DOI: 10.31038/NAMS.2020315

Abstract

Cyber hybrid warfare has been known since antiquity, it is not a new terminology nor a new practice. It can have an effect even more than a regular conventional war. The implementation of the cyber hybrid war aims to misinform, guide and manipulate citizens, disorganize the target state, create panic, overthrow governments, manipulate sensitive situations, intimidate groups, individuals and even shortened groups of the population, and finally to form an opinion according to the enemy’s beliefs. Creating online events designed to stimulate citizens to align with the strategy of governments or the strategy of the enemy government is a form of cyber hybrid warfare. The cyber hybrid warfare falls under the category of asymmetric threats as it is not possible to determine how, and the duration of the cyber invasion.The success or not of a cyber hybrid war depends on the organization, the electronic equipment, and the groups of actions they decide according to the means at their disposal to create the necessary digital entities. Finally, the cyber hybrid warfare is often used to show online military equipment aimed at downplaying its moral opponent.

Introduction

The cyber hybrid warfare also includes DeepFake, a practice mentioned in Christos Beretas previous research. The cyber hybrid war aims to disrupt and hurt the adversarial state in an organized and targeted manner, mainly regarding the organizational structure of the target state and its functioning.Digital media are used to intimidate citizens, target specific groups of people, disseminate false news between political and military leadership in order to spread hatred and resentment on both sides, to divide the people, and finally the fall of the government, followed by the anger and indignation of the people. The cyber hybrid warfare is not only and exclusively applied during a period of natural war, it is a kind of war that can be waged for years and of course in times of peace. It is difficult for citizens in a cyber hybrid war to understand the truth and lies.A well-organized cyber hybrid war is difficult for people to recognize as the facts presented are so convincing that it is impossible to recognize them as false. The ways to avoid and protect against such a war are numerous and require knowledge, experience, alertness, high morale, courage and professionalism to deal with such a cyber threat from its birth.Sovereign states around the world are using the cyber hybrid warfare to blackmail, trap, mislead, both foreign governments and citizens, achieving remote results without the use of physical violence and natural disasters. The cyber hybrid war has come to stay, and it is an emerging form of war – the pressure of the strong against the weak or better of the organized states against the disorganized. As mentioned above, a great DeepFake video is capable of stirring up enormous panic and hatred in a society. It is an asymmetric threat that is increasing day by day.

Characteristics

The cyber hybrid war is an asymmetric threat that is defined when an entity uses electronic means to disturb the peace or spread panic in the target state and launch hostilities or uproot social groups residing in it. A fake video, for example, that will be sent to targeted social groups is capable of sparking riots in the crowd with demonstrations and violence. By reading this one can easily understand the reader that the cyber hybrid war is the result of an entity preceding its onset.This entity is the digital asymmetric threat which if not handled properly then evolves into a cyber hybrid war. The cyber hybrid war is not tantamount to an isolated practice, that is, it is not a common attack on the adversarial state; rather, it consists of organized methods that are often impossible to identify, such an attack may include social media, online press, videos and hostilities from different events, etc.The difference between a cyber hybrid war and conventional warfare is that except there are no killings and conflicts, there is a constant low-level influx of information affecting the target state. That is, it does not follow the logic that an event has occurred, a number of people have risen and then the digital invasion process has ended, on the contrary, the digital presence is continuous and stable at the same level as possible.

Advanced stages of a cyber hybrid war include practices such as misinformation aimed at the financial loss of the target state, intra-country turmoil from pro-country groups that launched the cyber hybrid war to compel its citizens to withdraw. for the purpose of financial loss or even the overthrow of the government.In a cyber hybrid war, the invaders’ practical ways of attacking are not one-sided but two-sided, which means that in one field they can decrease and increase in another, for example a false bent can be seen in social media news and on the contrary the volume of fake videos is growing too.A cyber hybrid war is often won when combine electronic and physical attacks in the target state, which means that in the target state it requires the penetration of disturbing elements in order to revolt and destroy the target state’s infrastructure and economy.This includes increasing crime, which will then be used in the media and social media by the adversary state as a means of corrupting the target country with the ultimate aim of reducing its reputation, spreading fear to other countries. aimed at restricting travelers, other countries’ security reviews, further financial burden, withering and global isolation.

The success or not of a  cyber hybrid war in addition to the proper organization, hardware, and staff, requires and sufficient funding for the whole venture, funding is a key success factor, with insufficient funding the result will be the opposite, as it will unprofessionalism has emerged, and it is easy for social groups to understand that this is fake news, which is equivalent to project failure and redesign.Funding can come exclusively from the state that organizes the cyber hybrid threat, it can come from friendly countries in it, as well as from organizations that are scattered around the world, usually when a cyber hybrid war is funded by organizations around the world, the communication takes place through social media or smart phone applications that offer anonymous messaging services. At this point it should be noted that there is no formal single practice or specificity in the form of steps that need to be taken to be considered a threat as a cyber hybrid threat, so there is no legal framework defining the steps that characterize that this is a threat to the target state to take legal actions, the legal framework is incomplete and that is something that countries that are waging such wars are very aware of and they are washed.

As technology evolves, asymmetric threats increase as states with sufficient funding and equipment are able to wage such wars on a large scale, which is why the cyber hybrid wars will intensify. That is why governments and security agencies around the world are trying to organize and shield themselves against the cyber hybrid war, now knowing that its impact is greater than even conventional warfare.Preparing, organizing, and preventing such attacks are the basic prerequisites for dealing with the threat. This entails writing and implementing a cyber security policy that outlines the conditions, steps to be taken, education, definitions, and how to handle such incidents.The security policy should be updated annually and adapted to the needs and the level of risk that exists per period. It must adequately specify how government agencies must act in a period of digital asymmetric threat. Allied countries need to formulate a common cyber policy so that dealing with a digital asymmetric threat is unified. It is of no use to allies and friendly countries not to implement a common strategy against digital asymmetric threats. Friendly organized countries can easily trap the enemy and destroy the plans.

Conclusion

The cyber hybrid war is made up of several entities that, depending on the smooth functioning of all entities, are judged to be successful or unsuccessful. It is an asymmetric threat, no one can know the length or the size of the area it will take place. It is a kind of war that with the development of technology will see significant development. An important factor in success is financial support and therefore the amount of money each state is willing to spend to design and implement a credit cyber hybrid war. A well-organized and implementable cyber hybrid warfare can cause severe damage to a conventional one. It is not necessary for a cyber hybrid war to be designed exclusively by wealthy and developed countries, such a war can be created by any state that has the knowledge, money, and organization to mount an asymmetric threat. In the cyber hybrid war, the chances of convicting states for war crimes are minimized, as in the cyber hybrid war there is no clear legal framework defining the methods of intruders.Identifying a digital threat is difficult due to the complexity of its actions; identifying and neutralizing a cyber hybrid threat requires knowledge and experience of such threats. Some countries in the world have developed methods and teams to detect and manage such threats, but the measures they take to protect them are found to be incomplete and not fully effective and the reason is the rapid development of technology that new methods and techniques are constantly being discovered.Finally, as has been said above, the best defense is the organization of friendly states to provide a single aid and formulate a unified security policy that will lead to massive isolation of cyber hybrid threats. Unified repression by friendly countries against such attacks is the best organized defense against hybrid threats.

References

1. Christos Beretas(2020)DeepFake – Another One Cyber Threat.
2. Andreas Krieg, Jean-Marc Rickli(2019) Surrogate Warfare: The Transformation of War in the Twenty-First Century.
3. Andrew Fevery(2018) Hybrid Warfare.

DOI: 10.31038/NAMS.2020314

Opinion Article

This article is a personal opinion article and nothing more.

We are all living in the last days a state of panic wherever we are in the world. This panic is justified and owe it to the well-known Coronavirus pandemic (COVID-19) as it is officially called. The virus causes from mild to very severe symptoms, such as acute respiratory infection, that is, ARF pneumonia(AcuteRespiratoryFailure). Symptoms of the virus start from a cough, fever with tithing slowly, tiredness, and shortness of breath.

So far there is no vaccine or antibody that kills the virus. The treatments are based on antibodies created to treat the flu and pneumonia. Experimental tests of cocktail antibiotics are also used which act on a case-by-case basis in conjunction with the patient’s physical condition, concomitant illness, and age.

The way of virus actions have triggered a number of questions that I’m sure they will be of concern to you, whether you have been involved or have not found answers Important questions remain unanswered, such questions are:

• The first patient how has been infected by the virus?

• In the country where the virus first appeared, did they allow the sale of animals that had been used as experimental animals before?

• Is allowed  to sell laboratory cloned animals?

• Is it permissible to import from outside of the country laboratory created cloned animals?

• Is there evidence that animals or seafood , have the virus spread or been infected?

• Are there any announcements or publications from government sources about the study, safety and protection against such corona-viruses in the past?

• Does virus analysis show mutations based on the corona-virus structure?

Focusing on time actions of the virus, again causes questions, ranging from 2 to 14 days with an average of 5 days. Sound like the virus have hooks and try to get into the human body. By adding amino acids, a virus that could threaten humans could easily mutate.

Sound like the virus enters the human body, and after it enters, stays there and shows signs of existence after a few days, this could be concealed in such a way to protect itselfto not detected and kill it early before infect the human body, also by this way may hide the exact date of infection, so that the place of infection is not easily detected.

Analyzing the above reminds me a bit of the way HIV works, where after about a week those infected with HIV show flu or simply cold symptoms, then the symptoms subside and the carrier stays asymptomatic for years, spreading ignorant the virus.

Considering all of the above, to behave the way COVID-19 might behave, this virus may be made in the laboratory, as a biological weapon that was either accidentally escaped, or applied as a test under real conditions, or for some purpose, by whom; unknown.People who believe it is a biological weapon could think of two scenarios, the first being a real-life test, and the second being that the virus escaped from a laboratory by accident in an experimental animal or a worker.

Whatever the reality is, I hope very soon all of this will be over and everything will return to normal in our lives.

References

1. PhD Candidate in Cyber Security (Innovative Knowledge Institute) Paris, France.
2. Member of Alpha Beta Kappa Honor Society, Alpha of Ohio, USA.

DOI: 10.31038/NAMS.2020313

Abstract

The textile wastewaters could not be treated effectively with conventional treatment processes due to high polyphenol and aromatic compounds and colour content. In this study, by doping of NiCO₂O₄ to  Bi₂O₂CO₃ the generated NiCO₂O₄to Bi₂O₂CO₃nanocomposite was used for the photocatalytic oxidation of COD components (COD_total, CODdissolved, COD_inert), color, organophenols and organoaromatic compounds from a textile industry wastewaters (TW) at different operational conditions such as, at different photooxidation times (5 min, 15 min, 30 min, 60 min, 80 min and 100 min), at diferent NiCO₂O₄ratios (0.5wt% , 1wt%, 1.5wt%, 2wt%), at different NiCO₂O₄ / Bi₂O₂CO₃nanocomposite concentrations (1, 5, 15, 30 and 45 mg/L), under 10, 30, 50 and 100 W solar irradiations, respectively. The maximum COD_total, COD_inert, total flavonols, total aromatic amines (TAAs) and color photooxidation yields were 99%, 92%, 91%, 98% and 99% respectively, under the optimized conditions, at 30 mg/L Ni/BiO nanocomposite with a Ni mass ratio of 1.5 wt% under 50 W UV (ultraviolet) light, after 60 min photooxidation time, at 25°C. The photooxidation yields of kaempferol (KPL), quercetin (QEN), patuletin (PTN), rhamnetin (RMN) and rhamnazin (RHAZ) from flavonols and 2-methoxy-5-methylaniline (MMA), 2,4-diaminoanisole(DAA); 4,40-diamino diphenyl ether (DDE), o-aminoazotoluene (OAAT), and 4-aminoazobenzol (AAB) from polyaromatic amines were > 82%.The pollutants of textile industry wastewater were effectively degraded with Ni doped BiO nanocomposite.

