Identifying the acidification trend of the Balearic Sea using artificial intelligence

Sept. 5, 2022
  • This study highlights the use of machine learning to characterize the pH decline in the Balearic Sea, a key task in assessing the impacts of climate change on marine biodiversity. 
  • The results show a trend of pH decline similar to the rates of decline observed in other basins of the global ocean.

An interdisciplinary team from CSIC centers in the Balearic Islands has presented the first determination of the acidification rate in the coastal area of the Balearic Sea to elucidate the consequences of climate change in coastal areas of the archipelago. The aim of the study has focused on reconstructing incomplete time series of relevant pH through the use of artificial intelligence techniques. 

The results, published in the journal Scientific Reports, indicate that these coastal areas show a trend of pH decrease (acidification) of 0.0020±0.00054 pH units per year. This trend is similar to the one observed in other basins of the global ocean and is mainly due to the incorporation of atmospheric carbon dioxide into the seawater and the increase in temperature.

"This work is a valuable contribution to understanding the role of coastal zones and the effects of climate change on the ecosystems present here," says Dr. Hendriks, the project's lead researcher, "The decrease in seawater pH is due to the increase of carbon dioxide in the atmosphere and results in major alterations that have a major impact on marine ecosystems. For example, ocean acidification leads to reduced saturation levels of carbonate minerals, which increases difficulties in shell formation for calcifying marine organisms (plankton, molluscs, echinoderms and corals). So measuring how pH is changing in these areas is key to characterizing the problem," Hendriks explains.

The Mediterranean Institute for Advanced Studies (IMEDEA, CSIC-UIB), the Andalusian Institute of Marine Sciences (ICMAN, CSIC), the Institute of Interdisciplinary Physics and Complex Systems (IFISC, CSIC-UIB), the Balearic Islands Coastal Observation and Forecasting System (ICTS SOCIB), and the Institute of Marine Sciences (IIM, CSIC) have participated in the study. The management team of the Cabrera Archipelago Maritime-Terrestrial National Park and the Regional Ministry of the Environment and Territory have collaborated in the project. It has also been funded by the Ministry of Science and Innovation, the Govern de les Illes Balears and the BBVA Foundation.

The study has constituted a major operational effort that began in 2018 with the collection of pH data, along with other variables (water temperature, salinity and dissolved oxygen levels), at the monitoring stations of the Balearic Ocean Acidification Time Series (BOATS) network in the Bay of Palma and in the Maritime-Terrestrial National Park of the Cabrera archipelago, within the CSIC Water:iOS Interdisciplinary Thematic Platform. However, the maintenance of this type of stations involves several difficulties - financial costs, meteorological risks, deployment in areas with high shipping traffic, instrumental failures, etc. - which implies the appearance of gaps in the data and, therefore, a loss of quality when it comes to preparing global studies.

In order to fill these gaps and estimate the pH series over a wide time interval prior to monitoring, the team applied Deep Learning techniques, an emerging area of Machine Learning that has recently made substantial advances in the field of Artificial Intelligence. Specifically, several models of recurrent neural networks were developed which, when trained, allowed the pH series to be related to the set of environmental variables obtained, predicting the pH value when it is not available.

Thanks, therefore, to the work of obtaining a large amount of data and the subsequent application of these techniques, it has been possible to reconstruct the decadal trend of acidification of the Balearic Sea, which is the main result of the work.


Flecha, S., Giménez-Romero, À., Tintoré, J., Pérez, F. F., Alou-Font, E., Matías, M. A., & Hendriks, I. E. (2022). pH trends and seasonal cycle in the coastal Balearic Sea reconstructed through machine learning. Scientific Reports, 12(1), 1-11. DOI:

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