Adsorption of nitrate, phosphate, nickel and lead on soils: Risk of groundwater contamination

Ecotoxicology and Environmental Safety 179 (2019) 182–187

Contents lists available at ScienceDirect

Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv

Adsorption of nitrate, phosphate, nickel and lead on soils: Risk of groundwater contamination

T

Mohamed Abdelwaheb, Khaoula Jebali, Hatem Dhaouadi, Sonia Dridi-Dhaouadi∗ University of Monastir, Faculty of Sciences of Monastir, Research Unity of Applied Chemistry and Environment, 5000, Monastir, Tunisia

A R T I C LE I N FO

A B S T R A C T

Keywords: Adsorption Heavy metal Soil Clay Nitrates Phosphates Risk Isotherm model

Agricultural activities pose a significant risk of groundwater pollution. Indeed, fertilizers and treated wastewater used for irrigation are, in part, responsible for the deterioration of groundwater and surface water quality. In some cases, soil may provide a protective barrier against this pollution, but this depends on the nature of the soil and the contaminant. This work presents the effect of the soil clay content on the retention of four different pollutants in order to evaluate the risk they represent for the groundwater. These contaminants are generated by two main agricultural activities: 1/soil fertilization with phosphate and nitrate fertilizers and 2/irrigation with treated wastewater in which heavy metals such as nickel and lead are persistent. Firstly, the characterization of the sand and clay used in this work was performed and showed a cation exchange capacity of 1.24 and 25 meq/ 100 g, a specific surface area of 0.12 and 67.98 m2/g and a percentage of organic matter of 0.15 and 2% for sand and clay, respectively. The retention isotherms on all pollutants and the Langmuir, Freundlich, FreundlichLangmuir, Hill and Koble-Corrigan models were applied. All experimental isotherms have been successfully adjusted using the Koble-Corrigan expression. The amounts of nitrates, phosphates, nickel and lead retained by the sandy soil, for an initial pollutant concentration equal to 1 meq/L, were evaluated at 0.29, 3.89, 5.97 and 8.56 μeq/g respectively. In contact with a soil containing 30% clay, the adsorbed amounts were estimated at 3.55, 15.00, 6.97 and 8.79 μeq/g for nitrates, phosphates, nickel and lead, respectively. These results mean that the pollutants that pose the greatest risk of groundwater contamination when carried by water through sandy soil are classified as follows lead < nickel < phosphate < nitrate while for a clayey soil, the classification becomes as follows: phosphates < lead < nickel < nitrate.

1. Introduction For more than a century, agricultural activities have considerably increased their consumption of phytosanitary and fertilizers products. Unfortunately, these are often a source of groundwater pollution, especially for the more soluble ones, namely nitrates and phosphates (Borggaard et al., 2004; Serio et al., 2018). Although nitrates are not considered toxic as such, but their conversion to nitrites and then their combination with amines are known to be responsible of methemoglobinemia in newborns and other problematic diseases (Fan et al., 1987). On the other hand, phosphates are responsible for various health problems, such as cardiovascular disorders (Gérard, 2016; Komaba and Fukagawa, 2016). On the other side, using treated wastewater in agricultural irrigation increases the risk of soil contamination by persistent pollutants such as heavy metals. These represent a real danger to the groundwater that they can reach by the flow water infiltration. Lead

and nickel are among the most often encountered metals because of their various origins: domestic detergents, street run off, hospital waste, surface treatment industries, batteries manufacturer, foundries, etc. Although lead has toxic effects on the nervous system and the blood circulation system, nickel can damage some organs and is even suspected of being responsible of certain cancer types (Gupta and Bhattacharyya, 2008; Ebrahimi et al., 2018). Khan et al. (2008) have shown the direct correlation between the concentration of certain heavy metals, including lead, in soils and plants for human consumption. The authors concluded that the contamination exceeded the limits allowed by WHO. In the same context, Kumar et al. (2018) presented a review of the scientific literature from 1991 to 2018 on the presence of heavy metals in agricultural soils in India. They showed that concentrations of zinc and lead very often exceeded the recommended limits for Indian natural soils. On the other hand, and based on their assessment of the presence of heavy metals in agricultural soil samples,

∗ Corresponding author. University of Monastir, Institut Préparatoire aux Etudes d’Ingénieurs de Monastir, Research Unity of Applied Chemistry and EnvironmentFaculty of Sciences, 5000, Monastir, Tunisia. E-mail address: [email protected] (S. Dridi-Dhaouadi).

https://doi.org/10.1016/j.ecoenv.2019.04.040 Received 21 January 2019; Received in revised form 11 April 2019; Accepted 12 April 2019 Available online 29 April 2019 0147-6513/ © 2019 Elsevier Inc. All rights reserved.

