Abstract
The presence of arsenic in water bodies poses a significant risk to both human health and the environment. Arsenic (As) contamination in water sources is a global environmental concern caused by both natural processes and human activities. Due to its toxic and persistent nature, arsenic has detrimental effects on ecosystems and human wellbeing. This study aimed to elucidate the mechanisms behind arsenic accumulation in water bodies. In aquatic environments, arsenic concentrations in drinking water have been reported as high as 1,320 μg/L (Nicaragua), while groundwater levels exceeded 5,000 μg/L (Thailand), and wastewater contained up to 134,000 μg/L (landfill leachate in Brazil). Furthermore, bioaccumulation of arsenic (μg/g) in fish species ranges from 0.4 (catfish in the Paraná River Delta, Brazil) to 362 (Pteromylaeus bovinus, Northern Adriatic Sea). Recent research has predominantly focused on removing arsenic from aqueous solutions through adsorption methods. Notably, nanoparticle adsorbents and graphene-based adsorbents demonstrate a high capacity for arsenic removal from water bodies.
1 Introduction
As the global demand for clean water rises, the importance of water resources is increasing. Factors such as population growth, pollution, and climate change are reducing the usability of limited water resources. Urbanization, agriculture, and industry have a significant impact on water quality. Pollutants, such as heavy metals, disrupt aquatic ecosystems, thereby undermining their sustainability (Sener et al., 2023). The presence of metal pollution, such as arsenic (As), has a harmful impact on the ecosystem because of its toxic, non-biodegradable, and persistent characteristics. Arsenic rapidly accumulates in the soil, water, and sediment, posing a threat to the environment (Prasad Ahirvar et al., 2023). The presence of As in the water is a result of weathering processes that occur in rocks and sulfidic minerals. One of these processes involves the oxidation of sulfidic minerals like arsenopyrites, which releases arsenic into water sources. Additionally, anthropogenic activities such as agriculture, mining, and industrial waste also contribute to the release of As into the environment (Irunde et al., ).
Arsenic, a harmful contaminant, is a toxic substance that is both colorless and odorless (Abedi and Mojiri, ). It can be found in elevated concentrations in water bodies across different countries worldwide. Exposure to high levels of arsenic can result in severe immediate and long-term health issues, including diarrhea, vomiting, diabetes, heart disease, cancer, miscarriage, childhood cancer, and even fatalities (Nguyen and Mulligan, 2023). To ensure safe drinking water and protect public health, the World Health Organization (WHO) recommends a maximum allowable concentration (MAC) of As in drinking water at 10 μg/L. This limit is legally defined in the European Union Drinking Water Directive. However, certain countries like Denmark and Ireland have set even lower limits (5 μg/L and 7.5 μg/L, respectively). Arsenic concentrations in groundwater and surface water have been found to range from 100 to 5,000 μg/L. In certain cases, particularly near hydrothermal systems close to shorelines, extremely high concentrations of 1,386 and 5,850 μg/L have been reported. This widespread contamination of As in aquatic systems is recognized as a significant global environmental issue, with impacts observed worldwide (Zhang et al., 2022).
Increasingly, more states are adopting stricter As standards based on the severity of As issues, population exposure, and the availability of improved removal technologies (Nikić et al., 2023). Adsorption is a widely used and promising technique for removing As from water (Fang et al., ). Adsorption has gained significant recognition as a cost-effective method for As removal. It offers several advantages, including high removal efficiency, lower cost, and simpler operation compared to alternative methods (Moradi et al., 2023). In this review manuscript, our study aims to study the fate of As in water bodies around the world as well as comprehensively evaluate the effectiveness of adsorption as a method for As removal from water.
2 Arsenic contamination in water bodies
Once released into the environment, arsenic becomes part of the biogeochemical cycle and cannot be degraded. In aquatic environments, arsenic undergoes complex chemical speciation, resulting in various inorganic and organic arsenic species. Inorganic forms include arsenite As(III) and arsenate As(V), while organic forms encompass methylarsonate (MMA), dimethylarsinate (DMA), tetramethylarsine (TMA), trimethylarsine oxide (TMAO), arsenocholine (AsC), arsenobetaine (AsB), thiolated arsenic, arsenosugars (As-Sug), and arsenolipids (Zhang et al., 2022).
Commonly, in water, As exists in various redox states, predominantly as inorganic species such as As(III) and As(V). However, the presence of organic species is more common when there is anthropogenic contamination (Fuoco et al., ). As(III) is widely recognized for its higher toxicity compared to As(V) (Wu et al., 2023). The fate and mobility of arsenic in water bodies are influenced by various processes: (1) redox reactions, (2) adsorption and desorption, (3) competitive adsorption (ion exchange), (4) solid-phase precipitation and dissolution, and (5) biological activity. These interconnected processes, along with factors such as redox potential (Eh), pH, chemical composition, and reaction kinetics, collectively determine the behavior of arsenic under specific conditions (Mohan and Pittman, 2007; Cheng et al., ). This redox sensitivity of arsenic is depicted in Figure 1, which illustrates the Eh-pH diagram for arsenic specie. In oxidizing conditions, arsenate (H2AsO4) is the predominant form at low pH levels (below approximately 6.9), while at higher pH levels, it exists in the form of HAsO. Conversely, under reducing conditions at pH levels below approximately 9.2, the dominant form is the uncharged arsenite species (H3AsO3) (Smedley and Kinniburgh, 2002).
Figure 1
The problem of arsenic contamination in aquatic environments is a worldwide concern (Singh et al., 2020). In the area near Lomé (in Togo), surface water samples exhibited elevated concentrations of arsenic, reaching as high as 6,460 μg/L. Similarly, in Prestea, Ghana, a wide range of arsenic concentrations ranging from 150 to 8,250 μg/L were reported in surface waters (Ahoulé et al., ). Table 1 presents the concentration of As reported in aquatic environments worldwide. According to the data presented in Table 1, the highest recorded arsenic concentration (μg/L) in drinking water was 1320 in Nicaragua. In Taiwan and Thailand, the maximum arsenic concentrations (μg/L) in groundwater were 5,000 and 1,820, respectively. Furthermore, the highest arsenic concentration (μg/L) in surface water was 2,650 in Nicaragua. In Brazil, the maximum arsenic concentration (μg/L) in wastewater was 134,000, observed in landfill leachate. Additionally, a notable concentration of arsenic (14.8 μg/L) was reported in seawater near Tarut Island in the Persian Gulf.
