (þ) 0.1 () 3.2 (þ)

37.71 26.12 31.11

10 () 10 ()

30 min () 2 h (þ) 2 h (þ) 30 min () 2 h (þ) 2 h (þ)

8 h () 8 h () 24 h (þ) 8 h () 24 h (þ) 24 h (þ) 24 h (þ) 24 h (þ) 24 h (þ) 24 h (þ) 8 h ()

0.05 () 0.05 () 1 (þ)

30 (þ) 10 () 10 ()

1 h () 1 (þ) 1 h () 1 (þ) 1 h () 0.5 ()

10 () 10 () 10 ()

4 h (þ) 4 h (þ) 4 h (þ)

0.50 (þ) 0.04 () 0.04 ()

30 (þ) 30 (þ) 30 (þ)

1 (þ) 1 (þ)

30 (þ) 10 ()

1 h () 0.5 () 5 h (þ) 1 (þ)

10 () 10 ()

1 h () 1 h ()

0.50 (þ) 0.04 ()

0.05 () 0.05 ()

30 (þ) 30 (þ)

16 h (þ) 1 h ()

3.2 (þ) 3.2 (þ)

30.49 42.95

0.05 ()

30 (þ)

5 h (þ) 1 (þ)

30 (þ)

1 h ()

0.04 ()

30 (þ)

2 h (þ)

0.05 ()

30 (þ)

16 h (þ)

0.1 ()

32.07

0.05 ()

30 (þ)

5 h (þ) 0.5 ()

10 ()

1 h ()

0.04 ()

30 (þ)

0.50 (þ)

10 ()

16 h (þ)

3.2 (þ)

37.00

10 ()

4 h (þ)

0.50 (þ)

10 ()

0.05 ()

10 ()

16 h (þ)

0.1 ()

32.81

1 h () 0.5 ()

30 (þ)

4 h (þ)

0.04 ()

30 (þ)

30 min () 30 min () 2 h (þ)

0.05 ()

10 ()

5 h (þ) 1 (þ)

1 (þ)

30 (þ)

0.05 ()

10 ()

1 h ()

0.1 ()

31.84

1 (þ)

30 (þ)

5 h (þ) 0.5 ()

10 ()

4 h (þ)

0.50 (þ)

10 ()

2 h (þ)

0.50 (þ)

10 ()

1 h ()

0.1 ()

28.53

1 (þ)

30 (þ)

1 h () 1 (þ)

30 (þ)

1 h ()

0.04 ()

10 ()

0.50 (þ)

10 ()

16 h (þ)

0.1 ()

22.47

8 h () 0.05 () 8 h () 1 (þ) 8 h () 1 (þ)

10 () 10 () 10 ()

5 h (þ) 0.5 () 5 h (þ) 1 (þ) 5 h (þ) 0.5 ()

30 (þ) 30 (þ) 30 (þ)

1 h () 4 h (þ) 4 h (þ)

0.50 (þ) 0.04 () 0.50 (þ)

30 (þ) 10 () 30 (þ)

0.50 (þ) 0.50 (þ) 0.05 ()

10 () 10 () 30 (þ)

1 h () 16 h (þ) 16 h (þ)

3.2 (þ) 3.2 (þ) 0.1 ()

30.91 26.52 51.24

24 h 1 (þ) (þ) 8 h () 0.05 ()

10 ()

1 h () 1 (þ)

30 (þ)

1 h ()

0.50 (þ)

30 (þ)

0.05 ()

10 ()

1 h ()

3.2 (þ)

36.59

30 (þ)

5 h (þ) 0.5 ()

30 (þ)

4 h (þ)

0.04 ()

10 ()

0.05 ()

30 (þ)

1 h ()

3.2 (þ)

29.89

24 h 0.05 () (þ) 8 h () 0.05 ()

30 (þ)

1 h () 1 (þ)

30 (þ)

4 h (þ)

0.50 (þ)

10 ()

0.50 (þ)

30 (þ)

1 h ()

3.2 (þ)

41.53

10 ()

1 h () 0.5 ()

10 ()

1 h ()

0.04 ()

10 ()

0.05 ()

10 ()

1 h ()

0.1 ()

24.94

24 h (þ) 16 h (0)

0.05 ()

10 ()

1 h () 0.5 ()

30 (þ)

1 h ()

0.50 (þ)

10 ()

30 min () 2 h (þ) 2 h (þ) 30 min () 30 min () 30 min () 30 min () 30 min () 2 h (þ)

0.50 (þ)

30 (þ)

16 h (þ)

0.1 ()

28.24

0.52 (0)

20 (0)

3 h (0) 0.75 (0)

20 (0)

2h 30 min (0)

0.52 (0)

20 (0)

0.27 (0)

20 (0)

8h 30 min (0)

1.65 (0)

39.95 ± 4.20

1h 15 min (0)

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

1

C3: NaH2PO4 (M)

F4: carbonate

* The symbols of ‘þ‘, ‘-’ and ‘0’ represent high, low and middle levels of each variable.

