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Heavy metal contamination in mangrove sediments in Klang estuary, Malaysia: Implication of risk assessment
Estuarine, Coastal and Shelf Science 226 (2019) 106266
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Heavy metal contamination in mangrove sediments in Klang estuary, Malaysia: Implication of risk assessment
T
Mohammed ELTurka, Rosazlin Abdullaha,b, Rozainah Mohamad Zakariaa,c,∗, Nor Kartini Abu Bakard a
Institute of Biological Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia Centre for Research in Biotechnology for Agriculture, University of Malaya, 50603, Kuala Lumpur, Malaysia c Institute of Ocean and Earth Science, University of Malaya, 50603, Kuala Lumpur, Malaysia d Department of Chemistry, University of Malaya, 50603, Kuala Lumpur, Malaysia b
A R T I C LE I N FO
A B S T R A C T
Keywords: Pollution Soil Port Klang Selangor Ecological risk assessment Mobility heavy metals
Deterioration of environmental quality in estuaries leads to a high risk of destruction of marine life. Heavy metal (Mn, As, Cu, Zn, and Pb) concentration in mangrove sediment in the Klang estuary was evaluated to identify any potential risk of contamination resulting from the rapid development along the Klang river. Several indicators were used to evaluate the pollution status: geo-accumulation (Igeo), contamination factor (CF), degree of contamination (DC), potential ecological risk index (PERI) and risk assessment code (RAC). The results indicate that As and Pb concentrations exceeded the threshold effect level, while Mn, Zn, and Cu posed no adverse biological impacts. In addition, Igeo, CF, and PERI values showed minimal risk of heavy metal pollution in the study area. However, RAC for Zn exhibited high environmental risk. On the other hand, Cu and Mn showed medium environmental risk, while Pb and As showed no environmental risk. These results can increase awareness of the mitigating action that mangrove forests naturally provide against pollution, and provide information to design future plans and policies towards more sustainable development in the area.
1. Introduction Rapid development has many benefits, but it also poses a big challenge to maintaining the environment (Kaewtubtim et al., 2016). Environmental pollution poses a major threat to the natural environment (Rothlin and McCann, 2016). Reports show that in recent decades, the marine environment has been continuously loaded with various hazardous chemical pollutants from human activities, severely affecting environmental quality (de Vallejuelo et al., 2010; Karbassi et al., 2008; Miola et al., 2016). Heavy metals enter the environment through natural sources, for example erosion and weathering of rocks, as well as anthropogenic sources, for example, wastewater, agricultural and industrial activities (Alloway, 2013; Belkhiri et al., 2016; Li et al., 2016). The major sources of pollution are industry, mining, agriculture, and wastewater (Cox and Preda, 2005). Research notes that areas with industrial activities and human activities always have high concentrations of heavy metals (Caccia et al., 2003). Heavy metal concentration can pose a threat to the ecosystem and its adjacent environment. The impact is more pronounced in river estuaries and their associated mangrove forests and adjoining marine environment (de Vallejuelo
∗
et al., 2010; Marchand et al., 2006). Sediment assessment is more conservative compared to water assessment because sediments retain contaminants (Casas et al., 2003; Singh et al., 2005), which can reflect the historical variation of the pollutants (Olubunmi and Olorunsola, 2010; Singh et al., 2005). The Mangrove forest ecosystems are an essential intertidal zone in secured estuarine shores and can mainly be found in tropical and subtropical areas (Tam and Wong, 2000). They provide valuable resources for economic benefit as well as shoreline protection (Tamin et al., 2011). They are also very sensitive ecosystems, and many Mangrove zones across the world are declining or being lost because of contamination (Davari et al., 2010). The mangrove forests in the Klang area, which is located on the Straits of Malacca, is among the ecologically exposed zones that are threatened by human activities, such as chemical hazards, industries, shipping, fisheries, tourism, human settlements, and logging, all of which result in contamination and deterioration of environmental quality (Sany et al., 2013). The Klang estuary is at the end of Klang River that runs through the capital city of Malaysia, Kuala Lumpur, and is home to the largest and busiest port of Malaysia, Port Klang.
Corresponding author. Institute of Biological Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia. E-mail address: [email protected] (R. Mohamad Zakaria).
https://doi.org/10.1016/j.ecss.2019.106266 Received 30 January 2019; Accepted 23 June 2019 Available online 24 June 2019 0272-7714/ © 2019 Elsevier Ltd. All rights reserved.
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However, patches of mangrove forests still exist at the fringing coastal areas of the estuary, despite the overwhelming growth of human population and continuous development. Although quite a number of studies have investigated the concentration of heavy metals in this area, none have assessed the potential risk. A lack of studies on risk assessment and the mobility of heavy metals in the Klang estuary could lead to inadequate environmental management, as not enough information is available. Thus, it is necessary to evaluate the concentration and distribution of heavy metals in this critical area, so as to understand the present condition of its ecosystem and to compile baseline data for future monitoring. Many sediment quality guidelines have been designed to protect the environment from the harmful and toxic effects of sediment-bound pollutants (McCready et al., 2006). These guidelines measure the degree to which the chemical condition of sediments can have an adverse effect on aquatic organisms and are designed to evaluate the quality of the sediments. They are also used to group contaminated areas and prioritize them for further investigation (Díaz-de Alba et al., 2011). The study aims to investigate the concentration and deposition of selected heavy metals, i.e. Mn, As, Cu, Zn and Pb, in the mangrove sediment around the Klang estuary in Peninsular Malaysia, to elucidate the mobility of heavy metals and to evaluate the impact of anthropogenic activities on the coastal mangrove forest ecosystem. Information gathered in this study can enhance the understanding of heavy metal contamination in the Klang mangrove ecosystem. This information is important for decision makers involved in coastal ecosystem management in Malaysia, and will enable effective monitoring of both environmental quality and sustainable development.
Table 1 GPS Sampling points. Station No.
Latitude (N)
Longitude (E)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
3° 0′4.88" 3° 0′4.72" 3° 0′4.58" 3° 0′4.51" 3° 0′5.30" 3° 0′5.34" 3° 0′5.14" 3° 0′5.13" 3° 0′5.94" 3° 0′6.01" 3° 0′5.71" 3° 0′5.80" 2°59′30.34" 2°59′30.17" 2°59′30.12" 2°59′30.03" 2°59′29.64" 2°59′29.50" 2°59′29.18" 2°59′29.32" 2°59′28.97" 2°59′28.82" 2°59′28.62" 2°59′28.74"
101°22′19.81" 101°22′19.00" 101°22′18.36" 101°22′17.63" 101°22′19.64" 101°22′18.95" 101°22′18.17" 101°22′17.41" 101°22′19.48" 101°22′18.68" 101°22′17.87" 101°22′17.12" 101°22′32.93" 101°22′31.71" 101°22′30.01" 101°22′28.99" 101°22′33.13" 101°22′31.86" 101°22′30.20" 101°22′29.11" 101°22′33.30" 101°22′31.98" 101°22′30.39" 101°22′29.31"
2. Materials and methods 2.1. Study area This study was conducted in the remaining mangrove patches of the Klang estuary, close to Port Klang, as shown in Fig. 1 and GPS coordinators in Table 1. The area is characterized by massive and rapid
Fig. 1. Map of Peninsular Malaysia and the study area (Klang estuary). 2
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commercial development including ports, transportation, industrial areas, commercial and residential areas, tourism activities, and fisheries. The population of Klang District is 879,867 and the Port Klang forms an important part of the economy of the state of Selangor. It is home to about 95 shipping companies and agents, 300 custom brokers, 25 container storage centers, as well as more than 70 freight and transport companies. It handled almost 50% of Malaysia's sea-borne container trade in 2013 (Department of Statistics, Malaysia. 2018). The Klang River Basin covers an area of 1288 km2 and is approximately 120 km in length. The upper catchment is mountainous, and is still partly covered by tropical forest, and flows through the metropolitan city of Kuala Lumpur and eventually drains into the Straits of Malacca. The range of mean annual rainfall is 1701–1710 mm, while the range of mean annual temperature is 27.3–27.7 °C (Hemati et al., 2017).
