\makeatletter
\@ifundefined{HCode}
{\documentclass[screen,CRGEOS,Unicode,biblatex,published]{cedram}
\addbibresource{crgeos20250080.bib}
\newenvironment{Table}{\begin{table}}{\end{table}}
\newenvironment{noXML}{}{}
\let\citep\parencite
\let\citet\textcite
\def\xcitealp#1#2{\citeauthor{#1}, \citelink{#1}{#2}}
\newcommand*{\citelink}[2]{\hyperlink{cite.\therefsection @#1}{#2}}
\def\defcitealias#1#2{}
\let\citepalias\parencite
\let\citetalias\textcite
\def\thead{\noalign{\relax}\hline}
\def\endthead{\noalign{\relax}\hline}
\def\tabnote#1{\vskip4pt\parbox{.95\linewidth}{#1}}
\def\botline{\\\hline}
\def\dollar{\$}
\def\tbody{\noalign{\relax}}
\def\tsup#1{$^{{#1}}$}
\def\tsub#1{$_{{#1}}$}
\def\og{\guillemotleft}
\def\fg{\guillemotright}
\let\permil\textperthousand
\RequirePackage{etoolbox}
\usepackage{upgreek}
\usepackage{wasysym}
\usepackage{pifont}
\def\jobid{crgeos20250080}
%\graphicspath{{/tmp/\jobid_figs/web/}}
\graphicspath{{./figures/}}
\newcounter{runlevel}
\usepackage{multirow} 
\def\nrow#1{\@tempcnta #1\relax%
\advance\@tempcnta by 1\relax%
\xdef\lenrow{\the\@tempcnta}}
\def\morerows#1#2{\nrow{#1}\multirow{\lenrow}{*}{#2}}
\let\MakeYrStrItalic\relax
\def\refinput#1{}
\def\back#1{}
\csdef{Seqnsplit}{\\}
\def\myRef{\ref}
\newcommand*{\hyperlinkcite}[1]{\hyper@link{cite}{cite.#1}}
\DOI{10.5802/crgeos.300}
\datereceived{2025-01-29}
\daterevised{2025-05-13}
\datererevised{2025-06-19}
\dateaccepted{2025-06-20}
\ItHasTeXPublished
\def\bcaption#1#2#3{\caption{#1\unskip\break\\[-5pt]\hspace{\fill}}\end{figure*}\setcounter{figure}{#2}\begin{figure*}\caption{(\textbf{cont}.) #3}}
}
{
\PassOptionsToPackage{authoryear}{natbib}
\documentclass[crgeos]{article}
\usepackage[T1]{fontenc} 
\usepackage{pifont}
\def\CDRdoi{10.5802/crgeos.300}
\def\bcaption#1#2#3{\caption{#1#3}}
 \def\citelink#1#2{\citeyear{#1}}
 \def\xcitealp#1#2{\citealp{#1}}
\makeatletter
\def\CDRsupplementaryTwotypes#1#2{}
 \def\selectlanguage#1{} 
 \def\hyperlinkcite#1#2{\citeyear{#1}}
\let\newline\break
}
\makeatother

\dateposted{2025-07-15}
\begin{document}

\begin{noXML}

\TopicFR{Zone critique et socio-\'ecosyst\`emes}
\TopicEN{Critical zone and socio-ecosystems}

\CDRsetmeta{articletype}{review}

\title{A synthesis of copper isotope data for major continental and
marine reservoirs} 

\alttitle{Synth\`{e}se des donn\'{e}es d'isotopes du cuivre pour les
principaux r\'{e}servoirs continentaux et marins}

\author{\firstname{J\'{e}r\^{o}me} \lastname{Viers}\CDRorcid{0000-0003-3974-5993}}
\address{G\'{e}osciences Environnement Toulouse (GET), Universit\'{e}
de Toulouse, CNRS, IRD, 14 Avenue Edouard Belin, 31400, Toulouse,
France} 
\email{jerome.viers@get.omp.eu}

\keywords{\kwd{Copper isotopes}\kwd{Signatures}\kwd{Terrestrial and
marine reservoirs}}

\altkeywords{\kwd{Isotopes du
cuivre}\kwd{Signatures}\kwd{R\'{e}servoirs terrestres et marins}}

%\thanks{}

\begin{abstract} 
The objective of this work is to make an inventory of copper isotope
signatures (what amplitudes? what signs?) in the main reservoirs (rock,
soil, river, oceans). After almost 25 years of work carried out with
precise measurements of these isotopic ratios, it is important to
consider whether they can provide us useful constraints in the
understanding of the cycle of large-scale elements. The goal is not to
discuss all the hypotheses of each paper, of each site, but to present
the compilation of data in a neutral way and identify the significant
features. This work should provide a simple, hard-hitting overview of
the potential of this isotopic tool as a source tracer, but also of the
main mechanisms at play within and between reservoirs.
\end{abstract}

\begin{altabstract} 
L'objectif de ce travail est de dresser un inventaire des signatures
isotopiques du cuivre (quelles amplitudes ? quels signes ?) dans les
principaux r\'{e}servoirs (roches, sols, rivi\`{e}res, oc\'{e}ans).
Apr\`{e}s pr\`{e}s de 25 ann\'{e}es de recherches men\'{e}es avec des
mesures pr\'{e}cises de ces rapports isotopiques, il est l\'{e}gitime
de se demander s'ils peuvent r\'{e}ellement nous apporter des
contraintes utiles \`{a} la compr\'{e}hension du cycle des
\'{e}l\'{e}ments \`{a} grande \'{e}chelle. L'objectif n'est pas ici de
discuter toutes les hypoth\`{e}ses de chaque article ou de chaque site,
mais de pr\'{e}senter une compilation des donn\'{e}es de mani\`{e}re
neutre et d'en d\'{e}gager les caract\'{e}ristiques significatives. Ce
travail vise \`{a} offrir une vue d'ensemble simple et percutante du
potentiel de cet outil isotopique, \`{a} la fois comme traceur de
sources et comme r\'{e}v\'{e}lateur des principaux m\'{e}canismes
intervenant au sein des r\'{e}servoirs et entre eux.
\end{altabstract} 

\maketitle

{\vspace*{4pt}}

\twocolumngrid

\end{noXML}

\defcitealias{Wangetal2020}{ibid.}
\defcitealias{Oliasetal2006}{ibid.}

\section{Introduction}\label{sec1}

Copper (Cu) is an essential element for the physiological functioning
of living beings, but it can also be toxic in high concentrations
\citep{FlemmingTrevors1989,Brewer2010}. It is also an indispensable
metal for our societies, thanks to its many uses (transport, industry,
communication, construction, agriculture). Demand for Cu is growing
exponentially to meet the needs of both the world's growing population
and the energy transition. Demand is such that even in Europe, where
mining activity has been excessively low for the past thirty years,
former mining areas such as Andalusia (Huelva region, Spain) are
experiencing a significant mining revival \citep{BuuSao2021}.\looseness=1

Because of its economic importance, numerous works have been carried
out to understand the development of Cu-rich mineralization 
\citep[e.g.,][]{Mathuretal2009} and thus facilitate mineral
exploration. Given its massive use in all sectors of our \mbox{societies,}
many studies have been carried out on the dispersion of copper in the
continental and oceanic environments 
\citep[e.g.,][]{Littleetal2014,Blotevogeletal2019}.
At the end of the 90s, the first precise measurements of the copper
isotope ratio (i.e., \tsup{65}Cu/\tsup{63}Cu) were carried out by
MC-ICP-MS \citep{Marechaletal1999}, Cu having two stable isotopes
(\tsup{63}Cu and \tsup{65}Cu). The scientific community then began
systematic measurements of this isotopic ratio in various environments
(rocks, soils, rivers, sediments etc.)
\citep{Johnsonetal2004,Tengetal2017, Moynieretal2017}. The purpose of
these studies was initially to make measurements in these reservoirs,
and see if we could detect different isotope signatures. In a second
step, the objective was to see if these signatures could be used as a
source tracer or to understand the processes that induce them. In the
latter case, understanding these processes opens a window of
understanding on mechanisms that counteract the mobility of elements,
in this case copper, in the natural environment 
\citep[e.g.,][]{Borroketal2008,Navarreteetal2011,Coutaudetal2018,
Komareketal2021}. 

The objective of this work is to make an inventory of copper isotope
signatures (what amplitudes? what signs?) in the main reservoirs (rock,
soil, river, oceans). After almost 25 years of work carried out with
precise measurements of these isotopic ratios, it is important to
consider whether they can provide us useful constraints in the
understanding of the cycle of large-scale elements. The goal is not to
discuss all the hypotheses of each paper, of each site, but to present
the compilation of data in a neutral way and identify the significant
features. This work should provide a simple, hard-hitting overview of
the potential of this isotopic tool as a source tracer, but also of the
main mechanisms at play within and between reservoirs. All the isotopic
data presented are expressed in the form of $\updelta$\tsup{65}Cu (in
units of $\permil$) whose reference material is SRM-NIST 976: 
{\begin{equation}\label{eq1}
\updelta{}^{65}\mathrm{Cu} (\permil) = 
\left(
\left(\frac{
\left(\frac{{\,}^{65}\mathrm{Cu}}{{\,}^{63}\mathrm{Cu}}\right)_{\mathrm{sample}}}{
\left(\frac{{\,}^{65}\mathrm{Cu}}{{\,}^{63}\mathrm{Cu}}\right)_{\mathrm{NIST976}}}
\right)-1
\right)\times 1000.  
\end{equation}}\unskip

{\vspace*{4pt}}

\section{Rocks, mineralizations and alteration products
(Figure~\myRef{fig1})}\label{sec2}

{\vspace*{4pt}}

Rocks (upper continental crust, mantle) show relatively low isotopic
variability with the following mean values for ${\updelta}$\tsup{65}Cu
($\permil$): 0.02 ${\pm}$ 0.06 (granite), 0.41 ${\pm}$ 0.16 (granodiorite),
${-}$0.25 ${\pm}$ 0.44 (peridotite), 0.11 ${\pm}$ 0.08 (basalt), and 0.11
${\pm}$ 0.16 for sedimentary rocks. These data are consistent with the
value proposed by \citet{Savageetal2015} for the Bulk Silicate Earth
(0.07 ${\pm}$ 0.1). The most striking result is the high isotopic
variability of Cu-rich primary or secondary mineralization and
associated reservoirs (ore, mining waste, precipitates, river sediments
impacted by acid mine drainage). While primary sulfides (e.g.,
chalcopyrite) show an average value of 0.33 ${\pm}$ 1.66, variability is
extreme for secondary products in natural or mining contexts (${-}$16.49
${<}$\ ${\updelta}$\tsup{65}Cu($\permil$) ${<}$ 9.98)
\citep{Mathuretal2009} with an average value in the present data set of
0.07 ${\pm}$ 2.86. These secondary products may be either zones of
secondary Cu enrichment (e.g. secondary chalcocite) (1.32 ${\pm}$ 1.26)
or zones displaying the typical mineralogical assemblages of
environments impacted by acid mine drainage with, in particular,
ferrous minerals (e.g., goethite, hematite) or secondary sulfate
minerals (e.g., jarosite, schwertmannite) (${-}$1.59 ${\pm}$ 3.60).
However, it seems that zones with secondary enrichment (chalcocite) are
generally clearly positive, while leaching zones with a mixture of
goethite, hematite and sulfates are negative
\citep{Mirnejadetal2010,Mathuretal2012a,Mathuretal2012b}. 
Experimental works \citep[e.g.,][]{Ehrlichetal2004} have clearly shown that the
dissolution of primary sulfide minerals results in the preferential
departure of the heavy isotope (in the fluids), leaving an in-situ
light-enriched solid residue. This result may seem contradictory to the
synthesis of the data presented in Figure~\ref{fig1}, where secondary
products exhibit both positive or negative values. However, this high
variability can be explained by the complexity of the water--rock
interaction processes occurring in mineralized and/or mining areas.
Indeed, in these environments, the dissolution of sulfides results in
very charged percolating waters which can re-precipitate minerals, the
latter may be dissolved under favorable conditions and so on
\citep{Lottermoseretal1999, Oliasetal2006, Nietoetal2007}. This
succession of precipitation/dissolution may result in a high degree of
fractionation (isotopic variability) in secondary products, solids and
liquids, which can be both negative and positive
\citep{PerezRodriguezetal2013}. The strong isotopic amplitude in
mining areas make copper isotopes a potentially relevant tracer for the
study of metal transfer processes within these environments but also to
assess the impact of these mining districts on continental and marine\break
ecosystems.