Keywords

Flavonols; Nickel cobaltite NiCO₂O₄ nanocomposite; bismuth subcarbonate (Bi₂O₂CO₃) nanocomposite; Photooxidation; Polyaromatic amines; Ultraviolet (UV) light irradiation.

Biographical notes

Delia Teresa Sponza is a Professor at the Department of Environmental Engineering, Engineering Faculty, Dokuz Eylül University,İzmir, Turkey. She graduated her MSc and PhD degrees from Dokuz Eylül University, Turkey, in Environmental Engineering. Her research interests are environmental microbiology,environmental sciences and toxicity. She has published a number ofresearch papers at the national and international journals.

Rukiye Oztekin is a Researcher at the Department of Environmental Engineering, Engineering Faculty, Dokuz Eylül University, İzmir, Turkey. She graduated her MSc and PhD degrees from Dokuz Eylül University, Turkey, in Environmental Engineering. Her research interestsinclude environmetal sciences and toxic industrial wastewater treatment.

Introduction

Textile industry is one of those industries that consume large amounts of water in the manufacturing process [1] and, also, discharge great amounts of effluents with synthetic dyes to the environment causing public concern and legislation problems. Synthetic dyes that make up the majority (60–70%) of the dyes applied in textile processing industries [2] are considered to be serious health risk factors. Apart from the aesthetic deterioration of water bodies, many colorants and their breakdown products are toxic to aquatic life [3] and can cause harmful effects to humans [4,5]. Several physico-chemical and biological methods for dye removal from wastewater have been investigated [6-8] and seem that each technique faces the facts of technical and economical limitations [7]. The traditional physical, chemical and biologic means of wastewater treatment often have little degradation effect on this kind of pollutants. On the contrary, the technology of nanoparticulate photodegradation has been proved to be effective to them. Compared with the other conventional wastewater treatment means, this technology has such advantages as: (1) wide application, especially to the molecule structure-complexed contaminants which cannot be easily degraded by the traditional methods; (2) the nanoparticles itself have no toxicity to the health of our human livings and (3) it demonstrates a strong destructive power to the pollutants and can mineralize the pollutants into carbondipxide (CO₂) and water (H₂O) [9].

Bi₂O₂CO₃ has gained much attention due to its promising photocatalytic activity for wastewater treatment [10-12]. Although Bi₂O₂CO₃ has been widely studied in the photocatalytic degradation of wastewater, little attention has been poured to investigate the microwave catalytic performance of Bi₂O₂CO₃ for microwave catalytic oxidation degradation of wastewater, up to now. At the same time, the magnetic NiCO₂O₄ has intriguing advantages, such as excellent microwave absorption performance, low cost, magnetically separable property, and high stability [13]. To the best of our knowledge, NiCO₂O₄-Bi₂O₂CO₃ composite as microwave catalyst for degradation o more semiconductor photocatalysts have been found to be capable of photocatalytic degradation of organic macromolecular contaminants in wastewater [14, 15]. Therefore, photocatalytic degradation has become the most environmentally friendly, energy-saving, and efficient water pollution treatment method. In view of the fact that the traditional photocatalysts (such as TiO₂) have large band gap energy and low response to visible light, their application is greatly limited. Among these miconducting photocatalysts, bismuth molybdate (Bi₂MoO₆) as a ternary oxide compound of Aurivillius phase becomes one of the promising materials. This is because it has a unique layered structure sandwiched between the perovskite octahedral (MoO₄)₂⁻sheets and bismuth oxide layers of (Bi2O₂)²⁺ [16-18]. Its dielectric property, ion conductivity, and catalytic performance have obvious advantages in bismuth-based semiconductors [19, 20]. Nevertheless, the light absorption property of the pure Bi₂MoO₆ primarily appears in the ultraviolet light region, which is only a small part of the solar spectra. Meanwhile, it presents a high recombination rate of electronhole
pairs in the process of photocatalytic reaction [21]. Therefore, researchers have improved the performance of Bi₂MoO₆ by means of
morphology controlling, semiconductor compounding, and doping modification [22]. Among these measures, doping has proven to be an effective method to ameliorate the surface properties of photocatalysts and enhance photocatalytic performance.

It was reported that carbon-doped Bi₂MoO₆ exhibited significantly enhanced and stable photocatalytic properties compared with Bi₂MoO₆ [23], which carbon replaced the O₂⁻anion in the lattice of Bi₂MoO₆, resulting in lattice expansion and grain diameter reduction, enhancement of specific surface area [24]. prepared Graphene-Bi₂MoO₆ (G-Bi₂MoO₆) hybridphotocatalysts by a simple one-step process, and an increase in photocatalytic activity was observed for G-Bi₂MoO₆ hybrids compared with pure Bi₂MoO₆ under visible light. Xing et al., (2017) reported the photocatalytic activity of 0.5% Pd–3C/BMO was robustly enhanced about 5-fold for Rhodamine B (RhB) degradation within 40 min under UV + visible light irradiation and 29-fold for O-phenylphenol (OPP) degradation within 120 min under visible light irradiation in comparison with pristine Bi₂MoO₆, respectively. [25] prepared a B-doped Bi₂MoO₆ photocatalyst with hydrothermal method by using HBO₃ as a dopant source. It was found that B-doping increases the amount of Bi⁵⁺ and oxygen vacancies, so that the visible light absorption of catalyst is stronger, and the band gap energy is lower, which significantly improves the photocatalytic activity of Bi₂MoO₆. [26] successfully synthesized sulfur-doped copper-cobalt bimetal oxide by coprecipitation method, which significantly improved the catalytic performance and stability of the catalyst. [27] fabricated Bi₂MoO₆ surface co-doped with Ni²⁺ and Ti⁴⁺ ions through an incipient-wetness impregnation technology and calcination method, with the results suggesting Ni²⁺ and Ti⁴⁺ codoping increases visible-light absorption by Bi₂MoO₆ and promotes the separation of photogenerated charge carriers. Density functional theory calculations and systematical characterization results revealed that Biself-doping could not only promote the separation and transfer of photo generated electron-hole pairs of Bi₂MoO₆ but also alter the position of valence and conduction band without changing its preferential crystal orientations, morphology, visible light absorption, as well as band gap energy [28, 29] synthesized pure and various contents of Ce³⁺ doped Bi₂MoO₆ nano structures by a facile hydrothermal method. The 0.5%Ce³⁺ doped Bi₂MoO₆ exhibitsthe best photocatalytic activity of 96.6% within 20 min for RhB removal.

The photocatalytic performance of NiCO₂O₄-doped Bi₂MoO₆ nanoparticles has not been investigated extensively for the removals of aromatics and polyphenols from a textile industry. In this work, the phsicochemical properties of NiCO₂O₄ doped Bi₂O₂CO₃ nanocomposite was investigated using microscope (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), photoluminescence spectra (PL), N2 adsorption–desorption, elemental mapping, Raman and diffused reflectances pectra (DRS) analysis. The photocatalytic oxidation of pollutant parameters [COD components (COD_total, CODdissolved, COD_inert), flavonols (kaempferol, quercetin, patuletin, rhamnetin and rhamnazin), polyaromatic amines (2-methoxy-5-methylaniline, 2,4-diaminoanisole, 4,40-diamino diphenyl ether, o-aminoazotoluene, and 4-aminoazobenzol) and color] from the TW at different operational conditions such as, at increasing photooxidation times (5 min, 15 min, 30 min, 60 min, 80 min and 100 min), at diferent Ni mass ratios (0.5wt% , 1wt%, 1.5wt%, 2wt%), at different Ni-BiO photocatalyst concentrations (1, 5, 15, 30 and 45 mg/L), at different pH ranges (4, 6, 8, 10) under 10, 30, 50 and 100 W UV light irradiations, respectively, were investigated .

Materials and methods

Raw wastewater

The characterization of raw TW was given in Table 1.

Table 1. Characterization values of TW at pH=5.7 (n=3, mean values ± SD). (SD: standard deviation; n: the repeat number of experiments in this study).

Chemical structure of flavonols and poliaromatics present in the TW

The structure of flavonols in the TW was shown in Figure 1. The structure of polyaromatics in the TW was given Figure 2.

Figure 1. Chemical structure of flavonoids in the TW.

Figure 2. Chemical structure of polyaromatics in the TW

Preparation of photocatalysts

Ni-doped BiO nano particles were prepared by co-precipitation method using nickel nitrate hexahydrate [Ni(NO₃)₂.6H₂O] (Analytical grade, Merck ) and Bismuthnitrate hexahydrate [Bi(NO₃)₂·6H₂O] (Sigma, Aldrich) as the precursors of nickel and bismuth, respectively. Ni(NO₃)₂.6H₂O and sodium carbonate anhydrous (Na₂CO₃) were dissolved separately in double distilled H₂O to obtain 0.5 mol/Lsolutions. Nickel nitrate solution (250 mL of 0.5 mol/L) was slowly added into vigorously stirred 250 mL of 0.5 mol/L Na₂CO₃ solution. Nickel nitrate in the required stoichiometry was slowly added into the above solution and a white precipitate was obtained. The precipitate was filtered, repeatedly rinsed with distilled H₂O and then washed twice with ethanol. The resultant solid product was dried at 100°C for 12 h and calcined at 300°C for 2 h. BiO particles were also prepared by the same procedure without the addition of nickel nitrate solution. The doping Ni mass ratios of Bismuth are expressed as wt%.

X-Ray diffraction (XRD) analysis

XRD patterns of the samples are going to carry out using a D/Max-2400Rigaku X-ray powder diffractometer operated in the reflection mode with Cu Ka (λ = 0.15418 nm) radiation through scan angle (2θ) from 20° to 80°.

Scanning electron microscopy (SEM) analysis

The morphological structures of the Ni-BiO nanocomposites before photocatalytic degradation with UV light irradiations and after photocatalytic degradation with UV by means of a SEM.

Fourier transform infrared spectroscopy (FTIR)analysis

The FTIR spectra of Ni, BiO and Ni-BiO samples were measured with FTIR spectroscopy measurements.

Photocatalytic degradation reactor

A 2 L cylinder kuvars glass reactor was used for the photodegradation experiments in the TW under different UV powers, at different operational conditions. 1000 mL TW was filled for experimental studies and the photocatalyst were added to the cylinder glass reactor. The photocatalytic reaction was operated with constant stirring during the photocatalytic degradation process. 10 mL of the reacting solution were sampled and centrifugated (at 10000 rpm) at different time intervals.

Used chemicals

Ni(NO₃)₂.6H₂O (Analytical Grade, Merck, Germany) and Bi(NO₃)3·6H₂O (Analytical grade, Merck, Germany) were used as nickel and bismuth sources, respectively. Na₂CO₃ was purchased from Merck (Analytical grade). Helium, He(g) (GC grade, 99.98%) and nitrogen, N₂(g) (GC grade, 99.98%) was purchased from Linde, (Germany). Kaempferol (99%), quercetin (99%), patuletin (99%), rhamnetin (99%), rhamnazin (99%), 2-methoxy-5-methylaniline (99%), 2,4-diaminoanisole (99%), 4,40-diamino diphenyl-ether (99%), o-aminoazotoluene (99%), 4-aminoazobenzol (99%) were purchased from Aldrich, (Germany).