Table 2 NIST SRM 2586 value. No
Element
Certified value (mg/kg)
Mean of observed value (mg/ kg)
% Recovery
1 2 3 4 5
Cu Pb Mn As Zn
81 432 1000 8.7 352
88.83 ± 3.7 370 ± 8.3 917.54 ± 3.62 9.5 ± 0.9 286 ±
109.66 85.64 91.75 109.19 81.25
Table 3 The standard recovery values in the sediment sample.
2.2. Sediment sampling collection A total of 24 sediment samples (0–20 cm deep, triplicates for each sample), from sampling points in the study area within the remaining mangrove patches at the Klang estuary, were collected by utilizing auger tools. The reason to collect surface sediments so that the effects of recent depositional events (e.g., flooding or dredging) can clearly be delineated to test their influence on the contamination in the sediments (Smodis et al., 2003). The samples were kept in ziplock plastic bags under 4 °C and then transferred directly to the laboratory. In the lab, the samples were homogenized, air dried, sieved through 0.63-μm (to obtain heavy metal digestion and fractionation analysis) and 2-mm (to obtain physical properties) sieves, and kept for further analysis.
No.
Cu
Pb
As
Zn
Mn
F1 (acid soluble) F2 (reducible fraction) F3 (oxidizable fraction) F4 (residual fraction) sum F Total element Recovery
2.12 1.92 2.89 4.13 11.06 9.38 117.91
0.48 6.76 6.86 30.59 44.69 37.39 119.52
1.32 1.34 10.55 16.61 29.82 35.69 83.55
10 5.32 2.07 5.27 22.66 22.46 100.89
14.01 5.69 56.26 29.26 105.22 105 100.20
was used as a standard to check against the accuracy of the analysis. In this method, 1 g of standard reference material (SRM 2586) was digested in the same way the soil samples were digested, and as reported in Table 2. The sequential extraction recovery is presented in Table 3. The following equation is then applied to measure the Sequential exF1 + F2 + F3 + F4 traction recovery. Total element × 100%. 2.6. Risk assessment methods
2.3. Physical parameter
In order to compare current heavy metals level with local background values, and to understand the anthropogenic effects, Geoaccumulation Index (Igeo) was recommended (Müller, 1969); whereby Igeo = log2 Cn/1.5 Bn, where Cn is the concentration of metals in the test zone and Bn is the geochemical background in the upper crust (Turekian and Wedepohl, 1961). Muller (1969) interpreted the classification as follows: ≤ 0: uncontaminated sediment; 0–1: unpolluted sediment to moderately polluted; 1–2: moderately polluted; 2–3: Moderately polluted to highly polluted; 3–4: highly polluted; 4–5: highly polluted to extremely polluted; and > 5 indicates extremely polluted. Hakanson (1980) proposed Contamination Factor (CF) to assess the level of soil metal concentrations. The factor was determined as the division between metals level in the sample by the background value of heavy metals (Turekian and Wedepohl, 1961) (Hakanson, 1980). Contamination Factor (CF) is interpreted as follows: < 1: no or low contamination; 1–3: moderate contamination; 3–6: considerable contamination; and CF > 6 indicates very high contamination. Potential Ecological Risk Index (PERI) was also established by Hakanson (1980). The PERI formula indicates the heavy metal proprieties and the environmental behavior of metal pollution in the sediment. To calculate PERI, the following equations are used:
The pH of the homogenized sediment samples was analyzed using a glass electrode pH meter (EUTECH, 2700). Electrical conductivity was monitored using an EC meter (Hanna instruments HI, 2315). Sediment texture was analyzed using Particle Size Analyzer (PSA, Coulter, model L 230). 2.4. Analytical methods Concentration of Mn, As, Cu, Zn, and Pb was determined following EPA-ROC (1994) method, where a 0.5 g sediment sample was digested by Aqua Regia technique. The final digested mixture was filtered through Whatman filter paper, and the heavy metal concentration was detected using ICP- MS Agilent 7500. For the speciation study, a four-stage BCR (European Community Bureau of Reference) sequential extraction, modified by Rauret et al. (1999), was conducted to examine the mobility of metals in the soil. The BCR fractions were determined via four steps: Step one (F1): making an acid soluble fraction by adding 40 ml acetic acid to 1-g sediment and shaking for 16 h. Step two (F2): making a reducible fraction by adding 40 ml hydroxyl ammonium chloride to residual F1 and shaking for another 16 h. Step three (F3): making an oxidizable fraction by adding 10 ml hydrogen peroxide (30%) to residual F2, which is then digested at room temperature for 1 h. Another 10 ml of hydrogen peroxide was further added to this residue, which was digested at 85 °C using a water bath for 1 h. Finally, 50 ml of ammonium acetate, was added at pH = 2 and shaken for 16 h. Step four (F4): making a residual fraction using aqua regia, sequential extraction and analysis by ICP-MS, Agilent 7500.
