CHAPTER 5 IMPACT ASSESSMENT

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1 CHAPTER 5 IMPACT ASSESSMENT 5.1 Geoaccumulation Index and Pollution Load Index Knowledge of the natural concentration of metals in the bottom sediments of river, lakes,, and estuaries are important parameters to create an environmental database for 4 the system under study and has become a major feature all throughout the world. Sediments are considered as a suitable medium for regulating the concentrations of toxic metals and also for assessing the environmental quality as they perform an important function of buffering higher metal concentration of water by adsorption and precipitation. Adams et al., (1992) and Chapman (1998) stated that the sediments are the reflector and accumulator of the contaminant inputs into the aquatic ecosystem allowing the sources and sinks to be identified. Because of their high capability to, segregate the different kinds of metals, sediments can reflect the water quality and explain the effects of anthropogenic inputs ;(Chen, 1991; Rowlatt et al., 1994). Awadallah et al. (1996) indicated that the.weathering of surrounding rocks and soil, directly exposed to the surface water may become the largest natural source for certain metal ions in the River. A river system, which is situated in an area having less or no developmental activity, is considered as pristine and the data obtained may be considered as baseline data representing the natural concentration level (Donoghue et al., 1998). The advantage of establishing baseline concentration of metals in sediments for various aquatic environments is to determine if the observed metal concentrations are elevated above the background levels. On the other hand, these baseline concentrations are heeded to provide the regulators and managers with a scientific database to establish the available criteria to control the overlying water quality. A variety of approaches have been proposed to develop sediment quality criteria (SQC) based on the total concentrations of metals. - Baseline concentrations of metals in sediments are often considered as - s? equivalent to the geometric mean of the concentrations of a metal in as many samples as measurements are done (Kabata-Pendias et al., 1992). It may not be a true background or baseline concentrjation, but has been shown to be within 95% of the true value. In absence'of reliable background value, the geometric mean has been found to be very 209

2 convenient to calculate the pollution load index and hence to draw appropriate inferences about the state of the sediment quality. In an aquatic ecosystem, the assessment of sediment quality o f a river could be done ' v by various approaches. In the present study, the geo-accumulation index (Ige0), and the pollution load index (PLI) were used to evaluate the sediment quality. Statistical analysis requires complete numerical datasets. It has been established that the i Geometric means (GM) o f metal concentrations fit a log-normal distribution and therefore, these are considered as better estimates for most geochemical dkta (Ghen et 1 ' 1 al., 1999; Hseu et a!., 2002). The GM data of the metal concentrations in the sediments 1 are usually less than the corresponding arithmetic mean (AM) and the same could be used as the background concentration of metals in the sediments to evaluate the risk of 1 environmental contamination (Chen et al., 1999; Hseu et al., 2002). Heavy metal pollution in sediments can be measured quantitatively by applying the concept of Geoaccumulation Index of Muller (1979), expressed mathematically as Igeo = log2 C J 1.5 x B Where Cn is the measured concentration o f the element n in the sediment and Bn is the geochemical background value-(taken to be represented by the geometric mean o f the concentration values o f a metal). The factor 1.5 is used because o f the possible; variations in the. background data. Muller s geo-accumulation index is basically a single metal approach to quantify metal pollution in sediments. A positive Ige0 value indicates significant anthropogenic contribution to the metal contents o f the sediment while a negative value shows negligible or no contribution. On the basis o f the values o f the geoaccumulation index, the sediments can be classified as shown below with respect to their quality: s 2 1 0