Analytical methods

pH, T(°C), ORP, DO, BOD5, COD_total, CODdissolved, total suspended solids (TSS), Total-N, NH₃-N, NO₃-N, NO₂^–N, Total-P and PO₄-P measurements were monitored following the Standard Methods 2310, 2320, 2550, 2580, 4500-O, 5210 B, 5220 D, 2540 D, 4500-N, 4500-NH3, 4500-NO₃, 4500-NO₂ and 4500-P [30]. Inert COD was measured according to glucose comparison method [31]. The samples were analyzed by high pressure liquid chromatography (HPLC) with photodiode array and mass spectrometric detection using an Agilent 1100 high performance liquid chromatography system consisting of an automatic injector, a gradient pump, a Hewlett–Packard series 1100 photodiode array detector, and an Agilent series 1100 VL on-line atmospheric pressure ionization electrospray ionization mass spectrometer to detect flavonols namely kaempferol, quercetin, patuletin, rhamnetin, rhamnazin and polyaromatics namely, 2-methoxy-5-methylaniline, 2,4-diaminoanisole, 4,40-diamino diphenyl-ether, o-aminoazotoluene, 4-aminoazobenzol, respectively. All the  metabolites were measured in the same HPLC by mass spectrometric detections. Operation of the system and data analysis were done using ChemStation software, and detection was generally done in the negative ion [M − H]– mode, which gave less complex spectra, although the positive ion mode was sometimes used to reveal fragmentation patterns—especially patterns of sugar attachment. Separation of flavonol components was made on a Vydac C18 reversed phase column (2.1 μm dia. × 250 mm long; 5-μm particle size). Columns were eluted with acetonitrile-water gradients containing 0.1% formic acid in both solvents. The quality of the raw (un-treated) and photooxidated wastewater were determined by measuring the absorbances of the supernatans at wavelengths varying between 200 nm, 250 nm, 300 nm, 350 nm and 540 nmusing an Aquamate Termoelectron Corporation UV-vis spectrophotometer.

Measurement of photonic efficiency (lr) of Ni doped BiO

The relative photonic efficiency of the catalyst is obtained by comparing the photonic efficiency of Ni-doped BiO with that of the standard photocatalyst (BiO). In order to evaluate lr, a solution of 1-Methylcyclopropene-MCP (40 mg/L) with a pH of 10 was irradiated with 100 mg of BiO and Ni-doped BiO for 60 min. From the degradation results, Ir was calculated as follows (Eq. 1).

Operational conditions

Under 10-30-50 and 100 W  UV light powers the photocatalytic oxidation of the pollutant parameters in the TW at different operational conditions such as at increasing Ni mass ratios in the Ni-BiOnanocomposite(0.5wt% , 1wt%, 1.5wt%, 2wt%), at increasing photooxidation times (5 min, 15 min, 30, 60 min, 80 min and 100 min), at different Ni-BiOphotocatalyst concentrations (1, 5, 15, 30 and 45 mg/L), under acidic, neutral and basic conditions, respectively.

All the experiments were carried out following the batch-wise procedure. All experiments were carried out three times and the results were given as the means of triplicate sampling with standard deviation (SD) values.

Results and analysis

XRD Analysis results

The powder XRD patterns of BiO and Ni-doped BiO with different lanthanum mass ratios are shown in Figure 3. The XRD patterns of all the Ni-doped BiO catalysts are almost similar to that of BiO, suggesting that there is no change in the crystal structure upon Ni loading. This also indicates that Ni⁺² is uniformly dispersed on BiO nanoparticles in the form of small Ni2O₂ cluster. However the Ni-doped samples have a wider and lower intense diffraction peaks than pure BiO. Moreover, the XRD peaks of Ni-doped BiO continuously get broader with increasing the Ni loading up to a mass ratio of 2%wt.

Figure 3. XRD patterns of BiO and Ni doped BiO (a) pure BiO, (b) 2 wt% Ni doped BiO, (c) 0.5wt% Ni doped BiO, (d) 1.0wt% Ni doped BiO, and (e) 1.5wt% Ni doped BiO.

SEM Analysis results

The morphology of nanocomposite particles is analyzed by SEM. Figure 4 shows that the nanocomposite material is partly composed of clusters containing composite nanoparticles adhering to each other with a mean size of around 20-80 nm before photooxidation process (Figure 4a) while the size increased to 24-86 nm after photooxidation (Figure 4b) with intermediates and remaining not photodegraded pollutants.

Figure 4. SEM micrographs of pure and nickel modified BiO, (a) pure BiO at 25°C, (b) Ni doped BiO at 25°C.

FTIR Analysis results

Figure 5 shows the FTIR spectrum of BiO and Ni-doped BiO, BiO powder synthesized under laboratory conditions. The peak between 400 and 700 cm^-1 give the information of Bi–O and Ni–Bi–O on the FTIR spectra. The peak at 437–455 cm^-1 give the information about stretching vibration of crystalline hexagonal zinc oxide (Bi–O stretching, vibration) and the peaks from 902 to 1020 cm^-1 are attributed to the bond between lanthanum and oxygen (Ni–O). The broad peak between 3400 to 3900 cm^-1 indicate the OH groups, due to the H₂O which indicates the existence of atmospheric H₂O adsorbed on the surface of nanocrystalline powder. An absorption band and a peak have been observed at 2350 cm^-1, respectively, which arises from the absorption of atmospheric CO₂ on the metal cations.

Figure 5. FTIR Spectra of pure BiO and Ni-doped BiO nanoparticles, with different concentration of dopant

Results and Discussions

Effect of increasing Ni-BiOnanocomposite concentrations on the removals of TW pollutants

The effects of increasing Ni-BiO nanocomposite concentrations (1 mg/L, 5 mg/L, 15 mg/L, 30 mg/L and 45 mg/L), on the photocatalytic oxidation of polutant parameters in the TW was investigated. The preliminary studies showed that the maximum removal of COD with 20 mg/L Ni-BiO nanocomposite was 89% with 70 min photooxidation time at pH=7.8 with 40 W UV power (Data not shown). Based on these yields the operational conditions for photocatalytic time were choosen as 60 min at a power of 50 W and at a pH of 8. The maximum photocatalytic oxidation removals for all pollutants in the TW were observed at 30 mg/L Ni-BiO nanocomposite concentrations, at pH=8.0, after 60 min photooxidation time and at 25°C at a power of 50 W (Figure 6). Removal efficiencies slightly decreased at 45 mg/L Ni-BiO nanocomposite concentration, because over load of surface area of Ni-BiO nanocomposites (Figure 6). This limiting the power of UV irradiation. Lower photo-removal efficiencies was measured for 1, 5, and 15 mg/L Ni-BiO concentrations due to low surface areas in the nanocomposite. On the contrarily, the surface area is high at 30 mg/L Ni-BiO nanocomposite concentrations. Therefore, the maximum photodegradation yield was observed in this nanocomposite concentration. The COD_total, COD_inert, total flavonols, total aromatic amines and color removals increased linearly as the Ni-BiO nanocomposite concentrations were increased from 1 mg/L up to 5 mg/L, to 15 mg/L, and up to 30 mg/L, respectively (Table 2 and Figure 6). Furher increase of nanocomposite concentration to 45 mg/L affect negatively the all the pollutant yields. The reason for this is the optimum amount of catalyst increases the number of active sites on the photocatalyst surface, which in turn increase the number of OH^● and superoxide radicals (O₂^– ●) to degrade pollutant parameters (COD components, flavonols, polyaromatics, color). When the concentration of the catalyst increases above the optimum value, the degradation decreases due to the interception of the light by the suspension [32]. reported that as the excess catalyst (turbidity) prevent the illumination of light, OH^●, a primary oxidant in the photocatalytic system decreased and the efficiency of the degradation reduced accordingly. Furthermore, the increase in catalyst concentration beyond the optimum may result in the agglomeration of catalyst particles; hence, the part of the catalyst surface becomes unavailable for photon absorption, and thereby, photocatalytic oxidation efficiency decreases [33]. Maximum COD_total, COD_inert, total flavonols, TAAs and color removal efficiencies were obtained after 60 min photooxidation process with yields of 99%, 92%, 91%, 98% and 99%, respectively, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W power and at 25°C (Figure 6). Flavonols such as kaempferol, quercetin, patuletin, rhamnetin, rhamnazin removal efficiencies were 87%, 88%, 90%, 87% and 85% respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration and at 25°C temperature (Table 2). Polyaromatic amines such as, 2-methoxy-5-methylaniline, 2,4-diaminoanisole, 4,40-diamino diphenyl ether, o-aminoazotoluene, 4-aminoazobenzol removal efficiencies after photooxidation process were 93%, 95%, 87%, 84% and 82%, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 2).

Figure 6. Removal efficiencies of COD_total, COD_inert, total flavonols and TAAs at Ni-BiO=1 mg/L, BiO=5 mg/L, BiO=15 mg/L and BiO=30 mg/L.

Table 2. Effect of increasing Ni-BiO nanocomposite concentrations on the TW during photooxidation process after 60 min, at 50 W UV irradiation, at pH=8.0, at 25°C.

Kaempferol metabolies such as, 3-O-[2-O,6-O-bis(α-L-rhamnosyl)-( β-D-glucosyl] quercetin, 3-O-[6-O-(α-L -rhamnosyl)-( β-D-  glucosyl]quercetin, 3-O-{2-O-[6-O-(p-hydroxy-trans-cinnamoyl)-{ )-, β-D-glucosyl]- á-L-rhamnosyl}kaempferol decreased from 5.7 mg/L to 0.86 mg/L, from 5.7 mg/L to 1.08 mg/L, from 5.7 mg/L to 1.25 mg/L, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 3). Quercetin metabolites such as, 3-O-[6-O-( α-L -rhamnosyl)- )-(β-D-glucosyl]quercetin, 3-O-{2-O-[6-O-(p-hydroxy-trans-cinnamoyl)-( β-D -glucosyl]– á-L-rhamnosyl}quercetin reduced from 9.2 mg/L to 1.28 mg/L, from 9.2 mg/L to 2.30 mg/L, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 3). Patuletin metabolites such as, (E)-ascladiol, (Z)-ascladiol dropped off from 10.3 mg/L to 1.55 mg/L, from 10.3 mg/L to 1.85, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 3). Rhamnetin metabolites such as, methyl quercetin, tetrahydroxy-7-methoxyflavone decreased from 7.2 mg/L to 1.15 mg/L, from 7.2 mg/L to 1.44 mg/L, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 3). Rhamnazin metabolites such as, Rhamnazin-3-0-β-D-glucopyranosyl-(l →5)- α-L-arabinofuranoside, Rhamnazin-3-O-β-D- glucopyranosyl-(l—»5)-[β-D-apiofuranosyl-(-1→2)]-α -L-arabinofuranoside reduced from 6.5 mg/L to 1.42 mg/L, from 6.5 mg/L to 1.66 mg/L, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 3).

Table 3. The metabolites of flavonols in the TW.

