Environ Earth Sci DOI 10.1007/s12665-014-3645-3
ORIGINAL ARTICLE
Removal of arsenic from groundwater in West Bengal, India using CuO nanoparticle adsorbent Kyle J. McDonald • K. J. Reddy • Neha Singh Ravi Prakash Singh • Saumitra Mukherjee
•
Received: 24 September 2013 / Accepted: 20 August 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract Arsenic contamination in drinking water is a worldwide health crisis. Treatment of arsenic laden water in areas of the world such as West Bengal, India has proven to be an extremely difficult task. Practical and reliable treatment technologies that can overcome the socioeconomic and geochemical barriers present in these areas of the world must be developed. Cupric Oxide (CuO) nanoparticles have shown promising characteristics as a sorbent to remove arsenic from water. Presented in this study is, to our knowledge, the first time CuO nanoparticles have been used to treat groundwater from West Bengal that is naturally high in arsenic. Batch experiments were conducted by reacting CuO nanoparticles with sixteen groundwater samples from West Bengal that exceed 10 lg/L. All samples showed near complete removal of arsenic following the treatment with CuO nanoparticles. The removal of arsenic was unaffected by the presence of high concentrations of competing ions such as bicarbonate (HCO3-), phosphate (PO43-), and sulfate (SO42-). The CuO nanoparticle treatment showed no major affect on other water constituents. Overall, the results of this study suggest that CuO nanoparticles show potential as an effective sorbent of arsenic under diverse geochemical makeups. Keywords Arsenic removal Arsenic contamination Arsenate Arsenite Arsenic toxicity CuO sorbent
K. J. McDonald K. J. Reddy (&) Department of Ecosystem Science and Management, University of Wyoming, Laramie Wyoming, USA e-mail:
[email protected];
[email protected] N. Singh R. P. Singh S. Mukherjee School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India
Introduction Arsenic is a metalloid element that can be found ubiquitously throughout the Earth’s crust. It enters groundwater via natural and multiple anthropogenic pathways including smelting, combustion of fossil fuels, and various agricultural practices. However, the predominant source of arsenic contamination is through the weathering of geologic materials (Chakraborti 2011). Many human health problems are associated with exposure to elevated levels of arsenic including discoloration of skin, diabetes, intestinal maladies, carcinogenesis and ultimately death (Anawar et al. 2002; Pan et al. 2013). The widespread nature and severity of health issues associated with arsenic contamination of drinking water are chief global health concerns (Chakraborti et al. 2013). Many areas around the world are afflicted with extremely high levels of arsenic concentration including, Argentina, Bangladesh, Cambodia, Hungary, China, Chile, India, Iran, Italy, Mexico, Nepal, Taiwan, Vietnam, and some parts of the United States (Welch et al. 2005; Berg et al. 2007; Winkel et al. 2008; Rahman et al. 2009; Chowdhury et al. 2000; Martinson and Reddy 2009; Petrini et al. 2011; Rodrı´-guez-Lado et al. 2013; Pazand and Javanshir 2013). Identifying and confirming locations where high levels of arsenic occur are difficult tasks. This is due to the widespread occurrence of arsenic contamination, the high variability within small regions and the meticulous means required to accurately analyze arsenic concentrations. Further, existing arsenic removal techniques are hindered by many factors including pre and post treatment requirements, the disposal of byproducts, operational expertise, lack of onsite arsenic analysis techniques, and socioeconomic barriers (Siegel et al. 2007; Mohan and Pittman 2007; Chiew et al. 2009; Shafiquzzaman et al. 2009; Johnston et al. 2010; Chowdhury et al. 2011; Zhang et al. 2012).
