Geochemistry: Exploration, Environment, Analysis, Vol. 12, 2012, pp. 197 –209 DOI: 10.1144/1467-7873/10-RA-046

1467-7873/12/$15.00 © 2012 AAG/Geological Society of London

Global geochemical background values are the key to an impor-tant array of crucial questions. For example, Zoback (2001) stated ‘Documenting and understanding natural variability is a vexing topic in almost every environmental problem: How do we recognize and understand changes in natural systems if we don’t understand the range of baseline levels?’. Reliable element budgets, such as the definition of average element concentra-tions in soils, demand both high quality and representativeness of the underlying geochemical database. Using median values, rock geochemical data seem less critical due to a narrower range of values and less spatial variability than soils, and generalizing over larger geographical areas – such as global average values for granites or any other rock type without mineralization – appears acceptable due to the comparatively slow weathering rates. Soil geochemical data, however, show considerably higher spatial variability (e.g. Licht et al. 2006; Reimann et al. 2009; Salminen et al. 1998) and much faster dynamics. This is due to the fact that soils (S) can be defined as the qualitative func-tion: S = f (C, O, V, R, G, T) with C = climate, O = organisms,

V = vegetation, R = relief, G = geology (parent material), and T = time (after Jenny 1941).

From a global perspective, data from presumed or known contaminated sites or areas predominate in the literature. Little or no representative data exist for areas with little anthropo-genic impact, particularly for the southern hemisphere. Thus, many datasets contain a systematic bias – if interpreted as rep-resentative for a larger area – due to the limited spatial cover-age of related data and due to a focus on a small suite of elements, mainly motivated by environmental questions (sensu pollution; see discussion in Darnley 1998). Other problems relate to non-standardized sampling, sample preparation and analysis protocols, leading to severe difficulties in comparing data-sets from different sources (see discussion in Reimann & de Caritat 1998 and Licht et al. 2006). Hence, truly representa-tive, highly quality-controlled data are indispensable to repre-sent larger areas without a bias, particularly when background data are required (Reimann & Garrett 2005). Such data can be obtained through high-quality large-scale geochemical

A soil geochemical background for northeastern Brazil

Jörg Matschullat1*, Silke Höfle1, Juscimar da Silva2, Jaime Mello3, Germano Melo Jr.4, Alexander Pleßow1 & Clemens Reimann5

1Interdisciplinary Environmental Research Centre, TU Bergakademie Freiberg, Brennhausgasse 14, D-09599 Freiberg, Germany

2Embrapa Vegetable. Rodovia BR 060, km 09, Brasília/Anápolis, Caixa Postal 218, CEP 70359-270, Gama, DF, Brazil

3Federal University of Viçosa, Department of Soil Science, Av. P. H. Rolfs, s/n. CEP 36570-000 Viçosa, MG, Brazil4Federal University of Rio Grande do Norte (UFRN), Department of Geology, Lagoa Nova, Caixa Postal 1639,

CEP 59072-970, Natal, RN, Brazil5Norwegian Geological Survey, Leiv Eriksson vei 39, N-7002 Trondheim, Norway

*Corresponding author (e-mail: joerg.matschullat@ioez.tu-freiberg.de)

ABStRACt: Very few area-representative soil geochemical data exist for the south-ern hemisphere. A sub-continental scale (1.7 × 106 km2) geochemical sampling expe-dition in northeastern Brazil delivered 101 representative composite soil samples (30–50 cm depth) for non-anthropogenically influenced areas (mainly pasture land). Major, minor, and selected trace elements, determined by WD-XRF, are discussed with respect to lithology, soil type, biome type, climate and land use. These element concentrations vary up to two orders of magnitude, except for Si (factor 2.6). Silicon is strongly enriched compared to global averages, whereas most other com-ponents show a considerable deficiency. Significant deviations occur compared to results obtained from southern Brazil and from Australia – examples for the few representative data from the southern hemisphere. Anthropogenic influences appear negligible. All environmental parameters, except for land use, play an active role in shaping the geochemical composition. Lithology appears to be partly decoupled from the soils due to their age. The soil composition reflects soil type, biome type, and weathering influences. Most plant nutrients, despite their absolute depletion, show the highest values in Caatinga soils, and the lowest in Atlantic Forest soils. The new data form a robust and valuable tool to support future land use management.

KeywoRdS: low-density, soil geochemical mapping, major and minor elements, trace elements, lithology, soil type, climate, biome, land use

research-articleResearch article12X10.1144/1467-7873/10-RA-046J. MatschullatSoil Geochemical Background for Northeastern Brazil2012

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mapping (e.g. Smith 2009). The distinct relevance of scale with its implications for data interpretation has been demonstrated recently for a range of scales from local to continental (Reimann et al. 2009, 2010).

More than a decade ago, the Task Group on Global Geochemical Baselines (Reeder 2007) was established under the auspices of both the International Union of Geological Sciences and the International Association of Geochemistry to facilitate the generation of a much-needed global geochemical database (Darnley et al. 1995). Broad-scale geochemical map-ping programs conducted since that time have been more prevalent in the northern hemisphere than in the southern hemisphere. This geographical bias has resulted in the south-ern hemisphere having weaker and less representative geo-chemical sample coverage than currently found in the north. At the same time, many emerging economies in exactly these regions are increasingly active with their unprecedented devel-opment. Thus, time is of the essence if a database is to be cre-ated that intends to reflect a status that dominantly represents natural processes of element distribution. That database is also needed to assess possible anthropogenic impacts, and to evalu-ate and discern between changes due to natural versus man-induced processes. With the exigencies of global change, such a database may serve at the same time as an indicator to predict future changes in, for example, natural soil fertility as a result of changing regional climatic conditions and thus, in (agro)ecological zones (Ross 2006; Gerstengarbe & Werner 2008; Kurukulasuriya & Mendelsohn 2008).

The Brazilian–German project BraSol-2010, an International Year of Planet Earth (IYPE) contribution, was launched in 2008 to deliver a high-quality, spatially representative data-set for the southern hemisphere, and to support related regional activities of the Brazilian Geological Survey (CPRM: project PGAGEM; Silva 2008). The low-resolution soil geochemical sampling campaign targeted c. 1.7 x 106 km2 of northeastern and northern Brazil, jointly addressed as northeastern Brazil in this paper (Fig. 1). The project region is unusual with respect to

its climatological conditions and gradients, its current rapid development, and its (bio)diversity. It comprises 11 federal states, including Tocantins and a slice of southeastern Pará. (The latter are officially considered to be part of the administra-tive geographical unit ’Northern Region’, Região Norte; see, for example, Simielli 2006). Home to c. 52 million inhabitants, the region reflects the country’s lowest life expectancy (68 years) and the lowest per capita income (R$ 4.134 or c. 1.700 �; IBGE 2006). The principal aim of this study was to provide a robust regional soil geochemical background, and to identify and explain possible positive or negative anomalies. The prime questions addressed by this work are:

(1) What is the soil geochemical background variation for northeastern Brazil as determined by ultra low-density sampling?;

(2) How does the regional soil geochemical background relate to our current understanding of average soil geochemical values (such as World Soil Average values; Koljonen 1992);

(3) What are the driving forces for the geochemical signatures in these soils?

SuRvey AReAMajor regional environmental parameters influencing soil geochemistryThe enormous natural diversity of northeastern Brazil is easily illustrated (Table 1). Geological ’witnesses’ to this diversity reach from the early Proterozoic (with Archaean roots) to Cainozoic rocks. The climatological setting shows the semi-desert environments of the ’Sertão’ in the central eastern part and rainwater-rich coastal environments along the Atlantic Ocean. Six large drainage basins characterize the area. The tropical soils of the area are dominated by acrisols, arenosols, and ferralsols. Five major biomes are represented. This natural diversity is still present despite the current land use with domi-nating agricultural production, growing urban centres along

Fig. 1. Location of the sampling points in northeastern Brazil along the transects A (red, north) and B (blue, south); from Höfle (2009) after Scharpf (2010)

Soil Geochemical Background for Northeastern Brazil 199

the coast, and an ever-increasing transportation network of roads and train lines that link most of the regional centres.

