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Pelagic Sargassum inundation of coastlines across the North Atlantic is an ongoing challenge but presents new opportunities for value-added resource recovery. This study assessed the techno-economic feasibility and environmental impact of utilising these invasive brown seaweed, and food waste as feedstock for energy production and fertiliser recovery in Barbados. The biorefinery concept evaluated was designed with hydrothermal pretreatment (HTP) and anaerobic digestion (AD) technologies. Financial analyses of four varied feedstock and process scenarios (S1-S4) established a linear relationship between profitability and the sale of products (electricity and fertiliser). In all cases, simple sale of power generated to the national grid resulted in a negative cash flow and required the introduction of fertiliser sales to achieve positive cash flows. Moreover, the net loss in the electricity only scenarios exceeded that of the landfill disposal, the present operation employed on the island for Sargassum management. The addition of the solid digestate to the revenue stream increased the profit margin and financial attractiveness of the process. Maximum income generation could be attained through 100% supply of the digestate to international markets. However, this approach provides zero support to local food security. The preferred option involves the 50/50 split utilisation of the solid digestate in local and international agricultural practice. While HTP is energy-intensive technology, the recirculation of waste heat generated by a combined heat and power unit for HTP reduced the input energy demand. It also lowered the potential environmental impact by more than 10-fold, relative to landfill disposal. Recycling of the liquid digestate also reduced the fresh water demand and its associated costs. Despite the promising results, process scale-up and commercialisation remain a main challenge, primarily due to the seasonality and variability of Sargassum se

Industrial parks have a high potential for recycling and reusing resources such as water across companies by creating symbiosis networks. In this study, we introduce a mathematical optimization framework for the design of water network integration in industrial parks formulated as a large-scale standard mixed-integer non-linear programming (MINLP) problem. The novelty of our approach relies on i) developing a multi-level incremental optimization framework for water network synthesis, ii) including prior knowledge of water demand growth and projected water scarcity to evaluate the significance of water-saving solutions, iii) incorporating a comprehensive formulation of the water network synthesis problem including multiple pollutants and different treatment units and iv) performing a multi-objective optimization of the network including freshwater savings and relative cost of the network. The significance of the proposed optimization framework is illustrated by applying it to an existing industrial park in a water-scarce region in Kenya. Firstly, we illustrated the benefits of including prior knowledge to prevent an over-design of the network at the early stages. In the case study, we achieved a more flexible and expandable water network with 36% lower unit cost at the early stage and 15% lower unit cost at later stages for overall maximum freshwater savings of 25%. Secondly, multi-objective analysis suggests an optimum freshwater savings of 14% to reduce the unit cost of the network by half. Moreover, the significance of symbiosis networks is highlighted by showing that intra-company connections can only achieve a maximum freshwater savings of 17% with significantly higher unit cost (+45%). Finally, we showed that the values of symbiosis connectivity index in the Pareto front correspond to higher freshwater savings, indicating the significant role of the symbiosis network in the industrial park under study. This is the first study, where all the above elements ha

Wastewater-based epidemiology is an additional indicator of drug use that is gaining reliability to complement the current established panel of indicators. The aims of this study were to: (i) assess spatial and temporal trends of population-normalized mass loads of benzoylecgonine, amphetamine, methamphetamine and 3,4-methylenedioxymethamphetamine (MDMA) in raw wastewater over 7 years (2011-17); (ii) address overall drug use by estimating the average number of combined doses consumed per day in each city; and (iii) compare these with existing prevalence and seizure data. Analysis of daily raw wastewater composite samples collected over 1 week per year from 2011 to 2017. Catchment areas of 143 wastewater treatment plants in 120 cities in 37 countries. Parent substances (amphetamine, methamphetamine and MDMA) and the metabolites of cocaine (benzoylecgonine) and of Δ9 -tetrahydrocannabinol (11-nor-9-carboxy-Δ9 -tetrahydrocannabinol) were measured in wastewater using liquid chromatography-tandem mass spectrometry. Daily mass loads (mg/day) were normalized to catchment population (mg/1000 people/day) and converted to the number of combined doses consumed per day. Spatial differences were assessed world-wide, and temporal trends were discerned at European level by comparing 2011-13 drug loads versus 2014-17 loads. Benzoylecgonine was the stimulant metabolite detected at higher loads in southern and western Europe, and amphetamine, MDMA and methamphetamine in East and North-Central Europe. In other continents, methamphetamine showed the highest levels in the United States and Australia and benzoylecgonine in South America. During the reporting period, benzoylecgonine loads increased in general across Europe, amphetamine and methamphetamine levels fluctuated and MDMA underwent an intermittent upsurge. The analysis of wastewater to quantify drug loads provides near real-time drug use estimates that globally correspond to prevalence and seizure data.

