Abstract
The sustainability of the blue economy is at risk due to the harmful effects of marine plastic pollution on aquatic ecosystems and coastal communities. Plastics release toxic chemicals into the environment, pollute coastlines, and harm fisheries, aquaculture, and shellfish beds. The practical implementation of the recently proposed Integrated Marine Debris Observing System requires comprehensive knowledge of pollution inputs, which vary in space, time, and intensity. Plastic emissions from coastal populations in the Mediterranean were identified as the primary source of plastic pollution in the basin. Data from the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP) were used as indicators for population-related plastic fluxes into the Mediterranean. An original algorithm distributed a predefined total annual plastic flux proportionally to nighttime lights along a coastal belt, also considering country-specific correction factors based on Human Development Indices. The average plastic fluxes for 2015–2024, measured in kg per day, were represented at a horizontal resolution of 15 arcseconds. Our analysis showed that plastic fluxes from coastal populations of Italy, Spain, and Egypt mainly contributed to the Mediterranean Sea. To demonstrate the main algorithmic features, the spatial variability of fluxes along Sicily’s coastlines was examined closely. Comparison of our results with the mass budget components obtained independently along the Barcelona coastline showed good consistency. Following the international FAIR principles (Findable, Accessible, Interoperable, Reusable), the data is freely available and ready for use for modeling and source-specific observation planning. For the user’s convenience, two datasets were provided: one with and one without the country-specific correction. This allows for quick re-normalization of flux values when new information about the total annual fluxes or correction principles becomes available. Our datasets require caution when used, as they are not fully validated products but rather experimental. The reported methodology is applicable to any area and allows further implementation related to advances in the representation of plastic sources.
1 Introduction
The blue economy is considered a new frontier for economic development, which targets the conservation and sustainable use of oceans, seas, and marine resources. The Mediterranean blue economy is predicted to grow in ports and maritime equipment, fishing, marine aquaculture, shipbuilding, desalination sites, and maritime and coastal tourism (). However, the economic benefits of the blue economy are accompanied by threats to the health of marine ecosystems, including rising sea temperatures and sea levels, biodiversity and habitat loss, overfishing, and anthropogenic pollution.
The sustainability of the blue economy is at risk due to the destructive effects of marine plastic pollution on aquatic ecosystems and the businesses that depend on them. Plastics adsorb and release toxic chemicals into the environment (), penetrate the food chain (), alter the physiology of various taxa (), contaminate sediments, and damage fisheries, aquaculture, and shellfish beds ().
We focused on the Mediterranean Sea due to its vital economic significance and the growing anthropogenic threats it faces. Plastic pollution is especially intense in the basin because of its landlocked geography and limited exchange with nearby water bodies (). To sustain the blue economy and address these threats in the Mediterranean, effective management strategies for plastic pollution are necessary. Developing these strategies requires a deep understanding of the current state of plastic pollution and trends.
The projected increase in plastic consumption inevitably leads to greater pollution of the marine environment due to the lack of an international response (). Since the scale of plastic pollution exceeds the management capacity of any single country, region, or sector, it requires a dedicated global framework. The proposed Global Plastics Treaty was intended to cover the entire lifecycle of plastics, from upstream (production) and midstream (design) to downstream (waste management) stages. However, the treaty has not yet been signed because the latest round of UN negotiations, held in Geneva in August 2025, failed to reach a consensus.
To combat plastic pollution the Integrated Marine Debris Observing System was recently proposed (). Its practical implementation is based on comprehensive knowledge of pollution inputs, including where, when, and how much plastic is released. The ‘from-simple-to-complex’ principle, driven by accumulating knowledge, is commonly used to iteratively identify plastic sources.
In plastic pollution research, the Mismanaged Plastic Waste (MPW) concept, introduced by , is widely used, with MPW defined as ‘material that is either littered or inadequately disposed.’ The mechanisms of plastic mobilization include entry into the ocean via inland waterways and wastewater outflows, as well as transport by wind or tides.