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Recent studies have found that CuO is a novel and effective arsenic adsorbent that circumvents many of the obstacles other treatment techniques may encounter. Reddy and Attili (2005) established that CuO is able to adsorb both arsenate and arsenite in the presence of competing ions and does not require pH adjustments (Reddy and Attili 2005; Reddy 2007; Reddy 2011). These researchers attributed this to the high point of zero charge (PZC) for CuO, estimated to be 9.4 ± 0.4 (Yoon et al. 1979), which allows for adsorption of arsenic in most naturally occurring pH ranges of water. Subsequent studies found that nanoscaled CuO has a high maximum adsorption capacity (Martinson and Reddy 2009). Further, these studies have shown CuO nanoparticles are able to remove arsenic from many different groundwater samples suggesting that it is effective in a wide range of geochemical conditions (Roth and Reddy 2005; Martinson and Reddy 2009; Reddy and Roth 2012; Reddy et al. 2013). However, these studies have been conducted only with groundwater samples collected predominantly from western US. Studying the performance of CuO in areas where different geochemical conditions exist and where arsenic related health issues are more prevalent will establish a better understanding of the capabilities of this remediation technology. Regions within the lower Ganges Plain and the Assam Valley, including West Bengal, have been recognized for greater than two decades as some of the most significantly arsenic affected regions in the world (Chakraborti et al. 2009). In this study, samples collected were chosen based on the results of arsenic concentration in groundwater of several West Bengal tubewells. Details of the sampling locations and geohydrological conditions are reported by Singh et al. (2013). In their study, remotely sensed satellite imagery was used to identify the sampling points by studying landform features and inferring the surface manifestation of hydrogeochemical features. Analysis of multispectral sensor imagery can be used as a proxy for landcover (Ledwith 2002; Lunetta et al. 2006). This is useful to delineate the impact of urbanization on groundwater recharging in certain terrains (Mukherjee 2008) because vegetation plays key roles in the interactions between groundwater and surface water, due to its direct and indirect influence on recharge. In addition to locate areas which may contain high concentrations of arsenic, affordable, accurate and timely analysis of arsenic concentrations must be determined. The Arsenic Econo-Quick field testing kit suits all of these criteria and has been shown to produce accurate results in numerous studies (George et al. 2012). The treatment of arsenic laden groundwater samples with CuO nanoparticles will be the primary focus of this paper. The scope and objectives of this particular research were to (1) prepare CuO nanoparticles at the University of Wyoming, Laramie, Wyoming,
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USA and transport them in accordance with all hazmat regulations to Jawaharlal Nehru University (JNU), New Delhi, India, (2) collect groundwater samples identified with high concentrations of arsenic, and (3) assess the effectiveness of CuO nanoparticles in removing arsenic from groundwater under the geochemical conditions of West Bengal.
Materials and methods CuO nanoparticles The CuO nanoparticles used in this study were synthesized following the procedures developed by Martinson and Reddy (2009). This method combines ethanolic solutions of copper chloride and sodium hydroxide with a dispersant. The solution was then reacted in a GE model JES1358WL 1,200 W microwave that has been modified with a reflux condenser extending from the top at ambient air pressure for 10 min at 20 % power to generate uniformly sized particles. The supernatant was then cooled to room temperature, decanted, centrifuged and washed with a sequence of deionized (DI) water at 60–65 °C, ethanol and acetone. The washed precipitate was then placed in a 110 °C oven to dry. The resulting CuO nanoparticles are considered a class 9 unspecified environmentally hazardous substance, solid with a UN (United Nations) number 3077. The nanoparticles were shipped, following International Air Transport Association (IATA) regulations, from the University of Wyoming to JNU, New Delhi, India to conduct all batch experiments. Sample collection The study area is a part of North 24 Parganas and South 24 Parganas in West Bengal. Sampling locations were determined on the basis of spectral signatures of various geomorphologic landform features inferred from the satellite imagery conducted by Singh et al. (2013). Variance between geomorphic features can be inferred from satellite imagery on the basis of the variation between differing structures and differing reflectance. Selected well locations were qualitatively confirmed to have high concentrations of arsenic using the Arsenic Econo-Quick field test kits. These groundwater samples were collected at the wellhead and stored in polypropylene bottles. All bottles were filled with zero head space and were transported back to the lab and stored at 4 °C to avoid chemical alteration. A portion of sample aliquots was collected and acidified in the field with diluted HNO3 for cation and heavy metal analysis. All chemical analyses were conducted at JNU, New Delhi, India.
