Exploration of Polymetallic Nodules and Resource Assessment: A Case Study from the German Contract Area in the Clarion-Clipperton Zone of the Tropical Northeast Pacific

Comparing deep-sea polymetallic nodule mining technologies and evaluating their probable impacts on deep-sea pollution

Deep-sea polymetallic nodules (PMN) hold promise as a future resource, with various consortia like MOES, IOM, GSR, KORDI, and COMRA actively exploring mining possibilities. However, current technologies lack environmental sustainability. This study comprehensively compares the technologies proposed by different consortia for deep sea mining (DSM). It evaluates the designs and prototypes of key components like crawlers, conveyor belts, crushers, riser pipes, and slurry tailing discharge mechanisms for their technical feasibility and environmental impact. Environmental concerns regarding sediment disturbances, nodules pick-up methods, crushing, and tailing material filtration are addressed in this article. It is suggested that further research and development efforts are needed to optimize technologies and integrate effective environmental protection measures into DSM operations.

Multispectral multibeam backscatter response of heterogeneous rhodolith beds

Acoustic backscatter has been used as a tool to map the seafloor in greater detail and plays an increasingly important role in seafloor mapping to meet multiple ocean management needs. An outstanding challenge to the use of backscatter for seafloor mapping is the distinction between acoustically similar substrates, such as mixed sediments from rhodoliths. Rhodolith beds are a biogenic substrate that provides important ecological services, and are typically classified as a single categorical substrate type-though nodules coverage may be spatially variable. Recently, multispectral acoustic backscatter has demonstrated great potential to improve thematic seafloor mapping compared to single-frequency systems. This work employs multispectral multibeam backscatter and underwater imagery to characterize and map rhodolith beds in the Costa das Algas Marine Protected Area (Brazil). A support vector machine classifier was used to classify multifrequency backscatter mosaics according to rhodolith classes identified from underwater imagery. Results suggest that multispectral backscatter is effective both in providing information for mapping different proportions of rhodolith coverage and in predicting the presence or absence of these nodules. The backscatter of the lowest frequency was the most useful for distinguishing variable proportions of rhodolith coverage, and the two higher frequencies were better predictors of presence and absence.

Complete mitochondrial genome of the abyssal coral Abyssoprimnoagemina Cairns, 2015 (Octocorallia, Primnoidae) from the Clarion-Clipperton Zone, Pacific Ocean

The Clarion-Clipperton Zone (CCZ) in the tropical East Pacific is a region of interest for deep-sea mining due to its underwater deposits of polymetallic nodules containing economically important metals such as nickel, copper, and cobalt. It is also a region of extensive baseline studies aiming to describe the state of the environment, including the biodiversity of the benthic fauna. An abundant component of the abyssal plain ecosystem consists of sessile fauna which encrusts polymetallic nodules and are vulnerable to potential impacts arising from exploitation activities, particularly removal of substrate. Therefore, this fauna is often considered to have key species whose genetic connectivity should be studied to assess their ecological resilience. One such species is Abyssoprimnoagemina Cairns, 2015, a deep-sea coral from the CCZ whose presence in the Interoceanmetal Joint Organization (IOM) claim area has been confirmed during samplings. In this study, we used next-generation sequencing (NGS) to obtain the 18S nuclear rRNA gene and the complete mitochondrial genome of A.gemina from IOM exploration area. The mitogenome is 18,825 bp long and encodes for 14 protein coding genes, 2 rRNAs, and a single tRNA. The two phylogeny reconstructions derived from these data confirm previous studies and display A.gemina within a highly supported cluster of seven species whose mitogenomes are all colinear and of comparable size. This study also demonstrates the suitability of NGS for DNA barcoding of the benthic megafauna of the CCZ, which could become part of the IOM protocol for the assessment of population diversity and genetic connectivity in its claim area.

Comminution of Polymetallic Nodules with a High-Pressure Water Jet

This paper presents an original method for the comminution of polymetallic nodules in a hydro-jet mill of our own design, having the water jet pressure in the range of 70-200 MPa. The best comminution results are ensured by equipping the mill with a water jet having a diameter d_w = 0.7 mm and a comminution-homogenization tube having a diameter d_h = 2.4 mm, and by setting a distance s = 10 mm from the outlet of the homogenization tube to the comminuting disc. Particles of concretions comminuted under such conditions are characterized by a fairly regular isometric shape and favorable morphology represented by intense development of the specific surface.

Investigating the benthic megafauna in the eastern Clarion Clipperton Fracture Zone (north-east Pacific) based on distribution models predicted with random forest

The eastern Clarion Clipperton Fracture Zone (CCZ) is a heterogeneous abyssal environment harbouring relatively low abundances of highly diverse megafauna communities. Potential future mining of polymetallic nodules threatens these benthic communities and calls for detailed spatial investigation of megafauna. Based on the predicted probability of occurrence of 68 megafauna morphotypes, a seabed area extending over 62,000 km² was divided into three assemblages covering an eastern plain area, a deeper western plain area and an area covering both seamount and abyssal hill sites. Richness, estimated as the sum of morphotypes with a predicted probability of occurrence larger than 0.5, amounts to 15.4 of 68 morphotypes. Highest richness was predicted at seamount sites, and lowest richness in the western part of the study area. Combining the predicted probability of megafauna occurrences with bathymetric variables, two seamount habitats and two plain habitats could be defined. One of these megafauna plain habitats corresponds with contiguous nodule fields of high abundance that may be targeted for future mining, showing that prospective nodule fields have a clearly differentiated megafauna assemblage. Monitoring and management schemes, including the delineation of preservation and protection areas within contract areas, need to incorporate this geological and biological heterogeneity.

Importance of Spatial Autocorrelation in Machine Learning Modeling of Polymetallic Nodules, Model Uncertainty and Transferability at Local Scale

Machine learning spatial modeling is used for mapping the distribution of deep-sea polymetallic nodules (PMN). However, the presence and influence of spatial autocorrelation (SAC) have not been extensively studied. SAC can provide information regarding the variable selection before modeling, and it results in erroneous validation performance when ignored. ML models are also problematic when applied in areas far away from the initial training locations, especially if the (new) area to be predicted covers another feature space. Here, we study the spatial distribution of PMN in a geomorphologically heterogeneous area of the Peru Basin, where SAC of PMN exists. The local Moran’s I analysis showed that there are areas with a significantly higher or lower number of PMN, associated with different backscatter values, aspect orientation, and seafloor geomorphological characteristics. A quantile regression forests (QRF) model is used using three cross-validation (CV) techniques (random-, spatial-, and cluster-blocking). We used the recently proposed “Area of Applicability” method to quantify the geographical areas where feature space extrapolation occurs. The results show that QRF predicts well in morphologically similar areas, with spatial block cross-validation being the least unbiased method. Conversely, random-CV overestimates the prediction performance. Under new conditions, the model transferability is reduced even on local scales, highlighting the need for spatial model-based dissimilarity analysis and transferability assessment in new areas.