Background and aims: 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.Design: Analysis of daily raw wastewater composite samples collected over 1 week per year from 2011 to 2017.Setting and Participants: Catchment areas of 143 wastewater treatment plants in 120 cities in 37 countries.Measurements: 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.Findings: 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.Conclusions: The analysis of wastewater to quantify drug loads provides near real‐time drug use estimates that globally correspond to prevalence and seizure data.

Dependent on the excretion pattern, wastewater monitoring of viruses can be a valuable approach to characterizing their circulation in the human population. Using polyethylene glycol precipitation and reverse transcription-quantitative PCR, the occurrence of RNA of SARS-CoV-2 and influenza viruses A/B in the raw wastewater of two treatment plants in Germany between January and May 2022 was investigated. Due to the relatively high incidence in both exposal areas (plant 1 and plant 2), SARS-CoV-2-specific RNA was determined in all 273 composite samples analyzed (concentration of E gene: 1.3 × 10⁴ to 3.2 × 10⁶ gc/L). Despite a nation-wide low number of confirmed infections, influenza virus A was demonstrated in 5.2% (concentration: 9.8 × 10² to 8.4 × 10⁴ gc/L; plant 1) and in 41.6% (3.6 × 10³ to 3.0 × 10⁵ gc/L; plant 2) of samples. Influenza virus B was detected in 36.0% (7.2 × 10² to 8.5 × 10⁶ gc/L; plant 1) and 57.7% (9.6 × 10³ to 2.1 × 10⁷ gc/L; plant 2) of wastewater samples. The results of the study demonstrate the frequent detection of two primary respiratory viruses in wastewater and offer the possibility to track the epidemiology of influenza by wastewater-based monitoring.

Use of wastewater-based epidemiology as a tool to record and manage the course of SARS-CoV-2 infections in human populations requires information about the efficiency of methods to concentrate the virus from wastewater. In the present study, we spiked untreated wastewater with quantified SARS-CoV-2 positive clinical material and enriched the virus by polyethylene glycol precipitation and ultrafiltration with Vivaspin 10 kDa MWCO columns. SARS-CoV-2 was detected and quantified by reverse transcription quantitative PCR (E- and S-gene) and droplet digital PCR. The concentration of virus with precipitation resulted in mean recoveries between 59.4% and 63.7% whereas rates from 33.0% to 42.6% after ultrafiltration of samples were demonstrated. The results suggest that the use of both methods allows an effective and practicable enrichment of SARS-CoV-2 from raw wastewater.

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

Combined sewer overflows (CSOs) prevent surges in sewer networks by releasing untreated wastewater into nearby water bodies during intense storm events. CSOs can have acute and detrimental impacts on the environment and thus need to be managed. Although several gray, green and hybrid CSO mitigation measures have been studied, the influence of network structure on CSO occurrence is not yet systematically evaluated. This study focuses on evaluating how the variation of urban drainage network structure affects the frequency and magnitude of CSO events. As a study case, a sewer subnetwork in Dresden, Germany, where 11 CSOs are present, was selected. Scenarios corresponding to the structures with the lowest and with the highest number of possible connected pipes, are developed and evaluated using long-term hydrodynamic simulation. Results indicate that more meshed structures are associated to a decrease on the occurrence and magnitude of CSO. Event frequency reductions vary between 0% and 68%, while reduction of annual mean volumes and annual mean loads ranged between 0% and 87% and 0% and 92%. These rates were mainly related to the additional sewer storage capacity provided in the more meshed scenarios, following a sigmoidal behavior. However, increasing network connections causes investment costs, therefore optimization strategies for selecting intervention areas are needed. Furthermore, the present approach of reducing CSO frequency may provide a new gray solution that can be integrated in the development of hybrid mitigation strategies for the CSO management.