Environ Earth Sci
Analytical methods Transmission electron microscopy (TEM) and BrunauerEmmett-Teller (BET) surface area analysis were used to characterize the CuO nanoparticles. TEM images were obtained using a Hitachi H-7000 equipped with a high resolution cooled Gatan UltraScan 4000 CCD camera. Prior to imaging, the samples were placed on a carbon coated copper grid after being dispersed in ethanol and exposed to ultrasonic vibration for 30 min. Surface area was determined using a TriStar 3000 BET analyzer. These experiments were conducted at University of Wyoming, Laramie, Wyoming, USA. Groundwater samples were qualitatively analyzed for arsenic using the Arsenic Econo-Quick field test kits in the field as well as after the treatment with CuO nanoparticles. The analysis of cations and anions followed the methods as described by the American Public Health Association (APHA and WEF 2005). Arsenic was analyzed by a Thermo Scientific Atomic Absorption Spectrophotometer (AAS) to corroborate qualitative analysis. AAS was also used to analyze the concentrations of calcium (Ca), magnesium (Mg), iron (Fe), and copper (Cu). Concentrations of sodium (Na) and potassium (K) were analyzed using an Aimil Flame Photometer. Phosphate was measured on the basis of the deprotonation of malachite green by the molybdophosphate complex. Chloride (Cl) was estimated using Mohr’s titration method. Fluoride (F) was measured using an anion electrode on an Orion bench top meter. Bicarbonate was determined using the potentiometric titration method. The pH measurements of all samples were taken using a Hanna pH meter. All samples were subjected to the above analyses prior to and following the reaction with CuO nanoparticles. Geochemical modeling
paper with plastic syringes. The filtrate was analyzed qualitatively using the Arsenic Econo-Quick field testing kit and quantitatively for complete water chemistries using the methods as described previously.
Results and discussion CuO nanoparticle and groundwater characterization Physical properties of the synthesized CuO nanoparticles were analyzed using TEM and BET analysis. TEM analysis shows that the nanoparticles formed spherical and cylindrical shapes as shown in Fig. 1. These results suggest that residual byproducts formed in the process of synthesis of CuO nanoparticles are still present. For example, our earlier studies have shown that approximately 7 % is residual Cu2Cl(OH)3 salt in synthesized CuO NPs (Martinson and Reddy 2009). The BET surface area of the CuO nanoparticles gave a specific surface area of 62 m2/g. These nanoparticles are similar in size and surface area to the nanoparticles prepared by Martinson and Reddy (2009). Chemical characterization of the groundwater samples before CuO NPs treatment is reported in Table 1. Of the 38 total groundwater samples collected, 16 contained concentrations of arsenic above the United States Environmental Protection Agency (USEPA) and World Health Organization (WHO) limit of 10 lg/L. All of which were from tubewells located in West Bengal. Concentrations of arsenic ranged from 13 to nearly 70 lg/L. Arsenic analysis results from the semi-qualitative Arsenic Econo-Quick field test kit were very agreeable with the analysis by AAS. Several studies suggest that source of arsenic in West Bengal groundwater is a geogenic. However, the mechanism of release of arsenic into groundwater is not clearly understood. A recent study suggested that geohydrological processes such as dissolution of silicate minerals,
All groundwater chemical data were modeled by Visual MINTEQ 3.0 before and following treatment with CuO nanoparticles to predict ionic species distribution and potential solid phases controlling dissolved ionic species. Visual MINTEQ is a freeware geochemical model that uses code adapted from USEPA’s MINTEQA2 software and has since 2000 been maintained by John Peter Gustafsson at KTH, Sweden (Gustafsson 2011). Batch experiments 50 mL of unacidified groundwater samples were combined with 0.2 g of prepared CuO nanopaticles in 50 mL centrifuge tubes. The solution was allowed to react on an orbital shaker table for 30 min at 250 rpm. Following the reaction, the samples were filtered using 0.45 lm filter