3 Sediment Class. t IgsoValue Sediment aualitv 6» 5 Very highly polluted 5 1 ' >>4-5 1 Highly polluted 4.» 3-4 Moderately to highly polluted 3» 2-3 Moderately polluted 2» 1-2 Moderately polluted to unpolluted 1» 0 - l Unpc lluted 0 «0 Practically none (can be taken as the background) Along with the geoaccumulation index, another index has been used to characterize the sediments with' respect to their contamination load. Known as the Pollution Load Index (PLI), it is calculated (Tomlison et al., 1980) from the formula: PLI= (Product of contamination or enrichment factors for n number of metals)1/n For significant contamination, PLI is > 1. The PLI value represents the impact of urbanization on the river system and shows a direct linear relationship with urban center population (Singh et al., 2002). The contamination or the enrichment factor is calculated from the following formula, CF or = (Metal concentration in polluted sediments)/ (Background value of the metal). In the present work, CF values were calculated by using the geometric mean value method (Hseu et al., 2002) of arriving at the background values. The average value of the ten heavy metals along with the geometric mean values and the contamination factors, for the Siang river sediments are given in Table 5.1. The values were on the other hand calculated from the average shale concentrations given by Turekian and Wedepohl (1961), which have been accepted as standards worldwide. The geoaccumulation indices for the metals are shown in Table 5.2 along with the pollution load index (PLI) calculated on the basis of the background values of the metals obtained from the geometric means of the observed values. Muller s 211

4 geoaccumulation indices for all the metals were found negative except Zn and V.which showed positive values. It was also observed that the geoaccumulation indices of all the metals in general slightly decreases in downward direction from which it can be inferred that the metals arei not accumulated in the bed sediments rather they are regularly flushed out with the sediment to the river Brahmaputra due to the high gradient of the river. The Pollution loadlndex calculated from the contamination factors was found to be greater than 1 in case of S2 (1.20), and was just 1.00 for SI, S3, S4 and S5. Rest of the sites has PLI values less than 1. On the basis,of these values, it can be concluded that the sediments of site 2.have metal contamination. The pollution load index (PLI) was also evaluated for.each of the sampling stations from the enrichment factors () obtained by dividing :the mean concentration of a metal by the corresponding lithogenic background value (Turekian and Wedepohl, 1961). These are shown in Table 5.3. It was found that the PLIs evaluated from the enrichment factors is slightly less than the ones obtained from the contamination factors (CF). The site 2 showed highest PLI value (0.89).!, - Highest enrichment of Cd was observed at the site 1 (5.17), Co at site 5 (2.3), Cr at site 6 (1.10), Cu at ;site 6 (0.61), Hg at site 4 (0.23), Mn at site 2 (0,38), Ni at site 7 (0.61), Pb at site 9 (2.09), V atsite 2 (0.36) and Zn at site 1 (2.41). Enrichment factor for Cd, Co, Pb and Zn were found to be greater than >1 indicating their enrichment in the sediments. No specific trend was observed for the enrichment factors which can be inferred as absence of contamination due to anthropogenic sources, only Cd and Pb might have some anthropogenic input The enrichment of Cd and Pb in the river sediments was supported by the earlier results of Das (2004) for the Brahmaputra river sediments when enrichment of Cd and Pb was observed at Dibrugarh situated within 20 km of the confluence with the river Siang. 212

5 Table 5.1: metal concentrations and contamination factors (CF) for the Siang River sediments. (Concentrations are in mg/kg, except for Hg which is in pg/kg, MC mean concentration, CF contamination factor, GM geometric mean concentration, CF = MC/GM). Sites GM Cd Co Cr Cu Hg Mn Ni Pb Zn MC CF MC CF MC CF MC CF MC CF MC CF MC CF MC CF MC CF MC CF :

6 Table.5.2; Geoaccumulation index of 10 heavy metals for the 9 sediment sampling sites on the River Siang aiong with the pollution load index (PLI). The PLIs were calculated from the contamination factors obtained with the geometric means of metal concentrations as the background values. Sampling sites Cd Co Cr Cu Hg , Mn Ni Pb Zn PLI

7 Table 5.3: heavy metal concentrations, enrichment factors () and pollution load index (PLI) for the Siang River sediments, (concentrations of all the metals are in mg/kg, except mercury which is expressed in pg/kg) S a m p l i n g s i t e s C d C o C r C u H g M n N i P b L 6 t Z n P L I