2-methoxy-5-methylaniline metabolite such as, 5-nitro-o-toluidine decreased from 134.6 mg/L to 36.34, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 4). 2,4-diaminoanisole such as, 4-acetylamino-2-aminoanisole, 2,4-diacetylaminoanisole reduced from 275.8 mg/L to 22.06 mg/L, from 275.8 mg/L to 38.61 mg/L, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 4). 4,40-diamino diphenyl ether metabolites such as, N,NI-diacetyl-4,4I-diaminobenzhydrol, N,NI-diacetyl-4,4 I –diaminophenylmethane dropped off from 156 mg/L to 28.08 mg/L, from 156 mg/L to 40.56 mg/L, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 4). o-aminoazotoluene metabolites such as, hydroxy-OAT (I), 4′-hydroxy-OAAT,  2′-hydroxymethyl-3-methyl-4-aminoazobenzene, 4, 4′-bis(otolylazo)-2, 2′ -dimethylazoxybenzene decreased from 293.6 mg/L to 58.72 mg/L, from 293.6 mg/L to 79.27 mg/L, from 293.6 mg/L to 85.14 mg/L, from 293.6 mg/L to 117.44 mg/L, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L La-ZnO nanocomposite concentration at 50 W  UV power and at 25°C (Table 4). 4-aminoazobenzol metabolites such as phenylhydroxylamine, nitrosobenzol reduced from 178 mg/L to 39.16 mg/L, from 178 mg/L to 44.5 mg/L, respectively, after 60 min photooxidation time, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power and at 25°C (Table 4).

Table 4. The metabolites of polyaromatic amines in the TW.

[34] researched the photocatalytic activity of pure and Ni⁺²-doped BiO samples for the degradation of Rhodamine B (RhB). The effect of Ni⁺² doping concentration on the photocatalytic activity of RhB wasalso investigated. 9 g/L Ni⁺²-doped BiO with a mass ratio of 2wt% had high photocatalytic efficiency [34]. 4-nitrophenol degradation was studied in the presence of Ni doped BiO nanoparticles with a Ni mass ratio of 4% [35]. 78.26% 4-nitrophenol removal was observed the aforementioned nanocomposite after 195 min photodegradation time and at 30 W  UV light irradiation at pH=8 [35]. 68.57% Acid Yellow 29 55% Coomassie Brilliant Blue G250 and 37.27% Acid Green 25 degradations was obtained after 120 min of irradiation in the presence of 0.9% Ni-doped BiO, at 500 W UV light irradiation, under atmospheric oxygen, at 25°C, respectively [36]. The color and pollutant yields obtained in our study exhibited higher yields compared to the studies given above with low Ni-BiO nanocomposite concentrations.

Effect of increasing Ni mass ratios on 30 mg/L Ni doped BiO nanocomposite for photodegradation of TW pollutants

Were researched the effects of different La mass ratios (0.5wt%, 1wt%, 1.5wt% and 2wt% ) in 30 mg/L Ni-BiO nanocomposite concentrations on the photooxidation yields of all pollutants in the TW during photooxidation experiments. Maximum COD_total, COD_inert, total flavonols, TAAs and color removal efficiencies were 99%, 92%, 91%, 98% and 99%, respectively, after 60 min photooxidation time, at pH=8.0, at 1.5wt% Ni mass ratio and at 25°C (Table 5 and Figure 7). Removal efficiencies increased as the Ni mass ratio in the Ni doped BiO nanocomposite were increased from 0.5wt% to 1wt% and to 1.5wt%. Maximum removal efficiencies was measured at 1.5wt% Ni mass ratio in the nanocomposite. The photocatalytic degradation efficiency of BiO nanoparticles increases with an increase in the Ni loading and shows a maximum activity at 1.5 wt%. Then decreases in photooxidation yield was observed on further Ni doping (to 2 wt%). The reason of this can be explained as follows: excessive amounts of dopants can retard the photocatalysis process, because excess amount of dopants deposited on the surface of BiO increases the recombination rate of free electrons and energized holes, thus inhibiting the photodegradation process. Hence, further increase in Ni doping to 2wt% results in the decrease of photocatalytic degradation efficiency.

Table 5. Effect of increasing Ni mass ratios on the TW during photooxidation process after 60 min, at 50 W UV irradiation, 30 mg/L Ni-BiO nanocomposite concentrations, at pH=8.0, at 25°C.

Figure 7. Removal efficiencies of COD_total, COD_inert, total flavonols and TAAs at 0.5wt%, 1wt%, 1.5wt%, and 2 wt% Ni mass ratio.

The synthesized Ni-doped BiO catalyst possesses smaller particle size distribution than pure BiO nanoparticles. Apart from their small size, as Ni⁺² was doped in BiO, more surface defects are produced as reported by [37]. Consequently, the migration of the photo-induced electrons and holes toward surface defects is reasonable [37]. Thus, the separation efficiency of the electron–hole pairs of Ni-doped BiO with more oxygen defects should be more than that of the pure BiO nanoparticles. Therefore, the enhancement in the photocatalytic degradation efficiency of Ni doping BiO increases due to small particle size and higher defect concentration compared to BiO alone.

UV absorbances of Ni-doped BiO

The UV–vis absorption spectra of BiO and Ni-doped BiO are shown in Figure 8. It can be clearly seen from Figure 8. The maximum absorbance shifts is 410 nm for pure nano BiO while the maximum absorbance of Ni-doped BiO with a Ni mass ratio 0.5wt% is observed at a wavelentgh of 380 nm. The wave of absorbance of Ni-doped BiO also increases gradually with increasing the Ni loading and is much higher as compared to that of pure BiO. This could be mainly attributed to the quantum size effect as well as the strong interaction between the surface oxides of Bi and Ni. These observations strongly suggest that the Ni doping significantly affects the absorbance properties.

Figure 8. UV-vis absorption spectra of Ni doped BiO catalysts

The strong UV band gap emission (375–395 nm) results from the radiative recombination of an excited electron in the conduction band with the valence band hole. The broad visible or deep-trap state emissions (410–440 nm and 540–580 nm) are commonly defined as the recombination of the electron-hole pair from localized states with energy levels deep in the band gap, resulting in lower energy emission. These deep-trap emissions indicate the presence of defects or oxygen vacancies of BiO nanostructures [38]. Since the band gap excitation of electrons in BiO or Ni-doped BiO with 254 nm can promote electrons to the conduction band with high kinetic energy, they can reach the solid-liquid interface easily, suppressing electron–hole recombination in comparison with 365 nm. Hence, the observation of low rate at 254 nm is therefore unexpected [39]. The UV band gap emission of Ni-doped BiO nanostructures was increased between 380 and 410 nm after the photocatalytic process of pollutant parameters. The results show that 1.5 wt% Ni-doped BiO has maximum activity as compared to other photocatalysts.

Effect of increasing photooxidation time on the photooxidation yields of pollutants in the TW

Six different photooxidation times (5 min, 15 min, 30 min, 60 min, 80 min and 100 min) was examined during photocatalytic oxidation of the pollutants in the TW. To determine the optimum time for maximum removals these pollutant parameters in the TW. The maximum photocatalytic oxidation removals was observed at 60 min photooxidation time, at pH=8.0 using 30 mg/L Ni doped BiO with a La mass ratio of 1.5wt% at an UV power of 50 W (Figure 9). The removals of COD_total, COD_inert, total flavonols, total aromatic amines and color were found to increase linearly with increase in retention time from 5 min up to 80 min. A further increase in retention time to and 100 min lead to a decrease in yields of pollutant parameters. In other words the removal efficiencies of pollutant parameters (COD components, flavonols, polyaromatics, color) decreased for photooxidation time > 60 min since at long irradiation times since the surface energy of Ni doped – BiO decreases [40]. The photooxidation can form small molecules such as H₂O, carbonmonoxide (CO), CO₂ and benzene etc. after long irradiation; it will lead to the decrease of the polar groups and the oxygen content of pollutant surface. The dispersive component of surface energy, the density of polymer surface has great influence on dispersivity of pollutants in the TW. However, the rate of photodegradation of Ni doped-BiO blends increases with the increase of irradiation time, and is higher than that of photocrosslinking after long irradiation time, leading to the decrease of the density of the polymer surface and the dispersivity of COD, dyes and other pollutants to Ni doped–BiO [41]. The photooxidation can form small molecules such as H₂O, CO, CO₂ and benzene etc. after long irradiation; it will lead to the decrease of the polar groups and the oxygen content of polymer surface, therefore the dispersivity decreaeses resulting in low photooxidation yields [41]. Aromatic and phenolic metabolites which would adsorb strongly onto titania surface and block significant part of photoreactive sites.

Figure 9. Removal efficiencies of COD_total, COD_inert total flavonols and TAAs after 5, 15, 30, 60, 80 and 100 min retention times.

The maximum COD_total, COD_inert, total flavonols, total aromatic amines and color removal efficiencies were 99%, 92%, 91%, 98% and 99%, respectively, after 60 min photooxidation time, at 1.5 wt% Ni mass ratio in 30 mg/L Ni-BiO nanocomposite concentration, at pH=8.0 and at 25°C under 50 W irradiation (Table 6). Also, flavonols such as kaempferol, quercetin, patuletin, rhamnetin, rhamnazin removal efficiencies were 87%, 88%, 90%, 87% and 85%, respectively (Table 6). The photooxidation removals of polyaromatic amines such as, 2-methoxy-5-methylaniline, 2.4-diaminoanisole, 4.40-diamino diphenyl ether, o-aminoazotoluene, 4-aminoazobenzol were 93%, 95%, 87%, 84% and 82%, respectively, after 60 min at pH=8.0 and at 25°C (Table 6). Kaempferol, quercetin, patuletin, rhamnetin, rhamnazin concentrations decreased from 5.7 to 0.741 mg/L, from 9.2 to 1.104 mg/L, from 10.3 to 1.03 mg/L, from 7.2 to 0.936 mg/L, from 6.15 to 0.923 mg/L, respectively. 2-methoxy-5-methylaniline, 2.4-diaminoanisole, 4.40-diamino diphenyl ether, o-aminoazotoluene, 4-aminoazobenzol concentrations decreased from 134.6 to 9.422 mg/L, from 275.8 to 13.79 mg/L, from 156 to 5.46 mg/L, from 293.6 to 10.28, from 178 to 6.23 mg/L, respectively.

Table 6. Effect of increasing photooxidation time on the TW during photooxidation process, at 50 W UV irradiation, at pH=8.0, 30 mg/L Ni-BiO nanocomposite concentrations, 1.5 wt% Ni mass ratio, at 25°C.

The color yields obtained in this study for TW are higher than the studies given below: [42] investigated the effects of Bi0.95Ni0.05O and Bi0.90Ni0.10O on the treatment of Methylene Blue (MB) dyestuff removal under 18 UV irradiation for 1 h. 81% color yields were observed for the aforementioned Ni-Bi-O nanocomposites, respectively [42]. [43] found 80% color yields based on Reactive Black 5 after 60 min irradiation time under 90 W irradiation using Bi-Ni nanocomposite.