Ecotoxicology and Environmental Safety 179 (2019) 182–187

M. Abdelwaheb, et al.

The loss on ignition was determined by weighing the sand (or the clay) before and after its combustion at 525 °C in the muffle furnace for 24 h. The loss on ignition is given by:

Mehmood, A., et al. (2019) recommend that health authorities continuously monitor the quality of wastewater used for irrigating cropland. The literature distinguishes three different approaches when dealing with agricultural pollution: social (Evans et al., 2019; Yang et al., 2018), regulatory (Zaring, 1996) and economic approaches (Abler, 2005). Whatever the approach adopted, the environmental aspect remains at the center of the problem raised by agricultural activities. Studying this aspect of the problem will inevitably have positive consequences on the society, on the regulation and on the economy. Indeed, the contamination of groundwater, generally used for human consumption, often causes social protests. These incriminate the unsustainability of agricultural activities induced by the intensive use of fertilizers and the irrigation with wastewater whose treatment does not take into account the elimination of residual pollutants such as heavy metals (Klay et al., 2010). Treating this water inevitably induces additional costs that will be charged to consumers. These costs are directly related to the drinking water regulation which is becoming more and more severe (World Health Organization, 2004). The objective of this work is to quantify the risk of groundwater contamination by nitrates and phosphates anions and by nickel and lead cations brought to the soil surface by agricultural activities. The study was done by following the pollutant behavior onto the soil by using different isotherm adsorption models. The influence of soil clay content on groundwater contamination has been investigated in order to determine the relationship between soil composition and risk of groundwater contamination.

PF (%) =

m3 − m2 × 100 m

(3)

where m3 is the mass of the dry soil (sand or clay) after combustion (Rodier et al., 2009). The soil specific surfaces were evaluated by Sears method (Sears, 1956). 1.5 g of sand (or clay) is acidified with 0.1eq/L HCl until a pH of 3.0–3.5 is reached. To this solution are added 30 g of NaCl and then 150 mL of distilled water. The titration is carried out at a constant temperature (298 K) with 0.1eq/L NaOH to pH 4.0 and then to pH 9.0. The specific surface can be then calculated by the following formula:

S (m2/ g ) = 32V − 25

(4)

Where V is the volume in milliliters of NaOH 0.1 N required to raise the pH from 4.0 to 9.0 for 1.5 g of soil. The cation exchange capacity (CEC) of sand (or clay) was done by the following method (Ollat and Combeau, 1960): 500 mL of CaCl2 (1 eq/L) are injected from bottom to top through a column filled with 10 g of sand (or successive layers of 10 g of clay and 10 g of sand). Then, 150 mL of CaCl2 (0.05 eq/L) are injected followed by 500 mL of KNO3 (1eq/L). The percolate is collected in 500 mL flask and the total calcium titrated with EDTA (0.02eq/L) at pH 12 using the Eriochrome Black T as a colorful indicator. At the same time, the chloride is titrated with AgNO3 (0.05eq/L) using the K2CrO4 as a colorful indicator. The CEC is given by:

2. Materials and methods

CEC (meq/100g ) = 2v − 5V

2.1. Preparation of the pollutant solutions

where v is the volume (ml) of EDTA required for calcium titration and V the volume (mL) of AgNO3 required for chlorides titration. The pH of zero charge (pHzpc) was determined as follows: In 6 different beakers, known amounts of sand (or clay) were contacted with 100 mL of sodium chloride (0.1 M). A pH value was set for each beaker (from 2 to 12) by adding the necessary amounts of nitric acid or potassium hydroxide. After 24 h stirring at constant temperature (19 ± 1 °C), the pH was measured again and plotted as a function of the initial pH. The intersection of this curve with the straight-line pHi = pH corresponds to pHzpc (Kavitha and Thambavani, 2014). Fourier transform infrared spectroscopy (PerkinElmer spectrometer FTIR) was used to identify functional groups on the soil surface.