Conventionally, the sources are divided into land-based and marine inputs (). Following , global plastic sources are classified into three groups: plastics from coastal populations (around 40% of the total flux), plastics from inland populations transported by rivers (∼40%), and maritime inputs along shipping routes (∼20%). Therefore, the so-called plastic input ratio could initially be defined globally as 40/40/20 (). To date, riverine sources have been the most extensively studied because they are based on numerous observations [e.g (; ; ; )]. The greatest uncertainty originates from maritime inputs that lack empirical data. Parametrizing the coastal population-related sources is also quite challenging, as direct measurements of plastic fluxes tend to be site-specific and scarce ().
For modeling purposes, we linked previously plastic fluxes from the coastal population to the 505 largest Mediterranean nearshore cities, which comprised more than 20,000 inhabitants (). Another approach was performed by , who used NASA’s gridded population density data. Then, the densities within the predefined coastal zone were multiplied by the estimated specific mismanaged plastic waste generation rate in kg per person per day for each coastal country, as reported by .
The goal of our study is to estimate plastic inputs from the Mediterranean coastal population by directly using satellite-derived observations. We propose this alternative approach based on the hypothesis that humans cause both plastic and light pollution. Nighttime light (NTL) signals observed from space visually depict human presence on the coasts and can serve as indicators of plastic entering the ocean. We utilized the data from the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP) as proxies for population-related plastic inputs. NTL imagery offers high-resolution spatial data on a daily basis (), unlike census-based population datasets that are updated every five years. Not only does NTL provide information about human settlements and urbanization (; ), but also about infrastructure and land use (; ; ).
In this study, three main assumptions have been made: (1) the annual plastic flux from the coastal Mediterranean population is set at 1,000 tons (); (2) a 20-kilometer coastal zone is where plastic emissions from coastal populations originate and reach the Mediterranean; (3) country-specific plastic emission factors are based on Human Development Indices (HDI) introduced by .
Unlike riverine fluxes, population-related plastic fluxes into the Mediterranean Sea have never been published. For the first time, we provide gridded data and an algorithm for their production, resolving the current research gap.
The paper is organized as follows: Section 2 explains the data processing. Section 3 presents the results and their implications for various Mediterranean regions. In Section 4, we discuss the challenges of validation, parameter variation, and method limitations. Lastly, Section 5 summarizes the key findings and outlines directions for future research.
2 Materials and methods
Satellite-derived nighttime lights (NTL) are well-known data products that measure brightness from space and show the locations of artificial lighting. The method has evolved over more than 50 years and has required significant effort to reduce background noise. Solar and lunar contamination, data degradation caused by cloud cover, and features unrelated to electric lighting (e.g., fires, flares, volcanoes) were among them (). We process the monthly cloud-free composites1 provided by the Earth Observation Group at the National Geophysical Data Center in Boulder, Colorado, US ().
The corresponding workflow is schematically represented in Figure 1. First, NTL fields were clipped for the Mediterranean Sea. Then, an average NTL map covering the period 2015–2024 was created at a native horizontal resolution of 15 arcseconds (Figure 2). The resulting NTL distribution is highly irregular. The most prominent coastal metropolises, urban and economic centers are clearly visible: Algiers (Algeria); Tunis (Tunisia); Tripoli, Benghazi (Libya); Alexandria (Egypt); Tel Aviv-Yafo (Israel); Izmir (Türkiye); Athens (Greece); Rome, Naples, (Italy); Marseille (France); Barcelona, Valencia, and Alicante (Spain). Generally, the Western part of the coastal Mediterranean exhibited higher NTL levels than the Eastern part. Interestingly, the spatial gradients of NTL along the shores of Libya and Egypt are much sharper if we compare them with those of other Mediterranean countries.
Figure 1
Figure 2
An original algorithm was created to aggregate the spatial light pollution signals into a single-cell coastal belt along the basin (Figure 3A). Initially, for each cell i of the NTL field Li, the minimum distances over all the coastal cells k were calculated (Figure 3B):
Figure 3
Then, NTL signals Li were modified to using the predefined distance-dependent function
where R1 is the maximum influence distance, and R2 is the minimum influence distance.
Measuring in km and assuming R1 = 2 km and R2 = 20 km, the dimensionless function (Figure 4) preserves the original NTL values at a distance of 2 km from the coastlines. A gradual decrease extends offshore, reaching up to 20 km inland. Consequently, the NTL signals that were located more than 20 km away from the shore were excluded from the analysis.
Figure 4
While designing the distance-dependent function F(r), we adhered to the following principles:
The function should be easy to interpret.
It should be a positive-definite, fairly smooth function with transparent asymptotic behavior.
The number of parameters should be kept as low as possible to avoid overfitting in subsequent modeling.
In our approach, we use four parameters: 1, 0, R1, and R2. Obviously, F(r) in Equation 3 is not the only possible parameterization. In the distance-decay interval, the function can be exponential, for example.
Next, the modified light pollution values for each cell i were distributed over the coastal cells k according to the same distance-dependent function rewritten for brevity (Figure 3C):
In Equation 4, the conservativity requirement is satisfied for each i. Distribution from a cell i is schematically represented in Figure 3C. The thicker the arrows, the larger the contributions. Obviously, the cell closest to the coastline emits the most input, but other cells also contribute.
The algorithm (Equations 1–4) enabled the design of a conservative alongshore distribution Lk even when faced with complex coastal geometry. Additionally, uncertainties concerning the selection of the terrestrial belt-of-influence width, which typically ranges from 10 km () to 50 km (; ; ), were minimized.
Seaward plastic flux Pk [kg day−1] from coastal cell k is assumed to be proportional to the coastal light pollution Lk[nW cm−2 sr−1] and cell area Sk:
where am represent the country-specific correction factors listed in Table 1, following . For the Mediterranean countries, the values am range from 0.046 to 0.073.
Table 1
| Country | am | Country | am | Country | am |
|---|---|---|---|---|---|
| Morocco | 0.062 | Syria | 0.062 | Slovenia | 0.046 |
| Algeria | 0.057 | Türkiye | 0.051 | Italy | 0.047 |
| Tunisia | 0.058 | Cyprus | 0.047 | Malta | 0.046 |
| Libya | 0.059 | Greece | 0.047 | France | 0.047 |
| Egypt | 0.058 | Albania | 0.053 | Spain | 0.047 |
| Israel | 0.046 | Montenegro | 0.051 | ||
| Lebanon | 0.060 | Croatia | 0.049 |
Mediterranean country-specific correction factors after .
These dimensionless factors account for the country-specific differences in plastic emissions into the Mediterranean Sea. They could be derived from at least two different concepts. The former is based on Mismanaged Plastic Waste (MPW) values proposed by and then modified by ; ; . The latter relies on the Human Development Indices (HDIs), introduced by . Within the MPW concept, behavioral differences in plastic releases into the ocean were linked to a country’s economic status. While HDIs account for the combined effects of economic prosperity, education levels, and living standards. The comparison of correction factors obtained from different works is presented in Figure 5. It should be noted that the MPW concept proposed by was the first chronologically. In this work, we employed the HDI approach proposed by as a second approximation, based on a more accurate validation of the underlying data. Additionally, the HDI-based correction resulted in smoother country-specific normalization. In our subjective opinion, it looks more realistic for the culturally homogeneous Mediterranean coastal population.
Figure 5
The constants of proportionality in Equation 5 were derived from the total population-related Mediterranean annual plastic flux of tons year−1. Identically to the country-specific correction, this value is also highly uncertain. Furthermore, it varies by three orders of magnitude (Figure 6).
Figure 6
Similarly, the first estimate was derived from and was later reassessed in ; ; , and . The second approximation was computed by , who closed the Mediterranean plastic mass budget by inverse modeling of plastic sources and sinks. They determined that annually, between 2,100 and 3,400 tons of plastic were released. In terms of percentages, 61% come from the coastal population, 32% from rivers, and 6% from fisheries. That yields 1,281–2,074 tons year−1 from the coastal population. suggested values that were quite close to these limits.
Importantly, dedicated plastic observations and modeling in the Barcelona coastal region () also revealed much lower fluxes than those reported by . Given the crucial observational limitations () and the absence of reliable in situ gridded data on plastic distribution in the basin, an order-of-magnitude estimate of 1,000 tons year−1 has been used in this work.
Since our dataset is primarily designed for modelers who simulate the transport of plastic at sea, the plastic fluxes are assigned to the sea cells adjacent to the coast (Figure 3A). For the user’s convenience, two output options are available: with and without country-specific correction. This allows quick re-normalization of flux values when new information about the total annual population-related Mediterranean plastic flux and country-specific correction factors becomes available.