GeologyThe basement of the South American Platform consists of metamorphic rocks of amphibolite to granulite facies and granitoids of Archean age, associated with Proterozoic units, usually represented by folded strips of metamorphic rocks (green schist to amphibolite facies, volcanic and sedimentary sequences), and several younger granitoids. This basement is widely exposed in large shields that are separated by overlying Phanerozoic rocks. Prominent for the research region is a piece of the Central Brazilian shield in Tocantins, and much of the Atlantic shield. The Central Brazilian shield, or ’Guaporé’, extends to the interior of Brazil, crossing the border of Bolivia in the west. It is surrounded by the Amazon basin in the north, the Parnaíba basin in the east and the Paraná basin in the south. The Atlantic shield is exposed in the eastern section of the region, nearly reaching the Atlantic Ocean in Bahia, except for some narrow and shallow Phanerozoic coastal sedimentary covers, and runs parallel to the coast to Ceará. Its western bor-der is the Parnaíba basin. Sedimentary and volcanic deposits of Ordovician-Silurian age overlie rocks of the Atlantic shield. These deposits have partially filled five synclinal basins: Amazon, Parnaíba, Potiguar, Tucano-Jatobá, and Paraná. In this study, only the Parnaíba and Potiguar basins were sam-pled. In addition to these basins, several other smaller basins occur exposed on the platform (Beurlen 1970; de Oliveira 1978; Zeil 1986).

ClimateThe entire region is under tropical influence with four sub-units. In its NW, a thin swath of equatorial climate (1) with dominating westerly continental equatorial air masses corre-sponds to the Amazon basin with less than three months of dry season. The central northern part of the study area, and thus most of the A-transect (see below) are under tropical equatorial (2) climatological influence with a range of four to eleven dry months per year. Here, Atlantic equatorial air masses from the northeast and southeast (trade winds) domi-nate. The eastern part, basically following the extent of the Atlantic Forest biome, is typical for the tropical littoral climate of the oriental NE (3) with one to seven months of dry season. Again, equatorial air masses from the Atlantic Ocean domi-nate. The central southern part, and second largest area, mainly represented by the B-transect (see below), shows a tropical humid to dry climate (4), also referred to as the tropical climate of Central Brazil. Four to eight months of drought characterize the region, and air masses dominate from the southern Central Atlantic Ocean, coming mainly from southeasterly directions (Nimer 1989; Mendonça & Danni-Oliveira 2007).

Not considering recent variations, the annual average air temperatures (1961–2001) range from 22 to >26°C, with min-ima from 18 to 22°C, and maxima from 26 to >32°C. The annual average precipitation (1961–2001) yields from <500 to c. 2000 mm, with individual years showing considerably lower, but scarcely higher rainfall rates. While temperatures generally decrease with increasing latitude, the precipitation shows a dis-tinct minimum for the project region with the highest precipi-tation along the Atlantic coast and towards the Amazon basin and the lowest in Paraíba state and northern Bahia (DNM 1992; Mendonça & Danni-Oliveira 2007).

Based on a cluster analysis, 32 separate climate types were distinguished globally by Gerstengarbe & Werner (2008). The above mentioned four climate types for northeastern Brazil

appear as well, albeit not quite as precisely distinguished (28GW 2, 4; 29GW 2, 4; 30GW 3; and 32GW 1), and were stud-ied in a comparison between the longer time frame of 1901–2002, and the shorter but highly dynamic 15 year-period between 1988 and 2002. This comparison shows substantial shifts in climate types, most pronounced in the Caatinga biome area (Gerstengarbe & Werner 2008). These shifts will, if trends persist, lead to serious changes in precipitation behaviour and other climatological parameters that will challenge existing land use practice.

During the sampling campaign, daytime air temperatures between 20–38°C (median 29°C) were encountered, with max-imum soil surface temperatures of 27–51°C (median 37°C). The related air humidity varied between 20 and 100% rH (median 48% rH). A small but significant relative temperature decrease and humidity increase from the A to the B-transect was observed. The sampling period was more humid than the long-term average.

HydrologyThe region is divided into six large drainage systems (ANA 2009) from NW–SE: (1)Tocantins-Araguaia river basin (967 059 km2) with the Tocantins and Araguaia rivers (discharge c. 13 600 m3 s-1); (2)Occidental Atlantic northeastern basin (254 100 km2), mainly discharged by the Mearim and Itapicuru riv-ers (2514 m3 s-1); (3)Parnaíba basin (344 112 km2) with the Parnaíba as the most potent river (1270 m3 s-1); (4)Oriental Atlantic northeastern basin (287 348 km2) with the Jaguaribe river (200 m3 s-1 before the construction of reservoirs) as the most prominent river, and the Acaraú and Piranhas-Açu riv-ers; (5)São Francisco basin (630 000 km2) with the São Francisco river (3140 m3 s-1) as the most important and fourth largest river of Latin America; and (6)Atlantic eastern basin (374 677 km2) with a discharge of 1400 m3 s-1, with the Paraguaçu river as the most relevant in the northern part of this basin (project area).

The important rivers of these major catchments have their sources in the mountain ranges of northeastern Brazil, namely the Serra Dourada and Serra dos Pirineus (Basin 1), the Serras do Tiracambu, Gurupi and Alpercatas (Basin 2), the Espigão Mestre with the Chapada das Mangabeiras in the SW (Basins 1 and 3), the Serra Grande with the Chapada do Araripe in the N (Basin 4), the Planalto da Borborema in the NE (Basin 4), and the Serra Geral and Chapada Diamantina in the central south-ern part (Basin 5). The average annual precipitation is highest in the mountain ranges with up to 2000 mm, while minima occur in the Sertão with less than 500 mm (DNM 1992), where mean discharge is below 5 L s-1 km-2 (ANA 2009). Since pre-cipitation occurs in the rainy seasons only, weathering is rela-tively slow and about half of the research area experiences more than six months of drought each year.

SoilsWith high evapo(transpi)ration rates, tropical soils of north-eastern Brazil are generally more shallow (sometimes centime-tres to a few decimetres only) than in the humid tropics. The Cerrado-type biome represents an intermediate situation. Most soils of the study area are likely among the oldest on Earth with ages dated to at least 72 million years (Vasconselos et al. 1994). Basic geochemical information was compiled and dis-cussed in detail by Melfi & Pedro (1977, 1978), pointing out the hydrolytic genesis under mostly fully oxidizing conditions. A rather low cation exchange capacity (CEC) characterizes substantial parts of the western and southwestern part of the region with a similar distribution of base saturation: lowest in

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the west and southwest, and highest in the east and northeast (Melfi et al. 2004). Kaolinite (Al2[Si2O5(OH)4], gibbsite/hydrargillite (γ-Al(OH)3), goethite (α-FeOOH), and maghe-mite (Fe2O3, γ-Fe2O3) are typical minerals for the upper soil horizons, in equilibrium with the prevailing acidic pH values, and a low to moderate cation exchange capacity (Rösler & Lange 1972). In its western half, kaolinitic soils prevail, while smectitic ones dominate in the eastern part (Melfi et al. 2004). The region is dominated by ferralsols, acrisols and arenosols, but yields a considerably larger spectrum (Table 1). To date, geochemical data such as elemental composition did not exist for any larger area.

BiomesNortheastern Brazil is home to five different biomes that have developed over many millions of years (IBGE 2009a). Coastal mangrove forests (Manguezal) (1) cover a rather small area. This biome was excluded from this study since its dynamics at the interface of the Atlantic Ocean are decoupled from most terrestrial processes. Only very small fragments remain today of the Atlantic Forest (Mata Atlântica) biome (2), situated along the eastern coast of Brazil. The heritage of this unique and diverse biome sustains significant agricultural activities from Rio Grande do Norte to Bahia (and beyond). This biome represents the highest precipitation rates and thus the strong-est biogeochemical cycling along the coast. It is, therefore, fully represented in this study. Further inland, dry, xeric shrub-land (Caatinga, ‘white forest’) (3) emerges, featuring sparse small trees and partly dense cacti populations that cover sub-stantial areas of the region. The climate is characterized by a pronounced dry season, and only slightly more humid condi-tions in the rest of the year. The more densely covered Cerrado biome (4) consists of tropical savanna-type vegetation further to the west and SW (Oliveira & Marquis 2002; Gottsberger & Silberbauer-Gottsberger 2006; Schmidt et al. 2009). At the northwestern boundary (Maranhão, Pará), the landscape dips towards the Amazon Lowlands. Here, the Mata dos Cocais, a transitional palm forest and secondary vegetation, character-ized by members of the palm family, dominates. The biome Amazon Rainforest (5) has basically disappeared in the project area due to clear-cutting over the past few years. In general, land use changes over the last few years and decades have seri-ously altered the natural biomes in much of northeastern Brazil (Simielli 2006: 111; Scharpf 2010; Schulz 2010).

