EARTH SCIENCES CENTRE UNIVERSITY OF GOTHENBURG B547 2008

Department of Earth Sciences Geology

GÖTEBORG 2008

MINING ACTIVITY IMPACT ON FLUVIAL SEDIMENTS IN THE SW AMAZON

DRAINAGE BASIN, PERU

Martin Persson

GÖTEBORG UNIVERSITET Institutionen för geovetenskaper Avdelningen för geologi Geovetarcentrum ISSN 1400-3821 B547 Projektarbete Göteborg 2008

Postadress Besöksadress Telefon Telefax Earth Scienceses Centre Geovetarcentrum Geovetarcentrum 031-786 19 51 031-786 19 86 Göteborg University S 405 30 Göteborg Guldhedsgatan 5A S-405 30 Göteborg SWEDEN

MINING ACTIVITY IMPACT ON FLUVIAL SEDIMENTS IN THE SW AMAZON

DRAINAGE BASIN, PERU

Martin Persson

Abstract

There are environmental problems involved in large scale exploitation of natural recourses in

Peru. Mining and resulting tailings and smelters contribute to raised metal levels in the

surrounding sediments. The objective of this study is to consider the environmental impact of

mining activity on metal contents of fluvial sediments. One aim is to assess the spatial

distribution of possibly mine derived sediment metal and arsenic anomalies. The fieldwork

was done in the Peruvian regions Cusco, Madre de Dios and Loreto. These represent different

settings; mountainous Andean and tropical lowland forest of different latitudes and altitudes.

The primary data source is from analyzing samples collected during the Fall of 2007. The

samples have been subjected to two types of leaching, followed by ICP-MS multi elemental

analysis. Powder XRD, loss on ignition and grain size analysis by wet sieving and Sedigraph

were also carried out. The heavy metal (and arsenic) contents have been evaluated using

various normalization approaches and guideline comparisons.

Two of the investigated sediments are interpreted as mining impacted. Other anthropogenic

sources do also contribute to raised metal and arsenic levels, primarily in Cusco.

Keywords: Amazonia, Peru, Mining, trace metal distribution, Amazon River, Rio Vilcanota,

Rio Urubamba, Rio Manu, Loreto, Madre de Dios, and Cusco.

Sammanfattning

Miljöproblem finns då naturtillgångar stora som Perus exploateras. Gruvdrift och därmed

relaterade varphögar och smältverk bidrar till höjda metallhalter i omgivande sediment. Syftet

med denna studie är att undersöka miljöpåverkan från gruvindustrin och hur denna påverkar

metallinnehållet i fluviala sediment. Ett mål är också att uppskatta den geografiska

spridningen av möjliga gruvrelaterade anomalier i sedimentens metallhalt. Fältarbetet gjordes

i de peruanska regionerna Cusco, Madre de Dios och Loreto. Dessa representerar olika

miljöer; bergiga Anderna och tropisk låglandsskog av olika latitud och höjd.

Den primära datakällan är analyser av provmaterial som insamlades under hösten 2007.

Proverna har utsatts för två typer av lakning med efterföljande ICP-MS multielementanalys.

Pulver-XRD, glödgningsförlust samt kornstorleksanalys med våtsikt och Sedigraph har också

utförts. Tungmetall- och arsenikhalterna har utvärderats genom olika normaliseringstekniker,

statistikbehandling och jämförelser med befintliga sedimentbedömningsgrunder.

Två av de undersökta sedimenten tolkas som påverkade av gruvaktivitet. Andra mänskliga

källor bidrar också till höjda metall- och arsenikhalter, huvudsakligen i Cusco.

Nyckelord: Amazonas, Peru, gruvindustri, spårmetallspridning, Amazonfloden, Rio

Vilcanota, Rio Urubamba, Rio Manu, Loreto, Madre de Dios och Cusco.

2

Table of contents Introduction ................................................................................................................................ 4

Objectives ............................................................................................................................... 4

Theoretical and historical framework .................................................................................... 4 Andean mining history and extent ..................................................................................... 4 Trace and major elements in river sediment systems ......................................................... 6 Sulfide weathering and Acid mine drainage formation ..................................................... 7 River transport of sediment and trace elements ................................................................. 8

A summary of Peruvian environments ................................................................................... 9 The Peruvian Amazon basin ............................................................................................ 10 The Peruvian Andes ......................................................................................................... 14

Study areas ............................................................................................................................... 14 The state of Cusco ................................................................................................................ 14

Geology and sedimentology ............................................................................................. 15 Rio Urubamba .................................................................................................................. 15

Laguna Huarcarpay .......................................................................................................... 17

Climate ............................................................................................................................. 18 Mining activity intensity .................................................................................................. 18 Land use and coverage ..................................................................................................... 19

The state of Madre de Dios .................................................................................................. 19

Geology and sedimentology ............................................................................................. 20 Rio Manu .......................................................................................................................... 21

Rio Alto Madre de Dios ................................................................................................... 21 Climate ............................................................................................................................. 22 Land use and coverage ..................................................................................................... 22

The state of Loreto ............................................................................................................... 22 Geology and sedimentology ............................................................................................. 23

Rio Marañón ..................................................................................................................... 23

Climate ............................................................................................................................. 23

Land use and coverage ..................................................................................................... 24 Methods .................................................................................................................................... 24

Fieldwork and sampling ....................................................................................................... 24 Visual examination of soil samples .................................................................................. 25

Analytical procedures ........................................................................................................... 25 Grain size distribution analyses ........................................................................................ 25 Loss on ignition analysis .................................................................................................. 26

XRD ................................................................................................................................. 26 Geochemistry ................................................................................................................... 27

Acid digestion of samples ................................................................................................ 27 ICP-MS ............................................................................................................................. 28

Normalization and statistics ................................................................................................. 28 Statistics ............................................................................................................................... 28

Sediment quality guidelines and enrichment factors ........................................................ 29 Pollution Index ................................................................................................................. 29

Results ...................................................................................................................................... 29

Visual examination of soil samples ...................................................................................... 29 Grain size distribution .......................................................................................................... 30 XRD ..................................................................................................................................... 32 Loss on ignition analysis ...................................................................................................... 32 ICP-MS ................................................................................................................................. 33

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Normalization and statistics ................................................................................................. 35

Soil quality guidelines and Enrichment Factors ............................................................... 39 Discussion ................................................................................................................................ 42

Uncertainties ......................................................................................................................... 44

Conclusions .......................................................................................................................... 45 Further research .................................................................................................................... 45

References ................................................................................................................................ 46 Appendix A. Sample record and Field observations ................................................................ 50 Appendix B. Grain size distribution and statistics ................................................................... 54

Appendix C. ICP-MS results .................................................................................................... 57 Appendix D. Normalized metal content ................................................................................... 59 Appendix F. XRD results ......................................................................................................... 62 Appendix G. LOI results .......................................................................................................... 63 Appendix H. Population densities in study areas ..................................................................... 63

Appendix I. Sediment and Soil quality guidelines ................................................................... 64

Appendix J. List of Acronyms and words ................................................................................ 64

Appendix K. Epilogue .............................................................................................................. 65 Appendix L. Additional images cd-rom ................................................................................... 65

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Introduction Mining together with Colombian use of herbicides, Ecuadorian oil exploitation and Amazon

deforestation are the main environmental issues on the South American continent (ABN,

2006). The mining impact of sediments has traditionally been considered a short range

feature. This Master’s thesis aims at describing the regional affects of Peruvian mines.

Fieldwork was carried out in the states of Cusco, Madre de Dios and Loreto in August and

September 2007.

Objectives

This study was made to present how mining and other anthropogenic metal sources

contaminate fluvial sediments in Southwestern Amazonia, Peru. One aim of this study is to

understand the parameters controlling the distribution of trace metals and the interplay

between them. Natural and anthropogenic contribution will be studied. Part of the goal of this

Master’s thesis is to answer the following questions.

• What levels could be expected (mg/kg)?

– Background assessment

– Deviations from background

• What differences in metal/As content are there?

– Andean tributaries vs. downstream Amazon Rain forest

– Spatially close and distant samples

• What Parameters and Elements covary? – Are any correlations possible?

• Which parameters affect where high levels of trace and major elements are

found?

• What sources are there for raised metal and arsenic levels in sediments? – Anthropogenic vs. Natural factors

• Could the mining industry impact be assessed? – What chemical and mineralogical features could be considered evidence for

mining impact?

– What range of impact is likely?

Theoretical and historical framework

To understand and evaluate the problems stated under the previous headline it is necessary to

have an understanding in historical, sedimentological and geochemical aspects of the

Southwestern Amazon drainage.

Andean mining history and extent

The earliest archeological findings suggest that mining has occurred at least since 1,400 B.C

in the Peruvian Andes. Early Andean mining were limited primarily to copper and tin (Abbot

et al., 2003). The pre-Columbian and Incan empires as well as the Spanish conquistadors

have all been involved in mining activities. During colonial times mainly gold, silver and

mercury was extracted. Guano (bird and bat droppings used as fertilizer) and saltpeter were

popular mining products during early republican times. Since the mid 1970´s political actions

and a modernized legal system has made the mining industry open for foreign investments.

Relative political stability and rich natural recources have made Peru interesting for foreign

investments (Baker, 1996 and INGEMMET, 2007a). In 2006, more than 60% of the Peruvian

5

export revenue or 14,700 million US$ were generated by the mining industry (MEM, 2006b

and INGEMMET, 2007a). See figure 1 for metal production quantities.

Figure 1. Peruvian metal production in metric tons unless otherwise specified (Modified from USGS,

2006) .

Figure 2 shows the main distribution of Peruvian mines. Mining is fundamentally limited to

the Andes Mountains. The most widespread are the non-metallic mines which are located

mainly on an axis parallel to the strike of the Andes. Alluvial gold production occurs in the

southern parts (States of Madre de Dios, Puno, Arequipa and Ayacucho-Ica). Gold mining are

also well represented in the north-western corner in and around La Libertad state.

Polymetallic ores are extracted in a large part of the Peruvian Andes (INGEMMET, 2000). At

present there are close to 33,000 registered mining rights in Peru (INGEMMET, 2007a).

Mining is forbidden in the Peruvian rain forests. Roughly 45,000,000 hectares of land or

35.5% of the total area is protected by law and thus excluded from metal exploitation.

Authorities estimate the presence of about 1,200 illegal miners in the area (ABN, 2006 and

INGEMMET, 2007b).

1 10 100 1 000 10 000 100 000 1 000 000

Arsenic

Cadmium

Copper

Gold (kg)

Iron (MTx1000)

Lead

Silver (kg)

Tin

Zinc

6

Figure 2. Principal distribution of Peruvian mines (Modified from INGEMMET, 2000) .

Trace and major elements in river sediment systems

River and flood sediment have trace (and major) element compositions that are influenced by

a variety of factors. These factors are both natural and human induced. Some examples are

listed in Figure 3.

Point- as well as non-point sources exist. Many possible transport ways such as atmospheric,

riverine and groundwater aided transport are available. (Drever, 2005) Lakes and rivers derive

their trace and major element content from atmospheric deposition, soil processes (including

podogenesis and sorption to soil organic matter). The natural component can fluctuate

through time due to local catchment evolution or climate change.

To answer the questions in the introductory part of this report it is necessary to distinguish the

different metal sources. Mining alone do not account for all elevated metal levels in sediments

of the researched areas. Land usage and natural geological parameters also governs the levels.

Municipal waste, sewage and landfill leakage, air transported particles and boat traffic could

potentially result in elevated metal and arsenic levels. Differences in weathering, source rock,

climate and other parameters result in different metal levels. Background levels of metals

should be considered a local phenomenon. Slope and relief affects how the sediment is

transported. Greater relief produces more locally derived sediment in a stream. Small

gradients surrounding a flood plain cause for long transported material to dominate (Foster

and Charlesworth, 1996).

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Figure 3. Natural and anthropogenic sources of river sediment trace metal contents (Modified from Foster

and Charlesworth, 1996).

Sulfide weathering and Acid mine drainage formation

Acid mine drainage (AMD) is a product of sulfide weathering. AMD arises after the metals

have been extracted from the ore with waste rock piles remaining. A series of weathering

reactions are initiated (see equations 1-5 below) when previously buried rock material are

brought up to an oxidizing environment on the earth´s surface. This causes sulfuric acid to

form and metal oxides to be mobile. The metal concentrations could often reach toxic levels

(Robbed and Robinson, 1995; Butler, 2007; Baker, 1996).

AMD is analogous to natural weathering but the active time involved is shorter. AMD could

potentially release large amounts of acid and resulting soluble metals (and other elements) to

the environment. The AMD range of impact is often considered limited to a few kilometers.

Leached mine tailing trace metals have been proven to be transported over 3 km. Significant

levels of Zn and Cd were found 3.3 km downstream from the Daduk Au/Ag/Pb/Zn mine,

Korea (Lee et al., 2001). Few studies have investigated the regional scale degradation of

sediment. Four principal oxidation processes, or a combination, are responsible for the

formation of acid mine drainage.

Chemical oxidation

Electrochemical oxidation

Bacterial (i.e. Acidithiobacillus spp.) oxidation

A combination of chemical, electrochemical and bacterial oxidation.

(Ritcey, 2005)

The general AMD reaction involves the following components.

Sulfide mineral (often pyrite) + Moist/Water + Oxygen (Air) Metal ions + SO4 + Acidity

(Ritcey, 2005)

Iron disulfide mineral example weathering reactions:

2 FeS2 + 7 02 + 2 H2O → 2 Fe2+

+ 4 SO4 + 4 H+ (Equation 1)

4 Fe2+

+ O2 + 4 H+ → 4 Fe

3+ + 2 H2O (Equation 2)

4 Fe3+

+ 12 H2O → 4 Fe(OH)3 + 12 H+ (Equation 3)

FeS2 + 14 Fe3+

+ 8 H2O → 15 Fe2+

+2 SO42-

+ 16 H+ (Equation 4)

4Fe2+

+ O2+ 10 H2O → 4 Fe(OH) 3(s) + 8H+ (Equation 5)

Natural sources

Volcanic activity

Geothermal activity

Weathering

Soil/Plant interaction

Antropogenic sources

Transport

Industry

Mining

Municipal waste

Landfill leachates

Agriculture

Total sediment content

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The first part of the complex series of reactions (eqn. 1) involves the oxygen and water aided

production of ferrous iron, sulfate and acidity. If the Fe2+

is exposed to atmospheric oxygen it

could oxidize to form Fe3+

with even further acidity involved.

In the second reaction (eqn. 2) the ferrous iron is being transformed to ferric iron. In the third

step (eqn. 3) hydrolysis of the ferric iron with water produces solid ferrihydrite. Release of

further acidity is another effect of the hydrolysis. This reaction is pH dependent. The solid

mineral cannot be formed if pH is under 3.5 but the ferric iron remains in solution. The fourth

reaction does not require free oxygen as the oxidation of additional pyrite oxidizes with ferric

iron. The acid formation of iron is rapid since the cyclic nature of the reactions. The iron

conversion reaction described above can be accelerated by the bacterial species hiobacillus

ferroxidans by a factor 100 - 1 000 000 (Hulshof et al., 2006).