Fig. 1 TEM image of CuO nanoparticles at 30x magnification
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Environ Earth Sci Table 1 Groundwater characterization data prior to treatment with CuO nanoparticles Ca (mg/L)
HCO3 (mg/L)
Cl (mg/L)
PO4 (mg/L)
NO3 (mg/L)
SO4 (mg/L)
F (lg/L)
Cu (lg/L)
As (lg/L)
48.3
88.1
376.5
94.2
210.8
6.5
12.0
4.1
8.0
61.9
34.3
50.4
87.7
529.0
24.7
140.7
7.8
0.4
1.4
7.1
58.3
33.8
51.0
95.3
498.5
42.1
142.7
0.2
BDL
1.7
4.6
19.9
60.0
33.4
50.1
103.2
613.0
49.5
152.6
4.7
BDL
1.4
18.8
39.0
8.3
39.6
13.2
39.1
85.7
396.5
69.5
48.3
0.5
13.7
4.0
17.4
17.0
8.5
66.2
38.2
53.1
114.7
427.5
120.3
199.3
BDL
44.6
2.4
195.5
35.6
WB26 WB28
8.1 8.3
135.3 44.5
35.6 22.4
53.6 37.2
129.3 57.1
317.5 426.5
567.4 76.9
12.1 16.9
BDL 0.6
4.9 0.4
2.6 1.6
123.8 10.0
14.8 14.4
WB29
8.8
65.1
35.4
47.6
80.5
369.0
48.2
215.9
6.7
0.1
1.7
BDL
69.4
WB30
8.6
56.8
23.2
41.1
88.2
487.5
60.8
58.0
1.6
0.7
1.8
17.5
13.0
WB32
8.6
53.9
33.7
40.2
76.4
437.5
42.1
77.3
18.0
0.9
1.5
29.1
58.0
WB33
8.5
61.4
34.0
46.8
68.8
528.5
94.2
10.8
1.5
0.6
2.7
21.4
16.6
WB34
8.6
153.7
32.4
65.6
75.7
695.0
164.4
132.2
6.0
6.9
1.9
18.8
48.5
WB36
8.5
58.1
33.2
42.8
85.4
537.5
102.9
1.2
15.4
BDL
1.9
82.7
42.2
WB37
8.7
77.4
31.0
49.7
81.2
376.5
111.6
190.9
BDL
0.8
2.3
34.1
14.7
WB39
8.3
57.2
32.8
59.3
96.5
357.5
68.2
252.3
2.8
0.1
1.3
30.2
14.3
ID
pH
Na (mg/L)
K (mg/L)
Mg (mg/L)
WB19
8.4
75.9
33.9
WB20 WB21
8.3
48.5
8.4
49.0
WB22
8.5
WB23 WB25
BDL below detection limit
formation of clay minerals, and ion exchange processes in aquifer may be responsible for the release of arsenic into the groundwater of the study area (Singh et al.2013). The pH of these samples ranged from 8.1 to 8.8. Concentration of major elements in mg/L was between 39.6 to 153. 7 (Na), 13.2 to 38.2 (K), 37.2 to 59.3 (Mg), and 57.1 to 129.3 (Ca). Concentration of major anios in mg/L was between 317.5 to 695 (HCO3), 24.7 to 567.5 (Cl), BDL to 18.0 (NO3), and BDL to 44.6 (SO4) (Table 1). Fluoride (F) concentrations were low and ranged between 1.3 and 4.1 lg/L. Copper concentrations ranged from BDL to almost 0.2 mg/L. Further, many samples showed significant concentrations of PO43-, ranging from 1 to greater than 200 mg/L. High concentration of PO43- in groundwaters of lower Ganges area is not uncommon. For example, similar high concentrations of phosphate were found by the British Geological Survey in the groundwaters of comparable regions in Bangladesh (BGS and DPHE 2001). Arsenic removal by CuO nanoparticles Following the batch experiment process, all water samples were analyzed for complete water chemistries, reported in Table 2. The water samples showed slight decrease in pH and minor changes in major and trace element concentrations after the reaction with CuO nanoparticles. Minor changes were observed in concentrations of NO3- and SO42- following treatment with CuO nanoparticles. However, these changes are minimal and do not exceed
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2.5 mg/L for NO3- and 2 mg/L for SO42-. The HCO3showed much more variability in the concentration after the treatment. For most samples, HCO3- concentration decreased with the treatment of CuO NPs, which is expected from decrease in the pH following the treatment. However, for WB 25, 26, 29, 32, and 34 samples the HCO3- concentration increased substantially. It is difficult to explain these anomalies. Concentrations of Cu before and after treatment with CuO nanoparticles are of particular concern. Most of these samples show an increase in Cu concentrations following the batch experiment process ranging from 0.0195 mg/L to almost 0.189 mg/L (Table 2). However, among all treated samples Cu remained well below the EPA drinking water maximum contaminant level goal of 1.3 mg/L. Other studies with CuO nanoparticles also reported that Cu concentration in treated water remains below the US EPA MCL. For example, Martinson and Reddy (2009), Reddy and Roth (2012) studies have treated over fifty groundwater samples, collected from different states in western US, with CuO and found similar results. The dissolved Cu concentration in all above studies remained well below the US EPA MCL limit. Results of arsenic removal are shown in Fig. 2. After the treatment, arsenic analysis results from the semi-qualitative Arsenic Econo-Quick field test kit were very agreeable with the analysis by AAS. All samples showed significant reduction in arsenic concentrations to levels well below the USEPA and WHO standard value of 10 lg/L. The effectiveness of many arsenic sorbents are significantly limited
