Abstract

A study on contamination status and ecological risk of heavy metals in surface sediment at selected sites on Kelantan River and its nearshore area was carried out. Ten samples along Kelantan River and 25 samples from the nearshore were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) to determine heavy metal concentrations. Sediment samples were also analyzed for particle size compositions, total organic matter and pH content. The average concentrations for As, Cd, Cr, Cu, Mn, Ni, Pb, Zn were 7.38, 1.31, 17.71, 11.40, 507.15, 5.97, 22.61, 32.95 mg/kg for riverine and 14.14, 4.59, 29.79, 14.07, 389.96, 9.65, 62.21, 41.04 mg/kg for nearshore samples respectively. The potential ecological risk index showed stations Bekok, Manek Urai, and RH under considerable risk followed by station Pasir Mas under moderate risk. The pollution load index classified four nearshore sites (KW10, KW17, KW18, KW37) as polluted. The geo-accumulation index (Igeo) categorized moderate contamination for Cd and Pb. The enrichment factor (EF) along the river categorized extremely high enrichment for Cd, and significant enrichment for As, Pb and Mn while Pb and As were under very high and significant enrichment in nearshore areas. Pb, Cu, Zn, As, Ni and Cr showed significant correlations with each other.

INTRODUCTION

Environmental pollution associated with human activities along rivers, estuaries, and coastal waters is emerging as a global dilemma as these areas are vital for human survival. The global sediment flux from river to sea has increased significantly due to intense human activities and has resulted in material transport pattern variation in estuaries and adjacent sea areas. Over the years, anthropogenic activities in the river basin have caused the tropical rainforests to deteriorate. However, due to heavy precipitation sediment flux increases from rivers to sea in tropical areas, complicated biogeochemical cycling processes have resulted in tropical estuaries and adjacent shelf areas (Syvitski et al. 2005).

Sediment, as a carrier of heavy metals and pollution indicator of the aquatic environment, plays an important role in the assessment of metal contamination in natural waters. Heavy metal pollution that is largely caused by rapid industrialization, urbanization and most anthropogenic actions eventually builds up in soil and sediment, known as sinks for heavy metal accumulation (Zhu et al. 2012). However, physicochemical processes and change in environmental conditions, such as acidification, could transfer metal forms from solid to liquid phase to be directly released into the freshwater and, eventually, marine environments. Metal-polluted environments cause heavy metal bioaccumulation in lower trophic levels of the food chain, such as plankton and zoobenthos, which is then biomagnified towards higher trophic levels of the food chain, endangering public health, wild and agricultural flora as well as fauna. Furthermore, overexposure to heavy metals can cause birth defects, cancer, skin lesions, and organ damage as well as mental and physical retardation (Singh & Cameotra 2004). In the current era of rapid urbanization and industrial development, environmental pollution has been a major concern increasingly receiving public awareness concerning the restoration or remediation of metal-contaminated environments.

The Kelantan River runs through Kelantan State, Peninsular Malaysia, which has a population of more than 1.8 million in July 2018. In 1993, the physicochemical conditions of Kelantan River were good (Abas Kutty et al. 2009). However, the Malaysian Department of Irrigation and Drainage (2009) has revealed fair conditions of the Kelantan River and its tributaries, the Galas River and Lebir River, due to high turbidity, total suspended solids and total dissolved solids. Total suspended solids, turbidity and nitrate were detected at levels exceeding the Malaysian Interim Water Quality Standards (INWQS) in 2010 (Yen & Rohasliney 2013). Fish sampled from the Kelantan River were also detected with elevated concentrations of Cd, Ni and Pb but did not exceed the quality standards (Hashim et al. 2014).

A recent study, Wang et al. (2017) found that the Kelantan River had experienced moderate pollution of Pb and Cr metals. Studies to date on heavy metal distribution in the surface sediments of Kelantan River still remain scarce. Moreover, local agriculture, fishery, tourism, logging, and manufacturing industries primarily depend on the Kelantan River and its proximal water bodies for livelihood and population sustenance (Ibbitt et al. 2002). The growing economic importance and deteriorating environmental conditions of the river entail the need for recent in-depth assessment on the comprehensive pollution status of the Kelantan River. This present study aims to analyse the heavy metal distribution and ecological risk in the Kelantan River and its nearshore area.

METHODS

Study area

The Kelantan River (latitudes 4° 40′ to 6° 12′ north, longitudes 101° 20′ to 102° 20′ east), located in north-east Peninsular Malaysia, crosses a large area of the state of Kelantan (Figure 1) and flows directly into the south-west of the South China Sea through the capital city of Kota Bharu with a population of above 300 thousand in the year 2010 according to a decadal report by the Malaysian Department of Statistics (2010). The Kelantan River is 248 km long, 25 m deep with a catchment area of 150 km and 140 km in length and breadth respectively. The catchment area comprises tropical forest, lowland forest, mountains and limestone hills. Primary tropical rainforest mainly covers the upstream while agricultural plantation covers the midstream and downstream. During the rainy season from October to January, the north-east monsoon brings heavy precipitation rainfall and wind waves. During the dry season from February to September, the south-west monsoon reduces rainfall and increases the frequency of smooth sea conditions. The mean annual run-off and sediment load from the river into the sea are 1.8 × 1010 m3 and 2.5 × 106 t respectively.

Figure 1

(a) Riverine and (b) nearshore sampling sites on the Kelantan River.

Figure 1

(a) Riverine and (b) nearshore sampling sites on the Kelantan River.

Sampling location

The sediment sampling exercise was carried out in August 2016; ten and 25 sites were sampled from the Kelantan River and nearshore areas of the Kelantan River (Figure 1). Surface sediment samples of 10 cm depth were collected using a Van Veen grab sampler, sealed in ziplock plastic bags, and kept in an icebox and transported to the laboratory for storage at −20 °C prior to further analysis.