Land useWhile the biome types could still be distinguished during the sampling campaign, the land was mostly privately owned and, albeit sparsely inhabited, showed a distinct imprint of human usage. Several national parks preserve more original biome relics (Araguaia, Tocantins, TO; Lençóis Maranhenses, Maranhão, MA; Serra das Confusões, Serra da Capivara and Sete Cidades, Piauí, PI; Ubajara, Ceará; Chapada Diamantina, Bahia, BA), supported by a smaller and less well protected habitat (e.g. Parque das Dunas in Natal, Rio Grande do Norte, RN). The population density is generally low in the entire area (<1 to >200, avg.: 10–50 inhabitants km-2; Simielli 2006), with higher densities focussed mainly on fast-growing cities along the coast and around other local centres, such as Terezina (PI) and Palmas (TO). Intense agriculture is typical for the Atlantic Forest biome with sugarcane, cotton, dry rice, soy beans, fruit, and wood production (Schulz 2010). Large-scale pasture land for cattle farming was the best preserved, both in the Caatinga and Cerrado biomes. Here, only limited deforestation, but substantial removal of shrubs and other smaller surface

vegetation (generally thorny), was observed. In the transi-tional palm forest area, Babaçu (Attalea speciosa) and Carnaúba (Copernicia prunifera) palms dominate. The Cerrado biome and much of the transitional forest landscape is used for soy bean, millet, sugar-cane, corn, and dry rice production, often on very large, highly mechanized farms with sizes that may exceed several hundred square kilometres (Schmidt et al. 2009).

MAteRiAlS And MetHodSSamplingPrior to field work, geological, topographical, soil and land use maps were studied, and the most feasible routes defined. The aim was to properly represent those multiple criteria on the ultra-low density scale. With the selected A and B transects, a reasonable compromise was found between proper coverage and road accessibility. A total of 111 sites were selected, fol-lowing a statistical approach with one site for each 10 000 km2, avoiding urban and industrial environments. The sites, c. 50–100 km apart, were sampled in parallel by two teams (A and B), covering an area of from c. 2°S to 12°S, and from 34°W to 49°W (Fig. 1). Each site was situated at a minimum distance of 100 m (mostly several 100 m) from nearby roads or any other significant human influence (A-transect: 56 sites with 50 BOT samples, B-transect: 55 sites with 51 BOT samples). ‘BOT’ refers to a mineral soil layer depth of 30 to 50 cm.

Each site had an average area of c.10 000 m2. A minimum of five sub-sites were selected, where individual holes were manu-ally drilled (Edelman augers with standard handles, Ø 10 cm; Eijkelkamp, NL). At each sub-site, c. 1 m2 of surface area was thoroughly cleared with a brush and a dustpan, collecting the organic litter material (L-horizon = ‘ORG’ samples), and to remove material that could later interfere with drilling or intro-duce sample contamination (larger plant material, such as roots and branches; stones, animal scat). These ORG samples will be discussed in a separate paper.

After drilling and retrieving the top 20 cm of mineral soil (‘TOP’ samples, not part of this paper), the generally very sta-ble boreholes were overdrilled to 30 cm, and that material dis-carded. The BOT sample was retrieved from there to 50 cm depth. A total of 101 samples was gathered, fewer than the 111 predefined because of very shallow soil conditions at some sites. The BOT material from each site was hand-mixed on-site (laboratory gloves), and filled directly into pre-labelled sample bags (RILSAN® by Tub-Ex ApS, DK) after removing larger stones and root material. The advantage of this new biopoly-mer material (RILSAN®) consists in its high mechanical resil-ience, chemical inertness for standard samples, very low net weight, and the sealed bags are (gas)tight. Material from all related sub-sites was amalgamated in the bags to a composite sample of c. 1.5 kg total weight.

Prior to sealing, the bags’ contents were briefly but thor-oughly and manually homogenized. Small aliquots were col-lected in the field for colour definition (Munsell Soil Colour Chart; Wolf 2009), and the determination of electrical conduc-tivity and pHH2O. The latter parameters were determined from a sludge (soil:water = 1:5 V/V) with standard equipment. The BOT samples mostly showed distinctively brighter soil colours compared to the mineral topsoil (0–20 cm; Wolf 2009) due to even lower organic matter content. The determinations of electrical conductivity and pH values (both H2O and CaCl2) were repeated under controlled conditions in the laboratory (Mannschatz 2009). Because of higher accuracy and precision, these laboratory values are reported here. Following the field determinations, the bags were tightly sealed after carefully

Soil Geochemical Background for Northeastern Brazil 201

squeezing out the captured air, and readied for transport (dou-ble packing).

Sample preparationUpon return to the Laboratory of Environmental Geochemistry (UFRN, Natal, RN), all sample bags were opened and the material oven-dried in a clean laboratory environment for at least 24 h at no more than 40°C. Thereafter, the material of each bag was manually homogenized again, and split (three ali-quots for project partners). All bags were checked for proper function and packed for transport to the Freiberg laboratories. There, all samples were manually sieved (<2 mm) and ground, using agate mortars to fragment larger, freshly formed aggre-gates. Humid samples were oven-dried (40°C) to standard dry-ness prior to sieving and grinding. Fifty grammes of each dry and sieved sample were milled with a planetary mill with agate equipment (pulverisette 5, Fritsch, DE) to a maximum grain size of 63 µm.

AnalysisThe sample powder was used to produce pressed powder pel-lets (40-mm diameter: 10.000 g sample + 2.000 g wax Hoechst C, 210 kN, 40 s; hydraulic pelleting press; HTP 40, Herzog Maschinenfabrik, Osnabrück, DE) for the determination of volatile and trace elements, and glass fusion discs for major and minor components (Table 2) and some of the trace ele-ments (1.000 g sample + 8.000 g Li2B4O7 FX-X 100-2 Fluxana, Kleve; fusion machine, type VAA 2, HD Elektronik und Elektrotechnik GmbH, Kleve DE; maximum melt tempera-ture 1400°C). Using a wavelength-dispersive X-ray fluores-cence (XRF) spectrometer with a rhodium target 4-kW end window tube (S8 Tiger, Bruker AXS, Karlsruhe DE), both sample preparation types were analysed in vacuum with 34-mm masks and evaluated with standard software after thorough matrix-matched calibrations (41 elements with 56 reference materials for pressed powder pellets and 18 elements with 71 reference materials for fused discs).

A total of 29 trace elements (Ag, As, B, Ba, Br, Cd, Ce, Cl, Co, Cr, Cs, Cu, F, Ga, La, Mn, Mo, Nb, Ni, Pb, Rb, Sn, Sr, U, V, W, Y, Zn, and Zr) was measured by WD-XRF. Of these, Cd (10), Cl (50), and F (500) were impossible to quantify (<<DL, detection limit; determination limit in mg kg-1 in brackets). Due to their proximity to the DL (mg kg-1), Ag (15), As (10), B (40), Br (10), Co (10), Cs (10), Mo (100), Ni (12), Sn (10), U (10), Y (10) and W (15), were excluded from further discussion, since their median values may serve as ori-entation values only. Fully quantified results from ICP-AES and ICP-MS analysis shall be reported in a future paper (Schucknecht, pers. comm.). Thus, only Ba (57), Ce (101), Cr (57), Cu (75), Ga (69), La (95), Mn (69), Pb (77), Rb (95), Sr (80), V (64), Zn (88), and Zr (101) are being discussed further (Table 3). The values in brackets give the number of fully quantifiable samples. Non-quantifiable results were set half the DL (all trace elements except for Ce, La, Rb and Zr). Note that WD-XRF analysis delivers true total element con-centrations in contrast to an often partial aqua regia extrac-tion as often used in soil sciences.