5

6

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

61.42 ± 1.68 mg kg1 determined using the acid digestion method. The analytical accuracy of HGAAS for As detection was performed using CRM, which resulted in 96e100% of recovery indicating a negligible matrix effect for the detection method. 3.2. Plackett-Burman design for variable screening 3.2.1. First screening of 19 extraction variables Table 2 presents the experimental conditions of the constructed PBD model with 19 independent variables. The summation of the extracted As in F1 to F7 was used as the response. In this preliminary study, the amount of the As extracted from F1 to F7 ranged from 22.47 to 51.24 mg kg1 indicating a wide range of the As concentrations when different extraction conditions were used. Based on Multivariate Linear Regression (MLR) between the experimental data and the response, a mathematical equation of the constructed PBD was applied as follows:

y ¼ 34:12 þ 2:08A þ 1:41TE1  0:84TE2  2:56C2  1:69TW2 þ 1:61TE3 þ 0:54C3  0:48TW3 þ 1:56TE4 þ 0:24C4 þ 0:47TW4 þ 1:07TE5 þ 2:17C5 þ 1:82TW5  1:12TE6  2:39C6 þ 1:73TW6 þ 0:30TE7 þ 1:81C7 (3) In this case, y is the amount of extracted As. In general, the magnitude of the coefficients could be used to determine how much the variables were significant or influential on the amount of extracted As. The greater the size of the magnitude was, the more important the variable was likely to be associated with the response. The sign of the coefficient could be used to indicate the direction of the effect. For example, a positive (þ) value indicated a direct proportion relationship with the response and vice versa. Using the value of p < 0.5 as a general criterion, 11 variables including, SSR, TE1, C2, T W2, TE3, TE4, C5, T W5, C6, T W6 and C7, were determined to be significantly important. The SSR had a large and positive effect on the extraction process. This could be because this variable was directly related to the interface between the extraction solution and the soil sample (Borges et al., 2016). In the first fraction (F1), extraction time (TE1) displayed significant importance. The positive effect indicated that the amount of the extracted As increased with the prolonged period of time that corresponded to the Larios method (Larios et al., 2013) indicating the need for a relatively long period of time (24 h) for the extraction process in this step. In F2, the concentrations of NaOAc (C2) and washing time (TW2) were identified as important variables. NaOAc is an ionic salt that contains a replaceable cation which could be used to replace the weakly adsorbed As that is retained on the solid surface by relatively weak electrostatic interaction. However, the negative coefficient of NaOAc showed that an increase of NaOAc would reduce the amount of extracted As. This could be due to the interference effect (Filgueiras et al., 2002). In this case, the acetate salt could enhance the extraction of other metals leading to competition of the As target phase. The Tw2 variable was not ideally recognized as being significant since the deionized water simply had a low As extraction efficiency when compared with the other reagents used in the SEP schemes (Rodriguez et al., 2003). However, the negative coefficient indicated that the amount of extracted As would be reduced when the washing time was increased. This might have occurred as a result of a problem with As re-adsorption due to the low buffering capacity of the water (Rauret, 1998). In F3, only the extraction time (TE3) was identified as being