Effect of increasing UV powers on the yields of pollutants in the TW

In this study, four UV light powers were used (10 W, 30 W, 50 W and 100 W) to detect the optimum UV irradiation power for maximum photo-removal of the pollutant parameters in the TW using 30 mg/L Ni doped BiO nanocomposite with a Ni mass ratio of 1,5%w. The maximum photocatalytic oxidation removals was observed at 50 W  UV light irradiation, at pH=8.0, after 30 min photooxidation time and at 25°C (Table 7 and Figure 10). The COD_total, COD_inert total flavonols, total aromatic amines and color were found to increase linearly with increase in UV light irradiation from 10 W, up to 30 W, up to 50 W, respectively (Table 7 and Figure 10). Further increase of UV power up to 100 W did not affect positively the pollutant yields. Maximum COD_total, COD_inert, total flavonols, TAAs and color removal efficiencies after photooxidation process were 99%, 92%, 91%, 98% and 99%, respectively, for the aforementioned operational conditions (Figure 10). Flavonols such as kaempferol, quercetin, patuletin, rhamnetin, rhamnazin removal efficiencies were 87%, 88%, 90%, 87% and 85%, respectively, after 60 min photooxidation time, at 50 W  UV light, at pH=8.0, at 30 mg/L Ni-BiO nanocomposite concentration and at 25°C (Table 7). Polyaromatic amines such as, 2-methoxy-5-methylaniline, 2, 4-diaminoanisole, 4, 40-diamino diphenyl ether, o-aminoazotoluene, 4-aminoazobenzol removal efficiencies after photooxidation process were 93%, 95%, 87%, 84% and 82%, respectively (Table 7).

Table 7. Effect of increasing UV light irradiations on the TW during photooxidation process after 60 min, at 30 mg/L Ni-BiO photocatalyst concentration, at pH=8.0, at 25°C.

Figure 10. Removal efficiencies of COD_total, COD_inert, total flavonols and TAAs at 10 W, 30 W, 50 W and at 100 W.

The UV power determines the extent of light absorption by the semiconductor catalyst at a given wavelength. During initiation of photocatalysis, electron–hole formation in the photochemical reaction is strongly dependent on the optimum light intensity [44]. In this study, as the UV power increase from 10 W up to 50 W might favor a high-level surface defects, which account for the increase in the defect emission relative to the UV emission as reported by [39]. Higher UV powers > 50 W decrease the defects in the surface of the nanoparticle by disturbing the active holes.

Effect of increasing pH values on the pollutant yields in the TW

The effects of increasing pH values (4.0, 6.0, 8.0 and 10.0) on the photocatalytic oxidation of polutant parameters in TW was examined by considering the solubility of BiO nanoparticles in acidic as well as in highly basic solutions. The maximum photocatalytic oxidation removals was obtained at pH=8.0, after 60 min photooxidation time with a Ni mass ratio 1.5wt% using 30 mg/L Ni-BiO nanocomposite concentration at 50 W  UV power (Table 8 and Figure 11). In acidic medium, less photocatalytic degradation of pollutant parameters (COD components, flavonols, polyaromatics, color) was observed. The extent of photocatalytic degradation of polutant parameters was found to increase with increase in initial pH to 8.0 and a decrease in maximum photocatalytic degradation was found at pH 10. The possible explanation of this is that the pH at zero point charge (zpc) of BiO is 9.0 ± 0.3 [45]. Below pH 8.0, active sites on the positively charged catalyst surface are preferentially covered by pollutant molecules. Thus, surface concentration of the polutant parameters (COD components, flavonols, polyaromatics, color) is relatively high, while those of OH^– and OH^● are low. Hence, photocatalytic degradation decreases at acidic pH. On the other hand, above pH 8.0, catalyst surface is negatively charged by means of metal-bound OH^–, consequently the surface concentration of the polutant parameters (COD components, flavonols, polyaromatics, color) is low, and OH^● is high. In addition, polutant parameters are not protonated above pH 8.0. The electrostatic repulsion between the surface charges and Ni doped BiO nanocomposite hinders the amount of polutant parameters and the adsorption, consequently surface concentration of the polutant parameters decreases, which results in the decrease of photocatalytic degradation at pH 10.0. In conclusion, pH 8.0 can provide moderate surface concentration of polutant parameters which react with the holes to form OH^●.

Table 8. Effect of increasing pH values on the TW during photooxidation process, at 50 W UV irradiation, after 60 min, at 25°C.

Figure 11. Removal efficiencies of COD_total, COD_inert, total flavonols and TAAs at pH=4.0, pH=6.0, pH=8.0, pH=10.0.

Photocatalytic oxidation mechanisms of Ni doped BiOnanocomposite

The higher activity of Ni doped BiO can beattributed to successful e⁻–h⁺ separation and production of ●O₂⁻ and OH^●. Ni-modified BiO sample manifests the highest efficiency, which may be explained by the highest number of O₂ vacancies (related to the different charge and electronegativity of Ni and Bi ions) and as a result of stronger adsorption of OH⁻ions onto the BiO surface [46]. This favors the formation of OH^● by reaction of hole and OH⁻. The OH^● and photogenerated ●O₂⁻has extremely strong non-selective oxidants lead to the degradation of the organic pollutant at the surface of Ni modified BiO [41]. The photocatalytic degradation mechanism starts with the illumination of BiO nanoparticles and production of electron–hole pairs in Eq. (2):

Major roles of metal ions in this study are to increase the concentration of BiO on the surface of the catalyst and to prolong the individual life-time of electrons and holes and hence, inhibit their recombination. The ability of Ni⁺³ to scavenge photogenerated electrons is as follows: (Eq. 3):

However, stabilities of Ni⁺³ ions may be disturbed in their reduced forms (Ni⁺²). This can be achieved by transferring the trapped electron to O₂ [Eq. (4)]:

The produced O₂⁻● is responsible from the generation of OH^●, known as highly reactive electrophilic oxidants [Eqs. (5-7)]:

In the meantime, photogenerated holes may react with H₂O molecules and produce OH^● (Eq. 8):

The color removal by photooxidation of dyes reactions were given in Eqs (9-14):

Thus, loading of metal ions such as Ni on the surface of BiO matrix can suppress the recombination of photoinduced charge carriers either with only electron capture ability or with steps forward to produce OH^●. For Ni–BiO, electron accepting ability, production of more OH^●, the highest surface roughness value and the higher dark adsorption capacity result in pronounced photoactivity. The decay profile of the products includes the subsequent attacks of OH^●, known as highly reactive electrophilic oxidants. The main reaction pathway (60% of OH^●) is the addition of the OH^● to the double bond of the azo group, resulting in the rapid disappearance of color; however, addition to the aromatic ring also occurs (40% of OH^●) [47, 48]. Further OH^● attacks and the increment in OH^● concentration in the solution increase the yield of OH^–adduct in the degradation progress of each product. The opening of the dye aromatic rings due to consecutive oxidation reactions leads to low-molecular weight compounds [49].

Photonic efficiency of Ni doped BiO

In order to evaluate the relative photonic efficiency (Ir), a solution of MCP (40 mg/L) adjusted to pH 10 was irradiated with 100 mg BiO (Merck) and Ni-doped BiO, separately. The relative photonic efficiencies of light of wavelengths 254 and 365 nm for BiO and Ni-doped BiO are presented in Table 9. For comparison, the relative photonic efficiency of TiO₂ is also presented in Table 9. The relative photonic efficiencies of Ni-doped BiO are greater as compared to those of BiO and TiO₂, revealing the effectiveness of metal-doped systems. It is also interesting to note that the relative photonic efficiency for Ni-doped BiO for light of wavelength 254 nm are much higher as compared to that for 365 nm. The results are in good agreement with degradation and mineralization studies. Comparing the high efficiency of Ni doped BiO catalysts with standard BiO and TiO₂ catalyst, the photocatalytic efficiency of 1.5 wt% Ni-doped BiO is higher as compared to that of BiO and TiO₂ and other Ni doped BiO nanacomposites.

Table 9. Comparison of relative photonic efficiencies in the photodegradation of pollutants in TW by BiO and Ni-doped BiO photocatalysts (*)

Reusability of Ni doped BiO

As shown in Figure 12, after the first cycle of photocatalytic oxidation within 60 min, 99% of the Ni doped BiO with a mass ratio of 1.5wt% was recovered. After three cycles, the phoooxidation ability of Ni doped BiO nanocomposite was retained at 93% of the original value. After 8th cycles the nanocomposite was reatined at 80%. One of the reasons for the slight decline in photooxidation is that the surface of the reused photocatalysts may exist with some low residual substances which did not occupy the photocatalytic sites and did not block the adsorption. The presence of Ni significantly changed the binding site of the pollutant molecules. It is possible that the oxygen atom in Ni-BiO was bound to the dopant Ni [50]. The speedily recovering of the photodegradation capacity of Ni doped BiO for pollutans photodegradation will benefit to their photocatalytic activity.

Figure 12. The reusability of Ni doped BiO

Conclusions

By using 30 mg/L Ni-BiO with a Ni mass ratio of 1.5w% the COD_total, COD_inert, flavonols, polyaromatics and color were photodegraded with yields as high as 82-99% within 60 min photooxidation time, at 25°C under 50 W  UV power, at pH=8.0. The addition of Ni to BiO lead to enhance the photocatalytic activity by increasing the total surface area. The flavonoids and polyaromatic amines and their metabolites in the TW were firstly determined photodegraded with high rates and photonic efficiency using 30 mg/L Ni-BiO with a Ni mass ratio of 1.5w% at pH 8.