Cif = Cis/Cin
(1)
Eri =
(2)
Tir
x
Cif
PERI = ∑
Eri
Cif =
(3) Cis
each element contamination factor, = content of eleWhere, ment in samples, Cin = reference value of element, Eri = potential ecological risk index of a single element, and Tir = biological toxicity of a single element (As = 10, Cu = 5, Pb = 5, Zn = 1 and Mn = 1) (Soliman et al., 2015). According to the classification by Hakanson (1980): if PERI < 150 = low grade; 150 ≤ PERI < 300 = moderate grade; 300 ≤ PERI < 600 = severe grade; and PERI > 600 = serious
2.5. Quality analysis As shown in Table 2, the Standard Reference Material (SRM), developed by the National Institute of Standards and Technology (USA), 3
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Cu, Mn and Zn compared to the current study site. This is anticipated as the east coast region is not heavily industrialized, unlike the west coast region (Dasar et al., 2009; Sukri et al., 2018). This shows that the west coast posed a significant threat to the environment. Additionally, the present results in Klang estuary were also compared with the Kuala Selangor mangrove forest, where the Cu and As elements in the estuary were found to be higher than that of the forest area. A comparison with previous studies shows that the persistence of heavy metals such as Cu and As indicates that the source of pollution is from an anthropogenic source and is still ongoing, and that it may come from activities such as mining and smelting, fuel processing and combustion, wood protection, chemical production and application, and disposal and combustion of municipal and industrial wastes (Popovic et al., 2001; Wang and Mulligan, 2006).
grade. Perin et al. (1985) suggested the use of risk assessment code (RAC) to evaluate metal contamination in soil, by deploying the equation below:
RAC =
F1 ×100%. Total heavy metal concentration
Where, F1 equals Fraction step 1 (Li et al., 2016; Pan et al., 2013). Perin et al. (1985) classified RAC scale as follows: < 1%: no risk, 1–10%: low risk, 11–30%: medium risk, 31–50%: high risk, and RAC > 50%: very high risk. 2.7. Statistical analyses The SPSS software v23.0 was utilized to characterize the relationships between metals in the mangrove soil. The correlation between physical properties and heavy metals was tested using Pearson's correlation.
3.3. Heavy metal sequential extraction The chemical partitioning of modified BCR sequential extraction for each heavy metal is presented in Fig. 2. The heavy metal fractions in Klang estuary sediments are summarized below: The fractionation profile of Mn shows that it is typically bound to oxidizable fraction, residual fraction, and acid solubles, the rest being associated with the reducible fraction, suggesting create risk to marine life (Jain et al., 2007). Tessier et al. (1979) suggested that the relatively high degree of acid soluble Mn indicated that the metal exists in reduced state. Furthermore, Mn (II) oxidisation may be much slower than Fe (II) oxidisation in most natural waters (Tessier et al., 1979). This fraction is sensitive to pH changes, and metal release is achieved through dissolution of a fraction of the solid material at pH close to 5. The high bioavailability, mobility and potential toxicity of acid-soluble metals in aquatic organisms are of great concern. The fractionation profile of Cu showed the higher part of it being bound to the residual fraction in the surface sediments of Klang estuary. The Cu speciation distribution was in this order: Residual fraction > Oxidizable fraction > Acid soluble > Reducible fraction. An important part of Cu also correlates with the acid soluble fraction whereby it is easily available for aquatic organisms (Gadde and Laitinen, 1974). In Zn, the fractionation profile indicated that the acid soluble fraction is the most significant. This is followed by reducible fraction, where it could be associated with the Fe–Mn hydroxide fraction. The percentage of Zn speciation distribution was in the order: Acid soluble > Reducible fraction > Residual fraction > Oxidizable fraction. The acid soluble fraction is generally available in sediment. In addition, sediment also has the carbonate portion in the form of association with metallic element. The relatively high stability of ZnCO3 and co-precipitation with CaCO3 describes the link of heavy metals that may instantly remobilized in any environmental changes like salinity, redox, pH, etc. (Deurer et al., 1978; Kumar et al., 2012). As for As, the major part was bound to the residual fraction followed by Oxidizable fraction > Reducible fraction > Acid soluble. Kumar et al. (2012) indicated that a significant level of Oxidizable fraction shows that As is naturally bound to the sediments. Heavy metals in the residual fraction (R) are chemically stable and biologically inactive. The greater the percentage of metals present in this fraction, the smaller the risk of the metals because this portion of the metals cannot be re-released to water under normal conditions. Finally, Pb shows that the residual fraction is the major part in its fractionation profile. The Pb speciation distribution was in the order: Residual fraction > Oxidizable fraction > Reducible fraction > Acid soluble. A minor amount of the lead was found to be associated with exchangeable fractions and bound to carbonate fractions, and therefore does not pose a risk to aquatic life.
3. Results and discussion 3.1. Physical parameters Table 4 shows the mean, standard deviation, and minimum and maximum variation of physical parameters in Klang estuary. According to Table 4, the pH is neutral in the mangrove sediment with a mean pH of 7.01 in the study area. The neutral pH is probably due to the frequent tidal flooding, preventing it from becoming acidic in reducible condition (Li et al., 2007). The metallic cations show higher activity and mobility at lower pH. Electrical conductivity (EC) shows a mean of 9.50 ± 1.90. The grain size content in the sediment and sand was 12% and 80%, respectively. 3.2. Heavy metal distribution in sediment The concentration of Cu, Pb, As, Zn, and Mn in sediments from Klang River estuary is shown in Table 5. The heavy metal mean concentrations of the sediment follow this order: Mn > Zn > Pb > As > Cu. According to SQGs as outlined by (MacDonald et al., 2000) and the guidelines for the protection and the management in of aquatic sediment quality in Ontario, Canada (MacDonald et al., 2000; Persaud et al., 1993), our results indicate that the concentrations of As and Pb exceeded the threshold effect level (TEL) but are still less than the probable effects level (PEL). In addition, the results showed that Mn, Zn, and Cu were lower than the TEL and PEL values, indicating no adverse biological impacts. The high level of As and Pb is due to the nearness of the study area to industrial zones and industrial outlets in Port Klang (Sany et al., 2013). According to previous studies in the same study area, many contaminants such as wastewater, industrial waste, and domestic waste are discharged directly into Klang River (Sasekumar and Chong, 2005). The results were compared with previous studies (Haris and Aris, 2015) in the Klang area, and showed that the Cu and Pb concentration had doubled. This indicates continuing sources of pollution, which will produce further negative effects on the environment. On the other hand, the east coast of Peninsular Malaysia recorded very low value of Table 4 Physical properties, mean, and standard deviation.
pH ECa Grain size (%) Sand (%) a
Minimum
Maximum
Mean
Std. Deviation
6.17 4.50 2.00 80.00
7.86 13.00 20.30 98.00
7.01 9.50 11.68 88.30
0.41 1.90 4.96 5.36
EC = Electrical conductivity. 4
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Table 5 The mean and standard deviation of total metals concentration in mangrove sediments and Sediment Quality Guidelines as reference. Site
Cu (ppm)
Pb (ppm)
As (ppm)
Zn (ppm)
Mn (ppm)
Ref
Klang estuary Port Klang Kelantan river Terengganu river Kuala Selangor estuary a LEL b SEL c TEL d PEL
9.38 ± 10.73 6.77 6.74 5.33 3.55 16 110 31.6 149
37.39 ± 17.22 15.55 20.82 – 76.63 31 250 35.8 128
35.69 ± 27.99 – – – 20.74 6 33 9.79 33
22.46 ± 18.80 48.24 18.67 12.56 28.84 120 820 121 459
105 ± 16.17 136.99 39.4 15.95 153.30 460 1100 – –
This study Haris & Aris (2015) Dasar et al. (2009) Sukri et al. (2018) ELTurk et al. (2018) Persaud et al. (1993) Persaud et al. (1993) MacDonald et al. (2000) MacDonald et al. (2000)
a b c d
LEL = Lowest Effect Level. SEL = Severe Effect Level. TEL = threshold effect level, indicates concentrations below which adverse effects on biota are rarely observed. PEL = probable effects level, indicates concentrations above which adverse effects on biota are frequently observed.
