Topography, weather and human activity effects on the behavior of metallic elements in a tropical catchment

CHEMICAL GEOLOGY L oroP 5Eosc ENcL"

ELSEVIER

Chemical Geology 114 (1994) 69-82

Topography, weather and human activity effects on the behavior of metallic elements in a tropical catchment Jos6 Luis Mogoll6n, Claudio Bifano Instituto de Ciencias de la Tierra, Universidad Central de Venezuela, Apt. 3895, Caracas IO IO-A, Venezuela

(Received September 1, 1992; revision accepted September 17, 1993)

Abstract

The geochemical behavior of metals in 11 rivers of the Lake Valencia drainage system, north-central Venezuela, was investigated by repeated sampling. Pb, Zn, Ni, Cu and Cr discharges from domestic and industrial activities have resulted in concentration increases of 2-16 × above background levels, depending on the metal, as well as an increase of 13-27% of the labile fraction (% LF). No apparent effect on the metals' grain-size distribution was observed. Evidence suggests that chemical reactions, coupled with transport of sediments from the mountainous areas to lowland areas, produce: ( 1 ) an increase in metal concentration and % LF, which are concomitant with the pollution effect; and (2) a metals' grain-size redistribution toward fine fractions. Analysis of major elements, i.e. Na, K, Mg, Ca, AI and Fe, provides a suitable approach to discriminate the two effects mentioned above. The wet-dry season variability in metal concentration appeared to be controlled mainly by the input of flushed material from soil. This process can explain the large areal variability of concentrations compared with sampling and analytical variability. A physical dilution of Zn with less contaminated, flushed soil material was also observed.

I. Introduction

Contemporary contamination levels in the developing countries, many of them located in tropical areas, are high (Kouadio and Tefry, 1987; Miguel, 1991 ). They are a consequence of the development of new industrial zones and increases in population and agricultural activities accompanied by the discharge of untreated solid and liquid wastes into the environment (De Lacerda and Abrao, 1984; Pfeiffer et al., 1985; Ajmal et al., 1987; De Lacerda et al., 1987; Olade, 1987; Mogoll6n et al., 1990). However, there is a general lack of knowledge about the geochemical behavior of metals in tropical systems (Paul

and Pillai, 1983; Rivera et al., 1986; Olade, 1987). Studies of heavy metals show that, in general, the fine grain- size fractions of sediments are enriched relative to the coarse fraction, and that this material is largely responsible for the transport and storage of metals in non-acidic systems (Frrstner and Wittmann, 1981; Salomons and FSrstner, 1984). Thus, analysis of the fine fraction is a common approach to study heavy-metal pollution (Krumgalz, 1989; Prohic and Jurasic, 1989). However, high-gradient rivers, typical of mountainous areas, transport coarse fractions and present difficulties for studying metal contamination due to the heterogeneity of bed ma-

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70

J.L. Mogollon, C. Bifano / Chemical Geology 114 (1994)69-82

terials (Axtmann and Luoma, 1991 ). Moreover, in some tropical rivers the concentrations of heavy metals are not correlated with grain size (Mogoll6n et al., 1990) The abundance, distribution and speciation of metals in rivers can be affected by many natural factors, such as lithology, topography, vegetation and weather regime. Since these factors are interrelated, their individual influence has not been clearly established (F6rstner and Wittmann, 1981; Salomons and F6rstner, 1984). To achieve accurate reference values, therefore, study areas with similar natural characteristics to the potentially contaminated area have to be selected. However, this is not always possible and global reference values or statistical splits of the background and anomalous populations have been used (Davies, 1983; Salomons and F/Srstner, 1984), in some cases without geochemical criteria that support the chosen values. In Venezuela there has been a large population expansion in the last three decades, especially in the north, where a high degree of pollution had been reported (Lewis and Weibezahn, 1983; Mogoll6n and Bifano, 1989 ). These reports suggest that the Lake Valencia drainage system is being seriously affected by human activities and therefore it was chosen to conduct a geochemical study of its bottom sediments. The specific objectives of this study were: ( 1 ) to contribute to knowledge of background level metal concentrations in tropical river sediments in relation to topography and weather changes (dry-wet seasons); (2) to identify the major pollutants and anthropogenic enrichments; and (3) to determine the effects of the factors mentioned above on the geochemical behavior of metals. In order to achieve these goals, extractable concentrations in HNO3 ( 1 N) of Na, K, Mg, Ca, Fe, A1, Mn, Pb, Zn, Ni, Cu, Cr and Co were determined in the grain-size fraction of < 63/zm of 30 stream sediments collected during the dry season and 30 collected during the wet season. Physico-chemical parameters, i.e. pH, temperature and dissolved oxygen, of the associated waters in each sampling location were measured. In 15 sediment samples the total metal content

in the < 63-#m fraction were determined, as well as the extractable concentration in HNO3 in the <63-, 63-125-, 125-500- and 500-2000-/~m grain-size fractions. The coefficient of variation of measured concentrations is due to: (a) sampling methods; (b) analytical methods; and (c) differences between samples. These contributions were estimated in order to discriminate real trends from errors.

