Pollutant Flux Estimation of the Lijiang River Based on an Improved Prediction-Correction Method

Abstract

Pollutant flux estimation and the analysis of flux variations are the basis for water quality assessment and water pollution control. At present, pollution flux estimation has certain shortcomings, such as a low frequency of water quality monitoring and inadequate calculation methods. To improve the rationality and reliability of river pollution flux estimation results, an improved prediction-correction pollution flux estimation method was developed by combining the LOADEST model and the Kalman filtering algorithm. By establishing the regression equation between pollutant flux and daily discharge, the predicted pollution flux procedure can be calculated using the LOADEST model. In a subsequent step, the pollutant flux is corrected based on the Kalman filtering algorithm. The improved method was applied to estimate the fluxes of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorus (TP) at the Guilin Section of the Lijiang River from 2010 to 2019. The estimated fluxes were in good agreement with the measured ones, with relative deviation values for COD, NH3-N, and TP of 2.27, 3.20, and 1.39%, respectively. The improved method can reasonably estimate fluctuations in river pollution fluxes without requiring more data. The results in the present study provide powerful scientific basis for pollutant flux estimation under low-frequency water quality monitoring.