Fig. 2. Heavy metal sequential extraction in Klang estuary, expressed in percentage. (F1) Acid soluble fraction, (F2) Reducible fraction, (F3) Oxidizable fraction, (F4) residual fraction.
significantly polluted by these metals.
3.4. Risk assessment of sediment contamination 3.4.1. Geo-accumulation index (Igeo) Table 6 presented the Igeo values in Klang estuary. The dominance of the heavy metals in the Igeo sediment follows the order: As > Pb > Zn > Cu > Mn. According to the classification by Muller (1969), Igeo for As, Pb, Cu, Mn, and Zn in the sediments belongs to class zero, showing that the sediment in Klang mangrove estuary was not
3.4.2. Contamination factor (CF) Contamination factor (CF) was calculated to determine the contamination status in the sediment of Klang estuary. Table 6 shows the contamination factor result. Our results for contamination factor of a single heavy metal element is as follows: As > Pb > Zn > Cu > Mn. According to Hakanson (1980), these results show a low degree of contamination, except for As, which showed moderate contamination in mangrove sediment. Previous study in surface sediment in Port Klang area indicated a significant impact of heavy metals in Port Klang, marinas, and shipyards in the Klang estuary area, which attributed to the discharge and leaching of protective paint from the port area (Haris and Aris, 2015). This results indicates the rapid absorption of these metals into the sediment particles. There is also discharge of solid waste into the river from the nearby population, as well as sediments from neighboring streams that join the main course of the river (Patel et al., 2017).
Table 6 Evaluation on potential risk of heavy metals pollution and risk assessment in sediments from the Klang estuary. Index
As
Cu
Mn
Pb
Zn
a
0.38 2.74 27.45 4.96%
−5.73 0.037 0.187 28.62%
−6.77 0.013 0.0138 14.23%
−1.83 0.467 2.33 1.38%
−3.98 0.13 0.13 40.93%
Igeo CF Eri d RAC b c
a b c d
Igeo = Geoaccumulation index. CF = Contamination factor. Eri = Potential ecological risk index of a single element. RAC = Risk assessment code.
3.4.3. Potential ecological risk index (PERI) The mean of the potential ecological risk coefficient of PERI is 5
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Table 7 Correlation between soil properties and heavy metals in Klang estuary.
As Zn Pb Cu Mn pH ECa Grain size sand
As
Zn
Pb
Cu
Mn
pH
EC
Grain size
sand
1 .695** .773** 0.354 .620** −0.295 0.221 .493a −0.25
1 .906** .707** .893** −0.354 0.201 .406a −0.397
1 .576** .841** −0.254 0.212 .484a -.453a
1 .570** −0.169 0.12 0.315 −0.31
1 -.407a 0.143 .495a -.510a
1 .411a 0.037 0.065
1 0.198 −0.141
1 -.829**
1
**Correlation is significant at the 0.01 level (2-tailed). ** EC = Electrical conductivity. a Correlation is significant at the 0.05 level (2-tailed).
Assessment Code (RAC) showed a high environmental risk of Zn and Cu and a medium environmental risk of Mn. This research suggests that man-made pollution and contamination in the Klang estuary has been mitigated by the mangrove ecosystem. Therefore, maintaining the mangrove ecosystem is critical to ensure that its ecological role in biofiltering pollutants is protected. Authorities can use this evidence in support of policies for the protection of this unique environment, and for more sustainable development in the Klang coastal area.
follows: As > Pb > Zn > Mn > Cu, where all results were lower than 150, and therefore indicated low ecological risk. Even so, continuous monitoring of this site is suggested because it would benefit the relevant authorities and help them direct their actions toward the protection of this ecosystem. 3.4.4. Risk assessment code (RAC) The RAC results shown in Table 6, indicating that the metal content of Zn, Cu, and Mn in Klang sediment were the highest risk sources, particularly from the physical-chemical changes occurring in the environment. The mean RAC of Zn value is 40.93%, which showed a high ecological hazard release in the area. However, Cu and Mn with mean RAC values of 28.26% and 14.23%, respectively, showed medium environmental risk, while Pb (1.38%) and As (4.96%) showed no environmental risk in the study area. In general, the order of the potential risk is Zn > Cu > Mn > As > Pb.
Author contribution ElTurk collected the samples and analyzed the data. All authors contributed to the manuscript and gave final approval for publication. Acknowledgement The authors are grateful to the Forest Research Institute Malaysia (FRIM) for the financial support (GA006-2017). Appreciation is extended to the Forestry Department of Peninsular Malaysia, and Forest Department of Selangor, for permission to conduct the study.
3.5. Correlation analysis The correlation between the physical properties and metals is presented in Table 7, which shows the degree of correlation between As, Zn, Pb, Mn, and Cu and the related soil physical properties i.e. grain size, sand, pH and EC). A significant correlation was found between Pb, As, Cu, Mn, and Zn due to their anthropogenic origin while pH was negatively correlated with heavy metals. The study area recorded low pH, therefore, the metallic cations are more mobile and show more activity. Electrical conductivity (EC) was positively correlated with all studied metals. Heavy metals gain increased adsorption capacity as pH increases, because of the reduced competition with H+ ions and increase in negative surface potential.
