Variability of total alkalinity in coastal surface waters determined using an in-situ analyzer in conjunction with the application of a neural network-based prediction model

Li Qiu; Kunshan Jiang; Quanlong Li; Dongxing Yuan; Jinshun Chen; Bo Yang; Eric P Achterberg

doi:10.1016/j.scitotenv.2023.168271

Variability of total alkalinity in coastal surface waters determined using an in-situ analyzer in conjunction with the application of a neural network-based prediction model

Sci Total Environ. 2024 Jan 15:908:168271. doi: 10.1016/j.scitotenv.2023.168271. Epub 2023 Oct 31.

Authors

Li Qiu¹, Kunshan Jiang¹, Quanlong Li², Dongxing Yuan¹, Jinshun Chen³, Bo Yang⁴, Eric P Achterberg⁵

Affiliations

¹ State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China; College of the Environment and Ecology, Xiamen University, Xiamen, China.
² State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China; College of the Environment and Ecology, Xiamen University, Xiamen, China. Electronic address: liql@xmu.edu.cn.
³ State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China; College of Ocean and Earth Sciences, Xiamen University, Xiamen, China.
⁴ Department of Marine Biology and Ecology, Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, USA.
⁵ Marine Biogeochemistry, Chemical Oceanography, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany. Electronic address: eachterberg@geomar.de.

PMID: 37918720
DOI: 10.1016/j.scitotenv.2023.168271

Abstract

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.

Keywords: Artificial neural network; Coastal carbonate system; Diel variability; In situ analyzer; Seasonal variability; Total alkalinity.