3 Results
At a horizontal resolution of 15 arcseconds (∼ 400 m in the Mediterranean), seaward plastic fluxes related to the coastal population were computed (Figure 7).
Figure 7
Previously, we used a delta-function representation for the largest Mediterranean coastal cities within a 10 km coastal belt (). The current distribution appears smoother and does not have zero values in the domain. Hotspots where plastic fluxes exceed 0.3 kg day−1 are located along the European Mediterranean coast, including Barcelona (Spain), Athens (Greece), and Naples (Italy). Along the Asian coast, two Israeli clusters and Beirut (Lebanon) are clearly visible. In North Africa, cities such as Tangier (Morocco), Oran, Algiers, and Skikda (Algeria), Tunis (Tunisia), Tripoli and Benghazi (Libya), and Alexandria (Egypt) exhibit the highest plastic fluxes. Conversely, the Aegean and Adriatic Islands emit much less plastic (0.003–0.07 kg day−1).
Integrated along the coastline, plastic fluxes from coastal populations across Mediterranean countries can be compared (Figure 8). Within the HDI concept (), the largest contributions, at 586.0 kg day−1, come from Italy, followed by Spain (306.0 kg day−1) and Egypt (282.2 kg day−1). The smallest fluxes are emitted from Slovenia (3.5 kg day−1), followed by Montenegro (7.9 kg day−1) and Syria (10.1 kg day−1), despite its medium level of human development (Figure 5).
Figure 8
Visualizing high-resolution data across the Mediterranean basin is challenging. To demonstrate local-scale inhomogeneities, we use Sicily as an example. The primary features of the algorithm are shown in Figure 9.
Figure 9
We started with the NTL distribution (Figure 9A). The area affecting plastic fluxes is shown in Figure 9B, where a filtering effect of the function F(r) (Equation 3, Figure 4) is clearly visible. To better emphasize regional differences, plastic fluxes are shown at a more detailed scale (Figure 9C). Lastly, the graph of specific plastic fluxes along the shoreline (Figure 9D) allows for a visual comparison of contributions from Sicilian cities (Palermo, Catania, Syracuse, Gela, Trapani, and Messina) and specific land-use patterns.
4 Discussion
4.1 Challenge of validation
Validating basin-scale plastic fluxes from coastal populations is challenging. By definition, plastic flux is the time derivative of plastic mass concentration. Satellite-based plastic detection would be invaluable for such estimates, but we currently lack a well-established methodology. In fact, remote sensing detection is critically limited by the size and concentration of plastic on the sea surface, which are too low in the Mediterranean to significantly influence the water-surface reflectance signal (). A very promising optical sensing method has recently been developed by . The recent application by highlighted the growing importance of further proxy modeling to address the scarcity of direct measurements of both plastic concentrations and fluxes.
Currently, we have to rely on in situ monitoring or shipboard observations. These measurements represent quasi-instantaneous samplings, which hardly allow calculation of the derivative. Furthermore, many studies have reported only the abundance of plastic in item-count units [e.g., ; ], thereby introducing additional uncertainty arising from the count-to-mass conversion.
Thus, rigorous studies are necessary to obtain robust ground-truth estimates of plastic fluxes from the coastal population. Such an original approach was developed by , who closed the mass budget for plastic debris in the coastal Barcelona area. Monitoring was integrated with numerical modeling in the open-boundary domain. Manta trawl net sampling, measurements of combined sewer overflows, regression analysis to examine relationships between water discharges and plastic abundance, and Lagrangian particle tracking were conducted in the densely populated, industrial metropolitan area. Transport of virtual particles was driven by high-resolution hydrodynamics (sea currents and waves) and random-walk turbulence. Randomized particle beaching was also accounted for. As a result, the total discharged mass from the coastal Barcelona area was estimated at 2,538 ± 2,585 kg year−1, including inputs from combined sewer overflow, the port, land, river mouths, and the exported part ( () Table 3). After clipping the study area from , we extracted seven grid cells from our dataset. By integrating plastic fluxes across these cells, we found that the area emits approximately 868.6 kg year−1 of plastic, consistent with the range reported by . Plastic flux emitted from that Barcelona coastline segment () accounts for ∼0.1% of the total population-related Mediterranean plastic flux.