References Alloway, B.J., 2013. Sources of Heavy Metals and Metalloids in Soils, Heavy Metals in Soils. Springer, pp. 11–50. Belkhiri, L., Mouni, L., Narany, T.S., Tiri, A., 2016. Evaluation of potential health risk of heavy metals in groundwater using the integration of indicator kriging and multivariate statistical methods. Groundwater for Sustainable Development 4, 12–22. Caccia, V.G., Millero, F.J., Palanques, A., 2003. The distribution of trace metals in Florida Bay sediments. Mar. Pollut. Bull. 46, 1420–1433. Casas, J., Rosas, H., Solé, M., Lao, C., 2003. Heavy metals and metalloids in sediments from the Llobregat basin, Spain. Environ. Geol. 44, 325–332. Cox, M.E., Preda, M., 2005. Trace metal distribution within marine and estuarine sediments of western Moreton Bay, Queensland, Australia: relation to land use and setting. Geogr. Res. 43, 173–193. Davari, A., Danehkar, A., Khorasani, N., Poorbagher, H., 2010. Heavy metal contamination of sediments in mangrove forests of the Persian Gulf. J. Food Agric. Environ. 8, 1280–1284. de Vallejuelo, S.F.-O., Arana, G., de Diego, A., Madariaga, J.M., 2010. Risk assessment of trace elements in sediments: the case of the estuary of the Nerbioi–Ibaizabal River (Basque Country). J. Hazard Mater. 181, 565–573. Deurer, R., Förstner, U., Schmoll, G., 1978. Selective chemical extraction of carbonateassociated metals from recent lacustrine sediments. Geochem. Cosmochim. Acta 42, 425–427. Díaz-de Alba, M., Galindo-Riano, M.D., Casanueva-Marenco, M.J., García-Vargas, M., Kosore, C.M., 2011. Assessment of the metal pollution, potential toxicity and speciation of sediment from Algeciras Bay (South of Spain) using chemometric tools. J. Hazard Mater. 190 (1–3), 177–187. Dasar, K.N., Ahmad, A., Mushrifah, I., Shuhaimi-Othman, M., 2009. Water quality and heavy metal concentrations in sediment of Sungai Kelantan, Kelantan, Malaysia: a baseline study. Sains Malays. 38 (4), 435–442. Department of Statistics, 2018. General Report of Population Census. Kuala Lumpur, Malaysia. EPA-ROC, 1994. The Standard Methods for Determination of Heavy Metals in Soils and Plants. National Institute of Environmental Analysis of EPA-ROC, Taipei, Taiwan, ROC (In Chinese). ELTurk, M., Abdullah, R., Rozainah, M.Z., Bakar, N.K.A., 2018. Evaluation of heavy metals and environmental risk assessment in the Mangrove Forest of Kuala Selangor
4. Conclusion The heavy metals that posed the greatest threat to the Klang mangrove ecosystem that can be found in the sediments of Klang estuary, were As and Pb. Important correlations were observed between the heavy metals in the Klang mangrove sediments of the study area, indicating common contamination sources. The heavy metals assessed were strongly attributed to anthropogenic sources, such as industrial outlets and urban discharges. The risk assessment index used for this evaluation showed consistency in assessing the situation of sediment quality in Klang estuary as compared to the sediment quality guidelines. The results indicate that the remaining mangrove areas was not severely threatened by the pollutants and although the concentration of As sediment samples was higher than PEL, this is not predicted to affect the marine organisms in the study area and the heavy metal level is still below the level at which it would pose a risk to the environment. The risk assessment for sediment i.e. PERI, CF and Igeo results, showed a low heavy metal risk to the environment of Klang estuary. Also, the Risk 6
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7
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Heavy metal contamination in mangrove sediments in Klang estuary, Malaysia: Implication of risk assessment
T
Mohammed ELTurka, Rosazlin Abdullaha,b, Rozainah Mohamad Zakariaa,c,∗, Nor Kartini Abu Bakard a
Institute of Biological Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia Centre for Research in Biotechnology for Agriculture, University of Malaya, 50603, Kuala Lumpur, Malaysia c Institute of Ocean and Earth Science, University of Malaya, 50603, Kuala Lumpur, Malaysia d Department of Chemistry, University of Malaya, 50603, Kuala Lumpur, Malaysia b
A R T I C LE I N FO
A B S T R A C T
Keywords: Pollution Soil Port Klang Selangor Ecological risk assessment Mobility heavy metals
Deterioration of environmental quality in estuaries leads to a high risk of destruction of marine life. Heavy metal (Mn, As, Cu, Zn, and Pb) concentration in mangrove sediment in the Klang estuary was evaluated to identify any potential risk of contamination resulting from the rapid development along the Klang river. Several indicators were used to evaluate the pollution status: geo-accumulation (Igeo), contamination factor (CF), degree of contamination (DC), potential ecological risk index (PERI) and risk assessment code (RAC). The results indicate that As and Pb concentrations exceeded the threshold effect level, while Mn, Zn, and Cu posed no adverse biological impacts. In addition, Igeo, CF, and PERI values showed minimal risk of heavy metal pollution in the study area. However, RAC for Zn exhibited high environmental risk. On the other hand, Cu and Mn showed medium environmental risk, while Pb and As showed no environmental risk. These results can increase awareness of the mitigating action that mangrove forests naturally provide against pollution, and provide information to design future plans and policies towards more sustainable development in the area.
1. Introduction Rapid development has many benefits, but it also poses a big challenge to maintaining the environment (Kaewtubtim et al., 2016). Environmental pollution poses a major threat to the natural environment (Rothlin and McCann, 2016). Reports show that in recent decades, the marine environment has been continuously loaded with various hazardous chemical pollutants from human activities, severely affecting environmental quality (de Vallejuelo et al., 2010; Karbassi et al., 2008; Miola et al., 2016). Heavy metals enter the environment through natural sources, for example erosion and weathering of rocks, as well as anthropogenic sources, for example, wastewater, agricultural and industrial activities (Alloway, 2013; Belkhiri et al., 2016; Li et al., 2016). The major sources of pollution are industry, mining, agriculture, and wastewater (Cox and Preda, 2005). Research notes that areas with industrial activities and human activities always have high concentrations of heavy metals (Caccia et al., 2003). Heavy metal concentration can pose a threat to the ecosystem and its adjacent environment. The impact is more pronounced in river estuaries and their associated mangrove forests and adjoining marine environment (de Vallejuelo
∗
et al., 2010; Marchand et al., 2006). Sediment assessment is more conservative compared to water assessment because sediments retain contaminants (Casas et al., 2003; Singh et al., 2005), which can reflect the historical variation of the pollutants (Olubunmi and Olorunsola, 2010; Singh et al., 2005). The Mangrove forest ecosystems are an essential intertidal zone in secured estuarine shores and can mainly be found in tropical and subtropical areas (Tam and Wong, 2000). They provide valuable resources for economic benefit as well as shoreline protection (Tamin et al., 2011). They are also very sensitive ecosystems, and many Mangrove zones across the world are declining or being lost because of contamination (Davari et al., 2010). The mangrove forests in the Klang area, which is located on the Straits of Malacca, is among the ecologically exposed zones that are threatened by human activities, such as chemical hazards, industries, shipping, fisheries, tourism, human settlements, and logging, all of which result in contamination and deterioration of environmental quality (Sany et al., 2013). The Klang estuary is at the end of Klang River that runs through the capital city of Malaysia, Kuala Lumpur, and is home to the largest and busiest port of Malaysia, Port Klang.
Corresponding author. Institute of Biological Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia. E-mail address: [email protected] (R. Mohamad Zakaria).
https://doi.org/10.1016/j.ecss.2019.106266 Received 30 January 2019; Accepted 23 June 2019 Available online 24 June 2019 0272-7714/ © 2019 Elsevier Ltd. All rights reserved.
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However, patches of mangrove forests still exist at the fringing coastal areas of the estuary, despite the overwhelming growth of human population and continuous development. Although quite a number of studies have investigated the concentration of heavy metals in this area, none have assessed the potential risk. A lack of studies on risk assessment and the mobility of heavy metals in the Klang estuary could lead to inadequate environmental management, as not enough information is available. Thus, it is necessary to evaluate the concentration and distribution of heavy metals in this critical area, so as to understand the present condition of its ecosystem and to compile baseline data for future monitoring. Many sediment quality guidelines have been designed to protect the environment from the harmful and toxic effects of sediment-bound pollutants (McCready et al., 2006). These guidelines measure the degree to which the chemical condition of sediments can have an adverse effect on aquatic organisms and are designed to evaluate the quality of the sediments. They are also used to group contaminated areas and prioritize them for further investigation (Díaz-de Alba et al., 2011). The study aims to investigate the concentration and deposition of selected heavy metals, i.e. Mn, As, Cu, Zn and Pb, in the mangrove sediment around the Klang estuary in Peninsular Malaysia, to elucidate the mobility of heavy metals and to evaluate the impact of anthropogenic activities on the coastal mangrove forest ecosystem. Information gathered in this study can enhance the understanding of heavy metal contamination in the Klang mangrove ecosystem. This information is important for decision makers involved in coastal ecosystem management in Malaysia, and will enable effective monitoring of both environmental quality and sustainable development.
