Quantifying the fate and risk assessment of different antibiotics during wastewater treatment using a Monte Carlo simulation

Zhao, Lin; Ji, Yi; Yao, Jiayi; Long, Sha; Liu, Dong; Yang, Yongkui

doi:10.1016/j.jclepro.2017.09.065

Cited by 37 publications

(12 citation statements)

References 30 publications

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“…For the dataset where no data quality screening was performed, the importance of the chemical name as random factor highlights missing moderators specific to the chemical to explain the heterogeneity in removal. In fact, further parameters such as the surface area, the polarity, or the charge of the molecule could affect its interactions with other molecules and hence also its removal from wastewater 67,69,[73][74] . Fitting our model to a subset of our data following stricter quality criteria, showed, however, that the random effects of WWTP-specific properties played a more important role than physico-chemical properties compared to the model with all data points included.…”

Section: Heterogeneity In Removal Efficienciesmentioning

confidence: 99%

“…In fact, chemical-specific deconjugation has also already been identified in other studies as influencing the removal of chemicals in activated sludge WWTPs 15 . Further, the counterintuitive relationship of the RE to the KOC stresses the need for a better understanding of the electrochemical interactions of pharmaceuticals to activated sludge (see also Kummerer 68 , Zhao, et al 67 , Tolls 71 ).…”

Section: Model Performancementioning

confidence: 99%

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Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Douziech

Conesa

Benítez‐López

et al. 2018

Environ. Sci.: Processes Impacts

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Large variations in removal efficiencies (REs) of chemicals have been reported for monitoring studies of activated sludge wastewater treatment plants (WWTPs). In this work, we conducted a meta-analysis on REs (1539 data points) for a set of 209 chemicals consisting of fragrances, surfactants, and pharmaceuticals in order to assess the drivers of the variability relating to inherent properties of the chemicals and operational parameters of activated sludge WWTPs. For a reduced dataset (n = 542), we developed a mixed-effect model (meta-regression) to explore the observed variability in REs for the chemicals using three chemical specific factors and four WWTP-related parameters. The overall removal efficiency of the set of chemicals was 82.1% (95% CI 75.2-87.1%, N = 1539). Our model accounted for 17% of the total variability in REs, while the process-based model SimpleTreat did not perform better than the average of the measured REs. We identified that, after accounting for other factors potentially influencing RE, readily biodegradable compounds were better removed than non-readily biodegradable ones. Further, we showed that REs increased with increasing sludge retention times (SRTs), especially for non-readily biodegradable compounds. Finally, our model highlighted a decrease in RE with increasing K. The counterintuitive relationship to K stresses the need for a better understanding of electrochemical interactions influencing the RE of ionisable chemicals. In addition, we highlighted the need to improve the modelling of chemicals that undergo deconjugation when predicting RE. Our meta-analysis represents a first step in better explaining the observed variability in measured REs of chemicals. It can be of particular help to prioritize the improvements required in existing process-based models to predict removal efficiencies of chemicals in WWTPs.

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Section: Heterogeneity In Removal Efficienciesmentioning

confidence: 99%

Section: Model Performancementioning

confidence: 99%

Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Douziech

Conesa

Benítez‐López

et al. 2018

Environ. Sci.: Processes Impacts

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“…Therefore, the treatment of hospital effluents before their disposal into urban wastewater systems is very important in order to avoid problems of contamination and inhibition of biomass growth in conventional WWTPs, which are usually based on biological treatments that are unable to completely remove pharmaceutical products including antibiotics and their metabolites [10]. The removal rates of antibiotics in WWTPs are reported to range from 80 to 90%, and the removal is mainly due to adsorption on the sludge, rather than degradation [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous degradation of ciprofloxacin, amoxicillin, sulfathiazole and sulfamethazine, and disinfection of hospital effluent after biological treatment via photo-Fenton process under ultraviolet germicidal irradiation

Perini

Tonetti

Vidal

et al. 2018

Applied Catalysis B: Environmental

125

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A UVC-assisted photo-Fenton process was applied to hospital wastewater that had been submitted to anaerobic treatment. Low iron (10 μM; 0.56 mg L −1) and H 2 O 2 (500 μM; 17 mg L −1) concentrations were used at the natural pH of the effluent (pH ≈ 7.4). Citric acid was employed as a complexation agent, at a 1:1 ratio, in order to maintain Fe 3+ soluble at this pH, avoiding extra procedures and costs associated with acidification/basification of the final effluent. The anaerobic process quantitatively reduced the biochemical oxygen demand (BOD 5), chemical oxygen demand (COD) and total organic carbon (TOC), with low removal of antibiotics present in the wastewater. Degradation of the antibiotics ciprofloxacin, amoxicillin, sulfathiazole, and sulfamethazine was studied by spiking the anaerobic effluent at initial concentrations of 200 μg L −1. The antibiotics were efficiently degraded (80-95%) using UVC radiation alone, although under this condition, no DOC removal was observed after 90 min. Further additions of H 2 O 2 and iron citrate increased the degradation rate constant (k obs), and 8% of DOC was removed. A lower pH resulted in higher k obs , although this was not essential for application of the photo-Fenton process. Irradiation with a germicidal lamp resulted in greater degradation of the antibiotics, compared to use of a black light lamp or sunlight, since the overall degradation was influenced by photolysis of the antibiotics, photolysis of H 2 O 2 , and the Fenton reaction. The photo-Fenton treatment could also be applied directly to the raw hospital wastewater, since no significant difference in degradation of the antibiotics was observed, compared to the anaerobic effluent. The photo-Fenton process under UVA and solar radiation reduced total coliforms and E. coli after 90 min. However, quantitative disinfection of these bacteria present in the Hospital effluent was only accomplished under UVC radiation.

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“…Monte Carlo simulation (MCS) algorithms are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribu--tion. MCS is one of the most common methods used to accommodate the uncertainties associated with many risk---related problems [8,19,20]. It has been recognized as a means of quantifying variability and uncertainty in risk assessments by the National Academy of Sciences and USEPA.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

“…This method provides a quantitative way to estimate the proba--bility distributions for exposure risks and provides more information for making deci--sions related to risk protection. The widespread use of MCS in risk assessment promises a significant improvement in these assessments' scientific rigor [20]. The MCS method generally requires three main steps, which are intended as follows:…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlation of Air Pollution Global Risk Assessment

et al. 2022

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Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi-agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.

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Quantifying the fate and risk assessment of different antibiotics during wastewater treatment using a Monte Carlo simulation

Cited by 37 publications

References 30 publications

Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Simultaneous degradation of ciprofloxacin, amoxicillin, sulfathiazole and sulfamethazine, and disinfection of hospital effluent after biological treatment via photo-Fenton process under ultraviolet germicidal irradiation

A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlation of Air Pollution Global Risk Assessment

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