Sample preparation and analysis

Sediment samples were oven-dried at 40° C until a constant dry weight. The oven-dried samples were homogenized using a clean dry pestle and ceramic mortar, then sieved through a stainless steel sieve of 63-μm mesh size. Any form of gravel, coarse debris and other unwanted debris were removed manually using a pair of plastic forceps. A mass of 0.5 g homogenized sediment samples was digested in a 250 mL glass beaker using a 3:1 combination of ultra-pure HCl and HNO3. The digested samples were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES). Reagent blanks, sample replicates and Buffalo River Sediment Reference Material (SRM8704) sourced from the National Institute of Standards and Technology (USA) were analyzed for quality control and the percentage ranged from 82.60% to 101.78% of the certified value. For the quality assurance/control (QA/QC) procedure, all glassware was soaked in 5% nitric acid overnight, rinsed with distilled water and oven-dried to terminate potential contamination.

Loss on ignition (LOI) was used to determine the organic matter (OM) content in the sediment samples. Samples were oven-dried at 40 °C, ground with pestle and mortar, then sieved through a 2 mm mesh. The organic matter content was calculated by measuring weight LOI of the oven-dried sediment samples (LOI%, weight ∼5 g, 4 h at 500 °C). Three grams of dried samples were weighed and transferred into a 15 mL centrifuge tube, after which freshly prepared 2% Calgon solution (20 g sodium hexametaphosphate, warm deionized water topped up to 1 L) was added. The tubes containing the samples were shaken for 16 h at 15,000 rpm using an end-over-end shaker. The particle size was analysed using an MSS Hydro2000MU Particle Size Mastersizer (Malvern Instruments, UK). For the assessment of the pH value of the sediment samples, 10 g of sediment and 25 mL of distilled water were added into a glass beaker with a solid-to-liquid ratio of 1:2.5. The glass beaker was covered with a plastic film and placed in an orbital shaker for 4 h at 175 rpm. A digital electrode pH meter (Model WTW pH 330) was used to determine the pH value.

Ecological risk assessment

Contamination and potential ecological risk index

The potential ecological risk index (PERI) was used to evaluate aquatic pollution control and has been commonly employed in assessing heavy metal contamination of sediment samples (Håkanson 1980). It is used to deduce the potential risk resulting from ecological sensitivity exposure, concentration and toxicity of heavy metals. It is also practiced as a comprehensive potential risk assessment where Eir is the sum of the potential risk of individual metal elements. It represents the sensitivity of the biological community to the toxic substance and shows the potential risk produced by overall contamination. A common method for comparison is for sediment metal concentrations with pre-civilization background levels to be compared with the present-day metal concentration levels in standard earth materials. The abundances of the studied heavy metals as average shale values by Turekian & Wedepohl (1961) were used as the background values in this study. The estimation of PERI was calculated using the following equations: 
formula
where:
  • = contamination factor

  • = measured concentration of heavy metal in surface sediment

  • = concentration of heavy metal in reference natural background. 
    formula
where:
  • = potential ecological risk index value of a single heavy metal pollution

  • = contamination factor

  • = response coefficient for toxicity of a single heavy metal contamination.

Tif is known as the toxic-response factor or coefficient value for a single heavy metal contamination. The response coefficient values for Pb, Cd, Mn, Cu, Zn, As, Ni, and Cr were 5, 30, 1, 5, 1, 10, 5, and 2 respectively. The terminology used for PERI values are as follows: low (PER < 150), moderate (150 ≤ PER < 300), considerable (300 ≤ PER < 600), and very high ecological risk (PER≥ 600) (Håkanson 1980).

Pollution load index

The pollution load index (PLI) gives an estimation of the overall toxicity status of the sample and also an outcome of the contribution of all metals. PLI gives an accessible method to prove the deterioration of the soil conditions resulting from the accumulation of heavy metals. The contamination factor (CF) for each metal is equivalent to the metal concentration in the sediment divided by the base value of that metal. A number of contamination factors were obtained for different metals at each site, and the site pollution index was determined by taking the calculated CF and deriving it from the nth root of the factors multiplied together. In an integrated approach the PLI of the eight metals were calculated to evaluate the sediment quality (Tomlinson et al. 1980). The comparison factor is expressed as the ratio of the mean of the measured concentration with the average shale concentration given by Turekian & Wedepohl (1961), which is employed as global standard reference for uncontaminated sediment. The PLI for each sampling site is the nth root of the number n multiplying the contamination factors (CF) altogether. The PLI value for each single site was calculated as follows: 
formula
where:
  • CF = contamination factor

  • Cmetal = measured concentration of heavy metal in surface sediment

  • Cbackground = concentration of heavy metal in reference natural background

  • n = number of metals.

The PLI value >1 indicates that the area is in a polluted condition and <1 indicates no polluted condition (Tomlinson et al. 1980).

Geo-accumulation index

Geoaccumulation index (Igeo) is a common approach to estimate the enrichment of metal concentration above background concentrations. Igeo has been used to assess current heavy metal concentrations against pre-industrial concentrations. It is commonly used to assess the degree of contamination in soil and sediment. The Igeo method classifies the metal pollution degree in seven enrichment classes based on increasing numerical values for the index. The world average shale values were used as geochemical background values according to Turekian & Wedepohl (1961). The Igeo values were calculated using the following equation: 
formula
where:
  • Cn = measured concentration of heavy metal in surface sediment

  • Bn = geochemical background concentration of the metal (world average shale value)

  • 1.5 = background data caused by lithogenic effects.

The Igeo values were classified as: uncontaminated (<0), uncontaminated to moderately contaminated (0–1), moderately contaminated (1–2), moderately to highly contaminated (2–3), highly contaminated (3–4), highly to very highly contaminated (4–5), very highly contaminated (>5).