The stock solutions of metal cations were prepared from Pb(NO3)2 (FLUKA) and Ni(NO3)2.6H2O (Sigma Aldrich) while the stock solutions of the anion compounds were prepared from KNO3 (FLUKA) and Na2HPO4 (Sigma Aldrich). The solids were dissolved in distilled water to obtain ionic concentrations of 1000 mg per liter. Different dilution of the stock solutions were done with distilled water in order to obtain the desired concentrations. 2.2. Soil preparation and characterization The sand was collected from Skanes beach (Monastir, Tunisia) and the clay from Oued El Guelta (Ouardanine, Monastir, Tunisia). Soils with different clay contents were prepared by dry blending known masses of each solid material. The pH of the soil solutions was determined by contacting 20 g of dry sand (or clay) with 50 mL of distilled water. The solutions are stirred for one minute and then left for two hours before noting the pH value (pH-meter EUTECH instrument) (Kome et al., 2018). The humidity of the soil (sand and clay) was determined after weighing the sand (or clay) before and after placing it in an oven at 105 °C for 24 h. The expression of humidity is then given by:

Humidity (%) =

m − m1 × 100 m

2.3. Adsorption experiments Batch experiments were realized for the fourth ions (NO−3 , PO34− , Ni2 + and Pb2 +). These experiments were done by putting 2 g of sand with different clay contents (from 0 to 30%) in 20 mL of the ionic solution of lead, nickel, nitrate or phosphate. The solution concentrations vary from 0 to 1000 mg/L (9.66 meq/L), 0–100 mg/L (3.38 meq/L), 0–200 mg/L (2.10 meq/L) and 0–500 mg/L (24.18 meq/L) for lead, nickel, nitrate and phosphate, respectively. No pH adjustment was made and all solutions showed pH values between 7.0 and 7.8. Stirring was maintained for 24 h at 200 rpm in a constant temperature (19 ± 1 °C). The mixture was then centrifuged (3000 rpm) for 10 min, and the ion residual concentrations were analyzed using Atomic Absorption Spectrometer (Analytical Jena), spectrophotometer (UNICO 1200) and a specific electrode for the metallic cations, phosphates and nitrates, respectively. The amount of adsorbed ions per solid unit mass, qe (mg/g) is calculated as follows:

(1)

where m and m1 are the masses of the soil before and after its stay in the oven. The soil organic matter (OM) was evaluated after weighing the dry sand (or clay) before and after placing it in a muffle furnace for 16 h at 375 °C. The expression of OM is done as follows:

OM (%) =

m2 − m1 × 100 m1

(5)

qe =

(2)

Where m1 and m2 are the mass of the soil (sand or clay) after its stay in the oven (105 °C) and in the muffle furnace (375 °C), respectively.

(C0 − Ce ) ×V m

(6)

Where C0 and Ce (mg/L) are the initial and equilibrium ion concentrations, V (mL) is the solution volume and m (g) is the mass of the 183

Ecotoxicology and Environmental Safety 179 (2019) 182–187

M. Abdelwaheb, et al.

Table 1 Expression and characteristic of the Isotherm models [19]. Model Langmuir

Freundlich Langmuir-Freundlich Hill Koble-Corrigan

Expression

qe =

qmax KL Ce 1 + KL Ce

1

q e = KF CenF qe =

qmax (KLFCe)nLF 1 + (KLFCe)nLF

qe =

qmax (Ce)nH KH + CenH

qe =

qmax aKC CenKC 1 + bKC CenKC

Characteristic qmax: the maximum adsorbed amount of pollutant (mg.g−1) Ce: aqueous concentration of pollutant at equilibrium (mg.L−1) KL:Langmuir constant nF and KF: Freundlich constants

Monolayer adsorption. All active sites are equivalent. No interactions between the adsorbed molecules.

nLF and KLF: Langmuir-Freundlich constants nH, and KH: Hill constants

Active sites are heterogeneous. The expression tends to Freundlich model for low adsorbate concentrations and to Langmuir expression for high adsorbate concentrations. Cooperative adsorption that takes into account possible interactions between adsorbed molecules. Adsorption onto heterogeneous surfaces. The model includes Langmuir and Freundlich expressions.

nKC, aKC, bKC Koble-Corrigan constants

Empirical model for multilayer adsorption. Active sites are heterogeneous.