significant and as having a positive effect. This could be because the soil sample consisted of clay as the major component (Table S2). Consequently, large amounts of As were strongly adsorbed by surface adsorption onto the clay particles (Cai et al., 2002). Hence, the process required longer amounts of time to expel the As. In F4, extraction time (TE4) was found to be significantly important. This outcome agreed with that of the previous report (Tessier et al., 1979) where 5 h was found to be sufficient for the extraction of more than 99% of the metal contents that were associated with carbonate. In F5, the concentration of NH4Ox (C5) and the washing time (T W5) resulted in the positive effects that were observed. NH4Ox plays an important role in inducing the processes of protonation as well as in complexation and reduction causing the oxalate ion to form complexes with the ferric and aluminium ions. Thus, excess amounts of oxalate were required to bring the reaction to equilibrium (Pansu and Gautheyrou, 2006). The significance of Tw5 could be influenced by the re-adsorption effect (Loeppert et al., 2002; Wenzel et al., 2001). This could be because NH4Ox was not highly competitive with As(V) for the ligand-binding site, which was regarded as the main mechanism for As adsorption in this fraction. Therefore, As could be gradually re-adsorbed into the remaining Fe oxide. Consequently, the readsorbed As could then be extracted with water during the course of this washing step. In F6, concentrations of NH4Ox (C6) were also found to be significantly important. Since the coefficient had a negative value, this variable has a negative effect on the extraction efficiency. This may be influenced by other factors such as an incomplete reaction between oxalate-ascorbic and the solid phase (Silveira et al., 2006) or formation of the As sulfide phase (Kim et al., 2016). However, the washing time (TW6) was shown to have a significant and positive effect. This indicated that the remaining or precipitated As that had not dissolved in the C6 was then distributed into the water that was used in this washing step. In F7, concentrations of NH4OAc (C7) were significant and had a positive effect. Ammonium salt solution of a replaceable cation, such as NH4OAc, was employed to leach the metal fraction bound via electrostatic forces to the negative sites on the solid surface (Filgueiras et al., 2002). Therefore, increases of the NH4OAc concentration could enhance the efficiency of the As extraction in this step. 3.2.2. Second screening of 11 extraction variables Since many extraction variables were considered in this investigation, the variable screening process using PBD was performed twice. A total of 11 selected variables from the first PBD model were examined again using the second PBD model. The details of the second PBD model are provided in Table 3. In the second screening, most coefficients of the variables in the second screening were larger in size than those of the first screening indicating that some variables that were eliminated in the first model might have interfered with other significant variables. The mathematic equation for the PBD model with 11 variables is presented below:

y ¼ 27:95 þ 8:87A þ 1:81TE1  1:49C2 þ 0:57TW2 þ 0:27TE3  1:00TE4 þ 1:28C5  0:79TW5  1:98C6  0:66TW6 þ 0:42C7 (4) Based on the established model, SSR, TE1, C2 and C6 were identified as significant. The SSR had a positive effect on the response. Therefore, it was suggested that the SSR be utilized in a high ratio in order to prevent the extractant from being exhausted during the extraction process (Keon et al., 2001; Larios et al., 2013). The TE1

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

7

Table 3 Plackett-Burman design with 11 independent variables (second screening) and response (amount of As extracted from F1 to F7). Runs A: SSR (g:mL)

1 2 3 4 5 6 7 8 9 10 11 12 13e 17

1:10 () 1:100 (þ) 1:100 (þ) 1:10 () 1:10 () 1:100 (þ) 1:10 () 1:10 () 1:100 (þ) 1:100 (þ) 1:100 (þ) 1:10 () 1:55 (0)

F1: soluble

F2: loosely

TE1: Time

C2: NaOAc (M)

30 min () 30 min () 24 h (þ) 30 min () 24 h (þ) 30 min () 24 h (þ) 30 min () 24 h (þ) 30 min () 24 h (þ) 24 h (þ) 12 h 15 min (0)

0.005 () 0.5025 (þ) 0.5025 (þ) 0.5025 (þ) 0.5025 (þ) 0.5025 (þ) 0.5025 (þ) 0.005 () 0.005 () 0.005 () 0.005 () 0.005 () 0.2538 (0)

F3: strongly

F4: carbonate

F5: amorphous

TW2: Washing time (min)

TE3: Time

TE4: Time

C5: NH4Ox (M)

TW5: Washing time (min)

C6: NH4Ox (M)

TW6: Washing time (min)

C7: NH4OAc (M)

10 20 10 20 10 10 20 20 20 10 20 10 15

8 h () 8 h () 24 h (þ) 24 h (þ) 24 h (þ) 8 h () 8 h () 24 h (þ) 8 h () 24 h (þ) 24 h (þ) 8 h () 16 h (0)

1 h () 5 h (þ) 5 h (þ) 1 h () 1 h () 1 h () 5 h (þ) 5 h (þ) 1 h () 5 h (þ) 1 h () 5 h (þ) 3 h (0)

0.04 0.04 0.04 0.50 0.04 0.50 0.50 0.04 0.04 0.50 0.50 0.50 0.27

10 10 30 30 10 30 10 30 30 10 10 30 20

0.05 0.05 0.05 0.05 0.28 0.28 0.28 0.28 0.28 0.28 0.05 0.05 0.16

10 30 10 30 30 10 10 10 30 30 10 30 20

0.1 () 3.2 (þ) 0.1 () 0.1 () 3.2 (þ) 3.2 (þ) 0.1 () 3.2 (þ) 0.1 () 0.1 () 3.2 (þ) 3.2 (þ) 1.65 (0)

() (þ) () (þ) () () (þ) (þ) (þ) () (þ) () (0)

() () () (þ) () (þ) (þ) () () (þ) (þ) (þ) (0)

F6: crystalline

() () (þ) (þ) () (þ) () (þ) (þ) () () (þ) (0)

() () () () (þ) (þ) (þ) (þ) (þ) (þ) () () (0)

F7: organic

() (þ) () (þ) (þ) () () () (þ) (þ) () (þ) (0)

As extracted (mg kg1)

18.52 31.93 33.87 16.87 15.25 31.11 10.51 13.50 34.15 32.07 44.96 20.64 33.08 ± 3.06

* The symbols of ‘þ‘, ‘-’ and ‘0’ represent high, low and middle levels of each variable.