Underground mining operations in pyritic environments generally have Eh values ranging

from -0.04V to +0.08V and pH values of 5.4 and 3.9 respectively. Rich presences in Fe2+

have

been proven to cause pH as low as 3.5 and Eh values over 0.5. The air exposure causes open

pit mines to have higher Eh (Ritcey, 2005).

Sulfide minerals carry a latent acidity. Pyrite, [FeS2] and Pyrrhotite, [Fe1-xS] are two

examples. X is usually between 0 and 0.2. The sulfide mineral acidity is step by step

transformed to H+ by ferrous-iron oxidation by atmospheric O2. Mineral-dissolution increases

metal concentrations (e.g. Fe, Al, Cu, Pb, Zn, and Cd) in the pore water when exposed to the

Fe2+

and sulfide oxidation.

Pyrite bearing minerals found in mine tailings weather quickly (months-years) in neutral –

slightly alkaline conditions. One product of this weathering is extreme acidity. In addition

AMD usually is related to high levels of heavy metals. Dissolved metal ions (e.g. Cu2+

, Cd2+

and Al3+

) will be released from the tailings under these extreme conditions.

The worst case scenario in AMD is when the pyrite content is high in an environment where

the base content is low. Other factors, for example porosity, influence the acid formation

rates. Pyritic sandstones release its acid load more rapidly than do argillaceous rocks. If

present, acid consuming carbonate minerals and other basic minerals could prevent low pH

drainage. Also, below the zone of sulfide oxidation the carbonates cause a decline in pore

water metal content. Calcium carbonates are the most reactive of these and tend to react

readily with water. The peak acid production is 5 – 10 years after mining and a declination

occurs approximately 20-40 years after mining. (OSM, 2007)

River transport of sediment and trace elements

Sedimentological principles provide a basic understanding of the interaction between source

(e.g. mine tailings and landfill deposits) and recipients (downstream channels, flood plains

and the Atlantic Ocean). Water transported pollutants are contributed by direct runoff into the

streams and rivers. Surface runoff from mine waste piles, landfills and other similar facilities

could raise metal and arsenic contents. The elevated levels of major and trace elements may

have numerous influences. Pollutants entering the Amazon watershed are likely to be

transported and deposited elsewhere. This makes it hard to predict what effect a contaminant

will have and where it will have greatest impact. The meandering character and annual

floodings of rivers complicates pollution patterns.

Sediment is transported in moving water by three mechanisms. Suspended sediment transport

is the major way of transport for the Amazon Basin Rivers and tributaries. Saltation and bed

9

load transport is a minor way of transport. A majority of the Amazon sediment is transported

as suspended load. The percentages transported by each mechanism vary among tributaries

(Gibbs, 1967).

Fine material, especially clay particles (<2m) are of importance in transporting trace metals.

Clay particles have an ability to bind ions due to their negative electrical surface charge. This

attracts positively charged metal and metalloid ions (for example Cu2+

and As5+

). Since the

clay particles are small they could easily be transported (mainly as suspended load) and

potentially spread and dilute trace elements over a large area (Foley, 1999). This is especially

true for colloidal transport. Colloids generally range from 1 nm to 0.2 m. The term refers to

particulate matter too small to be easily deposited (Drever, 2005).

Sediment sources (with related trace metals) could be found both upstream and perpendicular

to the river channel itself. The downstream transport of sediment could potentially reach

several thousand km. The sideways or perpendicular transport of sediment and ions occurs

due to surface run off, groundwater movement and mass movement towards the channel low

point. Few theoretical limits except groundwater divides can limit the potential source-target

distance. The final recipient of the east-Andean river sediment is the Atlantic Ocean. Rivers

can also deposit sediment over vast areas of land. This effect could also be described as a

(more temporal) recipient. If crops are grown on the potentially contaminated fluvial plains

humans could also be regarded recipients.

A summary of Peruvian environments

Geologic, geomorphologic, sedimentary, climatic, chemical, geochemical and biological

information is necessary to fully recognize the large sedimentary systems of the Andes and

Amazon basin and how these are affected by metallic and arsenic pollution. Three general

type settings are found in Peru. These are, from west to east:

The arid coastal plain – La Costa

The foothills and high mountainous Andes area – La Sierra

The tropical rain forests of the Amazon basin – La Selva

There are transition forms between these settings. The Montane forest on the eastern side of

the Andes is one example. Large differences in altitude cause high erosion rates. Mass waste

actions with resulting colluvial deposits are common. Wedge shaped scars are a common

feature in the Andean landscape. The tropical forest are further divided into Tierra firme

(Spanish: Dry land) and the Várzea (annually flooded forest).

Due to the subduction of the oceanic Nazca plate under the continental South American plate

and resulting orogenic activity the different rocks show a complex pattern (Méndez, 2005).

Bed Rock types are principally distributed parallel to the subduction zone to the west. Plutons

and massifs are stretched in a southeast-northwesterly direction. This pattern is developed by

plate tectonic movements. Rocks are young in the western part of the Andes and generally

getting older to the east (INGEMMET, 2001).

Climate plays a role in the mine waste – environment interplay. Temperature, air moist

content and precipitation govern weathering intensity. Temperature and precipitation are often

considered less important than deposit geology (du Bray, 1996). The Peruvian climate varies

with the Humboldt Current, vicinity to the Andes, latitude and other local and regional

factors. The largest differences are found when comparing the main type settings (La Costa,

La Sierra and La Selva). The coastal strip is dominated by an arid climate affected by the

Pacific Ocean and the associated Humboldt Current to the west. The Pacific evens the

10

temperature throughout the year (yearly mean approximately 20°C).

Fogs (May-Nov) and absence of rain is typical features of the

Peruvian coastal weather. The yearly precipitation amounts vary

locally but are under 50 mm/year (Encarta, 2007). The Peruvian

Andes principally receives 300-1,000 mm/year. The eastern side of

the mountain range generally receives more precipitation. The

coldest mean temperature is in June, ~3ºC and the warmest period is

April-October with a 12º C mean. The eastern part of the Cordillera

is most humid. A few areas on the eastern slopes (at an altitude in

the 2,000 m a.s.l. range.) receive up to 7,000 mm precipitation a

year. Dry season in Cusco occurs from May to October. November

to April is the wet season characterized by an increase in humidity

and rain fall. The Peruvian Amazon forest receives 1,500-2,500 mm

rain/year. The year is divided into a rainy and a dry season which

roughly corresponds to the wet and dry season in the Andes. Peru is

subjected to El Niño events every 3-7 years. Heavy rainstorms

along the coast and can cause severe floodings (Agrotechnologica

Amazonica, 2005).

The human water consumption (Table 1) is predominated by

agriculture. Public and household use is roughly a tenth. Mining and other industry consumes

comparable amounts of water (Justo, 2006).

Table 1. Use of water in the Peruvian Amazon drainage in comparison to the total Peruvian use (Justo,

2006).

Million m3 Public

use

Livestock Agriculture Industrial Mining Total

consumption

Atlantic parts of

the Amazon

drainage

0.229 0.041 1.996 0.049 0.053 2.367

Peru 1.265 0.079 16.267 1.155 0.207 18.972

The Peruvian Amazon basin

The Amazon basin (Fig. 4.) is the largest fluvial system in the world in terms of drainage area

(6.1 x106 km

2), number of tributaries and water discharge (between 34,000 m

3/s and 121,000

m3/s) (Encarta, 2007). Its length is second only to the Nile. The exact length is debatable and

range from 6,240 km (Baumgartner and Reichel, 1975) to 6,850 km (Gelli, 2007). The

Atlantic part of the total Peruvian discharge is 97.8 %. The remaining percents are shared by

the Pacific Ocean and Lake Titicaca (Guyot, 2007).

The Amazon River width (Brazil) at low stage ranges between 1.6 and 10 km. During annual

floodings the width reaches 48 km or more. Ocean liners are able to navigate the first 2/3 of

the River course. The Manaus harbor (1,600 km upstream, Brazil) can receive Trans Atlantic

ships. Smaller ships can reach Iquitos at a distance of 3,700 km from the River mouth (Butler,

2007). Population density of the Amazon Basin is 4 inhabitants/ km2 (INEI, 2000).

The geographic extent of the Amazon basin is limited by the Rio Chamaya, Peru (79° W), Rio

Pará, Brazil (48° W), Rio Cotingo, Brazil (5° N), Rio Parapeti, Bolivia (20° S) and the

Atlantic Ocean. (Guyot et al., 2007) The two principal headstreams of the Rio Amazon are the

Ucayali and Marañón rivers. Both rivers originate from high Andean glacial meltwater. The

most distant source for the Rio Amazon is the peak Nevado Mismi, southern Peru. Melt water

Figure 4. Geographical

extent of the Amazon Basin

(Modified from WRI,

2002).

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feeds Rio Apurimac which in turn feeds Rio Ucayali. The origin of Rio Marañón is in the

Pasco watershed of the Cordillera Blanca.

The Rio Ucayali - Marañón joint is located near Nauta (90 km SSW of Iquitos). The Amazon

flow is primarily easterly towards the Atlantic Ocean (Encyclopædia Britannica, 2007). The

river delivers approximately 7 million km3 of fresh water a year to the Atlantic Ocean. This is

equivalent to 58% of the total South American runoff (Baumgartner and Reichel, 1975). The

Amazon estuary (estimated to be 240 km wide) is characterized by deltaic deposits. A

network of islands separates the river into branches. The mouth of the main stream, or the

Pará branch, is roughly 80 km wide.

The Floresta de Terra Firme, or the dry land forest unit covers the majority of the Amazon

forest (7 million km2). The Floresta de Terra Firme stretches across a large plain ranging from

130-200 m a.s.l. This forest never gets flooded even during the wet season. The extent of this

forest is largely equivalent of the Miocene - Pliocene great lake Belterra (25,000 – 1.8 million

ybp). Silt and clay deposits from this lake are still a notable landscape factor.

Another forested land cover type is the flooded forest closest to modern rivers and tributaries.

Fluctuations of water level involves a river water rise of >10 m in March-September (Fonseca

and Por, 2007).

The three principal suspended sediment sources according to the 2007 study by Guyot are:

The Andes Mountains, the Amazon Shields and Piedmont Basins. Andean sediment

comprises 87% of the total sediment load carried by the Amazon. This is in contrast to that

the Andean part of the watershed is only 12% of the total area. Also the classical study by

Gibbs (1967) recognizes the Andean part of the drainage as the principal sediment source.

The tropical tributaries are generally poor in suspended material and affect the system by

diluting the sediment concentration. The Andean sediment source area comprises only 10% of

the basin (Chakrapani, 2005). The majority (>95%) of the sediment carried by the Rio

Amazon is classed as suspended load (Ritchey et al., 1985). concludes that suspension is the

major mean of transport. Bedload on the other hand comprises only one or two percent. Most

of the suspended particles carried by the river are in the silt and clay fractions. The suspended

solid concentrations decrease as the river advances from the Andes to the ocean. During the

wet season typical concentrations in the mountainous tributaries are over 500 mg/l. East of

Xingu, Brazil concentrations are <200 mg/l (Gibbs, 1967). In the <2µm fraction of tropical

Amazon River tributaries kaolinite (88%) is the dominating mineral followed by quartz (6%)

and mica (5%). Mountainous (Andean) tributaries are subject to an even distribution of

kaolinite (29%), mica (30%) and montmorillonite (29%). The Rio Amazon mouth shares

similar characteristics as the mountainous tributaries. In the coarser 2-20µm fraction the

quartz content is between 40 and 48% for all classes. As in the <2 µm part tropical tributaries

tend to have a large portion of kaolinite (26%) with some mica (13%). Plagioclase represents

5%. Again, mountainous have the same character as Rio Amazon mouth. In the 2-20µm size

class the most common clay minerals are micas (26-27%), kaolinite (13-14%), k-feldspar (6-

7%) and plagioclase (7-8%) (Gibbs, 1967). Guyot et al. (2007) concludes that the clay

mineral signature of the Peruvian Andes is characterized by high illite and chlorite contents

(~75%). Further, there is a downstream smectite enrichment resulting from tributary input and

lateral bank erosion. Outside the mouth, Amazon fan clay minerals from depths ranging from

1,000-4,300 m were analyzed by Debrabant et al. (1997). Composition seems constant with

smectites (30-50%), illite (20-35%), kaolinite (15-25%), chlorite (8-20%) and random mixed

layer clays (trace-20%).

12

Clay percentage generally decreases as the Rio Urubamba descends from 4810 m a.s.l. (and

>60% clay) to 1890 m a.s.l. (20-30% clay). Where the Marañón joins the Ucayali the mean

clay percentage is 38.1. Downstream from Iquitos, where Rio Napa joins the Amazon River,

the clay fraction is 48.7% (Aufdenkempe, 2007).

Table 2. Mineralogic composition by size fraction and sedimentologic setting of solid material transported

(Wt %) (Gibbs, 1967).

Quartz Plagioclase K-feldspar Kaolinite Mica Montmorillonite Chlorite

Tropical tributaries (n=6)

<2µm 6 0 0 88 5 <1 <1

2-20

µm

48 5 9 26 13 0 <1

Mountainous tributaries (n=5

<2

µm

8 0 0 29 30 29 3

2-20

µm

42 8 6 13 26 0 5

Rio Amazon mouth

<2

µm

7 0 0 31 33 27 2

2-20

µm

40 7 7 14 27 0 5

Water color of the river waters in Amazonian Peru is an indicator for the drainage area and its

physical and chemical properties. Closely associated with color and trace metal transport is

organic content. Roughly 50% of the total organic carbon transported by the Rio Amazon is

dissolved organic carbon (DOC). This form is the most predominant in most tributaries. DOC

in the mainstem ranges from 2.7-4.7 mg/l. Black water tributaries can locally cause an

increase in DOC. (Rybicka et al., 1995) Total dissolved inorganic constituents transported by

the Amazon River are 232 x 106 t/year. (Richey et al., 1985) Generally the temperature

fluctuations in the Amazon basin are small throughout the year. The temperatures during the

days are approximately 26.5°C. The Amazonian lowlands are subjected to heavy rainfall all

year (average 2460 mm yr-1

). The wettest time is between January and June when vast areas

of land get flooded. Flow rates, discharge, and, river width are all affected by the seasonal

variability of precipitation. Of the total 2,044 million m3 precipitation that yearly falls on

Peru, 1,999 falls over the Atlantic part of the Amazon drainage (INRENA website, 2008).

13

Geologic history The proto-Amazon was formed during the Triassic – Cretaceous. The river system back tilted

and formed the Amazon as we know it. The modern Amazon drainage, or watershed, dates

back to the mid-Miocene. The most important geologic events related to the formation are

tectonic action or more specific: large scale convergence of the South American western

margin and the Pacific crustal plate (Potter and Hamblin, 2006).

The tectonic evolution of the Andes extends back into the Paleozoic. Plate tectonics is

ultimately responsible for the development of the Andes mountain range(s). The oceanic

Nazca plate is subducted beneath the continental South American plate with resulting trench

and present day volcanic arc and a foreland thrust-fold belt. The Peru-Chile oceanic trench is

visible evidence on the surface. The Nazca plate, being oceanic, is made up by young and

buoyant lithosphere. The buoyancy counter acts the subduction. The result of this is frequent

earthquakes (Zandt, 2002). Terrane accretion was the dominant process up to the Mesozoic.