Table 1
| Water sources | Concentrations (ranges or mean; μg/L) | Locations | References |
|---|---|---|---|
| Drinking water | 0–220 | Great Hungarian Plain, Hungary | Rowland et al., 2011 |
| 2 to 250 | Vojvodina, Serbia | Jovanovic et al., | |
| < 1 to 610 | Osijek-Baranja and Vukovar- Syrmia counties, Crovatia | Rowland et al., 2011 | |
| < 0.5–175 | Romania | Neamtiu et al., 2015 | |
| 30–105 | Kutahya, Western Turkey | Sener and Karakuş, 2017 | |
| < 1–118 | Spain | Medrano et al., 2010 | |
| 0.02–27.2 | Italy | Zuzolo et al., 2020 | |
| 0.03–25.3 | Aarhus, Denmark | Alarcón-Herrera et al., | |
| 0.88–17.9 | San Rafael Las Flores, Guatemala | Marcillo et al., 2020 | |
| 10–1,320 | Nicaragua | Delgado Quezada et al., | |
| Groundwater | 40.0 (Hand pump water) 49.0 (Tube Well water:) | District Jamshoro, Sindh, Pakistan | Baig et al., |
| 481 (Boreholes) | Vietnam | Sø et al., 2018 | |
| 209 (well) | Kandal, Cambodia | Lawson et al., 2016 | |
| 13.8 (well) | R. Y. Khan, Pakistan | Farooqi et al., | |
| 285.6 (Tube-well) | Nawalparasi, Nepal | Mueller and Hug, 2018 | |
| 36.8 (well) | Kurdistan, Iran | Sharifi and Sinegani, 2012 | |
| 104.3 (well) | Kuitun, China | Chen et al., | |
| 160 (tube-well) | Ganges, Bangladesh | Mukherjee et al., 2018 | |
| 1146 | Anhui, China | Sanjrani et al., 2019 | |
| up to 300 | South China | Luo et al., 2021 | |
| As(III): < 0.5–208 | Pannonian Basin, Hungary | Rowland et al., 2011 | |
| 0.10–168 | West Romania | Senila et al., 2017 | |
| < 0.05–2,230 | Tampere, Finland | Pedretti et al., 2019 | |
| < 0.4–431 | Italy | Carraro et al., | |
| 0.1–1320 | Nicaragua | Delgado Quezada et al., | |
| 0.1 | Platería Puno, Peru | George et al., | |
| 3.2–116.6 | Okavango delta, Botswana | Mladenov et al., 2014 | |
| 21.4–278 | Ethiopia | Rango et al., 2010 | |
| < 5.2–69.4 | Ghana | Bhattacharya et al., | |
| 0.5–123 | Central Tanzania | Nwankwo et al., 2020 | |
| 53.8 ± 93.4 | Sindh, Pakistan | Ahmed Baig et al., | |
| 10–1,820 1–>5,000 | Groundwater of 200,000 population, Taiwan Groundwater of 15 000 population, Thailand | Sarkar and Paul, 2016 | |
| < 2–1,300 | Verde Valley, Arizona, USA | Camacho et al., | |
| 450 (maximum) | Baja California Sur, Mexico | Wurl et al., 2014 | |
| 25.7–137.8 | Banat, Serbia | Devic et al., | |
| 41.4 | Bolivia | Ramos Ramos et al., 2012 | |
| As(III): 12.24 As(V): 6.54 | Rayong province, eastern Thailand | Boonkaewwan et al., | |
| 253 | Namaqualand, South Africa | Abiye and Bhattacharya, | |
| 6,150 | West of Johannesburg, South Africa | ||
| 500 | Karoo, South Africa | ||
| As(V): 20.29 Total As: 251 | Mexico | Ortiz Letechipia et al., 2022 | |
| Surface water (River Lake) | 0.23–105 | El Salvador | Bundschuh et al., |
| 14.6–42.5 | Platería Puno, Peru | George et al., | |
| 200–2,200 | Ankobra, Volta and Densu river, Ghana | Gbogbo et al., | |
| 4.0 ± 1.60 | Sindh, Pakistan | Ahmed Baig et al., | |
| 7.04 | Changjiang River, china | Wang et al., 2011 | |
| 23.88 | Diyarbakir, Turkey | Varol and Sen, 2012 | |
| 2.354 | Tigris River, Turkey | ||
| 1.08 (Upriver:) 1.93 (Downriver:) 2.14 (Wet season:) | Pardo River, Brazil | Alves et al., | |
| 0.938 | Oghji, river, Armenia | Gabrielyan et al., | |
| 2.72 ± 2.51 | 27 subtropical rivers, China | Deng et al., | |
| 0.66–4.01 | Bandama river, West Africa | Ouattara et al., 2018 | |
| 0.56–5.06 | Comoé river, West Africa | ||
| 1.45–6.19 | Bia river, West Africa | ||
| 13.4 ± 1.2–13.8 ± 1.8 | Sinos River (Brazil) | Weber et al., 2013 | |
| 9.7 | the Antequera River, Bolivia | Ramos Ramos et al., 2012 | |
| 4.6 | Lasha River (L.R.), downstream, China | Li et al., 2013 | |
| 52.75 | The middle parts of the Zarshuran stream, IRAN | Sharifi et al., 2016 | |
| 23.45 | the Sarouq River in the downstream station, IRAN | ||
| 130 | Yamuna River, Delhi, India | Rahman and Singh, 2018 | |
| 39 ± 3 | Snake River below Lewis River | Wang et al., 2010 | |
| 45 ± 4 | Snake River above Lewis River | ||
| < 6 | Yangzonghai Lake, Yunnan, China | McCleskey et al., 2022 | |
| 97.5 ± 28.5 | Manchar lake, Sindh, Pakistan | ||
| As(V): 2.16 As(III): 0.34 | Zhushan Bay, Lake Taihu, china | Yan et al., 2016 | |
| 4.18 | Waihai, Lake Dianchi, china | Zhang N. et al., 2013 | |
| 170 | Handle Lake, Canada | Palmer et al., 2019 | |
| 60 | Lower Martin, Canada | ||
| 35 | Long Lake, Canada | ||
| 0.99–2650 | Nicaragua | Delgado Quezada et al., | |
| < 1–188 | Okavango delta, Botswana | Mladenov et al., 2014 | |