2. Study area

The Lake Valencia catchment is situated in a graben formed by the La Victoria fault zone, 15 km south of the Caribbean Sea and drains an area of 3000 km 2 in the north-central part of Venezuela. The lake has an area of 300 km 2 and the surrounding lowland covers 1280 km 2. The basin is filled with sandy alluvial and lacustrine sediments of mostly Quaternary age, although the lowest part of the lake sequence may be of late Tertiary age (Peeters, 1971 ). A fault zone separates two important tectonic units: the Cordillera de la Costa belt in the north, comprising metamorphosed rocks of epidote-amphibolite facies, and the Caucagua-E1 Tinaco belt in the south, with rocks metamorphosed to greenschist facies. The northern lithology is largely metasedimentary rocks, such as quartz-mica-feldspar schist, calcareous schist, crystalline limestone and augen-gneiss, whereas in the south, quartz-albite-muscovitic schist, marble, chlorite and calcareous schist, hornblende gneiss phyllite and quartzites predominate (Men6ndez, 1966; Bell, 1971; Maresch, 1974; Gonzdlez de Juana et al., 1980). During the Holocene the maximum water level of the lake was 425-427 m above sea level (m a.s.1.). Since the 17th century, human occupation has resulted in a lowering of water levels. At present, the lake level is 405 m a.s.1, and this change has exposed lacustrine sediments over which soil has developed (Peeters, 1971 ). The climate of the area is tropical, with a mean annual precipitation of 1000-1500 m m and a dry season from November to April. Precipitation in

J.L. Mogollon, C. Bifano / Chemical Geology 114 (I 994) 69-82

71

encia

Fig. 1. Sampling site locations.

the driest month is 40 mm. Mean annual temperature is 24.6°C (Alvarrz, 1976). The modern vegetation around the lake in undisturbed areas is tropical deciduous forest at lower elevations and evergreen moist forest and cloud forest at higher elevations. The catchment has been subjected to considerable deforestation, and consequently large areas are covered by a fire climax vegetation of tough grasses and small fireresistant woody plants (Lewis and Weibezahn, 1981). The catchment has a population of 2- 106 inhabitants centered in the city areas of Maracay and Valencia (Fig. 1 ), and has the highest population density (670 individuals/km 2) for any Venezuelan catchment. The valley is an industrial as well as agricultural center, with about 1300 factories, many processing sugar, food, paper, paint, battery and metals. There is considerable automobile traffic, based on leaded gas, in the two major cities and on the road that follows the lake shoreline. The parent materials of the lowland soils are lacustrine, fluvial and colluvial sediments. These soils have been classified as Entisols, Inceptisols,

Mollisols and Vertisols (Chavrz et al., 1975 ). In the mountains, soils are not well developed (usually < 30 cm in depth ) and are classified as Inceptisols or Entisols (Chavrz et al., 1975 ). The mineralogy of the soils has been studied by Comerma (1968), Zinck et al. (1979), Elizalde and Mayorca (1982) and Rios and Elizalde (1982). The mountain soils are mainly composed of primary minerals, such as albite, chlorite, muscovite and quartz, as well as weathering products, e.g. iron oxides and smectites. Kaolinite, muscovite, quartz and smectites predominate in alluvial lowland soils, whereas in the lacustrine soil there is mostly calcite and opal of biogenic origin, as well as smectites, kaolinite and aragonite.

3. Methods

Bed sediment samples were collected at 11 rivers, during the wet season (July/August 1987) and during the dry season (March 1988 ). Each composite sample of 1 kg weight comprised 5 surface subsamples (0-5 cm) and was stored in

72

J.L. Mogollon, C. Bifano / Chemical Geology 114 (1994) 69-82

a labelled polythene bag. In the laboratory, samples were dried at room temperature ( ~ 25°C) for 7 days, gently disaggregated using a porcelain mortar and then sieved through a set of stainlesssteel sieves, with apertures: 63, 125,500 and 2000

ranted, since our data suggest they are composed of only one population.

/tm.