However, it is generally believed that the present day mountain belt was developed mainly

during the Mesozoic to Recent, due to the east dipping tectonic slab and the opening of the

Atlantic Ocean (Zandt, 2002).

Subduction has occurred on the western margin of South America since early Cambrian or

late Precambrian. The Peruvian part of the Andean cordillera evolved primarily from the

Triassic to present. Mariana type subduction (extension and crustal attenuation with resulting

ocean trench, island arcs and a back arc basin) occurred from the late Triassic to late

Cretaceous. Andean type subduction (pulses of compression) occurred from the late

Cretaceous to present.

Subduction along the South American western margin is considered one of the biggest events

affecting modern drainage.

Highland production and continental tilt away from active margins and mega

structures

Drainage parallel and following fold belts

Rivers following regional strike-slip and transform faults (Potter and Hamblin, 2006).

Cenosoic Era - Opposite direction of flow. Outlet in the pacific

Rise of the Andes (23-8Ma) -River dammed and formation of massive lake Pebas (Miocene) and associated wetlands

Ucayali Peneplaination due to sea level drop 14.5-11.3 Ma

Tidal sedimentation and Madre de Dios formation ended peneplaination during middle and late Miocene (11-8Ma). Renewed Andean compression/uplift.

8Ma: Lago Pebas drained due to plate tectonic action

Megalake Lago Amazon or series of mega lakes during latest Miocene-~2.5 Ma

Amazon function as one or a series of sedimentary basin. Development corrolated with Andean tectonics

Modern Amazon drainage established during the late pliocene at ~2.5 Ma

Deforestation since the mid 1960's. south-east Brazil

Dams built in for exampel Tucuruí and Curua-Una, Brazil

Figure 5. Natural and humanly induced factors of the Amazon basin evolution (Data from Rodaz et

al., 2005; Campbell, 2006).

14

The Peruvian Andes

With its 9,000 km the Andes is the longest mountain range in the world. It runs along the

entire western margin of South America. The northern extreme is marked by the Caribbean

submerged peak islands of Aruba, Bonaire, and Curaçao. The Andes range is approximately

200–300 km wide throughout its length, except in the Bolivian flexure where it is 640 km

wide. The average height is close to 4,000 m. The Andes is composed of two major parts, the

Cordillera Oriental and the Cordillera Occidental. These are often separated by an

intermediate depression. In Peru the mountain range splits further in the smaller units:

Cordillera Negra and Cordillera Blanca (Wikipedia, 2007).

Study areas Three areas (Fig. 6) representing

different settings were chosen for

the sampling and fieldwork. In the

Southeastern states (Spanish:

departemento) of Cusco and

Madre de Dios mainly the

Amazon headwater Rio Urubamba

and the Amazon tributary Rio

Manú were studied. A few

locations outside these rivers were

also researched (i.e. Laguna

Huarcarpay and Rio Alto Madre

de Dios). The bordering regions

Cusco and Madre de Dios differ in

topography, geology, sediment-

ology, climate, population density

and land use.

Approximately 1,000 km north of

Cusco the north-central part of the

Peruvian Amazon was visited.

Sampling here was limited to one

sample each in the Amazon

mainstem and the Rio Marañón.

See appendix A for details on

sample locations.

Figure 6. Locations treated in this paper: Upper left; Detail map over the Loreto research area, Lower

left; Detailed map over the Cusco - Madre de Dios research area. The sample sites are indicated by the

small, open stars.

15

The state of Cusco

The Cusco region is located in the southern Peruvian Andes. The regional capital Cusco city

is located roughly 500 km south-east of Lima at approximately 3,400 m a.s.l. The landscape is

hilly and slopes are generally moderate. Elevations on the Pampa are in the range of 4,200 –

4,600 m. Gorges are set at a height of 3,000 – 4,200 m. This area was chosen because of a)

the effect of man is contributed over a relative short distance compared to the Amazon

mainstem b) easy access due to the Cusco Airport and drivable roads to the sampling sites and

c) the high altitude (>3,000 m) which is different from the other areas in this study.

Figure 7. Sample map of the Cusco area.

Geology and sedimentology

The complex Andean geology contains intrusive igneous rocks (such as the white-gray

Paleozoic Vilcapampa batholith) as well as sedimentary and volcanic rocks. At Aguas

Calientes, not far from Maccu Picchu, Paleozoic plutons (Plutones Tardihercinicos) constitute

the local geology. The Rio Urubamba/Vilcanota at Urcos is right at the limit between early

Cretaceous - Paleocene bed rock formations on the western side and older Silurian - Devonian

metasediment on the eastern flank. Sedimentary rocks are the most common in the

investigated areas of Cusco. The oldest rocks are further to the north-east and vice versa. To

the west of these areas igneous and volcanic rocks are more common (INGEMMET, 1999b).

Mollisols, rocky land and inceptisols comprises most soils in the State of Cusco (USDA,

1998).

Rio Urubamba

Rio Urubamba is the largest river in the Cusco region. The total Urubamba watershed area is

7,000 km2 (MINAG, 2007). The river is partly navigable and a headwater of the Rio Ucayali.

The river passes south-east of Cusco near the Puno state border where it’s called the Rio

Vilcanota (Figure 8). In the Sacred valley between the villages of Pisac and Ollantaytambo

16

the local name is Rio Wilcamayu (Quechua for The Sacred River). After this point the

Urubama flow north-north-west for approximately 724 kilometers before joining with the

lower Rio Apurimac to form the Rio Ucayali. At Aguas Calientes (Figure 9) a steeper

gradient causes faster flow.

The bed rock at Urcos is dominated by Late Cretaceous Huaro and Lucre formations on the

southwestern side. The Paleozoic Urcos formation takes over on the other side of the river.

Further downstream, towards Cusco, the Urcos formation occupies both sides of the river

(MEM, 1999b).

Figure 8. View of the Rio Vilcanota in the rural areas of Urcos city. Samples were taken on the western

bank (the one closest to the camera; Author, 2007).

Rio Urubamba is often divided into Upper and Lower Urubamba. The divide separating the

two is the whitewater canyon of Pongo de Mainique. The Upper Urubamba (Alto Urubamba)

valley is more densely populated than the lower. The use of irrigation is also more frequent

here. The valley features ruins, including the Incan city Machu Picchu. Other than the town of

Sepahua population around the Lower Urubamba (Bajo Urubamba) is relatively sparse.

The river is affected by wastewaters from the Cusco city landfill. 310,000 kg of household

wastes daily affects the Rio Huatanay which directly feeds the Rio Urubamba. The Huatanay

– Vilcanota confluence is located near Laguna Huarcarpay at the city of Huambuito. Also, a

large portion of people living near the river throw their garbage into the river, partly due to

the lack of regulated landfills (USAID, 2007; Bolanos and Carranza, 2006). During sampling

in the Rio Vilcanota cars were being washed into the river and garbage was spread on the

river side beaches.

17

Figure 9. Rio Urubamba near the central parts of Aguas Calientes downhill the Machu Picchu world

heritage site, 150 km downstream Cusco (Author, 2007).

Laguna Huarcarpay

Laguna Huarcarpay (48 km drive S of Cusco at 3,170 m a.s.l.) is one of three lakes in the

3,800 Ha Lucre-Huarcarpay wetland systems. The lake depth is 8.6 m (Austermühle, 2002).

The wetland occupies the Rio Lucre sub-basin, characterized by being a sub narrow rocky

canyon. The basin bottom is wide (4km). The dominating soil type in the area is alluvial

deposits made up by layers of clay, silt, sand and gravel. Lacustrine sediments are inherited

from a Pleistocene lake covering large part of the basin. Colluvium and fluvial sediment

deposits are also present. Rocks are sedimentary and volcanic in origin and were formed from

the Paleozoic to the Quaternary. Conglomerates and sandstones dominate the surroundings

(Ihue, 1992). The rocks dominating in Laguna Huarcarpay area is the cretaceous Lucre

formation of the Chitapampa group (MEM, 1999).

Around the wetland, mountains dominate the view in all directions. The inclination reaches

25 to 50%. The land use and coverage includes Totora sedge, prairie type plants, thorny

shrubs and bushes. Small scale farming and grazing occurs in the region.

Typical for the governing climatic conditions is the Kaztanozem type soil with high organic

content, calcareous nature and a pH range from 6.8-7.18. A more or less pronounced salinity

characterizes most soils in the valley (ACUÑA, 2003).

18

Figure 10. Laguna Huarcarpay (Author, 2007).

Climate

The heterogeneous climate characteristics of the Cusco region originate from the topographic

and geographic properties. The year is divided into a rainy (November-April) and a dry

season (May-October). The driest month is June. Most precipitation events occur in January.

Cusco City receives approximately 700 mm of precipitation a year. The 24 hour temperature

mean varies little throughout the year and is typically around 12°C. The dry period is

generally colder (MINAG, 2007).

Mining activity intensity

Mining activities in the Cusco state are limited to a few (with Andean standards) copper, iron

and polymetallic mines. Fe- and Cu skarn and porphyry copper occurrences dominates. There

are 1,226 registered mining rights in the region. Mining projects and operations are mainly

located to the south or south-west of Cusco city. Morosayhuas (Cu, Au, Zn, Pb and Ag),

Cotabambas (Cu, Au), Accha (Titiminas) (Zn, Pb), The Cerro Ccopane are among the most

important. The Cusco state also has 44 passive or abandoned mines (INGEMMET, 2007a and

MEM, 2006). The copper producing San Antonio de Cobre Cjata mine could possibly affect

the sediments downstream Urcos (DGM, 2002).

Of the abandoned mines two are located close to the Rio Huatanay (which feeds the Rio

Urubamba). The first is the Cancharahuay mine (abandoned 1988). Minerals found at the site

are crisocola, malakite, quartz, limonite and hematite. The mine and tailings drain to larger

water bodies only in the wet season. The 2000 MEM survey of the area showed no signs of

acid mine drainage. The same study considered the environmental effect of the mine and

tailing piles small. The other abandoned mine is the Kasapata (abandoned in 1983). Redish

19

sedimentary rocks dominates local bedrock outcrops. The mineral assemblage is similar to

that of the Cancharahuay mine. No indications of acid mine drainage. The former mining

activity is considered too small to cause any serious environmental damage (MEM, 2000).

The Mayutincco mine (abandoned in 1993, altitude 3,625 m a.s.l.) and Viscapata mine

(abandoned 1988, altitude 3,680 m a.s.l.) could potentially affect the metal contents of the

Laguna Huarcarpay sediment. The former mines of Mantanay (abandoned in 1988, altitude

3,400m) and San Cristobal (abandoned in 1997, Altitude 4,220 m a.s.l.) are close to the

Urubamba river town of Aguas Calientes and could thus have an effect on the sediment trace

metal pattern (MEM, 1999A).

Land use and coverage

The Pampa (from Quechua meaning plain) is usually covered by grass species. At lower

elevations shrubs and trees are common although the grass cover is still dominant. Small

herds of sheep, llamas, alpacas and cattle graze the land. The population density in the state of

Cusco is 16 inhabitants/km2. In some urban areas (such as Cusco city) the density reaches

over 1,000 inhabitants/km2(INEI, 2000).

The state of Madre de Dios

The entire area of Madre de Dios is situated within the Amazon River basin. Here, the

research area is in the western parts of the region. The Madre de Dios watershed is the largest

Amazonian sub-watershed in the area. Most rivers are considered white water rivers. The

main reason for choosing this area is that the human impact is virtually non-existing.

Figure 11. Madre de Dios sample location map.

20

Geology and sedimentology

Sediment is deposited on a seasonal basis and forms alluvial plains along the modern rivers

and tributaries throughout the state. The meandering character of many of the rivers has left a

number of oxbow lakes on the river plains. The lowlands between the rivers have a hilly

topography with slopes between 15 and 50% (UNEP-WCMC, 1997).

The area is part of the Madre de Dios foredeep of the South Amazonian foreland basin

system. The sediment source according to REE patterns are probably Brazilian shied and

Paleozoic - Mesozoic terrains (Roddaz et al., 2005). Large areas are covered by Quaternary

colluvial deposits. The soil material is characterized as Leptosols and regosols. Fluvisols

occupies the zones next to rivers (INRENA, 2007).

The few bedrock outcrops are reddish and grey sedimentary rocks (MEM, 2000). Sedimentary

rocks (superior Tertiary-Recent Quaternary) principally comprise the bed rock up to 1500 m

a.s.l. Over this level sedimentary and metamorphic rocks of the Precambrian and Paleozoic

eras (>440 ma) take over. On the western side of the Madre de Dios basin Cretaceous and

Precambrian – Devonian sedimentary rocks lies side by side with Cretaceous−Tertiary

volcanic and further away Mesozoic−Cenozoic intrusives (Schenk et al., 1997). On the

eastern rim, Permian and Mesozoic batholitic rocks intrude the Precambrian unit called the

Arequipa massif, which in turn thrust eastwards over marginal Mesozoic rocks. It is generally

believed that the Arequipa massif has been part of the South American Shield at least since

the Paleozoic. This corresponds to the Peru flat slab segment of the subducting Nazca Plate.

Young (Holocene) alluvial deposits cover most of Madre de Dios department. Large recently

deposited quaternary to recent sediments in the vicinity of modern rivers and flood plains.

Also, tertiary deposits of colluvial character (Schenk et al., 1997). Ultisols covers most of the

low-lying areas of the Madre de Dios sub-basin. Inceptisols are in abundance when elevation

gets higher to the west (Andes). Some patches of oxisoils could also be present (USDA,

1998).

21

Figure 12. Rio Manu, near sample site 8 (Author, 2007).

Rio Manu

The total length of the river is approximately 300 km. High water is from December to March

(Ziesler and Ardizzone, 1979). Rio Manu (Figure 12) is joining the Rio Alto Madre de Dios at

Bocca Manu to form the Rio Madre de Dios (Spanish for Mother of God).

The river flow is from west to east before entering Bolivian territory. After this point the river

course is diverted to the north and joining with the Rio Beni. After yet another joint (with the

Rio Mamore) the River changes name to Rio Madeira. The Madeira – Amazon joint is located

some 120 km east of Manaus, Brazil. Rio Manu and Rio Madre de Dios are characterized by a

high degree of meandering, murky brown waters and numerous oxbow lakes (cochas).

Rio Alto Madre de Dios

Rio Alto Madre de Dios (Figure 13) translates to the High Mother of God. This name is a

consequence of its high altitude origin and course. The river gets its water from the narrow

Alto Madre de Dios Basin. Flow is generally from south-west to north-east before joining Rio

Manu at Bocca Manu.

The river is follows the border between the Pleistocene Madre de Dios formation and

Holocene alluvial and fluvial deposits. Through a long part of its course the Paleocene

Yahuarango and Chambira formations are present (INGEMMET, 1999a).

22

Figure 13. Rio Alto Madre de Dios

Climate

Most of Madre de Dios is characterized by a humid tropical climate. The period between

November and April is marked by heavy rainfall (7,000 mm/year locally) whereas May to

October is considered a dry period (MINAG, 2007).