4.2 Parameter variation
To evaluate the associated uncertainty levels, parameter variation was conducted as part of the sensitivity analysis. We imposed the total annual flux parameter from the Mediterranean coastal population of tons year−1 to emphasize the order-of-magnitude accuracy of the current knowledge of this variable. Expectedly, a three-order-of-magnitude uncertainty (Figure 6) penetrates from the total flux to the obtained gridded fluxes. Nevertheless, since this parameter is linear, the dataset can be easily renormalized using a simple scaling factor. The resulting map in Figure 7 will not change, except for the scale bar, which must be renormalized. The axis in Figure 8 needs to be modified in the same manner. We estimated variations in plastic fluxes from the Barcelona area () for total annual population-related Mediterranean plastic fluxes of 500 tons year−1 and 3,000 tons year−1 as well as R1 = 5 km and R2 = 50 km. The range of the observed plastic flux variations is so broad that it spans all variations in our NTL-based estimations (Table 2).
Table 2
| Variated parameter | Annual plastic flux from the coastal population of the Barcelona area |
|---|---|
| Total annual plastic flux from the Mediterranean coastal population: | |
| 1,000 tons year−1 (scaling factor: 1) | 868.6 kg year−1 |
| 500 tons year−1 (scaling factor: 0.5) | 434.3 kg year−1 |
| 3,000 tons year−1 (scaling factor: 3) | 2,605.8 kg year−1 |
| Parameters of the distant-dependent function F(r): | |
| R1 = 2 km, R2 = 20 km | 868.6 kg year−1 |
| R1 = 5 km, R2 = 50 km | 1,510.7 kg year−1 |
| Components of annual plastic mass budget in the Barcelona area (): Table 3 | |
| Combined sewer overflow (CSO) | 100 kg year−1 |
| Port | 744 kg year−1 |
| Land | 1,641 kg year−1 |
| CSO+Port+Land* | 2,485 kg year−1 |
| River mouths | 13 kg year−1 |
| Exported | 40 kg year−1 |
| Total | 2,538 ± 2,585 kg year−1 |
Results of parameter variation to be compared to the plastic mass budget components estimated independently for the Barcelona area by .
*Summarized by the authors.
Although the estimations corresponding to tons year−1 and R1 = 2 km, R2 = 20 km; and tons year−1 and R1 = 5 km, R2 = 500 km appear closer to the total and ‘CSO+Port+Land’ plastic fluxes from the Barcelona area, we believe that more observations are necessary to recalculate our basin-scale dataset with more accurate parameter tuning.
Country-specific correction factors significantly affect plastic flux values and reorder the integrals for coastal populations across different Mediterranean countries (Figure 10). This is particularly evident in the differences among Algeria, Tunisia, Libya, and Egypt on the African coast and among Greece, Italy, France, and Spain on the European coast of the Mediterranean. To avoid rhetoric beyond this study, we decided to publish an additional dataset without country-specific corrections, leaving the decision to users ().
Figure 10
To sum up, the total annual plastic flux from the Mediterranean coastal population, as well as the R1 and R2 values in the distance-dependent function F(r), can be considered freely tunable parameters. To properly tune them, it is necessary to have more background information. Biases among country-specific corrections reveal existing contradictions in the current understanding of behavioral differences among the different Mediterranean coastal populations.
4.3 Method limitations
The central hypothesis, that NTL intensity is proportional to plastic flux from coastal populations, is based on the two constituents: (1) artificial NTL is associated with human presence, and (2) human presence is linked to plastic pollution. Both issues were empirically tested and supported by the literature. The former was reported, for example, in and in . The latter was proven by ; , and . Our approach differs from the aforementioned works in that we implicitly accounted for the human population density.
However, there are many so-called confounding cases in which the logical connection between NTL and plastic pollution is broken. This is especially true for extreme events such as wars, humanitarian catastrophes, natural hazards, and human-made incidents. In these cases, our methodology does not work. Every extreme case should be treated uniquely, using Extreme Value Theory () rather than average-state concepts. On the one hand, small deviations from the central hypothesis could be treated as minor fluctuations in our ten-year averaged dataset. Conversely, biases may accumulate and motivate further model development and the formulation of new theories.