The measurement of the physico-chemical parameters of the waters may be the simplest way to identify compositional variations as a consequence of lithology, topography and anthropic factors. Furthermore, these parameters affect processes like sorption, solubilization and chemical reactions. According to Table l, percentages of dissolved oxygen saturation (% O2) of river water in the mountain area (low human activity) show a small percentage coefficient of variation (% CV), i.e. 8-18, and their average values are close to 100%, corroborating the pristine character of the zone. In the lowland area, % 02 average values are 34% during the dry season and 76% during the wet; the coefficients of variation are larger than in the mountain area. These features are likely due to the input of waste water, which increases the chemical and biological oxygen demands (Goitia and L6pez, 1987) and the amount of N and P transported by the rivers (Lewis and Weibezahn, 1983 ). During the dry season only three locations showed % 02 values greater than 60%,

Sediments particles with < 63-/tin grain were treated with H N O 3 (1 N ) for 72 hr with occasional shaking at room temperature in closed plastic bottles, with a solid/liquid ratio of 3 g to 100 ml. The acid extracts were centrifuged at 3500 rpm for 10 min, placed in plastic bottles and analysed by flame atomic absorption spectrometry (AAS). All the glass and plastic ware used were decontaminated by rinsing with HNO3 (10% v / v ) and deionized water. Standards for AAS analysis were prepared in a matrix containing all major metals analysed. Standards for major elements were prepared by dilution of the bulk matrix. All reagents used were analytical grade and the water was deionized. Particle size distribution analysis was conducted on samples 1-5, 12, 15, 20, 22, 32 and 34 collected during the dry season, and in samples 15, 32 and 35 collected during the wet season (see Fig. 1 ). The <63-, 63-125-, 125-500- and 5002000-/tm size fractions were also extracted by the method previously described. Fractions of < 63 /~m were digested in a mixture of hot concentrated acids (HNO3-HC104-HF, 6:4:1) as described by Agemian and Chau ( 1976 ). Acid extracts were analysed by AAS. Analytical precisions of the methods were checked by the coefficient of variation of 8 replicates from 12 randomly selected samples. This typically ranged between 4% and 10%. Duplicate or triplicate determinations were made on all samples.

4.1. Physico-chemical parameters of waters

Table 1 Physico-chemical parameters of the waters Dry season % 02

Based on the characteristics of the study area, the data were split into lowland and upland groups. Each of these groups were divided into wet and dry season subgroups. As will be discussed later, subsequent geographical subdivision of the subgroups does not appear to be war-

T

% 02

pH

T

7.7 5 9

23 9 9

100 18 l0

7.2 6 11

23 ll 11

7.8 9 19

28 8 21

76 31 20

7.7 8 19

27 13 20

(oc)

(of)

Mountain: x

4. Results and discussion

pH

Wet season

% CV n

86 8 9

Lowland: x %CV n

34 82 21

x=average; % CV=percentage coefficient of variation; n = number of measurements,

J.L. Mogollon, C. Bifano / Chemical Geolog)" 114 (1994) 69-82

suggesting that human activities are affecting most of the path along the rivers' courses in the plain area. In consequence, it is not possible to chose reference sampling locations to get background values. The average pH-values of the lowland and mountain rivers are very similar, in spite of the variable acidity discharge present in the lowland area where the industrial activity is high. This fact could be explained by the buffer capacity of the fluvial systems which are carbonate rich (Gonz~ilez de Juana et al., 1980). The water temperatures of the lowland rivers are slightly higher than in mountain rivers, presumably due to lower altitudes. 4.2. Metal concentration variations The ability to discriminate real trends, related to natural or anthropogenic causes, from those that result from factors such as sampling and analytical error is of great importance in the interpretation of environmental geochemical data. Some studies concerning geochemical errors and their influence on data interpretation have been published (Howard and Lowestein, 1971; Chork, 1977), but they do not have general usefulness, since they depend on the particular sampling and analytical techniques used. In this study the percentage coefficient of variation (% CV) of the concentration of each element was partitioned as follows: (a) by sampling methods, % CV of three samples collected at sampling site 18 (mountain) and three samples collected at sampling site 26 (lowland); (b) by analytical methods, the average % CV of the concentration of 8 replicates of 12 samples randomly selected; and (c) by difference between samples, the % CV of all the samples ( n = 3 0 ) collected during the dry season. It must be emphasized that analytical errors cannot be removed, thus the reported sampling % CV corresponds, to the sum of analytical and sampling % CV. In the same way, the all samples' % CV include the analytical and the sampling % CV. The results presented in Table 2 show that analytical error ranges between _+4% and + 10%.