3.3. Central composite design for optimization

variable was identified as being significant with a positive effect. This indicated that the As in the soil sample was present as the most labile compound and would be affected by the soil texture. The soil texture contained a large amount of clay particles causing lower releases of As than the sand and silt particles (Javed et al., 2013). On the other hand, the C2 and C6 variables were detected as having a significantly negative effect. This result is similar with that of the first screening, as has been described in the previous section. These variables will be included in the optimization process using CCD in the next step.

A total of four significant variables (k ¼ 4) from the second PBD screening experiment were chosen as independent variables for the CCD model: SSR (A), extraction time in F1 (TE1), NaOAc (pH 8.2) concentration in F2 (C2) and NH4Ox (pH 3.25) concentration in F6 (C6). The CCD model consisted of 16 factorial points, 8 axial points and 7 replicates in the central point. The experimental conditions used for the CCD study of the selected variables are given in Table 4. In Table 4, the summations of the extracted As ranged from

Table 4 Extraction conditions for the central composite design of selected variables and amount of extracted As calculated from the sum of As concentrations in F1 to F7. Runs

A: SSR (g:mL)

F1: soluble

F2: loosely

TE1: Time

C2: NaOAc (M)

C6: NH4Ox (M)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25e31

1:30 (1) 1:30 (1) 1:70 (1) 1:55 (0) 1:55 (0) 1:70 (1) 1:70 (1) 1:70 (1) 1:30 (1) 1:30 (1) 1:55 (0) 1:30 (1) 1:100 (2) 1:70 (1) 1:55 (0) 1:55 (0) 1:10 (2) 1:70 (1) 1:30 (1) 1:30 (1) 1:70 (1) 1:30 (1) 1:70 (1) 1:55 (0) 1:55 (0)

18 h 7 min (1) 6 h 22 min (1) 18 h 7 min (1) 12 h 15 min (0) 12 h 15 min (0) 18 h 7 min (1) 18 h 7 min (1) 6 h 22 min (1) 6 h 22 min (1) 6 h 22 min (1) 12 h 15 min (0) 18 h 7 min (1) 12 h 15 min (0) 18 h 7 min (1) 12 h 15 min (0) 30 min (2) 12 h 15 min (0) 6 h 22 min (1) 18 h 7 min (1) 18 h 7 min (1) 6 h 22 min (1) 6 h 22 min (1) 6 h 22 min (1) 24 h (2) 12 h 15 min (0)

0.19 (1) 0.19 (1) 0.19 (1) 0.005 (2) 0.25 (2) 0.19 (1) 0.07 (1) 0.19 (1) 0.07 (1) 0.19 (1) 0.13 (0) 0.07 (1) 0.13 (0) 0.07 (1) 0.13 (0) 0.13 (0) 0.13 (0) 0.19 (1) 0.19 (1) 0.07 (1) 0.07 (1) 0.07 (1) 0.07 (1) 0.13 (0) 0.13 (0)

0.08 0.08 0.08 0.11 0.11 0.13 0.13 0.08 0.08 0.13 0.05 0.13 0.11 0.08 0.16 0.11 0.11 0.13 0.13 0.08 0.13 0.13 0.08 0.11 0.11

* The symbols of ‘þ‘, ‘-’ and ‘0’ represent high, low and middle levels of each variable.

F6: crystalline

(1) (1) (1) (0) (0) (1) (1) (1) (1) (1) (2) (1) (0) (1) (2) (0) (0) (1) (1) (1) (1) (1) (1) (0) (0)

As extracted (mg kg1)

36.69 28.91 24.19 36.64 35.16 43.32 48.93 57.29 33.01 43.57 45.32 36.70 48.76 35.04 40.06 35.99 23.42 48.41 40.94 29.61 41.21 38.65 36.47 39.33 47.33 ± 3.44

8

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

Fig. 1. Response surface plots used to investigate the effects of Soil Solution Ratio (SSR, g:mL), extraction time in F1 (TE1, h), concentration of NaOAc (C2, M) and NH4Ox (C6, M) in F2 and F6. The responses are shown as the amount of As extracted from F1 to F7 in the soil sample.