The 10-year NTL average is the first step in the temporal representation of gridded plastic fluxes. Seasonal and monthly products are planned for release after thorough validation and calibration of the averaged datasets in the Mediterranean. The lack of ground-truth data on plastic fluxes from the coastal population in the Mediterranean means our datasets are experimental rather than fully validated, metrics-based products. Consequently, they should be used with caution.
It is important to understand that we kept the high spatial resolution (∼400 m) of our dataset even though it is not accompanied by high predictive accuracy. This helps with further modeling, which typically requires re-gridding data to a user-defined mesh.
Since we currently do not have gridded ground-truth data on plastic fluxes in the Mediterranean, our approach primarily presents a spatial downscaling or allocation scheme rather than a predictive emission model.
Currently, the code is being run in the NCAR Command Language (NCL2). The monthly products will be provided with an upgraded Python-based code if the presented steady-state dataset is of interest to users.
5 Conclusions
A freely available dataset provides a new estimate of seaward plastic fluxes from the coastal Mediterranean population. For the first time, the algorithm for generating gridded data is described. Unlike previous assessments, the data are directly based on satellite-derived NTL observations. Given the high uncertainty in the underlying hypotheses, the dataset provides a second approximation to the MPW approach developed by and their early followers. Our results rely on the total annual Mediterranean plastic flux computed by and country-specific corrections based on Human Development Indices (). We confirm the significant roles of coastal megacities and urban-industrial agglomerations, such as Barcelona, Tel Aviv-Yafo, Athens, Naples, Alexandria, Tripoli, Beirut, and Tangier. Compared to earlier delta-function-like models (), the distribution of plastic fluxes appears smoother. The impact extends beyond major cities to suburbs and various land-use types. In the HDI framework (), the primary sources of plastic inputs from the coastal population are Italy, Spain, and Egypt, as shown by integrating flux data along the coastlines of these countries.
In the context of the proposed algorithm, the total population-related Mediterranean plastic flux and the F(r) parameters should be considered freely tunable parameters. The Mediterranean country-specific differences in MPW emissions remain understudied and require further independent research.
We advise caution when using our datasets, as they are not firmly validated products but rather experimental. Further testing and validation can be achieved through targeted source-focused observations and modeling, as was done by .
The work aligns with the international FAIR principles, which maximize accessibility and enable easy reuse. The methodology developed is applicable to any area and allows further implementation related to advances in the representation of plastic emission and progress in the Integrated Marine Debris Observing System ().
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.1594/PANGAEA.987840.
Author contributions
SL: Formal analysis, Methodology, Writing – original draft, Writing – review & editing. GC: Project administration, Writing – original draft, Writing – review & editing. SC: Investigation, Project administration, Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was conducted as part of the Space It Up Project funded by the Italian Space Agency (ASI) and the Ministry of University and Research (MUR) – contract n. 2024-5-E.0–CUP n. I53D24000060005.
Acknowledgments
The assistance provided by the PANGAEGA team () in data processing and storage is highly appreciated. The authors express their gratitude to the reviewers for their insightful comments and suggestions, which have greatly improved this manuscript.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Footnotes
1.^https://eogdata.mines.edu/products/vnl/ (Accessed online: December 2025).
2.^http://dx.doi.org/10.5065/D6WD3XH5 (Accessed online: April 2026).
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Summary
Keywords
plastic pollution, marine environment, NASA/NOAA nighttime lights, plastic fluxes, country-specific correction, Mediterranean Sea
Citation
Liubartseva S, Coppini G and Causio S (2026) Gridded plastic litter fluxes from the Mediterranean coastal population obtained from satellite-derived nighttime lights. Front. Mar. Sci. 13:1837939. doi: 10.3389/fmars.2026.1837939
Received
24 March 2026
Revised
27 April 2026
Accepted
30 April 2026
Published
18 May 2026
Volume
13 - 2026
Edited by
Olaf Duteil, Duteil Environmental Numerics, Germany
Reviewed by
Olga Lobchuk, Atlanticeskoe Otdelenie FGBUN Instituta Okeanologii imeni P P Sirsova Rossijskoj Akademii Nauk, Russia
Sabastian Simbarashe Mukonza, Pingtung University of Science and Technology Bookstore, Taiwan
Updates
Copyright
© 2026 Liubartseva, Coppini and Causio.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Svitlana Liubartseva, svitlana.liubartseva@cmcc.it
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