Land use and coverage

The remote locations make the sediments rather untouched by human activities. Some

downstream alluvial gold mining and very limited tourism occurs. With a population density

of <1 inhabitants/km2 the direct human impact is small (INEI, 2000). The state of Madre de

Dios has 1,334 registered mining rights. Mostly small scale and informal gold mining of

placer deposits occupy the mining sector in this region. In 2006, there were 22 abandoned

mines in the entire state (MEM, 2006).

Manu National Park, located in the Department of Madre de Dios is one of the most

biologically diverse protected areas in the world. Tropical lowland forest, montane forests and

puna (grasslands) all give good conditions for 850 bird species, 15,000 species of plants and

at least 13 threatened wild life species makes Manu an area of preservation value (IUCN

Technical Evaluation, 1989).

The state of Loreto

The state of Loreto occupies the northwestern corner of Peru. This is the largest region in the

country. Loreto shares borders with Ecuador, Colombia and Brazil. The area is completely

within the Amazon basin. The area studied in this work is in the Iquitos – Pacaya Samiria

23

reserve area in the central parts of the region. The goal of the sampling here were to

investigate an area which receives water from the majority of the Peruvian mining areas.

Geology and sedimentology

Flood plain deposits are common are

common throughout Loreto. The

geomorphology is hilly topography

(McElwee, 1999). The Loreto samples

are located in the Putamayo-Orient-

Marañón basin which is dominated by

Quaternary colluvial and alluvial

sediments. Tertiary rocks and sediments

could be found away from the modern

rivers (Schenk et al., 1997).

The river is constantly changing its path

and thus dumping silt over a large area.

Lower lying areas are flooded as the

river rises 10-15 meters during the wet

season. The loose soil of Loreto is

composed mainly of sand, with little clay

and silt content. The Amazon River

islands are composed mainly of silt

(McElwee, 1999).

Oxisoils covers large areas of the Loreto

state (and the rest of the Amazon basin).

Inceptisols are found near the present

fluvial channels (USDA, 1998). Highly

erodible Lixisols are related to

subsurface kaolinitic deposits. Acrisols that is naturally low in plant nutrients and Aluminum.

Alisols could be found in areas with higher temperature and precipitation. The Alisols

typically have high and mobile Al-contents (INRENA, 2007). The Amazon River is already

described above.Sampling was carried out near Iquitos, about 3,700 km from the river mouth.

Rio Marañón

Rio Marañón (together with Rio Ucayali) is the main headwater for the Amazon River. The

river reaches 1,600 km in length. Flow is more or less from south to north and in the latter

part of its course turning east to join Rio Marañón near the city of Nauta.

Climate

The Loretian climate is uniform throughout the year. There is no formal rain season since the

rainfall in the driest month exceeds 60 mm of precipitation. The wettest period occurs from

November - March. The yearly mean temperature is 26.5ºC. Annual precipitation is over

2,500 mm. Annual mean humidity is close to 60% (McElwee, 1999). The Iquitos climate is

hot and humid, with an average relative humidity of 85%. The wet season lasts from

approximately November to May, with the river reaching its highest point in May. The river

is at its lowest in October.

Figure 14. Loreto sample location map.

24

Land use and coverage

Land use is composed of secondary succession tropical forests, industrial areas near and

around Iquitos. Deforestation is a problem in the region. Iquitos is an urban centre (260,000

inhabitants) in the middle of the Amazon rain forest. The high population density (with

Amazonian standards) causes problems in waste management and water resource policies.

When sampling in Iquitos, Loreto household waste was observed on the river beaches (even

more so than in the Urubamba samples). Municipal waste is let out in the Amazon River on a

continuous basis. Population density in the Loreto region is 2.39 people/km2 (INEI, 2000).

88% of the region is covered by primary and secondary forests and the remaining part is

largely used for agricultural activities (MINAG, 2007). The Loreto state has 40 registered

mining rights. Mining is virtually non-existing and limited to the south western margin of the

region and could thus be left out of this study.

Figure 15. Western Rio Amazon beach close to Iquitos (Author, 2007).

Methods

Fieldwork and sampling

12 sediment samples and 4 bedrock pieces were retrieved in mid-August to mid-September.

Most samples were taken in the fluvial channel-fluvial plain near the river (<25 meters). In

meandering river setting the depositional inner curve was selected to prevent erosion effects.

Here fine material (such as silt and clay) accumulates during seasonal flooding events marked

by discoloration of nearby tree stems. Discrete levees were observed in Madre de Dios. These

were marked with differences in vegetation and a gentle topographic bulge. The average

amount of sediment in each sediment sample was approximately 200±50 grams dry weight. A

diary (Appendix A. Sample record and Field observations) was used to keep track of the

geological settings, altitude, locations and other important details involved. All samples were

25

kept in zip-lock bags at room temperature except when traveling in tropical areas. Positioning

of the sample sites was done by using a handheld Magellan Xplorist 100 GPS-unit (reported

accuracy of 3m).

Figure 16. Approximate sample location and environment features.

Visual examination of soil samples

Samples were described in the field. Sediment colors were described using the Munsell rock

color chart. The color and smell were used to characterize differences in the redox status.

Grain size distributions and grain shapes for the coarse fractions (>sand) of the samples were

also assessed.

The field determination was complemented by laboratory analyses (see p.30). Also, a more

precise grain shape determination was done using a stereo microscope. Basic grain

mineralogy was done using the same equipment. If available, the >2 mm fraction from the

wet sieving has been used. In practice, fractions ranging from 250 µm to 1 mm have been

used since the coarser material is very limited or not available. For each sample 200 grains

have been counted.

Analytical procedures

All analyses listed below were carried out in the University of Gothenburg Quaternary

Geology laboratory at Earth Science Centre, Gothenburg in the fall and winter of 2007-2008.

Grain size distribution analyses

Wet sieving and Sedigraph were used to construct histograms and plots. Descriptive statistics

were calculated using the Gradistat v5 Excel spreadsheet. The geometric Folk and Ward

(1957) method was used.

The distribution of fractions coarser than 63 μm (i.e. the silt-sand transition using the Udden-

Wentworth scale) has been determined by wet sieving. Following removal of organics (H2O2)

and dispersion (Na6(PO3)6) the 2000, 1000, 500, 250,180, 125, 90, 63 μm 3 inch diameter

sieves were used to isolate each size class. A Micrometrics 5100 sedigraph was used for the

Annual flood limit

Levee

Erosion (outer

curve) vs.

Deposition

(inner curve)

Sample point

26

sizes smaller than 63μm. Residual material from the wet-sieving was used for the distribution

analysis of the silt and clay fractions of the total sediment. The Sedigraph uses X-ray

absorption to measure changes in mass concentrations as used, particles settling in a liquid

with known properties. The liquid in this study was deionized water.

The sedimentation analysis is based on Stokes’ Law.

The method assumes that a single solid sphere settling in a fluid has a terminal settling

velocity which is uniquely related to its diameter.

Aluminum contents from the ICP-MS analysis have been used as a grain size proxy in

normalizations.

Loss on ignition analysis

The sediment samples were subjected to the Loss on ignition (LOI) procedure described by

Dean (1974). The LOI analysis gives only a rough estimation of organic content. The organic

content is well correlated to LOI though (R2=>0.9). Samples were first dried overnight at

105°C and weighed. After this, samples were burnt at 550°C for three hours, cooled in a

dessicator and weighed. To incinerate the CaCO3 carbon dioxide the samples were further

heated to 950°C for another three hours. By calculating the weight loss after each of the steps

organic and inorganic carbonate contents could be roughly approximated. Each LOI sample

contained 20 g±1g dry weight.

LOI550 = ((DW105–DW550)/DW105)*100 (Equation 7)

LOI950 = ((DW550–DW950)/ DW105)*100 (Equation 8)

A correction factor of 1.36 was applied to the LOI950 results to account for the difference in

CO3 and CO2 molecular weights. Samples were made in duplicates to assure satisfying

results.

XRD

A Siemens D5005 X-ray diffractometer were used to determine and quantify the mineralogy

for four selected samples (3, 4, 7 and 12). Samples were chosen to cover all settings with a

limited amount of samples. The fine sand fraction (125-250µm) was separated, grind and 2g

of sample was mounted in a plastic sample holder. The analyzed interval was 2θ = 5-65°, in

0.02° steps. The samples were rotated at 30 rpm. The samples used are listed in Appendix F.

Siemens EVA was used for the mineral identification. Quantitative estimation of mineral

assemblages was done by using the SIROQUANT™ software. Typical detection limits for

this setup is generally 1%.

2

1

1s

stg)(

V18D

Equation 6. Dst = Stokes’ diameter

η = fluid viscosity

ρs = de ensity of the solid ρ1 = density of the liquid

V = settling velocity

g = acceleration due to gravity

27

Geochemistry

The geochemical work of this report is limited to the acid digestion and ICP-MS

multielemental analysis described below. Various normalizers have been used and statistical

techniques have been applied to evaluate the results.

Acid digestion of samples

Duran 100 ml sample bottles were soaked in 10% HNO3 and thoroughly rinsed before use.

The <63m samples for the inductively coupled mass spectrometer (ICP-MS) were divided in

two series. These were subjected to two different treatments.

Nitric acid digestion: 1g of sample dry weight exposure to 20 ml 1:1 HNO3/Deionized water

(Milli-Q) for 3 hours at 121˚C in an Autoclave. The residual matter was removed by using a

centrifuge for 5 minutes at 4000 rpm. The method used is a modified version of SIS (1997).

(du Bray, 1996)

Aqua Regia digestion: 1 g of sample were subjected to 20 ml (3:1 HCL/HNO3 ratio) at

121˚C 3hrs followed by 16 hours at room temperature. The centrifuge procedure for particle

removal is described above. The method used does not digest the total metal contents of the

samples. Aqua Regia will dissolve all “environmentally available” elements. Refer to US

EPA, (1996) for more detailed information.

(du Bray, 1996)

HNO3

Easily soluble phases

Humic materials

Carbonate phases

Aqua Regia

Easily soluble phases

Humic materials

Carbonate phases

Organic matter

Maximize mobility

Sulfide minerals

28

The basis for using the first method (see above) is to extract the biologically available part of

the total sediment content of the analyzed metals and arsenic. The second method aims at

extracting the total metal content of the sediment. Aqua Regia extraction leaves undissolved

residual material which comprises the most immobile phases. Recovery rate, accuracy and

precision are further discussed elsewhere in the discussion.

ICP-MS

The prepared samples were run through a HP 4500 series ICP-MS. Acid leachable

multielement analysis included eight elements. The investigated metals and metalloids were

Al, Cr, Fe, Cu, Zn, As, Cd and Pb. A blank sample was used to account for metal

contamination by the acids used. The raw data from the ICP-MS analysis were recalculated

and conversed from µg/l to mg/kg. The initial 1 g of sample and 20 ml acid was put in the

beaker as stated in the Swedish Standard (SIS, 2003). Typical detection limits for the HP

4500 series is in the ppt range. Conversion of raw data (µg/l) to desired unit (mg/kg):

𝐴2 = 𝐶2 ∗ 𝐿2

𝑀𝐷+

𝑀𝐶

100 (Equation 9)

Extraction with Aqua Regia is element dependent but 70-80% has been reported by Davies et

al. (1999). Additional content could be extracted using hydrofluoric acid. A true total content

is not available using this digestion agent.

Normalization and statistics

The ICP-MS results have been normalized to selected parameters to avoid natural variability

of metal concentrations. Since positive metal ions bond extensively to organic matter and clay

sized particles these parameters have been taken into account for the normalization. Loss on

ignition serves here as substitute for true organic content. Grain size normalization have been

done using two fraction intervals, clay (<2µm) and mud (<63µm). Aluminum has been used

because most anthropogenic activities do not cause enrichment of this conservative metal

(refractory lithophilic element). Population density has also been used in normalizations. All

elements have finally been plotted against each other to see which show similarities in their

relative distribution. Five different normalizers were tested to statistically evaluate relations

between the above mentioned parameters and metal concentrations:

([Me+]sample / Alsample) * Alaverage (Equation 10)

([Me+] sample / LOIsample) * LOIaverage (Equation 11)

([Me+] sample / Clay%sample)* Clay%average (Equation 12)

([Me+] sample / <63µm %sample)*<63µmaverage (Equation 13)

([Me+] sample /Pop. density sample)*Pop. densityaverage (Equation 14)

Element contents have been plotted against population density and altitude to further

determine any significant anthropogenic or naturally occurring trends.

Statistics

Statistical parameters like R2 and 95% confidence limits (2) were used to quantify deviations

from what is considered a natural content. The confidence limits have been used in ICP MS to

find data outliers. Sample points outside these limits are considered impacted by man. The

main statistical analysis conducted in this study is a principal component analysis to evaluate

29

which elements and parameters are related to each other. A correlation matrix was constructed

to evaluate which parameters to include in the following analytical steps.

Grain size statistics like kurtosis and skewness have been calculated to be able to tell

something about transport and deposition of the sediments.

Statistical analyses were made using SPSS release 13 for Windows. Microsoft Excel 2007 and

The Gradistat v5 and Sediment Quality Index 1.0 spreadsheet were used for grain size

statistics.

Sediment quality guidelines and enrichment factors

At present there are no available guidelines for the tropical Americas. The Canadian Sediment

Quality Guidelines, CCME (1999), have been used as a substitute. The method uses scope,

frequency and amplitude to rank the samples from 0=bad to 100=good. Below The Interim

Sediment Quality Guidelines threshold biological effects are not likely. The probable effects

level defines the bio affecting metal content (CCME 1999). The Swedish SEPA (1999) and

the American NOOA, (1999) sediment quality guidelines have been employed for comparison

of CCME results. Enrichment factors have been calculated. The lowest sample concentration

of each element has been used in the element background assessment. Refer to Appendix E

for suggested background contents used in the enrichment factor calculations.

Enrichment factor = Element contentsample / Element contentbackground

(Equation 15)

Pollution Index

Although slightly controversial the PI-method (employed by Kloke 1979) has been used to

get an idea of the total toxicity of the samples. The idea is to account for the effect of the total

metal cocktail of the sediments rather from the single metallic elements toxicity.

P. I. = Cd

3+Cu

100+Pb

100+Zn

300 /4

(Equation 16)

Results The ICP-MS results are crucial to assess the contaminant levels at the various sampling

localities. The other results (i.e. LOI, GSD and XRD) were mainly used to put the metal

concentrations into a context.

Visual examination of soil samples

The visual examination shows that the grain assembly matures lower in the drainage. The

largest variation in mineralogy exists in the Cusco region samples. Madre de Dios samples

have a larger variety of minerals than the Loreto samples. Rock fragments were found in the

Rio Urubamba samples and to some degree in the Rio Alto Madre de Dios. The Rio Marañón

sample is less mature than the Rio Amazon sample and has more in common with Rio Alto

Madre de Dios in terms of grain roundness and shape.

30

Table 3. Mineralogic features observed using hand lens or stereo microscope. The 1-2 mm fraction was

examined if not otherwise noted.

