Estimating the transport parameters of propyphenazone, caffeine and carbamazepine by means of a tracer experiment in a coarse-gravel unsaturated zone

Koroša, Anja; Brenčič, Mihael; Mali, Nina

doi:10.1016/j.watres.2020.115680

Cited by 19 publications

(11 citation statements)

References 68 publications

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“…Hence, the degradation kinetics in the stream water microcosms may be related to the physicochemical properties of each micropollutant (SI, Section A), where the faster kinetics were observed for the most soluble compounds. This is in agreement with the fast caffeine biodegradation kinetics in the stream water microcosms (Figure 2) and previous lysimeter experiments (Koroša et al, 2020). Caffeine biodegradation was the main attenuation process as abiotic transformation was insignificant in the stream water microcosms (SI, Sections H and I) and sorption is expected to be insignificant due to low organic content (<0.01%) in the quartz sand matrix of the aquifers.…”

Section: Kinetics Of Micropollutants Biotransformation In Stream Wate...supporting

confidence: 91%

Reactive transport of micropollutants in laboratory aquifers undergoing transient exposure periods

Prieto-Espinoza

Chiara

Belfort

et al. 2023

Science of The Total Environment

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Section: Kinetics Of Micropollutants Biotransformation In Stream Wate...supporting

confidence: 91%

Reactive transport of micropollutants in laboratory aquifers undergoing transient exposure periods

Prieto-Espinoza

Chiara

Belfort

et al. 2023

Science of The Total Environment

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“…Caffeine has been observed to degrade relatively rapidly, with half lives of less than 104 h (Dvory et al, 2018b;Hillebrand et al, 2012a; Table 2), and an estimate of 12 days for degradation in a lake (Buerge et al, 2003). However, Hillebrand et al (2012b) suggested wastewater leakage and the subsurface redox condition may have contributed to the high biodegradation of caffeine that they observed; and longer degradation times of 30 to 69 days have been estimated for caffeine in intergranular aquifers (Gonçalves et al, 2017;Koroša et al, 2020).…”

Section: Degradation Properties Of Eocsmentioning

confidence: 98%

Emerging organic contaminants in karst groundwater: A global level assessment

Reberski

Terzić

Maurice

et al. 2022

Journal of Hydrology

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A quarter of the world's population uses groundwater from karst aquifers. A range of emerging organic contaminants (EOCs) are considered a potential threat to water resources and dependant ecosystems, and karst aquifers are the most vulnerable groundwater systems to anthropogenic pollution. This paper provides the first global compilation (based on 50 studies) of EOCs in karst aquifers and explores EOC occurrence and the use of EOCs to understand karst systems. Of the 144 compounds detected in the reviewed studies, the vast majority in karst groundwater are pharmaceuticals and pesticides. Maximum concentrations of compounds varied over five orders of magnitude, and nearly half of the detected compounds exceed 100 ng/L. Karst groundwater is shown to have lower frequency of detection and lower concentrations compared to surface waters and local shallow intergranular aquifers, but overall higher concentrations compared to other major aquifer types. A growing number of studies have demonstrated the utility of EOCs and some legacy compounds for groundwater quality assessment and as tracers for characterising karst systems. They can improve understanding of vulnerability, storage, attenuation mechanisms, and in some cases have been used to assist with catchment delineation. This is a growing research area for karst hydrogeology, and more research is needed to understand EOC contamination of karst aquifers, and to develop EOCs as tracers within karst to improve our understanding of this critical water resource.

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“…Triangular, trapezoidal or Gaussian membership functions can be selected according to the size of the nonlinear measure in the divided region Ev [14][15]. The quasi-steady-state wind speed Vmean is selected as the scheduling parameter, that is, θ=Vmean.…”

Section: T-s Fuzzy Linearization Of Nonlinear Processesmentioning

confidence: 99%

Calculation and Application of Important Parametersin Wind Energy Resource Assessment of Wind Farm Based on T-S Fuzzy Linearization

2021

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In recent years, the construction of wind farms has increased rapidly, and the evaluation of wind energy resources is an important basis for wind farm construction and one of the key factors for success or failure. The accuracy of wind energy resource evaluation, that is, the study of wind energy evaluation methods is particularly important. The purpose of this article is to calculate and apply the important parameters in wind farms based on fuzzy T-S linearization, to study and analyze the calculation process and algorithm of important parameters of wind energy resource evaluation, and to design and develop the corresponding wind energy resource evaluation system. The energy resource evaluation system and the corresponding parameter algorithm are implemented to make the system easy to use, accurate in calculation and accurate in evaluation. Finally, the system is used to calculate the simultaneous wind speed and annual average wind speed of each wind height (15m, 55m, 75m) of a wind farm in 2020. The annual average wind speed at the wind height is 7.16 m/s, and the annual average wind speed at the wind marker at 75 m is 8.07 m/s. In addition, the average wind speed of each anemometer in the first year first increased and then decreased with time, reaching a maximum value at 8:00 or 12:00.

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Estimating the transport parameters of propyphenazone, caffeine and carbamazepine by means of a tracer experiment in a coarse-gravel unsaturated zone

Cited by 19 publications

References 68 publications

Reactive transport of micropollutants in laboratory aquifers undergoing transient exposure periods

Reactive transport of micropollutants in laboratory aquifers undergoing transient exposure periods

Emerging organic contaminants in karst groundwater: A global level assessment

Calculation and Application of Important Parametersin Wind Energy Resource Assessment of Wind Farm Based on T-S Fuzzy Linearization

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