\begin{figure*}
\includegraphics{fig01}
\bcaption{\label{fig1}Type of sample and associated bibliographic
reference for rocks, mineralization (ore) and alteration products; all
data are presented in the form of $\updelta$\tsup{65}{Cu} (in units of
$\permil$) whose reference material is SRM-NIST976 
(see Equation~(\ref{eq1})). This
is the case for all figures. For each reference, is reported a mean
value associated with a standard deviation and the number of samples.
Sometimes there is only one data. The points surrounded by a dotted
line are from the same study and the reference (ex: S-6) allows to find
the author and the associated values in the legend.
R1: Granite from Lachlan Fold Belt (SE Australia) 
\citep[0.08 ${\pm}$ 0.34 ($n=32$);][]{Lietal2009b}; 
R2: Granite from porphyry-type ore bodies (Lhasa, Tibet) 
\citep[0.04 ${\pm}$ 0.23 ($n=4$);][]{Zhengetal2019}; 
R3: Diorite from Central Asian Orogenic Belt (West China) 
\citep[${-}$0.06 ($n=1$);][]{Zhaoetal2022}; 
R4: Granodiorite (Colorado, USA) (${=}$\ GSP-2, Certified Reference
Material) \citep[0.3 ($n=1$);][]{Wangetal2022};
R5: Granodiorite from porphyry-type ore bodies (Lhasa, Tibet) 
\citep[0.34 ${\pm}$ 0.24 ($n=36$);][]{Zhengetal2019}; 
R6: Granodiorite from Qulong porphyry copper deposit (Tibet) 
\citep[0.59 ${\pm}$ 0.13 ($n=14$);][]{Nieetal2012}; 
R7: Mafic enclave (Lhasa, Tibet) 
\citep[0.42 ${\pm}$ 0.3 ($n=12$);][]{Zhengetal2019}; 
R8: Lherzolite from Central Asian Orogenic Belt (West China) 
\citep[${-}$0.12 ${\pm}$ 0.27 ($n=11$);][]{Zhaoetal2022};
R9: Hornblende Lherzolite from Central Asian Orogenic Belt (West China)
\citep[${-}$0.63 ${\pm}$ 0.42 ($n=6$);][]{Zhaoetal2022};
R10: Olivine Gabbro from Central Asian Orogenic Belt (West China) 
\citep[${-}$0.3 ${\pm}$ 0.2 ($n=8$);][]{Zhaoetal2022}; 
R11: Cratonic peridotite (China) \citep[${-}$0.64 ${\pm}$ 0.68
($n=30$);][]{Liuetal2015}; 
R12: Basalt (Iceland) (${=}$\ BIR, Certified Reference Material) 
\citep[${-}$0.02 ($n=1$);][]{Lietal2009b}; 
R13: Columbia River Basalt (USA) (${=}$\ BCR-1/2, Certified Reference
Material) \citep[0.19 ($n=1$);][]{Sullivanetal2022}; 
R14: Diabase (Virginia, USA) (${=}$\ W-2a Certified Reference
Material) \citep[0.04 ($n=1$);][]{Sullivanetal2020}; 
R15: Hawaiian basalt (USA), (${=}$\ BHVO-2, Certified Reference
Material) \citep[0.08 ($n=1$);][]{Wangetal2022}; 
R16: Basalt from Country Rock (Huangshannan, Huanshandong, Hulu and
Tulaergen deposits, Eastern Tianshan, China) \citep[0.22 ${\pm}$ 0.04
($n=2$);][]{Zhaoetal2022}; 
R17: Basalt (Southwest Pacific Ocean, Niuatahi volcan, Tonga) 
\citep[0.13 ${\pm}$ 0.10 ($n=3$);][]{Wangetal2019}; 
R18: Dacite (Southwest Pacific Ocean, Niuatahi volcan, Tonga)
\citep[0.21 ${\pm}$ 0.1 ($n=9$);][]{Wangetal2019}; 
R19: Basalt (Hainan Island, China) \citep[0.07 ($n=1$);][]{Liuetal2022}; 
R20: Andesite (Oregon, USA), (${=}$\ AGV-2, Certified Reference
Material) \citep[0.06 ($n=1$);][]{Sullivanetal2022}; 
R21: Andesitic breccia tuff from Country Rock (Huangshannan,
Huanshandong, Hulu and Tulaergen deposits, Eastern Tianshan, China)
\citep[0.10 ${\pm}$ 0.03 ($n=3$);][]{Zhaoetal2022}; 
R22: Cambrian micashist (southwest France) 
\citep[0.07 ($n=1$);][]{ElAzzietal2013}; 
R23: Biotite quartz schist from Country Rock (Huangshannan,
Huanshandong, Hulu and Tulaergen deposits, Eastern Tianshan, China)
\citep[0.46 ${\pm}$ 0.04 ($n=2$);][]{Zhaoetal2022}; 
R24: Shale (Marcellus formation, USA)
\citep[0.04 ${\pm}$ 0.16 ($n=8$);][]{Mathuretal2012b}; 
R25: Black shales (South Africa) 
\citep[${-}$0.05 ${\pm}$ 0.42 ($n=11$);][]{ChiFruetal2016}; }{0}{R26: Black shales (Gabon) \citep[0.23 ${\pm}$ 0.09
($n=8$);][]{ChiFruetal2016}; 
R27: Black shales (Maokou formation, China)
\citep[0.14 ${\pm}$ 0.09 ($n=5$);][]{Lvetal2016}; 
R28: Cody Shale (USA) (${=}$~SCo-1, Certified Reference Material) 
\citep[${-}$0.16 ($n=1$);][]{Sossietal2014}; 
R29: Loess (China) \citep[0.01 ${\pm}$ 0.03 ($n=2$);][]{Lietal2009b}; 
R30: Greywacke (Goa state, India) 
\citep[0.03 ($n=1$);][]{Littleetal2019}; 
R31: Sandstone \citep[0.26 ${\pm}$ 0.12 ($n=2$);][]{Zhaoetal2022}; 
R32: Bedrock (Fluvio-lacustrine sediments) (China) 
\citep[0.12 ${\pm}$ 0.01 ($n=2$);][]{Renetal2022}; 
R33: Tuffite (Slovakia) \citep[0.14 ($n=1$);][]{Bigalkeetal2010b}; 
R34: Bulk Silicate Earth (MORB ${+}$ OIB ${+}$ Komatite ${+}$ Peridotite) 
\citep[0.07 ${\pm}$ 0.1;][]{Savageetal2015}; 
R35: Mix of chalcopyrite, chalcocite, djurleite, and covellite from
Canariaco district (Peru) \citep[0.03 ${\pm}$ 0.85
($n=37$);][]{Mathuretal2012a}; 
R36: Mix of goethite and hematite from Canariaco district (Peru)
\citep[${-}$5.35 ${\pm}$ 2.28 ($n=7$);][]{Mathuretal2012a}; 
R37: Mix of chalcopyrite and pyrite from Urumieh-Dokhtar magmatic belt
(Iran) \citep[0.43 ${\pm}$ 0.4 ($n=10$);][]{Mirnejadetal2010};
R38: Mix of hematite and goethite from Urumieh-Dokhtar magmatic belt
(Iran) \citep[${-}$3.84 ${\pm}$ 4.49 ($n=10$);][]{Mirnejadetal2010}; 
R39: Supergene chalcocite from Urumieh-Dokhtar magmatic belt (Iran)
\citep[3.16 ${\pm}$ 1.29 ($n=11$);][]{Mirnejadetal2010}; 
R40: Cu--Co ore (${=}$\ SU-1, Certified Reference Material) 
\citep[${-}$0.02;][]{Chapmanetal2005}; 
R41: Mix of Pyrite, chalcopyrite, galene, and sericite from Prospect
Gulch abandoned mine (Colorado, USA) 
\citep[${-}$0.13 ${\pm}$ 0.1 ($n=2$);][]{FernandezBorrok2009}; 
R42: Chalcopyrite ore (skarn type) (China) 
\citep[${-}$1.61 ${\pm}$ 0.55 ($n=3$);][]{Mathuretal2014}; 
R43: Chalcopyrite and pyrite (massive sulfide, Spain) 
\citep[${-}$1.91 ${\pm}$ 2.6 ($n=15$);][]{Mathuretal2014}; 
R44: Chalcopyrite and pyrite (porphyry copper deposit, Chile) 
\citep[3.98 ${\pm}$ 2.38 ($n=4$);][]{Mathuretal2014}; 
R45: Chalcopyrite and pyrite from 9 porphyry copper deposits (USA,
Chile, Turkey) \citep[1.21 ${\pm}$ 2.74 ($n=7$);][]{Mathuretal2009};
R46: Secondary chalcocite \citep[0.53 ${\pm}$ 4.89
($n=15$);][]{Mathuretal2009}; 
R47: Leach cap (mix of hematite, jarosite and goethite) 
\citep[${-}$2.49 ${\pm}$ 4.01 ($n=5$);][]{Mathuretal2009}; 
R48: Oxide \citep[1.98 ${\pm}$ 4.81 ($n=10$);][]{Mathuretal2009}; 
R49: Chalcopyrite (USA, Peru) \citep[0.04 ${\pm}$ 0.52
($n=38$);][]{Larsonetal2003};
R50: Pyrite from Mining district of Taxco (southern Mexico) 
\citep[0.18 ${\pm}$ 0.28 ($n=4$);][]{DotorAlmazanetal2017}; 
R51: Tailings and precipitates from mining district of Taxco (southern
Mexico) \citep[${-}$0.18 ${\pm}$ 0.70 ($n=14$);][]{DotorAlmazanetal2017};
R52: Chalcopyrite (Colorado, USA) \citep[${-}$0.01
($n=1$);][]{Kimballetal2009}; 
R53: Enargite (Colorado, USA) \citep[0.29
($n=1$);][]{Kimballetal2009}; 
R54: Chalcopyrite \citep[0.37 ${\pm}$ 0.23 ($n=29$);][]{Zhengetal2019};
R55: Chalcopyrite \citep[0.36 ${\pm}$ 0.2;][]{Lietal2009a};
R56: Chalcopyrite (Schwarzwald area, South Germany) 
\citep[0.014 ${\pm}$ 0.5 ($n=25$);][]{Markletal2006};
R57: Oxidized chalcopyrite (Schwarzwald area, south Germany) 
\citep[${-}$1.14 ${\pm}$ 0.89 ($n=3$);][]{Markletal2006}; 
R58: Chalcopyrite (Dexing mine, China) \citep[0.33 ${\pm}$ 0.57
($n=9$);][]{Songetal2016}; 
R59: Pyrite from Dexing mine (China) \citep[2.81 ${\pm}$ 4.47
($n=15$);][]{Songetal2016}; 
R60: Tailings from Dexing mine (China) \citep[5.1 ${\pm}$ 0.7
($n=4$);][]{Songetal2016}; 
R61: Chalcopyrite (Lau Basin, Pacific) \citep[0.32
($n=1$);][]{Marechaletal1999}; 
R62: Chalcopyrite (Chuquicamata, P\'{e}ru) \citep[0.27
($n=1$);][]{Marechaletal1999}; 
R63: Chalcopyrite (Morenci, USA) \citep[5.74
($n=1$);][]{Marechaletal1999}; 
R64: Malachite (Zaire) \citep[${-}$0.24 ($n=1$);][]{Marechaletal1999}; 
R65: Chrysocolla (Bagdad, USA) \citep[${-}$0.12
($n=1$);][]{Marechaletal1999}; 
R66: Azurite (Chessy, France) \citep[2.05
($n=1$);][]{Marechaletal1999}; 
R67: Hypogene zone from Kerman Porphyry Copper Belt (Iran) 
\citep[1.49--7.31 (range);][]{Sarjoughianetal2024}; 
R68: Leached zone from Kerman Porphyry Copper Belt (Iran) 
\citep[${-}$3.41 to 5.82 (range);][]{Sarjoughianetal2024}; 
R69: Supergene enriched zone with secondary~minerals (Kerman Porphyry
Copper Belt, Iran) \citep[5.18--8.71
(range);][]{Sarjoughianetal2024}; 
R70: Chalcopyrite (Cu--Pb--Zn deposit from Mount Isa, Australia) 
\citep[${-}$0 ${\pm}$ 0.26 ($n=12$);][]{Mahanetal2023}; 
R71: Cu sulphide minerals within porphyries \citep[${-}$2.31 ${\pm}$ 0.68
($n=9$);][]{Asaeletal2007}; 
R72: Cu sulphide minerals within Cambrian dolomite \citep[${-}$2.03
${\pm}$ 0.6 ($n=13$);][]{Asaeletal2007}; 
R73: Chalcopyrite \citep[0.13 ${\pm}$ 0.47 ($n=3$);][]{Kimetal2023}; 
R74: Chalcopyrite (Chile) \citep[0.09 ${\pm}$ 0.38
($n=24$);][]{Mathuretal2005}; 
R75: Chalcopyrite from the skarn ore bodies (Hongshan-Hongniu deposit,
China) \citep[0.34 ${\pm}$ 0.22 ($n=28$);][]{Wangetal2017}; 
R76: Chalcopyrite from Dabu porphyry Cu--Mo deposit (Tibet)
\citep[${-}$0.10 ${\pm}$ 0.60 ($n=22$);][]{Wuetal2017}; 
R77: Secondary minerals from bed rivers and open pits impacted by
Tharsis Mine (Huelva, Spain) \citep[${-}$1.82 ${\pm}$ 3.18
($n=9$);][]{Viersetal2023}.} 
\end{figure*}

