Light attenuation in photobioreactors and algal pigmentation under different growth conditions – model identification and complexity assessment

Microalgae are photosynthetic organisms, and thus one of the most important factors affecting their growth is light. Yet, effective design and operation of algal cultivation systems still lacks robust numerical tools. Here, a comprehensive and mathematically consistent simulation model is presented in the ASM-A framework that can accurately predict light availability and its impact on microalgae growth in photobioreactors (PBR). Three cylindrical column reactors, mimicking typical open pond reactors, with different diameters were used to conduct experiments where the light distribution was monitored inside the reactor. A batch experiment was conducted where the effect of nutrients and light availability on the pigmentation of the microalgae and light distribution was monitored. The effect of reactor size and cultivation conditions on the light distribution in PBRs was evaluated. Moreover, we assessed the effect of using different simulation model structures on the model prediction accuracy and uncertainty propagation. Results obtained show that light scattering can have a significant effect on light distribution in reactors with narrow diameter (typical to panel-type PBRs) and under cultivation conditions that promote low pigmentation or low biomass concentration. The light attenuation coefficient was estimated using the Lambert-Beer equation and it was compared to Schuster's law. The light attenuation was found to be dependent on biomass concentration and microalgae pigmentation. Using a discretized layer model to describe the light distribution in PBRs resulted in the most accurate prediction of microalgal growth and lowest uncertainty on model predictions. Due to model complexity a trade-off needs to be made between accuracy of the prediction ac simulation time.</p

Light attenuation in photobioreactors and algal pigmentation under different growth conditions – model identification and complexity assessment

Microalgae are photosynthetic organisms, and thus one of the most important factors affecting their growth is light. Yet, effective design and operation of algal cultivation systems still lacks robust numerical tools. Here, a comprehensive and mathematically consistent simulation model is presented in the ASM-A framework that can accurately predict light availability and its impact on microalgae growth in photobioreactors (PBR). Three cylindrical column reactors, mimicking typical open pond reactors, with different diameters were used to conduct experiments where the light distribution was monitored inside the reactor. A batch experiment was conducted where the effect of nutrients and light availability on the pigmentation of the microalgae and light distribution was monitored. The effect of reactor size and cultivation conditions on the light distribution in PBRs was evaluated. Moreover, we assessed the effect of using different simulation model structures on the model prediction accuracy and uncertainty propagation. Results obtained show that light scattering can have a significant effect on light distribution in reactors with narrow diameter (typical to panel-type PBRs) and under cultivation conditions that promote low pigmentation or low biomass concentration. The light attenuation coefficient was estimated using the Lambert-Beer equation and it was compared to Schuster's law. The light attenuation was found to be dependent on biomass concentration and microalgae pigmentation. Using a discretized layer model to describe the light distribution in PBRs resulted in the most accurate prediction of microalgal growth and lowest uncertainty on model predictions. Due to model complexity a trade-off needs to be made between accuracy of the prediction ac simulation time.</p

Hindered and compression solid settling functions – sensor data collection, practical model identification and validation

Secondary settling tanks (SSTs) are the most hydraulically sensitive unit operations in activated sludge water resource recovery facilities (WRRF). Mathematical models for predicting activated sludge solids settling velocity include parameters that show irreducible epistemic uncertainty. Therefore, reliable and periodic calibration of the settling velocity model is key for predicting activated sludge process capacity, thus averting possible failures under wet-weather flow- and filamentous bulking conditions. The two main knowledge gaps addressed here are: (1) Do constitutive functions for hindered and compression settling exist, for which all velocity parameters can be uniquely estimated? (2) What is the optimum sensor data requirement of developing reliable settling velocity functions? Innovative settling column sensor and full-scale data were used to identify and validate amended Vesilind function for hindered settling and a new exponential function for compression settling velocity using one-dimensional and computational fluid dynamics simulations. Results indicate practical model identifiability under well-settling and filamentous bulking conditions

Diffusion and sorption of trace organic micropollutants in biofilm with varying thickness

