Summary: | Sediment quality receives little concern in aquatic monitoring programs due to its expensive analytical procedures. However, sediment contaminations can influence the overall quality of aquatic environment. Sediment provides habitats to benthic organisms, which some of them are the food sources to human. Due to the acidic condition in digestion track, sediment contaminants can become soluble to the organisms and lead to the occurrence of bioaccumulations. It also serves as diffuse sources of contamination to the overlying water body; slowly releasing the contaminant back into the water column (Marcus, 1991). Therefore, ensuring a good sediment quality is crucial to maintain a healthy aquatic ecosystem, which ensuring good protection of human health and aquatic life. The aim of this study was to analyse and assess the riverbed sediment quality in a typical Malaysian river. Sungai Linggi river basin at Negeri Sembilan had been chosen as the study area because of the multiple developments (commercial, indrustrial, agricultural etc.) within its basin boundary. Metals (Cd, Cr, Co, Fe, Pb, Mn, Hg, Zn, As and Al), nutrients (TKN and TP) and carbon content (loss of ignition) were selected to be the analysis parameters. The result was then being compared with the effect levels in published SQGs. In addition, sediment enrichment factors (SEFs) were calculated to assess the metal contaminants in collected sediment samples with relative to the Al content. SQGs and SEFs provided different conclusions of the major contaminants in the sediment samples. The SQGs approach indicated Hg and As were two major contaminants in this basin because many samples exceeded the severe effect level (SEL) (Hg = 80%; As = 26%). Conversely, Zn and As were the critical contaminants signified by SEFs approach. Percentages of samples with SEFs more than 2 for Zn and As were 56.52% and 43.48% respectively. These differences were expected because using SQGs in sediment quality assessment can be subjective due to the origin of SQGs; while SEFs provide more tailored approach because they were calculated based on the background concentrations of each sample.
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