{\vspace*{4pt}}

\section{Soils (Figure~\myRef{fig2})}\label{sec3}

{\vspace*{4pt}}

\begin{figure*}
\includegraphics{fig02}
\bcaption{\label{fig2}Type of sample and associated bibliographic
reference for soils; The points surrounded by a dotted line are from
the same study.
S1: Fluvisol (Glen Feshie chronosequence, Scotland, United Kingdom)
\citep[${-}$0.25 ${\pm}$ 0.11 ($n=20$);][]{Vanceetal2016}; 
S2: Andisol from Hawaii (USA) \citep[${-}$0.08 ${\pm}$ 0.38
($n=54$);][]{Vanceetal2016}; 
S3: Lateritic soils from Amazonia (Brazil)  \citep[${-}$0.39 ${\pm}$ 0.12
($n=8$);][]{Guinoiseauetal2017}; 
S4: Lateritic soils from Goa state (India)  \citep[${-}$0.37 ${\pm}$ 0.33
($n=14$);][]{Littleetal2019}; 
S5: Lateritic soils from Penglai weathering profile (Hainan Island,
China) \citep[${-}$0.05 ${\pm}$ 0.15 ($n=19$);][]{Liuetal2022}; 
S6: Saprolitic soils on basalt (South Carolina, USA) 
\citep[0.4 ${\pm}$ 1 ($n=17$);][]{Liuetal2014}; 
S7: Soils on basalt from Hainan Island (China) 
\citep[0.01 ${\pm}$ 0.16 ($n=19$);][]{Liuetal2014}; 
S8: Arenosol from the French Soil Quality Monitoring (France)
\citep[${-}$0.15 ($n=1$);][]{Fekiacovaetal2015}; 
S9: Cambisol (on basalt) from the French Soil Quality Monitoring
(France) \citep[0.09 ($n=1$);][]{Fekiacovaetal2015}; 
S10: Cambisol (on granit) from the French Soil Quality Monitoring
(France) \citep[0.01 ($n=1$);][]{Fekiacovaetal2015};
S11: Podzol from the French Soil Quality Monitoring (France)
\citep[${-}$0.14 ($n=1$);][]{Fekiacovaetal2015}; 
S12: Soils on Marcellus formation (Pennsylvania, USA)
\citep[${-}$0.5 ${\pm}$ 0.19 ($n=33$);][]{Mathuretal2012b};
S13: Paddy soil, gley horizon (Suzhou, Eastern China)
\citep[${-}$0.09 ${\pm}$ 0.08 ($n=33$);][]{Wangetal2022}; 
S14: Liaohe plain Soil (${=}$\ GBW07425 (GSS-11, Certified Reference
Material) \citep[${-}$0.07 ($n=1$);][]{Sullivanetal2022}; 
S15: Paddy soil (GBW07443) (GSF-3, Certified Reference Material)
\citep[${-}$0.04 ($n=1$);][]{Sullivanetal2022}; 
S16: Soil from North China Plain (GBW07389) (GSS-33, Certified
Reference Material) \citep[${-}$0.06 ($n=1$);][]{Sullivanetal2022}; 
S17: Deeper horizon from retisol (Yonne Plateau, France)
\citep[${-}$0.38 ${\pm}$ 0.15 ($n=19$);][]{Kusonwiriyawongetal2017}; 
S18: Carbonatic alluvial soil (floodplain, Switzerland)
\citep[${-}$0.08;][]{Kusonwiriyawongetal2016};
S19: Skeletic cambisol developed on slate (Germany)
\citep[${-}$0.15 ${\pm}$ 0.13 ($n=4$);][]{Bigalkeetal2011}; 
S20: Dystric cambisol on sandstone (Slovakia) 
\citep[${-}$0.06 ${\pm}$ 0.12 ($n=7$);][]{Bigalkeetal2011}; 
S21: Haplic podzol (Germany) \citep[${-}$0.20 ${\pm}$ 0.31
($n=10$);][]{Bigalkeetal2011}; 
S22: Haplic podzol (Slovakia) 
\citep[${-}$0.15 ${\pm}$ 0.08 ($n=6$);][]{Bigalkeetal2011}; 
S23: Cultivated paddy soils (Mun River Basin, Thailand) 
\citep[0.04;][]{Zhengetal2023}; 
S24: Abandoned paddy soils (Mun River Basin, Thailand) 
\citep[0.06 ($n=1$);][]{Zhengetal2023}; 
S25: Vertic cambisol (Vineyard catchment, Italia) 
\citep[0.26 ${\pm}$ 0.08 ($n=8$);][]{Blotevogeletal2018}; 
S26: Calcaric cambisol (Vineyard catchment, Italia) 
\citep[0.18 ${\pm}$ 0.03 ($n=7$);][]{Blotevogeletal2018}; 
S27: Fungicides (Cu sulfates) 
\citep[0.85 ${\pm}$ 0.05 ($n=2$);][]{Blotevogeletal2018}; 
S28: Fungicides (Cu hydroxydes) 
\citep[0.09 ${\pm}$ 0.27 ($n=8$);][]{Blotevogeletal2018}; 
S29: Fine clays fraction from Cambisol (Vineyard catchment)
\citep[${-}$0.23 ${\pm}$ 0.16 ($n=8$);][]{Babcsanyietal2016}; 
S30: Silt fraction from Cambisol (Vineyard catchment) 
\citep[0.23 ${\pm}$ 0.04 ($n=4$);][]{Babcsanyietal2016}; 
S31: Fungicides (Cu sulfates) 
\citep[${-}$0.07 ${\pm}$ 0.01 ($n=2$);][]{Babcsanyietal2016}; 
S32: Fungicides (Cu hydroxydes)
\citep[0.36 ${\pm}$ 0.36 ($n=4$);][]{Babcsanyietal2016}; }{1}{S33: 
Soils from Bordeaux (Vineyard catchment, France) 
\citep[${-}$0.28 ${\pm}$ 0.03 ($n=3$);][]{Petitetal2013}; 
S34: Soils from Banyuls (Vineyard catchment, France)
\citep[${-}$0.03 ${\pm}$ 0.03 ($n=2$);][]{ElAzzietal2013}; 
S35: Cu fungicides 
\citep[${-}$0.34 ($n=1$);][]{ElAzzietal2013}; 
S36: Dystric Cambisol (1~km--3.8~km from smelter, Slovakia)
\citep[0.15 ${\pm}$ 0.13 ($n=12$);][]{Bigalkeetal2010b}; 
S37: Stagni eutric cambisol 
\citep[0.14 ${\pm}$ 0.13 ($n=6$);][]{Bigalkeetal2010b}; 
S38: Waste products (slag, ash, solid waste) (Slovakia) 
\citep[0.59 ${\pm}$ 0.75 ($n=5$);][]{Bigalkeetal2010b}; 
S39: Top soils from Panhihua city area (Sichuan province, China)
\citep[${-}$0.01 ${\pm}$ 0.11 ($n=19$);][]{Xiaetal2023}; 
S40: Ore from Panhihua city area (Sichuan province, China) 
\citep[1.34 ($n=1$);][]{Xiaetal2023}; 
S41: Fly ash from Panhihua city area (Sichuan province, China) 
\citep[0.16 ${\pm}$ 0.16 ($n=3$);][]{Xiaetal2023}; 
S42: Coal from Panhihua city area (Sichuan province, China) 
\citep[2.44 ($n=1$);][]{Xiaetal2023}; 
S43: Smelting slag from Panhihua city area (Sichuan province, China)
\citep[1.29 ($n=1$);][]{Xiaetal2023}; 
S44: Contaminated soils (Tsumeb district, Namibia) 
\citep[0.4 ${\pm}$ 0.22 ($n=8$);][]{Kribeketal2018}; 
S45: Slag flotation tailings, smelter dust {\ldots} (Tsumeb district,
Namibia) \citep[0.43 ${\pm}$ 0.3 ($n=18$);][]{Kribeketal2018}; 
S46: Polluted soils from Copperbelt (Zambia)
\citep[${-}$0.23 ${\pm}$ 0.18 ($n=13$);][]{Mihaljevicetal2018}; 
S47: Concentrates processed in the smelter (Zambia)
\citep[${-}$0.55 ${\pm}$ 0.14 ($n=4$);][]{Mihaljevicetal2018}; 
S48: Contaminated soils from coal mining area (Suixi County, soutwest
Huaibei city, China) \citep[0.72 ${\pm}$ 0.16 ($n=17$);][]{Renetal2022}; 
S49: Flying ash coal mining area (Suixi County, soutwest Huaibei city,
China) \citep[0.92 ($n=1$);][]{Renetal2022}; 
S50: Soils from mining region of Taxco area (southern Mexico) 
\citep[0.94 ${\pm}$ 0.57 ($n=5$);][]{DotorAlmazanetal2017}; 
S51: Tailing and precipitates from mining region of Taxco area
(southern Mexico) \citep[${-}$0.13 ${\pm}$ 0.7
($n=15$);][]{DotorAlmazanetal2017}; 
S52: Soil close to abandoned Dalseong Cu--W mine (Republic of Korea)
\citep[${-}$0.46 to 0.32 (range);][]{Kimetal2023};
S53: Soil downstream the adit seepage (abandoned Dalseong Cu--W mine,
Republic of Korea) \citep[0.41--1.12 (range);][]{Kimetal2023}; 
S54: Adit seepages from Dalseong Cu--W mine (Republic of Korea)
\citep[1.94${\pm}$ 1.15 ($n=9$);][]{Kimetal2023}; 
S55: Soils (colluviosol or leptosol) on limestone Marseille
\citep[${-}$0.41 ${\pm}$ 0.2 ($n=9$);][]{Gellyetal2019};
S56: Soil from Dexing mine (China) 
\citep[3.94 ${\pm}$ 2 ($n=2$);][]{Songetal2016}; 
S57: Tailings from Dexing mine (China) 
\citep[5.1 ${\pm}$ 0.7 ($n=4$);][]{Songetal2016}.}
\vspace*{-2pt}
\end{figure*}