73

Table 2 Percentage variation coefficient of <63-~tm fraction sampling and replicate analyses compared to coefficient for all sediments samples Sampling

Na K Mg Ca A1 Fe Mn Pb Zn Ni Cu Co Cr

Analytical

All samples

(1)

(2)

(3)

(4)

12 4 2 2 11 7 7 2 6 14 6 4 3

3 4 8 6 5 8 9 0 6 8 4 10 6

5 5 7 6 4 4 8 5 5 4 8 10

130 49 50 160 32 39 120 140 110 70 90 40 190

( l ) corresponds to 3 samples collected in sampling site 26; (2) corresponds to 3 samples collected in sampling site 18; (3) corresponds to the average coefficient of variations of 8 replicates of 12 samples; (4) corresponds to all sediments samples.

Since sampling % CV ranges from 0 to 14 it is concluded that this has not introduced additional significant variations in the concentration data. Furthermore, the sampling and analytical errors are not large enough to obscure the difference between samples (% CV 32-190) in agreement with the work of Howard and Lowestein ( 1971 ) and Chork ( 1977 ) in temperate rivers. The relatively large samples' % CV for most of the elements could be attributed to the inputs of material with quite different sources. In particular, high % CV of Ca and Na may be caused by input of enriched materials from plain soils (Mogoll6n et al., 1994a), and those of Pb, Zn and Cr by anthropogenic inputs, as will be shown below.

4.3. Background values

As mentioned above, lowland-area dissolvedoxygen values indicate that human activities are

J.L. Mogollon, C. Bifano / Chemical Geology 114 (1994) 69-82

74

Zn and Pb, the shale values agree with the average concentrations measured in recent shallowwater sediments in less polluted regions. However, for elements such as Cr, Ni, Co and Cu, which are especially enriched in basic rocks, the shale values appear to be too high for comparison with sediments from inland water (Salomons and F6rstner, 1984). The average concentration of Co in shale is 190 X higher than in carbonate, 11 X for Cu and 8 X for Cr; thus, anthropogenic enrichments of these magnitudes would be neglected if shale values are used as reference for high carbonate content sediments. Based on the above discussion, it seems that reference values for lowland sediments have to be inferred from local data, based on geochemical criteria. The main inputs to the lowland sediments are mountain sediments and flushed material from lowland soils. Therefore, it can be expected that lowland sediment background values range between the values for these two materials. The main inputs to Lake Valencia are those coming from the lowland river; hence, background values for the lake sediments are an additional reference. Table 3 shows metal average concentration values in lowland sediments compared to moun-

affecting most of the rivers. In consequence, it is not possible to chose a priori sampling locations, within the area, for background values; the values obtained from low human activity areas cannot be used either, due to dissimilar parent materials and topography. In order to split background and pollution data populations, statistical methods should be used. Frequency distributions of A1, Mn, Fe, Mg and K concentrations in lowland sediments are log normal with only one population present, as can be expected for these elements; Na and Co follow the same pattern, though four anomalous values were identified. For the potentially polluting elements, i.e. Cu, Ni, Cr, Zn and Pb, nonnormal and complex distributions were found, the result of superimposed populations of data that withhold identification of the background values. Lepeltier (1969) and Sinclair (1974) successfully dealt with mixed populations of data, using accumulative percentage frequency plots. However, this method is useful only when the number of data is > 50. An average shale composition reported by Turekian and Wedepohl (1961 ) has been used as a global basis for comparison for metal-contaminated sediments. For some metals, e.g. Mn,

Table 3 Average concentration (#g g - l ) of metals in the < 63-/~m fraction of soils and sediments from the Lake Valencia catchment ( 1 N HNO3 extraction ) Na

K

Mg

Ca

Fe

A1

Mn

Pb

Zn

Ni

Cu

Cr

Co

Mountain: Soils [1] River sediment wet season dry season

41

850

845

1,650

4,980

7,700

230

34

31

10

20

5

8

65 75

880 730

2,150 3,000

3,400 2,750

9,060 9,260

7,070 6,200

310 410

32 11

51 38

13 10

23 12

6 9

9 10

113

1,800

3,900

5,100

11,970

8,060

400

39

67

20

32

15

13

120 210 1,450

1,200 830 1,540

3,200 3,400 3,240

7,300 10,600 100,000

13,500 13,000 13,600

7,900 5,600 6,500

290 196 710

46 47 26

107 12(1 68

21 19 31

45 30 34

37 17 17

13 10 19

Low&n~ Soils [1] River sediment wet season dry season Lake sediment

[2] Data from: [ 1] = Mogoll6n et al. ( 1993a); [2] = Mogoll6n et al., (1993b).