Table 1 GPS Sampling points. Station No.
Latitude (N)
Longitude (E)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
3° 0′4.88" 3° 0′4.72" 3° 0′4.58" 3° 0′4.51" 3° 0′5.30" 3° 0′5.34" 3° 0′5.14" 3° 0′5.13" 3° 0′5.94" 3° 0′6.01" 3° 0′5.71" 3° 0′5.80" 2°59′30.34" 2°59′30.17" 2°59′30.12" 2°59′30.03" 2°59′29.64" 2°59′29.50" 2°59′29.18" 2°59′29.32" 2°59′28.97" 2°59′28.82" 2°59′28.62" 2°59′28.74"
101°22′19.81" 101°22′19.00" 101°22′18.36" 101°22′17.63" 101°22′19.64" 101°22′18.95" 101°22′18.17" 101°22′17.41" 101°22′19.48" 101°22′18.68" 101°22′17.87" 101°22′17.12" 101°22′32.93" 101°22′31.71" 101°22′30.01" 101°22′28.99" 101°22′33.13" 101°22′31.86" 101°22′30.20" 101°22′29.11" 101°22′33.30" 101°22′31.98" 101°22′30.39" 101°22′29.31"
2. Materials and methods 2.1. Study area This study was conducted in the remaining mangrove patches of the Klang estuary, close to Port Klang, as shown in Fig. 1 and GPS coordinators in Table 1. The area is characterized by massive and rapid
Fig. 1. Map of Peninsular Malaysia and the study area (Klang estuary). 2
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commercial development including ports, transportation, industrial areas, commercial and residential areas, tourism activities, and fisheries. The population of Klang District is 879,867 and the Port Klang forms an important part of the economy of the state of Selangor. It is home to about 95 shipping companies and agents, 300 custom brokers, 25 container storage centers, as well as more than 70 freight and transport companies. It handled almost 50% of Malaysia's sea-borne container trade in 2013 (Department of Statistics, Malaysia. 2018). The Klang River Basin covers an area of 1288 km2 and is approximately 120 km in length. The upper catchment is mountainous, and is still partly covered by tropical forest, and flows through the metropolitan city of Kuala Lumpur and eventually drains into the Straits of Malacca. The range of mean annual rainfall is 1701–1710 mm, while the range of mean annual temperature is 27.3–27.7 °C (Hemati et al., 2017).
Table 2 NIST SRM 2586 value. No
Element
Certified value (mg/kg)
Mean of observed value (mg/ kg)
% Recovery
1 2 3 4 5
Cu Pb Mn As Zn
81 432 1000 8.7 352
88.83 ± 3.7 370 ± 8.3 917.54 ± 3.62 9.5 ± 0.9 286 ±
109.66 85.64 91.75 109.19 81.25
Table 3 The standard recovery values in the sediment sample.
2.2. Sediment sampling collection A total of 24 sediment samples (0–20 cm deep, triplicates for each sample), from sampling points in the study area within the remaining mangrove patches at the Klang estuary, were collected by utilizing auger tools. The reason to collect surface sediments so that the effects of recent depositional events (e.g., flooding or dredging) can clearly be delineated to test their influence on the contamination in the sediments (Smodis et al., 2003). The samples were kept in ziplock plastic bags under 4 °C and then transferred directly to the laboratory. In the lab, the samples were homogenized, air dried, sieved through 0.63-μm (to obtain heavy metal digestion and fractionation analysis) and 2-mm (to obtain physical properties) sieves, and kept for further analysis.
No.
Cu
Pb
As
Zn
Mn
F1 (acid soluble) F2 (reducible fraction) F3 (oxidizable fraction) F4 (residual fraction) sum F Total element Recovery
2.12 1.92 2.89 4.13 11.06 9.38 117.91
0.48 6.76 6.86 30.59 44.69 37.39 119.52
1.32 1.34 10.55 16.61 29.82 35.69 83.55
10 5.32 2.07 5.27 22.66 22.46 100.89
14.01 5.69 56.26 29.26 105.22 105 100.20
was used as a standard to check against the accuracy of the analysis. In this method, 1 g of standard reference material (SRM 2586) was digested in the same way the soil samples were digested, and as reported in Table 2. The sequential extraction recovery is presented in Table 3. The following equation is then applied to measure the Sequential exF1 + F2 + F3 + F4 traction recovery. Total element × 100%. 2.6. Risk assessment methods
2.3. Physical parameter
In order to compare current heavy metals level with local background values, and to understand the anthropogenic effects, Geoaccumulation Index (Igeo) was recommended (Müller, 1969); whereby Igeo = log2 Cn/1.5 Bn, where Cn is the concentration of metals in the test zone and Bn is the geochemical background in the upper crust (Turekian and Wedepohl, 1961). Muller (1969) interpreted the classification as follows: ≤ 0: uncontaminated sediment; 0–1: unpolluted sediment to moderately polluted; 1–2: moderately polluted; 2–3: Moderately polluted to highly polluted; 3–4: highly polluted; 4–5: highly polluted to extremely polluted; and > 5 indicates extremely polluted. Hakanson (1980) proposed Contamination Factor (CF) to assess the level of soil metal concentrations. The factor was determined as the division between metals level in the sample by the background value of heavy metals (Turekian and Wedepohl, 1961) (Hakanson, 1980). Contamination Factor (CF) is interpreted as follows: < 1: no or low contamination; 1–3: moderate contamination; 3–6: considerable contamination; and CF > 6 indicates very high contamination. Potential Ecological Risk Index (PERI) was also established by Hakanson (1980). The PERI formula indicates the heavy metal proprieties and the environmental behavior of metal pollution in the sediment. To calculate PERI, the following equations are used:
The pH of the homogenized sediment samples was analyzed using a glass electrode pH meter (EUTECH, 2700). Electrical conductivity was monitored using an EC meter (Hanna instruments HI, 2315). Sediment texture was analyzed using Particle Size Analyzer (PSA, Coulter, model L 230). 2.4. Analytical methods Concentration of Mn, As, Cu, Zn, and Pb was determined following EPA-ROC (1994) method, where a 0.5 g sediment sample was digested by Aqua Regia technique. The final digested mixture was filtered through Whatman filter paper, and the heavy metal concentration was detected using ICP- MS Agilent 7500. For the speciation study, a four-stage BCR (European Community Bureau of Reference) sequential extraction, modified by Rauret et al. (1999), was conducted to examine the mobility of metals in the soil. The BCR fractions were determined via four steps: Step one (F1): making an acid soluble fraction by adding 40 ml acetic acid to 1-g sediment and shaking for 16 h. Step two (F2): making a reducible fraction by adding 40 ml hydroxyl ammonium chloride to residual F1 and shaking for another 16 h. Step three (F3): making an oxidizable fraction by adding 10 ml hydrogen peroxide (30%) to residual F2, which is then digested at room temperature for 1 h. Another 10 ml of hydrogen peroxide was further added to this residue, which was digested at 85 °C using a water bath for 1 h. Finally, 50 ml of ammonium acetate, was added at pH = 2 and shaken for 16 h. Step four (F4): making a residual fraction using aqua regia, sequential extraction and analysis by ICP-MS, Agilent 7500.