Enrichment factor

Enrichment factor (EF) is commonly used in the literature as a way of identifying and quantifying human interference with global element cycles. The concept of normalizing element concentrations to an average crustal value is discussed in detail by Wang et al. (2017). The EF is calculated to diminish the metal variability associated with variations in mud/sand ratios. EF also acts as a convenient tool to plot geochemical trends over large geographical areas which possibly have substantial variations in the mud-to-sand ratios. The EF value was calculated for each element to estimate the anthropogenic impact on heavy metals in the sediment. In this study, Al was used for normalization in order to eliminate the grain size effect (Wang et al. 2017). The EF values of the heavy metals analyzed in this study were calculated using the following equation (Sutherland 2000): 
formula
where:
  • (Me/Al)sample = ratio of the metal (Me) to Al in samples of interest

  • (Me/Al)reference = ratio of metal and Al concentrations in the background.

The EF values are classified as: deficiency to minimal enrichment (<2), moderate enrichment (2–5), significant enrichment (5–20), very high enrichment (20–40), extremely high enrichment (>40) (Wang et al. 2017).

Statistical analysis

Statistical analysis was performed using IBM-SPSS software version 20 (International Business Machines Corporation, USA). The Pearson test was performed to identify significant correlation between heavy metal concentration, organic matter content, and pH value, as well as percentages of clay, silt, and sand.

RESULTS AND DISCUSSION

Physicochemical distribution

The sediment grain size analysis showed that along the Galas River (Kuala Gris and KBU) and Lebir River (Manek Urai and Bekok), the surficial sediments are mainly composed of silt. The sand content gradually decreased overall from the upstream to the downstream. Similarly, the overall clay content reduced moderately from the upstream to the downstream and was found to peak at the Kuala Krai and Bekok stations, at the confluence of both Galas and Lebir rivers. On the other hand, the overall silt content elevated gradually and the highest was recorded at the Tanah Merah station, which contributed 70% to the total sediment composition in that area. In general, the overall pattern for sediment composition was as follows: silt > sand > clay (Figure 2(a)–2(c)). The surface sediments at the river channel were mainly dominated by a silty clay type of sediments. The surface sediments at the nearshore area mainly consisted of silt, with an average percentage of 72.11% overall sediment composition, followed by sand and clay with average percentages of 34.67% and 17.34% respectively (Figure 3(a)–3(c)). The highest sand content was present at the KW33 station and the lowest was found at the KW2 station. The sediments that were near to shore (stations KW2 and KW7) mainly consisted of sand and the clay content increased overall with increasing distance from the shore. The distribution patterns showed that most of the surficial sediments at the nearshore were dominated by clayey silt and the sand content increased as the distance increased from the shore. Regions with denser human population, such as towns, urban districts (Tanah Merah), the capital city (RH, Pasir Mas), and along the Lebir River, harboured higher clay content and were thus comprised of the silty clay type of sediment composition. Other areas along the Kelantan River typically consisted of the silt type of sediment composition. Areas near industrial, residential, fishery, agricultural or aquacultural activities have also been reported to contain relatively more clay than areas dominated by other activities (Sekabira et al. 2012). Logging activities have been present at the upstream area of the Lebir River and Galas River. Also, the local rainfall typical of the tropical rainforest climate is an average annual rainfall of 2,000 mm, reaching 5,000 mm during the monsoon. The logging activity and tropical rainfall could contribute to the excessive silting and discolouration of the Kelantan River. Since heavy metals are absorbed onto clays and other fine-grained materials, the sediment of the Kelantan River that is abundant in clay and silt could become a carrier of heavy metals (Ho et al. 2010).

Figure 2

Distribution of (a) sand, (b) silt, (c) clay, (d) total organic matter content and (e) pH in the riverine and estuarine areas of the Kelantan River.

Figure 2

Distribution of (a) sand, (b) silt, (c) clay, (d) total organic matter content and (e) pH in the riverine and estuarine areas of the Kelantan River.

Figure 3

Distribution of (a) sand, (b) silt, (c) clay, (d) total organic matter content and (e) pH in the nearshore area of the Kelantan River.

Figure 3

Distribution of (a) sand, (b) silt, (c) clay, (d) total organic matter content and (e) pH in the nearshore area of the Kelantan River.

The LOI data revealed the organic matter content (OM), which can be used to qualitatively assess the depositional nature of the sampling location. The OM content in surficial sediment ranged from 0.17% to 1.95% with an average of 0.94% at the Kelantan River and ranged from 0.16% to 3.50% with an average of 1.84% at the nearshore. The OM content decreased gradually from the upstream of the river to the downstream and the enrichment occurred from the estuary (Kuala Besar station) to nearshore stations KW25, KW32 and KW37 with 3.36%, 3.11% and 3.50% OM content (Figures 2(d) and 3(d)). High organic depositions were detected in surface sediments at the capital city, confluence and upstream areas of both tributaries, and adjacent shelf areas, and Wang et al. (2017) also detected higher OM along the adjacent shelf areas. Mildly acidic to neutral pH levels were detected at the tributaries, confluence, midstream, capital city and estuary, whereas alkaline pH levels were detected at coastal marine areas and Tanah Merah, the midstream urban district (Figures 2(e) and 3(e)). The observed trend was expected since the pH in saltwater habitats is typically mildly alkaline but acidic in anthropogenically populated riverine areas (Kulthanan et al. 2013).