J.L. Mogollon, C. Bifano / Chemical Geology 114 (1994) 69-82

tain sediments, lowland soils and lake sediments. It can be seen that average concentrations of lowland sediments are higher than those of mountain sediments. This could be the result of either a mixture of flushed materials from the lowland soils, which are metal-enriched (Mogol16n et al., 1994a) with mountain sediments, or a pollution effect. Elements, such as A1 and Fe, commonly used as a reference in pollution studies (Salomons and Frrstner, 1984; Kouadio and Tefry, 1987), in lowland sediments exhibit values quite similar to or lower than lowland soils, as expected based on the above discussion. Because of this, lowland soils background values can be used as a proxy for lowland sediment background. The elements Na, Ca, Pb, Zn, Cu and Cr in lowland sediments showed higher values than in lowland soils, at least during the dry season, presumably due to pollution. Mn is peculiar in that its concentration in lowland sediments is lower than in lowland soils and mountain sediments. This could be due to the low oxygen concentration in the lowland river water (Table 1 ) which increases its solubility (Brookins, 1988). Lowland soil has been reported to have Pb pollution (Mogoll6n et al., 1994a), which could explain its relatively high background value. Hence, for this element it seems preferable to use the lake background value as reference. For the other trace elements, soil backgrounds are fairly similar to or smaller than lake sediment values. This fact strengthens the selection of our background values and suggests that, in the worst case, the Pb background value was overestimated.

4.4. Anthropogenic enrichment factors The enrichment factors (EF) of trace metals, shown in Table 4, were obtained by calculating the ratio between the metal concentration in each lowland sediment and its background value (lowland soils) (Table 3 ). Only concentrations outside the 95% probability range (x_+ 2tr) were taken into account. In order to reject the influence of natural enrichments, it was verified that none of the calculated EF coincide with an increase of the percentage of the < 63-/tin grain-

75

size fraction, or rise of Fe and A1 concentrations. The trace metals Pb, Zn, Ni, Cu and Cr show EF of > 2 in 12 of 17 wet season sampling sites, and in 12 of 21 dry season sampling sites. This feature suggests that 60-70% of the path along the river courses is polluted, as was previously confirmed based on physico-chemical parameters of the water. Sampling sites influenced by industrial waste discharges, e.g. 43-45 in the Los Guayos river; 38 in the Cabriales river; 14 in the Giiey river; 16 in the Aguas Blancas river; and 22 in the Turmero river, show EF>~ 5 at least for one of the studied trace metals, whereas sampling sites affected only by domestic waste discharges show EFt<3. Pb is present as a pollutant in a high number of sampling sites and shows the highest EF. The wide distribution of Pb contamination, under physico-chemical conditions in which its mobility is relatively low (Brookins, 1988) as was found in soils of the area (Mogoll6n et al., 1994a), suggests that several pollution sources (e.g., leaded gas) are affecting the area. Therefore, Pb should be considered as one of the most important pollutants.

4.5. Metal distribution as a function of grain size In order to estimate the mass and metal concentration enrichments as a function of grain size, average and standard deviation values for each granulometric fraction were calculated for mountain sediment samples 1, 2, 15, 32 and 33 (Table 5 ), and for lowland sediments samples 35, 34, 35 and 44 (Table 6). In Fig. 2 % mass and Ni concentration distribution are shown as examples of the general trends found. Mountain sediments :show a very high percentage of medium (125-500/~m) and coarse (500-2000 /zm) percent weight fractions, whereas lowland sediments show a more homogeneous distribution (Fig. 2). This feature may be attributed to physical breaking and sorting of sediment particles during their transport from upland to lowland areas. The granulometric distribution pattern of the studied metals tends to be shifted towards fine

J.L. Mogollon, C. Bifano / Chemical Geology 114 (1994) 69-82

76 "Fable 4

E n r i c h m e n t f a c t o r s in l o w l a n d s e d i m e n t s ( < 6 3 - ~ m f r a c t i o n , 1 N H N O 3 e x t r a c t i o n ) Sampling

Wet season

Dry season

site Pb

3 4 5 7 8 10 ll 12 I4 16 2O 22 26 27 30 31 34 35 36 38 39 42 43 44 45

Zn

Ni

3

Cu

3

Cr

3

Pb

Zn

Ni

Cu

....................................

n.a

......................................

....................................

n.a

......................................

Cr

2 3 .................................... n . a ......................................

3 ....................................

n,a

...................................... ....................................

10 6 3 5 5 .................................... n . a ...................................... 4 4 3 4 2 3 ....................................

n.a

10 8 3

9 7 4 6

3

3

n.a

......................................