Sample Description

1 Quartz grains sub angular – surrounded semi spheres. Sample dominated by arkose and

sandstone rock fragments. Feldspars surrounded spheroids. Less than 5% dark mineral

grains.

2 A mixture between a number of sedimentary rock fragments. Calcite not as abundant as

sandstone and arkose. Quartz grains immature and sub angular. Dark minerals constitute

7%

3 A bit enriched in rounded sphere shaped quartz compared to other Andean samples.

Feldspars well rounded. Dark minerals make up <10%.

4 Quartz angular rods-blades constitute ~ 50% of sample. Feldspars sub rounded-sub

angular and rod shaped. Dark minerals and slate 25%. Sedimentary rock fragments 25%.

5 40% dark minerals. Magnetite about 10% of the dark portion. Quartz sub rounded and

spheroid-disc shaped. Feldspar sub angular spheroid-rods.

6 About 50 % dark minerals. Magnetite about 10% of the dark portion. Quartz sub

rounded and spheroid-disc shaped. Some sulfur coating on grains. Feldspar sub angular

spheroid-rods.

7 About 50 % dark minerals. Quartz sub rounded and spheroid-disc shaped. Some

sulfurcoated grains. Feldspar sub angular spheroid-rods.

8 Quartz sub rounded-sub angular and spherical. Feldspar rounded disks. Sedimentary

rock fragments of various kinds. Dark and silvery mineral predominance.

9 Beige sandstone fragments predominate. Quartz grains sub angular-rounded and shaped

like partly spheroidal rods.

10 Mud dominated sample. Dark minerals and rock fragments constitutes 25% respectively.

11 75 % of sediment is spheroid formed and rounded – well rounded quartz. Few dark

minerals, feldspars and rock fragments.

12 Quartz grains dominate in the 250-500µm grain size fractions. These are rounded-well

rounded and spheroid-disc shaped. Few dark minerals of which about half magnetite.

Generally more immature than sample 11.

Grain size distribution

The clay content is higher in the lower altitude river samples researched. Rio Madre de Dios,

Rio Amazon and Rio Marañón have similar mud levels (<63µm). The smallest fine material

fractions are found in the Andean samples of the Cusco region. Further details and statistical

parameters listed in Appendix B. Grain size distribution and statistics.

31

Figure 17. Clay, silt, sand and gravel distributions of samples.

Sample 1 has a large fraction that is coarser than sand. The remaining fine material portion is

too small to obtain satisfying results. The sample 1 skewness and other statistics could not be

correctly determined. A sieve with coarser meshes and larger sample quantity is necessary too

obtain more accurate results. Samples 2 and 3 have dominating sand fractions (>90%). Stream

velocity prevents fine material from accumulation in sample 3. Sample 4 and 5 have a

dominant silt fraction and lack a significant gravel fraction. Rio Manu samples (6-8) are not

dominated by a single size fraction. The large silt and clay fraction in sample 10 is explained

by lee side deposition of fine material due to large rocks and complex channel form that

creates calm portions of the river. Differences in samples 11 and 12 are due to different terrain

sample locations rather than any large sedimentological variations

Mean grain size (µm)

Sorting ()

Skewness

Kurtosis

Figure 18. Descriptive statistics for the grain size samples.

0% 20% 40% 60% 80% 100%

1

2

3

4

5

6

8

9

10

11

12

Gravel >

2mm

Sand

63μm-

2mm

Silt 2-

63μm

Clay

<2μm

1.0

10.0

100.0

1000.0

1 2 3 4 5 6 8 9 10 11 12

0.0

10.0

20.0

1 2 3 4 5 6 8 9 10 11 12

-1.5

-1.0

-0.5

0.0

0.5

1 2 3 4 5 6 8 9 10 11 12

0.0

1.0

2.0

3.0

1 2 3 4 5 6 8 9 10 11 12

32

XRD

Some minerals not included in the SIROQUANT database have been excluded from the

quantification. Quartz and feldspars dominate all samples. Heavy minerals are more common

and the mineralogy is more diverse in the Andean samples than in samples from the other

environments.

Table 4. SIROQUANT results.

1Low.

2Monoclinic.

Sample Mineral Formula Contrast corrected weight % Error Chi2

3 Albite1 NaAlSi3O8 12.90 0.54 2.36

Quartz SiO2 66.60 0.79

Anorthite2 CaAl2Si2O8 2.50 0.51

Jarosite KFe3(SO4)2(OH)6 1.20 0.25

Muscovite KAl2(AlSi3O10)(F,OH)2 16.80 0.71

Σ 100.00

4 Microcline KAlSi3O8 35.00 0.74 2.27

Quartz SiO2 65.00 0.74

Σ 100.00

7 Quartz SiO2 91.50 1.58 2.33

Microcline KAlSi3O8 7.50 1.26

(Wollastonite) CaSiO3 1.00 1.05

Σ 100.00

12 Albite NaAlSi3O8 46.00 2.32 3.00

Quartz SiO2 54.00 2.32

Σ 100.00

Sample 3 is the most mineralogical diverse sample in this study. Quartz (~67%) predominates

accompanied by plagioclase (mainly albite and anorthite) and muscovite. Jarosite is a heavy

mineral found in this sample. Madre de Dios samples (4 and 7) are comprised mainly of

Quartz (65 and 91.5 % respectively). The K-feldspar Microcline dominates the residual part

of the mineralogy. The Amazon sample (p.12) is principally all albite and quartz. The quartz

fraction (54%) is the lowest in the study.

Loss on ignition analysis

Loss on ignition generally ranges from a couple

percent to 12% (See Appendix G. LOI results).

Sample 1 and 2 are anomalously high though with

percentages loosed of 62 and 23% respectively.

Both samples are affected by ongoing biological

production. Sample 6 and 8 have low LOIs of about

1%.

0 20 40 60

123456789

101112

LOI %

Sa

mp

le

550°C 950°CFigure 19. Loss on ignition at 550 and 950 ºC.

33

Trends in the various sedimentological settings are weak. Samples of Andean origin have the

highest mean organic content. There are large variations in both the organic and the CaCO3

fractions. Not surprisingly, the Madre de Dios samples show similarities in CaCO3 content

although the organic content varies from next to nothing to about 8 wt%. The Loreto samples

have similar contents of organics and CaCO3 of about 9 wt% and 11 wt%, respectively.

ICP-MS

The Aqua Regia and the HNO3 extractable phases show similar element distribution patterns

between the samples. Al, Cr and Fe are more immobile than the other elements and only

~70% of the Aqua Regia contents are extracted in the HNO3 extraction compared to 80-100%

for the other elements. Arithmetical means for each state of the HNO3 and Aqua Regia

extractable contents are provided below.

Areas with a large human presence generally have sediments with a higher metal and As

contents. Samples with low population or little land use have low copper content (roughly one

third - half) even if the normalization techniques employed. Zn is nearly three times higher in

populated areas. As is higher in samples taken near active or abandoned mines. Comparably

high Cd and Pb levels are found at sites near larger roads and heavily used river sections.

Figure 20. Left: Arithmetical means for the HNO3/H2O leachable fractions. Right: Arithmetical means of

Aqua Regia extractable content. Rio Alto Madre de Dios samples are included in the Cusco values.

Cusco state including Rio Alto Madre de Dios

The metal contents of sample 1 is close to the mean of the Cusco samples although low in Zn,

Cd and As. Sample 2 has a relatively low element contents, comparable with the Madre de

Dios samples. Sample 3 has the study’s highest Fe concentration and is second only to sample

10 in Zn, As and Pb. The locations of these samples are characterized by a high population

density and a short distance from mining activities. The sample is lower than the Cusco mean

in Al, Cr and Cd. Sample 4 has the highest contents of Cr and Cu. Al, Fe, Cd are also rather

high in comparison to the other Cusco sediments. Sample 9 is comparable to sample 4

although slightly lower in all element contents. Sample 10 is high in all elements except Al

0.01 0.1 1 10 100

Al (%)

Cr

Fe (%)

Cu

Zn

As

Cd

Pb

mg/kg

Cusco Madre de Dios Loreto

0.01 0.1 1 10 100

Al (%)

Cr

Fe (%)

Cu

Zn

As

Cd

Pb

mg/kg

34

and Cr. This site has the study’s highest Zn, As, Cd and Pb contents (2-3 times the Cusco

mean respectively). The fact that the Rio Alto Madre de Dios samples are here included in

Cusco lowers the Cusco mean metal and As contents.

The state of Madre de Dios

Sample 6, 7 and 8 are only separated by kilometers. Differences in metal content could be

observed despite the limited spatial distribution. While sample site 8 generally has the lowest

metal and arsenic contents site 7 show moderate to high levels. Sample 5 and 6 have low

contents of all analyzed elements and are close to the Madre de Dios mean. Sample 7 has the

highest contents of all elements compared to the other Madre de Dios samples (excluding the

Rio Alto Madre de Dios). Sample 8 has the lowest total metal and As load of the state.

The state of Loreto

Sediment metal content in the two Loreto samples (sample 11 and 12) does not vary

significantly. All elements except As are above the study mean.

Figure 21. HNO3 extractables. Note the logarithmic scale and refer to Appendix C for complete data.

If the HNO3 fraction is correlated to the Aqua Regia fraction R2-values are over 0.9 (Lower

for Fe and Pb). The HNO3 fraction constitutes close to 80% of the Aqua Regia extractable

metals. The differences between Aqua Regia and HNO3 extractables are element specific.

0.01 0.1 1 10 100

1

2

3

4

5

6

7

8

9

10

11

12

mg/kg

Sam

ple

Al (%) Cr Fe (%) Cu Zn As Cd Pb

35

Figure 22. Aqua Regia extractable metal and arsenic content. Note the logarithmic scale and refer to

Appendix C for complete data.

Normalization and statistics

Similar patterns among sampling stations are visible for most elements. Andean samples 3

and 10 are generally higher than the other samples whereas the Rio Manu samples 8, 7 and 6

are considerably lower. Cr does not have a clear Andean peak. Cu is decreasing downstream

except for an Andean anomaly. Pb has a weak linear trend with little downstream effect.

0.01 0.1 1 10 100

1

2

3

4

5

6

7

8

9

10

11

12

mg/kg

Sam

ple

Al Cr Fe Cu Zn As Cd Pb

36

Cr (mg/kg)

Fe (%)

Cu

Zn

As

Cd

Pb

Figure 23. Al normalized, Aqua regia extracted element concentrations (mg/kg). Samples ordered with the

highest elevation (3799 m a.s.l.) to the left and the lowest to the right (110 m a.s.l.).

0

20

40

1 3 2 10 4 9 6 7 8 5 12 11

0

10

20

1 3 2 10 4 9 6 7 8 5 12 11

0

50

100

1 3 2 10 4 9 6 7 8 5 12 11

0

200

400

1 3 2 10 4 9 6 7 8 5 12 11

0

100

200

1 3 2 10 4 9 6 7 8 5 12 11

0

0.5

1

1 3 2 10 4 9 6 7 8 5 12 11

0

50

1 3 2 10 4 9 6 7 8 5 12 11

37

Cr (mg/kg)

Fe (%)

Cu (mg/kg)

Zn (mg/kg)

As (mg/kg)

Cd (mg/kg)

Pb (mg/kg)

Figure 24. Al normalized, Aqua regia extracted element concentrations (mg/kg). Samples ordered with the

highest population densities (15.69) to the left and the lowest to the right (0.9).

0

20

40

3 2 1 10 11 12 4 5 6 7 8 9

0

10

20

3 2 1 10 11 12 4 5 6 7 8 9

0

50

100

3 2 1 10 11 12 4 5 6 7 8 9

0

200

400

3 2 1 10 11 12 4 5 6 7 8 9

0

100

200

3 2 1 10 11 12 4 5 6 7 8 9

0

0.5

1

3 2 1 10 11 12 4 5 6 7 8 9

0

50

3 2 1 10 11 12 4 5 6 7 8 9

38

Two element groupings could be observed in the correlation matrix below. The first group

contains Pb, As, Cd and Zn. The second grouping contains Cr, Cu and Al. Fe could best be

placed in group 1 although this element seems related to both groups. The second group has a

weaker relationship than the first (see table 7).

Table 5. Elements and parameters statistics. 2 anomalous LOI samples have been replaced with the

variable mean.

Mean Std. Deviation Data Outliers

Al 16784.44 9086.567 s.11

Cr 23.59167 10.43348 s.4

Fe 36399.64 19443.46 s.3

Cu 26.44833 17.31098 s.4

Zn 78.2025 52.04336 s.10

As 17.18917 26.83757 s.10

Cd 0.168333 0.152901 s.10

Pb 14.9475 8.796153 s.10

LOI 4.86432 3.150748 -

Population 6.06 7.13046 -

Mud 28.69246 32.24506 -

Table 6. Correlation matrix of metal, arsenic and associated parameters.

Al Cr Fe Cu Zn As Cd Pb LOI Pop. Mud

Al 1.00 0.84 0.26 0.61 0.34 -0.08 0.49 0.41 0.58 -0.23 -0.08

Cr 0.84 1.00 0.45 0.83 0.33 -0.05 0.31 0.27 0.39 -0.23 0.19

Fe 0.26 0.45 1.00 0.63 0.73 0.66 0.38 0.65 0.26 0.24 0.18

Cu 0.61 0.83 0.63 1.00 0.51 0.25 0.34 0.39 0.45 0.11 0.14

Zn 0.34 0.33 0.73 0.51 1.00 0.89 0.88 0.96 0.78 0.38 0.46

As -0.08 -0.05 0.66 0.25 0.89 1.00 0.70 0.86 0.58 0.62 0.42

Cd 0.49 0.31 0.38 0.34 0.88 0.70 1.00 0.91 0.94 0.27 0.43

Pb 0.41 0.27 0.65 0.39 0.96 0.86 0.91 1.00 0.82 0.42 0.30

LOI 0.58 0.39 0.26 0.45 0.78 0.58 0.94 0.82 1.00 0.37 0.35

Population -0.23 -0.23 0.24 0.11 0.38 0.62 0.27 0.42 0.37 1.00 0.08

Mud -0.08 0.19 0.18 0.14 0.46 0.42 0.43 0.30 0.35 0.08 1.00

Table 7. Communalities for parameters used in the principal component analysis. The communality

indicates the proportion of the variance explained by the factor structure.

Initial Extraction Initial Extraction

Al 1 0.90775 Cd 1 0.97444

Cr 1 0.93684 Pb 1 0.92569

Fe 1 0.90366 LOI 1 0.91952

Cu 1 0.86212 Population 1 0.5795

Zn 1 0.96495 Mud 1 0.29155

As 1 0.98311

39

Table 8. Tested principal components and statistics.

PC Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 5.7 52.2 52.2 5.7 52.2 52.2

2 2.4 21.6 73.8 2.4 21.6 73.8

3 1.1 10.3 84.1 1.1 10.3 84.1

4 0.9 8.6 92.7

5 0.6 5.6 98.2

6 0.2 1.4 99.6

7 0.0 0.2 99.8

8 0.0 0.1 99.9

9 0.0 0.1 100.0

10 0.0 0.0 100.0

11 0.0 0.0 100.0

Figure 25. Principal components with eigenvalues >1.