Solid-liquid partitioning is one of the main fate processes determining the removal of micropollutants in wastewater. Little is known on the sorption of micropollutants in biofilms, where molecular diffusion may significantly influence partitioning kinetics. In this study, the diffusion and the sorption of 23 micropollutants were investigated in novel moving bed biofilm reactor (MBBR) carriers with controlled biofilm thickness (50, 200 and 500 μm) using targeted batch experiments (initial concentration = 1 μg L−1, for X-ray contrast media 15 μg L−1) and mathematical modelling. We assessed the influence of biofilm thickness and density on the dimensionless effective diffusivity coefficient f (equal to the biofilm-to-aqueous diffusivity ratio) and the distribution coefficient Kd,eq (L g−1). Sorption was significant only for eight positively charged micropollutants (atenolol, metoprolol, propranolol, citalopram, venlafaxine, erythromycin, clarithromycin and roxithromycin), revealing the importance of electrostatic interactions with solids. Sorption equilibria were likely not reached within the duration of batch experiments (4 h), particularly for the thickest biofilm, requiring the calculation of the distribution coefficient Kd,eq based on the approximation of the asymptotic equilibrium concentration (t &gt; 4 h). Kd,eq values increased with increasing biofilm thickness for all sorptive micropollutants (except atenolol), possibly due to higher porosity and accessible surface area in the thickest biofilm. Positive correlations between Kd,eq and micropollutant properties (polarity and molecular size descriptors) were identified but not for all biofilm thicknesses, thus confirming the challenge of improving predictive sorption models for positively charged compounds. A diffusion-sorption model was developed and calibrated against experimental data, and estimated f values also increased with increasing biofilm thickness. This indicates that diffusion in thin biofilms may be strongly limited (f ≪ 0.1) by the high biomass density (reduced porosity)

Diffusion and sorption of trace organic micropollutants in biofilm with varying thickness

Solid-liquid partitioning is one of the main fate processes determining the removal of micropollutants in wastewater. Little is known on the sorption of micropollutants in biofilms, where molecular diffusion may significantly influence partitioning kinetics. In this study, the diffusion and the sorption of 23 micropollutants were investigated in novel moving bed biofilm reactor (MBBR) carriers with controlled biofilm thickness (50, 200 and 500 μm) using targeted batch experiments (initial concentration = 1 μg L−1, for X-ray contrast media 15 μg L−1) and mathematical modelling. We assessed the influence of biofilm thickness and density on the dimensionless effective diffusivity coefficient f (equal to the biofilm-to-aqueous diffusivity ratio) and the distribution coefficient Kd,eq (L g−1). Sorption was significant only for eight positively charged micropollutants (atenolol, metoprolol, propranolol, citalopram, venlafaxine, erythromycin, clarithromycin and roxithromycin), revealing the importance of electrostatic interactions with solids. Sorption equilibria were likely not reached within the duration of batch experiments (4 h), particularly for the thickest biofilm, requiring the calculation of the distribution coefficient Kd,eq based on the approximation of the asymptotic equilibrium concentration (t &gt; 4 h). Kd,eq values increased with increasing biofilm thickness for all sorptive micropollutants (except atenolol), possibly due to higher porosity and accessible surface area in the thickest biofilm. Positive correlations between Kd,eq and micropollutant properties (polarity and molecular size descriptors) were identified but not for all biofilm thicknesses, thus confirming the challenge of improving predictive sorption models for positively charged compounds. A diffusion-sorption model was developed and calibrated against experimental data, and estimated f values also increased with increasing biofilm thickness. This indicates that diffusion in thin biofilms may be strongly limited (f ≪ 0.1) by the high biomass density (reduced porosity)

Transformation and sorption of illicit drug biomarkers in sewer biofilm

In-sewer transformation of drug biomarkers (excreted parent drugs and metabolites) can be influenced by the presence of biomass in suspended form as well as attached to sewer walls (biofilms). Biofilms are likely the most abundant and biologically active biomass fraction in sewers. In this study, 16 drug biomarkers were selected, including the parent forms and the major human metabolites of mephedrone, methadone, cocaine, heroin, codeine, and tetrahydrocannabinol (THC). Transformation and sorption of these substances were assessed in targeted batch experiments using laboratory-scale biofilm reactors operated under aerobic and anaerobic conditions. A one-dimensional model was developed to simulate diffusive transport, abiotic and biotic transformation, and partitioning of drug biomarkers. Model calibration to experimental results allowed estimating biotransformation rate constants in sewer biofilms, which were compared to those obtained for suspended biomass. Our results suggest that sewer biofilms can enhance the biotransformation kinetics of most selected compounds. Through scenario simulations, we demonstrated that the estimation of biotransformation rate constants in biofilm can be significantly biased if the boundary layer thickness is not accurately estimated. This study complements our previous investigation on the transformation and sorption of drug biomarkers in the presence of only suspended biomass in untreated sewage. A better understanding of the role of sewer biofilmsalso relative to the in-sewer suspended solidsand improved prediction of associated fate processes can result in more accurate estimation of daily drug consumption in urban areas in wastewater-based epidemiological assessments

