Screening and Quantification of Micro(Nano)Plastics and Plastic Additives in the Seawater of Mar Menor Lagoon

In this work a suspect-screening approach was employed to assess the polymers and plastic additives of micro(nano)plastics (NPL/MPLs) of size ranges from the nm range to 20 μm present in seawater from the top 5 cm of the Mar Menor lagoon during two sampling campaigns (summer and winter), as well of other potentially adsorbed compounds onto the plastic particles surfaces and suspended material. The identification of NPL/MPLs has been based on characteristic Kendrick Mass Defect analysis for each polymer type in mass spectra. The applied methodology allowed to identify NPLs/MPLs of polystyrene (PS), polyethylene (PE), polyisoprene (PI), polybutadiene (PBD), polypropylene (PP), polyamides (PA), polyvinylchloride (PVC), n-isopropylacrylamide (PNIPAm), and polydimethylsiloxanes. In addition, PS, PE, PI, PBD, PP, PA, and PVC were confirmed with standards, and the equivalent concentrations were quantified. The results of this study showed that most frequently found compounds were PP, PE, PA and PNIPAm, while the compound found at higher concentrations was by far PP reaching the 9,303 ± 366 ng/mL in one of the samples. A total number of 135 chemical compounds were tentatively identified, 74 of them plastic additives and compounds used in the polymers manufacture or coming from the polymer’s decomposition. In relation to plastic additives, the more frequently tentatively identified compounds were plasticizers such as phthalates group; stabilizers such as antioxidants (e.g., distearyl 3,3′-thiodipropionate, 2,5-di-tert-butylhydroquinone), and UV filters as benzotriazoles. Several flame retardants of the group of phosphates were as well detected. The other compounds tentatively identified in the samples were pharmaceuticals, pesticides, food additives, flavors and natural products that were attached onto the plastic particles and particulate matter from surrounding waters. In regards to the seasonal variation, during the summer a major number of compounds were tentatively detected, while de concentrations of polymers were slightly higher in winter. The spatial distribution showed higher contamination in the southern part of the coastal lagoon.

Monitoring Lipophilic Toxins in Seawater Using Dispersive Liquid—Liquid Microextraction and Liquid Chromatography with Triple Quadrupole Mass Spectrometry

The use of dispersive liquid–liquid microextraction (DLLME) is proposed for the preconcentration of thirteen lipophilic marine toxins in seawater samples. For this purpose, 0.5 mL of methanol and 440 µL of chloroform were injected into 12 mL of sample. The enriched organic phase, once evaporated and reconstituted in methanol, was analyzed by reversed-phase liquid chromatography with triple-quadrupole tandem mass spectrometry. A central composite design multivariate method was used to optimize the interrelated parameters affecting DLLME efficiency. The absence of any matrix effect in the samples allowed them to be quantified against aqueous standards. The optimized procedure was validated by recovery studies, which provided values in the 82–123% range. The detection limits varied between 0.2 and 5.7 ng L−1, depending on the analyte, and the intraday precision values were in the 0.1–7.5% range in terms of relative standard deviation. Ten water samples taken from different points of the Mar Menor lagoon were analyzed and were found to be free of the studied toxins.

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R2CV (cross-validated coefficient of determination) for the best-fit models.

Assessing the Hydrodynamic Response of the Mar Menor Lagoon to Dredging Inlets Interventions through Numerical Modelling

The Mar Menor lagoon has been subjected to high anthropogenic pressures. Among them, in the early 1970s, dredging and enlargement of one of the inlets to make a navigable channel has had strong consequences on the hydrology, ecology, and fisheries of the lagoon. In recent years, changes in agricultural practices have induced an eutrophication process, leading to loss of water quality. As a solution, some management proposals have included dredging of the inlets in order to increase the water renewal. However, these proposals did not take into account the negative effects of previous experiences nor the consequences on environmental conditions of the lagoon and therefore on biological processes. The purpose of this work is to assess the effect that proposed mitigation measures, could have on the hydrodynamic conditions and discuss its possible ecological consequences. A three-dimensional (3D) numerical model has been used to simulate the lagoon under different dredging scenarios, covering different dredging depths and extensions. The simulated current fields and fluxes through the inlets, as well as water renewal times have been compared for the different scenarios. It is found that some of the considered scenarios take the system beyond the threshold of sustainability, where the modified current dynamics could affect sediment transport, beach dynamics and fishing capacities in a significant way. Water exchange with the Mediterranean is also strongly affected, with consequences for species connectivity, and a homogenization of the water renewal times that could lead to loss of ecosystem heterogeneity and structural complexity. The study demonstrates the utility of numerical models as effective tools for the management of coastal areas.