3

4

4 2

......................................

.................................... n . a ...................................... 3 3 2 3 2 2 2 2 .................................... n . a ...................................... .................................... n.a ...................................... .................................... n.a ...................................... 11 3 3 2 6 .................................... n.a ......................................

4

2

14 3

3

4

3

16

.................................... n.a ...................................... 9 5 3 3 3

n.a. = n o t a n a l y s e d .

particles (Fig. 2; Table 5), in contrast to the granulometric distribution of metals in upland soils (Mogoll6n et al., 1994a) which did not show any tendency of accumulation. This dissimilarity in metal distribution suggests that chemical weathering processes are coupled with transport of particles from soils to rivers. There is a general decrease in the relative concentration of metals in coarse fractions in the lowland sediments compared to the upland sediments (Tables 5 and 6). Since no differences were observed between pollutant and non-pollutant elements, this feature may be simply due to the effect of weathering during transport. It must be stressed that metals tend to accumulate in the fine fraction of the studied sediments, in spite of a particular percent weight of particle-size class distribution. For this reason, and due to its easier homogenization, the < 63-

/~m grain fraction seems to be a good choice to ascertain non-residual contents, background values, anthropic enrichments and the effect of precipitation, as was used for the Clark Fork river, Montana, U.S.A. (Axtmann and Luoma, 1991 ).

4.6. Labile and refractory fractions The proportion (%) of metals extracted with HNO3 ( 1 N) in relation to total metal content is a measure of the fraction of the metal that resides in the labile fraction (% LF) (Agemian and Chau, 1976; Zielinski et al., 1983; Chester et al., 1985 ). It can be related to anthropogenic inputs or activities (Fillipek and Owen, 1979; Tessier et al., 1982; Reaves and Berrow, 1984; Peinado et al., 1987; Brook and Moore, 1988 ). The % LF were determined for the fine grain fraction ( < 63 /tm) from mountain sediments

J.L. Mogollon, C. Bifano / Chemical Geology 114 (1994) 69-82

77

Table 5 Granulometric distribution and metal concentration (in jzg g- ~) as a function of grain size in mountain sediments (wet season, l N HNO3 extraction)

Mass(%) Na K Mg Ca A1 Fe Mn Pb Zn Ni Cu Cr Co

2000-500~m (n=5)

500-125 pm (n=5)

125-63pm (n=5)

<63 pm (n=5)

30 41 440 390 8,800 4,600 8,900 230 32 40 15 16 10 8

35 43 510 540 10,400 5,100 9,100 230 28 47 13 18 12 9

8 46 550 400 14,400 5,100 9,200 260 26 48 14 22 12 10

7 68 700 330 20,600 7,500 12,000 390 37 67 17 30 16 12

n = number of samples. Table 6 Granulometric distribution and metal concentration (in #g g- 1) as a function of grain size in lowland sediments (dry season, 1 N HNO3 extraction )

Mass (%) Na K Mg Ca A1 Fe Mn Pb Zn Ni Cu Cr Co

2000-500 pm (n=6)

500-125 pm (n=6)

125-63 #m (n=6)

< 63 pm (n=6)

8

35

27

30

4 138 40 22,000 720 1,730 87 13 28 4 11 14 2

57 490 640 37,700 5,800 12,100 350 31 105 20 46 24 12

57 360 304 34,000 6,800 13,300 360 30 98 27 43 30 12

77 350 426 59,000 9,300 18,000 510 45 132 31 59 43 17

n = number of samples. ( s a m p l e s 1, 2, 15, 32 a n d 3 3 ) a n d f o r l o w l a n d s e d i m e n t s ( s a m p l e s 3 - 5 , 12, 20, 22, 34 a n d 4 4 ) . It c a n b e s e e n in Fig. 3 t h a t a b s o l u t e d i f f e r e n c e s b e t w e e n % L F in m o u n t a i n a n d l o w l a n d a r e a s a r e < 10% f o r t h e n o n - p o l l u t a n t s e l e m e n t s , e.g. Al, F e a n d Co, w h e r e a s , f o r t h e p o l l u t a n t e l e m e n t s , i.e. Z n , N i , C u a n d Cr, s p r e a d s i n % L F r a n g e

f r o m 13% t o 27%. E v e n t h o u g h P b is a p o l l u t a n t , its s p r e a d in % L F is o n l y 6%, which suggests t h a t its a n t h r o p o g e n i c i n p u t h a s a s i m i l a r p a r t i t i o n between labile and refractory fractions to the sediments. I n g e n e r a l , t h e s t u d i e d e l e m e n t s s h o w a t least a slight c h a n g e o f % L F f r o m m o u n t a i n to low-

J.L. Mogollon, C. B(fano / Chemical Geology 114 (1994) 69-82

78 40

. _ . J ~ _

1_

i

i

i

L

I

I

I

I

I

~

1 . ~ _

_

Ni (nag/ kg)

% Mass

35 30 25 20 15 10 5 0

i

i

i

i

i

Mountain

i

!

