Unveiling the existence and ecological hazards of trace organic pollutants in wastewater treatment plant effluents across China

Liu, Jianchao; Yang, Fang; Cai, Yuanfei; Lu, Guanghua; Li, Yiping; Li, Ming; Fan, Linhua; Gao, Li

doi:10.1016/j.eehl.2023.09.006

Cited by 10 publications

(1 citation statement)

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“…It was found that the types of organic pollutants in downstream rivers were relatively similar to those in the e uent of sewage treatment plants (Jiang et al 2023), which showed that wastewater treatment plant e uents are a non-negligible source of organic pollutants in surface runoff. A survey of 46 wastewater treatment plant e uents in China were found to contain 302 trace organic pollutants, accounting for 59 percent of the total organic matter (Liu et al 2024). Organic pollutants pose a serious threat to the environment due to their toxic characteristics over prolonged period (Kumar et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Driving analysis and prediction of COD based on frequency division

Li,

Chen,

Wang

et al. 2024

Preprint

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COD (Chemical Oxygen Demand) is an important indicator to measure organic pollution of water body. To strengthen in-depth analysis and prediction of COD, a new method was proposed in this paper. A frequency division method, Variational Mode Decomposition (VMD) was used to complete time domain decomposition of COD data before model simulation. The original data was separated into five signals with different frequency bands, IMF1, IMF2, IMF3, IMF4 and IMF5, with which the influence of meteorological factors and water quality factors on COD were explored. The long-term COD content is mainly driven by nutrient factors phosphorus and nitrogen, while the immediate fluctuation characteristics exhibit relatively stability. Random Forest, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to predict COD with the original data and the signal data processed by VMD. It is found that frequency division can improve simulation stability and accuracy of GRU and LSTM more significantly than Random Forest. VMD-GRU and VMD-LSTM models can be used reliably for COD analyzation and prediction in Chengdu area.

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Section: Introductionmentioning

confidence: 99%