Assessing city-wide pharmaceutical emissions to wastewater via modelling and passive sampling.

With increasing numbers of chemicals used in modern society, assessing human and environmental exposure to them is becoming increasingly difficult. Recent advances in wastewater-based epidemiology enable valuable insights into public exposure to data-poor compounds. However, measuring all >26,000 chemicals registered under REACH is not just technically unfeasible but would also be incredibly expensive. In this paper, we argue that estimating emissions of chemicals based on usage data could offer a more comprehensive, systematic and efficient approach than repeated monitoring. Emissions of 29 active pharmaceutical ingredients (APIs) to wastewater were estimated for a medium-sized city in the Netherlands. Usage data was collected both on national and local scale and included prescription data, usage in health-care institutions and over-the-counter sales. Different routes of administration were considered as well as the excretion and subsequent in-sewer back-transformation of conjugates into respective parent compounds. Results suggest model-based emission estimation on a city-level is feasible and in good agreement with wastewater measurements obtained via passive sampling. Results highlight the need to include excretion fractions in the conceptual framework of emission estimation but suggest that the choice of an appropriate excretion fraction has a substantial impact on the resulting model performance.

Characterization of urban sources of antibiotics and antibiotic-resistance genes in a Dutch sewer catchment.

A one year study was conducted in the city of Nijmegen, The Netherlands, to characterize various urban sources of antibiotics and antibiotic resistant genes (ARGs) in wastewater within a single sewer catchment. Prevalence of ermB, tet(W), sul1, sul2, intl1, and 16S rRNA gene was determined at 10 locations within the city. Sampling locations included a nursing home, a student residence, a hospital and an industrial area, among others. Wastewater concentrations of 23 antibiotics were measured using passive sampling. Additionally, excreted loads of 22 antibiotics were estimated based on ambulatory prescription and clinical usage data. Genes sul1 and intl1 were most abundant across most locations. Ciprofloxacin and amoxicillin together contributed over 92 % of the total estimated antibiotic selective pressure at all sampling points. The present study highlights the prominent role that hospitals can have in the prevalence and proliferation of ARGs in urban wastewater. Furthermore, results suggest that even short-term changes in the therapeutic regimen prescribed in hospitals may translate into shifting ARG abundance patterns in hospital wastewater. The methods applied present an opportunity to identify emission hotspots and prioritize intervention options to limit ARG spread from urban wastewater to the environment.

Predicting and improving the microbial removal of organic micropollutants during wastewater treatment: A review.

Organic micropollutants (OMPs) consist of widely used chemicals such as pharmaceuticals and pesticides that can persist in surface and groundwaters at low concentrations (ng/L to µg/L) for a long time. The presence of OMPs in water can disrupt aquatic ecosystems and threaten the quality of drinking water sources. Wastewater treatment plants (WWTPs) rely on microorganisms to remove major nutrients from water, but their effectiveness at removing OMPs varies. Low removal efficiency might be the result of low concentrations, inherent stable chemical structures of OMPs, or suboptimal conditions in WWTPs. In this review, we discuss these factors, with special emphasis on the ongoing adaptation of microorganisms to degrade OMPs. Finally, recommendations are drawn to improve the prediction of OMP removal in WWTPs and to optimize the design of new microbial treatment strategies. OMP removal seems to be concentration-, compound-, and process-dependent, which poses a great complexity to develop accurate prediction models and effective microbial processes targeting all OMPs.

The role of the sewer system in estimating urban emissions of chemicals of emerging concern.

The use of chemicals by society has resulted in calls for more effective control of their emissions. Many of these chemicals are poorly characterized because of lacking data on their use, environmental fate and toxicity, as well as lacking detection techniques. These compounds are sometimes referred to as contaminants of emerging concern (CECs). Urban areas are an important source of CECs, where these are typically first collected in sewer systems and then discharged into the environment after being treated in a wastewater treatment plant. A combination of emission estimation techniques and environmental fate models can support the early identification and management of CEC-related environmental problems. However, scientific insight in the processes driving the fate of CECs in sewer systems is limited and scattered. Biotransformation, sorption and ion-trapping can decrease CEC loads, whereas enzymatic deconjugation of conjugated metabolites can increase CEC loads as metabolites are back-transformed into their parent respective compounds. These fate processes need to be considered when estimating CEC emissions. This literature review collates the fragmented knowledge and data on in-sewer fate of CECs to develop practical guidelines for water managers on how to deal with in-sewer fate of CECs and highlights future research needs. It was assessed to what extent empirical data is in-line with text-book knowledge and integrated sewer modelling approaches. Experimental half-lives (n = 277) of 96 organic CECs were collected from literature. The findings of this literature review can be used to support environmental modelling efforts and to optimize monitoring campaigns, including field studies in the context of wastewater-based epidemiology. Supplementary Information: The online version contains supplementary material available at 10.1007/s11157-022-09638-9.

Risk-management tool for environmental prioritization of pharmaceuticals based on emissions from hospitals.

Over the past decade, the health care sector has become increasingly aware of the impact of pharmaceutical emissions to the environment. Yet, it remains unclear which compounds are the most relevant to address and at what point emission control is most effective. This study presents a modelling framework to prioritize pharmaceuticals based on their relative risks for aquatic organisms, using purchase and prescription data from hospitals. The framework consists of an emission prediction module and a risk prioritization module. The emission prediction module accounts for three different routes of intake (oral, intravenous, rectal), for non-patient consumption, and for delayed athome excretion due to relatively long half-lives or prescription durations of selected pharmaceuticals. We showcase the modelling framework with 16 pharmaceuticals administered at two Dutch academic hospitals. Predictions were validated with experimental data from passive sampling in the sewer system. With the exception of metformin, all predictions were within a factor of 10 from measurements. The risk prioritization module ranks each pharmaceutical based on its predicted relative risk for aquatic organisms. The resulting prioritization suggests that emission mitigation strategies should mainly focus on antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs).