Variations and behavior of wastewater-marking pharmaceuticals influenced under hydrodynamic conditions in urban river systems

Zhou, Haidong; Chen, X.; Ying, Tianqi; Xuan, Yumei; Wangjin, Yadan; Liu, X.

doi:10.1007/s13762-018-2038-7

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…Spatial features are a new and significant type of feature used in the model for remote sensing water quality monitoring in this paper. The water's functional zone [21] and hydrodynamics [23] are two key factors that affect the water's quality. We proved that the spatial features improve the grading accuracy.…”

Section: Feature Analysismentioning

confidence: 99%

“…Streamflow and discharge rates are two of the important factors. The channel morphology also affects this capacity because it is affected by streamflow and discharge patterns [23]. Thus, in addition to the spectral features of the water, water quality remote sensing should also include the spatial features of the stream and the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

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Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality

et al. 2021

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Urban river networks have the characteristics of medium and micro scales, complex water quality, rapid change, and time–space incoherence. Aiming to monitor the water quality accurately, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters. In this paper, we describe a spectral- and spatial-feature-integrated ensemble learning method for urban river network water quality grading. We proposed an in situ sampling method for urban river networks. Factor and correlation analyses were applied to extract the spectral features. Moreover, we analyzed the maximum allowed bandwidth for feature bands. We demonstrated that spatial features can improve the accuracy of water quality grading using kernel canonical correlation analysis (KCCA). Based on the spectral and spatial features, an ensemble learning model was established for total phosphorus (TP) and ammonia nitrogen (NH3-N). Both models were evaluated by means of fivefold validation. Furthermore, we proposed an unmanned aerial vehicle (UAV)-borne water quality multispectral remote sensing application process for urban river networks. Based on the process, we tested the model in practice. The experiment confirmed that our model can improve the grading accuracy by 30% compared to other machine learning models that use only spectral features. Our research can extend the application field of water quality remote sensing to complex urban river networks.

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Section: Feature Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality

et al. 2021

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“…Experimental setup. The experiment was carried out in a circulating flume simulating urban water system [10]. The main body of the river simulation device was made up of two parts: straight part and an arc part.…”

Section: Chemicals and Materialsmentioning

confidence: 99%

Removal of typical antibiotics from the symbiotic system of Microcystis aeruginosa and emergent plants

Lidan Pang,

Haidong Zhou,

Mengwei Li

et al. 2023

Environ. Protect. Eng.

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In a simulated urban river system, the conversion and distribution of six typical antibiotics were investigated under the following conditions: no plant, only Microcystis aeruginosa (algae) and algae combined with Juncus effusus, Cyperus alternifolius, and Acorus calamus. Through the calculation of the mass balance, the quantitative distribution of antibiotics in the water phase, sediment, Microcystis aeruginosa, and plant tissues, and the total elimination efficiency of the antibiotics were determined. The results showed that higher concentrations of sulfathiazole (STZ) and sulfamethoxazole (SMZ) were detected in the water phase of the non-plant group, which were 52.81% and 56.88%, respectively, and ciprofloxacin (CIP) and tetracycline (TCY) were detected higher in the sediment, up to 1562 ng/g and 1829 ng/g, respectively. The antibiotic removal rates have been greatly improved, and those in the system containing Microcystis aeruginosa were higher than that in the system without aquatic plants or algae. The calculation of the mass balance showed that the removal effect of algae combined with Juncus effusus was the best, and the removal rates (azithromycin (AZM) and clarithromycin (CLM)) were the highest, reaching 68.88% and 61.96%. It seems that algae and plants play an important role in the removal of antibiotics.

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Removal of antibiotics and antibiotic resistance genes from urban rivers using a photocatalytic-and-bionic artificial ecosystem

Zhou

Cui

Pang

et al. 2022

Journal of Cleaner Production

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Variations and behavior of wastewater-marking pharmaceuticals influenced under hydrodynamic conditions in urban river systems

Cited by 3 publications

References 41 publications

Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality

Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality

Removal of typical antibiotics from the symbiotic system of Microcystis aeruginosa and emergent plants

Removal of antibiotics and antibiotic resistance genes from urban rivers using a photocatalytic-and-bionic artificial ecosystem

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