Enantiomeric profiling of chiral illicit drugs in a pan-European study

The aim of this paper is to present the first study on spatial and temporal variation in the enantiomeric profile of chiral drugs in eight European cities. Wastewater-based epidemiology (WBE) and enantioselective analysis were combined to evaluate trends in illicit drug use in the context of their consumption vs direct disposal as well as their synthetic production routes. Spatial variations in amphetamine loads were observed with higher use in Northern European cities. Enantioselective analysis showed a general enrichment of amphetamine with the R-(−)-enantiomer in wastewater indicating its abuse. High loads of racemic methamphetamine were detected in Oslo (EF = 0.49 ± 0.02). This is in contrast to other European cities where S-(+)-methamphetamine was the predominant enantiomer. This indicates different methods of methamphetamine synthesis and/or trafficking routes in Oslo, compared with the other cities tested. An enrichment of MDMA with the R-(−)-enantiomer was observed in European wastewaters indicating MDMA consumption rather than disposal of unused drug. MDA's chiral signature indicated its enrichment with the S-(+)-enantiomer, which confirms its origin from MDMA metabolism in humans. HMMA was also detected at quantifiable concentrations in wastewater and was found to be a suitable biomarker for MDMA consumption. Mephedrone was only detected in wastewater from the United Kingdom with population-normalised loads up to 47.7 mg 1000 people−1 day−1. The enrichment of mephedrone in the R-(+)-enantiomer in wastewater suggests stereoselective metabolism in humans, hence consumption, rather than direct disposal of the drug. The investigation of drug precursors, such as ephedrine, showed that their presence was reasonably ascribed to their medical use

Comparison of pharmaceutical, illicit drug, alcohol, nicotine and caffeine levels in wastewater with sale, seizure and consumption data for 8 European cities

Background: Monitoring the scale of pharmaceuticals, illicit and licit drugs consumption is important to assess the needs of law enforcement and public health, and provides more information about the different trends within different countries. Community drug use patterns are usually described by national surveys, sales and seizure data. Wastewater-based epidemiology (WBE) has been shown to be a reliable approach complementing such surveys. Method: This study aims to compare and correlate the consumption estimates of pharmaceuticals, illicit drugs, alcohol, nicotine and caffeine from wastewater analysis and other sources of information. Wastewater samples were collected in 2015 from 8 different European cities over a one week period, representing a population of approximately 5 million people. Published pharmaceutical sale, illicit drug seizure and alcohol, tobacco and caffeine use data were used for the comparison. Results: High agreement was found between wastewater and other data sources for pharmaceuticals and cocaine, whereas amphetamines, alcohol and caffeine showed a moderate correlation. methamphetamine and 3,4- methylenedioxymethamphetamine (MDMA) and nicotine did not correlate with other sources of data. Most of the poor correlations were explained as part of the uncertainties related with the use estimates and were improved with other complementary sources of data. Conclusions: This work confirms the promising future of WBE as a complementary approach to obtain a more accurate picture of substance use situation within different communities. Our findings suggest further improvements to reduce the uncertainties associated with both sources of information in order to make the data more comparable.Jose Antonio Baz Lomba, Stefania Salvatore, Richard Bade, Erika Castrignanò, Ana Causanilles, Juliet Kinyua, Ann-Kathrin McCall, Pedram Ramin, Nikolaos I. Rousis, and Yeonsuk Ryu acknowledge the EU Marie-Skłodowska Curie Initial Training Network SEWPROF (Marie Curie-FP7-PEOPLE, grant number 317205) for their Early Stage Researcher grant and Emma Gracia-Lor for her Experienced Researcher grant. We thank the people and agencies who assisted in the collection of the wastewater samples, in particular Pia Ryrfors and colleagues at Vestfjorden Avløpselskap (VEAS, Oslo, Norway)

Impacts of competitive inhibition, parent compound formation and partitioning behavior on the removal of antibiotics in municipal wastewater treatment

We present a process model that predicts the removal of the antibiotic micropollutants, sulfamethoxazole (SMX), tetracycline (TCY), and ciprofloxacin (CIP), in an activated sludge treatment system. A novel method was developed to solve the inverse problem of inferring process rate, sorption, and correction factor parameter values from batch experimental results obtained under aerobic and anoxic conditions. Instead of spiking the batch reactors with reference substances, measurements were made using the xenobiotic organic micropollutant content of preclarified municipal sewage. Parent compound formation and removal were observed, and the model developed using the simulation software West showed limited efficiency to describe the selected micropollutants profiles, when growth substrate removal occurs. The model structure was optimized by accounting for competitive inhibition by readily biodegradable substrates on the cometabolic micropollutant biotransformation processes. Our results suggest that, under anoxic conditions, hydrophobicity-independent mechanisms can significantly impact solid-liquid partitioning that our model takes into account by using the sorption coefficient as a lumped parameter. Forward dynamic simulations were carried out to evaluate the developed model and to confirm it for SMX using data obtained in a full-scale treatment plant. Evaluation of measured and simulation results suggest that, robust model prediction can be achieved by approximating the influent load of chemicals biodegrading via a given parent compound, e.g., human conjugates, as an antibiotic mass that is proportional to the parent compound load