| 0.21–3 | Ethiopia | Dsikowitzky et al., | |
| < 1–82 | Tanzania | Nyanza et al., 2014 | |
| 3.26–22.7 | Rift lake (Lake Elementaita and Naivasha), Kenya | Yang et al., 2017 | |
| 1.47–1.89 | Lake Victoria, Kenya | Outa et al., 2020 | |
| 12.0 ± 2.20 | Sindh, Pakistan | Ahmed Baig et al., | |
| 305.6 20.6 | Mapam Yumco, China Yangzuoyongcuo, China | Li et al., 2013 | |
| 972 | Ontario, Canada | Sprague and Vermaire, 2018 | |
| 200.1 1,588.3 | River Niger Upstream, Onitsha, Nigeria River Niger Central Drainage, Onitsha, Nigeria | Ezeabasili et al., | |
| Total As: < 6.0 | Taihu, Chaohu, and Dianchi lakes, China | Yang et al., 2016 | |
| As(III): 0.8 | Utinoie lake (hot-springs), Mutnovsky volcano on the Kamchatka peninsula, Russia | Ilgen et al., | |
| Wastewater and landfill leachate | Gohagoda leachates: ND-2 Acetogenic leachates: < 100–300 Methanogenic leachates: < 1–2 | Sri Lanka | Wijesekara et al., 2014 |
| 45.2 | Simpang Renggam Landfill Site, Johor, Malaysia | Mohd-Salleh et al., 2020 | |
| 1.6520 | Backwash Sludge, West Bengal, India | Koley, | |
| 1,360 | China | Liu et al., 2018 | |
| As(V): 42,000 | South Korea | Lee et al., 2015 | |
| 134,000 | the Maringá landfill (Paraná, Brazil) | Scandelai et al., 2021 | |
| Minimum:0.2 Maximum: 928 | Romania | Schiopu and Gavrilescu, 2010 | |
| Landfill A: 1,180 Landfill B: 2,870 | South Africa | Mosai et al., 2022 | |
| As(III): 108 | Pennsylvania (PA), USA (effluent) | Zhao et al., 2013 | |
| Seawater | T-As: 1.71 ± 0.35 | Kagoshima Bay, southwestern Japan | Tomiyasu et al., 2021 |
| 0.46–1.55 | Jordanian Gulf of Aqaba | Al-Taani et al., | |
| 1.2 to 4.3 | southern Taiwan | Lin et al., 2013 | |
| 8.55–14.88 | Tarut Island, Arabian Gulf | El-Sorogy et al., | |
| As(III): 0.09–0.51 Total As: 0.20 to 0.66 | Todos os Santos Bay, Salvador, Bahia, Brazil | dos Santos et al., | |
| 0.6 | China Sea | Wang et al., 2022 | |
| 0.6–1.6 | Atlantic Ocean | ||
| 0.8–1.1 | Indian Ocean | ||
| 0.94–1.56 | Atlantic Ocean | ||
| 1.0 | Pacific Ocean | ||
| 1.4 | Galway Bay, Ireland | ||
| 1.8 | Krka estuary, Yugoslavia | ||
| 2.81–4.48 1.78–3.42 | surface seawater, subtropical bay of China bottom seawater, subtropical bay of China | Zhang et al., 2018 | |
| 1.26 | Caspian Sea | Abadi et al., | |
| 2.41 | Al-Khobar | Alharbi et al., | |
| 0.300 | Mediterranean Sea | El-Sorogy and Attiah, | |
| 1.18 | Dingzi Bay, Yellow Sea | Pan et al., 2014 | |
| 2.19 | Jinzhou Bay | Wang et al., 2012 | |
| 2.60 | World Ocean (mean) | Famil Yusif et al., | |
| 7.420 ± 0.742 | the Jordanian coastline of the Gulf of Aqaba, Red Sea | Al-Absi et al., |
Occurrence of As in water bodies.
When aquatic organisms come into contact with arsenic through their diet and various sources like water, they have the ability to accumulate, retain, and convert different forms of arsenic within their bodies (Azizur Rahman et al., ). The primary bioaccumulation processes encompass the uptake, assimilation, biotransformation, and elimination of arsenic (Figure 2). Once absorbed by an organism, arsenic has the potential to undergo biotransformation, converting into a less toxic form. Notably, AsB is recognized for its low toxicity among aquatic organisms across different trophic levels (Zhang et al., 2022).
Figure 2
The toxicity of arsenic varies among living organisms depending on their resistance capabilities and detoxification mechanisms. In terms of toxicity, inorganic arsenic (iAs) is more harmful than organoarsenic and has been classified as a proven carcinogen for humans. Arsenite (AsIII) is typically more toxic than arsenate (AsV), whereas dimethylarsinous acid (DMAAIII) and monomethylarsonous acid (MMAAIII) exhibit higher toxicity compared to their respective parent compounds (Azizur Rahman and Hasegawa, ). Azizur Rahman et al. () stated that while it is commonly known that iAs is typically more toxic than organoarsenic species, the toxicity of iAs species toward aquatic organisms is still a topic of debate. Interestingly, there are exceptions to this generalization. Marine phytoplankton, for instance, often exhibit higher sensitivity to arsenite (AsIII), whereas freshwater phytoplankton are highly sensitive to arsenate (AsV). As an illustration, the marine phytoplankton species Dunaliella sp. and Polyphysa peniculus demonstrate greater sensitivity to AsV compared to AsIII. Table 2 shows the bioaccumulation As in aquatic organisms, which indicates that up to 362 μg/g of As reported in fish tissues.