References

1. Lin SH, Chen ML (1997) Treatment of textile wastewater by chemical methods for reuse.Water Research31: 868-876.
2. Van der Zee FP, Bouwman RHM, Strik DP, Lettinga G, Field JA (2001) Application of redox mediators to accelerate the transformation of reactive azo dyes in anaerobic bioreactors.Biotechnology and Bioengineering75: 691-701.[Crossref]
3. Chung KT, Stevens SEJ (1993) Degradation of azo dyes by environmental microorganisms and helminthes.Environmental Toxicology and Chemistry 12: 2121-2132.
4. Weisburger JH (2002) Comments on the history and importance of aromatic and heterocyclic amines in public health.Mutation Research-Fundamental and Molecular Mechanisms of Mutagenesis506: 9-20.[Crossref]
5. Oliveira DP, Carneiro PA, Sakagami MK, Zanoni MVB, Umbuzeiro GA (2007) Chemical characterization of a dye processing plant effluent–identification of the mutagenic components.Mutation Research-Fundamental and Molecular Mechanisms of Mutagenesis626: 135-142.
6. Robinson T, McMullan G, Marchant R, Nigam P (2001) Remediation of dyes in textile effluent: a critical review on current treatment technologies with a proposed alternative.Bioresource Technology77: 247-255.[Crossref]
7. Van der Zee FP, Villaverde S (2005) Combined anaerobic–aerobic treatment of azo dyes—a short review of bioreactor studies.Water Research39: 1425-1440.[Crossref]
8. Roosta M, Ghaedi M, Shokri N, Daneshfar A, Sahraei R, Asghari A (2014) ‘Experimental design based response surface methodology optimization of ultrasonic assisted adsorption of safaranin o by tin sulfide nanoparticle loaded on activated carbon. Spectrochimica Acta – Part A: Molecular and Biomolecular Spectroscopy 122:  223-231.
9. Tavakkoli H, Moayedipour T (2014) Fabrication of perovskite-type oxide La0.5Pb0.5MnO₃ nanoparticles and its dye removal performance. Journal of Nanostructure in Chemistry4: 116-124.
10. Qi X, Zhou J, Yue Z, Gui Z, Li L (2003) Auto-combustion synthesis of nanocrystalline LaFeO₃.Materials Chemistry and Physics78: 25-29.
11. Deganello F, Marcì G, Deganello G (2009) Cipate–nitrate auto-combustion synthesis of perovskite-type nanopowders: A systematic approach.Journal of the European Ceramic Society29: 439-450.
12. Jadhav LD, Patil SP, Chavan AU, Jamale AP, Puri VR (2011)Solution combustion synthesis of Cu nanoparticles; a role of oxidant-to-fuel ratio.Micro & Nano Letters6:812-815.
13. Shea LE, McKittrick J, Lopez OA, Sluzky E (1996) Synthesis of red-emitting, small particle size luminescent oxides using an optimized combustion process.Journal of the American Ceramic Society 79: 3257-3265.
14. Dong SY, Feng JL, Fan MH, Pi Y, Hu L, et al. (2015) Recent developments in heterogeneous photocatalytic water treatment using visible light-responsive photocatalysts: A review.RSC Advances5: 14610-14630.
15. Wang HL, Xu LJ, Liu CL, Lu Y, Feng Q, et al. (2019) Composite magnetic photocatalyst Bi5O7I/MnxZn1-xFe2O₄:Hydrothermal-roasting preparation and enhanced photocatalytic activity.Nanomaterials 9: 118-131.
16. Li HH, Li KW, Wang H (2009) Hydrothermal synthesis and photocatalytic properties of bismuth molybdate materials.Materials Chemistry and Physics116: 134-142.
17. Ren J, Wang W, Shang M, Sun S, Gao E (2011) Heterostructured bismuth molybdate composite: Preparation and improved photocatalytic activity under visible-light irradiation.ACS Applied Materials & Interfaces 3: 2529-2533. [Crossref]
18. Zhang T, Huang JF, Zhou S, Ouyang HB, Cao LY,et al. (2013) Microwave hydrothermal synthesis and optical properties of flower-like Bi₂MoO₆ crystallites.Ceramics International39: 7391-7394.
19. Meng XC, Zhang ZS (2016) Bismuth-based photocatalytic semiconductors: Introduction, challenges and possible approaches.Journal of Molecular Catalysis A-Chemical423: 533-549.
20. Jia YL, Ma Y, Tang JZ, Shi W (2018) Hierarchical nanosheet-based Bi₂MoO₆ microboxes for efficient photocatalytic performance.Dalton Transactions47: 5542-5547.
21. Zhou TF, Hu JC, Li JL (2011) Er³⁺ doped bismuth molybdate nanosheets with exposed {010} facets and enhanced photocatalytic performance.Applied Catalysis B: Environmental110: 221-230.
22. Wang QY, Lu QF, Wei MZ, Wei M, Guo E, et al. (2018) ZnO/-Bi₂MoO₆ heterostructured nanotubes: Electrospinning fabrication and highly enhanced photoelectrocatalytic properties under visible-light irradiation.Journal of Sol-Gel Scıence and Technology85: 84-92.
23. Zhong Y, He ZT, Chen DM, Hao D, Hao W, (2019) Enhancement of photocatalytic activity of Bi₂MoO₆ by fluorine substitution.Applications of Surface Science467: 740-748.
24. Wang PF, Ao YH, Wang C, Hou J, Qian J (2012) A one-pot method for the preparation of graphene- Bi₂MoO₆ hybrid photocatalysts that are responsive to visible-light and have excellent photocatalytic activity in the degradation of organic pollutants.Carbon50: 5256-5264.
25. Wang M, Han J, Guo PY, Sun MZ, Zhang Y, et al. (2018) Hydrothermal synthesis of B-doped Bi₂MoO₆ and its high photocatalytic performance for the degradation of Rhodamine B.Journal of Physics and Chemistry of Solids 113: 86-93.
26. Chen C, Liu L, Guo J, Zhou LX, Lan YQ (2019) Sulfur-doped copper-cobalt bimetallic oxides with abundant Cu (I): A novel peroxymonosulfate activator for chloramphenicol degradation.Chemical Engineering Journal361: 1304-1316.
27. Wang J, Sun YG, Wu CC, Cui Z, Rao PH(2019) Enhancing photocatalytic activity of Bi₂MoO₆ via surface co-doping with Ni2+ and Ti4+ ions.Journal of Physics and Chemistry of Solids129: 209-216.
28. Ding X, Ho WK, Shang J, Zhang LZ (2016) Self doping promoted photocatalytic removal of no under visible light with Bi₂MoO₆: Indispensable role of superoxide ions.Applied Catalysis B: Environmental182: 316-325.
29. Zhang XH, Zhang HR, Jiang HT, Yu F, Shang ZR (2019) Hydrothermal synthesis and characterization of Ce³⁺ doped Bi₂MoO₆ for water treatment.Catalysis Letters1-11.
30. Eaton AD, Clesceri LS, Rice EW, Greenberg AE, Franson MAH (2005) Standard Methods for the Examination of Water and Wastewater’, Ed. by Franson MAH, (21th ed.), American Public Health Association (APHA), American Water Works Association (AWWA), Water Environment Federation (WEF), American Public Health Association (APHA) 800 I Street, NW, Washington, DC: 20001-3770, USA.
31. Germirli F, Orhon D, Artan N (1991) Assessment of the initial inert soluble COD in industrial wastewater.Water Science and Technology 23: 1077-1086.
32. Sun J, Qiao L, Sun S, Wang G (2008) Photocatalytic degradation of orange g on nitrogen-doped TiO₂ catalysts under visible light and sunlight irradiation.Journal of Hazardous Materials 155: 312-319.[Crossref]
33. Huang M, Xu C, Wu Z, Huang Y, Lin J, et al. (2008) Photocatalytic discolorization of methyl orange solution by Pt modified TiO₂ loaded on natural zeolite.Dyes and Pigments77: 327-334.
34. Jia T, Wang W, Long F, Fu Z, Wang H, Zhang Q (2009) Fabrication, characterization and photocatalytic activity of La-doped ZnO nanowires.Journal of Alloys and Compounds484: 410-415.
35. Khatamian M, Khandar AA, Divband B, Haghighi M, Ebrahimiasl S (2012) Heterogeneous photocatalytic degradation of 4-nitrophenol in aqueous suspension by Ln (La³⁺, Nd³⁺ or Sm³⁺) doped ZnO nanoparticles.Journal of Molecular Catalysis A: Chemical365: 120-127.
36. Raza W, Haque MM, Muneer M (2014) Synthesis of visible light driven ZnO: characterization and photocatalytic performance. Applications of Surface Science322: 215-224.
37. Zheng Y, Chen C, Zhan Y, Lin X, Zheng Q, et al. (2007) Luminescence and photocatalytic activity of ZnO nanocrystals:  correlation between structure and property.Inorganic Chemistry 46: 6675-6682.
38. Bohle DS, Spina CJ (2009) Cationic and anionic surface binding sites on nanocrystalline zinc oxide: surface influence on photoluminescence and photocatalysis.Journal of the American Chemical Society131: 4397-4404.
39. Selvam NCS, Vijaya JJ, Kennedy LJ, (2013) Comparative studies on influence of morphology and La doping on structural, optical, and photocatalytic properties of zinc oxide nanostructures.Journal of Colloid and Interface Science407: 215-224.
40. Fox MA, Dulay MT (1993) Hetereogeneous photocatalysis.Chemical Reviews93: 341-357.
41. Korake PV, Dhabbe RS, Kadam AN, Gaikwad YB, Garadkar KM (2014) Highly active lanthanum doped ZnO nanorods for photodegradation of metasystox.Journal of Photochemistry and Photobiology B-Biology130: 11-19.
42. Suwanboon S, Amornpitoksuk P, Bangrak P, Muensit N (2013) Optical, photocatalytic and bactericidal properties of Zn1-xLaxO and Zn1-xMgxO nanostructures prepared by a sol–gel method.Ceramics International39: 5597-5608.
43. Kaneva N, Bojinova A, Papazova K, Dimitrov D (2015) Photocatalytic purification of dye contaminated sea water by lanthanide (La³⁺, Ce³⁺, Eu³⁺) modified ZnO.Catalysis Today252: 113-119.
44. Cassano AE, Alfano OM (2000) Reaction engineering of suspended solid heterogenous photocatalytic reactors.Catalysis Today58: 167-197.
45. Anandan S, Vinu A, Venkatachalam N, Arabindoo B, Murugesan V (2006) Photocatalytic activity of ZnO impregnated Hâ and mechanical mix of ZnO/Hâ in the degradation of monocrotophos in aqueous solution.Journal of Molecular Catalysis A: Chemical256: 312-320.
46. Anandan S, Vinu A, Mori T, Gokulakrishnan N, Srinivasu P, et al. (2007) Photocatalytic degradation of 2,4,6-trichlorophenol using lanthanum doped ZnO in aqueous suspension.Catalysis Communications8: 1377-1382.
47. Joseph JM, Destaillats H, Hung H, Hoffmann MR (2000) The sonochemical degradation of azobenzene and related azo dyes:  rate enhancements via fenton’s reactions.Journal of Physical Chemistry A104: 301-307.
48. Spadaro JT, Isabelle L, Renganathan V (1994) Hydroxyl radical mediated degradation of azo dyes: evidence for benzene generation.Environmental Science & Technology28: 1389-1393.
49. Galindo C, Jacques P, Kalt A (2000) Photodegradation of the aminobenzene Acid Orange 52 by three advanced oxidation processes: UV/H₂O₂, UV/TiO₂ and VIS/TiO₂: comparative mechanistic and kinetic investigations.Journal of Photochemistry and Photobiology A-Chemistry 130: 35-47.
50. Pala RGS, Metiu H (2007) The structure and energy of oxygen vacancy formation in clean and doped, very thin films of ZnO.Journal of Physical Chemistry C111: 12715-12722.
51. Battle PD, Cheetham AK, Goodenough JB (1979)A neutron diffraction study of the ferrimagnetic spinel NiCO₂O₄.Materials Research. Bulletin14: 1013-1024.
52. Roosta M, Ghaedi M, Daneshfar A, Darafarin S, Sahraei R,et al. (2014b) Simultaneous ultrasound-assisted removal of sunset yellow and erythrosine by ZnS:Ni nanoparticles loaded on activated carbon: optimization by central composite design.Ultrasonics Sonochemistry21: 1441-1450.
53. Verma S, Joshi HM, Jagadale T, Chawla A, Chandra R, et al. (2008) Nearly monodispersed multifunctional NiCO₂O₄ spinel nanoparticles: magnetism, infrared transparency, and radio frequency absorption.The Journal of Physical Chemistry C112: 15106-15112.
54. Wu Z, Zhu Y, Ji X (2014) NiCO₂O₄-based materials for electrochemical supercapacitors.Journal of Materials Chemistry A2: 14759-14772.
55. Xing YX, Zhan J, Liu ZL,Du CF (2017) Steering photoinduced charge kinetics via anionic group doping in Bi₂MoO₆ for e_cient photocatalytic removal of water organic pollutants.RSC Advances7: 35883-35896.

DOI: 10.31038/NAMS.2020312

Abstract

Marine litter is a widespread problem affecting all the oceans of the world. Plastics represent around 90% of marine litter, and it is estimated that there are between 15 and 51 trillion plastic particles floating on the surface of the oceans. The objectives of this study are to: (i) identify and characterize the main categories of floating items sampled in surface waters off the Cap Corse peninsula, (ii) provide estimates of the occurrence of floating items in this area, and (iii) get an overview of the potential areas of litter accumulation. We highlighted a heterogeneous distribution of floating litter as the plastic density characterizing the area between Bastia and Macinaggio (27 027 items/km²) was, on average, 2.31 times higher than the density estimated between Macinaggio and Pino (11 688 items.km²). Several studies highlighted that spatio-temporal variability of plastic densities and sizes of plastics (micro, meso, macroplastics) could be tightly linked with hydrodynamics and wind regime, distance to land, coastal human population and maritime traffic. Beyond the need to further raise awareness, providing more evidences and information regarding such marine pollution may hopefully foster urgent management strategies, whereby the most effective mitigation strategy implies reducing the input at its source.