Cif = Cis/Cin
(1)
Eri =
(2)
Tir
x
Cif
PERI = ∑
Eri
Cif =
(3) Cis
each element contamination factor, = content of eleWhere, ment in samples, Cin = reference value of element, Eri = potential ecological risk index of a single element, and Tir = biological toxicity of a single element (As = 10, Cu = 5, Pb = 5, Zn = 1 and Mn = 1) (Soliman et al., 2015). According to the classification by Hakanson (1980): if PERI < 150 = low grade; 150 ≤ PERI < 300 = moderate grade; 300 ≤ PERI < 600 = severe grade; and PERI > 600 = serious
2.5. Quality analysis As shown in Table 2, the Standard Reference Material (SRM), developed by the National Institute of Standards and Technology (USA), 3
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Cu, Mn and Zn compared to the current study site. This is anticipated as the east coast region is not heavily industrialized, unlike the west coast region (Dasar et al., 2009; Sukri et al., 2018). This shows that the west coast posed a significant threat to the environment. Additionally, the present results in Klang estuary were also compared with the Kuala Selangor mangrove forest, where the Cu and As elements in the estuary were found to be higher than that of the forest area. A comparison with previous studies shows that the persistence of heavy metals such as Cu and As indicates that the source of pollution is from an anthropogenic source and is still ongoing, and that it may come from activities such as mining and smelting, fuel processing and combustion, wood protection, chemical production and application, and disposal and combustion of municipal and industrial wastes (Popovic et al., 2001; Wang and Mulligan, 2006).
grade. Perin et al. (1985) suggested the use of risk assessment code (RAC) to evaluate metal contamination in soil, by deploying the equation below:
RAC =
F1 ×100%. Total heavy metal concentration
Where, F1 equals Fraction step 1 (Li et al., 2016; Pan et al., 2013). Perin et al. (1985) classified RAC scale as follows: < 1%: no risk, 1–10%: low risk, 11–30%: medium risk, 31–50%: high risk, and RAC > 50%: very high risk. 2.7. Statistical analyses The SPSS software v23.0 was utilized to characterize the relationships between metals in the mangrove soil. The correlation between physical properties and heavy metals was tested using Pearson's correlation.
3.3. Heavy metal sequential extraction The chemical partitioning of modified BCR sequential extraction for each heavy metal is presented in Fig. 2. The heavy metal fractions in Klang estuary sediments are summarized below: The fractionation profile of Mn shows that it is typically bound to oxidizable fraction, residual fraction, and acid solubles, the rest being associated with the reducible fraction, suggesting create risk to marine life (Jain et al., 2007). Tessier et al. (1979) suggested that the relatively high degree of acid soluble Mn indicated that the metal exists in reduced state. Furthermore, Mn (II) oxidisation may be much slower than Fe (II) oxidisation in most natural waters (Tessier et al., 1979). This fraction is sensitive to pH changes, and metal release is achieved through dissolution of a fraction of the solid material at pH close to 5. The high bioavailability, mobility and potential toxicity of acid-soluble metals in aquatic organisms are of great concern. The fractionation profile of Cu showed the higher part of it being bound to the residual fraction in the surface sediments of Klang estuary. The Cu speciation distribution was in this order: Residual fraction > Oxidizable fraction > Acid soluble > Reducible fraction. An important part of Cu also correlates with the acid soluble fraction whereby it is easily available for aquatic organisms (Gadde and Laitinen, 1974). In Zn, the fractionation profile indicated that the acid soluble fraction is the most significant. This is followed by reducible fraction, where it could be associated with the Fe–Mn hydroxide fraction. The percentage of Zn speciation distribution was in the order: Acid soluble > Reducible fraction > Residual fraction > Oxidizable fraction. The acid soluble fraction is generally available in sediment. In addition, sediment also has the carbonate portion in the form of association with metallic element. The relatively high stability of ZnCO3 and co-precipitation with CaCO3 describes the link of heavy metals that may instantly remobilized in any environmental changes like salinity, redox, pH, etc. (Deurer et al., 1978; Kumar et al., 2012). As for As, the major part was bound to the residual fraction followed by Oxidizable fraction > Reducible fraction > Acid soluble. Kumar et al. (2012) indicated that a significant level of Oxidizable fraction shows that As is naturally bound to the sediments. Heavy metals in the residual fraction (R) are chemically stable and biologically inactive. The greater the percentage of metals present in this fraction, the smaller the risk of the metals because this portion of the metals cannot be re-released to water under normal conditions. Finally, Pb shows that the residual fraction is the major part in its fractionation profile. The Pb speciation distribution was in the order: Residual fraction > Oxidizable fraction > Reducible fraction > Acid soluble. A minor amount of the lead was found to be associated with exchangeable fractions and bound to carbonate fractions, and therefore does not pose a risk to aquatic life.
3. Results and discussion 3.1. Physical parameters Table 4 shows the mean, standard deviation, and minimum and maximum variation of physical parameters in Klang estuary. According to Table 4, the pH is neutral in the mangrove sediment with a mean pH of 7.01 in the study area. The neutral pH is probably due to the frequent tidal flooding, preventing it from becoming acidic in reducible condition (Li et al., 2007). The metallic cations show higher activity and mobility at lower pH. Electrical conductivity (EC) shows a mean of 9.50 ± 1.90. The grain size content in the sediment and sand was 12% and 80%, respectively. 3.2. Heavy metal distribution in sediment The concentration of Cu, Pb, As, Zn, and Mn in sediments from Klang River estuary is shown in Table 5. The heavy metal mean concentrations of the sediment follow this order: Mn > Zn > Pb > As > Cu. According to SQGs as outlined by (MacDonald et al., 2000) and the guidelines for the protection and the management in of aquatic sediment quality in Ontario, Canada (MacDonald et al., 2000; Persaud et al., 1993), our results indicate that the concentrations of As and Pb exceeded the threshold effect level (TEL) but are still less than the probable effects level (PEL). In addition, the results showed that Mn, Zn, and Cu were lower than the TEL and PEL values, indicating no adverse biological impacts. The high level of As and Pb is due to the nearness of the study area to industrial zones and industrial outlets in Port Klang (Sany et al., 2013). According to previous studies in the same study area, many contaminants such as wastewater, industrial waste, and domestic waste are discharged directly into Klang River (Sasekumar and Chong, 2005). The results were compared with previous studies (Haris and Aris, 2015) in the Klang area, and showed that the Cu and Pb concentration had doubled. This indicates continuing sources of pollution, which will produce further negative effects on the environment. On the other hand, the east coast of Peninsular Malaysia recorded very low value of Table 4 Physical properties, mean, and standard deviation.
