The South Atlantic Ocean is currently undergoing significant alterations due to climate change. This region is important to the global carbon cycle, but marine carbon data are scarce in this basin. Additionally, this region is influenced by Agulhas eddies. However, their effects on ocean biogeochemistry are not yet fully understood. Thus, we aimed to model the carbonate parameters in this region and investigate the anthropogenic carbon (Cant) content in 13 eddies shed by the Agulhas retroflection. We used in situ data from the CLIVAR/WOCE/A10 section to elaborate total dissolved inorganic carbon (CT) and total alkalinity (AT) models and reconstruct those parameters using in situ data from two other Brazilian initiatives. Furthermore, we applied the Tracer combining Oxygen, inorganic Carbon, and total Alkalinity (TrOCA) method to calculate the Cant, focusing on the 13 identified Agulhas eddies. The CT and AT models presented root mean square errors less than 1.66 and 2.19 μmol kg−1, indicating Global Ocean Acidification Observing Network climate precision. The Cant content in the Agulhas eddies was 23% higher than that at the same depths of the surrounding waters. We observed that Agulhas eddies can play a role in the faster acidification of the South Atlantic Central Water.
GIS and spatial data science (SDS) tools have been recently approaching each other by establishing bridge technologies between them. R as one of the most prominent programming languages used in SDS projects has been granted access to GIS infrastructure, while R scripts can be integrated and executed in GIS functions. Unfortunately, the treatment of spatial fuzziness has so far not been considered in SDS projects and bridge technologies due to a lack of software packages that can handle fuzzy spatial objects. This paper introduces an R package named fsr as an implementation of the fuzzy spatial data types, operations, and predicates of the Spatial Plateau Algebra that is based on the abstract Fuzzy Spatial Algebra. This R package solves the problem of constructing fuzzy spatial objects as spatial plateau objects from real datasets and describes how to conduct exploratory spatial data analysis by issuing geometric operations and topological predicates on fuzzy spatial objects. Further, fsr provides the possibility of designing fuzzy spatial inference models to discover new findings from fuzzy spatial objects. It optimizes the inference process by deploying the particle swarm optimization to obtain the point locations with the maximum or minimum inferred values that answer a specific user request.
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