A dynamic water quality index model based on functional data analysis

A water quality index (WQI) incorporates two shortcomings in the dynamic assessment of water quality, namely: (1) the sampling time series must be identical for each indicator and no missing data should occur, and (2) stationary weights cannot represent the changes in the pollutant importance. To solve these problems, the present study introduces the functional data analysis method into WQI research and establishes a dynamic WQI (D-WQI) model. D-WQI is a generalization of the conventional WQI. In the D-WQI model, the changes of water quality and pollutant importance are represented in the form of dynamic functional curves. The generation methods of the concentration curves, sub-index curves, dynamic weight curves, and WQI curves are discussed. As an illustration, the D-WQI model is applied in the water quality assessment of the Changjiang River in Sanjiangying in 2012. Result shows that the river can be classified as II (good) throughout the year, which can satisfy the requirement of the Chinese South-to-North Water Diversion Project.