Keywords

Plastic, marine litter, manta-net, Mediterranean Sea.

Introduction

Marine litter is a widespread problem affecting all the oceans of the world. Plastic pollution has gained attention by scientists and public perception in the last decade [1, 2]. Plastics represent around 90% of marine litter [3], and it is estimated that there are between 15 and 51 trillion plastic particles floating on the surface of the oceans [4].Global plastic production increased from 5 million tonnes in 1950 to 322 million tonnes in 2015 [5] It is considered that, on average, around 80–90% of ocean plastic comes from land-based sources, including via rivers, with a smaller proportion arising from ocean-based sources such as fisheries, ships and aquaculture. Plastic inputs from land to ocean was estimated to represent at least 4.8 to 12.7 million tonnes in the year 2010 [6]. Because of their abundance, durability and persistence, marine plastics constitute today a major threat to the marine environment [7].

The most visible impacts of marine plastics are the ingestion, suffocation and entanglement of hundreds of marine species, including species listed on the IUCN Red List as near threatened or above [8]. Microplastics (less than 5 mm in diameter), in particular, have recently become a source of concern, as their ingestion has been observed in a wide variety of taxa including zooplankton, marine invertebrates, fish, turtles, seabirds, and marine mammals [9–11]. Once ingested, microplastics can cause starvation, alterations in intestinal functions, a reduction in feeding capacity, energy reserves and reproductive output [12]. Also, contaminated preys can be consumed by predators leading to the transfer of plastics accross trophic levels [13]. Moreover plastics are polymers that may contain a large variety of chemical additives and contaminants, including organic pollutants and endocrine disruptors, that pollute the environment [14] and can be harmful to marine biota [15, 16].

Floating marine litter also plays an important role in the spread of marine or terrestrial organisms, including the dispersal of invasive species that may pose a threat to local ecosystems [17]. Also, the hydrophobic nature of plastics stimulates the formation of biofilms and allows the establishment of numerous organisms, called “epiplastic” organisms, which constitute a new marine ecosystem called “plastisphere”[18]. It can host different groups, in particular microbial organismsincluding pathogenic viruses or bacteria but also fungi, algae, molluscs, cnidarians, and crustaceans [19–21]. The surge in the number of litter introduced into the marine environment currently offers a great variability of objects that may serve as « novel habitat » or as « hitch-hiking raft »  [22, 23].

In addition to causing harm and threatening marine ecosystems, marine litter, especially plastic, can also negatively affect human wellbeing, food security and socioeconomic sectors such as tourism, aquaculture, fishing and navigation [24-26].

The Mediterranean Sea is considered as a biodiversity hotspot [27], besieged by multiple human pressures. Indeed, its shores are home to around 10% of the world’s coastal population and around 100 million people live within 10 km of the coast [28]. Moreover, the Mediterranean basin is one of the busiest shipping routes in the world and receives the waters of densely populated watersheds, e.g. the Nile, the Ebro, the Po [29]. It is therefore not surprising that the basin is nowadays described as one of the areas most affected by marine litter in the world, whereby the average density of plastic as well as its frequency of occurrence throughout the basin were comparable to accumulation zones in ocean gyres [29–31]. In Europe, various regulatory directives have been put in place to limit and reduce this pollution, such as the Marine Strategy Framework Directive (MSFD). In order to define the concept of « Good Environmental Status », the MSFD proposed 11 descriptors including descriptor n° 10 describing good status for marine litter as follows: “Properties and quantities of marine litter do not cause harm to the coastal and marine environment” [32]. Also new knowledge on this topic may have impact in the implementation of other environmental regulations such as the new European Strategy for Plastics in a Circular Economy (COM/ 2018/028 final), which has recently agreed on banning certain single use plastic (SUP) items.

To develop and validate indices of ecosystem status or pollution, it is necessary to have access to areas with low human impact and then validate them along local pressure gradients [33]. Corsica, is a privileged area in the North-Western Mediterranean. It is at the centre of one of the most touristic regions in the world still sheltered from heavy pollution of anthropic origin. This area should come closer to the concept of Good Ecological Status in terms of pollution by plastic marine litter. Our study intends to provide further information on marine litter regarding the northeastern waters of Corsica, part of the Cap Corse and Agriate Marine Natural Park (PNMCCA). This area is also comprised within the Pelagos Sanctuary, a large marine area subject to an agreement between Italy, Monaco and France for the protection of marine mammals.

In detail, the objectives of this study are to: (i) identify and characterize the main categories of floating items sampled in surface waters off the Cap Corse peninsula, (ii) provide estimates of the occurrence of floating items in this area, and (iii) get an overview of the potential areas of litter accumulation.

Material and method

In August 2019, the Corsican Blue Project team carried out a marine litter sampling campaign  within the perimeter of the Cap Corse and Agriate Marine Natural Park (PNMCCA). A total of six manta-net tows were conducted along the coast of the Cap Corse peninsula between Bastia and Pino, including three transects along the eastern coast (Bastia-Macinaggio) and three transects along the northwestern coast (Pino-Macinaggio). The manta-net, characterized by a rectangular opening of 86 x 46 cm and a net opening of 330 μm, was deployed at the surface beyond the boats’ wake at an average speed of 2.2 to 2.4 knots and for about 30 min per tow. General characteristics of each transect were recorded. After each sampling, the entire net was thoroughly rinsed with seawater from the opening to the collection bag to ensure that all the debris were concentrated in the cod end before being retrieved.

Collected samples were sent for analysis to the STARESO (STAtion de REcherches Sous-marines et Océanographique) research station.In the laboratory, the samples were rinsed with seawater on a 300 μm sieve.Then the mesh was rinsed with tap water and the sample was collected. Sampling consists of direct extraction from the environment of items that are recognizable by the naked eye. Several steps have been taken to separate the litter from the biological matrix and water [34]. Litter was manually separated from natural debris by removing the largest pieces of biological material (leaves, algae, wood …) and rinsing them with water that underwent a second sorting to avoid any loss of debris [35]. Items that were visually identified as litter were collected using fine forceps and then counted. To avoid misidentification and underestimation of microplastics it is necessary to standardize the plastic particle selection, following certain criteria to guarantee proper identification [34]. Plastics were identified according morphological characteristics and physical response features (e.g. response to physical stress; microplastics were bendable or soft, colors) [34, 36]. Identified plastic items were measured over their largest cross-section (total length) in order to be classified into three categories: microplastics (<5 mm), mesoplastics (5-200 mm) and macroplastics (> 200 mm) [37]. Plastic items were also classified, as proposed by [21], into six categories according to their visual characteristics:

1. Fragment: this category is generally the most abundant. They are rigid, thick, with sharp, pointed edges and an irregular shape. They can be of different colors.

2. Film: they also appear in irregular shapes, but compared to the fragments, they are fine and flexible and generally transparent.

3. Pellet: they are generally from the plastics industry. These are irregular round shapes, about 5 mm in diameter. They are generally flat on one side and can be of different colors.

4. Granule: they have a regular round shape and generally smaller, around 1 mm in diameter. They appear in natural colors (white, beige, brown).

5. Filament: these are, with the fragments, the most abundant type of particles. They can be short or long, with different thicknesses and colors.

6. Foam: they most often come from large particles of polystyrene foam. They have a soft, irregular shape and are white to yellow in color.

The surface of the surveyed area was estimated by multiplying the observation width by the transect distance, and litter density (items/km²) was calculated by dividing the items count with the surveyed area surface [29].

Results and discussion

In this study, plastic litter was encountered in 100% of the hauls made off Cap Corse, representing a total of 238 itemsand a mean density of 19357 items/ km².

We highlighted a heterogeneous distribution of floating litter as the plastic density characterizing the area between Bastia and Macinaggio (27 027 items/km²) was, on average, 2.31 times higher than the density estimated between Macinaggio and Pino (11 688 items.km²) (Fig.1). Several studies highlighted that spatio-temporal variability of plastic densities and sizes of plastics (micro, meso, macroplastics)could be tightly linked withhydrodynamics and wind regime [31, 38], distance to land [29, 39], coastal human population [40, 41] and maritime traffic [23]. In our case, the difference in densities found between the regions Bastia-Macinaggio and Macinaggio-Pino, could be consistent with coastal human population pressurewhereby the area with an estimated higher density is located close to Bastia, a city of more than 44 000 inhabitants.As an example, a sampling campaign conducted in the Ligurian Sea, mainly along the French continental coast, reported that highest densities were found in front of Nice [40]. Corsica has been identified as a reference area where contamination by microplastics was lower compared to other regions off the French Mediterranean coasts (e.g. Antibes, Marseille, Toulon) [42].

Figure 1. Average concentration of plastic litter, expressed as debrits per km², in surface water off Cap Corse.

Moreover, the area between Corsica and Elba has been identified as displaying the highest natural marine debris concentration throughout the Central Western Mediterranean, suggesting therefore that the area is under great influence of terrigenous input [43]. However, the identification of the source of the collected litter remains challenging without the consideration of dispersion models, and items being subject to surface water circulation [44] may drift from their source of input. Indeed, studies investigating on debris transport and dispersion due to marine circulation strongly suggest that floating litter might drift during periods ranging from a few weeks to several years before eventually sinking or beaching [45]. Taking into account thatthe Tyrrhenian Sea has been identified as an important accumulation zone [23] and that the eastern coast of Corsica  is under the influence of the Tyrrhenian sea current which flows along the Italian coast before entering the Corsica channel [46] it is very likely that the area between Bastia and Macinaggiois supplied by debris drifting northwards.

It remains particularly challenging to compare our litter concentrations with those reported inother studies given the large variety of sampling methodologies. However, based on the same sampling methodology, sampled in the northern coast of Corsica, at the same season and at a rather similar distance to land, and concentrations were found to be in a same order of magnitude, ranging from tens of thousands to hundreds of thousands of items per square kilometer [30, 31, 40] (Table 1). However, in our case, densitieswere overall lower, andhigherdensities in the Bay of Calvi might be linked tothe different demographic pressures. Indeed, during summer season, population in Calvi may rise up to 60 000 inhabitants while the northwestern part of Cap Corse does not have such large population centre. However, these densities might also be linked to thedifference inwind stress during the sampling campaign [38], and also, as previously mentionned, to the water circulation properties of the considered coastal areas.

Table 1. Literature data on floating plastic densities for stations along the northen Corsican coast.

Regarding size classes, 62% of the total number of items was smaller than 5mm (microplastics), 26% measured between 5 and 200mm (mesoplastics), and 12% was bigger than 200mm (macroplastics) (Fig.2). In other words microplastics were 2.4 times more abundant than mesoplastics,and 5.3 times more abundant than macroplastics (> 200 mm).Such proportion is very similar to the study carried out in the Bay of Calvi, reporting that microplastics, mesoplastics and macroplastics accounted for 54%, 28% and 18% of total collected items, respectively [38].

Figure 2. Size class of the litter found during the sampling campaign.