and Si-O-Fe silicon oxides, whose characteristic bands appear at 692796 cm−1 and 465-463 cm−1, respectively, also, characteristic bands attributed to Si-O-Si at 1083-1001 cm−1 can be reported for sand and for clay, respectively. Unlike sand, clay presents a broad band at 3620 cm−1 for Al-OH and another band at 1633 cm−1 which indicates the presence of water molecule in the adsorbent (Preeti and Singh, 2007; Marie Hélène et al., 2013). The pH of the aqueous solutions of sand and clay, pHs, as well as the pH of zero charge, pHpzc, measured as a function of the clay content in the sandy soil, are shown in Table 2 pHs values are estimated at 7.25 and 7.78 for sand and clay, respectively, whereas pHpzc vary from 6.60 to 7.10 depending on the clay content. These results indicate that solid surfaces are negatively charged suggesting that electrostatic interactions may occur during the sorption process (Hao et al., 2018). The adsorption isotherms on the soil as a function of its clay content are illustrated in Figs. 1 and 2 for cationic (Pb2 +, Ni2 + ) and anionic (NO−3 , PO34−) water contaminants, respectively. The five models, whose expressions appear in Table 1, were applied to all experimental isotherms. The parameters of these models are given in Tables 3 and 4 for the retention of cations and anions, respectively. The results given in Tables 3 and 4 show that most experimental isotherms cannot be correlated with the Langmuir model. In fact, the nonlinear correlation coefficients for this model range from 0.92 to 0.96 for nickel, from 0.57 to 0.88 for lead, from 0.79 to 0.90 for phosphates and from 0.55 to 0.90 for nitrates, depending on the clay content of the soil. This suggests that Langmuir assumptions are not valid for the adsorbate/adsorbent pairs that are the subject of this work. Indeed, the Langmuir hypothesis that retention sites have equivalent sorption energies does not apply for adsorbents as complex as the clayey soils. The same is true of Freundlich's empirical model which, despite the hypothesis of surface heterogeneity, does not specify a limit for the adsorbed quantities. The failure of Langmuir and Freudlich modeling justifies the use of multi-parameter models such as those of Koble-Corrigan, LangmuirFreundlich and Hill (Table 1). Figs. 1 and 2, together with the high values of the nonlinear regression coefficients given in Tables 3 and 4, show that all experimental isotherms, without any exception, were successfully adjusted using Koble-Corrigan expression. This model describes an adsorption on heterogeneous surfaces and includes in its expression the Langmuir and Freundlich equations. So, for low solute concentrations, the Koble-Corrigan equation is reduced to that of Freundlich: qe = qmax aKC CenKC where the Freundlich parameters are 1 given as follows: KF = qmax aKC and nF = n . However, for high solute KC concentration, Koble-Corrigan model allows the determination of the a maximum amount of monolayer adsorption: lim (qe ) = qmax bKC .

dry solids (Gupta and Bhattacharyya, 2008; Karapinar and Donat, 2004; Kaya and Ören, 2005). In order to predict the behavior of the pollutants according to the soil composition, six models were investigated to fit the experimental isotherms: Langmuir, Freundlich, Langmuir-Freundlich, Hill, KobleCorrigan. Table 1 gives the expression and the characteristics of these models (Syafiuddin et al., 2018). Nonlinear regression was adopted to minimize errors between experimental data and isotherm models. The regression coefficient was calculated by the following formula (Zakhama et al., 2011):

R2 =

∑ |qe − qeave |2 ∑ qe − qeave 2 + |qe − qemod |2

(7)

where qe is the experimental adsorbed amount at equilibrium (mg/g), qemod is the adsorbed amount predicted by the model (mg/g) and qeave is the average of the experimental adsorbed amount. 3. Results and discussion The results of the adsorbent characterization are given in Table 2. The specific surface area, the organic matter content (OM) and the cation exchange capacity (CEC) of clay material were evaluated at 67.98 m2/g, 4.90% and 25.0 meq/100 g, respectively, while for sand, the values of these parameters were only 0.13 m2/g, 0.15% and 3.5 meq/100 g, respectively. These results show that the clay materials used have interesting surface properties compared to those reported in the literature. Thus, Kabata-Pendias (2004) estimated the cation exchange capacities and the specific surfaces according to the type of clay between 5 and 200 meq/100 g. and between 7 and 800 m2/g, respectively. On the other hand, Grupta and Bhattacharyya (2008) worked on kaolinite and montmorillonite whose exchange capacities were estimated at 13.4 and 225.0 meq/100 g, respectively, while specific surfaces were evaluated at 3.1 and 18, 7 m2/g, respectively. In addition, the IR spectra (shown in supplementary data), show that the surface chemical functions of sand and clay are mainly Si-O-Al Table 2 Sand and clay characterization. Soil nature

Clay

Sand

Surface area pH Humidity Organic Matter Loss on ignition Cationic Exchange Capacity pHpzc

67.97 m2/g 7.25 4.90% 2.00% 3.36% 25.0 meq/100 g

0.13 m2/g 7.78 0.06% 0.15% 0.35% 3.5 meq/100 g

Sand

Sand + 20%Clay 6.60

7.10

Sand + 10%Clay 6.60

Ce →∞

Sand + 30%Clay 6.70

KC

Looking at Tables 3 and 4, it can be noted that some experimental isotherms are also correctly modeled by Langmuir-Freundlich and Hill models in addition to that of Koble-Corrigan which is the best model 184

Ecotoxicology and Environmental Safety 179 (2019) 182–187

M. Abdelwaheb, et al.

Fig. 1. Lead and Nickel adsorption isotherms (T = 19 °C; 200 rpm, m/V = 0.1 g/mL, pH = 7.0–7.8).