Concentration evaluation of heavy metals

The average concentration of heavy metals (in descending order) in Kelantan River and nearshore surface sediment concentrations were as follows: Mn > Zn > Pb > Cr > Cu > As > Ni > Cd, and Mn > Pb > Zn > Cr > As > Cu > Ni > Cd (Figures 4 and 5). In this study, the highest level of Pb was detected at the coastal station KW28 located in front of the estuary (99.50 mg/kg). The high accumulation of Pb at KW28 is suspected to be caused by the entrapment zone that is created from the mixing of freshwater and saltwater. Relatively high Pb levels were detected in the densely populated capital city, Tanah Merah and Kuala Krai urban districts, since urban runoff, sewage effluent, oil spillage, and boating observed at the studied areas could contribute to elevated levels of Pb in the surrounding environment. Comparison of heavy metal concentrations in the Kelantan River from the present study with previous reports revealed an increasing concentration of Pb, implying the need for continuous monitoring of Pb (Table 1).

Table 1

Comparison of average heavy metal concentrations in previous and present studies

MetalAverage concentration (ppm)YearReference
Pb 20.82 1993 Abas Kutty et al. (2009)  
42.22 2014 Wang et al. (2017)  
50.89 2016 This study 
Cd 1.82 1993 Abas Kutty et al. (2009)  
0.04 2014 Wang et al. (2017)  
3.66 2016 This study 
Mn 394.00 1993 Abas Kutty et al. (2009)  
423.44 2016 This study 
Cu 6.74 1993 Abas Kutty et al. (2009)  
16.74 2014 Wang et al. (2017)  
13.30 2016 This study 
Zn 18.67 1993 Abas Kutty et al. (2009)  
47.63 2014 Wang et al. (2017)  
38.73 2016 This study 
Ni 22.06 2014 Wang et al. (2017)  
8.59 2016 This study 
Cr 56.74 2014 Wang et al. (2017)  
26.34 2016 This study 
As 12.21 2016 This study 
MetalAverage concentration (ppm)YearReference
Pb 20.82 1993 Abas Kutty et al. (2009)  
42.22 2014 Wang et al. (2017)  
50.89 2016 This study 
Cd 1.82 1993 Abas Kutty et al. (2009)  
0.04 2014 Wang et al. (2017)  
3.66 2016 This study 
Mn 394.00 1993 Abas Kutty et al. (2009)  
423.44 2016 This study 
Cu 6.74 1993 Abas Kutty et al. (2009)  
16.74 2014 Wang et al. (2017)  
13.30 2016 This study 
Zn 18.67 1993 Abas Kutty et al. (2009)  
47.63 2014 Wang et al. (2017)  
38.73 2016 This study 
Ni 22.06 2014 Wang et al. (2017)  
8.59 2016 This study 
Cr 56.74 2014 Wang et al. (2017)  
26.34 2016 This study 
As 12.21 2016 This study 
Figure 4

Distribution of heavy metals (a) As, (b) Cd, (c) Cr, (d) Cu, (e) Mn, (f) Ni, (g) Pb, and (h) Zn in the riverine and estuarine areas of the Kelantan River.

Figure 4

Distribution of heavy metals (a) As, (b) Cd, (c) Cr, (d) Cu, (e) Mn, (f) Ni, (g) Pb, and (h) Zn in the riverine and estuarine areas of the Kelantan River.

Figure 5

Distribution of heavy metals (a) As, (b) Cd, (c) Cr, (d) Cu, (e) Mn, (f) Ni, (g) Pb, and (h) Zn in the nearshore area of the Kelantan River.

Figure 5

Distribution of heavy metals (a) As, (b) Cd, (c) Cr, (d) Cu, (e) Mn, (f) Ni, (g) Pb, and (h) Zn in the nearshore area of the Kelantan River.

Relatively higher Mn levels were detected at the confluence, Lebir River and nearshore areas closer to the estuary (Figures 4(e) and 5(e)). The Mn content in the Kelantan River was largely attributed to upstream logging activity at Lebir River, which leaches soil minerals and reduces its availability for trees and vegetation to utilize Mn for growth. In contrast, a relatively lower Mn level was detected at the post-ait area. This may be caused by Mn uptake by the mangrove topography on the ait, since Mn is an essential nutrient for plant growth.

The levels of Ni were found to be relatively higher along the Lebir River, at the confluence and in the capital city, as well as relatively distant coastal areas (Figures 4(f) and 5(f)). However, the relatively consistent Ni levels along the river were indicative of mostly lithogenic sources. Nevertheless, the common use of agricultural chemicals, building materials and paints at proximal urban areas could worsen Ni levels, especially without continuous and regular monitoring of heavy metals (Kabata-Pendias & Pendias 1992).

Relatively higher Cu levels were also detected in the capital city riverine area, confluence and upstream tributaries. The upstream logging and mid-to-downstream agricultural activities along the Kelantan River could contribute to elevated Cu levels. Cu is released via plant decomposition, soil exposure, weathering, boating and forest fires (Shaari et al. 2015). Also, negligible Cd was detected along the Galas River, indicating minuscule lithogenic input from natural erosion or weathering. Higher Cd levels were found along the Lebir River and in the capital city area, which are both populated areas. The increased Cd levels could be contributed by proximal boating, mining, smelting and recycling of metals or ores (Ayres et al. 2003). The Cr metal levels were significant along the Galas River and in the capital city area. Elevated Cr levels indicate high release of industrial and urban discharge, including the untreated effluent of the petroleum, textile and agricultural industries (Mohiuddin et al. 2012).

Additionally, higher As levels were detected in urban areas, which were the capital city, Kuala Krai urban district and Lebir sub-district. This finding was indicative of anthropogenic input, whereby arsenic-consuming industries included smelting, wood and glass manufacturing that were observed along the Kelantan River (Brooks 2010). Lastly, higher levels of Zn were detected at the confluence, in the capital city area and along the Galas River. Nearby natural and man-made geography could have contributed to the elevated Zn levels, such as urbanization and agriculture (Ramessur & Ramjeawon 2002). The coastal distribution of heavy metals was mostly concentrated up to a certain distance offshore from the estuary, which may be caused by a common region of particle settling and enhanced particle concentration, known as the entrapment zone or null zone (Kimmerer 1992).

