An integrative model to assess water quality in China's Lake Taihu: Comparing single‐factor and multifactor assessments. (9th October 2018) - Zhu, Huaqing; Lu, Xiwu
ABSTRACT: To determine the differences between single‐factor assessment (SFA) and multifactor assessment (MFA) of the water quality in Lake Taihu Basin in China, a software program was developed to perform absolute distance (AD) computations between SFAs and MFAs that refer to the Nemerow comprehensive index (NCI) and fuzzy comprehensive assessment (FCA). Symbolic models were established to describe the computation types and sequences that are involved in the models above. Water data that were obtained weekly from 7 monitoring sites (MSs) in the basin over 10 years were tested to generate water quality grades and ADs. Our results corroborated that the MFAs would approximate the SFA when each water quality indicator (WQI) is in its worst or best state. In addition to supporting that SFA ≥ NCI ≥ FCA, the ADs illustrated that FCA was inappropriate for process integration unless all WQIs had the same grading standards. The annual water quality grades of most MSs of Lake Taihu Basin and time could be fitted to quintic polynomials with relative average deviations (RADs) of below 5%. Integr Environ Assess Manag 2019;15:135–141. © 2018 SETAC Key Points: Symbolic models were established to describe computation types and sequences involved in hybrid models. Software was built to perform computations of absolute difference (AD) that reveal the difference between single‐factor assessment (SFA) and multifactor assessment (MFA). Two deductions that made up the difference between SFAs and MFAs were corroborated by AD. Tendencies of water quality for most monitoring sites on Lake Taihu Basin were fitted to quintic polynomials, with relative average deviation (RAD) below 5%.