scor

FacebookTwitter

Influence of particle composition on the rate constants of thorium adsorption

Chemical species are constantly exchanged between seawater (solution, D) and particles (solid material, P). This continuous D-P exchange is a key process determining the chemical composition of the ocean. Particles are heterogeneous materials, made of (i) biological material from the surface ocean, (ii) lithogenic material from external inputs to the ocean, and (iii) authigenic (oxyhydr)oxides precipitation in the water column. Understanding the role of each of these phases in driving the D-P exchange is therefore a major issue.

Lerner and co-workers (2018, see reference below) propose to disentangle the particle composition effect on the thorium adsorption rate constant k1 using two different regression models. Model I considers biogenic particles, lithogenic particles, Mn (oxyhydr)oxides, and Fe (oxyhydr)oxides as regressors, and k1 as the regressand. Model II considers ln(biogenic particles), ln(lithogenic particles), ln(Mn (oxyhydr)oxides), and ln(Fe (oxyhydr)oxides) as regressors, and ln(k1) as the regressand, where ln() denotes the natural logarithm. Thus, models I and II posit that the effects of particle phases on k1 are, respectively, additive and multiplicative. Regressions are considered separately in two regions of the North Atlantic: an upwelling region off the western margin of Mauritania, and an open-ocean region east of Bermuda.

The authors find that model II better describes the effect of particle composition on k1. Based on this regression model, the authors find that Mn (oxyhydr)oxides have a stronger effect on k1 in the open-ocean region, and biogenic particles have a stronger effect on k1 in the upwelling region.

18 Lerner

Figure: Relative Importance (RI) of particle phases for influencing the thorium adsorption rate constant, k1, under the additive model (upper panels) and the multiplicative model (lower panels). Results shown at all stations (a,d), open-ocean stations (b,e), and Mauritanian upwelling stations (c,f). The red and blue bars show two different methods to obtain RI values. Biogenic particles and Mn (oxyhydr)oxides have the strongest relationship to k1, depending on the model and stations considered. Click here to view the figure larger.

Reference:

Lerner, P., Marchal, O., Lam, P. J., & Solow, A. (2018). Effects of particle composition on thorium scavenging in the North Atlantic. Geochimica et Cosmochimica Acta, 233, 115–134. http://doi.org/10.1016/J.GCA.2018.04.035

Isotopes Atlantic Ocean Iron Global scale Pacific Ocean Neodymium Neodymium isotopes Particles Multiple TEIs Southern Ocean Zinc Thorium Land-ocean inputs Hydrothermal Arctic Ocean Analysis Modelling Circulation Cadmium Land-ocean input Thorium isotopes Data compilation Indian Ocean Cycles Mercury Radium Speciation Barium Silicon Aerosol input Iron isotopes Copper Manganese Hypoxia Radium isotopes Phosphate Cobalt Rare Earth Element Lead Lead isotopes Aluminium Protocol Mediterranean Sea Aerosols Boundary Exchange Protactinium Thorium-Protactinium Paleoceanography Environmental change Organic matter Nepheloids Aerosol Cadmium isotopes Zinc isotopes International Polar Year Uranium Microbial Rare Earth Elements Benthic Limitation Phytoplankton Oxygen Silicon isotopes Chromium Chronium isotopes BioGEOSCAPES Particulate Organic Carbon Export fluxes Residence times Methylmercury Surface waters Helium Paleocirculation Proxy Nickel Remineralization Nitrogen Sediments Climate change Lanthanum Yttrium Scandium Intercalibration Lithogenic Macronutriments Micronutriments Hafnium Hafnium isotopes Ice Sea ice Helium isotopes Particle fluxes Barium isotopes Biological pump Iodine Uranium isotopes Artificial Intelligence Cadmium sulfide Antarctic geology Beryllium Mammals Phosporus Time Series Productivity Red Sea Distribution coefficient Mesoscale transport Fertilisation Processes Estuaries Mesopelagic Anoxia Black Sea ICPMS Ecosystem CO2 degassing Transmissiometer Eddy Kinetic Energy Fate Scavenging Fractionation Distribution Iron sulfide Precipitation Shelf Inputs River Pitzer equations Gadolinium Intercomparison Coastal area Gallium Submarine Ground Water Discharge Cooper isotopes Total Hg Fertilization Experiments Behavior Budget Atmospheric Dynamic SAFE samples Boundary Scavenging Procedure Osmium Arsenic Aerosols input Nitrate Nutrients Deep water Copper isotopes Dissolved concentations

 Data Product (IDP2017)

eGEOTRACES Atlas

 Data Assembly Centre (GDAC)

 Outreach

Subscribe Mailing list

Contact us

To get a username and password, please contact the GEOTRACES IPO.

This site uses cookies to offer you a better browsing experience. Find out more on how we use cookies and how you can change your settings.