Table 2
| Fish/shrimp | Concentrations (μg/g) | Remarks | Location | References |
|---|---|---|---|---|
| Micropterus salmoides | 1.39 ± 0.22 | Liver | Emory River mile 1.0; U.S. | Otter et al., 2012 |
| Pomoxis annularis | 4.66 ± 0.82 | |||
| Lepomis macrochirus | 1.76 ± 0.21 | |||
| Lepomis microlophus | 6.83 ± 0.726 | |||
| Rabbitfish S. fuscescens | 1.04 ± 0.09 | Muscle | China, under control conditions | Zhang et al., 2016 |
| Seabass L. japonicus | 2.59 ± 0.67 | |||
| Pteromylaeus bovinus | 36.3–362 | Muscle | Northern Adriatic Sea | Šlejkovec et al., 2014 |
| Myliobatis aquila | 32.4–69.8 | |||
| Freshwater fish | 0.748 ± 0.651 | Muscle | Xiang River, | Jia et al., |
| Green tiger shrimp (Penaeus semisulcatus) | 29.254 ± 0.473 | Muscle | Iskenderun Bay, Turkey | Kaya and Turkoglu, |
| Common sole (Solea solea) | 4.397 ± 0.311 | |||
| Whiting fish (Merlangius merlangus) | 10.320 ± 0.518 | |||
| E. suratensis | 3.0–3.4 | Kidney | Sri Lanka | Perera et al., 2016 |
| A. testudineus | 4.1–4.7 | |||
| C. striata | 4.0–4.3 | |||
| Meretrix lusoria | 7.6–10.7 | NR* | Putai county | Liu et al., 2007 |
| Catfish | 0.45–4.93 | Gill | Paraná River Delta (PRD, Argentina) and in Paranaguá Estuarine Complex (PEC, Brazil) | Avigliano et al., |
| Anadara sp. | 13–23 | American Samoa | Peshut et al., 2008 | |
| Gafarium sp. | 20-68 | |||
| Labeo calbasu (Calbasu) | 1.86 ± 0.02 | Muscle (wet weight) | Manchar Lake | Shah et al., 2009 |
| Cirrhinus mrigala (Mrigala) | 0.54 ± 0.04 | |||
| Mystus gullio (Gullio) | 1.72 ± 0.24 | |||
| Oreochromis niloticus | 5.70 ± 2.10 As(III); 8.79 ± 4.46 As(IV) | Liver; after 1 week analysis | NR | Ferreira et al., |
Bioaccumulation of As in fish/shrimp.
*NR, Not reported.
Garai et al. () stated that prolonged exposure of freshwater fish to low concentrations of arsenic leads to the accumulation of this toxic element primarily in the liver and kidney tissues. The exposure to arsenic causes histopathological changes in the gills and liver tissue of tilapia (Oreochromis mossambicus), a type of freshwater fish. The observed alterations in the gills include epithelial hyperplasia, lamellar fusion, epithelial lifting, edema, desquamation, and necrosis.
3 Removal of As with Adsorption method
In recent decades, various treatment techniques have been used to remove heavy metals like As, including chemical precipitation, membrane filtration, extraction, and electrochemistry. However, these methods have drawbacks such as poor selectivity, high energy consumption, and high costs, limiting their application in engineering. As a result, adsorption has become a favored choice for heavy metal removal due to its broad applicability, low cost, and environmental friendliness (Di et al., ). On the other hand, the main advantages of the adsorption technique include its adaptability, user-friendly nature, cost-effectiveness, and its ability to accommodate a diverse range of adsorbents sourced from minerals, as well as biological and organic origins (Rajendran et al., 2022). Adsorption begins by transferring heavy metal ions from the aqueous solution to the surface of adsorbents. Subsequently, these ions bind to the surface through physical or chemical interactions. To facilitate this process, adsorbents must possess a large accessible surface area, which ensures the presence of numerous exposed active sites for effective and selective binding with heavy metal ions (Fei and Hu, ). A wide range of adsorbents have been employed for the adsorption of heavy metals (such as As) from wastewater and natural water (Chakraborty et al., ). These include engineered adsorbents like nano-adsorbents and metals coated adsorbents, as well as low-cost options. Among low-cost adsorbents, natural materials such as zeolites, and clay are commonly used. Additionally, agricultural waste materials and biochar serve as prominent sources of bio-adsorbents (Abdollahi et al., ; Oladoye, 2022). According to Ariffin et al. (), the most advantageous aspects of adsorption methods include their simplicity, flexibility in design, ease of operation, and resistance to toxic contaminants. However, a notable disadvantage of this method is the need for regeneration processes. The adsorption mechanism can involve physical entrapment (absorption) or chemical binding through weak Van der Waals forces, dipole-dipole and ion-dipole interactions, cation exchange, or strong covalent bonding (Mojiri et al., 2020). Physisorption occurs when the absorbent and adsorbate are united by van der Waals forces. Chemisorption takes place when the adsorbate forms chemical bonds with the surface of the adsorbent. Electrostatic attraction occurs when the adsorbent surface carries negative or positive charges. Ion exchange happens between divalent metal cations and oxygen-containing functional groups, taking advantage of the cation exchange capacity. Surface complexation occurs when heavy metals exceed the available sites on the adsorbent surface, and this involves the formation of multiatom assemblies during the reaction activities (Mahesh et al., 2022). The removal of As through adsorption methods is presented in Table 3. According to Table 3, the maximum adsorption capacity of engineered adsorbents is significantly higher than that of single adsorbents. For example, engineered nanoparticles can exhibit adsorption capacities exceeding 100 mg/g.
