Simulation of Three PPCPs Existed in Major Pearl River with an ASM Model Including a Separate Degrading Microorganism

Hundreds of different pharmaceuticals and personal care products (PPCPs) are usually detected in urban riverine waters, and have long been attracting attention for their potential toxic effect to river ecosystems and even to human beings. Biodegradation and sorption are known as the main mechanisms to remove PPCPs in activated sludge systems, the characteristics of their removal process in rivers, however, are still less known. We developed an ASM-based process model including aerobic growth and decay of a specific PPCPs-degrading microorganism and try to simulate the transformation and conversion process of three main endocrine-disrupting phenols (nonylphenol, bisphenol-A, triclosan) in Major Pearl River Delta, South China. Firstly we aquired the data of river conditions and substances concentrations, then set up a model by WEST software which contains ASM theories, here the form of river system was simplified. Subsequently conducted parameter calibration and operated the simulating process using steady-state background concentrations. Validation results show that when the river is seen as series of aerobic tanks the removal of PPCPs in the system is similar to that traditional organic matters degrading by activated sludge process, and that the transport fate of PPCPs is sensitive to three model parameters of the hypothesized microorganism: concentration in the system, half saturation coefficient and maximum specific growth rate, and that the main effect of PPCPs removal in river is aerobic growth of organisms and that adsorption can be nearly ignored during the process. The proposed model shows considerable success in capturing important characteristics of the observed PPCPs transformation, thus this method could acquainting the knowledge of PPCPs changes in river aquatic environment by adjusting the model parameters according to practical conditions. Further mechanism analysis should be conducted to optimize the model.