Risk assessment

PERI provides an estimation of the contamination degree of the sediment in the presence of metals other than major elements such as Fe, Al, and Mn. This index is estimated using the toxicity effect of the metals along with the measured concentration of the sediment in comparison with heavy metal reference values in the Earth's crust. The range of the potential ecological risk index (RI) in the study area was found to be between 26.96 and 3,619.78, indicating low to very high ecological risk in the metal-contaminated surface sediment according to the risk classification. The highest RI found at the five nearshore sampling sites (KW3, KW10, KW17, KW18, KW37), which is classified as having very high ecological risk, was apparently due to Cd contribution (Table 2). Cd contamination had caused major freshwater lakes in China to be at moderate potential risk at national scale due to anthropogenic activity discharges. Other sampling sites were found to be in the range of low to moderate ecological risk contamination. Individual single elements (Eir) for other heavy metals (Cr, Pb, As, Cu, Ni, Mn) were lower than 40, suggesting that the sediments showed a low potential risk. However, Cd showed the highest Eir with a range of values of 130.83, contributing 80.5% at the river and 459.50, about 88.56%, at the nearshore respectively (Table 3). The sewage that discharged from cities along the east coast of Peninsular Malaysia has been attributed with a higher concentration of Cd (Rezaee Ebrahim Saraee et al. 2011). The risk posed by individual heavy metals (Eir) at different sampling sites descended in the order of Cd > Cr > As > Pb > Cu > Mn > Ni > Zn for the river and Cd > Cr > Pb > As > Cu > Ni > Mn > Zn for the nearshore. Other metals such as Cr, As and Pb showed mean Eir values of 17.71, 5.68, 5.65 for the river and 29.79, 10.88, 15.55 at the nearshore respectively. The higher levels of Cr in estuary and coastal waters are due to industrial wastewater discharge into the Kelantan River estuary delta in addition to its natural sources. The increment of Pb levels in surficial sediment from river to nearshore indicates the sources of it, originating from natural weathering and also from erosion of upstream materials. Furthermore, the release from agricultural production activities into the river, urbanization at the plain area and industrialization activities at the estuary delta area have also contributed to the elevated Pb levels in the study area (Wang et al. 2017). Agricultural industry and the fertilizer used may also have caused the As contamination in the surficial sediments (Rezaee Ebrahim Saraee et al. 2011).

Table 2

Potential ecological risk index (PERI) and pollution load index (PLI) of heavy metals in the Kelantan River

StationPotential ecological risk index (PERI)
Pollution load index (PLI)
IndexRisk classificationIndexPollution classification
Bekok 376.25 Considerable 0.53 No pollution 
Kuala Gris 36.97 Low 0.00 No pollution 
Tanah Merah 106.17 Low 0.32 No pollution 
Kuala Besar 85.99 Low 0.24 No pollution 
Tok Bali 47.00 Low 0.09 No pollution 
Manek Urai 447.70 Considerable 0.50 No pollution 
Pasir Mas 113.04 Moderate 0.52 No pollution 
RH 342.88 Considerable 0.66 No pollution 
KBU 42.24 Low 0.00 No pollution 
Kuala Krai 26.96 Low 0.00 No pollution 
KW2 53.14 Low 0.00 No pollution 
KW3 1,386.73 Very high 0.76 No pollution 
KW4 586.72 Considerable 0.77 No pollution 
KW5 130.52 Low 0.23 No pollution 
KW7 263.50 Moderate 0.47 No pollution 
KW8 386.77 Considerable 0.56 No pollution 
KW9 143.90 Low 0.64 No pollution 
KW10 3,619.78 Very high 1.09 Polluted 
KW14 51.25 Low 0.00 No pollution 
KW16 58.91 Low 0.00 No pollution 
KW17 1,704.35 Very high 1.37 Polluted 
KW18 1,177.73 Very high 1.01 Polluted 
KW19 408.84 Considerable 0.92 No pollution 
KW21 89.28 Low 0.58 No pollution 
KW22 55.49 Low 0.00 No pollution 
KW23 103.03 Low 0.59 No pollution 
KW24 384.47 Considerable 0.41 No pollution 
KW25 67.99 Low 0.52 No pollution 
KW27 79.67 Low 0.60 No pollution 
KW28 243.31 Moderate 0.80 No pollution 
KW29 51.02 Low 0.25 No pollution 
KW31 123.34 Low 0.72 No pollution 
KW32 68.64 Low 0.00 No pollution 
KW33 85.51 Low 0.29 No pollution 
KW37 1,648.21 Very high 1.02 Polluted 
StationPotential ecological risk index (PERI)
Pollution load index (PLI)
IndexRisk classificationIndexPollution classification
Bekok 376.25 Considerable 0.53 No pollution 
Kuala Gris 36.97 Low 0.00 No pollution 
Tanah Merah 106.17 Low 0.32 No pollution 
Kuala Besar 85.99 Low 0.24 No pollution 
Tok Bali 47.00 Low 0.09 No pollution 
Manek Urai 447.70 Considerable 0.50 No pollution 
Pasir Mas 113.04 Moderate 0.52 No pollution 
RH 342.88 Considerable 0.66 No pollution 
KBU 42.24 Low 0.00 No pollution 
Kuala Krai 26.96 Low 0.00 No pollution 
KW2 53.14 Low 0.00 No pollution 
KW3 1,386.73 Very high 0.76 No pollution 
KW4 586.72 Considerable 0.77 No pollution 
KW5 130.52 Low 0.23 No pollution 
KW7 263.50 Moderate 0.47 No pollution 
KW8 386.77 Considerable 0.56 No pollution 
KW9 143.90 Low 0.64 No pollution 
KW10 3,619.78 Very high 1.09 Polluted 
KW14 51.25 Low 0.00 No pollution 
KW16 58.91 Low 0.00 No pollution 
KW17 1,704.35 Very high 1.37 Polluted 
KW18 1,177.73 Very high 1.01 Polluted 
KW19 408.84 Considerable 0.92 No pollution 
KW21 89.28 Low 0.58 No pollution 
KW22 55.49 Low 0.00 No pollution 
KW23 103.03 Low 0.59 No pollution 
KW24 384.47 Considerable 0.41 No pollution 
KW25 67.99 Low 0.52 No pollution 
KW27 79.67 Low 0.60 No pollution 
KW28 243.31 Moderate 0.80 No pollution 
KW29 51.02 Low 0.25 No pollution 
KW31 123.34 Low 0.72 No pollution 
KW32 68.64 Low 0.00 No pollution 
KW33 85.51 Low 0.29 No pollution 
KW37 1,648.21 Very high 1.02 Polluted 
Table 3