To determine pH values and electrical conductivity, a sludge of 1:5 (V/V) was made with 1 g sample (< 2 mm) and 5 ml of distilled water. The electrical conductivity (µS cm-1) was determined at 23°C in the laboratory with standard equip-ment (LF 39 with LTC 1/21 probe, Sensortechnik Meinsberg, automatic temperature compensation). The pH determination used the same solution for pHH2O and a 10 mmol l-1 CaCl2-solution for pHCaCl2 measurements.

Quality controlSampling was performed with utmost care and following a strict protocol to prevent sample contamination or alteration of any kind. This protocol continued to the last analytical step and was compiled using the experience gained in the large-scale geochemical mapping projects: the European North (Äyräs & Reimann 1995), the FOREGS (Salminen et al. 1998) and GEMAS (EuroGeoSurveys Geochemistry Working Group 2008) procedures. Analytical quality was verified using certified reference materials as unknown samples (granite GM, Central Geological Institute of GDR, ZGI; BRP-1 and GBW 07602), a project-internal in-house standard (BRASOL; tropi-cal soil from Campinas, São Paulo state), another non-certified material (ORIS) and repeated determinations of sample dupli-cates as unknown samples. The variation coefficient for stand-ard reference materials was always below 7%, mostly <2%, and recovery always within the permissible limits (100 ± 5%). Four randomly chosen samples were sent without further information to another laboratory and these delivered practi-cally identical results (see Höfle 2009).

Conductivity and pH calibrations were done with standard solutions for all determinations, and reproducibility checked with four repetitions for every fifth sample. EC reproducibility was within ± 10%, for both pH determinations ± 2% (Mannschatz 2009).

ReSultS And diSCuSSionFollowing a discussion of the geochemical composition of the BOT samples, the results are assessed with respect to lithology and soil type (Figs 2a, b), biome type and climatological param-eters (Figs 2c–e). Land use was deemed a priori less promising as an influence on soil composition since related changes are comparatively recent (years to decades, in very few places up to 300 years; Ross 2006: 98ff.) and will thus most likely not be noticeable in the deeper soil horizon outside of urban environ-ments.

Geochemical composition of the Bot samplesIn general, the BOT samples reflect deeper soil conditions, less likely influenced by rapid surface processes, such as erosion and transport by wind and water, plant and animal interactions, and direct/indirect anthropogenic activities. Soil pHH2O values (laboratory determinations) range from 4.4–8.7 (acidolytic to hydrolytic) with a median value of 5.4 (hydrolytic). The median pHH2O values yield 5.5 for the A-transects (range 4.6—7.7) and 5.3 for the B-transects (range 4.4—8.7). Soil solution conduc-tivities show median values for A of 13 µS cm-1 (range 4–390 µS cm-1), and for B of 15 µS cm-1 (range 3–510 µS cm-1) (Mannschatz 2009). The character of the soils generally reflects fully oxidizing conditions. Dominantly acidic conditions and low electrical conductivities are typical for tropical soils and reflect their age, long-term weathering, and the related deple-tion of major cations such as alkaline and alkaline earth ele-ments.

Major and minor elementsSilicon is clearly enriched in most of the transect soils, com-pared to world soil and upper crustal average values. Titanium shows similar values to world averages, while all other major and minor elements are depleted (Table 2). While this deple-tion is moderate for Al and Fe, it reaches a full order of mag-nitude for Mn, P and S, and two orders of magnitude for Ca, Mg, Na and K (expressed as oxides in Table 2). A comparison with a major study of Australian laterites (Smith et al. 1992)

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Fig. 2. Sampling sites and their relation to (a) lithology (top) and (b) soil type (bottom), and to the climate variables (1961–1990): (c) annual aver-age temperature (top), (d) annual average precipitation (centre), and (e)to biomes (bottom) in northeastern Brazil. Every second site is labelled for better legibility.

Soil Geochemical Background for Northeastern Brazil 203

shows very similar median values for Si, Al, Fe, Ca, Mg, and S. Yet, the median concentrations of Na, K, and P are consider-ably lower in northeastern Brazil; only the Ti values are signifi-cantly higher (Table 2). When comparing the BraSol-results with data from other projects in Brazil, larger differences emerge, although sampling and subsequent processing are mostly comparable. Soil data from Bahia and Goiás, nearest to our study region, were obtained by Kronberg et al. (1979) using WD-XRF and spark-source spectrometry (total concentra-tions). They show similar ranges (min–max), except for K, but deviate in their median values. Silicon shows considerably higher values in the NE, while Al, Fe, Mn, and Ti show distinc-tively lower median values as compared to the data from Bahia and Goiás. The ’nutrient group’, comprising Ca, Mg, Na, K, and P, is very similar to the Goiás samples but considerably lower than the Bahia samples. Unfortunately, Kronberg et al. (1979) did not publish details on their sampling strategy, which might explain at least some of the differences.

The concise work of Licht et al. (2006) on soils in the State of Paraná, southern Brazil (25°S), again shows distinct differ-ences from this work. Their B-horizon samples roughly com-pare with our BOT samples. Both protocol and quality control followed the same procedures, albeit with a higher sample den-sity. Major, minor and some trace components were deter-mined by WD-XRF. Silicon shows almost double the median in the northeast as compared to the Paraná value, Al and K half of those values, and Fe, Mn, Na, P, and Ti one order of magnitude lower values than the soils in Paraná. Calcium and Mg are almost an order of magnitude lower in the northeast, and S values almost identical (Table 2). Paraná has quite differ-

ent conditions in its geology, lithology and climatological parameters. About half of the rocks belong to the Paraná basin, with its basaltic rocks showing a complex composition from picrites to rhyolites. Other alkaline rocks along the bor-der of the Paraná basin add to the observed low Si and high Al, Fe, and Mn levels. The State receives considerably more pre-cipitation (1560 mm a-1 versus 1130 mm a-1 in the NE) and shows a distinctively lower evaporation (930 mm a-1, as com-pared to 1,770 mm a-1 in the NE; DNM 1992). The tempera-tures in Paraná may range from 0° to 36°C, while they hardly ever fall below 10°C in the NE. These contrasts jointly with the different lithologies – the Paraná being dominated by the Paraná sedimentary basin and the northeast defined half by the Parnaíba sedimentary basin and half by very old to old crystal-line rocks from the basement – may explain the differences in major element soil geochemistry.

Minor differences occur between the A and the B-transect (Table 2). Silicon, Al, Fe, and Ti occur in slightly lower concen-trations in the northern samples (A-transect). This pattern could be interpreted as an even stronger or longer weathering further south, due to higher humidity.

Trace elementsIn comparison with the World Soil average values, there is a relative depletion of Ba, Ce, Cr, Cu, La, Mn, Pb, Rb, Sr, V, and Zn, particularly strong for Ba, Mn, Rb, Sr and Zn. This deple-tion is also evident in comparison with the Brazilian soil data with individual exceptions (Table 3). The other trace elements may coincide with other literature data, but no clear relation,

table 1. Number of sites per unit of the influential parameters lithology, soil type, biome and climatological conditions (averaged annual precipitation and temperature), and the information source for the allocations (ntotal = 101). Individual ‘n’ in brackets

Lithological unit Soil type Precipitation (mm) Temperature (°C) Biome

Alluvial sediments = recent and young pre-Holocene terraines (11)

Acrisols [Argisols] – reddish, acidic soils with clay enriched in B horizon (Bt); n = 22

1,500 – 2,100 (36) 26 – 28 (51) Atlantic Forest (18)

Carbonate-rich sediments = sandy and clayey, sometimes slightly metamorphosed rocks, may include tertiary carbonates (18)

Arenosols [Quartzarenic Neosols] – little or no evidence for the development of a pedogenic horizon (e.g., sandy soils); n = 21

1,200 – 1,500 (27) 24 – 26 (43) Caatinga (38)