Environ Earth Sci Table 2 Groundwater characterization data following treatment with CuO nanoparticles Ca (mg/L)
HCO3 (mg/L)
Cl (mg/L)
PO4 (mg/L)
NO3 (mg/L)
SO4 (mg/L)
F (lg/L)
Cu (lg/L)
As (lg/L)
48.3
87.7
294.5
92.4
198.9
6.4
11.0
3.9
100.8
1.6
34.2
49.8
86.9
294.5
23.9
138.3
7.5
0.2
1.4
114.6
0.2
33.6
51.0
94.9
355.5
42.0
139.2
0.2
BDL
1.6
89.6
54.8
33.2
49.8
101.0
485.0
48.5
150.8
4.7
BDL
1.3
114.3
BDL
7.7
36.2
13.0
39.0
84.9
366.0
68.4
48.0
0.4
11.5
3.9
117.9
BDL
WB25
7.9
65.9
37.9
51.7
114.0
457.0
116.0
195.9
BDL
42.7
2.2
75.9
BDL
WB26 WB28
7.5 7.7
131.8 41.9
35.2 22.1
50.7 36.6
128.8 55.8
719.5 275.0
565.8 76.8
10.2 15.1
BDL 0.5
4.7 0.4
2.5 1.5
86.7 77.5
BDL BDL
WB29
8.2
63.2
35.1
46.3
80.0
668.0
47.5
209.0
6.5
0.1
1.3
36.1
1.9
WB30
8.0
55.8
23.0
40.6
87.9
305.5
60.0
52.9
1.6
0.6
1.7
89.4
BDL
WB32
7.9
51.9
33.1
39.9
75.9
569.0
41.9
75.1
15.5
0.8
1.4
67.8
0.1
WB33
7.8
59.5
33.8
46.2
67.9
417.0
94.1
10.1
1.2
0.5
2.6
125.1
BDL
WB34
8.3
148.6
32.2
65.0
75.0
1104.5
162.5
130.5
5.9
5.4
1.8
189.1
BDL
WB36
7.9
55.8
33.1
41.9
84.8
316.5
100.1
1.0
14.8
BDL
1.9
81.1
BDL
WB37
7.8
75.4
29.2
48.9
80.8
386.0
110.8
185.7
BDL
0.6
2.1
111.1
BDL
WB39
7.9
55.4
32.5
59.0
93.7
557.5
65.4
250.5
1.8
0.1
1.2
19.5
BDL
ID
pH
Na (mg/L)
K (mg/L)
Mg (mg/L)
WB19
7.7
72.6
33.8
WB20 WB21
7.8
46.7
7.9
42.8
WB22
7.9
WB23
BDL
BDL below detection limit
Fig. 2 Concentration of arsenic in West Bengal groundwater samples prior to and following treatment with CuO nanoparticles
Concentration of As before and after CuO nanoparticle treatment 70 Before Treatment
Concentration of As (µg/L)
60
After Treatment
50
40
30
20
10
0
Sample ID
by the presence of competing ions such as phosphate, bicarbonate, and sulfate (Jain and Loeppert 2000; Meng et al. 2000; Zhang et al. 2012). Results shown in Fig. 3 suggest no discernible effect on the rate of arsenic removal by CuO nanoparticles due to high concentrations of potential competing ions such as HCO3-(Fig. 3a), PO43(Fig. 3b) or SO42- (Fig. 3c). Conventional arsenic removal technologies have proven ineffective due to multiple socioeconomic barriers as well as other problems such as the
disposal of spent media (Johnston et al. 2010). CuO nanoparticles have shown potential for regeneration and reuse which could drastically reduce the cost associated with its use as well as reduce the problems associated with disposal of spent media (Reddy et al. 2013). Further, various sorbent technologies show a significant decline in effectiveness of arsenic removal in the presence of competing ions such as HCO3-, PO43-, and SO42- (Meng et al. 2000; Siegel et al. 2007; Zhang et al. 2012). However,
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A 100%
800
90%
700
80% 600 70%
As Removal
Fig. 3 Concentration of competing ions vs. arsenic removal. a HCO3- vs. %As removed, b PO43- vs. %As removed, and c SO42- vs. %As removed
60%
500
50%
400
40%
300
Concnetraton (mg/L)
Environ Earth Sci
200 20% 100
10%
0%
0 1
B
HCO
30%
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16 300
100%
200
60% 50%
150
40% 100
30% 20%
50
10% 0%
0 2
3
4
5
6
8
7
9
10
11
12
13
14
15
16
50
90%
45
80%
40
70%
35
60%
30
50%
25
40%
20
30%
15
20%
10
10%
5
As Removal
C 100%
0%
0 1
2
3
these competing ions have shown no discernible effect on the effectiveness of arsenic removal by CuO nanoparticles at naturally occurring concentrations. These results suggest that CuO nanoparticles are an effective sorbent for arsenic and are able to perform well under a wide variety of geochemistries.