fitted. In fact, these models can be considered as special cases of the 1 nLF = K = aKC = bKC , where KLF and nLF Koble-Corrigan model since KLF H are the Langmuir-Freundlich isotherm parameters, KH the Hill isotherm parameter and aKC, bKC the Koble-Corrigan isotherm parameters. The Koble-Corrigan equation is a three parameter model capable of representing the adsorption phenomenon on surfaces as complex as those exhibited by clay materials. Thus, the parameters aKC and bKC are empirical adsorption constants related to the thermodynamic equilibrium while the exponent nKC expresses the affinity between the adsorbate and the adsorbent (Shahbeig et al., 2013; Ayawei et al., 2017). This model is valid too when the constant “nKC” is greater than 1, which is the case of all isotherms except that of the Ni (II)/soil at 10% clay. Otherwise, this means that the model is not able to define the experimental data, even if the regression coefficient is high. (Ayawei et al., 2017). Fig. 3 compares the adsorbed amounts of the four pollutants as a function of soil clay content, the initial concentrations of the four ionic species being the same (1 meq/L). The unit used, the milliequivalent, is the most appropriate unit for comparing elements of different molecular weights and different electrical charges. In fact, the classification of pollutants according to the risk they pose for groundwater depends on the unit with which they are expressed. The results show that the adsorbed quantities of anionic pollutants increase with the clay content whereas those of the cationic pollutants remain invariable. However, the classification of pollutants that pose the greatest risk of groundwater contamination depends on the nature of the soil that these pollutants cross when transported by water. For a sandy soil, this classification is given as follows:

Lead < nickel < phosphate < nitrate While for a clayey soil, the classification is: Phosphates < lead < nickel < nitrate For anionic species, Fig. 3 shows that the retention of phosphates is much higher than that of nitrates, which implies that the risk of groundwater contamination by nitrates is greater than that of phosphates. This can be explained by interactions with iron and aluminum oxides, whose presence in clay soils has been revealed by infrared analyzes. These interactions may be represented by one of the following mechanisms (Sigg and Stumm, 1981): 3−

2−

nPO4 ⎧ nPO4 + OH ,Or Fe or Al − OH + ⎧ − → Fe or Al − ⎨ ⎨ nNO 3 ⎩ nNO3 ⎩ ⎧ nFe or Al − OH + PO3 − 4 ⎨ ⎩ nFe or Al − OH

nFe or Al⋱ − → ⎧ PO4 + 2OH− ⎨ ⎩ nFe or Al⋰

Therefore, the monovalent anion does not have access to the second mechanism, which probably explains its low retention. On the other hand, Fig. 3 shows that the adsorption of nickel and lead is very little affected by the clay content of soils. This is explained by the fact that all the experiments were carried out in neutral pH (7–7.8). Indeed, several works in the literature have clearly demonstrated the effect of pH on the adsorption mechanism of metal cations (Tokarský, 2018; Nimisha et al., 2016; Ghorbel-Abid and TrabelsiAyadi, 2015). It has thus been shown that the retention by clays of a cationic species is generally attributed to the exchange between an ion

Fig. 2. Nitrate and Phosphate adsorption isotherms (T = 19 °C, 200 rpm,m/V = 0.1 g/mL, pH = 7.0–7.8). 185

Ecotoxicology and Environmental Safety 179 (2019) 182–187

M. Abdelwaheb, et al.

Table 3 Isotherm model parameters for the metallic cations. Isotherm model

Langmuir

Freundlich

Langmuir -Freundlich

Hill

Koble-Corrigan

Clay content

qmax KL R2 KF nF R2 qmax KLF nLF R2 qmax KH nH R2 qmax aKC bKC nKC R2

(mg/g)

(mg/g)

(mg/g)

(mg/g)

Nickel

Lead

0%

10%

20%

30%

0%

10%

20%

30%

0.26 0.45 0.92 0.08 0.42 0.90 0.26 0.45 1.19 0.94 0.26 0.95 0.39 0.94 0.26 0.38 0.33 1.00 0.94