Table 3
| Arsenic | Adsorbent | Maximum adsorption capacity (mg/g) | Adsorption isotherm | Remarks | References |
|---|---|---|---|---|---|
| As(III) | Activated carbon (AC) | 184 | Langmuir | As concentration was 20 mg/L; Anion exchange and physiochemical adsorption were the primary mechanisms of adsorption; The removal of As(III) reached 92% with powdered activated carbon (PAC). | Sabzehmeidani et al., 2021 |
| As(III); As(V) | Granular activated carbon (GAC) | 0.03; 0.05 | NR* | As(V) undergoes adsorption on an oxide surface through a ligand exchange mechanism, forming an inner sphere surface complex. The removal of As(III) and As(V) reached 44% and 71% respectively with GAC. | Mondal et al., 2008 |
| As(III) | AC | 1.0 | Langmuir | Ion exchange, complexation and physiochemical adsorption were the primary mechanisms of adsorption | Budinova et al., |
| As(III) | AC | 6.5 | Langmuir | The adsorption of As(III) by AC is a complex process that is not solely controlled by electrostatic attraction. | Mondal et al., 2007 |
| As(III); As(V) | Fe-modified-AC | 38.8; 51.3 | Langmuir | NR | Chen et al., |
| As(III) | Fe–Zr@AC | 556 | Langmuir | Surface functional groups –OH, NH2, COO–, Fe–O, ZrO were involved for As (III) removal. | Sahu et al., 2021 |
| As(V) | Fe-AC | 17 | Langmuir | Surface functional groups were involved for As(V) removal | Lodeiro et al., 2013 |
| As(III); As(V) | Biochar (derived from peanut shell) | 7.6; 7.9 | Langmuir | Both hydroxyl (-OH) and aromatic surface functional groups (such as C=O, C=C-C, and -C-H) played a significant role in the sorption of both species of arsenic. The removal of As(III) and As(V) reached 95% and 99% respectively with biochar. | Sattar et al., 2019 |
| As(III) | Magnetic Fe3O4/Douglas fir biochar | 5.4 | Sips isotherm | Both surface complexation and pH-dependent chemisorption mechanisms played a significant role in the sorption of As(III). | Navarathna et al., 2019 |
| As(V) | Fabrication of engineered biochar (derived from paper mill sludge) | 22.8 | Langmuir | Specific surface interactions played a significant role in the sorption of As(V) | Yoon et al., 2017 |
| As(V) | Activated biochar | 24.4 | Langmuir | The primary sorption mechanism is the chemical interaction between the metal ions and the surface functional groups of biochars. | Jin et al., |
| As | Activated biochar | 8.6 | Langmuir | The main adsorption mechanism was the functional groups. | Saikia et al., 2017 |
| As(V) | Biochar (derived from sewage sludge) | 0.03 | Langmuir | 53% of As(V) was removed. The primary mechanisms responsible for heavy metal immobilization were the electrostatic interactions between the negative surface charge of biochar and metal cations, as well as metal precipitation in the case of anionic metals. | Agrafioti et al., |
| As(V) | Zerovalent iron composited biochars | 26.5 | Langmuir | Electrostatic interactions, surface complexation and diffusion were sorption mechanisms. | Ahmad et al., |
| As(V) | Magnetic graphene oxide | 59.6 | Langmuir | The adsorption of As(V) on MGO is mainly governed by the electrostatic attraction or repulsion interaction of As(V) species with the MGO surface. | Sheng et al., 2012 |
| As(III) | Fabricated crumpled graphite oxides | 47.3 | Langmuir | >98% of As(III) was removed. The both electrostatic interaction and surface complexation were the main sorption mechanism. | Son et al., 2018 |
| As(III); As(V) | Graphite oxides | 19.0; 28.0 | Langmuir | Both surface complexation mechanism, rather than electrostatic interactions were the main sorption mechanisms. | Su et al., 2017 |
| As(III) | Combination of Graphene Oxide (GO) and Granular Ferric Hydroxide (GFH) | 0.2 | Langmuir | Chemisorption was the main sorption mechanism. | Tolkou et al., 2023 |
| As | Modified bentonite | 0.6–2.8 | Langmuir | The electrostatic adsorption, the ligand exchange and precipitation were the main sorption mechanism. | Hua, |
| As(III) | Modified bentonite (hydroxyapatite-bentonite clay-nanocrystalline cellulose) | 51.0 | Langmuir | >95% of As(III) was removed. Ion-exchange mechanism was the main sorbent mechanism. | Hokkanen et al., |
| As | Co-modified bentonite with manganese oxides and poly(dimethyldiallylammonium chloride) | 9.1–99.9 | Langmuir | Both electrostatic attraction and inner-sphere complexation were main sorption mechanisms. | Hua, |
| As(V) | Bentonite-Anthracite@Zetag (BT-An@Zetag) composite | 38.6 | Freundlich | The electrostatic, ion exchange and functional surface groups were main sorption mechanism. | El-Aassar et al., |
| As(III) | Fe-exchanged zeolite | 100 | Langmuir | The ion exchange was the main sorption mechanism. | Li et al., 2011 |
| As(III); As(V) | Copper exchange zeolite | 1.3; 1.4 | Langmuir | The ion exchange was the main sorption mechanism. | Pillewan et al., 2014 |
| As(III) | Zeolitic imidazolate frameworks | 108.1–122.0 | Langmuir | Hydroxyl substitution was the main mechanism for arsenic removal. | Liu et al., 2015 |
| As(III); As(V) | Cellulose@iron oxide nanoparticles | 23.1; 32.1 | Langmuir | Both electrostatic and functional groups governed the sorption mechanism. | Yu et al., 2013 |
| As | Copper (II) oxide nanoparticles | 10.8 | Langmuir | Chemisorption was the main sorption mechanism. | Goswami et al., |
| As(III); As(V) | Iron oxide nanoparticles | 42.0; 83.0 | Langmuir | The removal of arsenic on nanosized iron oxide occurs through chemisorption, specifically via inner-sphere surface complexation. | Cheng et al., |
Removal of arsenic with adsorption method.
*NR, Not reported.
3.1 Removal of As with carbon-based adsorbents
Carbon-based adsorbents possess significant potential as adsorbent materials due to their large specific surface area, high porosity, substantial pore volume, and adjustable morphological and functional group properties. These characteristics make them suitable for various applications, including water purification. The adsorption efficiency of carbonaceous materials is influenced by factors such as the raw material, production technique, and environmental variables (Mahesh et al., 2022). The main mechanisms of As removal with carbon-based adsorbents are shown in Figure 3. Sabzehmeidani et al. (2021) reported that activated carbon, biochar, and graphene-based adsorbents are commonly utilized for the removal of arsenic (As) from water sources.
Figure 3
Activated carbon (AC) is a versatile and widely available carbon material used for adsorption. It comes in different forms, such as granular, powder, pellet, and spheres. These materials exhibit efficient and selective adsorption of toxins and heavy metals. Compared to other adsorbents, activated carbon-based materials are cost-effective, highly effective, and easy to use (Lan et al., 2023). AC has a structure that includes well-defined micro, meso, and macropores, as well as diverse surface functional groups. The surface area of activated carbon ranges from 500 to 3,000 m2/g (Sultana et al., 2022). In a study, Gao et al. () utilized modified AC to effectively remove both As(III) and As(V). The maximum adsorption capacities were found to be 10.9 mg/g for As(III) and 16.0 mg/g for As(V). The maximum adsorption capacity (mg/g) observed for As(V) removal using As was 1.2 (Yürüm et al., 2014).