The first observation we can make is that soils exhibit a very low
isotopic variability (${-}$0.11 ${\pm}$ 0.19) and are generally enriched in
the light isotope compared to the mean value of the rocks in the upper
continental crust (see Section~\ref{sec2}). However, we find that some
soils may have much higher variability \citep{Liuetal2014,
Vanceetal2016}. The soil profile studied by \citet{Liuetal2014} in
North Carolina (S6, Figure~\ref{fig2}) reveals a high isotopic
amplitude with values that can be high (0.4 ${\pm}$ 1) compared to the
average soil. If we look at this study more closely, it appears that it
is the surface soils which show strong joint enrichments of iron and
copper that show these high isotopic values (up~to~3.63). These data
agree with the work of \citet{Balistrierietal2008} and
\citet{Pokrovskyetal2008} showing that the sorption of Cu onto
amorphous Fe(III) metallic oxyhydroxides favors the heavy isotope. Note
also that some soils (Andisol) in Hawaii may have high signatures which
are explained by an input of Asian dust that contributes significantly
to the Cu budget \citep{Vanceetal2016}. However, in general, the global
isotopic value we obtained for soils suggests that the pedogenetic
processes induce the preferential leaching of the heavy isotope
(\tsup{65}Cu). This signifies that the products (dissolved and
particulate matter) of these weathering processes exported out of the
soil should therefore exhibit an isotopic signature higher than that of
soils. In other words, this suggests that the material transported by
rivers must have a heavier overall signature than ``average''\break soils.

Soils impacted by strong anthropogenic activities also show differences
in isotopic composition compared to the average soil. Among soils
impacted by winegrowing, it appears that they can present a negative
signature \citep[e.g., Bordeaux region, France, ${-}$0.37 to ${-}$0.28;
S33, Figure~\ref{fig2};][]{Petitetal2013} or positive \citep[e.g.,
Soave region, Italy; 0.18--0.26; S25--S26, 
Figure~\ref{fig2};][]{Blotevogeletal2018}. This contrast can be
explained by
the high isotopic variability of copper brought into vineyards as a
fungicide (${-}$0.34 ${<}$\ $\updelta$\tsup{65}Cu($\permil$) ${<}$ 0.85; 0.3
${\pm}$ 0.4). We obtained an average value of 0.02 ${\pm}$ 0.24 for soils
impacted by winegrowing. Another particularity concerns soils impacted
by mining activities and/or processing (in particular pyrometallurgy)
which generally show a significantly heavier signature than average
soils (0.96 ${\pm}$ 1.5). However, if this general trend is emerging, it
should not obscure the fact that differences can be found locally, at
certain sites. This can be illustrated by the work of
\citet{Mihaljevicetal2018}. Indeed, on this site of Zambia impacted by
mining activity, the soils are isotopically negative (${-}$0.43 ${\pm}$
0.06{$\permil$}) as are the mine tailings (${-}$0.75 to
${-}$0.45{$\permil$}; \xcitealp{Mihaljevicetal2018}{2018}). These soils
impacted by viticulture or mining activities do not seem to follow a
general rule regarding the sign and amplitude of fractionation. 
It~is~\mbox{therefore} important to properly characterize their reservoirs/sources
and their isotopic signatures at a local scale. However, the magnitude
of these fractionations makes the use of copper isotopes relevant in
such contexts. Their use in natural contexts for the study of
pedogenetic processes seems more\break limited.