The first principal component contributes with 52.2% of the variance. LOI, Pb, Cd, As, Zn

and Cu are all included as key variables. Population correlations are weak but are highest for

component 1. Component 2 (21.6 % of the variance) derives its properties mainly from Cr

and Al. Also Cu could be included here. Component 3 has a total impact of 10.3 % and is less

well defined than component 1 and 2. Fe shares characteristics with component 1 and 3.

Soil quality guidelines and Enrichment Factors

Loreto and the populated areas of the Andes have the highest P.I. values (Pollution Indexes).

Remote areas of the Andes and the Madre de Dios region are generally low. The variability

within each setting could be as large as a factor 2. The lowest P.I. is found in the Madre de

Dios sample 8. The highest is from the Aguas Calientes (sample 10) of the Cusco region.

Sample 3 and 10 have the highest general Enrichment Factors.

-1.00 -0.50 0.00 0.50 1.00

Al

Cr

Fe

Cu

Zn

As

Cd

Pb

LOI

Pop

Mud

1 2 3

40

Table 9. Enrichment factors for each element and sample. Shaded cells marks EFs >5.

Sample: 1 2 3 4 5 6 7 8 9 10 11 12

Al 3.8 1.4 1.9 4.3 2.0 1.8 3.8 1.0 2.7 2.9 6.4 5.6

Cr 1.4 1.0 1.7 1.6 1.6 1.2 1.1 1.6 2.0 1.3 0.9 1.1

Fe 1.5 1.0 7.7 2.0 2.4 1.9 1.4 3.1 2.9 3.0 1.1 1.3

Cu 2.9 2.2 5.5 4.6 2.5 1.0 1.3 1.0 2.7 3.4 1.5 2.0

Zn 1.0 1.7 5.9 1.6 1.8 1.7 1.3 2.1 1.9 5.8 1.4 1.5

As 1.2 3.1 24.8 1.1 1.0 1.2 1.5 1.0 1.4 21.9 1.2 1.7

Cd 1.0 2.7 3.6 1.3 1.4 1.7 1.9 2.0 1.5 9.0 2.2 2.8

Pb 1.0 1.5 3.9 0.7 1.3 1.5 1.2 2.0 1.0 3.7 1.1 1.2

For Andean samples copper is generally rated as class 3 according to the SEPA system. Zn

has a larger variability among samples. The highest content is in samples 3 and 10 (both rated

class 2). Cadmium and lead is very low in all samples. Chromium is rated class 1 or 2 in all

samples. Arsenic is rated high in samples 3 and 10, otherwise low.

The Madre de Dios samples have very low Cu contents except for sample 4 (moderately

high). Zn is highest in sample 5 but all samples are all rated class 1. Cd and Pb contents are

very low in all samples. Cr is rated moderately high in sample 4 otherwise low. Samples 4

and 9, originating from the Alto Madre de Dios have a higher content of As and heavy metals

than the other Madres de Dios samples (Rio Manu).

The two Loreto samples are moderately high in copper and chromium but low in Arsenic.

All samples are very low in Zn, Cd and Pb compared to SEPA (1999).

Figure 26. Pollution index results. Left: Samples ordered from highest to lowest population. Right:

Samples ordered from highest to lowest altitude.

Canadian sediment quality guidelines

6 samples are over the ISQG (Interim Sediment Quality Guidelines) for As. Samples 3 and 10

have contents over the PEL (Probable effects level). No samples reach the Cd ISQG. Sample

4 is slightly over the ISQG limit for Cr. Cu for sample 1, 4 and 12 are higher than ISQG. No

samples except number 10 are close to ISQG for Pb. Samples 3 and 10 are over the Zn ISQG.

R² = 0.216

00.10.20.30.4

3 2 1 10 11 12 4 5 6 7 8 9

P.I

.

Sample

R² = 0.0179

00.10.20.30.4

1 3 2 10 4 9 6 7 8 5 12 11

P.I

.

Sample

41

Table 10. Canadian Sediment Quality index. ISQG= PEL=Probable effects level (CCME, 1999).

Sample Freshwater,

ISQG

Freshwater,

PEL

1 88 100

2 100 100

3 50 75

4 63 100

5 100 100

6 100 100

7 88 100

8 100 100

9 100 100

10 45 75

11 86 100

12 74 100

Al contents are close to the NOOA (1999) background in samples 2, 6 and 8. Only the two

samples from Loreto have Al contents over the Hyalella azteca (freshwater amphipod used

for sediment toxicity analysis) treshold effects level (TEL). Only sample 8 is close to the As

background. Samples 3 and 10 are well over the Probable effects limit. Sample 10, 11 and 12

are close to the upper limit or over the Cd background. All other samples are low. Samples 2,

6 and 8 are within the background level for Cr. All other samples are higher than the

established background level. Only sample 4 reaches the H. azteca TEL. Samples 1, 4 and 12

are over the Cu threshold effects level. Other samples are considered equal or slightly over the

background level. Only samples 2 and 8 have Fe contents within the background interval.

Samples 3, 10, 11 and 12 have Pb contents over the background. Only sample 10 is close to

the threshold effects level. Four samples (3, 10, 11 and 13) are over the H. azteca TEL. Only

samples 2, 6 and 8 are within the background limits.

Guidelines summary

The SEPA guidelines agrees with NOOA SQuiRT card limits in that Cr, Cu is enriched in

most samples in relation to natural background. NOOA guidelines rate all samples except

sample 8 over As background whereas SEPA rates 5, 6, 8 and 9 class 1. Both methods rates

Pb and Cd low in most samples. NOOA rates Pb and Cd over background in samples 10, 11

and 12 though (and 3 for Pb).

42

Discussion The Andean landscape has more relief than the rain forest of the States of Loreto and Madre

de Dios. The amount of short transported sediment (such as mass movements and surface

runoff) is greater in steep slope areas. This is visible in the samples as a general increase in

roundness, sphericity and quartz contents downstream. Grains from the Andean samples are

less rounded and have less pronounced quartz content than samples originating further

downstream in the Amazon drainage (i.e. Rio Manu and Rio Amazon). The Amazon main

stem sample and Rio Manu share characteristics in that quartz constitutes a larger portion of

the bulk sediment than the other samples. Both samples taken in Loreto are within the Iquitos

forebulge depozone part of the North Amazonian foreland basin. According to REE patterns

Neogene sediments of the area derived from Andean volcanic rocks and cratonic shield rocks

(Roddaz et al., 2005). Tropical rain forest samples (Cusco; Loreto) are significantly more

rounded than the Andes ones. This agrees with surveys by Gibbs (1967) who concludes that

the main portion of the material carried by the Amazon River has an Andean provenance.

The Rio Alto Madre de Dios samples are recognized as a gradual transition between the two

main sedimentary settings described above. The Rio Marañón sediment maturity is visibly

lower and the mineralogy is more diverse than in the Iquitos sample (Rio Amazon). The

resulting cause of this is that the Marañón branch is more proximal to the Andes than the

Ucayali/Urubamba. Areas with high precipitation are generally densely forested and are

protected against erosion (Bridge, 2003). The highly meandering character of Rio Manu and

Rio Madre de Dios are likely to erode previously deposited sediment. Effects hereof are not

considered in this report. Rio Alto Madre de Dios has a more “Andean” signature in mineral

composition and grain shape and are more related to the Cusco area than the Madre de Dios

state samples.

Grain size distribution patterns usually govern the trace metal concentrations. This is the case

in this study too. Other factors also seem to have a large importance as the relationship

between fine material (<0.2µm or <63µm) and metal content are weaker than excepted for

some samples and elements. Natural geological properties and anthropogenic factors govern

the metal and arsenic contents at the scale of this work. Local factors affect the deposition and

transport of various size fractions. Tropical Madre de Dios samples are governed by a

stronger relationship between grain size and element content (correlation R2>0.8 for most

elements). This suggests that the human contribution disturbs the grain size signal in the other

samples. The human impact in the state of Madre de Dios is very limited compared to other

localities. The poor sorting of sediments in the environments sampled indicates that the

energy has been inconsistent. This has promoted dumping rather than washing. The limited

sample quantity affects the possibilities in grain size trend assessments. Considering the

floodplain nature and the grain size distribution in the samples several factors are likely to

affect the investigated samples. These could be flooding intensity, stagnation of water and

erosion. GSD (poor – very poor sorting) gives a clue that river processes is dominating the

river bank environments. Muddy sediments have most likely been transported as suspended

load whereas the coarser fractions have been moved by bedload actions. Each overbank

flooding event generally deposits a unit of mm-dm thickness. (Bridge, 2003) The floodplains

of this study are considered active in this study since they are annually flooded.

The metal contents variability associated with principal component 1 is partly explained by

human population density differences. Al, Cr and Cu are relatively constant throughout the

samples. Sample 2 is comparably low in most metals. This is explained by the very limited

possible impact from anthropogenic sources, as the sample location is a remote Andean lake.

43

The lowest concentrations of sediment metal and Arsenic contents have been used as a

geochemical baseline. Rio Manu samples together with little impacted sites in the Andes

serves as principal background sources. Loreto samples have been excluded due to the vast

geographical distance from the other samples. The deviation from the Al normalized

background content is element specific. The content diversity is largest in the As content. A

factor 20 separates the lowest from the highest Al normalized contents. Cr has the lowest

variability of all analyzed elements. The highest sample concentration is two times the lowest.

All samples have sediment chemistry with naturally low contents of Cu, Cr, Zn compared to

Upper continental crust (Taylor and McLennan, 1985). This suggest that sediment quality

guidelines needs to be local and at least some element classes should be lower than in the

SEPA (1999) or the CCME (1999) in the study areas. It is worth mention that the nature of

the bedload material is heterogeneous because of natural processes such as sorting and

deposition of heavy minerals. The correlation between population density and sediment

element content is positive and strong. Some elements seem related to one another. Cd, As,

Zn and Pb covary with each other in most samples. This is substantiated by the principal

component analysis. Varying Al content appears related to Cu, Zn, Cd and Cr variability. Cr

and Cu variations are closely linked to Al contents (R2>0.8). The Cu-Al relationship is

disturbed by samples 3 and 4, which are believed to be largely anthropogenic in sediment

chemistry character. Zn also has a connection to Al. However three samples (3, 10 and 5)

interfere and lower the total R2. These samples have slightly high metal content that could not

fully be explained by Al-variations alone. As and Cu behave more independent from grain

size and organics than do the other examined metals. A mining induced factor is a plausible

explanation. Arsenic concentrations are higher in more populated areas.

Rio Marañón and Rio Amazon mainstem samples are characterized by high Al and Fe

contents. Al is about 2 times and Fe 1.3 times the geometric mean of all samples. The element

could be released during weathering and transport. It is also possible that the Loreto samples

are too few to exclude local anomalies. The moderately high Fe levels in Loreto are rated

lower in the Al normalization. The Al-normalized content reveals that Cr varies little if

grainsize effects are considered. Each element investigated is related to different properties.

Pb, Cd and Zn covary with organic contents (e.g. LOI). The normalizations to LOI show a

weaker correlation than for the Al and grain size. The organic content of these samples have

been severely enriched by human and/or natural processes. Al and Al normalized Cr

decreases throughout the drainage. The Laguna Huarcarpay sediment (Sample 2) is here used

as natural background. The other elements also have a tendency to increase downstream but

the trend is weaker. Zn, As, Cd and Pb increase from the Laguna Huarcarpay to the Agua

Calientes and then decrease towards the Iquitos site. Cu is the least affected metal during

downstream transport. Clay % has a large impact on the Al content of the sediment. This is

not surprisingly as Al is often used as a grain size proxy. Also Cd is largely governed by the

Clay fraction. Population density has an effect on the two first principal components and thus

has an impact on all analyzed elements. The LOI is governed by the population density. The

other parameters effect on Cr is weak and therefore interpreted as being dominated by natural

geological factors not included in the analysis.

The low Madre de Dios contents are explained by low regional population density and human

presence. The alluvial gold mining activities near the Bolivian border are located downstream

and do not directly affect the sediment at the sample stations. A liable explanation for higher

concentrations in Rio Alto Madre de Dios than in Rio Manu sediment is higher population

density and shorter distance to the principal sediment source. If the less impacted sediments

are compared with the sites at Urcos and Aguas Calientes it is evident that aluminum is not

44

significantly enriched by human activities and are thus useable as a lithophilic element in

normalizations.

The Jarosite occurrence in sample 3 could be indicative of mining activities and AMD.

Jarosite – AMD associations are described in McCubbin and Lang (1999). The quantity

determined by Siroquant is near the detection limit (1.2%) but the EVA peaks are well

defined. Previous workers have concluded that mining derived sediment metal concentrations

are usually not measurable more than a few kilometers downstream (i.e. Lee et al., 2001).

This makes the Loreto samples useful for comparison since there are no mining activities in

the area. Municipal waste treatment (or lack of it) has similar effects as in the Cusco region

and the sediment source is the same (Andes) even though the Cusco landfill is a major point

source. Most element contents of the ICP-MS samples plots well within the 95 % confidence

limits (2). Samples within these boundaries are often explained by a natural geological

variability. Mining induced elevated metal levels would plot well out of the confidence limit.

Samples 3 and 10 stands out with high metal and arsenic contents and are believed to be

mining impacted. The samples stand out with a significantly higher levels of primarily As and

Cd. The levels in these samples are however much lower than samples investigated closer to

mine tailings (described in Méndez, 1999, Lee et al., 2001)

As a developing country, waste management leaves much to be desired. Andean samples 3

and 10 are interpreted as being impacted by diffuse and point human sources. Rio Urubamba

samples are affected by poor waste management in Cusco and the surrounding Sacred Valley.

Samples are considerably higher than the suggested geochemical background. This is

supported by Bolaños and Carranza, (2006) and Allende Ccahuana (2002) who investigated

the pollution sources in the Urubamba area. Samples are comparably high in element

contents if compared with the Pluspetrol (2006) samples. Except the general municipal waste

effect this could be explained by Cu and polymetallic mining activities. Short distances to the

closest mines upstream support this (fig. 7). Also, the sediment element patterns are different

from the Loreto samples. The soluble sulfate mineral jarosite occurrence in sample 3 together

with high metal and As contents than other samples is indicative of a mining impact more

distant from the tailings than is usually recorded in similar studies. Also, both Loreto samples

comparably high metal and arsenic consents. If the ICP-MS data is normalized to Al the

levels are among the studies lowest Cr, Fe, Pb and As. The mentioned four samples are

characterized by high Pb and Cd likely due to use of leaded gasoline (phased out 2005; El

Comercio, 2004). The Loreto samples are potentially affected by diffuse natural and

anthropogenic sources from several thousands of kilometers of headwaters and tributaries.

Uncertainties

It was not possible to process the samples before arrival in Sweden (approximately 4 weeks

after the initial sampling). The sediment samples were not kept in a freezer or fridge during

the field stay. ICP MS sample 5 should be regarded with caution as the rubber seal gave up

during the end of the extraction procedure. Also, spectral interference caused by the HCl Cl-

could affect primarily Cr and As of the Aqua Regia leached samples. LOI samples 1 and 2

contain insufficient material to be righteously compared to the other samples. The two

duplicates do not deviate significantly though. Exceptional results could be explained by a

large local factor in organic content of the samples. This pattern is also apparent in the study

by Aufdenkempe et al. (2007). LOI is hard to use for element-organic content correlations in

Andean samples since the sites at Urcos and Aguas Calientes are clearly organically polluted

by municipal waste. The samples analyzed are sufficient to characterize the overall situation

in the state of Cusco.