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Concnetraton (mg/L)
1
SO
As Removal
70%
PO
250
80%
Concnetraton (mg/L)
90%
4
5
6
7
8
9
10
11
12
13
14
15
16
Geochemical model The modeled species distribution of arsenic ions is shown in Table 3. Results of the Visual MINTEQ model show ionic arsenic speciation is dominantly in the form of HAsO-2 4 . The distribution of arsenic speciation shifts with
Environ Earth Sci Table 3 Percent distribution of ionic arsenic species based on geochemical modeling through Visual MINTEQ No.
ID
Species
Table 3 continued No.
Pre-treatment
2
WB19
WB20
4
5
WB21
WB22
WB23
Pre-treatment
WB25
8
9
10
11
12
13
WB26
WB28
WB29
WB30
WB32
WB33
WB34
0.1
0.0
96.6
92.2
HAsO4-2
97.3
88.0
H2AsO4-
3.3
7.8
H2AsO4AsO4-3
2.6 0.1
12.0 0.0
HAsO4-2
96.6
90.0
-
3.3
10.0
0.1
0.0
HAsO4-2
97.3
92.0
H2AsO4
-
2.7
8.0
AsO4-3
0.1
0.0
HAsO4-2
97.8
92.1
H2AsO4-
2.1
7.9
AsO4-3
0.1
0.0
-2
96.6
87.8
H2AsO4-
3.4
12.2
AsO4-3
0.1
0.0
-2
97.8
92.3
H2AsO4AsO4-3
2.1 0.0
7.7 –
HAsO4-2
95.2
83.8
H2AsO4-
4.7
16.2
AsO4-3
-3
AsO4
0.1
0.0
HAsO4-2
96.5
87.5
H2AsO4-
3.4
12.5
AsO4-3
0.2
0.0
HAsO4-2
98.7
95.9
H2AsO4-
1.1
4.1
-3
AsO4
0.1
0.0
HAsO4-2
98.2
93.5
H2AsO4-
1.7
6.5
AsO4-3
0.1
0.0
HAsO4-2
98.2
92.0
H2AsO4-
1.7
8.0
AsO4-3 HAsO4-2
0.1 97.8
0.0 90.1
H2AsO4-
2.1
9.8
AsO4-3
0.1
0.1
-2
98.3
96.9
H2AsO4-
1.6
3.0
AsO4-3
0.1
0.0
HAsO4-2
97.8
92.0
H2AsO4-
2.1
8.0
AsO4-3
HAsO4 14
15
WB36
WB37
AsO4
Post-treatment
HAsO4-2
HAsO4 7
WB39
-3
0.0
HAsO4 6
Percent species distribution
0.1
AsO4-3
H2AsO4 3
Species
Post-treatment 16
1
ID
Percent species distribution
0.1
0.0
-2
98.5
90.2
H2AsO4-
1.4
9.7
HAsO4
Table 4 Average saturation index of crystal tenorite based and pH for pre and post-treatment samples based on Visual MINTEQ model Pre-treatment
Post-treatment
Tenorite SI
pH
Tenorite SI
pH
2.57 ± 4.23
8.46 ± 0.18
1.57 ± 0.73
7.87 ± 0.19
a slight decrease in HAsO-2 and a slight increase in 4 HAsO4 following treatment with CuO. However, the dominant species post treatment remains HAsO-2 4 . The average pH of the water samples prior to and following treatment with CuO nanoparticles was 8.5 and 7.9, respectively. In naturally occurring waters, the dominant species of arsenic is HAsO4- above a pH of 8 and HAsO-2 4 below a pH of 8 (Masscheleyn et al. 1991). The arsenic in these groundwater samples occurs as negatively charged ions. At this pH range, CuO develops a positive surface charge because the PZC for CuO is 9.4 ± 0.4 (Yoon et al. 1979). This reinforces adsorption as the mechanism of arsenic removal. Table 4 shows the average saturation index (SI) for crystal tenorite (CuO) in pre and post-treatment samples. The SI is a calculated value that is useful in predicting the stability of ions in water. A SI with a calculated value of greater than 1 is deemed oversaturated and predicts precipitation, less than 1 is undersaturated and predicts dissolution. The SI value for tenorite in pre-treatment samples is highly variable with an average value of 2.6 while the average SI value for tenorite in post-treatment samples is considerably less variable averaging at 1.6. This suggests that the concentrations of dissolved Cu in the post-treatment samples are controlled by the CuO nanoparticles.