pH ECa Grain size (%) Sand (%) a
Minimum
Maximum
Mean
Std. Deviation
6.17 4.50 2.00 80.00
7.86 13.00 20.30 98.00
7.01 9.50 11.68 88.30
0.41 1.90 4.96 5.36
EC = Electrical conductivity. 4
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Table 5 The mean and standard deviation of total metals concentration in mangrove sediments and Sediment Quality Guidelines as reference. Site
Cu (ppm)
Pb (ppm)
As (ppm)
Zn (ppm)
Mn (ppm)
Ref
Klang estuary Port Klang Kelantan river Terengganu river Kuala Selangor estuary a LEL b SEL c TEL d PEL
9.38 ± 10.73 6.77 6.74 5.33 3.55 16 110 31.6 149
37.39 ± 17.22 15.55 20.82 – 76.63 31 250 35.8 128
35.69 ± 27.99 – – – 20.74 6 33 9.79 33
22.46 ± 18.80 48.24 18.67 12.56 28.84 120 820 121 459
105 ± 16.17 136.99 39.4 15.95 153.30 460 1100 – –
This study Haris & Aris (2015) Dasar et al. (2009) Sukri et al. (2018) ELTurk et al. (2018) Persaud et al. (1993) Persaud et al. (1993) MacDonald et al. (2000) MacDonald et al. (2000)
a b c d
LEL = Lowest Effect Level. SEL = Severe Effect Level. TEL = threshold effect level, indicates concentrations below which adverse effects on biota are rarely observed. PEL = probable effects level, indicates concentrations above which adverse effects on biota are frequently observed.
Fig. 2. Heavy metal sequential extraction in Klang estuary, expressed in percentage. (F1) Acid soluble fraction, (F2) Reducible fraction, (F3) Oxidizable fraction, (F4) residual fraction.
significantly polluted by these metals.
3.4. Risk assessment of sediment contamination 3.4.1. Geo-accumulation index (Igeo) Table 6 presented the Igeo values in Klang estuary. The dominance of the heavy metals in the Igeo sediment follows the order: As > Pb > Zn > Cu > Mn. According to the classification by Muller (1969), Igeo for As, Pb, Cu, Mn, and Zn in the sediments belongs to class zero, showing that the sediment in Klang mangrove estuary was not
3.4.2. Contamination factor (CF) Contamination factor (CF) was calculated to determine the contamination status in the sediment of Klang estuary. Table 6 shows the contamination factor result. Our results for contamination factor of a single heavy metal element is as follows: As > Pb > Zn > Cu > Mn. According to Hakanson (1980), these results show a low degree of contamination, except for As, which showed moderate contamination in mangrove sediment. Previous study in surface sediment in Port Klang area indicated a significant impact of heavy metals in Port Klang, marinas, and shipyards in the Klang estuary area, which attributed to the discharge and leaching of protective paint from the port area (Haris and Aris, 2015). This results indicates the rapid absorption of these metals into the sediment particles. There is also discharge of solid waste into the river from the nearby population, as well as sediments from neighboring streams that join the main course of the river (Patel et al., 2017).
Table 6 Evaluation on potential risk of heavy metals pollution and risk assessment in sediments from the Klang estuary. Index
As
Cu
Mn
Pb
Zn
a
0.38 2.74 27.45 4.96%
−5.73 0.037 0.187 28.62%
−6.77 0.013 0.0138 14.23%
−1.83 0.467 2.33 1.38%
−3.98 0.13 0.13 40.93%
Igeo CF Eri d RAC b c
a b c d
Igeo = Geoaccumulation index. CF = Contamination factor. Eri = Potential ecological risk index of a single element. RAC = Risk assessment code.
3.4.3. Potential ecological risk index (PERI) The mean of the potential ecological risk coefficient of PERI is 5
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Table 7 Correlation between soil properties and heavy metals in Klang estuary.
As Zn Pb Cu Mn pH ECa Grain size sand
As
Zn
Pb
Cu
Mn
pH
EC
Grain size
sand
1 .695** .773** 0.354 .620** −0.295 0.221 .493a −0.25
1 .906** .707** .893** −0.354 0.201 .406a −0.397
1 .576** .841** −0.254 0.212 .484a -.453a
1 .570** −0.169 0.12 0.315 −0.31
1 -.407a 0.143 .495a -.510a
1 .411a 0.037 0.065
1 0.198 −0.141
1 -.829**
1
**Correlation is significant at the 0.01 level (2-tailed). ** EC = Electrical conductivity. a Correlation is significant at the 0.05 level (2-tailed).
Assessment Code (RAC) showed a high environmental risk of Zn and Cu and a medium environmental risk of Mn. This research suggests that man-made pollution and contamination in the Klang estuary has been mitigated by the mangrove ecosystem. Therefore, maintaining the mangrove ecosystem is critical to ensure that its ecological role in biofiltering pollutants is protected. Authorities can use this evidence in support of policies for the protection of this unique environment, and for more sustainable development in the Klang coastal area.
follows: As > Pb > Zn > Mn > Cu, where all results were lower than 150, and therefore indicated low ecological risk. Even so, continuous monitoring of this site is suggested because it would benefit the relevant authorities and help them direct their actions toward the protection of this ecosystem. 3.4.4. Risk assessment code (RAC) The RAC results shown in Table 6, indicating that the metal content of Zn, Cu, and Mn in Klang sediment were the highest risk sources, particularly from the physical-chemical changes occurring in the environment. The mean RAC of Zn value is 40.93%, which showed a high ecological hazard release in the area. However, Cu and Mn with mean RAC values of 28.26% and 14.23%, respectively, showed medium environmental risk, while Pb (1.38%) and As (4.96%) showed no environmental risk in the study area. In general, the order of the potential risk is Zn > Cu > Mn > As > Pb.
Author contribution ElTurk collected the samples and analyzed the data. All authors contributed to the manuscript and gave final approval for publication. Acknowledgement The authors are grateful to the Forest Research Institute Malaysia (FRIM) for the financial support (GA006-2017). Appreciation is extended to the Forestry Department of Peninsular Malaysia, and Forest Department of Selangor, for permission to conduct the study.
3.5. Correlation analysis The correlation between the physical properties and metals is presented in Table 7, which shows the degree of correlation between As, Zn, Pb, Mn, and Cu and the related soil physical properties i.e. grain size, sand, pH and EC). A significant correlation was found between Pb, As, Cu, Mn, and Zn due to their anthropogenic origin while pH was negatively correlated with heavy metals. The study area recorded low pH, therefore, the metallic cations are more mobile and show more activity. Electrical conductivity (EC) was positively correlated with all studied metals. Heavy metals gain increased adsorption capacity as pH increases, because of the reduced competition with H+ ions and increase in negative surface potential.
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