Sustainable growth of industries in fast developing but water-scarce countries like Kenya can only be achieved by optimizing water usage and reducing wastewater discharge. Besides internal recycling, industries can greatly reduce their freshwater intake by exchanging wastewater streams with neighboring industries and creating symbiosis relations. Previous studies have attempted to design an optimal network of water exchange streams in industrial parks by applying a global multi-objective optimization approach (e.g., [1]). However, only a few have considered the complexity of used water streams in terms of flow rate variation [2] and number of significant pollutants [3]. In this study, we present the outcome of the optimization analysis, covering the above-mentioned knowledge gaps, for water exchange between 10 companies in an industrial park in Ruaraka, Nairobi. A combination of clarification, physico-chemical, membrane-based (Inside® by Aquaporin A/S, Denmark) and disinfection treatments was considered in the optimization exercise as decentralized fit-for-purpose treatment units. Information on volumes and quality of water usage and wastewater discharge was gathered through on-site visits and interviews. The analysis forms part of a research project, Gecko (Green and Circular Innovation for Kenyan Companies), investigating circular solutions within water, materials and energy by specifically focusing on wastewater reuse and targeting industrial symbiosis potentials. The formulation of the water reuse network is shown in the superstructure (an example for three processes) in Figure 1 using a series of multiple treatment units prior to reuse. The multi-objective evolutionary algorithm NSGA II [4] was applied, considering (i) an economic objective (i.e. capital and operational cost of treatment, piping cost and freshwater consumption volume); (ii) an environmental objective (i.e. CO2 emission, wastewater discharge pollution load); (iii) social constraints, con

The study presents a global optimization strategy in designing a water reuse network for industrial symbiosis. Two optimization approaches, stochastic and deterministic, are compared based on the level of complexity of the network such as the number of contaminants, treatment unit selection, flow variation. Preliminary results on a general superstructure formulated as a mixed integer nonlinear programing shows promising performance of a stochastic model using a non-dominated sorting genetic algorithm. The developed optimization methodology will be extended to perform uncertainty and flexibility assessments of water reuse networks.

In this work a step forward in investigating the use of prescription drugs, namely erectile dysfunction products, at European level was taken by applying the wastewater-based epidemiology approach. 24-h composite samples of untreated wastewater were collected at the entrance of eight wastewater treatment plants serving the catchment within the cities of Bristol, Brussels, Castellón, Copenhagen, Milan, Oslo, Utrecht and Zurich. A validated analytical procedure with direct injection of filtered aliquots by liquid chromatography-tandem mass spectrometry was applied. The target list included the three active pharmaceutical ingredients (sildenafil, tadalafil and vardenafil) together with (bio)transformation products and other analogues. Only sildenafil and its two human urinary metabolites desmethyl- and desethylsildenafil were detected in the samples with concentrations reaching 60 ng L−1. The concentrations were transformed into normalized measured loads and the estimated actual consumption of sildenafil was back-calculated from these loads. In addition, national prescription data from five countries was gathered in the form of the number of prescribed daily doses and transformed into predicted loads for comparison. This comparison resulted in the evidence of a different spatial trend across Europe. In Utrecht and Brussels, prescription data could only partly explain the total amount found in wastewater; whereas in Bristol, the comparison was in agreement; and in Milan and Oslo a lower amount was found in wastewater than expected from the prescription data. This study illustrates the potential of wastewater-based epidemiology to investigate the use of counterfeit medication and rogue online pharmacy sales.

Temperature is one of the key factors, influencing the transformation kinetics of organic chemicals. In the context of wastewater-based epidemiology, however, temperature differences among sewer catchments and within the same catchment (due to, e.g., seasonal variations) have been neglected to date as a factor influencing the estimation of illicit drug consumption. In this study, we assessed the influence of temperature on the transformation of biomarkers in wastewater and its ensuing implications on the back-calculation of chemical consumption rate in urban catchments using the example of selected illicit drugs. Literature data, obtained in laboratory-scale experiments, on the stability of drug biomarkers in untreated wastewater at trace levels was systematically reviewed, and transformation rates obtained at different temperatures were collected. Arrhenius-based equations were fitted to empirical data and identified to describe the transformation of selected cocaine and morphine biomarkers at applicability temperature range (from 2–9 °C to 30–31 °C), with estimated exponential Arrhenius coefficients between 1.04 and 1.18. These empirically-derived relationships were used to assess the influence of temperature on the transformation of drug biomarkers during in-sewer transport and its effect on the back-calculation of drug consumption rate in hypothetical urban catchment scenario simulations. Up to 4-fold increase in removal efficiency was estimated when wastewater temperature increased from 15 °C to 25 °C. Findings from this study can help reducing the uncertainty intrinsic to wastewater-based epidemiology studies, and will be beneficial in comparing chemical consumption estimates from different catchments worldwide.

Caffeine metabolites in wastewater were investigated as potential biomarkers for assessing caffeine intake in a population. The main human urinary metabolites of caffeine were measured in the urban wastewater of ten European cities and the metabolic profiles in wastewater were compared with the human urinary excretion profile. A good match was found for 1,7-dimethyluric acid, an exclusive caffeine metabolite, suggesting that might be a suitable biomarker in wastewater for assessing population-level caffeine consumption. A correction factor was developed considering the percentage of excretion of this metabolite in humans, according to published pharmacokinetic studies. Daily caffeine intake estimated from wastewater analysis was compared with the average daily intake calculated from the average amount of coffee consumed by country per capita. Good agreement was found in some cities but further information is needed to standardize this approach. Wastewater analysis proved useful to providing additional local information on caffeine use.