Conclusions CuO nanoparticles effectively removed arsenic from sixteen groundwater samples collected in West Bengal, India. All samples contained naturally high levels of arsenic reaching concentrations as high as 70 lg/L––seven times more than the drinking water guideline held by the USEPA
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and WHO. Furthermore, many samples contained high concentrations of potential competing ions such as HCO3-, PO43-, and SO42-. The effectiveness of CuO nanoparticles in removing arsenic was unchanged by the presence of these potential competing ions. The treatment of natural groundwater samples suggests that CuO nanoparticles are effective across a wide range of geochemical conditions. The combination of these characteristics lend to the viability of CuO nanoparticles as an effective adsorbent to remove arsenic from groundwater that merits further study and development for field application. Future research should be focused on the regeneration and reuse of CuO nanoparticle sorbents, the capture and disposal of desorbed arsenic from regeneration, and ultimately the field implementation of column filtration using a field arsenic analytical technique as a real-time monitor of arsenic concentrations. Acknowledgments The authors would like to thank the University of Wyoming and Jawaharlal Nehru University for providing the facilities and resources used in this research.
References Anawar HM, Akai J, Mostofa KMG, Safiullah S, Tareq SM (2002) Arsenic poisoning in groundwater: health risk and geochemical sources in Bangladesh. Environ Int 27(7):597–604. doi:10.1016/ S0160-4120(01)00116-7 APHA, WEF (2005) Standard methods for the examination of water and wastewater 1965, 21 Berg M, Stengel C, Trang PTK, Hung Viet P, Sampson ML, Leng M, Samreth S, Fredericks D (2007) Magnitude of arsenic pollution in the Mekong and Red River Deltasaˆ€’’Cambodia and Vietnam. Sci Total Environ 372(2):413–425 BGS, DPHE (2001) Arsenic contamination of groundwater in Bangladesh. In: Kinniburgh DG, Smedley PL (eds) British geological survey technical report WC/00/19. British Geological Survey, Keyworth Chakraborti D (2011) Arsenic: occurrence in groundwater. In: Jerome ˆA ˆ , Nriagu O (eds) Encyclopedia of environmental health. A Elsevier, Burlington, pp 165–180 Chakraborti D, Das B, Rahman MM, Chowdhury UK, Biswas B, Goswami AB, Nayak B, Pal A, Sengupta MK, Ahamed S, Hossain A, Basu G, Roychowdhury T, Das D (2009) Status of groundwater arsenic contamination in the state of West Bengal, India: a 20-year study report. Mol Nutr Food Res 53(5):542–551. doi:10.1002/mnfr.200700517 Chakraborti D, Mohammad MR, Das B, Nayak B, Pal A, Sengupta MK, Hossain MA, Ahamed S, Sahu M, Saha KC, Mukherjee SC, Pati S, Dutta RN, Quamruzzaman Q (2013) Groundwater arsenic contamination in Ganga-Meghna-Brahmaputra plain, its health effects and an approach for mitigation. Environ Earth Sci 70(5):1993–2008 Chiew H, Sampson M, Huch S, Ken S, Bostick BC (2009) Effect of groundwater iron and phosphate on the efficacy of arsenic removal by iron-amended BioSand filters. Environ Sci Technol 43(16):6295–6300 Chowdhury UK, Biswas BK, Chowdhury TR, Samanta G, Mandal BK, Basu GC, Chanda CR, Lodh D, Saha KC, Mukherjee SK
123
(2000) Groundwater arsenic contamination in Bangladesh and West Bengal, India. Environ Health Perspect 108(5):393 Chowdhury SR, Yanful EK, Pratt AR (2011) Arsenic removal from aqueous solutions by mixed magnetite-maghemite nanoparticles. Environ Earth Sci 64(2):411423 George CM, Zheng Y, Graziano J, Hossain Z, Rasul SB, Mey J, Van Geen A (2012) Evaluation of an arsenic test kit for rapid well screening in Bangladesh. Environ Sci Technol 46(20):11213–11219 Gustaffson JP (2011) Visual MINTEQ. 