0.57 0.52 0.95 0.19 0.34 0.97 0.57 0.49 0.75 0.96

0.58 0.45 0.96 0.18 0.41 0.93 0.58 0.41 0.80 0.98

0.19 2.04 0.94 0.11 0.72 0.99 0.19 1.76 2.78 0.99

2.10 0.20 0.88 0.34 0.60 0.93 2.10 0.17 1.50 0.96

1.80 0.25 0.57 0.13 1.20 0.98 1.80 0.25 2.44 0.99

1.80 0.11 0.59 0.09 1.03 0.87 1.80 0.12 2.39 0.85

1.85 0.12 0.64 0.04 1.39 0.95 1.85 0.13 1.46 0.95

0.57 1.71 0.75 0.96 0.57 0.43 0.25 0.52 0.98

0.58 2.03 0.80 0.98 0.58 0.71 1.00 1.46 0.99

0.19 0.21 2.80 0.99 0.58 0.87 0.43 1.05 0.99

in solution and a labile ion already fixed on the site of the exchanger. As Table 2 shows that the pH of the solutions is greater than pHpzc then, this means that the surface of the adsorbents is not protonated. It is rather the “natural” cations of clay, such as Ca2+ and Na2+, which are involved in the exchange with metal cations. This exchange is therefore less favorable than what could have been done with the proton, which is known to be the most labile cation. (Kannan and Verma, 2013).

2.10 9.85 1.52 0.98 2.10 0.06 0.06 1.67 0.96

1.80 120.80 3.62 0.98 1.80 0.03 0.02 2.31 0.99

1.80 166.10 2.39 0.86 1.80 0.02 0.05 1.54 0.88

1.85 199.60 2.51 0.88 1.85 0.02 0 1.39 0.95

maximum amount of adsorbate that can be retained. Thus, we have shown that nitrates present the greatest risk of groundwater contamination due to their low retention by sandy and clayey soils. This is not the case for phosphates whose interactions with iron and aluminum oxides explain their adsorption by clays. On the other hand, the results of this work have shown that clays cannot constitute a barrier to groundwater pollution by metal cations when they are carried by aqueous solutions at neutral pH.

4. Conclusion Acknowledgments

This study showed the impact of the clay nature of a soil on the risk of groundwater contamination by four ionic contaminants: nitrates, phosphates, lead and nickel. For this purpose, the retention isotherms on a sandy soil with different clay contents were made for all pollutants and the Langmuir, Freundlich, Freundlich-Langmuir, Hill and KobleCorrigan models were applied. All experimental isotherms, without exception, have been successfully adjusted using the Koble-Corrigan expression. This model, which includes in its expression the Langmuir and Freundlich equations, describes adsorption on heterogeneous surfaces by specifying the

The authors thank the Tunisian Ministry of Higher Education and Scientific Research for its financial support.

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2019.04.040.

Table 4 Isotherm model parameters for the anionic compounds. Isotherm model

Langmuir

Freundlich

Langmuir -Freundlich

Hill

Koble-Corrigan

Clay content

qmax KL R2 KF nF R2 qmax KLF nLF R2 qmax KH nH R2 qmax aKC bKC nKC R2

(mg/g)

(mg/g)

(mg/g)

(mg/g)

Phosphate

Nitrate

0%

10%

20%

30%

0%

10%

20%

30%

0.14 0.190 0.90 0.41 0.031 0.90 0.14 0.190 1.56 0.97 0.14 2.1013 16.36 0.97 0.14 0.012 0.015 2.83 0.90

1.10 0.075 0.85 0.47 0.140 0.88 1.10 0.067 4.59 0.99

1.26 0.058 0.84 0.62 0.089 0.93 1.26 0.056 5.01 0.96

1.28 0.120 0.79 1.26 0.026 0.99 1.28 0.110 1.86 0.95 1.28 57.69 1.86 0.95 1.28 0.019 210–8 1.26 0.99

0.25 0.004 0.74 1.81 4.1005 0.99 0.25 0.003 5.50 0.97 0.25 4.1013 5.50 0.99 0.25 2.10–5 5.10−9 1.75 0.99

0.66 0.017 0.90 0.64 0.187 0.98 0.66 0.025 5.35 0.99

1.24 0.016 0.65 0.84 0.140 0.91 1.24 0.024 7.36 0.99

1.60 0.015 0.55 0.95 0.120 0.90 1.60 0.020 4.24 0.99

1.10 2.4.108 4.58 0.99 1.10 8.2.10−6 8.6.10−6 4.32 0.99

1.26 2.9 108 4.35 0.95 1.26 4.1 10−6 4.6 10−6 4.32 0.96

186

0.66 5.108 5.39 0.99 0.66 3.10–5 3.10–5 2.64 0.99

1.24 7.1011 7.34 0.99 1.24 2.10–5 2.10–5 2.47 0.99

1.60 107 4.24 0.99 1.60 0.008 1.10–9 0.95 0.90

Ecotoxicology and Environmental Safety 179 (2019) 182–187

M. Abdelwaheb, et al.

Fig. 3. Effect of soil clay content on Nitrate, Phosphate, Lead and Nickel adsorption (C0 ≈ 1 meq/L for all pollutants). Karapinar, N., Donat, R., 2004. Adsorption behaviour of Cu2+ and Cd2+ onto natural bentonite. Desalination 249, 123–129. https://doi.org/10.1016/j.desal.2008.12.046. Kaya, A., Ören, A.H., 2005. Adsorption of zinc from aqueous solutions to bentonite. J. Hazard Mater. 125, 183–189. https://doi.org/10.1016/j.jhazmat.2005.05.027. Kabata-Pendias, A., 2004. Soil–plant transfer of trace element: an environmental issue. Geoderma 122, 143–149. https://doi.org/10.1016/j.geoderma.2004.01.004. Kannan, T., Verma, H., 2013. Application of polymer-based ion exchangers for heavy metal and radioactive waste removal, water purification, protein separation and catalysis. In: Naushad, M., ALOthman, Z.A. (Eds.), A Book on Ion Exchange, Adsorption and Solvent Extraction. Nova Science Publishers, 978-1-62417-887-0, pp. 93–110. Marie-Hélène, Bernier, Levy, Guy J., Fine, Pinchas, Borisover, Mikhail, 2013. Organic matter composition in soils irrigated with treated wastewater: FT-IR spectroscopic analysis of bulk soil samples. Geoderma 209, 233–240. https://doi.org/10.1016/j. geoderma.2013.06.017. Nimisha, K.V., Aparna, Mohan, Janardanan, C., 2016. Pectin–Tin (IV) molybdosilicate: an ecofriendly cationic exchanger and its potential for sorption of heavy metals from aqueous solutions. Resour. Effic. Technol. 2, 153–164. https://doi.org/10.1016/j. reffit.2016.11.016. Ollat, C., Combeau, A., 1960. Méthode de détermination de la capacité d'échange et du pH d'un sol; relations entre le complexe absorbant et le pH. Sols Afr. 5, 343–372. Preeti, Agar Nayak, Singh, B.K., 2007. Instrumental characterization of clay by XRF, XRD and FTIR. 30. Indian Academy of sciences, pp. 235–238. Rodier, J., Legube, B., Merlet, N., Brunet, R., 2009. Analyse d’un dépôt et d’un sédiment, L'analyse de l'eau, ninth ed. DUNOD, France, pp. 1174. Serio, F., Miglietta, P.P., Lamastra, L., Ficocelli, S., Intini, F., De Leo, F., De Donno, A., 2018. Groundwater nitrate contamination and agricultural land use: a grey water footprint perspective in Southern Apulia Region (Italy). Sci. Total Environ. 645, 1425–1431. https://doi.org/10.1016/j.scitotenv.2018.07.241. Sears, G.W., 1956. Determination of specific surface area of colloidal silica by titration with sodium hydroxide. Anal. Chem. 28, 1981–1983. Syafiuddin, A., Salmiati, S., Jonbi, J., Fulazzaky, M.A., 2018. Application of the kinetic and isotherm models for better understanding of the behaviors of silver nanoparticles adsorption onto different adsorbents. J. Environ. Manag. 218, 59–70. https://doi. org/10.1016/j.jenvman.2018.03.066. Shahbeig, H., Bagheri, N., Ghorbanian, S.A., Hallajisani, A., Poorkarimi, S., 2013. A new adsorption isotherm model of aqueous solutions on granular activated carbon. World J. Model. Simulat. 9, 243–254. Sigg, L., Stumm, W., 1981. The interaction of anions and weak acids with the hydrous goethite (α-FeOOH) surface. Colloids Surface. 2, 101–117. https://doi.org/10.1016/ 0166-6622(81)80001-7. World Health Organization, 2004. Guidelines for drinking-water quality: recommendations. In: World Health Organization, Geneva, 2004, third ed. . Yang, Q., Li, Z., Lu, X., Duan, Q., Huang, L., Bi, J., 2018. A review of soil heavy metal pollution from industrial and agricultural regions in China: pollution and risk assessment. Sci. Total Environ. 642, 690–700. https://doi.org/10.1016/j.scitotenv. 2018.06.068. Zaring, D., 1996. Agriculture, nonpoint source pollution, and regulatory control: the Clean Water Act's bleak present and future. Harv. Envtl. L. Rev. 20, 515. Zakhama, S., Dhaouadi, H., M’henni, F., 2011. Nonlinear modelisation of heavy metal removal from aqueous solution using Ulva Lactuca alage. Bioresour. Technol. 102, 786–796. https://doi.org/10.1016/j.biortech.2010.08.107.