Graphene is a 2D lattice of carbon atoms with high surface area, thermal conductivity, Young's modulus, and charge carrier mobility. It exhibits the quantum Hall effect due to electron confinement in 2D materials (Sabzehmeidani et al., 2021). Graphene oxide (GO), a derivative of graphene, is a single sheet of graphite with a 2D honeycomb crystal plane structure. It exhibits a high specific surface area of up to 2,620 m2/g (Bian et al., ). GO is characterized by diverse functional groups on its surface, including epoxy, lactol, carboxyl, phenol, and hydroxyl groups, along with large π-stacking. These features enable GO to possess a high sorption capacity through strong interactions such as hydrogen bonding, electrostatic forces, and π-π interactions (Gabris et al., ). In the study (Bian et al., ), GO was synthesized using a modified Hummers method. A solution containing 1.0 g of NaNO3 dissolved in 50 ml of concentrated H2SO4 was prepared, and then 1.0 g of natural graphite was slowly added to the solution while stirring in an ice-water bath. After complete dissolution, 18.0 g of KMnO4 was gradually added over a period of 30 min. The mixture was stirred for 2 h at 309 ± 1 K and then heated in a water bath at 368 K for 30 min. Following this, 180 ml of deionized water was added, and a suspension of 20 ml of H2O2 (30 wt%) was slowly introduced. The desired products were washed with 10% v/v HCl and ultrapure water, undergoing centrifugation and ultrasonication until a constant pH value was reached. The resulting GO was dried at 333 K for 24 h. Chandra et al. () removed As(III) and As(V) using modified GO (magnetic GO). The maximum adsorption capacity (mg/g) based on the Langmuir isotherm was found to be 10.2 for As(III) and 5.2 for As(V), respectively. Their study revealed that the adsorption process of As using magnetic-GO primarily followed surface complexation. In a study conducted by Wu et al. (2018b) GO-based adsorbent (GO/CuFe2O4) was used to remove both As(III) and As(V). The maximum adsorption capacities (mg/g) achieved were 51.64 for As(III) and 124.69 for As(V) removal. Their study demonstrated that both As(III) and As(V) adsorptions on the GO-CuFe2O4 adsorbent followed an inner-sphere complex mechanism. In another study conducted by Su et al. (2017), a modified GO called iron oxide-graphene oxide was used to successfully remove 99.9% of both As(III) and As(V). The maximum adsorption capacities were 147 mg/g for As(III) and 113 mg/g for As(V). The results of their study showed that the adsorption of As onto modified-GO takes place through a mechanism known as surface complexation.
Biochar is produced through the thermal treatment of natural organic feedstocks in an oxygen-limited environment. Huang et al. () emphasized that biochar offers numerous advantages, such as its ion exchange capacity, expansive surface area, high porosity, and presence of surface functional groups, which make it promising for wastewater treatment. Biochar is composed of various elements, including carbon, sulfur, hydrogen, oxygen, nitrogen, and minerals found in the ash fraction. Although biochar shares similar properties and structure with AC, AC has a considerably larger surface area. The primary surface functional groups found in biochar include carboxyl, hydroxyl, phenolic hydroxyl, and carbonyl groups (Mojiri and Zhou, 2023). In a research, Wang et al. (2015) used a modified version of biochar called manganese oxide-modified biochars to successfully remove As(V). They discovered that the biochar had a maximum adsorption capacity of 0.59 mg/g, with the main adsorption mechanism being the interaction between the biochar and As(V). Navarathna et al. (2019) employed a modified biochar known as magnetic Fe3O4/Douglas fir biochar composites to effectively remove over 68% of As(III). The maximum adsorption capacity of the modified biochar was determined to be 6.9 mg/g. Their study suggested that the adsorption process was primarily governed by a chemisorption mechanism. In a research study, wood biochar exhibited remarkable efficiency in removing 92–100% of As. The maximum adsorption capacity for As(III) removal was measured at 3.1 mg/g, while for As(V) removal, it was 3.8 mg/g. The main identified adsorption mechanisms were chemisorption and physicosorption (which involve the filling of pores in the biochar) (Niazi et al., 2018).
One of the methods to analyze the structural properties and identify functional groups on biochar and other sorbents is FTIR spectroscopy. It enables the differentiation of functional groups between the original feedstock material and the resulting biochar, as well as the assessment of any changes in functional groups before and after As sorption (Amen et al., ). The FTIR spectrum of Oak wood biochar (OW-BC) before and after As removal is shown in Figure 4. In OW-BC, spectral bands indicated the presence of -OH and non-ionic carboxyl groups, as well as aliphatic methylene/methyl groups and C-O/C-O-C surface functional groups. After As sorption, slight shifts and intensity changes were observed. Notably, in As(III)-loaded OW-BC, peak shifts and transformations were observed at specific wavelengths. These changes were relatively smaller in As(V)-loaded OW-BC (Niazi et al., 2018).
Figure 4
3.2 Removal of As with mineral-based adsorbents
Researchers have been increasingly drawn to natural mineral-based adsorbents as an alternative source. These adsorbents have garnered significant attention due to their low cost, abundance, easy retrievability, and exceptional adsorption capabilities. Among the numerous natural minerals found on Earth, zeolite and clays, particularly bentonite, have been extensively studied for their effectiveness in removing heavy metals (Zaimee et al., 2021). Clay minerals are phyllosilicates composed of T (tetrahedral) and O (octahedral) sheets in a 1:1 or 2:1 ratio. Due to isomorphous substitution, the layers of certain 2:1 clay minerals (such as smectite and vermiculite) carry a negative charge, which is balanced by cationic counterions in the interlayer space. These counterions can be exchanged, making clay minerals efficient adsorbents for cationic contaminants (Zhu et al., 2016). Bentonite, a clay with a high content of montmorillonite, is widely utilized for ion removal from water due to its bi-dimensional tetrahedral and octahedral sheets and isomorphic substitutions. It is both cheap and abundant, making it an ideal choice for synthesizing functionalized clay (Barakan and Aghazadeh, ). Arsenic was effectively removed using modified bentonite, achieving a maximum adsorption capacity of 9.14–9.99 mg/g. The removal mechanisms involved a combination of electrostatic attraction and inner-sphere complexation, indicating a mixed removal mechanism (Hua, ). A modified bentonite [Fe(III)-modified bentonite] successfully removed As(III), exhibiting a maximum adsorption capacity of 0.3 mg/g (Baigorria et al., ). A significant removal efficiency of 84.5% was achieved for As(V) using modified bentonite (lanthanum-modified bentonite), with a maximum adsorption capacity of 3.8 mg/g (Cui et al., ).