Sedimentary rocks = mainly psammitic sequences, can include pyroclastic material (52)

Cambisols [Cambisols] – soils with cambic horizon or ochric epipedon; n = 3

900 – 1,200 (15) 22 – 24 (7) Cerrado (33)

Metamorphic rocks = ortho and paragneiss (16)

Chernozems [Chernosols] – very dark, base-rich, mineral soils. Many have chernozemic A horizon overlying argillic or calcic horizons; n = 1

700 – 900 (18) Amazon Rainforest (12)

Magmatic rocks = granites and magmatic rocks with felsic and mafic composition (4)

Ferralsols [Latosols] – deeply weathered, acidic, low in fertility, yellowish to reddish soils; n = 31

≤ 500 – 700 (5)

Gleysols [Gleisols] – seasonally or permanently wet soils; n = 3

Luvisols [Luvisols] –strong texture contrast between A and B horizon, base-rich, well-draining soils; n = 7

Planosols [Planosols] – soils with a distinct or abrupt Bt horizon, the upper part of which is strongly acidic and may or may not be sodic; pH < 6.5.; n = 4

Plinthosols [Plintosols] – soils with plinthic horizon, concretions, Fe-rich and mixture of kaolinite clay with quartz and other constituents, similar to laterites; n = 9

Information sourceIBGE (2009b) EMBRAPA (2006) INMET (2009b) INMET (2009a) IBGE (2009a)

All unit allocations have been made according to the information sources and the geographical position of the sampling sites. On-site verification was not possible at most sites. The soil type names depict the FAO terminology, followed in square brackets by the Brazilian System of Soil Classification

J. Matschullat et al.204ta

ble

2. M

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tatis

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Med

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4 (1

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2 (0

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DL

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eter

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l cru

st (a

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om tw

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92);

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4); A

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terit

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mith

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92; n

= 1

072

to 2

434,

dep

endi

ng o

n el

emen

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500

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BA

= B

ahia

soils

(n =

17)

and

GO

= G

oiás

soils

(n =

16;

bot

h K

ronb

erg

et al.

197

9); M

G =

Min

as G

erai

s and

G

oiás

soils

(Mar

ques

et a

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04a)

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ta.

for example, as to geographical position, emerges. Barium and Mn yield significantly higher median con-centrations in the northern transect (Table 3). A notice-able positive Zr anomaly appears in most samples, as compared with upper crust, World Soil averages, and the other Brazilian soil data, except for Paraná (Licht et al. 2006). At this point and until truly large-scale (conti-nental) geochemical mapping projects have presented their results, even the data from the sub-continental BraSol-project seem insufficient to unmistakably dis-cern large-scale processes (Reimann et al. 2009, 2010).

Statistical studiesA classification of the elements based upon Spearman rank correlation, the elements’ individual spatial behav-iour, and a cluster analysis produce five groups of ele-ments: (i) major elements; (ii) residual elements; (iii) life-essential elements; (iv) Ce and Zr; and (v) a ’no correlation’ group (Fig. 3). These five groups provide an approximation only; intermediate behaviour was evident.

Major elements (i) are represented by Si, Al, and Fe. Silicon shows the highest absolute concentrations and var-ies roughly by a factor of 2.6. Only two samples show Si concentrations below 40%. Their sites were on ferralsols over (carbonate-rich) sediments. The Al concentrations range over two orders of magnitude (factor 50), with most samples between 2 and 20%, and exceeding 20% in only one sample from the same site where the lowest Si con-centration was detected. These anomalies over carbonate-rich sediments were also encountered in soil surveys in the Potiguar Basin, RN State. The unexpectedly high Al values were interpreted as residual Al from clays in the carbonate-rich rocks. These rocks were observed to have a significant clay component in their composition and hence are not pure carbonate-rich sediments (pers. comm., Germano Melo Jr.). Iron concentration ranges also cover two orders of magnitude (factor 30) and show a rather heterogeneous spatial distribution. Only five sites deliver values above 10%, most of them on soils over sediments. The high Al and Fe versus low Si ratios were also shown by Marques et al. (2004a). The strong Si-enrichment with parallel relative depletion of various other crustal elements indicates a strong long-term weathering signal with Si residing as residual quartz and clay minerals.

The ‘residual elements’ (ii) comprise Cr and V, but also show distinct similarities with Fe. Aluminium plots with Ga, and shows similarities with Fe, V and Cr (Fig. 3). Somewhat weaker but still prominent is the ele-ment Ti, which may justify a group of Al, Cr, Fe, Ga, Ti and V, apart from Si, above. All of these are key com-ponents of silicates and oxides, and thus again evidence for weathering processes.

A third group, addressed as life-essential elements (iii), comprise Ba, Ca, Mg, Mn, P, S, Sr, and Zn, which are mostly elements of the first and second groups of the Periodic System of the Elements, and important nutri-ents. Strontium is not essential but may substitute for Ca in organisms. The fourth group (iv) with Cu, Pb and Y is referred to as a ‘no correlation’ group. Their distribution does not reveal any meaningful pattern. Lead (Pb) is set off from the other three in the dendrogram (Fig. 3), but still is allocated to this group because of its behaviour. Cerium and Zr are not discussed since we had a problem with some samples that were milled in ZrO-material and led to a distinct bias for some samples.

Soil Geochemical Background for Northeastern Brazil 205

table 3. Trace element descriptive statistics in the A and B-transect BOT soil samples (n = 101; pressed powder pellets) in comparison with published data. Only those elements are displayed where more than 50% of the samples delivered results above the determination limit (DL). Values are rounded to relevant digit

Element

Ba Ce Cr Cu Ga La Mn-------------------------------------------------------------------------------- mg kg-1 ---------------------------------------------------------------------------------

Med A 85 43 43 15 13 19 112Med B 50 43 37 11 15 20 63Min 7.5 32 5 5 <5 11 <5Max 3,046 400 220 86 47 237 1,180DL 15 10 10 10 10 10 10

UC 610 65 35 20 16 31 560WS 500 65 80 25 n.d. 35 530GS 500 (50) 200 20 (n 10) n.d. 850WS-2 362 49 42 14 1.2 26 418AL 13–1,260 (228) 0.6–120 (16) <3–4,500 (81) <2–145 (14) 1.4–44 (11) 1.7–70 (11) <80–1,160 (155)PB 9–575 (173) 7–128 (52) 1–112 (14) 1–41 (7) n.d. 0.5–32 (10) 41–971 (285)MG 67 ± 127 83 ± 43 112 ± 69 33 ± 55 25 ± 13 23 ± 17 455 ± 583BA 7–6,700 (700) 5–1,800 (55) 5–1,700 (50) 6–600 (60) 6–2,000 (20) 0.7–850 (25) 20–2,400 (775)PR 43–502 (147) 41–220 (89) 43–62 (87) 20–319 (117) 12–47 (31) 16–72 (35) 135–1,980 (538)

Pb Rb Sr V Y Zn Zr -------------------------------------------------------------------------------- mg kg-1 ---------------------------------------------------------------------------------Med A 12 18 23 49 25 11 448Med B 13 14 28 49 21 11 458Min <5 13 5 <5 5 5 97Max 60 260 1,370 524 69 102 4,780DL 10 10 10 10 10 10 10

UC 19 111 330 31 21 62 214WS 17 65 240 90 20 70 230GS 10 60 300 100 n.d. 50 300WS-2 25 10–140 147 60 <2–70 (23) 62 300AL 0.3–83 (14) 2.4–260 (43) 1.4–260 (27) 10–820 (48) <1–17 (4) <2–110 (24) 7–142 (70)PB 2–25 (7) n.d. 4–148 (35) 5–158 (28) 0.2–42 (7) 6–129 (34) n.d.MG 26 ± 120 14 ± 32 9 ± 8 257 ± 231 20 ± 9 38 ± 54 375 ± 96BA 5–500 (20) 0.5–>4,500 (15) 3–330 (30) 10–400 (40) 1–370 (10) 1–3,600 (40) 60–2,000 (200)PR 15–45 (22) 7–124 (19) 12–126 (25) 81–856 (360) 10–50 (27) 24–124 (77) 299–776 (431)

DL = determination limit; UC = upper continental crust (average from two given values after Reimann & Caritat 1998); WS = World Soil Average (estimated means from Koljonen 1992). GS = Global soils by Vinogradov (1954); WS-2 = world soil compilation by Kabata-Pendias & Pendias (2001); AL = Australian laterite (Smith et al. 1992; n = 1072 to 2434, depending on element, area c. 500000 km2); PB = Paraíba topsoils (Schucknecht 2009; n = 30); MG = Minas Gerais Cerrado soils > 0,6 m (Marques et al. 2004b; and pers. comm. 08/2009; n = 48; BA = Bahia soils (Kronberg et al. 1979; n = 17); PA = Paraná B-horizon soils (Licht et al. 2006; n = 307); n.d. = no data.