Average potential ecological risk of single heavy metal in the Kelantan River and nearshore sediment

MetalThe value of at Kelantan RiverThe value of at nearshore
Pb 5.65a 15.55a 
Cd 130.83b 459.50c 
Mn 0.60a 0.46a 
Cu 1.27a 1.56a 
Zn 0.35a 0.43a 
As 5.68a 10.88a 
Ni 0.44a 0.71a 
Cr 17.71a 29.79a 
MetalThe value of at Kelantan RiverThe value of at nearshore
Pb 5.65a 15.55a 
Cd 130.83b 459.50c 
Mn 0.60a 0.46a 
Cu 1.27a 1.56a 
Zn 0.35a 0.43a 
As 5.68a 10.88a 
Ni 0.44a 0.71a 
Cr 17.71a 29.79a 

aClassified as low potential ecological risk.

bClassified as considerable ecological risk.

cClassified as very high ecological risk.

On the other hand, the calculated PLI data classified only four nearshore stations (KW10, KW17, KW18, KW37) as polluted (PLI >1), ranging from 1.01 to 1.37. The PLI gives some useful information on the quality of the environment to the inhabitants. It also gives beneficial information on the status of pollution in the area to decision makers. The higher PLI values indicate heavy metal input might be due to urban activities and anthropogenic sources (Yen & Rohasliney 2013). Among the studied metals, the Igeo values show a decreasing order Cd > Pb > As > Mn > Zn > Cu > Cr > Ni for the river that range from −3.72 to 1.40 and Cd > Pb > As > Mn > Zn > Cr > Cu > Ni for the nearshore that range from −3.49 to 1.53. The Igeo values reveal moderate Cd contamination status in the surface sediment of both riverine and nearshore areas, but moderate Pb contamination status at the nearshore area only (Table 4). However, the Igeo for As, Mn, Zn, Cu, Cr, and Ni shows an uncontaminated status of sediment quality for both river and nearshore environments with minimal values.

Table 4

Geo-accumulation index (Igeo) and enrichment factor (EF) of heavy metals in the Kelantan River

MetalGeo-accumulation index (Igeo)
Enrichment factor (EF)
RiverNearshoreRiverNearshore
As −0.90f −0.60f 7.55c 7.11c 
Cd 1.40e 1.53e 69.86a 93.54a 
Cr −2.66f −2.44f 2.17d 1.95e 
Cu −2.61f −2.53f 3.02d 1.70e 
Mn −1.89f −1.84f 7.28c 3.01d 
Ni −3.72f −3.49f 1.02e 0.96e 
Pb −0.57f 1.01e 14.20c 24.94b 
Zn −1.98f −1.89f 4.56d 2.78d 
MetalGeo-accumulation index (Igeo)
Enrichment factor (EF)
RiverNearshoreRiverNearshore
As −0.90f −0.60f 7.55c 7.11c 
Cd 1.40e 1.53e 69.86a 93.54a 
Cr −2.66f −2.44f 2.17d 1.95e 
Cu −2.61f −2.53f 3.02d 1.70e 
Mn −1.89f −1.84f 7.28c 3.01d 
Ni −3.72f −3.49f 1.02e 0.96e 
Pb −0.57f 1.01e 14.20c 24.94b 
Zn −1.98f −1.89f 4.56d 2.78d 

aClassified as very high contamination (Igeo); extremely high enrichment (EF).

bClassified as high to very high contamination; very high enrichment.

cClassified as high contamination; significant enrichment.

dClassified as moderate to high contamination; moderate enrichment.

eClassified as moderate contamination; deficiency to minimal enrichment.

fClassified as no contamination.

Note: there are no values corresponding to the classification of no to moderate contamination.

The distributions of calculated EF for each of the studied metals are displayed in Table 4. A higher EF value indicates severe anthropogenic contribution in contamination. Similarly to the Igeo results, the EF values also show that the surface sediment of most of the study areas were severely enriched with Cd. The calculated EFs show Cd in the river and at the nearshore with values of 69.86 and 93.54 respectively, indicating that the surface sediments in the study area were extremely enriched with this metal. The average EF value for As, Pb and Mn in the river shows a significant enrichment of these metals. The metals in the nearshore area classified under very high and significant enrichment were Pb and As respectively. However, the EF values for Ni, Cu, Zn and Cr were found to be minimally to moderately enriched in surface sediment of both riverine and nearshore areas. An early study on heavy metals in the surface sediment of Kelantan River mainly reported heavy metal input from upstream nature-induced erosion and weathering with minimal anthropogenic influence (Abas Kutty et al. 2009). However, the data of this study revealed that anthropogenic development had discharged metal contaminants into the surrounding river and sea.

