ALBERT

Description: Total alkalinity (TA) is an important variable of the ocean carbonate system. In coastal oceans, carbonate system dynamics are controlled by a range of processes including photosynthesis and respiration, calcification, mixing of water masses, continental inputs, temperature changes, and seasonal upwelling. Assessments of diel, seasonal and interannual variations in TA are required to understand the carbon cycle in coastal oceans. However, our understanding of these variations remains underdeveloped due to limitations in observational techniques. Autonomous TA measurements are therefore required. In this study, an in situ TA analyzer (ISA-TA) based on a single-point titration with spectrophotometric pH detection was deployed in Tong'an Bay, Xiamen, China, over a five-month period in 2021 to determine diel and seasonal TA variations. The TA observations were combined with an artificial neural network (ANN) model to construct TA prediction models for this area. This provided a simple method to investigate TA variations in this region and was applied to predict surface water TA between March and April 2021. The in situ TA observations showed that TA values in Tong'an Bay varied within a range from 1931 to 2294 μmol kg−1 over the study period, with low TA in late winter, early summer and late summer, and high TA in early winter. The TA variations in late summer and early winter were mainly controlled by mixing of water bodies. The diel variations of TA were greatly determined by tides, with a diel amplitude of 9 to 247 μmol kg−1. The ANN model used temperature, salinity, chlorophyll, and dissolved oxygen to estimate TA, with a root-mean-square error (RMSE) of ∼14 μmol kg−1, with salinity as the input variable with the greatest weight. The approach of combining ISA-TA observations with an ANN model can be extended to study the carbonate system in other coastal regions.