Pharmaceutical emissions on the example of the Baltic Sea catchment: comparing measurements with multi-tier predictive models.

Currently, there is uncertainty about emissions of pharmaceuticals into larger closed ecosystems that are at risk such as the Baltic Sea. There is an increasing need for selecting the right strategies on advanced wastewater treatment. This study analysed 35 pharmaceuticals and iodinated X-ray contrast media in effluents from 82 Wastewater Treatment Plants (WWTPs) across Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland and Sweden. Measured concentrations from Finland and Denmark were compared to predicted effluent concentrations using different levels of refinement. The concentrations predicted by the Total Residue Approach, as proposed by the European Medicines Agency, correlated with R2 of 0.18 and 0.031 to measured ones for Denmark and Finland, respectively and the predicted data were significantly higher than the measured ones. These correlations improved substantially to R2 of 0.72 and 0.74 after adjusting for estimated human excretion rates and further to R2 = 0.91 and 0.78 with the inclusion of removal rates in WWTPs. Temporal analysis of compound variations in a closely monitored WWTP showed minimal fluctuation over days and weeks for most compounds but revealed weekly shifts in iodinated X-ray contrast media due to emergency-only operations at X-ray clinics during weekends and an abrupt seasonal change for gabapentin. The findings underscore the limitations of current predictive models and findings (...) demonstrate how these methodologies can be refined by incorporating human pharmaceutical excretion/metabolization as well as removal in wastewater treatment plants to more accurately forecast pharmaceutical levels in aquatic environments.

A new workflow for assigning removal credits to assess overall performance of managed aquifer recharge (MAR).

Pathogen removal in managed aquifer recharge (MAR) systems is dependent upon numerous operational, physicochemical water quality, and biological parameters. Due to the site-specific conditions affecting these parameters, guidelines for specifying pathogen removal have historically taken rather precautionary and conservative approaches in order to protect groundwater quality and public health. A literature review of regulated pathogens in MAR applications was conducted and compared to up-and-coming indicators and surrogates for pathogen assessment, all of which can be gathered into a toolbox from which regulators and operators alike can select appropriate pathogens for monitoring and optimization of MAR practices. Combined with improved knowledge of pathogen fate and transport obtained through lab- and pilot-scale studies and supported by modeling, this foundation can be used to select appropriate, site-specific pathogens for regarding a more efficient pathogen retention, ultimately protecting public health and reducing costs. This paper outlines a new 10 step-wise workflow for moving towards determining robust removal credits for pathogens based on risk management principles. This approach is tailored to local conditions while reducing overly conservative regulatory restrictions or insufficient safety contingencies. The workflow is intended to help enable the full potential of MAR as more planned water reuse systems are implemented in the coming years.

Quantitative microbial risk assessment of a non-membrane based indirect potable water reuse system using Bayesian networks.

Risk-based approaches are used to define performance standards for water and wastewater treatment to meet health-based targets and to ensure safe and reliable water quality for desired end use. In this study, a screening level QMRA for a non-membrane based indirect potable reuse (IPR) system utilizing the sequential managed aquifer recharge technology (SMART) concept was conducted. Ambient removals of norovirus, Campylobacter and Cryptosporidium in advanced water treatment (AWT) steps were combined in a probabilistic QMRA utilizing Bayesian networks constructed in Netica. Results revealed that all pathogens complied with disease burden at the 95th percentile, and according to the assumptions taken about pathogen removal, Cryptosporidium was the pathogen with the greatest risk. Through systematic sensitivity analysis, targeted scenario analysis, and backwards inferencing, critical control points for each pathogen were determined, demonstrating the usefulness of Bayesian networks as a diagnostic tool in quantifying risk of water reuse treatment scenarios.

Varying attenuation of trace organic chemicals in natural treatment systems - A review of key influential factors.

The removal of trace organic chemicals (TOrCs) from treated wastewater and impacted surface water through managed aquifer recharge (MAR) has been extensively studied under a variety of water quality and operating conditions and at various experimental scales. The primary mechanism thought to dictate removal over the long term is biodegradation by microorganisms present in the system. This review of removal percentages observed in biologically active filtration systems reported in the peer-reviewed literature may serve as the basis to identify future indicators for persistence, as well as variable and efficient removal in MAR systems. A noticeable variation in reported removal percentages (standard deviation above 30%) was observed for 24 of the 49 most commonly studied TOrCs. Such variations suggest a rather inconsistent capacity of biologically active filter systems to remove these TOrCs. Therefore, operational parameters such as the change in dissolved organic carbon (ΔDOC) during treatment, hydraulic retention time (HRT), filter material, and redox conditions were correlated to the associated TOrC removal percentages to determine whether a data-based relationship could be elucidated. Interestingly, 11 out of the 24 compounds demonstrated increased removal with increasing ΔDOC concentrations. Furthermore, 10 compounds exhibited a positive correlation with HRT. Based on the evaluated data, a minimum HRT of 0.5-1 day is recommended for removal of most compounds.

Differentiating between adsorption and biodegradation mechanisms while removing trace organic chemicals (TOrCs) in biological activated carbon (BAC) filters.

Efficient adsorption of certain trace organic chemicals (TOrCs) present in secondary treated municipal wastewater treatment plant (WWTP) effluents onto granular activated carbon (GAC) has already been demonstrated at lab- and full-scale. Due to high organic matter concentrations in WWTP effluents, GAC filters eventually develop a biofilm and turn into biological activated carbon filters (BAC), where removal of organic compounds is governed by biodegradation as well as by adsorption. However, determining TOrC breakthrough by conducting a long-term BAC column experiment to discern between the removal mechanisms is not possible due to competition for adsorption sites, fluctuating water quality, and other variables. Therefore, a rapid small scale column test (RSSCT) was conducted to determine the contribution of adsorption for select chemicals at 10,000 bed volumes treated (BVT). These results were then used in the pore surface diffusion model (PSDM) to model adsorption behavior at 40,000 BVTs. Pseudo-Freundlich K values obtained from the PSDM model were compared with K values obtained from an integral mass balance calculation. This comparison revealed that the modeling was most accurate for moderately to poorly adsorptive compounds. In comparing RSSCT results to long-term BAC columns, the modeling approach best predicted BAC removal of well adsorbing compounds, such as atenolol, trimethoprim, metoprolol, citalopram, and benzotriazole. However, differences in predicted vs observed BAC removal for the removals of venlafaxine, tramadol and carbamazepine revealed that BAC adsorption capacity was not yet exhausted for these compounds. Therefore, a comparison was not possible. The approach would be improved by operation at longer EBCT and improved calculation of compound fouling indices.