References Abler, D., 2005. Economic evaluation of agricultural pollution control options for China. J. Integr. Agric. 14, 1045–1056. https://doi.org/10.1016/S2095-3119(14)60988-6. Ayawei, N., Ebelegi, A.N., Wankasi, D., 2017. Modelling and interpretation of adsorption isotherms. J.Chem. 2017. https://doi.org/10.1155/2017/3039817. Borggaard, O.K., Szilas, C., Gimsing, A.L., Rasmussen, L.H., 2004. Estimation of soil phosphate adsorption capacity by means of a pedotransfer function. Geoderma 118, 55–61. https://doi.org/10.1016/S0016-7061(03)00183-6. Ebrahimi, R., Hayati, B., Shahmoradi, B., Rezaee, R., Safari, M., Maleki, A., Yetilmezsoy, K., 2018. Adsorptive removal of nickel and lead ions from aqueous solutions by poly (amidoamine) (PAMAM) dendrimers (G4). Environ. Technol. Innovat. 12, 261–272. https://doi.org/10.1016/j.eti.2018.10.001. Evans, A.E., Mateo-Sagasta, J., Qadir, M., Boelee, E., Ippolito, A., 2019. Agricultural water pollution: key knowledge gaps and research needs. Curr. Opin. Environ. Sustain. 36, 20–27. https://doi.org/10.1016/j.cosust.2018.10.003. Fan, A.M., Willhite, C.C., Book, S.A., 1987. Evaluation of the nitrate drinking water standard with reference to infant methemoglobinemia and potential reproductive toxicity. Regul. Toxicol. Pharmacol. 7, 135–148. https://doi.org/10.1016/02732300(87)90024-9. Gérard, F., 2016. Clay minerals, iron/aluminum oxides, and their contribution to phosphate sorption in soils: a myth revisited. Geoderma 262, 213–226. https://doi.org/ 10.1016/j.geoderma.2015.08.036. Gupta, S.S., Bhattacharyya, K.G., 2008. Immobilization of Pb (II), Cd (II) and Ni (II) ions on kaolinite and Montmorillonite surfaces from aqueous medium. J. Environ. Manag. 87, 46–58. https://doi.org/10.1016/j.jenvman.2007.01.048. Ghorbel-Abid, I., Trabelsi-Ayadi, M., 2015. Competitive adsorption of heavy metals on local landfill clay. Arab. J. Chem. 8 (1), 25–31. https://doi.org/10.1016/j.arabjc. 2011.02.030. Hao, W., Flynn, S.L., Alessi, D.S., Konhauser, K.O., 2018. Change of the point of zero net proton charge (pHPZNPC) of clay minerals with ionic strength. Chem. Geol. 493, 458–467. https://doi.org/10.1016/j.chemgeo.2018.06.023. Tokarský, Jonas, 2018. Ghassoul-Moroccan clay with excellent adsorption properties. Mater. Today: Proc. 5, 78–87. https://doi.org/10.1016/j.matpr.2018.05.060. Khan, S., Cao, Q., Zheng, Y.M., Huang, Y.Z., Zhu, Y.G., 2008. Health risks of heavy metals in contaminated soils and food crops irrigated with wastewater in Beijing, China. Environ. Pollut. 152, 686–692. https://doi.org/10.1016/j.envpol.2007.06.056. Kumar, V., Sharma, A., Kaur, P., Sidhu, G.P.S., Bali, A.S., Bhardwaj, R., Cerda, A., 2018. Pollution assessment of heavy metals in soils of India and ecological risk assessment: A state-of-the-art. Chemosphere 216, 449–462. https://doi.org/10.1016/j. chemosphere.2018.10.066. Komaba, H., Fukagawa, M., 2016. Phosphate: a poison for humans? Kidney Int. 90, 753–763. https://doi.org/10.1016/j.kint.2016.03.039. Klay, S., Charef, A., Ayed, L., Houman, B., Rezgui, F., 2010. Effect of irrigation with treated wastewater on geochemical properties (saltiness, C, N and heavy metals) of isohumic soils (Zaouit Sousse perimeter, Oriental Tunisia). Desalination 253, 180–187. https://doi.org/10.1016/j.desal.2009.10.019. Kome, G.K., Enang, R.K., Yerima, B.P.K., Lontsi, M.G.R., 2018. Models relating soil pH measurements in H2O, KCl and CaCl2 for volcanic ash soils of Cameroon. Geoderma Reg. 14, 185. https://doi.org/10.1016/j.geodrs.2018.e00185. Kavitha, B., Thambavani, D.S., 2014. Characterization of riverbed sand from mullai periyar, tamilnadu by FT-IT, XRD and SEM/EDAX. Asian J. Chem. 27, 54–56.

187