Assessment of emerging pollutants in Skudai river and its treatability at downstream water treatment plant

Emerging Pollutants (EPs) are synthetic or naturally occurring compounds currently detected in water environment. These chemicals such as surfactants, pharmaceuticals, personal care products (PCPs) and pesticides could cause adverse ecological and human health effects which include alteration of the normal function of endocrine systems of human and animals. With variation of potential sources, determination of their presence is also a difficult and costly. Different treatment technologies to remove the EPs for drinking water have been studied, which include adsorption, chemical oxidation and membrane filtration. Nevertheless, these technologies are relatively costly in terms of capital, operation and maintenance. This study was carried out to identify the best technique in extracting the EPs from water for screening purposes, to assess their presence in River Skudai and to determine the ability of downstream water treatment plant (WTP) to remove the EPs. Identification approach and solvent was carried out through extensive literature review and trial tests. Samples were taken from eight sampling points in Skudai River and five points in the WTP. Samples were pretreated using solid phase extraction (SPE) method and were analysed using Liquid Chromatography-Mass Spectrometry Detection (LCMSQTOF) for the river water sample and using Gas Chromatography-Mass Spectrometry (GCMS) for the treatment plant water sample. It was surmised that the extraction of EPs is largely based on polarity. The acetonitrile and methanol are highly polar solvents that can achieve high yields of EPs. EPs detected in Skudai River can be categorized into three groups, namely pharmaceutical (decylamine, hexadecyl isocyanate, methotrexate, butirosin A, tridodecylamine and 4-vinylcyclohexene), PCPs (tetradecylamine, limonene, oleylamine, and diethanolamine) and EDCs (styrene, ethylbenzene, phthalic and alfa-methyl styrene). The concentration of styrene ranged from 45 µg/L to 203 µg/L with an increasing trend towards downstream of the river. All the EPs detected are classified as toxic and carcinogenic compounds. As for the WTP, the coagulation process successfully removed endosulfan, chlorothalonil, and ethylbenzene while sedimentation removed 50% of benzene, 50% of triazine, along with 100% of ibuprofen and bisphenol A (BPA). Filtration and chlorination process did not remove styrene or triazine. Trihalomethanes (THMs) which are classified as EDCs were formed after chlorination process. Using polynomial multivariate, the removal rate of triazine in the water treatment plant was modelled. A nonlinear regression design was successfully applied to model the response as a polynomial function based on selected independent. Polynomial multivariate was further used to conduct and evaluate the effectiveness of coagulation and sedimentation process. The findings of this study indicate that different types of EPs can be found in Skudai River. While many can be successfully removed in the conventional water treatment plant, more efforts are needed to ensure that the environment and human health are protected from the hazardous EPs

Optimal designs for estimating the control values in multi-univariate regression models

This paper considers a linear regression model with a one-dimensional control variable x and an m-dimensional response vector . The components of are correlated with a known covariance matrix. Based on the assumed regression model, it is of interest to obtain a suitable estimation of the corresponding control value for a given target vector on the expected responses. Due to the fact that there is more than one target value to be achieved in the multiresponse case, the m expected responses may meet their target values at different respective control values. Consideration on the performance of an estimator for the control value includes the difference of the expected response E(yi) from its corresponding target value Ti for each component and the optimal value of control point, say x0, is defined to be the one which minimizes the weighted sum of squares of those standardized differences within the range of x. The objective of this study is to find a locally optimal design for estimating x0, which minimizes the mean squared error of the estimator of x0. It is shown that the optimality criterion is equivalent to a c-criterion under certain conditions and explicit solutions with dual response under linear and quadratic polynomial regressions are obtained.Calibration c-criterion Classical estimator Control value Equivalence theorem Locally optimal design Scalar optimal design