Lowland

i

i

i

i

i

i

Mountain

i

Lowland

Fig. 2. Average % mass and Ni concentrations ( 1 N HNO3 extractable) as a function of grain size in sediments from the Lake Valencia catchment. (Each histogram shows from right to left the fractions: <63, 63-125, 125-500 and 500-2000 /~m. )

[ ] Labile fraction 1201 . •,

[ ] Refractory fraction

. . . . . . . . . . . . . . . . . . . . . . . . . K

,,

Ca

.,

Fe

..

Co

~

Zn

,,,

Cu

100 80

lowland water, which increases its solubility (Brookins, 1988 ). The relative order of % LF for the studied elements cannot be explained in detail, since they are dependent on the type and concentration of minerals and amorphous phases, and on the amount of metals presen! in parent materials, information that is generally unavailable. However, the order can be explained in a broad sense. The highly weathering-resistant feldspars, such as plagioclase, are common host minerals of elements possessing the lowest % LF, i.e. Na, K and A1. Conversely, the trace elements usually accumulate in easy weathered minerals like pyroxenes, amphiboles and olivine (Henderson, 1982 ). Finally, Mn, Mg and Ca, in primary minerals, usually occupy a position more susceptible to weathering than Na, K and A1. This would explain their higher % LF. In addition, Mn, Mg and Ca are found in highly soluble compounds such as carbonate, which are common in the study area (Comerma, 1968; Gonz~ilez de Juana et al., 1980; Elizalde and Mayorca, 1982 )

4.7. Effects of the rainfall variation upon metal concentration and percentage of labile fraction

60 40 20

0 ML

ML

ML

ML

ML

ML

ML

ML

ML

ML

ML

ML

ML

Fig. 3. Average percentage of labile ( 1 N HNO3 extraction) and refractory fractions in mountain (M) and lowland (L) sediments from the Lake Valencia catchment ( < 63-/zm grainsize fraction ).

land, which could be attributed, as it was for the granulometric distribution changes, to chemical weathering processes during the transport of particles which redistribute the metals among the geochemical phases. Nonetheless, these increases are lower than those found when mountain and lowland soils of the Valencia catchment were compared (Mogoll6n et al., 1994a). The decrease o f M n % LF could be due to the remarkably low dissolved oxygen concentration in the

For each site, in which samples were collected during both seasons, the metal concentration ratio, as well as the percentage weight of the < 63/~m grain-size fraction ratio between dry and wet seasons, were calculated. This allowed an average for mountain sediments and lowland sediments to be estimated (Fig. 4). There is a predominance of the < 63-/1m particle size fraction during the dry season in both areas, as a result of lower water velocity which reduces the suspended load ability and hence increase the amount of fine material in the bed load. Since in mountains Na, Mg, Ca, Fe, Mn, Cr and Co showed a trend to accumulate in fine fractions, their dry to wet season ratios of > 1 (higher concentrations during the dry season) were expected. Other mechanisms, such as increased dissolution rates during the wet season commonly used to explain the feature described above (Rivera et al., 1986; Sakai et al., 1986;

J.L. Mogollon, C. Bifano / Chemical Geology 114 (1994) 69-82

0

~

2.0

~

Na

cO

1.5 O9

/:

1.0-

O

0.5. 0.0 ML ML

ML

ML

ML

ML ML

ML

ML

ML

ML

ML

ML

ML

Fig. 4. Average dry to wet season ratios of % o f mass and concentration ( 1 N H N O 3 extraction) in m o u n t a i n ( M ) and lowland ( L ) sediments from the Lake Valencia catchment ( < 63-#m grain-size fraction).