Correlation with bedrock, soil type, biome and climateA soil generally derives from its lithological base. Its petrologi-cal and geochemical composition, jointly with the prevailing climatological conditions, vegetation, etc. defines the soil type

that evolves. This in turn acts with climatological conditions to create the foundation for biome formation and anthropogenic land use. While the study region has been settled by man for many thousands of years, the first settlers were neither numer-ous nor have likely left a perceivable geochemical impact. Post-Columbian settlement focused on the coastal areas and ventured inland only as of the 17th Century with local impact only (Meneses et al. 2011). All of the information regarding bedrock, biomes, soil types and climatological conditions (average annual precipitation and temperatures) were derived from official maps (Table 1) and verified as best as possible in the field.

The 101 samples allow for a statistical evaluation of geo-chemical results with the on-site conditions of the influential parameters, since various combinations can be tested (Figs 2a–e; Höfle 2009). The grouping was done using the respective median values. In the next interpretative step, and including the TOP data, a more in-depth statistical interpretation is in preparation that includes the transformation of qualitative data (bedrock, soil type, biome, climate, temperature, precipitation) into more quantitative data, and applies suitable methods (e.g. correspondence analysis and principal component analysis), to quantify the observed below.

Fig. 3. Dendrogram (nearest neighbour, squared Euclidean) showing all elements except for Zr (because of 82 from 191 Zr-values were accepted – non-contaminated by grinding equipment)

J. Matschullat et al.206

BedrockThe geological development has left a sequence of bedrock types (Table 1 and Fig. 2a). Aluminium, Cu, Ga and K over magmatic rocks show the highest, and over (meta)sedimentary rocks and partly over alluvial sediments the lowest concentra-tions, in agreement with the standard distribution of these ele-ments in the respective rock types. Calcium, Cr, and Ti are highest over carbonate-rich (meta)sediments, but lowest over gneiss (Ca) and alluvial sediments (Cr, Ti). While the high val-ues over carbonates are lithogeochemically logical for Ca, and even Ti, the elevated Cr concentrations may be due to clays, as explained above for the unusually high Al values in the carbon-ate-rich sediments. Barium, Ce, Cr, Fe, La, Mg, Mn, Na, Ni, P, Pb, Rb, S, Sr, V, Y, and Zn are highest over metamorphic rocks, and show the lowest concentrations over sedimentary rocks, sometimes alluvial sediments. This distribution appears logical, with leached sediments and still relatively enriched soils over metamorphic sequences. Silicon and Zr show the highest values over alluvial sediments and sedimentary rocks, while the lowest occur over magmatic rocks, obviously the result of weathering-related enrichment. Only Ba, Mn and Sr show a significant deviation between individual rock types (based on median values), while all other trace elements remain within an order of magnitude between lowest and highest group median.

Soil typeThe distribution of soil types is not homogenous (Table 1). Nevertheless, a few patterns emerge. The lowest concentra-tions for Al, Ce, Cr, Fe, Ga, La, Mn, P, Ni, Pb, Rb, Ti, V, Zn and Zr were encountered in arenosols, followed by acrisols and ferralsols. As to be expected, Si shows the highest concen-trations in arenosols, the lowest in luvisols. Reversely, Al, Fe, Ga, P, Pb, (Rb), (V) and (Y) were dominant in luvisols (the latter three with their highest median values in cambisols with only three cases. Copper and Ti show the highest concentra-tions in gleysols. Planosols are characterized by the highest concentrations of Ba, Ca, Ce, Cr, K, La, Mn, Na, S, Sr and Zn (plant nutrients). Cerium, Cr, Mg, Mn, Y, and Zn dominate cambisols, followed by planosols (Cr, Mg, Mn), acrisols (Ce), and luvisols (Zn), and show the lowest concentrations in arenosols (Cr, Mg, Mn, Zn), ferralsols (Y).

Barium, Mn, Rb, Sr and Zn show more than an order of magnitude difference between individual soil type median val-ues, while the other trace elements remain within a factor of 10 (generally much less). This distribution represents the statisti-cal behaviour of the elements, as displayed in Figure 3, better than the parameter bedrock, and supports a partial de-coupling of soils from their underlying lithology. This clearly emerges from Figure 2b, as no relationship between any of the bedrock types and specific soil types could be observed.

ClimateHöfle (2009) distinguished five precipitation groups: from <500 mm to 700 mm a-1 (Group 5) to 1500–2100 mm a-1 (Group 1), and three temperature regimes, from 22–24°C (Regime 3) to 26–28°C annual average temperature (Regime 1) (Fig. 2c). The elements Al, Ba, Ca, K, Mg, Na, Pb, S, Y, and Zn show their highest concentrations with the lowest precipitation (Group 5), while the lowest concentrations occur in high annual precipitation regions (mainly Group 2). Only Ga has a medium precipitation range with its lowest concentrations (Group 3). Iron and V show their highest concentrations under wet conditions (Group 1), and their lowest values in Group 2 (Fe) and Group 5 (V). Cerium, Cr, Si, Ti, and Zr show

their highest concentrations also under wet conditions (Group 2), and their lowest generally under the driest average condi-tions (Group 5). Copper and P do not show precipitation-related signals.

The temperature regimes cover a rather narrow range. Therefore, seeing no distinction with temperature for Al, Cr, P, Ti, and Zr is not surprising. The signals are also not very strong for most other elements. Nevertheless, some pattern emerges, and Ba, Ce, Cu, and Si show the highest values with the lowest temperatures, whereas Ca, Ga, K, Mg, Na, S, Sr, V, and Zn show the reverse, the lowest concentrations with the highest average air temperatures.

In general, and not surprisingly, the climate indicator yields signals that are not much different from the parameter biome (Fig. 2d). Precipitation is a more sensitive indicator as com-pared to temperature, most likely because of the generally ele-vated to high ambient temperatures, and the fact that the surface temperatures are rapidly moderated with soil depth.

BiomeWhen observing at the element distribution in soils within a specific biome, the graphical representation (Figs 2b, 2e) does not support any unanimous distinction. Obviously, spatial soil type distribution is not bound to biome distribution. Looking at the element median values, however, allows discerning sev-eral groups amongst the biome types. The elements Al, Ba, Ca, K, Mg, Na, P, Pb, S, Sr, Y, and Zn show the highest values within the Caatinga biome, and the lowest in the Atlantic Forest (Al, Ba, Sr, and Y), and the Cerrado (Ca, K, Mg, Na, Pb, S, Sr, and Zn). The dry Caatinga biome preserves the nutrients better than the more water-rich, more dynamic (turnover) environments. Cerium, Ga, Si, Ti, and Zr are highest in the Atlantic Rainforest biome, and lowest in the Caatinga (Si, Ti), or in the Cerrado (Ga, Ce, Zr). The strong hydrolysis better preserves the weathering-resistant minerals and explains the behaviour well. Highest in the Cerrado are Cr and Cu, which show their lowest concentrations in the Caatinga (Cr) and the Atlantic Forest (Cu). The Amazon Rainforest biome is seen in the highest values of Fe, Mn, and V, with their lowest concen-trations in Atlantic Forest (Fe, V), and Cerrado (Mn).

