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Studying the variability of PH in the coastal zone using deep learning

Sea water has a pH of around 8.2 although it can vary between 7.5 and 8.5 depending on local salinity, it is estimated to have dropped by an average of 0.1 since l industrial era. This downward trend associated with increasing levels of CO2 in the atmosphere is a matter of concern due to the possible negative consequences for marine organisms, in particular calcifiers (corals, shell molluscs…). A team of Spanish researchers conducted a study to assess the seasonal variability of pH. titled ” pH trends and seasonal cycle in the coastal Balearic Sea reconstructed through machine learning”, it appeared in the magazine nature last July 28.

Susana Flecha, Àlex Giménez-Romero, Joaquín Tintoré, Fiz F. Pérez, Iris E. Hendriks, Manuel A. Matías, Eva Alou-Font are the authors of this study which aims to study the variability of the PH of the Balearic coastal zone thanks to deep learning.

Atmospheric carbon dioxide emissions have increased exponentially since the beginning of the industrial revolution, mainly due to the use of fossil fuels, industry and land use change. Previous studies have estimated that while approximately 46% of CO2 remains in the atmosphere, the remaining 54% is found in the biosphere and oceans. When CO2 dissolves in seawater, chemical reactions occur, causing the pH of the seawater to decrease.

Coastal areas are important land-ocean transition zones with complex interactions between biological, physical and chemical processes. Researchers assessed pH variability at two sites in the coastal zone of the Balearic Sea, Palma and Cabrera. They used environmental datasets of temperature, salinity and dissolved oxygen, obtained by autonomous sensors from 2018 to 2021, and trained recurrent neural networks to predict pH and fill data gaps. . Longer environmental time series (2012-2021) were used to obtain the pH trend using reconstructed data. The best predictions show a rate of change of −0.0020±0.00054 pH units per year, consistent with other observations of pH levels in coastal areas. Their methodology can make it possible to obtain pH trends even though the available pH data is not very numerous, it suffices that other variables are accessible.

This work has highlighted the capabilities of the techniques of deep learning, in particular the BD-LSTM (BiDirectional Long Short-Term Memory) architecture, to reconstruct relevant pH data to assess seasonal pH variability and to elucidate the consequences of climate change, such as the OA effect, in a coastal area of ​​the Balearic Sea, which can be extended to the coastal areas of the western Mediterranean basin. Nevertheless, future research is needed to assess and confirm these regional trends, so it is important to maintain time series monitoring networks.

Sources of the article:

“pH trends and seasonal cycle in the coastal Balearic Sea reconstructed through machine learning” https://doi.org/10.1038/s41598-022-17253-5
Nature, 07/28/2022

Authors:

Susana Flecha, Instituto de Ciencias Marinas de Andalucía (ICMAN-CSIC),Instituto Mediterráneo de Estudios Avanzados (IMEDEA-UIB-CSIC),
Alex Giménez-Romero, Instituto de Fisica Interdisciplinar y Sistemas Complejos, (IFISC-UIB-CSIC),
Joaquín Tintoré, Instituto Mediterráneo de Estudios Avanzados (IMEDEA-UIB-CSIC), Balearic Islands Coastal Observing and Forecasting System (SOCIB,
Eva Alou-Font, Balearic Islands Coastal Observing and Forecasting System (SOCIB),
Iris E. Hendriks, Instituto Mediterráneo de Estudios Avanzados (IMEDEA-UIB-CSIC),
Manuel A. Matías, Instituto de Fisica Interdisciplinar y Sistemas Complejos, (IFISC-UIB-CSIC),
Fiz F. Perez, Instituto de Investigaciones Marinas (IIM-CSIC).