\section{World rivers (Figure~\myRef{fig3})}\label{sec4}

\begin{figure*}
\includegraphics{fig03}
\bcaption{\label{fig3}Type of sample and associated bibliographic
reference for river waters.
R1: Amazon River (Brazil) 
\citep[0.69 ${\pm}$ 0.09 ($n=3$);][]{Vanceetal2008}; 
R2: Brahmaputra River (India) 
\citep[0.64 ($n=1$);][]{Vanceetal2008};
R3: Nile River (Sudan, Egypt) 
\citep[0.58 ${\pm}$ 0.22 ($n=18$);][]{Vanceetal2008}; 
R4: Chang-Jiang (Yangtze) River (China) 
\citep[1.32 ${\pm}$ 0.1 ($n=13$);][]{Vanceetal2008}; 
R5: Missouri River (USA) 
\citep[0.15 ${\pm}$ 0.16 ($n=4$);][]{Vanceetal2008}; 
R6: Kalix River (Sweden) 
\citep[0.53 ${\pm}$ 0.07 ($n=7$);][]{Vanceetal2008}; 
R7: River from Kamtchanka (Russia) 
\citep[0.6 ($n=1$);][]{Vanceetal2008}; 
R8: Ottawa River (Canada) 
\citep[0.45 ${\pm}$ 0.06 ($n=2$);][]{Vanceetal2008};
R9: Tocantins River (Brazil)
\citep[0.54 ${\pm}$ 0.09 ($n=3$);][]{Vanceetal2008}; 
R10: Volga River (Russia) 
\citep[0.64 ${\pm}$ 0.01 ($n=2$);][]{Vanceetal2008};
R11: Itchen River (England) 
\citep[0.64 ${\pm}$ 0.09 ($n=19$);][]{Vanceetal2008}; 
R12: Beaulieu River (England)
\citep[0.79 ${\pm}$ 0.17 ($n=7$);][]{Vanceetal2008}; 
R13: Suspended matter from Itchen River (England) 
\citep[${-}$0.55 ${\pm}$ 0.22 ($n=16$);][]{Vanceetal2008}; 
R14: Global River Water \citep[0.68;][]{Vanceetal2008}; 
R15: Suspended matter from Lot River (France) 
\citep[${-}$0.19 ($n=1$);][]{Petitetal2013}; 
R16: Suspended matter from Gironde (France) 
\citep[${-}$0.22 ($n=1$);][]{Petitetal2013}; 
R17: Suspended matter from Garonne River (France)
\citep[${-}$0.24 ${\pm}$ 0.08 ($n=13$);][]{Petitetal2013}; 
R18: Garonne River (France) 
\citep[0.12 ($n=1$);][]{Petitetal2013}; 
R19: Scheldt Estuary (Belgium) 
\citep[0.01 ($n=1$);][]{Petitetal2008}; 
R20: Bed sediment from Scheldt Estuary (Belgium) 
\citep[${-}$0.07 ${\pm}$ 0.21 ($n=7$);][]{Petitetal2008}; 
R21: Palojoki River (Karely, Russia) 
\citep[0.42 ${\pm}$ 0.05 ($n=3$);][]{Ilinaetal2013}; 
R22: Yangtze River (China) [1.68 ($n=1$); Viers, unpublished data]; 
R23: River from China [0.20 ($n=1$); Viers, unpublished data]; 
R24: River from Canada [0.27 ($n=1$); Viers, unpublished data]; 
R25: river from Siberia (Russia) [0.08 ($n=1$); Viers, unpublished data]; 
R26: River from Siberia (Russia) [0.05 ($n=1$); Viers, unpublished data]; 
R27: Rivers from Khamtchanka Peninsula (Russia) [${-}$0.25 ${\pm}$ 0.06 ($n=5$); Viers, unpublished data]; 
R28: Suspended matter from Negro River (Brazil) 
\citep[0.14 ${\pm}$ 0.14 ($n=16$);][]{Guinoiseauetal2017}; 
R29: St Lawrence River (Canada) 
\citep[0.38 ($n=1$);][]{Sullivanetal2022}; 
R30: Floodplain sediment (China) 
\citep[${-}$0.02 ($n=1$);][]{Sullivanetal2022}; 
R31: Tawara River (Japan) 
\citep[1.24 ${\pm}$ 0.05 ($n=6$);][]{Takanoetal2021}; 
R32: Uji River (Japan) \citep[0.78;][]{Takanoetal2021}; 
R33: Glen Feshie River (Scotland)
\citep[0.03 ${\pm}$ 0.09 ($n=5$);][]{Vanceetal2016}; 
R34: Alluvium from Glen Feshie River (Scotland)
\citep[${-}$0.23 ($n=1$);][]{Vanceetal2016};
R35: Chang-Jiang (Yangtze) River (China) 
\citep[1.18 ${\pm}$ 0.3 ($n=20$);][]{Wangetal2020}; 
R36: Tributaries of the Yangtze River (China)
\citep[0.77 ${\pm}$ 0.28 ($n=7$);][]{Wangetal2020}; 
R37: Suspended matter from Zhujiang River (China) 
\citep[0.17 ${\pm}$ 0.02 ($n=22$);][]{ZengHan2020};
R38: Bed sediments from Huangpu River (tributary of the Yangtze river, China)
\citep[${-}$0.37 to 0.18 (range);][]{Tuetal2023}; 
R39: Bed sediment from Baillaury River (Banyuls, France)
\citep[${-}$0.03 ${\pm}$ 0.08 ($n=3$);][]{ElAzzietal2013}; 
R40: Suspended matter from Baillaury River (Banyuls, France)
\citep[0.09 ($n=1$);][]{ElAzzietal2013}; 
R41: Baillaury River (Banyuls, France) 
\citep[0.31 ($n=1$);][]{ElAzzietal2013}; 
R42: River from vineyard catchment (Alsace, France)
\citep[0.95 ${\pm}$ 0.25 ($n=9$);][]{Babcsanyietal2016}; 
R43: Suspended matter from vineyard catchment (Alsace, France) 
\citep[${-}$0.20 ${\pm}$ 0.11 ($n=9$);][]{Babcsanyietal2016}; 
}{2}{R44: Sediment from urbanized area (Guanabara bay, Rio de Janeiro, Brazil) 
\citep[${-}$0.42 ${\pm}$ 0.05 ($n=5$);][]{Barreiraetal2024}; 
R45: Sediment from slightly urbanized area (Guanabara bay, Rio de Janeiro, Brazil) 
\citep[${-}$0.15 ${\pm}$ 0.18 ($n=5$);][]{Barreiraetal2024}; 
R46: Smelter impacted sediment from Ballinger lake (USA) 
\citep[0.94 ${\pm}$ 0.1 ($n=11$);][]{Thapaliaetal2010}; 
R47: Sediment from Ballinger lake (USA) 
\citep[0.77 ${\pm}$ 0.06 ($n=x$);][]{Thapaliaetal2010}; 
R48: Spanish River (Great Lakes region, Canada) 
\citep[${-}$0.24 ${\pm}$ 0.35 ($n=8$);][]{Junqueiraetal2023}; 
R49: Mine waste seepage (Spanish River, Great Lakes region, Canada)
\citep[0.88 ${\pm}$ 0.1 ($n=2$);][]{Junqueiraetal2023};
R50: Trent River (Great Lakes region, Canada)
\citep[${-}$0.26 ${\pm}$ 0.27 ($n=8$);][]{Junqueiraetal2023}; 
R51: Fischer Creek (USA) 
\citep[1.43 ${\pm}$ 0.32 ($n=13$);][]{Borroketal2008}; 
R52: Tinto River (Iberian Pyrite Belt, Spain) 
\citep[${-}$0.45 ${\pm}$ 0.66 ($n=5$);][]{Borroketal2008}; 
R53: Odiel River (Iberian Pyrite Belt, Spain) 
\citep[${-}$0.74 ${\pm}$ 0.27 ($n=5$);][]{Borroketal2008}; 
R54: West Branch Ompampanoosuc River 
\citep[${-}$0.41 ($n=1$);][]{Balistrierietal2008}; 
R55: Meca River (Odiel River tributary, Iberian Pyrite Belt, Spain)
\citep[${-}$0.31 ${\pm}$ 0.17 ($n=17$);][]{Masbouetal2020}; 
R56: Meca River tributary (Iberian Pyrite Belt, Spain) 
\citep[${-}$2.72 ($n=1$);][]{Masbouetal2020}; 
R57: Rivers from mining region of Colorado (USA) 
\citep[1.59 ${\pm}$ 0.09 ($n=14$);][]{Kimballetal2009}; 
R58: Cobica River (Iberian Pyrite Belt, Spain) 
\citep[0.73 ${\pm}$ 0.71 ($n=4$);][]{Viersetal2018}; 
R59: Meca River (Odiel River tributary, Iberian Pyrite Belt, Spain) 
\citep[${-}$0.21 ${\pm}$ 0.47 ($n=7$);][]{Viersetal2023}; 
R60: Rivers from Dexing mine region (China) 
\citep[4.96 ${\pm}$ 4.58 ($n=20$);][]{Songetal2016}.}
{\vspace*{-1pt}}
\end{figure*}

In Figure~\ref{fig3}, are plotted river data for dissolved phase 
({${<}$}0.22 ${\upmu}$m or 0.45 ${\upmu}$m), suspended sediment
({${>}$}0.22~${\upmu}$m or 0.45 ${\upmu}$m) and bottom sediments. If we
consider unpolluted rivers, that is to say ``just'' subject to global
diffuse pollution, it emerges that the dissolved phase of rivers shows
relatively high isotopic variability (between
${-}0.25<\updelta{}^{65}\mathrm{Cu}_{\mathrm{Dissolved}}$ ($\permil$)
${<}$ 1.68). In 2008, Vance et~al.\ proposed a global average value for the
dissolved fraction of rivers of 0.68{$\permil$}. \citet{Vanceetal2008}
suggest that the high value for the dissolved phase of rivers is
related to the preferential complexation of heavy copper with organic
ligands, as shown by the work of \citet{Ryanetal2014} and
\citet{Bigalkeetal2010a}. Compared to this average value proposed by
\citet{Vanceetal2008}, it  appears that some rivers may have much more
negative values [${-}$0.25{$\permil$} (Khamchanka, Russia); R27,
Figure~\ref{fig3}; Viers (unp.\ value)] or much more positive ones 
\citep[1.18 ${\pm}$ 0.3{$\permil$} for Yangtze River, China; R35, 
Figure~\ref{fig3};][]{Wangetal2020}. \citetalias{Wangetal2020} recognized that
the values obtained for the Yangtze River and its tributaries are the
highest ever measured; moreover, unpublished measurement (Viers)
confirm these high values for the Yangtze River (R22,
Figure~\ref{fig3}). \citetalias{Wangetal2020} attribute these high values to
the effect of the Three Gorges Dam; the accumulation of sediment within
the dam would favor the preferential sorption of the light isotope onto
mineral surface leaving a heavier copper in the dissolved phase
complexed with organic matter. Even if another average value is
proposed in this paper (0.53 ${\pm}$ 0.45) for the dissolved phase of
world rivers, to my opinion, proposing a rigorous weighted average flow
value requires a broader sampling of other major rivers (e.g., Orinoco,
Mekong, Congo, Yenisey, Lena) which provide a significant contribution
of the Cu budget to the world ocean and for which we don't have any
data. It also seems necessary to see whether the \mbox{isotopic} signature in
copper for these large river systems varies during the seasons, that is
to say the hydrological year. There is, however, another aspect to be
considered here. Some studies suggest that the flow of Cu to the World
Ocean via rivers could be largely underestimated and in particular the
transfer of Cu through coastal mining areas \citep{Oliasetal2006}. This
is the case, for example, of the Iberian Pyritic Belt (Huelva region,
Spain) where the Tinto and Odiel rivers would export to the Atlantic
Ocean almost 10\% of the world's copper flow \citepalias{Oliasetal2006} due
to the presence of nearly 90 abandoned mines in the watersheds of these
rivers. We understand that given the particular signature of these
rivers \citep[${-}$0.74 ${\pm}$ 0.27 and ${-}$0.45 ${\pm}$ 0.66 for
Odiel and Tinto rivers, respectively; R52 and R53,
Figure~\ref{fig3};][]{Borroketal2008},  this region (and similar
coastal regions) may influence the global isotopic signature of the
world's ocean. It is important to note that the rivers draining areas
impacted by mining activities are those with the highest isotopic
amplitude (0.28 ${\pm}$ 1.84). Given what we observed for solids
(precipitates, sediments, ore) in these mining areas, it is not
surprising to find this high amplitude for watercourses.