Zeolites, whether naturally occurring or artificially synthesized, are hydrated aluminosilicate minerals. Their porous and cage-like structure enables them to provide significant internal and external surface areas for efficient ion exchange. This characteristic makes zeolites highly effective in adsorbing arsenic from water, highlighting their potential as a valuable tool for eliminating this toxic substance (Salem Attia et al., 2014; Velarde et al., 2023). The application of a modified zeolite, the Copper Exchange Zeolite, resulted in the removal of over 98% of both As(III) and As(V). The maximum adsorption capacity for As(III) was measured at 1.3 mg/g, while for As(V) it was 1.4 mg/g (Pillewan et al., 2014). Electrostatic Interaction and ion substitution (Figure 5) are key mechanisms involved in the removal of arsenic (As) using zeolite (Shevade and Ford, 2004; Liu et al., 2015). The maximum adsorption capacity (mg/g) was 0.5, at pH=6, in As(V) removal with a modified zeolite (Macedo-Miranda and Olguín, 2007).
Figure 5
3.3 Removal of As with metal oxide nano-adsorbents
Nanoparticles are tiny particles ranging in size from 1 to 100 nanometers. They exist in the transitional zone between individual molecules and larger materials. Due to their small size, nanoparticles possess unique physicochemical properties, including a high specific surface area, significant energy, and the confinement of quantum effects (Jjagwe et al., ). Metal oxide nanoparticles (NPs) as adsorbents offer numerous benefits, including high specificity and capacity attributed to the quantum size effect and their large surface area. Moreover, these NPs possess unique structures, diverse pore sizes, low solubility, and can be easily synthesized using cost-effective techniques. Their low solubility, strong mechanical properties, and remarkable stability against organic dyes make metal oxides highly effective adsorbents (Hosny et al., ). Zinc oxide (ZnO) nanoparticles, silver oxide (Ag2O) nanoparticles, copper oxide (CuO) nanoparticles, titanium dioxide (TiO2) nanoparticles, and iron oxide nanoparticles are widely used metal oxide nanoparticles (Naseem and Durrani, 2021). Numerous iron-based materials and processes have been developed to address the issue of arsenic removal from drinking water. Iron oxides, oxyhydroxides, and hydroxides, such as amorphous hydrous ferric oxide (Fe(O)OH), goethite (FeO–OH), and hematite (Fe2O3), have demonstrated their efficacy in removing arsenic from aqueous solutions (Dhanasekaran and Sahu, ). Besides, in a study (Siddiqui and Chaudhry, 2017), a Fe2O3 nano-composite adsorbent has been functionalized to enhance arsenic (As) removal. As illustrated in Figure 6, Fe2O3 nano-composites have proven to be efficient adsorbents for arsenic cleanup from water. This efficacy is attributed to the presence of a large number of hydroxyl and other functional groups on the surface, enabling a higher capacity for the oxidation of As(III) to As(V). The functional groups attached to the composite material's surface provide strong trapping sites for arsenic across various pH levels.
Figure 6
In a study (Pham et al., 2020), As(V) was removed by a ferric hydroxide-based adsorbent, the maximum removal capacity was 2.9 (mg/g). In a separate study (Asadi Haris et al., ), superparamagnetic iron oxide nanoparticles (SPIONs) and alginate-encapsulated SPIONs (SPIONs-Alg) were synthesized for arsenic (As) removal from water. Figure 7 illustrates key bands: 3,400 cm−1 (O–H symmetric vibration), 2,936 cm−1 (aliphatic C–H stretching), 600 cm−1 ([Fe–O] intrinsic stretching), and 400 cm−1 (octahedral [Fe–O] vibration), confirming ferrite formation in spinel form. Notably, SPIONs-Alg exhibited a distinct 585 cm−1 band, indicating the presence of nanoferrite.
Figure 7
Due to the formation of stable bidentate binuclear surface complexes on its surface, TiO2 shows great promise as a material for the removal of As from industrial wastewater (Qiu et al., 2019). In a study (Deng et al., ), the maximum adsorption capacities (mg/g) for the removal of As(III) and As(V) using a TiO2-based adsorbent were 7.7 and 18.2, respectively. A study utilized a modified metal oxide nanoparticle (Ce0.8Ti0.2O2−y) to remove arsenic. The adsorbent demonstrated a maximum adsorption capacity of 2 × 105 mg/g (Mishra and Rai, 2019). Their study revealed that the removal procedure was primarily governed by two mechanisms. Firstly, the redox reaction involving ceria and titania metals facilitates the partial oxidation of the more toxic As(III) to the less toxic As(V). The resulting As(V) can readily form monodentate and bidentate complexes. Secondly, the adsorption of As(III) occurs through the interaction between the surface hydroxyl groups of the synthesized adsorbent and the As(III) species. In the study (Mishra and Rai, 2019), Titania-doped cerium oxide nanoparticles (Ce1−xTixO2−y) were synthesized using a single-pot aerogel process. Cerium nitrate hexahydrate (40 mM) and stoichiometric oxalic acid were dissolved in a mixture of ethanol and toluene. Titanium isopropoxide (10% and 20% w/w of cerium nitrate hexahydrate) in 50 mL of ethanol, along with distilled water, were gradually added to the precursor mixture. Vigorous stirring at 300 rpm resulted in a light yellowish gel. The gel was transferred to a Parr reactor and heated at a rate of 1°C per minute. The solvents were supercritically dried under elevated temperature and high pressure, yielding a light yellow, low-density powder of Ce1−xTixO2−y oxide nanoparticles. These nanoparticles were then used as adsorbents for arsenic removal. The use of various metal oxide nanoparticles resulted in the removal of over 92% of As(V), with an impressive adsorption capacity of up to 305 mg/g (Hocaoglu et al., ). They mentioned that the primary adsorption mechanism of metal oxides relies on the formation of strong bonds between the surface –OH groups of the metal oxides and the –OH group of the arsenic(V) species. In another study (Sunil et al., 2018), more than 96% of As was removed with Al-Ti2O6 nanoparticles. In another study (Powell et al., 2020), carbon-coated iron carbide (Fe3C@C) was used to remove arsenic (As) from groundwater. The maximum adsorption capacity was 168 micrograms per gram (μg/g). Figure 8 illustrates the interactions between arsenic and metal oxide-based nanoadsorbents under neutral pH conditions. Table 4 shows the removal of As using various nanoparticle adsorbents.
