Article
Statistical Calibration of a Portable pH Sensor for Coastal Monitoring: A Case Study in Mauritius
Yadhav A. Imrit 1, Roshan T. Ramessur 1 and Kishore Boodhoo 1,*
1 University of Mauritius, Reduit 80837, Mauritius
Correspondence: kishore.boodhoo@uom.ac.mu
Abstract: The application of sensors for measuring physico-chemical parameters like sea surface temperature and pH enables rapid, reliable long-term data collection. This is particularly crucial for Small-Island Developing States, whose marine ecosystems are vulnerable to climate change and ocean acidification. This study compared the pH data recorded by a portable pH sensor to those determined in the laboratory (UV-Vis spectrophotometric method) at Flic-en-Flac, a lagoon in the west coast of Mauritius. The two methods were compared using the Bland-Altman analysis and a multiple linear regression. It was noted that without adjusting for temperature differences between the pH sensor and a temperature probe, the pH results indicate a Pearson’s correlation coefficient of 0.473 (r2 = 0.224, p = 0.00135). In contrast, when temperature discrepancies between the pH sensor and the temperature probe were accounted for, the two methods yielded comparable results (Pearson’s r = 0.919, r2 = 0.959, p < 0.001). With the temperature adjustments, the data provided by the pH sensor are considered reliable and can be a complementary method to laboratory-based pH measurement.
Keywords: Ocean acidification; portable pH sensor; UV-Vis spectroscopy; SIDS; Mauritius
https://doi.org/10.59711/jims.12.110023
(This article belongs to the Special Issue Developing Resilient Prosperity and Sustainable Islands for the Next Decade)
References
1. Thomas, A.; Ramkumar, A.; Shanmugam, A. CO2 Acidification and Its Differential Responses on Aquatic Biota – a Review. Environ. Adv. 2022, 8, 100219, doi: 10.1016/j.envadv.2022.100219. [Crossref]
2. Wei, H.; Deng, Y.; Epa, U.P.K.; Belle, B.D.; Sharma, B.; Zhang, H.; Sa, H. Scientific Advances and Future Trends in Ocean Carbon Sink: An Interdisciplinary Review. Front. Mar. Sci. 2025, 12, 1658207, doi:10.3389/fmars.2025.1658207. [Crossref]
3. Sumaila, U.R.; Dyck, A.; Cheung, W.W.L. Fisheries Subsidies and Potential Catch Loss in SIDS Exclusive Economic Zones: Food Security Implications. Environ. Dev. Econ. 2013, 18, 427–439, doi:10.1017/S1355770X13000156. [Crossref]
4. Zitoun, R.; Sander, S.G.; Masque, P.; Perez Pijuan, S.; Swarzenski, P.W. Review of the Scientific and Institutional Capacity of Small Island Developing States in Support of a Bottom-up Approach to Achieve Sustainable Development Goal 14 Targets. Oceans 2020, 1, 109–132, doi:10.3390/oceans1030009. [Crossref]
5. Santana-Casiano, J.M.; González-Dávila, M. pH Decrease and Effects on the Chemistry of Seawater. In Oceans and the Atmospheric Carbon Content; Duarte, P., Santana-Casiano, J.M., Eds.; Springer Netherlands: Dordrecht, 2011; pp. 95–114 ISBN 978-90-481-9820-7. [Crossref]
6. Haugan, P.M.; Drange, H. Effects of CO2 on the Ocean Environment. Energy Convers. Manag. 1996, 37, 1019–1022, doi:10.1016/0196-8904(95)00292-8. [Crossref]
7. Caldeira, K.; Berner, R. Seawater pH and Atmospheric Carbon Dioxide. Science 1999, 286, 2043–2043, doi:10.1126/science.286.5447.2043a. [Crossref]
8. Elver, H.; Oral, N. Food Security, Fisheries and Ocean Acidification: A Human Rights Based Approach. In Research Handbook on Ocean Acidification Law and Policy; VanderZwaag, D.L., Oral, N., Stephens, T., Eds.; Edward Elgar Publishing, 2021 ISBN 978-1-78990-014-9. [Google Scholar]
9. Cinner, J.E.; Pratchett, M.S.; Graham, N.A.J.; Messmer, V.; Fuentes, M.M.P.B.; Ainsworth, T.; Ban, N.; Bay, L.K.; Blythe, J.; Dissard, D.; et al. A Framework for Understanding Climate Change Impacts on Coral Reef Social–Ecological Systems. Reg. Environ. Change 2016, 16, 1133–1146, doi:10.1007/s10113-015-0832-z. [Crossref]
10. Doney, S.C.; Fabry, V.J.; Feely, R.A.; Kleypas, J.A. Ocean Acidification: The Other CO2 Problem. Annu. Rev. Mar. Sci. 2009, 1, 169–192, doi:10.1146/annurev.marine.010908.163834. [Crossref]
11. Wang, Z.A.; Moustahfid, H.; Mueller, A.V.; Michel, A.P.M.; Mowlem, M.; Glazer, B.T.; Mooney, T.A.; Michaels, W.; McQuillan, J.S.; Robidart, J.C.; et al. Advancing Observation of Ocean Biogeochemistry, Biology, and Ecosystems With Cost-Effective in Situ Sensing Technologies. Front. Mar. Sci. 2019, 6, 519, doi:10.3389/fmars.2019.00519. [Crossref]
12. United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development Available online: https://digitallibrary.un.org/record/3923923 (accessed on 8 March 2025).