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

23.42 to 57.29 mg kg1. The maximum value of the extracted As was higher than those of the other screenings. For the model fitting, MLR analysis with the quadratic and interaction terms was applied to the experimental data. In the response, y was related to the tested variables as follows:

y ¼ 47:37 þ 4:06A  1:06TE1 þ 0:86C2 þ 2:08C6  2:67A2    2  2:28TE1  2:72C22  1:02C62  1:98 A  TE1  0:04 A         C2  0:17 A  C6  2:12 TE1  C2 þ 1:76 TE1  C6    0:14 C2  C6 (5) The significance of each variable was determined using the student’s t-test and p-values are shown in Table S3. Based on the coefficients of the linear terms, SSR played the most important role in the extraction efficiency. The interaction terms among TE1, C2 and C6 were found to be non-significant (p > 0.05) indicating that the selected variables displayed less interactions with each other. This could be due to the fact that the selection of the extractants in SEP was based on the different levels of leaching selectivity of the As that was bound at different strengths to the soil particles (Filgueiras et al., 2002). Therefore, the extraction conditions in each of the extraction steps should have a minimal effect on the other steps and conditions. In order to investigate the effects that persist between the studied variables, response surface plots are presented in Fig. 1. The interactions of SSR with TE1 (Fig. 1(a)), C2 (Fig. 1(b)) and C6 (Fig. 1(c)) revealed a predictive response ranging from 30 to 40 mg kg1 of the extracted As. To summarize, with a selected set of independent variables, the response surface (Fig. 1) that was generated by a quadratic equation (Eq. (5)) indicates that the maximum extracted As would be achieved in conditions with SSR of 1:75 g:mL, TE1 for 7 h 20 min, 0.16 M of C2 and 0.11 M of C6. SSR enhances the amount of extracted As. This finding is in agreement with that of the mass transfer principle where a greater amount of available solvent facilitates are transferred from the solid matrix into the solution (Borges et al., 2016). Taking into account that extracted As produced satisfactory results in the SSR of higher proportions such as 1:70 g:mL, the SSR of 1:75 g:mL was observed to be stable during validation of the optimized method. The optimum conditions of the SEP scheme for As fractionation in the soil is displayed in Table S4(a).

9

3.4. Comparison of optimized methods for As fractionation To demonstrate the application of the optimized SEP, the optimized SEP was used to extract As in soil samples collected from a contaminated area located in the north of Thailand. The extraction results were compared to those that had been analyzed using the previously reported SEP method (Srithongkul et al., 2019). The details of the optimized and reference methods are shown in Table S4 and the extraction results are compared in Table 5. In Table 5, the amounts of the extracted As in F4, F5 and F7 using the optimized conditions were similar to those obtained using the reference method. However, the concentrations of As in F1, F2 and F6 of the optimized method were significantly higher. In the first two fractions (F1 and F2), the amount of As extracted in the optimized method produced significantly higher amounts (2.78 ± 0.08 and 6.13 ± 0.81 mg kg1) than did the reference method (0.81 ± 0.04 and 3.57 ± 0.09 mg kg1). This indicates that the As in F1 and F2 required longer extraction times to complete the extraction process in the soil particles. This could be probably caused by the alkaline soil pH used in this study. The findings could be different when other acidic or neutral soils are used in the test (Romero-Freire et al., 2014). The concentrations of NaOAc (C2) seem to be less effective on As extraction since the C2 values between those two methods were found to be nearly 7 times different (0.16 vs 1 M). On the contrary, the amount of As extracted for F3 using the optimized method produced significantly lower amounts (7.57 ± 0.25 mg kg1) than the reference method (12.25 ± 0.20 mg kg1). However, when comparing the summation of extracted As in the first three fractions (F1 to F3), the extracted As amounts were not found to be significantly different (16.48 vs 16.63 mg kg1, respectively) for the optimized and reference methods. This would indicate that the As might not be completely extracted from F1 and F2 when using the reference method. In F4, F5 and F7, the results revealed a similar trend among the two methods. The amounts of extracted As were not significantly different from each other; however, the conditions were quite different such as with lower extraction times (1 vs 5 h and 8.50 vs 16 h in F4 and F7, respectively) and lower concentrations (0.04 vs 0.2 M and 0.1 vs 1 M, in F5 and F7, respectively). In F6, the amount of extracted As in the optimized method was higher than the reference method (16.55 ± 0.45 vs 13.10 ± 1.34 mg kg1). This indicated that the As in F6 required a longer amount of time to dissolve into the solution form. For residual fraction (F8), the concentration of As was determined using