The popularity of new psychoactive substances (NPS) has grown in recent years, with certain NPS commonly and preferentially consumed even following the introduction of preventative legislation. With the objective to improve the knowledge on the use of NPS, a rapid and very sensitive method was developed for the determination of ten priority NPS (N-ethylcathinone, methylenedioxypyrovalerone (MDPV), methylone, butylone, methedrone, mephedrone, naphyrone, 25-C-NBOMe, 25-I-NBOMe and 25-B-NBOMe) in influent wastewater. Sample clean-up and pre-concentration was made by off-line solid phase extraction (SPE) with Oasis MCX cartridges. Isotopically labelled internal standards were used to correct for matrix effects and potential SPE losses. Following chromatographic separation on a C18column within 6 min, the compounds were measured by tandem mass spectrometry in positive ionization mode. The method was optimised and validated for all compounds. Limits of quantification were evaluated by spiking influent wastewater samples at 1 or 5 ng/L. An investigation into the stability of these compounds in influent wastewater was also performed, showing that, following acidification at pH 2, all compounds were relatively stable for up to 7 days. The method was then applied to influent wastewater samples from eight European countries, in which mephedrone, methylone and MDPV were detected. This work reveals that although NPS use is not as extensive as for classic illicit drugs, the application of a highly sensitive analytical procedure makes their detection in wastewater possible. The developed analytical methodology forms the basis of a subsequent model-based back-calculation of abuse rate in urban areas (i.e. wastewater-based epidemiology).

The information obtained from the chemical analysis of specific human excretion products (biomarkers) in urban wastewater can be used to estimate the exposure or consumption of the population under investigation to a defined substance. A proper biomarker can provide relevant information about lifestyle habits, health and wellbeing, but its selection is not an easy task as it should fulfil several specific requirements in order to be successfully employed. This paper aims to summarize the current knowledge related to the most relevant biomarkers used so far. In addition, some potential wastewater biomarkers that could be used for future applications were evaluated. For this purpose, representative chemical classes have been chosen and grouped in four main categories: (i) those that provide estimates of lifestyle factors and substance use, (ii) those used to estimate the exposure to toxicants present in the environment and food, (iii) those that have the potential to provide information about public health and illness and (iv) those used to estimate the population size. To facilitate the evaluation of the eligibility of a compound as a biomarker, information, when available, on stability in urine and wastewater and pharmacokinetic data (i.e. metabolism and urinary excretion profile) has been reviewed. Finally, several needs and recommendations for future research are proposed.

The objective of this paper is to show the results of an industrial project dealing with modelling of anaerobic digesters. A multi-scale mathematical approach is developed to describe reactor hydrodynamics, granule growth/distribution and microbial competition/inhibition for substrate/space within the biofilm. The main biochemical and physico-chemical processes in the model are based on the Anaerobic Digestion Model No 1 (ADM1) extended with the fate of phosphorus (P), sulfur (S) and ethanol (Et-OH). Wastewater dynamic conditions are reproduced and data frequency increased using the Benchmark Simulation Model No 2 (BSM2) influent generator. All models are tested using two plant data sets corresponding to different operational periods (#D1, #D2). Simulation results reveal that the proposed approach can satisfactorily describe the transformation of organics, nutrients and minerals, the production of methane, carbon dioxide and sulfide and the potential formation of precipitates within the bulk (average deviation between computer simulations and measurements for both #D1, #D2 is around 10%). Model predictions suggest a stratified structure within the granule which is the result of: 1) applied loading rates, 2) mass transfer limitations and 3) specific (bacterial) affinity for substrate. Hence, inerts (XI) and methanogens (Xac) are situated in the inner zone, and this fraction lowers as the radius increases favouring the presence of acidogens (Xsu,Xaa, Xfa) and acetogens (Xc4,Xpro). Additional simulations show the effects on the overall process performance when operational (pH) and loading (S:COD) conditions are modified. Lastly, the effect of intra-granular precipitation on the overall organic/inorganic distribution is assessed at: 1) different times; and, 2) reactor heights. Finally, the possibilities and opportunities offered by the proposed approach for conducting engineering optimization pr

This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method developed was compared to parameter estimation methods commonly encountered in literature (i.e., estimation of all parameters at the same time and parameter estimation with fix values for upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncertainty analysis. Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters. This method can also offer a platform to promote a closer interaction between analytical chemists and modellers to identify models for biochemical transformation pathways, being a prominent example for the emerging field of wastewater-based epidemiology.

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 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 transformation rate constants in sewer biofilms, which were compared to those obtained using in-sewer suspended biomass. Our results suggest that sewer biofilms can enhance the transformation of most compounds. Through scenario simulations, we demonstrated that the estimation of transformation 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 lead to more accurate estimation of daily drug consumption in urban areas in wastewater-based epidemiological assessments.