8 Lithogenic and anthropogenic contribution for the heavy metals such as Cd, Co, Pb and Zn for which, enrichment factor was found to be >1, were calculated by comparing their seasonal average concentrations with average background concentration (geometric mean value) by applying the following relations, (i) (ii) % of Lithogenic contribution = 100 x (Shale concentration/background value) % of Anthropogenic contribution =100 Lithogenic contribution. The percentage lithogenic and anthropogenic contributions for the four heavy metals, Cd, Co, Pb and Zn,(Table.5.4) with observed enrichment (>1) in the Siang river sediments, are given in Table 6.4. High percentage of the anthropogenic contribution indicates possible easy availability of the toxic metals to biotic components of the river s ecosystem and their likely entry into the food chain under suitable physico chemical conditions. Considering the lithogenic and anthropogenic contributions it can be concluded that: (i) The river sediments are contaminated with respect to Cd, Co, Pb and Zn in all the sites. Higher anthropogenic contribution for Cd was observed at site 1 (13.4 %), for Co at site 5 (6.4 %), for Pb at site 9 (13.5 %) and for Zn at site 1 (39.2%), which is the highest. (ii) Share of anthropogenic contribution for Pb remains almost constant throughout the study stretch while that for other three heavy metals viz.cd, Co and Zn were found to decrease in downstream direction which indicates that these three metals are mainly coming from the weathering of the mineralized belt in the upper stretch and in downstream direction, the contribution decreases due to dispersion and dilution effect. (iii) Out of ten heavy metals studied, only four metals (Pb, Cd, Co and Zn) have higher abundance than the average shale values given by Truekian and Wedepohl (1964). 216

9 Table 5.4: Percentage o f lithogenic (L) and anthropogenic (A) contributions of the five heavy metals in the Siang River sediment. Stations Cd Co Ni Pb Zn L A L A L A L A L A SI S S S S S S : S8' S

10 5.2 Sediment pollution index Muller s system of indexing the metal pollution in sediments is a single metal approach. There is another method to assess the quality of the sediment based on multi-metal approach with respect to heavy metal concentrations along with proper consideration to relative metal toxicity. This is known as the method of Sediment Pollution Index (SPI), which is defined as the linear sum of the enrichment factors along with the account of metal toxicity weights. The metal toxicity weights are based on the relative toxicity of different metals and are inversely proportional to lithogenic or average shell value. It is considered that metal concentrations in unpolluted sediments must not exceed, the values of the average shale values. A weight of 1 was assigned to Gr and Zn, the less toxic metals. The weights for Ni, Cu, Pb and Cd were respectively 2, 2, 5 and 300. The Sediment Pollution Index can be expressed as follows: SPI = (mxwm)/ W m where = enrichment factor of a metal M in a particular sample,.and W = Toxicity weight of metal M. According to sediment pollution index there are five classes of sediments (SP10 to SPI4) grading sediment quality from natural to highly polluted level as follows: 1. SPI0 Natural sediments (0-2) 2. SPI 1 Low polluted sediments (2-5) 3. SPI2 Moderately polluted sediments (5-10) 4. SPI3 Highly polluted sediments (10-15) 5. SP14 Dangerously polluted sediments >20. i Fig. 5.1 represents the variation of SPI classes of the Siang River in the study area. It is evident that the sediment of the Siang River can be classified as low polluted sediment with respect to the six heavy metals. Although the whole stretch of the River undertaken for study fails under low polluted zone (SPI < 5), SPI is slightly high in the upstream 218

11 region and low in the downstream direction. This trend indicates high flushout rate of the contaminants in the downstream direction. o> CO it1". -J--.'-V '. ' Sediment Pollution Index K) CO itk 01 0> -» t. - e - - ' *-*. r-..**» I - ; ' " J-. ' 'k i -* * ' s:. v a \J*% *> 00 CO m CM Sampling sites Fig. 5.1 Variation of Sediment Pollution Index based on Cr, Ni, Cu, Zn, Pb and Cd of the Siang river in the down stream direction 219

Assessment of Heavy Metals in Sediments from Coastal Al-Hodiedah Governorate, Yemen

All Rights Reserved Euresian Publication 212 eissn 2249 256 Available Online at: www.environmentaljournal.org Volume 2, Issue 3: 168-173 Open Access Research Article Assessment of Heavy Metals in Sediments