45

Conclusions

Populated Andean areas are affected by elevated metal concentrations due to human

activities.

The highest levels of Fe, Zn, Cd, As and Pb documented in this study are found in the

Aguas Calientes and Urcos urban samples.

Madre de Dios ICP-MS samples have no raised levels of arsenic or metals.

The Amazon mainstem sample has elevated metal and arsenic levels from diffuse

sources. Loreto samples are characterized by metal levels higher than could be

explained by natural processes.

No analyzed sediment sample is considered severely impacted or toxic.

Samples 3 and 10 motivate further research in the Cusco region of the Peruvian

Andes. Higher levels of metals and arsenic could be expected locally. Mining

alteration of sediment geochemistry could be seen as a large amplitude but short

distance phenomenon whereas household waste and landfills potentially act as more

diffuse sources with a relatively small impact but geographically extensive.

Further research

The results presented in this study motivate further research to gain a deeper understanding of

the weathering, transport and deposition of trace metals in the complex Amazon watershed. It

could potentially be a massive work with a denser sampling pattern including stratigraphy and

more geochemical samples. To fully understand the principal contaminant patterns a tighter

sampling pattern is necessary. Undisturbed cores could provide more accurate geochemical

background concentrations than the ones suggested in Appendix E. Results from global

geochemical background assessments could prove useful. The XRD analysis should be done

on more samples (ranging from the actual tailing and a few kilometers downstream) to spot

pyrite weathering minerals (i.e. Jarosite and Goethite) closely associated with AMD. The LOI

method used in this study could be excluded in favor of a more accurate method. More

elements (like Li) could be included in the ICP MS analysis. PH measurements could

accompany the geochemical studies to help pointing out severe AMD sites. There could be an

problem in that there are several countries involved. This complicates for national geological

surveys to undertake such a project. Rio Marañón and Rio Ucayali contribute with

approximately equal amounts of water (and sediment) to the Rio Amazon. To gain better

understanding also the Rio Ucayali branch should be covered. Airborne imaging spectroscopy

and other advanced remote sensing techniques have been used successfully on other areas (i.e.

in Kuosmanen, 2003 and Mineo, 2003) and could be an alternative to in situ studies.

46

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50

Appendix A. Sample record and Field observations Table A-1. Summary of the environments at the sample sites.

ID: Location: Locality: Setting: Sediment: Land usage and coverage: Sediment

Color:

r1 13°25.727'S

071°51.766'W

Alt. 3200 m

a.s.l.

Rio Urubamba Valley look out, not

far from the Incan ruins at Pisac.

Southern side of the Rio Urubamba.

Stony, gravelly hill. Frequent

wedge shaped scars and

resulting colluvial deposits in

the area.

Bedrock sample Unimportant due to the elevation and

sample type

N/A

r2 13°29.024'S

071°57.780'W

Alt. 3795 m

a.s.l.

North of Cusco City on the valley

side. West of Rio Urubamba. Near

Puca Pucara ruins (Puka=Quechua

for red fortress)

Road cut Cemented and hard bed

rock sample

Not much. A few grazing cattle N/A

1 13°28.776'S

071°57.965'W

Alt. 3799 m

a.s.l.

Located at the modern Indian

market. Bottom sediment from an

unnamed stream (width=2m during

sampling). Stream is feeding the

Urubamba.

Fresh, rapid water movement.

Mountain stream of limited

size (width=1m). Abundant

biologic matter such as twigs

and leaves is present in and

around the river. Marginal

smell of decomposing organic

matter

Stream sediment taken at

d=0-15 cm. Loads of

organic matter in various

decomposed stages.

Grazing cattle and limited tourist

activities.

5 YR 2/2

2 13°36.925'S

071°43.463'W

Alt. 3090 m

a.s.l.

Half way between Cusco and

Urcos. Laguna Huarcarpay,

downhill from the main road by the

school

Andean wetland lake. Size

approximately 200x200

meters during sampling.

Fine sandy with some finer

contents. Obvious and

large peaty/organic

content.

Lake surrounded by wetlands and

mountains. Limited farming and

grazing. Asphalted and by Andean

standards well trafficated road, 150

m from the lake. Reed and other

typical wetland vegetation occupies

the lake margin.

5 YR 2/2

3 13°41.097'S Rio Vilcanota (Urubamba), at the River, around 50 meters in River beach sediment. Steep valley sides exploited by small 10 YR 5/2

51

071°36.723'W

Alt. 3713 m

a.s.l.

urban border of Urcos city.

Approximately 40 km south east of

Cusco City. Sample taken in the

vicinity of the bridge.

width Sediment taken 5 meters

from the water-land

interface. Decimeter sized

slate pieces and stones and

boulders available in the

river sediments. Sample

mainly sand and coarser.

scale farmers. The river is used for

every household activity imaginable

(i.e. car washing).

R3 13°23.284'S

071°35.626'W

Alt. 3520 m

a.s.l.

200 meters from pre-Columbian

tombs located near Rio Pichihua

approximately 70 km East-north-

east of Cusco.

Arid Andean with shrub and

grass vegetation..

Heavily weathered bed

rock sample. Loose

sediment in the area

mostly fine sand-silt. Silty

flour in conjunction with

bedrock.

Grazing sheep and occasional

donkeys and cattle.

4 12°49.586'S

071°23.334'W

Alt. 488 m a.s.l.

Upper course of Rio Alto Madre de

Dios

Tropical rain forest. Clear

water. Boulder (=0,3-0,6

m) accumulation in the river –

bank transition.

A stone removed from

sample because of weight.

6*5*2 cm sub angular

disc. Otherwise sandy with

a minor gravel content.

Primary rain forest. Absence of local

human impact.

5 Y 3/2

5 12°16.268'S

070°53.271'W

Alt. 303 m a.s.l.

Downstream Boca Manu, Rio

Madre de Dios

Tropical rain forest. River

width roughly 60 m.

Muddy Beach sediment. Small river boat dock for 2-3

motorized canoes surrounded by

primary rain forest. Population of

Bocca Manu approximated to a few

hundred. Last outpost of civilization.

5 Y 5/2

6 12°02.288'S

071°11.473'W

Alt. 317 m a.s.l.

Rio Manu Tropical rain forest. Less see

through water ability than in

Rio Alto Madre de Dios.

Stones (=0,1m) common in

the river sediment.

Sampling done in

conjunction with a

200x40m river beach. Fine

sand + finer.

Primary rain forest. Absence of local

human impact. Nearest village is

Boca Manu some 40km downstream.

Strictly regulated and limited boat

traffic.

5 YR 4/4

7 12° 00.230'S

071°13.459'W

Alt. 307 m a.s.l.

Rio Manu Tropical rain forest Beaches are common

during dry period. The

river sand is chocolate

colored.

River bank sand deposits

8 km upstream S.9. Primary rain

forest. Absence of local human

impact.

5 YR 4/2

52

>10 m thick. The surface

sand is darker than a few

centimeters down. Beaches

are made up by fine to

coarse sands.

8 12°00.260'S

071°14.071'W

Alt. 306 m a.s.l.

Rio Manu Tropical rain forest. River beach. Poorly sorted

Gravelly sand.

1 km upstream sample 7. Primary

rain forest. Absence of local human

impact.

10 YR 3/2

9 12°25.203'S

071°05.471'W

Alt. 321 m

a.s.l.

Rio Alto Madre de Dios Tropical rain forest. Fine silt accompanied with

gravel and stone bars in

the central channel. Water

relatively clear. 0,3-0,4

meter depth of sight. River

bank loose deposits >7 m

thick.

Primary rain forest. Absence of local

human impact.

10 YR 3/2

10 13°09.264'S

072°31.470'W

Alt. Approx.

2060 m a.s.l.

Rio Urubamba. Downstream Aguas

Calientes 200-300 m west of the

train station (i.e. city border).

Andean tributary. Very poorly sorted

sediment. Grains ranging

from =0,2-10m.

Sample taken in a calm

muddy area of the section.

Building debris (bricks)

have filled up one of the

river sides. Sediment is

dark Cafe latte colored on

the surface, changing

abrupt to almost black at

d>1cm. Smell is descent.

River clearly impacted by waste

resulting from Aguas Calientes.

10YR 4/2

11 3°71.758'S

073°22.421'W

Alt. 110

M a.s.l.

Rio Amazon, Iquitos urban border Urban impact secondary rain

forest.

Previously flooded area.

Large grass covered areas

surrounds the sampling

site. Waste in the form of

plastic bottles and general

household waste occupy

Except for the urban centre of Iquitos

both river banks are cultivated.

10 YR 3/2

53

the river banks. Mainly

sand/silt.

12 4°48.319'S

073°43.301'W

113 M a.s.l.

Piranha lodge. Rio Marañón, 180

km upstream Iquitos only a few

kilometers from the Ucayali-

Marañón joint at the rim of the

Pacaya Samiria National Reserve.

Primary and secondary Rain

forest

River banks shows no sign

of human activities. Rice

plantations a few

kilometers away on the

opposite side. Gravelly

sand.

Rice plantations within 100m from

the river channel.

10 YR 3/2

54

Appendix B. Grain size distribution and statistics

Sample: 1

Sample type: Trimodal,

Very Poorly

Sorted

Textural group: Muddy Sand

Sediment name: Very Coarse

Silty Very

Coarse Sand

MEAN, )(x 521.0

SORTING, σ 5.048

SKEWNESS, Sk -1.226

KURTOSIS, K 2.789

Clay (%) 4

Total fines (<63

µm)

15.6

Sample: 2

Sample type: Bimodal,

Moderately

Sorted

Textural group: Sand

Sediment name: Moderately

Sorted Fine

Sand

MEAN, )(x 183.7

SORTING, σ 1.702

SKEWNESS, Sk 0.029

KURTOSIS, K 0.770

Clay (%) 1.4

Total fines (<63

µm)

3.7

Sample: 3

Sample type: Unimodal,

Moderately

Sorted

Textural group: Sand

Sediment name: Moderately

Sorted

Medium

Sand

MEAN, )(x 396.0

SORTING, σ 1.987

SKEWNESS, Sk -0.134

KURTOSIS, K 1.993

Clay (%) 1.6

Total fines (<63

µm)

5.3

Sample: 4

Sample type: Polymodal,

Very Poorly

Sorted

Textural group: Sandy Mud

Sample: 5

Sample type: Polymodal,

Very

Poorly

Sorted

Sample: 6

Sample type: Bimodal,

Very Poorly

Sorted

Textural group: Muddy sand

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

55

Sediment name: Fine Sandy

Coarse Silt

MEAN, )(x 11.64

SORTING, σ 7.433

SKEWNESS, Sk 0.002

KURTOSIS, K 0.978

Clay (%) 19.6

Total fines (<63

µm)

80.1

Textural group: Sandy mud

Sediment name: Very Fine

Sandy

Very

Coarse Silt

MEAN, )(x 11.81

SORTING, σ 7.248

SKEWNESS, Sk -0.280

KURTOSIS, K 0.808

Clay (%) 20.9

Total fines (<63

µm %)

76.5

Sediment name: Coarse Silty

Medium

Sand

MEAN, )(x 72.07

SORTING, σ 6.966

SKEWNESS, Sk -0.611

KURTOSIS, K 0.885

Clay (%) 7.1

Total fines (<63

µm)

36

Sample 7 N/A

Sample: 8

Sample type: Polymodal,

Very Poorly

Sorted

Textural group: Muddy sand

Sediment name: Muddy Very

Coarse Sand

MEAN, )(x 108.4

SORTING, σ 15.18

SKEWNESS, Sk -0.570

KURTOSIS, K 0.606

Clay (%) 13.1

Total fines (<63

µm)

39.2

Sample: 9

Sample type: Trimodal,

Very Poorly

Sorted

Textural group: Mud

Sediment name: Coarse Silt

MEAN, )(x 5.511

SORTING, σ 5.586

SKEWNESS, Sk -0.228

KURTOSIS, K 0.839

Clay (%) 27.2

Total fines (<63

µm)

95.3

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

56

Sample: 10

Sample type: Unimodal,

Poorly Sorted

Textural

group:

Mud

Sediment

name:

Fine silt

MEAN, )(x 3.471

SORTING, σ 3.734

SKEWNESS,

Sk

-0.201

KURTOSIS,

K

1.055

Clay (%) 27.9

Total fines

(<63 µm)

99.4

Sample: 11

Sample type: Trimodal,

Very Poorly

Sorted

Textural group: Sandy Mud

Sediment name: Fine Sandy

Coarse Silt

MEAN, )(x 20.04

SORTING, σ 7.395

SKEWNESS, Sk -0.030

KURTOSIS, K 0.776

Clay (%) 12.7

Total fines (<63

µm)

66.8

Sample: 12

Sample type: Bimodal, Very

Poorly Sorted

Textural

group:

Muddy sand

Sediment

name:

Very Coarse

Silty Very

Coarse Sand

MEAN, )(x 328.0

SORTING, σ 4.740

SKEWNESS,

Sk

-0.250

KURTOSIS,

K

0.911

Clay (%) 3.4

Total fines

(<63 µm)

11.9

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

57

Appendix C. ICP-MS results Table C-1. Metals leachable by 1M HNO3 (mg/kg).

Sample Al Cr Fe Cu Zn As Cd Pb

1 15311 21.19 13378 34.27 48.42 6.157 0.0988 9.942

2 5785 7.78 6604 10.34 33.77 6.240 0.0756 6.341

3 7097 11.19 38129 30.89 200.78 32.824 0.1240 13.149

4 18662 31.89 38512 66.31 77.52 6.577 0.1163 9.802

5 8958 16.56 22176 16.20 154.35 4.606 0.0728 7.507

6 6011 9.99 13322 5.88 30.68 3.639 0.0484 6.714

7 13065 18.35 24277 16.54 56.88 5.692 0.1250 12.853

8 3908 6.85 9180 3.25 26.68 2.470 0.0324 4.783

9 11287 20.27 28342 22.42 51.90 5.005 0.0582 7.907

10 9703 13.17 29003 24.88 165.24 68.378 0.4087 21.286

11 24424 27.83 35213 33.68 109.47 9.136 0.3125 21.036

12 19997 26.16 33973 34.12 96.88 12.243 0.2688 18.517

Table C-2. Metals leachable by Aqua Regia (3:1 HNO3:HCl) (mg/kg).