Bird, 1987; Mogollrn et al., 1990), are quite unlikely to take place for elements like Na and Cr, because of their low percentage of labile fraction. The behavior of K, A1, Pb, Zn, Ni and Cu in mountain sediments, i.e. ratios > 1, which show higher concentrations during the wet season, was unexpected. In fact, this feature is quite unlike observations in other northern Venezuela rivers (Mogoll6n, 1989), temperate rivers (Bird, 1987) and subtropical rivers (Sakai et al., 1986) and only agree with the behavior observed in the Periyar river, India (Paul and Pillai, 1983). Like the elements mentioned above, these anomalously behaving elements show a trend to accumulate in the < 63-/tm fraction; hence, an additional cause other than an increase in the amount of fine fraction has to be invoked. The concentrations of K, A1, Pb, Zn, Ni and Cu in mountain soils are higher than in dry season mountain sediments (Table 3). Therefore, an input of flushed material from upland soils would explain the increase of concentration during the wet season and hence, the ratios are < 1. It must be noted that this process could also explain the behavior of Na, Mg, Ca, Fe and Mn, but not that of Cr and Co. The concentrations of Na and Ca in the lowland sediments are higher during the dry season than during the wet season, but concentrations of K, AI, Mn and Co are lower, i.e. ratios < 1

79

(Fig. 4). Again, this feature could be explained by input of materials flushed from soils. In fact, concentrations of Na and Ca are lower in the lowland soils than in the dry season lowland sediments and conversely for the latter elements. The pollutant elements, i.e. Pb, Zn, Ni, Cu and Cr, as a result of the lack of seasonality of waste discharges, were not expected to fulfill the relation between the wet/dry season concentration ratio and the concentration in soils. This is indeed the case for Cu and Cr. For Pb and Ni the similarity in the sediment and soil concentrations forbids us to draw any conclusions. Only Zn, which has a remarkably lower concentration in soil than in sediment, shows a decrease of concentration during the wet season due to the dilution effect of flushed materials from the soil. To ascertain the influence of rainfall variations on the amount of the metals' labile fractions, the latter values for wet and dry seasons were compared in three sampling sites (Table 7). The variations have no specific tendency. In fact, elements show opposed behavior when different sites were compared and for any specific site some elements increased their labile fraction whereas others decreased. The high variations observed suggest that Table 7 Wet to dry season variability of the percentage of the labile fraction in sediments from Lake Valencia catchment ( < 63# m grain-size fraction ) LF* Sampling site: Na K Mg Ca AI Fe Mn Pb Zn Ni Cu Co Cr LF*=

15 0 40 '.27 :57 56 41 - 10 - 28 -19 100 -2 - 160 100

( % L F wet . . . . .

32

35 20 33 17

29 l6 100 100 5 - 15 -23 12 - 92

- 286 29 21 -48 - 13 24 - 13 - 19 14 -4 11 -5 - 56

-- oV0 L F dry se . . /. o~/o . L. F w 1 season ) X 1 0 0 .

80

J.L. Mogollon, C Bifano / Chemical Geology 114 (1994) 69-82

rainfall has an important influence over the percentage of labile fractions, but further studies to this subject are needed to establish the behavior of metals in this phase.

discharge distorts the soil-sediment relation for the pollutants in the lowland.

Acknowledgements 6. Conclusions In areas in which most of the path along a river's course are affected by human activities, the metal concentration background values in sediments should be inferred based on the background values of the main input materials. We chose the upland sediments and lowland soils for our study. The similarity between the inferred background and the concentration in sediments of elements like AI and Fe, commonly used as a reference in pollution studies (Salomons and F~Srstner, 1984; Kouadio and Tefry, 1987), strengthens the assumption made. This approach seems to have a lower risk of incorrectly estimating background values than the use of a reported average shale composition or recent shallow-water sediments as reference. Domestic inputs in the lowland produce increases of up to 3 times the background values of Pb, Zn, Ni, Cu and Cr, whereas industrial wastes produce increases that range from 5 to 16 times. The estimated increase due to anthropogenic inputs of the percentage of the labile fraction ranges from 13% to 27%, depending on the element. Reactions coupled with transport of sediments from upland to lowland areas cause a redistribution of metals toward the fine particle fractions and can induce slight changes of the percentage of labile fraction and an increase in metal concentration. Inputs of material flushed from the soil control the wet-dry season concentration variability of the metals studied. For elements enriched in the soil relative to dry season sediments, there is an increase of their concentrations in sediments during the wet season. This feature is quite unlike the observations in other rivers in which a decrease of concentrations during the wet season has been explained by leaching of metals (Paul and Pillai, 1983; Sakai et al., 1986; Bird, 1987; Mogoll6n, 1989 ). The lack of seasonality of waste

We are grateful to M. Davis and R. Mauriello for their constructive review of the manuscript as well as to S. LoMonaco for helpful discussions. This work has been supported by a grant from Fundaci6n Polar. Part of the paper was written during a sabbatical leave of J.L.M. at Yale University funded by Fundaci6n Gran Mariscal de Ayacucho.

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