13. Monnereau, I.; Mahon, R.; McConney, P.; Nurse, L.; Turner, R.; Vallès, H. Fisheries Sector Vulnerabilities to Climate Change in Small Island Developing States. In Small Island Developing States; Moncada, S., Briguglio, L., Bambrick, H., Kelman, I., Iorns, C., Nurse, L., Eds.; The World of Small States; Springer International Publishing: Cham, 2021; Vol. 9, pp. 233–255 ISBN 978-3-030-82773-1. [Google Scholar]
14. Sutton, A.J.; Battisti, R.; Carter, B.; Evans, W.; Newton, J.; Alin, S.; Bates, N.R.; Cai, W.-J.; Currie, K.; Feely, R.A.; et al. Advancing Best Practices for Assessing Trends of Ocean Acidification Time Series. Front. Mar. Sci. 2022, 9, 1045667, doi:10.3389/fmars.2022.1045667. [Crossref]
15. Benway, H.M.; Lorenzoni, L.; White, A.E.; Fiedler, B.; Levine, N.M.; Nicholson, D.P.; DeGrandpre, M.D.; Sosik, H.M.; Church, M.J.; O’Brien, T.D.; et al. Ocean Time Series Observations of Changing Marine Ecosystems: An Era of Integration, Synthesis, and Societal Applications. Front. Mar. Sci. 2019, 6, 393, doi:10.3389/fmars.2019.00393. [Crossref]
16. Enochs, I.C.; Formel, N.; Shea, L.; Chomiak, L.; Piggot, A.; Kirkland, A.; Manzello, D. Subsurface Automated Samplers (SAS) for Ocean Acidification Research. Bull. Mar. Sci. 2020, 96, 735–752, doi:10.5343/bms.2020.0018. [Crossref]
17. Pinto, V.C.; Araújo, C.F.; Sousa, P.J.; Gonçalves, L.M.; Minas, G. A Low-Cost Lab-on-a-Chip Device for Marine pH Quantification by Colorimetry. Sens. Actuators B Chem. 2019, 290, 285–292, doi:10.1016/j.snb.2019.03.098. [Crossref]
18. Pérez-Rojas, C.A.; Martínez-Martínez, C.A.; Palacios-Mechetnov, E.; Lora-Vilchis, M.C. Design of a Low-Cost pH-Stat to Study Effects of Ocean Acidification on Growth and Nutrient Consumption of Diatoms. Aquac. Eng. 2022, 99, 102300, doi:10.1016/j.aquaeng.2022.102300. [Crossref]
19. Byrne, R.H. Measuring Ocean Acidification: New Technology for a New Era of Ocean Chemistry. Environ. Sci. Technol. 2014, 48, 5352–5360, doi:10.1021/es405819p. [Crossref]
20. Yang, B.; Patsavas, M.C.; Byrne, R.H.; Ma, J. Seawater pH Measurements in the Field: A DIY Photometer with 0.01 Unit pH Accuracy. Mar. Chem. 2014, 160, 75–81, doi:10.1016/j.marchem.2014.01.005. [Crossref]
21. Wiehe, E.C.; Gray, N.J.; Silver, J.J. The Blue Economy in Mauritius: Strategies and Imaginaries of a Large Ocean State. Geoforum 2025, 167, 104434, doi:10.1016/j.geoforum.2025.104434. [Crossref]
22. Rumley, D.; Chaturvedi, S.; Sakhuja, V. Fisheries Exploitation in the Indian Ocean: Threats and Opportunities; Institute of Southeast Asian Studies & Indian Ocean Research Group, 2009; ISBN 978-981-230-986-0. [Google Scholar]
23. Dickson, A.G.; Sabine, C.L.; Christian, J.R. Guide to Best Practices for Ocean CO2 Measurements; North Pacific Marine Science Organization, 2007. [Google Scholar]
24. Clayton, T.D.; Byrne, R.H. Spectrophotometric Seawater pH Measurements: Total Hydrogen Ion Concentration Scale Calibration of m-Cresol Purple and at-Sea Results. Deep Sea Res. Part Oceanogr. Res. Pap. 1993, 40, 2115–2129, doi:10.1016/0967-0637(93)90048-8. [Crossref]
25. Liu, X.; Patsavas, M.C.; Byrne, R.H. Purification and Characterization of Meta-Cresol Purple for Spectrophotometric Seawater pH Measurements. Environ. Sci. Technol. 2011, 45, 4862–4868, doi:10.1021/es200665d. [Crossref]
26. Ernie Lewis; Doug Wallace Program Developed for CO2 System Calculations Available online: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/CO2SYS/co2rprt.html (accessed on 4 March 2025).
27. Altman, D.G.; Bland, J.M. Measurement in Medicine: The Analysis of Method Comparison Studies. The Statistician 1983, 32, 307, doi:10.2307/2987937. [Crossref]
28. Aiken, L.S.; West, S.G.; Pitts, S.C. Multiple Linear Regression. In Handbook of Psychology; Weiner, I.B., Ed.; Wiley, 2003; pp. 481–507 ISBN 978-0-471-17669-5. [Google Scholar]
29. Shapiro, S.S.; Wilk, M.B. An Analysis of Variance Test for Normality (Complete Samples). Biometrika 1965, 52, 591–611, doi:10.1093/biomet/52.3-4.591. [Crossref]
30. Breusch, T.S.; Pagan, A.R. A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica 1979, 47, 1287, doi:10.2307/1911963. [Crossref]
31. Durbin, J.; Watson, G.S. Testing for Serial Correlation in Least Squares Regression: I. Biometrika1950, 37, 409, doi:10.2307/2332391. [Crossref]