Table 5 Comparison of As concentration between optimized and reference methods. Fractions

Names

As concentrations (mg kg1)

F1 F2 F3 F4 F5

Soluble arsenic Loosely adsorbed arsenic Strongly adsorbed arsenic Carbonate bound with arsenic Arsenic co-precipitated with amorphous Fe, Al and Mn oxyhydroxide Arsenic co-precipitated with crystalline Fe and Al oxyhydroxide Organic matter and secondary sulfides Residual arsenic Sum Recovery (%)

2.78 ± 0.08 6.13 ± 0.81 7.57 ± 0.25 0.26 ± 0.02 4.94 ± 0.46

Optimized

F6 F7 F8

Reference a a a a a

0.81 ± 0.04 b 3.57 ± 0.09 b 12.25 ± 0.20 b 0.25 ± 0.03 a 4.54 ± 0.23 a

16.55 ± 0.45 a

13.10 ± 1.34 b

0.29 ± 0.03 a 19.39 ± 0.86 a 57.92 ± 2.28 a 94.30 ± 3.71 a

0.30 ± 0.03 a 21.23 ± 1.36 a 56.06 ± 1.38 a 91.27 ± 2.25 a

* There were significant differences (p < 0.05) in As concentrations observed in the sequential extraction procedure. Mean ± standard deviation (n ¼ 3) within each column followed by the same letter are not considered significantly different (p > 0.05) according to the Least Significant Difference (LSD) test. ** Recovery of As was calculated from the sum of the As obtained from F1 to F8 and then divided by the total As present in the soil sample (61.42 ± 1.68 mg kg1).

10

C. Srithongkul et al. / Chemosphere 242 (2020) 125230

the di-acid digestion method as described in Section 2.1. The results were not significantly different between the two methods. However, the residual As in the reference method (21.23 ± 1.36 mg kg1) was slightly higher than when the optimized method was used (19.39 ± 0.86 mg kg1). This could be because the As in F6 was not completely extracted in the SEP process and was still retained within the soil sample. Lastly, the degree of As recovery, which was the summation of As concentrations from all fractions (F1 to F8) when compared to total As concentrations (61.42 ± 1.68 mg kg1), was calculated. In Table 5, the recovery levels of 94 ± 3% and 91 ± 2% revealed only non-significant differences between the results of the two methods indicating that the proposed SEP method was applicable for As fractionation in soil samples. In addition, the satisfactory recovery of the extraction could imply the small error due to the As associated with colloids (Ma et al., 2019). 4. Conclusion In this research study, the extraction variables that were known to affect the extraction performance of the SEP were investigated. The study revealed the important variables in relation to the amount of extracted As. Notably, SSR, extraction time in F1, concentrations of sodium acetate (NaOAc) in F2 and ammonium oxalate (NH4Ox) in F6 were identified as significantly important variables. Of those screened variables, SSR played the most important role in yielding the amount of extracted As in the CCD model. Based on the information provided from the experimental design of the study, the optimized method could be operated with significantly less operation time and lower concentrations of the extraction solutions. However, the operational conditions could change when the studied model was applied to the soils with different properties. Therefore, more soil samples should be tested and investigated to improve the accuracy of the extraction model. Acknowledgements This research study was financially supported by the Highland Research and Development Institute (Public Organization) (HRDI) and was partially supported by Chiang Mai University. The Center of Excellence for Innovation in Chemistry (PERCH-CIC), Office of the Higher Education Commission, Ministry of Education (OHEC) is also gratefully acknowledged. C. Srithongkul would like to thank the Science Achievement Scholarship of Thailand (SAST) and the Graduate School of Chiang Mai University. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.chemosphere.2019.125230. References Adriano, D.C., 2001. Arsenic. In: Adriano, D.C. (Ed.), Trace Elements in Terrestrial Environments: Biogeochemistry, Bioavailability and Risks of Metals. Springer New York, New York, pp. 219e261. Bagherifam, S., Lakzian, A., Fotovat, A., Khorasani, R., Komarneni, S., 2014. In situ stabilization of as and Sb with naturally occurring Mn, Al and Fe oxides in a calcareous soil: bioaccessibility, bioavailability and speciation studies. J. Hazard Mater. 273, 247e252. Borges, P.R.S., et al., 2016. Obtaining a protocol for extraction of phenolics from açaí fruit pulp through PlacketteBurman design and response surface methodology. Food Chem. 210, 189e199. Brereton, R.G., 2003. Chemometrics: Data Analysis for the Laboratory and Chemical Plant. John Wiley and Sons. Cai, Y., Cabrera, J.C., Georgiadis, M., Jayachandran, K., 2002. Assessment of arsenic mobility in the soils of some golf courses in South Florida. Sci. Total Environ. 291, 123e134. Chao, T.T., Zhou, L., 1983. Extraction techniques for selective dissolution of amorphous iron oxides from soils and sediments. Soil Sci. Soc. Am. J. 47, 225e232.