Figure 8
Table 4
| Adsorbent | Dia. (nm) | Removal (%) | adsorption capacity mg/g | Initial con. of As mg/L | pH | References | |
|---|---|---|---|---|---|---|---|
| As (III) | As (V) | ||||||
| Starch Functionalized Maghemite Nanoparticles (γ-Fe2O3@starch) | 9.65 | - | 8.88 | - | 1-6 | 7 | Siddiqui et al., 2020 |
| Iron-modified activated carbon fiber (Fe2O3/ACF) | - | - | 20.33 | 3 | 7 | Chen et al., | |
| Zinc Oxysulfid Nanomaterials (ZnOxS1-x) | 5–6 | 99.9 | 299.4 | 299.4 | 0.01 | 6-8 | Uppal et al., 2019 |
| Activated Carbons Modified with Iron hydro(oxide)F400-M | 3–36 | - | - | 0.3-1.2 | 0.025-5 | 6-8 | Vitela-Rodriguez and Rangel-Mendez, 2013 |
| SiO2@Fe3O4@MBT | 40 | 95.77 As(V) | - | 1 | 6.55 | Sheikhmohammadi et al., 2018 | |
| Fe3O4 loaded activated carbon | - | - | - | 204.2 | - | 8 | Liu et al., 2010 |
| Graphene oxide-MnFe2O4 nanohybrid | - | - | 146 | 207 | - | 4-6.5 | Kumar et al., 2014 |
| Zirconium-nanoscale carbon | 50–70 | - | - | 110 | 100 | 2.5 | Mahanta and Chen, 2013 |
| Nano zero valent iron supported on Activated carbon | < 100 | - | 18.2 | 12.0 | 2 | 6.5 | Zhu et al., 2009 |
| Nanocrystal line Titanium dioxide | 6 | 95% AS(III) >98% As(V) | - | - | 1 | 4-9.5 | Pena et al., 2006 |
| Functionalized Graphene Sheets | - | - | 139 | 142 | 300 | 6-7 | Mishra and Ramaprabhu, 2011 |
| Core/Shell structural nZVI/Mn Oxide | - | - | 29.4 | 35.7 | 5 | 4.8 | Bui et al., |
| Fe (III)-Cu (II) binary Oxide | 50 | 123.3 | 82.7 | 10 | 7 | Zhang G. et al., 2013 | |
| Ultra-small nanoparticles (IONPs) within Electrospun Hydrophilic Poly(vinyl alcohol) (PVA) nanofibers | < 5 | - | - | 3.5 ± 0.3 | 0.01 | 5 | Torasso et al., 2023 |
| Copper oxide nanoparticles | 12–18 | - | 26.9 | 22.6 | 0.1-100 | 6-10 | Martinson and Reddy, 2009 |
| Zr-UiO-66-SH-A | - | 90.7 | 98.8 | - | Shao et al., 2019 | ||
| Nano-Alumina wrapped Carbon Microspheres (Al-CMs) | - | - | - | 68.0 | - | 2-12 | Raj et al., 2023 |
| ZnO Nanorods Coated Porous Ceramic Monolith | 100 | 98 As(V) | - | 0.2 | 7 | Muensri and Danwittayakul, 2017 | |
| Alginate coated Superparamagnetic Iron Oxide Nanoparticles (SPIONs) | 25 | 99 | 240.08 | - | 6.5 | 7 | Asadi Haris et al., |
| Zr-metal-organic framework (MOF) UiO-66 | - | - | 352.1 | 147.5 | - | 3-11 | Qu et al., 2022 |
| Zeolite-supported nanoscale zero-valent iron (Z-NZVI) | 40–60 | - | 11.52 | - | 10 | 6 | Li et al., 2018 |
| Copper Ferrite (CuFe2O4) Foam (CFF) | - | - | 44 | 85.4 | 10 | 8.1 | Wu et al., 2018a |
| Amine functionalization of iron-based metal-organic frameworks MIL-101 [NH2-MIL-101(Fe)] | - | - | 153.4 | 147.7 | - | 7 | Fang et al., |
| Copper-doped ZIF-8 nanomaterials (Cu-ZIF-8) | - | - | - | 238.11 | 10-350 | 3-11 | Wang et al., 2023 |
| Cerium oxide (CeO2) nanoparticles (NPs) | 40–50 | - | 451 As(III) 119 As(V) | - | - | 3.6 | Bi et al., |
Nanoparticle adsorbents for As removal.
4 Conclusions
The presence of arsenic in water bodies poses a significant risk to both human health and the environment. Arsenic contamination in water sources is a global environmental concern caused by both natural processes and human activities. The high concentrations of arsenic found in drinking water, groundwater, wastewater, and aquatic organisms highlight the urgent need for effective removal methods. This review manuscript critically evaluated the effectiveness and limitations of adsorption methods for arsenic removal from water bodies. Adsorption emerged as a promising technique due to its cost-effectiveness, high removal efficiency, and simplicity of operation. Various adsorbents, including nanoparticle adsorbents and graphene-based adsorbents, demonstrated a high capacity for arsenic removal. Additionally, low-cost adsorbents such as zeolites, clay, and chitosan, as well as agricultural waste materials and biochar, have shown potential for arsenic adsorption. Future research should focus on optimizing adsorbent materials, understanding the mechanisms of arsenic adsorption, and developing sustainable and efficient regeneration techniques.
Statements
Author contributions
AM: Conceptualization, Investigation, Resources, Supervision, Writing—original draft, Writing—review & editing. ER: Conceptualization, Validation, Writing—review & editing. BK: Conceptualization, Validation, Writing—original draft, Writing—review & editing. SR: Writing—review & editing. NK: Funding acquisition, Writing—review & editing. MV: Writing—review & editing. HF: Writing—review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Conflict of interest
ER and HF were employed by Envirowise Consultant Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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Summary
Keywords
adsorption, arsenic removal, bentonite, graphene, nanoparticles, zeolite
Citation
Mojiri A, Razmi E, KarimiDermani B, Rezania S, Kasmuri N, Vakili M and Farraji H (2024) Adsorption methods for arsenic removal in water bodies: a critical evaluation of effectiveness and limitations. Front. Water 6:1301648. doi: 10.3389/frwa.2024.1301648
Received
25 September 2023
Accepted
22 January 2024
Published
07 February 2024
Volume
6 - 2024
Edited by
Ioannis D. Manariotis, University of Patras, Greece
Reviewed by
Amita Nakarmi, University of Arkansas at Little Rock, United States
Humaira Qadri, Government of Jammu & Kashmir, India
Xubo Gao, China University of Geosciences Wuhan, China
Updates
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© 2024 Mojiri, Razmi, KarimiDermani, Rezania, Kasmuri, Vakili and Farraji.
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*Correspondence: Amin Mojiri amin.mojiri@gmail.com
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