Thus, most plant nutrients, despite their absolute depletion, show the highest values in Caatinga soils, and the lowest in Atlantic Forest soils. This result may not be surprising when considering the more rapid element cycles under wet tropical conditions (see Amazon Rainforest) as compared with the semi-dry condition of the Caatinga. This interpretation is cor-roborated by the inverse enrichment of Ce, Ga, Si, Ti and Zr, all prone to forming weathering-resistant oxides. Obviously, biome information can be discerned at a depth of 30–50 cm in these old tropical soils, albeit certainly not as strong as in top-soil material (Schucknecht, pers. comm.).

large-scale transects – large-scale trends?Låg (1968), Låg & Steinnes (1976, 1978) and Reimann et al. (2000) describe strong concentrations gradients with distance to coast (up to several hundred kilometres) for a number of elements (e.g. Na, B, Cl, Ca, Mg, Se, Sr) in forest surface soils from Norway. While those soils are young (postglacial), it is worth testing whether the very old soils in northeastern Brazil show respective signals in deeper layers. Therefore, three large-distance E–W and four N–S transects were selected (Fig. 4). In effect, no trend emerges for any of the observed elements. Consequently, ocean distance (with dom-inantly easterly winds) seems not to have any significant influence on element distribution at the investigated scale

Soil Geochemical Background for Northeastern Brazil 207

Fig. 4. (a) North–south and (b) east–west transects (from Höfle 2009). See text for details

and soil depth. The only significant signal seems to be related to long-term weathering and becomes visible in the decreas-ing Si concentration when going south, opposite to the trends of Al and Fe.

ConCluSionSThe general approach of large-scale, low-density soil geochem-ical mapping has been applied for decades and has shown its validity in various major projects, primarily in the northern hemisphere. Results presented in this paper are the first for Latin America on a sub-continental scale (no continental-scale work exists yet).

In this work, major and minor elements, and selected trace elements determined from the lower surface soil layer (30– 50 cm depth) clearly establish a sound soil geochemical data-base for northeastern Brazil. High-quality data based on a strict protocol and tight quality control will serve as a reference for future work with higher spatial resolution in the region.

The new data show basically no evidence for anthropogenic influence at any of the selected sites, demonstrating the suc-cessful site selection aiming at non-contaminated areas for the establishment of reliable background data. This situation changes when higher resolution sampling is applied and anthropogenic influences cannot be avoided along a pre-defined transect (Schucknecht et al. 2011). Thus, the data can

J. Matschullat et al.208

be used as a regional geochemical background at least for deeper mineral soil, and clearly underline that global averages may be of limited value when studying regional or even sub-continental phenomena and conditions. The new data at the same time support the experience gained from other geochem-ical mapping projects, where similarly high variances occur at such spatial scales. There is, therefore, a need to establish local or regional backgrounds such as this one, depending on the intended purpose. None of the global soil data averages com-ply with the presented BraSol data, highlighting the unique geochemical signature of soils in northeastern Brazil. The same kind of data would be needed for a variety of acid extractions, especially for the widely used aqua regia extraction.

All of the investigated parameters, except land use, do have an active influence on the geochemical composition of the BOT (30–50 cm) soils. Lithology leaves its imprint, although the soils appear partly decoupled from the bedrock geochem-istry. Soil type, biome, and climatological conditions are prom-inent influential parameters for this large region. With respect to climate indicators, precipitation gives the strongest response, confirming the relevance of weathering processes, mainly hydrolysis.

Dedicated to late Prof. Dr. Hans Jürgen Rösler (deceased 12.01.2009). The authors thankfully acknowledge the financial support of the In-ternational Office (IB) of the German Federal Ministry of Education and Research, the German Academic Exchange Service (DAAD), the Chancellor of TU Bergakademie Freiberg, Dr. Andreas Hand-schuh, and numerous smaller sponsors in successfully realizing this work. A very special thank you goes to Anja Bräuer, Pauline Geier, Sebastian Heintschel, Jörg Hunger, Theresa Mannschatz, Christin Müller, Katrin Nordmann, Christian Scharpf, Nadja Schmidt, Ste-phan Schulz und Marlen Wolf, all team members of the sampling expedition in July/August 2008, and to Anne Schucknecht for their invaluable contributions. Without their disciplined engagement, fine spirit and dedication, this project would not have been possible. We also wish to acknowledge the helpful information generously pro-vided by Dr. Otavio Licht from MINEROPAR and by Prof. Dr. João José Marques from the Federal University of Lavras, Minas Gerais, both Brazil. Last, but not least, the authors thankfully acknowledge the language-polishing effort by Marie and Anne Marie de Grosbois. This paper is dedicated to a geochemist who from early on had a keen understanding of larger-scale processes in the earth sciences: the late Hans Jürgen Rösler. He strongly supported the broader view and interpretation needed in environmental geosciences. Last, but certainly not least, we wish to thank the referees and the chief editor, Gwendy Hall, wholeheartedly for their engaged, very thorough and helpful work.

ReFeRenCeSANA. 2009. Hydrologial basin information. http://www.ana.gov.br/ Agência

Nacional das Águas, Brasília (last search 14.06.2011).ÄyrAs, M. & reimANN, C. 1995. Ecogeochemistry Kola. Field Manual. Norwegian

Geological Survey, Trondheim; Report 95.111, 1–33.BeurleN, K. 1970. Geologie von Brasilien. Beiträge zur regionalen Geologie der

Erde 9. Borntraeger, Stuttgart (in German).DArNley, A.G. 1998. A global geochemical reference network: the foundation

for geochemical baselines. Journal of Geochemical Exploration, 60, 1, 1–5.DArNley, A.G., BjörkluND, A. et al. 1995. A global geochemical database for

environmental and resource management. Recommendations for international geochemical mapping. Final report of IGCP Project 259. UNESCO Publishing, Paris.

De OliveirA, A.I. 1978. Geologia do Brasil. 3rd ed. SUDENE, Escola Superior de Agricultura de Mossoro; Coleção Mossoroense, 72, 1–813. (in Portuguese).

DNm. 1992. Normais climatológicas (1961–1990). Ministério da Agricultura e Reforma Agrária, Secretaria Nacional de Irrigação, Departamento Nacional de Meteorologia (DNM); Brasília (in Portuguese).

emBrAPA. 2006. Sistema brasileiro de classificação de solos. 2nd ed. (Empresa BRAsileira de Pesquisa Agropecuária. Rio de Janeiro.

eurOGeOsurveys GeOCHemisTry WOrkiNG GrOuP. 2008. Geochemical mapping of agricultural and grazing land soil of Europe (GEMAS) – Field manual. Geological Survey of Norway Report 2008-038.

GersTeNGArBe, F.W. & WerNer, P.C. 2008. Climate development in the last century – global and regional. Internat. Journal of Medical Microbiology, 298, 5–11.

GOTTsBerGer, G. & silBerBAuer-GOTTsBerGer, I. 2006. Life in the Cerrado, as South American tropical seasonal ecosystem. I. Origin, structure, dynamics and land-use. Reta, Ulm.

Höfle, S. 2009. Geochemical composition of tropical soils in north-eastern Brazil. Unpublished MSc thesis, TU Bergakademie Freiberg.

iBGe. 2006. Brasil em números 2005. National statistics from the Institute of Geography and Statistics, Brasília.

iBGe. 2009a. Map of the Brazilian biomes with descriptions. ftp://geoftp.ibge.gov.br/mapas/tematicos/mapas_murais/biomas.pdf (last search 14.06.2011).

iBGe. 2009b. Map of lithology. http://mapas.ibge.gov.br/geologia/viewer.htm (last search 14.06.2011).

iNmeT. 2009a. Map of annual medium temperature of Brazil of the years 1961 to 1990. http://www.inmet.gov.br/html/clima/mapas/?mapa=tmed (last search 14.06.2011).

iNmeT. 2009b. Map of annual medium precipitation of Brazil of the years 1961 to 1990. http://www.inmet.gov.br/html/clima/mapas/?mapa=prec (last search 14.06.2011).

jeNNy, H. 1941. The factors of soil formation. McGraw Hill, New York.kABATA-PeNDiAs, A. & PeNDiAs, H. 2001. Trace elements in soils and plants. 3rd ed.