Chunguo, C., Zihui, L., 1988. Chemical speciation and distribution of arsenic in water, suspended solids and sediment of Xiangjiang river, China, Sci. Total Environ 77, 69e82. Dejaegher, B., Vander Heyden, Y., 2011. Experimental designs and their recent advances in set-up, data interpretation and analytical applications. J. Pharm. Biomed. Anal. 56, 141e158. Eckert, D., Sims, J.T., 1995. Recommended soil pH and lime requirement tests, Recommended Soil Testing Procedures for the Northeastern United States, third ed. Northeast Regional Publication No. 493, pp. 11e16. Estefan, G., Sommer, R., Ryan, J., 2013. Methods of Soil, Plant and Water Analysis, A Manual for the West Asia and North Africa Region, third ed. ICARDA, pp. 133e136. Filgueiras, A.V., Lavilla, I., Bendicho, C., 2002. Chemical sequential extraction for metal partitioning in environmental solid samples. J. Environ. Monit. 4, 823e857. Gerayeli, F., Ghojavand, F., Mousavi, S.M., Yaghmaei, S., Amiri, F., 2013. Screening and optimization of effective parameters in biological extraction of heavy metals from refinery spent catalysts using a thermophilic bacterium. Separ. Purif. Technol. 118, 151e161. Gimbert, L.J., Haygarth, P.M., Beckett, R., Worsfold, P.J., 2005. Comparison of centrifugation and filtration techniques for the size fractionation of colloidal material in soil suspensions using sedimentation field-flow fractionation. Environ. Sci. Technol. 39, 1731e1735. Goh, K.-H., Lim, T.-T., 2005. Arsenic fractionation in a fine soil fraction and influence of various anions on its mobility in the subsurface environment. Appl. Geochem. 20, 229e239. Huang, J.H., Kretzschmar, R., 2010. Sequential extraction method for speciation of arsenate and arsenite in mineral soils. Anal. Chem. 82, 5534e5540. Hughes, M.F., Beck, B.D., Chen, Y., Lewis, A.S., Thomas, D.J., 2011. Arsenic exposure and toxicology: a historical perspective. Toxicol. Sci. 123, 305e332. Jain, C.K., Ali, I., 2000. Arsenic: occurrence, toxicity and speciation techniques. Water Res. 34, 4304e4312. Javed, M.B., Kachanoski, G., Siddique, T., 2013. A modified sequential extraction method for arsenic fractionation in sediments. Anal. Chim. Acta 787, 102e110. Keon, N.E., Swartz, C.H., Brabander, D.J., Harvey, C., Hemond, H.F., 2001. Validation of an arsenic sequential extraction method for evaluating mobility in sediments. Environ. Sci. Technol. 35, 2778e2784. Kim, E.J., Jeon, E.-K., Baek, K., 2016. Role of reducing agent in extraction of arsenic and heavy metals from soils by use of EDTA. Chemosphere 152, 274e283. Kreidie, N., et al., 2011. An integrated geochemical and mineralogical approach for the evaluation of arsenic mobility in mining soils. J. Soils Sediments 11, 37e52. ndez-Martínez, R., Rucandio, I., 2013. Assessment of a sequential Larios, R., Ferna extraction procedure for arsenic partitioning and application to samples from different pollution sources. Anal. Methods 5, 4096e4104.  Larios, R., Fern andez-Martínez, R., Alvarez, R., Rucandio, I., 2012. Arsenic pollution and fractionation in sediments and mine waste samples from different mine sites. Sci. Total Environ. 431, 426e435. Lindsay, W.L., Norvell, W.A., 1978. Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Sci. Soc. Am. J. 42, 421e428. Loeppert, R.H., Jain, A., El-Haleem, M.A.A., Biswas, B.K., 2002. Quantity and Speciation of Arsenic in Soils by Chemical Extraction, Biogeochemistry of Environmentally Important Trace Elements. American Chemical Society, pp. 42e56. Lundstedt, T., et al., 1998. Experimental design and optimization. Chemometr. Intell. Lab. Syst. 42, 3e40. Ma, L., Wang, L., Tang, J., Yang, Z., 2016. Optimization of arsenic extraction in rice samples by PlacketteBurman design and response surface methodology. Food Chem. 204, 283e288. Ma, J., et al., 2019. Fractions and colloidal distribution of arsenic associated with iron oxide minerals in lead-zinc mine-contaminated soils: comparison of tailings and smelter pollution. Chemosphere 227, 314e323. Miller, W.P., Miller, D.M., 1987. A micro-pipette method for soil mechanical analysis, Commun. Soil Sci. Plant Anal. 18 (18), 1e15. ~ eiro, J., Lo pez-Mahía, P., Muniategui-Lorenzo, S., Fern Moreda-Pin andezFern andez, E., Prada-Rodríguez, D., 2002. Determination of As, Bi and Se in acidified slurries of marine sediment, soil and coal samples by hydride generation electrothermal atomic absorption spectrometry. J. Anal. At. Spectrom. 17, 721e726. nez, E., Esteban, E., Pen ~ alosa, J.M., 2012. The fate of arsenic in soil-plant Moreno-Jime systems. Rev. Environ. Contam. Toxicol. 215, 1e37. Naseri, E., Reyhanitabar, A., Oustan, S., Heydari, A.A., Alidokht, L., 2014. Optimization arsenic immobilization in a sandy loam soil using iron-based amendments by response surface methodology. Geoderma 232e234, 547e555. Pansu, M., Gautheyrou, J., 2006. Handbook of Soil Analysis, Mineralogical, Organic and Inorganic Methods. Springer, Berlin. Pawluk, S., 1972. Measurement of crystalline and amorphous iron removal in soils. Can. J. Soil Sci. 52, 119e123. Rauret, G., 1998. Extraction procedures for the determination of heavy metals in contaminated soil and sediment. Talanta 46, 449e455. Rodriguez, R.R., Basta, N.T., Casteel, S.W., 2003. Chemical extraction methods to assess bioavailable arsenic in soil and solid media. J. Environ. Qual. 32, 876e884.  n, M., Ortiz-Bernad, I., Martín-Peinado, F.J., 2014. Romero-Freire, A., Sierra-Arago Toxicity of arsenic in relation to soil properties: implications to regulatory purposes. J. Soils Sediments 14, 968e979. Sarabia, L.A., Ortiz, M.C., 2009. Response surface methodology. In: Brown, S.D.,

