Bivalve organisms are more vulnerable to a variety of aquatic pollution. It has high adaptability to various levels of contaminations. It can accumulate pollutants such as heavy metal in its tissues and cause major concern on potential risk of heavy metal especially to the consumers. The objectives of this study were to determine heavy metal (Cu, Zn, Pb and Cd) concentrations in five most consumed and popular bivalve species among Kota Kinabalu community and to compare with the Food Regulation Malaysia (1985) and Food and Agriculture Organization. Five most consumed bivalves species are Meretrix meretrix (Kepah), Anadara granosa (Kerang), Tridacna squamosa (Kima), Polymesoda erosa (Lokan) and Crassostrea gigas (Tiram). Health risk associated with these heavy metals in the five bivalves species were estimated based on target quotients (THQs). The results indicated that the metal concentrations in the bivalves ranged from 3.21 to 36.22 mg/kg for Cu, 28.62-1771.12 mg/kg for Zn, 0.20-3.43 mg/kg for Pb and 0.44-7.27 mg/kg for Cd. These concentrations were significantly correlated with species and the size of bivalves. Some of the heavy metal concentrations exceeded the permissible level by Food Regulation Malaysia (1985) and Food and Agriculture Organization. However, based on the THQs value for adults, only metal concentrations in Crassostrea gigas (Tiram) exceeded, which indicates potential health risks associated with the consumption of this species.
Bootstrap is one of the random sampling methods with replacement, that was proposed to address the problem of small samples whose distributions are difficult to derive. The distribution of bootstrap samples is empirical or free and due to its random sampling with replacement, the probability of choosing a specific observation may be equal to one. Unfortunately, when the original sample data contains an outlier, there is a serious problem that leads to a breakdown OLS (Ordinary Least Squares) estimator, and robust regression methods should be recommended. It is well known that the best robust regression method has a high breakdown point is not more than 0.50, so the robust regression method would break down when the percentage of outliers in the bootstrap sample exceeds 0.50. It is well known that fixed-x bootstrap is resampled the residuals which probably are having outliers. Moreover, the leverage point(s) is an outlier that occurs in X-direction, so the effects of it on fixed-x bootstrap samples would be existence. However, the decision-making about the null hypothesis of bootstrap regression coefficients could not be reliable. In this paper, we propose using weighted fixed-x bootstrap with a probability approach to guarantee the percentage of outliers in the bootstrap samples will be very low. And then weighted M-estimate should be to tackle the problem of outliers and leverage points and taking a more reliable decision about bootstrap regression coefficients hypothesis test. The performance of the suggested method has been tested with others by using real data and simulation. The results show our proposed method is more efficient and reliable than the others.
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