3.0 Jain A, Loeppert RH (2000) Effect of competing anions on the adsorption of arsenate and arsenite by ferrihydrite. J Environ Qual 29(5):1422–1430 Johnston RB, Hanchett S, Khan MH (2010) The socio-economics of arsenic removal. Nat Geosci 3(1):2–3 Ledwith M (2002) Land cover classification using SPOT Vegetation 10-day composite imagesaˆ€‘‘Baltic Sea catchment basin Lunetta RS, Knight JF, Ediriwickrema J, Lyon JG, Worthy LD (2006) Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens Environ 105(2):142–154 Martinson CA, Reddy K (2009) Adsorption of arsenic (III) and arsenic (V) by cupric oxide nanoparticles. J Colloid Interface Sci 336(2):406–411 Masscheleyn PH, Delaune RD, Patrick WH Jr (1991) Effect of redox potential and pH on arsenic speciation and solubility in a contaminated soil. Environ Sci Technol 25(8):1414–1419 Meng X, Bang S, Korfiatis GP (2000) Effects of silicate, sulfate, and carbonate on arsenic removal by ferric chloride. Water Res 34(4):1255–1261 Mohan D, Pittman CU (2007) Arsenic removal from water/waste water using adsorbents: a critical review. J Hazard Mater 142(1–2):1–53 Mukherjee S (2008) Role of satellite sensors in groundwater exploration. Sensors 8(3):2006–2016 Pan WC, Seow WJ, Kile ML, Hoffman EB, Quamruzzaman Q, Rahman M, Mahiuddin G, Mostofa G, Lu Q, Christiani DC (2013) Association of low to moderate levels of arsenic exposure with risk of type 2 diabetes in Bangladesh. Am J Epidemiol 178(10):1563–1570. doi:10.1093/aje/kwt195 Pazand K, Javanshir AR (2013) Hydrogeochemistry and arsenic contamination of groundwater in the Rayen area, southeastern Iran. Environ Earth Sci 70(6):2633–2644 Petrini R, Slejko F, Lutman A, Pison S, Franceschini G, Zini L, Italiano F, Galic A (2011) Natural arsenic contamination in waters from the Pesariis village. NE Italy. Environmental Earth Sciences 62(3):481–491 Rahman MM, Ng JC, Naidu R (2009) Chronic exposure of arsenic via drinking water and its adverse health impacts on humans. Environ Geochem Health 31:189–200 Reddy K, Attili V (2005) How to Remove Arsenic from Water. ASA, CSSA, SSSA, Annual Meeting abstracts Reddy KJ (2007) Method for Removing Arsenic from Water. US Patent No, 7,235,179. Washington, DC: U.S. Patent and Trademark Office Reddy KJ (2011) U.S. Patent No, 7,897,052. Washington, DC: U.S. Patent and Trademark Office Reddy K, Roth T (2012) Arsenic removal from natural groundwater using cupric oxide. Ground Water. doi:10.1111/j.1745-6584. 2012.00926.x Reddy K, McDonald K, King H (2013) A novel arsenic removal process for water using cupric oxide nanoparticles. J Colloid Interface Sci 397:96–102 Rodrı´-guez-Lado L, Sun G, Berg M, Zhang Q, Xue H, Zheng Q, Johnson CA (2013) Groundwater arsenic contamination throughout china. Science 341(6148):866–868
Environ Earth Sci Roth TR, Reddy K (2005) Regional assessment of arsenic in domestic wells of small communities Shafiquzzaman M, Azam MS, Mishima I, Nakajima J (2009) Technical and social evaluation of arsenic mitigation in rural Bangladesh. J Health Popul Nutr 27(5):674 Siegel M, McConnell P, Ilges A, Chen HW, Ghassemi A, Thompson R (2007) Development and evaluation of innovative arsenic adsorption technologies for drinking water by the arsenic water technology partnership (SAND2006-0113C) Singh N, Singh RP, Mukherjee S, McDonald KJ, Reddy KJ (2013) Hydro-geological processes controlling the release of arsenic in part of 24 Parganas district West Bengal. Environ Earth Sci. doi:10.1007/s12665-013-2940-8
Welch AH, Westjohn D, Helsel DR, Wanty RB (2005) Arsenic in ground water of the United States: occurrence and geochemistry. Ground Water 38(4):589–604 Winkel L, Berg M, Amini M, Hug SJ, Johnson CA (2008) Predicting groundwater arsenic contamination in Southeast Asia from surface parameters. Nat Geosci 1(8):536–542 Yoon RH, Salman T, Donnay G (1979) Predicting points of zero charge of oxides and hydroxides. J Colloid Interface Sci 70(3):483–493 Zhang G, Li X, Wu S, Gu P (2012) Effect of source water quality on arsenic (V) removal from drinking water by coagulation/ microfiltration. Environ Earth Sci 66(4):1269–1277
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