C. Srithongkul et al. / Chemosphere 242 (2020) 125230 Tauler, R., Walczak, B. (Eds.), Comprehensive Chemometrics. Elsevier, Oxford, pp. 345e390. Sarkar, D., Datta, R., Sharma, S., 2005. Fate and bioavailability of arsenic in organoarsenical pesticide-applied soils.: part-I: incubation study. Chemosphere 60, 188e195. Sarkar, D., Makris, K.C., Parra-Noonan, M.T., Datta, R., 2007. Effect of soil properties on arsenic fractionation and bioaccessibility in cattle and sheep dipping vat sites. Environ. Int. 33, 164e169. Schulte, E.E., 1995. Recommended soil organic matter tests. In: Rhodes, R. (Ed.), Recommended Soil Testing Procedures for the Northeastern United States, third ed. Northeastern Regional Publication No. 493, pp. 52e60. Silveira, M.L., Alleoni, L.R.F., O’Connor, G.A., Chang, A.C., 2006. Heavy metal sequential extraction methodsda modification for tropical soils. Chemosphere 64, 1929e1938. Srithongkul, C., Wongsaipun, S., Krongchai, C., Santasup, C., Kittiwachana, S., 2019. Investigation of mobility and bioavailability of arsenic in agricultural soil after

11

treatment by various soil amendments using sequential extraction procedure and multivariate analysis. Catena 181, 104084. Tessier, A., Campbell, P.G.C., Bisson, M., 1979. Sequential extraction procedure for the speciation of particulate trace metals. Anal. Chem. 51, 844e851. Vanaja, K., Shobha Rani, R.H., 2007. Design of experiments: concept and applications of Plackett Burman design. Clin. Res. Regul. Aff. 24, 1e23. Wenzel, W.W., et al., 2001. Arsenic fractionation in soils using an improved sequential extraction procedure. Anal. Chim. Acta 436, 309e323. Wolf, A., Beegle, D., 2011. Recommended soil tests for macro and micronutrients and cation exchange capacity by summation. In: Rhodes, R. (Ed.), Recommended Soil Testing Procedure for the Northeastern United States, third ed. Northeastern Regional Publication No.493, pp. 39e48. Yoon, I.-H., et al., 2010. Mechanism for the stabilization/solidification of arseniccontaminated soils with Portland cement and cement kiln dust. J. Environ. Manag. 91, 2322e2328.