A number of calculation methods were developed to quantify the degree of metal enrichment in sediments. Pollution impact scales have been proposed by various authors to convert the calculated numerical outcomes into a broad range of descriptions from high to low intensity. Application of Igeo would be a good approach to identify and quantify metal concentrations compared with EF. Since the overall EF values for the Kelantan River and nearshore were high, application of Igeo would be a better approach. Based on previous studies, EFs may result in indistinct output because of the naturally existing lower concentration of Al and/or higher concentration of heavy metals in sediment. Despite the high EF values in the Kelantan River and nearshore area, the Igeo results still showed that the sediments in this area were still uncontaminated except for the four sampling sites KW10, KW17, KW18 and KW37. Thus, the application of Igeo may provide more accurate results than that of EF.

Correlation of heavy metals and physicochemical properties of sediment

The results of correlation analysis showed that Mn, Cu, Zn and As correlated significantly with clay and silt (Table 5). The tendency of fine-grained sediments to adsorb much more heavy metal due to high specific surface area characteristics results in a higher concentration of heavy metals in sediments. This could suggest a natural origin of these components from weathering and erosion; the metals may have been contributed by agriculture, logging and land development. However, other heavy metals did not show any significant relationship with the finer-sized particles, suggesting that there might be other possible factors that determined their distribution. One of the controlling factors of spatial distribution in the sediments is the organic matter content. The geochemical behavior of heavy metals can be regulated by organic matter content via adsorption, desorption and complexation. In this study, the total organic matter content showed a significant positive correlation with the heavy metals indicating that it might control the distribution of the analyzed metals in the sediments. Also, the significant correlation between total organic matter content and As, Zn, Cu implied a contribution of heavy metals from domestic or agricultural sources.

Table 5

Correlation coefficient between sand, silt, clay, total organic matter content, pH and metals

pHLOIClaySiltSandPbCdMnCuZnAsNiCr
pH             
LOI 0.15            
Clay − 0.389a 0.371a           
Silt −0.115 0.551b 0.674b          
Sand 0.223 − 0.531b − 0.847b − 0.964b         
Pb 0.470b 0.500b −0.110 0.111 −0.040        
Cd −0.041 0.298 0.091 0.110 −0.112 0.239       
Mn 0.005 0.275 0.607b 0.477b 0.563b −0.011 −0.073      
Cu −0.226 0.680b 0.607b 0.611b − 0.659b 0.396a 0.308 0.300     
Zn −0.060 0.693b 0.470b 0.695b − 0.670b 0.444b 0.198 0.294 0.839b    
As 0.104 0.738b 0.397a − 0.449b − 0.467b 0.623b 0.185 0.394a 0.739b 0.731b   
Ni 0.132 0.614b 0.154 0.439b − 0.372a 0.572b 0.260 0.074 0.772b 0.761b 0.623b  
Cr 0.016 0.398a 0.155 0.298 −0.270 0.338a 0.317 0.109 0.650b 0.427a 0.386a 0.739b 
pHLOIClaySiltSandPbCdMnCuZnAsNiCr
pH             
LOI 0.15            
Clay − 0.389a 0.371a           
Silt −0.115 0.551b 0.674b          
Sand 0.223 − 0.531b − 0.847b − 0.964b         
Pb 0.470b 0.500b −0.110 0.111 −0.040        
Cd −0.041 0.298 0.091 0.110 −0.112 0.239       
Mn 0.005 0.275 0.607b 0.477b 0.563b −0.011 −0.073      
Cu −0.226 0.680b 0.607b 0.611b − 0.659b 0.396a 0.308 0.300     
Zn −0.060 0.693b 0.470b 0.695b − 0.670b 0.444b 0.198 0.294 0.839b    
As 0.104 0.738b 0.397a − 0.449b − 0.467b 0.623b 0.185 0.394a 0.739b 0.731b   
Ni 0.132 0.614b 0.154 0.439b − 0.372a 0.572b 0.260 0.074 0.772b 0.761b 0.623b  
Cr 0.016 0.398a 0.155 0.298 −0.270 0.338a 0.317 0.109 0.650b 0.427a 0.386a 0.739b 

aCorrelation is significant at the 0.05 level (2-tailed).

bCorrelation is significant at the 0.01 level (2-tailed).

CONCLUSION

This study showed the deteriorating environmental quality in surface sediments of the Kelantan River and its nearshore area in terms of heavy metal pollution and ecological risk assessment status. The present study showed the higher concentration overall of heavy metals in sediments at the nearshore area compared with the river. Based on the average values of EF and Igeo the contamination degree in the river and at the nearshore was defined as extremely enriched for Cd, significant to very high enrichment for Pb, moderate to significant enrichment for Mn, Cu, Zn, As and moderate to minimal enrichment for Ni and Cr. However, PLI showed that the level of overall sediment pollution as highest at four stations, namely KW10, KW17, KW18, KW37. From the PERI values, each single element has low potential ecological risk and Cd shows higher ecological risk. Organic matter content shows positive correlation with clay and silt content and negative correlation with sand composition. Besides that, significant and positive correlations were observed between organic matter content and Pb, Cu, Zn, As, and Ni. In addition, strong relationships between Cu and Zn (r = 0.84), Zn and Ni (r = 0.76), Ni and Cr (r = 0.74) suggested potential enrichment mechanisms and similar sources for these heavy metals. The heavy metal content in surficial sediments overall showed a decreasing pattern from the river to the nearshore. The heavy metals concentration was observed to peak at the convergence of the river stream (Kuala Krai and Bekok) and also at the low land plain areas (Pasir Mas and RH). The heavy metal pollutants that are among the most enriched, thus requiring monitoring efforts, are Cd, Pb and As. The potential for heavy metal contamination to worsen in the future, by anthropogenic activities and domestic and industrial wastewater influx, calls for attention from the public and scientific community towards continual quality analysis and remediation planning.