It also emerges from this synthesis that sediments show more negative
signatures than the dissolved phase of rivers
(${-}0.55<\updelta{}^{65}\mathrm{Cu}_{\mathrm{Sediment}}$ ($\permil$) ${<}$
0.23) with an average value of ${-}$0.09 ${\pm}$ 0.20. If we consider,
based on literature data \citep[e.g.,][]{Boyleetal1977,Viersetal2009,
Littleetal2014,Gaillardetetal2014,Takanoetal2014,Lietal2020,Machuetal2024}
that 90\% of the copper is exported in particulates, a simple mixing
calculation reveals that rivers export to the world ocean a whole
copper close to 0 or slightly negative depending on whether one
considers the value proposed by \citet{Vanceetal2008} or that of this
article. Considering such a distribution, this isotopic signature of
this whole material transported by rivers seems contradictory because
it was thought to find the missing fraction of the soils and therefore
a rather positive pool was expected. This apparent bias may be linked
to a still too incomplete database that does not properly represent the
reality of the isotopic signatures of these reservoirs (soils, river
sediments) or the isotopic signatures observed in watercourses are
impacted by secondary exchange/redistribution processes fractionating
copper isotopes within watercourses.

\section{Oceans (Figure~\myRef{fig4})}\label{sec5}

\begin{figure*}
\includegraphics{fig04}
\bcaption{\label{fig4}Type of sample and associated bibliographic
reference for Oceans; the points surrounded by a dotted line
proviennent d'une m\^{e}me zone g\'{e}ographique (China sea, Pacific
Ocean). O1: Mediterranean sea 
\citep[0.51 ${\pm}$ 0.2 ($n=96$);][]{Baconnaisetal2019}; 
O2: South Western Mediterranean sea 
\citep[0.48 ${\pm}$ 0.09 ($n=23$);][]{Baconnaisetal2019}; 
O3: South Eastern Mediterranean sea 
\citep[0.55 ${\pm}$ 0.14 ($n=19$);][]{Baconnaisetal2019}; 
O4: North Western Mediterranean sea 
\citep[0.50 ${\pm}$ 0.08 ($n=21$);][]{Baconnaisetal2019}; 
O5: North Eastern Mediterranean sea
\citep[0.54 ${\pm}$ 0.09 ($n=14$);][]{Baconnaisetal2019}; 
O6: North Atlantic Ocean 
\citep[0.50 ${\pm}$ 0.09 ($n=20$);][]{Baconnaisetal2019}; 
O7: Sapropel \citep[${-}$0.09 ($n=1$);][]{Marechaletal1999};
O8: South Atlantic 
\citep[0.64 ${\pm}$ 0.09 ($n=63$);][]{Littleetal2018}; 
O9: Particles from South Atlantic 
\citep[0.18 ${\pm}$ 0.15 ($n=17$);][]{Littleetal2018}; 
O10: Leachable pool from particles from South Atlantic 
\citep[0.4 ${\pm}$ 0.11 ($n=16$);][]{Littleetal2018};
O11: Near shore seawater (Halifax Harbour, North Atlantic) (${=}$\ CASS-5
reference material) \citep[0.46;][]{Sullivanetal2022}; 
O12: Seawater (Nova Scotia, North Atlantic) (NASS-6 reference material)
\citep[0.42;][]{Sullivanetal2022}; 
O13: English channel \citep[0.7 ($n=1$);][]{Berminetal2006}; 
O14: Sediment from Atlantic Ocean 
\citep[0.22 ${\pm}$ 0.1 ($n=8$);][]{Marechaletal1999};
O15: Suspended particle (lower fraction, ${<}250~\upmu$m) from
continental shelf of northern Bay of Biscay (north eastern Atlantic
Ocean) \citep[${-}$0.42 ${\pm}$ 0.04 ($n=2$);][]{Araujoetal2022}; 
O16: Suspended particle (coarse fraction, ${>}250~\upmu$m) from inner
continental shelf of northern Bay of Biscay (north eastern Atlantic
Ocean) \citep[0.09 ${\pm}$ 0.03 ($n=5$);][]{Araujoetal2022}; 
O17: Plankton, BCR 414 Certified Reference Material) 
\citep[${-}$0.27 ($n=1$);][]{Araujoetal2022}; 
O18: Surface sediments from the Loire estuary (France) 
\citep[${-}$0.08 ${\pm}$ 0.08 ($n=1$7);][]{Araujoetal2019}; 
O19: Paleovase from the Loire estuary (France)
\citep[0.03 ${\pm}$ 0.03 ($n=1$2);][]{Araujoetal2019}; 
O20: Marine sediments from Beaufort sea (MESS-3 reference material)
\citep[0.01;][]{Sullivanetal2020}; 
O21: Marine sediments from British Columbia (Canada) (PACS Certified
Reference Material) \citep[0.03;][]{Sullivanetal2020}; 
O22: Modern marine sediments (Cariaco basin)
\citep[0.14 ${\pm}$ 0.08 ($n=2$);][]{Littleetal2017}; 
O23: Modern marine sediments (California borderland basin) 
\citep[0.14 ${\pm}$ 0.08 ($n=4$);][]{Littleetal2017}; 
O24: Modern marine sediments (Black Sea) 
\citep[0.2 ${\pm}$ 0.01 ($n=4$);][]{Littleetal2017}; 
O25: Modern marine sediments (Peru margin) 
\citep[0.32 ($n=1$);][]{Littleetal2017}; 
O26: Modern marine sediments (Mexican margin)
\citep[0.16 ${\pm}$ 0.03 ($n=3$);][]{Littleetal2017}; 
O27: North Atlantic marine sediment (HISS-1 Certified Reference
Material) \citep[0.05 ${\pm}$ 0.02;][]{Takanoetal2020}; 
O28: South Central Indian Ocean (${<}$100~m) 
\citep[0.5 ${\pm}$ 0.01 ($n=4$);][]{Takanoetal2014}; 
O29: South Central Indian Ocean (${>}$300~m) 
\citep[0.64 ${\pm}$ 0.03 ($n=12$);][]{Takanoetal2014}; 
O30: South Central Indian Ocean
\citep[1.1 ${\pm}$ 0.16 ($n=1$2);][]{Vanceetal2008}; 
O31: Western North Pacific Ocean (${<}$100~m) 
\citep[0.47 ${\pm}$ 0.03 ($n=6$);][]{Takanoetal2014}; }{3}{O32: 
Western North Pacific Ocean (${>}$300~m) 
\citep[0.65 ${\pm}$ 0.04 ($n=12$);][]{Takanoetal2014}; 
O33: Eastern North Pacific Ocean (${<}$100~m) 
\citep[0.49 ${\pm}$ 0.06 ($n=7$);][]{Takanoetal2014}; 
O34: Eastern North Pacific Ocean (${>}$300~m) 
\citep[0.73 ${\pm}$ 0.06 ($n=18$);][]{Takanoetal2014}; 
O35: North East Pacific Ocean 
\citep[1.14 ${\pm}$ 0.12 ($n=11$);][]{Vanceetal2008}; 
O36: North East Pacific 
\citep[0.85--1.35 (range);][]{Berminetal2006}; 
O37: North Tasman sea (SW Pacific Ocean) 
\citep[0.61 ${\pm}$ 0.16 ($n=15$);][]{ThompsonEllwood2014}; 
O38: Mid Tasman sea (SW Pacific Ocean) 
\citep[0.7 ${\pm}$ 0.2 ($n=17$);][]{ThompsonEllwood2014}; 
O39: South Tasman sea (SW Pacific Ocean)
\citep[0.78 ${\pm}$ 0.17 ($n=16$);][]{ThompsonEllwood2014}; 
O40: Particles from Tasman sea (SW Pacific Ocean) 
\citep[0.17 ${\pm}$ 0.15 ($n=7$);][]{ThompsonEllwood2014}; 
O41: Surface water (${<}$200~m) from Kuroshio current along the East
China Sea \citep[0.47 ${\pm}$ 0.05 ($n=37$);][]{Takanoetal2022}; 
O42: Deep water (${>}$600~m) from Kuroshio current along the East
China Sea \citep[0.68 ${\pm}$ 0.02 ($n=17$);][]{Takanoetal2022}; 
O43: Northern South China Sea (${<}$400~m)
\citep[0.47 ($n=1$);][]{Takanoetal2020}; 
O44: Northern South China Sea (${>}$400~m) 
\citep[0.68 ${\pm}$ 0.03 ($n=4$);][]{Takanoetal2020}; 
045: Particles from Northern South China Sea
\citep[0.13--0.36;][]{Takanoetal2020}; 
O46: Fe--Mn nodules from Pacific Ocean 
\citep[0.54 ${\pm}$ 0.04 ($n=8$);][]{Littleetal2014};
O47: Fe--Mn nodules from Indian Ocean 
\citep[0.28 ${\pm}$ 0.39 ($n=8$);][]{Littleetal2014}; 
O48: Fe--Mn nodules from Atlantic Ocean
\citep[0.33 ${\pm}$ 0.07 ($n=8$);][]{Littleetal2014}; 
O49: Natural background sediment from Sepetiba Bay (Brazil) 
\citep[0.2 ($n=1$);][]{Jeongetal2023a}; 
O50: Sediment from Sepetiba bay (Brazil), impacted by smelter
\citep[0.32 ${\pm}$ 0.18 ($n=13$);][]{Jeongetal2023a}; 
O51: Sediment from Northern Sepetiba bay (Brazil) 
\citep[0.23 ${\pm}$ 0.1 ($n=12$);][]{Jeongetal2023a}; 
O52: Metallurgic slag from Sepetiba bay (Brazil)
\citep[0.4 ${\pm}$ 0.2;][]{Jeongetal2023a}; 
O53: Sediments from Busan Harbor (South Korea)
\citep[0.27 ${\pm}$ 0.04 ($n=$7);][]{Jeongetal2023b}; 
O54: Domestic Antifoulings paints (South Korea) 
\citep[0.48 ${\pm}$ 0.06 ($n=5$);][]{Jeongetal2023b}; 
O55: Imported antifouling sources paints (South Korea) 
\citep[0.39 ${\pm}$ 0.16 ($n=4$);][]{Jeongetal2023b}; 
O56: Cu-contaminated Mediterranean sediments from marina of Port Camargue
(France) \citep[0.07 ${\pm}$ 0.12 ($n=25$);][]{Briantetal2022}; 
O57: Antifouling paints Cu-contaminated Mediterranean marina of Port
Camargue \citep[0.54 ${\pm}$ 0.05 
($n=3$);][]{Briantetal2022}.\looseness=1}
{\vspace*{-2pt}}
\end{figure*}