CRC Press, Boca Raton.kOljONeN, T. (ed.) 1992. The geochemical atlas of Finland. Part 2: Till. Geological

Survey of Finland, Espoo, Finland.krONBerG, B.I., fyfe, W.S., leONArDOs, Jr. O.H. & sANTOs, A.M. 1979. The

chemistry of some Brazilian soils: element mobility during intense weather-ing. Chemical Geology, 24, 211–229.

kurukulAsuriyA, P. & meNDelsOHN, R. 2008. How will climate change shift agro-ecological zones and impact African agriculture? World Bank Policy Research working paper, 4717, 1–31.

låG, J. 1968. Relationship between the chemical composition of the precipita-tion and the contents of exchangeable ions in the humus layer of natural soils. Acta Agriculturae Scandinaviae, 18, 148–152.

låG, J. & sTeiNNes, E. 1976. Regional distribution of halogens in Norwegian forest soils. Geoderma, 16, 317–325.

låG, J. & sTeiNNes, E. 1978. Regional distribution of selenium and arsenic in humus layers of Norwegian forest soils. Geoderma, 20, 3–14.

liCHT, O.A.B., XuejiNG, X., QiN, Z., miyAzAWA, M., ferreirA, F.J.F. & PlAWiAk, R.A.B. 2006. Average reference values of geochemical and geo-physical variables in stream sediments and soils, State of Paraná, Brazil. Boletim Paranaense de Geociências, 58, 59–87.

mANNsCHATz, T. 2009. Wildfeuer und ihre Konsequenzen auf Böden in Nordost-Brasilien. Unpubl. MSc thesis, TU Bergakademie Freiberg (in German).

mArQues, J.J., sCHulze, D.G., Curi, N. & merTzmAN, S.A. 2004a. Major ele-ment geochemistry and geomorphic relationships in Brazilian Cerrado soils. Geoderma, 119, 179–195.

mArQues, J.J., sCHulze, D.G., Curi, N. & merTzmAN, S.A. 2004b. Trace ele-ment geochemistry in Brazilian Cerrado soils. Geoderma, 121, 31–43.

melfi, A.J. & PeDrO, G. 1977. Estudo geoquímico dos solos e formações superficiais do Brasil. Parte 1 Caracterização e repartição dos principais tipos de evolução pedogeoquímica. Revista Brasileira da Geociencia, 7, 271–286 (in Portuguese).

melfi, A.J. & PeDrO, G. 1978. Estudo geoquímico dos solos e formações superficiais do Brasil. Parte 2 Considerações sobre os mecanismos geo-químicos envolvidos na alteração superficial e sua repartição no Brasil. Rev. Brasil Geociencias, 8, 11–22 (in Portuguese).

melfi, A.J., mONTes, C.R., CArvAlHO, A. & fOrTi, M.C. 2004. Use of pedo-logical maps in the identification of sensitivity of soils to acidic deposi-tion: application to Brazilian soils. Anais da Academia Braileira de Ciências, 76, 139–145.

meNDONçA, F. & DANNi-OliveirA, I.M. 2007. Climatologia. Noções básicas e climas do Brasil. Oficina do Texto, São Paulo; 206 p. (in Portuguese).

meNeses, I., reNGer, F.E., DesCHAmPs, E. 2011. History and economy – Iron Quadrangle. In: DesCHAmPs, E. & mATsCHullAT, J. (eds) Arsenic: nat-ural and anthropogenic. Arsenic in the environment, 4, 73–80. CRC Press, Balkema.

Nimer, E. 1989. Climatologia do Brasil. 2nd ed. Fundação Instituto Brasileiro de Geografia e Estatística (IBGE), Rio de Janeiro (in Portuguese).

OliveirA, P.S. & mArQuis, R.J. (eds) 2002. The Cerrados of Brazil. Ecology and natural history of a neotropical savanna. Columbia University Press, New York.

reeDer, S. 2007. Global geochemical baselines. Episodes, 30, 69–72.reimANN, C. & De CAriTAT, P. 1998. Chemical elements in the environment. Factsheets

for the geochemist and environmental scientist. Springer, Heidelberg.

Soil Geochemical Background for Northeastern Brazil 209

reimANN, C. & GArreTT, R.G. 2005. Geochemical background – concept and reality. The Science of the Total Environment, 350, 12–27.

reimANN, C., BANks, D. & kAsHuliNA, G. 2000. Processes influencing the chemical composition of the O-horizon of podzols along a 500 km north-south profile from the coast of the Barents Sea to the Arctic Circle. Geoderma, 95, 113–139.

reimANN, C., mATsCHullAT, J., Birke, M. & sAlmiNeN, R. 2009. Arsenic dis-tribution in the environment: the dimensions of scale. Applied Geochemistry, 24, 1147–1167.

reimANN, C., mATsCHullAT, J., Birke, M. & sAlmiNeN, R. 2010. Antimony in the environment – lessons from geochemical mapping. Applied Geochemistry, 26, 175–198.

rösler, H.J. & lANGe, H. 1972. Geochemical tables. Elsevier, Amsterdam.rOss, J.L.S. 2006. Ecogeografia do Brasil. Subsídios para planejamento ambiental.

Oficina de Textos, São Paulo (in Portuguese).sAlmiNeN, R., TArvAiNeN, T. et al. 1998. FOREGS Geochemical mapping field

manual. Guide 47. Geological Survey of Finland, Espoo.sCHArPf, C. 2010. Georeferencing-methods – tools to differentiate landscape characteristics

in NE-Brazil. Unpublished BSc thesis, TU Bergakademie Freiberg.sCHmiDT, N., Geier, P., mANNsCHATz, T. & mATsCHullAT, J. 2009. The Cerrado

biome in central Brazil – natural ecology and threats to its diversity. Forum Geoökologie, 20, 44–50.

sCHuCkNeCHT, A. 2009. Stable Pb isotopes and selected geochemical parameters as process tracers in tropical soils. Unpublished MSc thesis, TU Bergakademie Freiberg.

sCHuCkNeCHT, A., mATsCHullAT, J. & reim, C. 2011. Lead (Pb) and stable Pb-isotope characteristics of tropical soils in north-eastern Brazil – indicators for bioproductivity and weathering? Applied Geochemistry, 26(12), 2191–2220.

sCHulz, S. 2010. Land use and C/N dynamics in soils of the Atlantic Rainforest biome in NE Brazil. Unpublished BSc thesis TU Bergakademie Freiberg.

silvA, C.R. 2008. Programa Nacional de Pesquisa em Geoquímica Ambiental e Geologia Médica (PGAGEM): Resultados 2003 – 2008. Congresso Brasileiro de Geologia, 44, 374. Curitiba, Livro de Resumos (in Portuguese).

simielli, M.E. 2006. Geoatlas. 2nd ed. Editora Ática, São Paulo (in Portuguese).

smiTH, D.B. 2009. Geochemical studies of North American soils: results from the pilot study phase of the North American soil geochemical landscapes project. Applied Geochemistry, 24, 1355–1356.

smiTH, R.E., ANAND, R.R. et al.1992. Laterite geochemistry for detecting concealed mineral deposits. Yilgarn craton, Western Australia. Summary report for the CSIRO-AMIRA Project P240, Exploration Geoscience Restricted Report 236R, CSIRO, Perth, Australia.

vAsCONselOs, P.M., reNNe, P.R., BrimHAll, G.H. & BeCker, T.A. 1994. Direct dating of weathering phenomena by 40Ar/39Ar and K-Ar analy-sis of supergene K-Mn oxides. Geochimica et Cosmochimica Acta, 58, 1635–1665.

viNOGrADOv, A.P. 1954. Geochemie seltener und nur in Spuren vorhandener chemischer Elemente im Boden. Akademie Verlag, Berlin (in German).

WOlf, M. 2009. Soil colour and mineralogy – basis for remote sensing applications. Unpublished BSc thesis, TU Bergakademie Freiberg.

zeil, W. 1986. Südamerika. Geologie der Erde, 1. Enke, Stuttgart (in German).

zOBACk, M.L. 2001. Grand challenges in Earth and Environmental Sciences – Science stewardship, and service for the twenty-first century. GSA Today, 11, 41–47.

Received 19 August 2010; revised typescript accepted 11 July 2011.