Sample Al. % Cr Fe. % Cu Zn As Cd Pb

1 2.017 31.15 3.0902 36.37 45.68 5.896 0.0804 12.499

2 0.732 7.86 0.7690 10.22 27.86 5.629 0.0798 6.791

3 1.031 18.90 8.3063 35.48 137.10 63.330 0.1479 24.423

4 2.318 39.65 4.7771 65.67 85.70 6.219 0.1229 10.503

5 1.087 19.20 2.7335 16.84 45.55 2.720 0.0615 8.817

6 0.953 12.65 1.9305 6.06 36.80 2.768 0.0640 8.547

7 2.056 24.47 3.0327 17.14 61.55 7.401 0.1549 14.814

8 0.535 8.96 1.7357 3.33 24.94 1.327 0.0426 6.345

9 1.434 30.96 4.3230 24.39 62.85 4.815 0.0864 8.683

10 1.542 22.01 4.8066 32.31 201.03 83.678 0.5526 34.592

11 3.451 33.38 4.0363 32.79 110.02 10.030 0.2973 22.215

12 2.980 33.91 4.1387 36.78 99.35 12.436 0.3378 21.150

Table C-3. ICP-MS statistics for the Andean samples.

Andean Samples.

n=4

g/kg Al Cr Fe Cu Zn As Cd Pb

Mean 9473.96 13.33 21778.79 25.09 112.05 28.40 0.18 12.68

S.D 4218.10 5.69 14380.14 10.58 83.42 29.46 0.16 6.38

Min 5785.20 7.78 6604.42 10.34 33.77 6.16 0.08 6.34

Max 15310.62 21.19 38129.48 34.27 200.78 68.38 0.41 21.29

Table C-4. ICP-MS statistics for the Madre de Dios state samples.

Madre de Dios samples. n=6

g/kg Al Cr Fe Cu Zn As Cd Pb

Mean 10315.20 17.32 22634.81 21.77 66.34 4.66 0.08 8.26

S.D 5283.60 8.78 10538.73 22.97 46.93 1.46 0.04 2.78

Min 3908.34 6.85 9179.67 3.25 26.68 2.47 0.03 4.78

Max 18661.79 31.89 38511.63 66.31 154.35 6.58 0.12 12.85

Table C-5. ICP-MS statistics for the Loreto state samples.

Loreto samples. n=2

g/kg Al Cr Fe Cu Zn As Cd Pb

58

Mean 22210.23 26.99 34593.22 33.90 103.18 10.69 0.29 19.78

S.D 3130.23 1.18 876.93 0.31 8.90 2.20 0.03 1.78

Min 19996.83 26.16 33973.14 33.68 96.88 9.14 0.27 18.52

Max 24423.64 27.83 35213.30 34.12 109.47 12.24 0.31 21.04

Table C-6. ICP-MS statistics for all samples in this study.

All samples. n=12

g/kg Al Cr Fe Cu Zn As Cd Pb

Mean 16784.44 23.59 36399.64 26.45 78.20 17.19 0.17 14.95

S.D 3130.23 1.18 876.93 0.31 8.90 2.20 0.03 1.78

Min 19996.83 26.16 33973.14 33.68 96.88 9.14 0.27 18.52

Max 24423.64 27.83 35213.30 34.12 109.47 12.24 0.31 21.04

Figure C-1. Geometric mean and spread (%) of elements analyzed.

1

100

10000

Al Cr Fe Cu Zn As Cd Pb

Min

Max

Geometric mean

59

Appendix D. Normalized metal content Table D-1. Element contents normalized to Al.

Al Cr Fe Cu Zn As Cd Pb

1 1.68 25.91 2.57 30.26 38.00 4.90 0.07 10.40

2 1.68 18.02 1.76 23.42 63.86 12.90 0.18 15.56

3 1.68 30.77 13.52 57.75 223.17 103.09 0.24 39.76

4 1.68 28.70 3.46 47.55 62.05 4.50 0.09 7.60

5 1.68 29.62 4.22 25.99 70.28 4.20 0.09 13.60

6 1.68 22.28 3.40 10.67 64.80 4.88 0.11 15.05

7 1.68 19.97 2.48 13.99 50.24 6.04 0.13 12.09

8 1.68 28.09 5.44 10.43 78.20 4.16 0.13 19.89

9 1.68 36.23 5.06 28.54 73.54 5.63 0.10 10.16

10 1.68 23.95 5.23 35.16 218.73 91.05 0.60 37.64

11 1.68 16.23 1.96 15.94 53.50 4.88 0.14 10.80

12 1.68 19.09 2.33 20.71 55.94 7.00 0.19 11.91

Table D-2. Element contents normalized to LOI.

Al Cr Fe Cu Zn As Cd Pb

1 0.82 12.62 1.25 14.74 18.51 2.39 0.03 5.06

2 0.30 3.23 0.32 4.19 11.44 2.31 0.03 2.79

3 5.64 103.34 45.41 193.96 749.55 346.24 0.81 133.53

4 3.66 62.56 7.54 103.62 135.23 9.81 0.19 16.57

5 8.04 141.84 20.20 124.45 336.59 20.10 0.45 65.15

6 11.26 149.52 22.82 71.62 434.87 32.72 0.76 101.02

7 3.71 44.10 5.47 30.90 110.94 13.34 0.28 26.70

8 17.17 287.43 55.67 106.73 800.09 42.56 1.37 203.51

9 7.92 171.00 23.87 134.67 347.07 26.59 0.48 47.95

10 1.55 22.12 4.83 32.47 202.02 84.09 0.56 34.76

11 2.34 22.66 2.74 22.25 74.68 6.81 0.20 15.08

12 2.44 27.74 3.39 30.10 81.28 10.17 0.28 17.30

Table D-3. Element contents normalized to Clay%.

Al Cr Fe Cu Zn As Cd Pb

1 4.12 63.68 6.32 74.36 93.39 12.05 0.16 25.55

2 6.49 69.66 6.81 90.51 246.82 49.87 0.71 60.16

3 6.03 110.48 48.55 207.36 801.34 370.17 0.86 142.75

4 1.91 32.67 3.94 54.11 70.62 5.12 0.10 8.65

5 0.57 10.04 1.43 8.80 23.81 1.42 0.03 4.61

6 2.12 28.20 4.30 13.51 82.02 6.17 0.14 19.05

7 2.30 27.35 3.39 19.16 68.80 8.27 0.17 16.56

8 0.49 8.14 1.58 3.02 22.66 1.21 0.04 5.76

9 0.59 12.67 1.77 9.98 25.72 1.97 0.04 3.55

10 0.72 10.21 2.23 14.99 93.24 38.81 0.26 16.04

11 4.45 43.04 5.20 42.27 141.87 12.93 0.38 28.64

12 10.59 120.50 14.71 130.73 353.07 44.20 1.20 75.17

60

Table D-4. Element contents normalized to % material <63µm.

Al Cr Fe Cu Zn As Cd Pb

1 6.26 96.67 9.59 112.88 141.76 18.30 0.25 38.79

2 9.25 99.33 9.71 129.07 351.95 71.11 1.01 85.79

3 9.15 167.70 73.70 314.77 1216.41 561.90 1.31 216.69

4 1.36 23.27 2.80 38.54 50.30 3.65 0.07 6.16

5 0.68 12.01 1.71 10.54 28.50 1.70 0.04 5.52

6 1.24 16.49 2.52 7.90 47.96 3.61 0.08 11.14

7 2.76 32.89 4.08 23.05 82.75 9.95 0.21 19.92

8 0.62 10.44 2.02 3.88 29.06 1.55 0.05 7.39

9 0.71 15.28 2.13 12.03 31.01 2.38 0.04 4.28

10 0.73 10.42 2.27 15.29 95.13 39.60 0.26 16.37

11 2.43 23.48 2.84 23.07 77.40 7.06 0.21 15.63

12 11.80 134.26 16.39 145.65 393.38 49.24 1.34 83.75

Table D-5. Element contents normalized to population density.

Al Cr Fe Cu Zn As Cd Pb

1 0.89 13.81 1.37 16.12 20.25 2.61 0.04 5.54

2 0.32 3.49 0.34 4.53 12.35 2.50 0.04 3.01

3 0.38 6.98 3.07 13.11 50.65 23.40 0.05 9.02

4 51.39 878.82 105.89 1455.71 1899.71 137.85 2.73 232.81

5 24.11 425.51 60.59 373.34 1009.72 60.29 1.36 195.45

6 21.13 280.42 42.79 134.33 815.63 61.36 1.42 189.46

7 45.58 542.34 67.22 380.01 1364.36 164.05 3.43 328.38

8 11.87 198.64 38.47 73.76 552.93 29.41 0.95 140.64

9 31.80 686.38 95.83 540.58 1393.14 106.74 1.92 192.48

10 0.57 8.13 1.78 11.94 74.27 30.91 0.20 12.78

11 3.83 36.99 4.47 36.34 121.94 11.12 0.33 24.62

12 3.30 37.58 4.59 40.77 110.11 13.78 0.37 23.44

61

Appendix E. Geochemical background assessment results

Table E-1. Normalized metal and As concentrations

Al

normalized

Background

Derived

from

sample

Al. % 0.54 8

Cr. mg/kg 18.02 2

Fe. % 1.76 2

Cu. mg/kg 10.43 8

Zn. mg/kg 38.00 1

As. mg/kg 4.16 8

Cd . mg/kg 0.07 1

Pb . mg/kg 7.60 9

62

Appendix F. XRD results

Figure F-1. Sample 3, XRD pattern of the fine sand fraction

Figure F-2. Sample 4, XRD pattern of the fine sand fraction

Figure F-3. Sample 7, XRD pattern of the fine sand fraction

FigureF-4. Sample 12, XRD pattern of the fine sand fraction

63

Appendix G. LOI results Table G-1. Loss on ignition results

Series 1 Series 2 Series 1 Series 2 Mean Mean

Sam

ple

Loss on

550°C, 2h,

Corrected

Loss on

550°C, 2h,

Corrected

Loss on

950°C, 2h,

Corrected

Loss on

950°C, 2h,

Corrected

Loss on

550°C, 2h,

Corrected

Loss on

950°C, 2h,

Corrected

1 27.09 18.69 11.47 12.03 22.89 11.75

2 68.44 56.00 0.00 0.00 62.22 0.00

3 3.33 3.31 10.46 10.96 3.32 10.71

4 4.93 5.29 14.57 4.99 5.11 9.78

5 2.67 1.99 14.68 16.02 2.33 15.35

6 1.24 2.08 15.38 13.98 1.66 14.68

7 5.14 4.96 8.40 9.62 5.05 9.01

8 1.04 1.14 4.98 4.92 1.09 4.95

9 2.68 2.04 5.83 6.35 2.36 6.09

1

0 11.17

12.97

14.74

13.24 12.07 13.99

1

1 7.88

8.08

15.49

13.89 7.98 14.69

1

2 8.10

7.24

33.62

31.44 7.67 32.53

Appendix H. Population densities in study areas Table H-1. Population densities of sampled areas (INEI, 2000)

Sample Population density

1 100

2 100

3 120

4 2

5 2

6 2

7 2

8 2

9 2

10 120

11 40

12 40

64

Appendix I. Sediment and Soil quality guidelines

Table I-1. Environmental quality guideline values (mg/kg) for sediment contamination. N/A = Not

available (Modified from SEPA, 1999).

Class Contents Cu Zn Cd Pb Cr As Fe Al

1 Very low <15 <150 <0.8 <50 <10 <5 N/A N/A

2 Low 15-25 150-300 0.8-2 50-150 10-20 5-10 N/A N/A

3 Moderately high 25-100 300-1000 2-7 150-400 20-100 10-30 N/A N/A

4 High 100-500 1000-5000 7-35 400-2000 100-500 30-150 N/A N/A

5 Very high >500 >5000 >35 >2000 >500 >150 N/A N/A

Table I-2. Canadian Soil Quality Guidelines (mg/kg) for Protection of Environmental and Human Health

(CCME, 1999).

Land Use Cu Zn Cd Pb Cr As Fe Al

Agricultural 63 200 1.4 70 64 12 N/A N/A

Residential 63 200 10 140 64 12 N/A N/A

Industrial/

Commercial

91 360 22 600 87 12 N/A N/A

Table I-3. Concentrations in g/kg (recalculated from NOOA, 1999).

Class Al As Cd Cr Cu Fe Pb

Background 0.26% 1.1 0.100-

0.300

7.000-

13.000

10.000-

25.000

0.99-

1.88%

4.0-17.0

Lowest ARCS

H. Azteca

TEL

2.55% 10.798 0.583 36.286 28.012 18.84% 37.0

Threshold

effects level.

TEL

N/A 5.900 0.596 37.300 35.700 N/A 35.0

Probable

effects level.

PEL

N/A 17.000 3.530 90.000 19.700 N/A 91.3

Upper effects

threshold.

UET

N/A 17.000 3.000 95.00 86.000 4% 127.0

Appendix J. List of Acronyms and words AMD – Acid mine drainage

Cochas – Oxbow lake (Spanish)

GSD – Grain size distribution

INGEMMET - Instituto Geológico Minero y Metalúrgico

LOI - Loss on ignition

M a.s.l. – Meters above sea level.

MCE – Multi Criteria Evaluation

MEM – Peruvian Ministry of Energy and Mining

PEL – Probable effects level

Ppt – parts per thousand

REE – Rare earth elements

Skarn - Old Swedish term for silicate gangue also including rock composed of calcium or

magnesium-bearing silicates derived from contact metamorphic and metasomatic processes.

UCC – Upper Continental Crust (Taylor and McLennan, 1985)

65

Appendix K. Epilogue Arrival in Lima was one day after the 8.0 magnitude magnitude earthquake that struck outside

the coast of Ica approximately 100 km south of Lima the 15th

of August 2007. This caused for

some rescheduling of plans. Other than this most went smoother than expected. Well, a few

incidents during the route were inevitable. One lost cell phone (including my music), a few

hundred mosquito bites (with resulting fever) and a minor landslide blocking the road while

crossing the Andes in off-road equipped bus. Another close call was in the Pacaya-Samiria

reserve (Loreto region) where a Fer-de-Lance snake tried its best. But some machete work

from the guide made me and my 2 companion travelers safe.

The biggest issue in working or travelling in Peru was my lack of Spanish. Another problem

is their poor English (or Swedish for that matter). You could have a hard time explaining that

you need a permit for bringing soil samples back to Sweden. The government officials at

Jorge Javez international airport had an interesting time as they guessed what my sediment

samples were. Among their guesses were orchids, insects, bromelias and only god knows

what else.

A tight budget made using high tech gadgets during sampling impossible. As a substitute for

large and heavy equipment creativity had to been used. Public busses are an excellent way of

transport where available. There are no regular stops and the driver lets you off wherever you

like. Also, motorized canoes, although expensive were used to get to the localities.

The reference site in the Nauta-Iquitos area had to be reached by plane since no roads could

take you there and going by boat would have ruined my time schedule.

All in all I am very satisfied with the expedition which got me to know people from around

the globe. It is also satisfying to see that the skills received at University of Gothenburg were

useable on such a different location than Sweden.

I wish to thank the following people, in no particular order. Ylva Ståhl, Sven Ardung and

Micke Persson for nice chitchat in the lab. My advisor, Professor Rodney Stevens for advice

and guidance. Nicasio Miranda for helping getting me around in the Cusco region. Andy and

his crew in the Manu area and excellent guidance from Mr. Israel in the Pacaya Samiria

reserve. And, thank you Anna.

Martin Persson, January 2008

Appendix L. Additional images cd-rom Run

Browse

F:\ (or your DVD/CD player of choice)

66