Given the limited data available, it appears that the World Ocean has
an average value of 0.63 ${\pm}$ 0.12{$\permil$} in agreement with
literature data \citep{Littleetal2018}. This isotopic signature of the
oceans ranges from 0.50{$\permil$} (Mediterranean Sea and Atlantic
Ocean) to 0.85{$\permil$} (North Pacific Ocean). The most striking
result, however, is the difference in isotopic composition between the
dissolved phase (0.63 ${\pm}$ 0.12{$\permil$}) and the particulate phase
(0.07 ${\pm}$ 0.17{$\permil$}), enriched in the light isotope. We find
here a similarity with the dissolved and particulate phases of rivers,
for which we have also an isotopically dissolved phase heavier than the
particulate phase. A number of articles 
\citep[e.g.,][]{Vanceetal2008,Takanoetal2014,
Littleetal2014,ShermanLittle2020}
have attempted to explain the copper cycle within the oceans and the
associated isotopic fractionations during intra-oceanic processes
(inputs from rivers and the atmosphere, intra-oceanic exchange between
dissolved and particulate matter, role of planktonic matter and Fe--Mn
nodules). In particular, these authors 
\citep[e.g.,][]{Littleetal2018} point out that if the signature of the
ocean is similar (0.65{$\permil$}) to that provided by rivers
(0.68{$\permil$}) a mass balance in stationary state requires an
additional source of light Cu. This source of light Cu could be
hydrothermalism or the dissolution of terrigenous particles, brought by
rivers or the atmosphere. However, the establishment of these balance
sheets is subject to high uncertainties regarding flows and associated
isotopic signatures. While the signature of the particles brought by
the rivers seems homogeneous and well constrained, it is not the same
for the dissolved phase. There is also considerable uncertainty about
the isotopic signature of atmospheric particles. If it seems that
particles in cities have isotopic signatures between 0 and 0.6 \citep[global
diffuse pollution of urban environments;][]{SoutoOliveiraetal2018,
Dongetal2017,OchoaGonzalezetal2016}, the few measurements available
for non-anthropized environments (Sahel Region (Africa), Taklamakan
desert (Asia), equatorial eastern North Atlantic region of West Africa,
eastern tropical Atlantic; South China Sea) reveal values in a more
negative range (${-}$0.24 to 0.83) \citep{Dongetal2013, Packmanetal2022,
Takanoetal2020}. Aerosols values obtained for sites heavily impacted by
mineral processing activities in Central Europe appear very negative
(${-}$3.63 to 0.42) \citep{Novaketal2016,Mihaljevicetal2018}. Another
striking fact resulting from this synthesis is that there is no major
isotopic difference between the signature of marine particles and those
of harbor sediments impacted by anthropogenic activities 
\citep{Briantetal2022,Jeongetal2023a,Jeongetal2023b}.  These data
appear similar to the mean value for terrestrial\break rocks.

Beyond these uncertainties on flux or isotopic signatures, work must be
continued on particle dissolution processes  
\citep[e.g.,][]{Littleetal2018,ThompsonEllwood2014}
as well as on processes occurring in estuarine zones. There is no data
on these environments.

\section{Conclusion}\label{sec6}

The objective of this work was to make an inventory of copper isotope
signatures in the main reservoirs (rock, soil, river, oceans). What
amplitudes, what signs? After almost 25 years of work carried out with
precise measurements of these isotopic ratios, it is important to
consider whether they can provide us useful constraints in the
understanding of the cycle of large-scale elements. Some results can be
confirmed while others today have too many uncertainties: (1) the
isotopic signature of rocks shows a low amplitude; (2) the regions with
sulfides or affected by acid mine drainage are those that exhibit the
most important fractionations either for solids (rocks, precipitates,
sediments) or for waters; (3) soils not impacted by human activities
(industry, mine, winegrowing) have low variability; (4) Cu leached from
the soils is heavier than the residual soil; (5) the global isotopic
signature used today for the dissolved phase of rivers should, to my
opinion, be improved by considering seasonal sampling and unsampled
large river systems; (6) the particulate matter of rivers is more
negative than the dissolved phase; (7) coastal mining areas are
potentially a major source of copper to the global Ocean and can impact
the isotopic signature of the global Ocean; (8) the dissolved phase of
the Oceans is much heavier than the particulate phase; (9) the work of
modelling Cu isotopic signatures in the World Ocean remains hazardous
due to the uncertainty of the isotopic signature of river and
atmospheric\break flows.

\section*{Declaration of interests}

The authors do not work for, advise, own shares in, or receive funds
from any organization that could benefit from this article, and have
declared no affiliations other than their research organizations.

\section*{Acknowledgement}

I would like to deeply thank you Nolwenn Lemaitre for helpful
discussion.

\back{}

\printbibliography
\refinput{crgeos20250080-reference.tex}

\end{document}
