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.
Highlights
Water pollution is one of the most important environmental issues
The pollutant flux regression Eqs 14–16 were obtained by using the daily average discharge, measured time, and water pollutant concentration data at the Guilin section of the Lijiang River; the parameters were calibrated by the optimization of Akaike Information Criterion (AIC) and SPCC
The relative deviation between estimated and measured fluxes of total phosphorus (TP) was reduced from 9.76 to 1.39% after pollutant flux correction. These results indicate that the fluxes of chemical oxygen demand (COD), NH3-N, and TP of the Lijiang River from 2010 to 2019, estimated by using the improved prediction-correction method based on the Load Estimator (LOADEST) model and the Kalman filtering algorithm, are reasonable and reliable
Summary
Water pollution is one of the most important environmental issues. Various pollutants, impacted by physical, chemical, biological, ecological, climatic, and other factors, can cause eutrophication, acidification, or alkalization, posing a threat to river ecosystem health (Aparicio et al, 2016; Steward et al, 2018). Pollutants in rivers can be rapidly transported through surface and subsurface routes, directly influencing the landscape water quality and regional water safety (Zhang et al, 2017; Qin et al, 2019). The river pollutant flux can directly reflect the total pollution load in the watershed above the river section, representing the production and transportation characteristics of pollutants in the watershed, which is the basis for formulating pollution control plans and measures (Halliday et al, 2014). Lowfrequency and discrete water quality monitoring data series pose great challenges to the reliable quantification of river pollutant fluxes (Li and Guo, 2017)
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