Beyond the size-based classification,items were additionnaly classified according to their shape and appearance and it was found that fragments were largely predominant (62%), followed by films (26%), filaments (9%), foams (2%) and pellets (1%) (Fig.3). No granule was found. Such proportion is consistent with previous results reported [39] and it was highlighted that the proportion of fragments was especially important in the north of Corsica compared to other stations throughout the Western Mediterranean Sea [31].

Figure 3. Main categories of litter found during the sampling campaign.

While on land, the island of Corsica is currently facing a major litter crisis,  due to overloaded garbage dumps, its coastal marine waters are not exempted from marine litter pollution. In this context and despite a relatively small sampling effort compared to other major sea campaigns, our study provides information regarding the type and occurrence of marine litter and plastics in the local coastal waters off the Cap Corse peninsula. While the average density and types of litter items were in agreement with other comparable studies, we highlighted a potential heterogeneous distribution within the Cap Corse marine waters, with higher densities found in the transects of the eastern coast than in those from the northwestern area of the peninsula. To this, different explanations were proposed, such as terrestrial inputs and the influence of large population centres or the influx of debris through the marine circulation and wind forcing.

Conclusion

We highly advise further marine litter sampling campaigns to be conducted within these waters that are positionned at a bio-geogaphical crossroad where several major currents meet. Moreover such study would be scientifically supported and potentially technically facilitated given the fact that the Cap Corse waters are included within the Cap Corse and Agriate Marine Natural Park and the Pelagos Sanctuary. Field information could, for example, be useful in the risk assessment of whale or mammals exposure to microplastics by validating simulated plastic distribution, in parallel to whale habitat models [47]. Moreover, Corsica is also subject to high tourist flows during summer which also translates into an increment of boating, maritime shipping and passengers transport. It would therefore be advisable to conduct sampling all year round, to highlight any annual variability. This study also intends to promote and encourage further « participatory sciences » by which citizens are involved in data collection, in association with the scientific world.

Beyond the need to further raise awareness, providing more evidences and information regarding such marine pollution may hopefully foster urgent management strategies, whereby the most effective mitigation strategy implies reducing the input at its source.

Acknowledgments

This research was funded by the “Agence de l’eau Rhône Méditerranée Corse” and the “Collectivité de Corse” (CdC), as part of the STARE CAPMED project research.

References

1. De-la-Torre GE (2019) Microplastics: an emerging threat to food security and human health. J Food Sci Technol.
2. Gabriel EnriqueDe-la-Torrea, Diana CarolinaDioses-Salinas, JasminMarlithCastro, RosabelAntay, NaomyYupanquiFernández, et al (2020) Abundance and distribution of microplastics on sandy beaches of Lima, Peru. Marine Pollution Bulletin 151:110877.
3. Laurent C.M.Lebreton, Joost van der Zwet, Jan-Willem Damsteeg, Boyan Slat, Anthony Andrady, et al (2017) River plastic emissions to the world’s oceans. Nat Commun 8:15611.
4. Van Sebille E, Wilcox C, Lebreton L,Maximenko N, Denise Hardesty B, et al (2015) A global inventory of small floating plastic debris. Environ Res Lett 10:124006.
5. PlasticsEurope E (2016) Plastics-the facts 2016. An analysis of European plastics production, demand and waste data. Brussels Belgium.
6. Jambeck JR, Geyer R, Wilcox C,Theodore Siegler R, Perryman M, et al (2015) Plastic waste inputs from land into the ocean. Science 347: 768-771.
7. Gregory MR (2009) Environmental implications of plastic debris in marine settings—entanglement, ingestion, smothering, hangers-on, hitch-hiking and alien invasions. Philosophical Transactions of the Royal Society B.Biological Sciences 364: 2013-2025.
8. Gall SC, Thompson RC (2015) The impact of debris on marine life. Marine pollution bulletin 92: 170-179.
9. Amélineau F, Bonnet D, Heitz O, Mortreux V, Harding AMA, et al (2016) Microplastic pollution in the Greenland Sea: Background levels and selective contamination of planktivorous diving seabirds. Environmental Pollution 219: 1131-1139. [Crossref]
10. Duncan EM, Broderick AC, Fuller WJ,Galloway ST, Godfrey HM,et al (2019) Microplastic ingestion ubiquitous in marine turtles. Global change biology 25: 744-752.
11. Lusher AL, Hernandez-Milian G, O’Brien J,Berrow S, O’ Connor I et al (2015) Microplastic and macroplastic ingestion by a deep diving, oceanic cetacean: the True’s beaked whale Mesoplodonmirus. Environmental Pollution 199: 185-191.
12. Guzzetti E, Sureda A, Tejada S, Faggio C (2018) Microplastic in marine organism: Environmental and toxicological effects. Environmental toxicology and pharmacology 64: 164-171.
13. Setälä O, Fleming-Lehtinen V, Lehtiniemi M (2014) Ingestion and transfer of microplastics in the planktonic food web.Environmental pollution 185: 77-83.
14. Gallo F, Fossi C, Weber R, Santillo D, Sousa J, et al (2018) Marine litter plastics and microplastics and their toxic chemicals components: the need for urgent preventive measures. Environmental Sciences Europe 30:13. [Crossref]
15. Hellmann JJ, Byers JE, Bierwagen BG, Dukes JS (2008) Five Potential Consequences of Climate Change for Invasive Species. Conservation Biology 22: 534-543. [Crossref]
16. Orsi AH, Whitworth III T, NowlinJr WD (1995) On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Research Part I: Oceanographic Research Papers 42: 641-673.
17. Miralles L, Gomez-Agenjo M, Rayon-Viña F, Gyraite G, Garcia-Vazquez E (2018) Alert calling in port areas: Marine litter as possible secondary dispersal vector for hitchhiking invasive species. Journal for Nature Conservation 42: 12-18.
18. Zettler ER, Mincer TJ, Amaral-Zettler LA (2013) Life in the “Plastisphere”: Microbial Communities on Plastic Marine Debris. Environ SciTechnol 47: 7137-7146.
19. Quero GM, Luna GM (2017) Surfing and dining on the “plastisphere”: Microbial life on plastic marine debris. Advances in Oceanography and Limnology.
20. Reisser J, Shaw J, Hallegraeff G,Proietti M, David Barnes KA, et al (2014) Millimeter-Sized Marine Plastics: A New Pelagic Habitat for Microorganisms and Invertebrates. PLoS ONE 9.
21. Viršek M, Palatinus A, Koren Š, Peterlin M, Horvat P,et al (2016) Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis. JoVE 16: 118.[Crossref]
22. Aliani S, Molcard A (2003) Hitch-hiking on floating marine debris: macrobenthic species in the Western Mediterranean Sea. Hydrobiologia 503: 59-67.
23. Mansui J (2015) Observation etmodélisation des macro-déchets en merMéditerranée, de la large échelle aux échellescôtière et littorale.
24. Rochman CM, Browne MA, Halpern BS, Hentschel BT, Hoh E, et al (2013) Classify plastic waste as hazardous. Nature 494: 169-171.
25. Sheavly SB, Register KM (2007) Marine debris & plastics: environmental concerns, sources, impacts and solutions. Journal of Polymers and the Environment 15: 301-305.
26. Werner S, Budziak A, Van Franeker JA, GALGANI Francois, HANKE Georg, et al (2016) Harm caused by marine litter.
27. Giorgi F (2006) Climate change hot‐spots. Geophysical research letters 33.
28. Richards LM (2008) CIESIN, Center for International Earth Science Information Network at Columbia University. Reference Reviews.
29. Cózar A, Sanz-Martín M, Martí E, Ignacio González-Gordillo J, Bárbara Ubeda, et al (2015) Plastic accumulation in the Mediterranean Sea. PLoS One 10.
30. Amandine Collignon, Jean-Henri Hecq, François Glagani, Pierre Voisin, France Collard, et al (2012) Neustonicmicroplastic and zooplankton in the North Western Mediterranean Sea. Marine pollution bulletin 64: 861-864.
31. Faure F, Saini C, Potter G, Galgani F, de Alencastro LF, et al (2015) An evaluation of surface micro-and mesoplastic pollution in pelagic ecosystems of the Western Mediterranean Sea. Environmental Science and Pollution Research 22: 12190-12197. [Crossref]
32. EU Commission (2010) On criteria and methodological standards on good environmental status of marine waters. Official Journal of the European Union, L 232: 14-24.
33. Gobert S, Richir J (2019) Des indices pour la définition de l’état des masses d’eau en milieu marin: mises au point, applications et aide à la gestion. Geo-Eco-Trop 353-364.
34. Hidalgo-Ruz V, Gutow L, Thompson RC, Thiel M (2012) Microplastics in the Marine Environment: A Review of the Methods Used for Identification and Quantification. Environ SciTechnol 46: 3060-3075.
35. Herrera A, Garrido-Amador P, Martínez I,María Dolores Samper, Juan López-Martínez, et al (2018) Novel methodology to isolate microplastics from vegetal-rich samples. Marine pollution bulletin 129:61-69.
36. Desforges J-PW, Galbraith M, Dangerfield N, Ross PS (2014) Widespread distribution of microplastics in subsurface seawater in the NE Pacific Ocean. Marine Pollution Bulletin 79: 94-99.
37. Ana LacerdaLdF, Rodrigues lds, Erik van Sebille, Fábio Rodrigues L, LourençoRibeiro, et al (2019) Plastics in sea surface waters around the Antarctic Peninsula. Scientific reports 9:1-12.
38. Collignon A, Hecq J-H, Galgani F, et al (2014) Annual variation in neustonic micro-and meso-plastic particles and zooplankton in the Bay of Calvi (Mediterranean–Corsica). Marine Pollution Bulletin 79: 293-298.
39. Zeri C, Adamopoulou A, Varezić DB,Fortibuoni T, KovačViršek M, et al (2018) Floating plastics in Adriatic waters (Mediterranean Sea): From the macro-to the micro-scale. Marine pollution bulletin 136:341-350.
40. Pedrotti ML, Petit S, Elineau A,Bruzaud S, Crebessa JC, et al (2016) Changes in the floating plastic pollution of the Mediterranean Sea in relation to the distance to land. PloS one 11.
41. Ruiz-Orejón LF, Sardá R, Ramis-Pujol J (2016) Floating plastic debris in the Central and Western Mediterranean Sea. Marine Environmental Research 120: 136-144. [Crossref]
42. Gérigny O, Maryvonne H, Tomasino C, Galgani F (2018) Rapport IndicateurMicroplastiquesdans le cadre des campagnes Directive Cadre Eau MéditerranéeOccidentale (DCE) 2018.
43. Suaria G, Aliani S (2014) Floating debris in the Mediterranean Sea. Marine Pollution Bulletin 86: 494-504.
44. Millot C, Taupier-Letage I (2005) Circulation in the Mediterranean sea. The Mediterranean Sea29-66.
45. Zambianchi E, Trani M, Falco P (2017) Lagrangian transport of marine litter in the Mediterranean Sea. Frontiers in Environmental Science 5:5.
46. Gerigny O (2010) Hydrologieethydrodynamismedans les bouches de Bonifacio: mesures in-situ, modélisation, influence sur la biomasse. 441.
47. Fossi MC, Romeo T, Baini M,Cristina Panti, LetiziaMarsili, et al (2017) Plastic debris occurrence, convergence areas and fin whales feeding ground in the Mediterranean marine protected area Pelagos sanctuary: A modeling approach. Frontiers in Marine Science 4:167.