ACKNOWLEDGEMENTS

This research is funded by the Geology Department, University of Malaya (GPF017B-2018), Institution Centre of Excellence (HiCOE) Phase II Fund, Ministry of Higher Education (IOES-2014E), Bilateral Cooperation of Maritime Affairs of China (Contract No. HC140502) and China-ASEAN Maritime Cooperation Fund Project ‘Monitoring and conservation of the coastal ecosystem in the South China Sea’.

REFERENCES

REFERENCES
Abas Kutty
A.
Idris
M.
Shuhaimi-Othman
M.
2009
Water quality and heavy metal concentrations in sediment of Sungai Kelantan, Kelantan, Malaysia: a baseline study
.
Sains Malaysiana
38
,
435
442
.
Ayres
R. U.
Ayres
L. W.
Råde
I.
2003
The Life-Cycle of Copper, Its Co-Products and Byproducts
.
Springer
,
Dordrecht, The Netherlands
, pp.
135
141
.
Brooks
W. E
.
2010
Arsenic
. In:
2007 Minerals Yearbook
,
United States Geological Survey (USGS)
,
Washington, DC
,
USA
, pp.
7.1
7.6
.
Department of Irrigation and Drainage
2009
Study on the River Water Quality Trends and Indexes in Peninsular Malaysia. Water Resources Publication 21, Water Resources Management and Hydrology Division, Department of Irrigation and Drainage, Ministry of Natural Resources and Environment, Malaysia. Available from: https://www.water.gov.my/index.php/pages/view/1300 (accessed 7 November 2018)
.
Department of Statistics Malaysia
2010
Population Distribution by Local Authority Areas and Mukims. Available from: https://newss.statistics.gov.my/newss-portalx/ep/epFreeDownloadContentSearch.seam (accessed 7 November 2018)
.
Hashim
R.
Song
T. H.
Muslim
N. Z. M.
Yen
T. P.
2014
Determination of heavy metal levels in fishes from the lower reach of the Kelantan River, Kelantan, Malaysia
.
Tropical Life Sciences Research
25
,
21
39
.
Ho
H. H.
Swennen
R.
Van Damme
A.
2010
Distribution and contamination status of heavy metals in estuarine sediments near Cua Ong Harbor, Ha Long Bay, Vietnam
.
Geologica Belgica
13
,
37
47
.
Ibbitt
R.
Takara
K.
Desa
M. N. M.
Pawitan
H.
2002
Catalogue of Rivers for Southeast Asia and the Pacific
,
Vol. 4
.
UNESCO-IHP
,
Jakarta, Indonesia
.
Kabata-Pendias
A.
Pendias
H.
1992
Trace Elements in Soil and Plants
.
CRC Press
,
Boca Raton, FL, USA
.
Kimmerer
W.
1992
An Evaluation of Existing Data in the Entrapment Zone of the San Francisco Bay Estuary
,
Report FS/BIO-IATR/92-33, Interagency Ecological Studies Program
.
California Department of Water Resources
,
USA
.
Mohiuddin
K. M.
Otomo
K.
Ogawa
Y.
Shikazono
N.
2012
Seasonal and spatial distribution of trace elements in the water and sediments of the Tsurumi River in Japan
.
Environmental Monitoring and Assessment
184
,
265
279
.
Rezaee Ebrahim Saraee
K.
Abdi
M. R.
Naghavi
K.
Saion
E.
Shafaei
M. A.
Soltani
N.
2011
Distribution of heavy metals in surface sediments from the South China Sea ecosystem, Malaysia
.
Environmental Monitoring and Assessment
183
,
545
554
.
Sekabira
K.
Oryem-Origa
H.
Basamba
T. A.
Mutumba
G.
Kakudidi
E.
2012
Grain size and source apportionment of heavy metals in urban stream sediments
. In:
Water Pollution
(
Balkis
N.
, ed.),
IntechOpen
,
Rijeka, Croatia
, pp.
69
88
.
Shaari
H.
Mohamad Azmi
S. N. H.
Sultan
K.
Bidai
J.
Mohamad
Y.
2015
Spatial distribution of selected heavy metals in surface sediments of the EEZ of the east coast of Peninsular Malaysia
.
International Journal of Oceanography
2015
,
618074
.
Singh
P.
Cameotra
S. S.
2004
Enhancement of metal bioremediation by use of microbial surfactants
.
Biochemical and Biophysical Research Communications
319
,
291
297
.
Syvitski
J. P. M.
Vörösmarty
C. J.
Kettner
A. J.
Green
P.
2005
Impact of humans on the flux of terrestrial sediment to the global coastal ocean
.
Science
308
,
376
380
.
Tomlinson
D. C.
Wilson
J. G.
Harris
C. R.
Jeffery
D. W.
1980
Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index
.
Helgoländer Wissenschaftliche Meeresuntersuchungen
33
,
566
575
.
Wang
A. J.
Bong
C. W.
Xu
Y. H.
Hassan
M. H. A.
Ye
X.
Bakar
A. F. A.
Li
Y. H.
Lai
Z. K.
Xu
J.
Loh
K. H.
2017
Assessment of heavy metal pollution in surficial sediments from a tropical river-estuary-shelf system: a case study of Kelantan River, Malaysia
.
Marine Pollution Bulletin
125
,
492
500
.
Yen
T. P.
Rohasliney
H.
2013
Status of water quality subject to sand mining in the Kelantan River, Kelantan
.
Tropical Life Sciences Research
24
,
19
34
.
Zhu
H. N.
Yuan
X. Z.
Zeng
G. M.
Jiang
M.
Liang
J.
Zhang
C.
Yin
J.
Huang
H. J.
Liu
Z. F.
Jiang
H. W.
2012
Ecological risk assessment of heavy metals in sediments of Xiawan Port based on modified potential ecological risk index
.
Transactions of Nonferrous Metals Society of China
22
,
1470
1477
.