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With the global concerns on antibiotic resistance (AR) as a public health issue, it is pivotal to have data exchange platforms for studies on antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the environment. For this purpose, the NORMAN Association is hosting the NORMAN ARB&ARG database, which was developed within the European project ANSWER. The present article provides an overview on the database functionalities, the extraction and the contribution of data to the database. In this study, AR data from three studies from China and Nepal were extracted and imported into the NORMAN ARB&ARG in addition to the existing AR data from 11 studies (mainly European studies) on the database. This feasibility study demonstrates how the scientific community can share their data on AR to generate an international evidence base to inform AR mitigation strategies. The open and FAIR data are of high potential relevance for regulatory applications, including the development of emission limit values / environmental quality standards in relation to AR. The growth in sharing of data and analytical methods will foster collaboration on risk management of AR worldwide, and facilitate the harmonization in the effort for identification and surveillance of critical hotspots of AR. The NORMAN ARB&ARG database is publicly available at: https://www.norman-network.com/nds/bacteria/.

Identification of bioaccumulating contaminants of emerging concern (CECs) via suspect and non-target screening remains a challenging task. In this study, ion mobility separation with high-resolution mass spectrometry (IM-HRMS) was used to investigate the effects of drift time (DT) alignment on spectrum quality and peak annotation for screening of CECs in complex sample matrices using data independent acquisition (DIA). Data treatment approaches (Binary Sample Comparison) and prioritisation strategies (Halogen Match, co-occurrence of features in biota and the water phase) were explored in a case study on zebra mussel (Dreissena polymorpha) in Lake Mälaren, Sweden’s largest drinking water reservoir. DT alignment evidently improved the fragment spectrum quality by increasing the similarity score to reference spectra from on average (±standard deviation) 0.33 ± 0.31 to 0.64 ± 0.30 points, thus positively influencing structure elucidation efforts. Thirty-two features were tentatively identified at confidence level 3 or higher using MetFrag coupled with the new PubChemLite database, which included predicted collision cross-section values from CCSbase. The implementation of predicted mobility data was found to support compound annotation. This study illustrates a quantitative assessment of the benefits of IM-HRMS on spectral quality, which will enhance the performance of future screening studies of CECs in complex environmental matrices.

The Spanish Mediterranean basin is particularly susceptible to climate change and human activities, making it vulnerable to the influence of anthropogenic contaminants. Therefore, conducting comprehensive and exhaustive water quality assessment in relevant water bodies of this basin is pivotal. In this work, surface water samples from coastal lagoons or estuaries were collected across the Spanish Mediterranean coastline and subjected to target and suspect screening of 1,585 organic micropollutants by liquid chromatography coupled to ion mobility separation and high resolution mass spectrometry. In total, 91 organic micropollutants could be confirmed and 5 were tentatively identified, with pharmaceuticals and pesticides being the most prevalent groups of chemicals. Chemical analysis data was compared with data on bioanalysis of those samples (recurrent aryl hydrocarbon receptor (AhR) activation, and estrogenic receptor (ER) inhibition in wetland samples affected by wastewater streams). The number of identified organic contaminants containing aromatic rings could explain the AhR activation observed. For the ER antagonistic effects, predictions on estrogenic inhibition potency for the detected compounds were used to explain the activities observed. The integration of chemical analysis with bioanalytical observations allowed a comprehensive overview of the quality of the water bodies under study.

There is growing worry that drinking water can be affected by contaminants of emerging concern (CECs), potentially threatening human health. In this study, a wide range of CECs (n = 177), including pharmaceuticals, pesticides, perfluoroalkyl substances (PFASs) and other compounds, were analysed in raw water and in drinking water collected from drinking water treatment plants (DWTPs) in Europe and Asia (n = 13). The impact of human activities was reflected in large numbers of compounds detected (n = 115) and high variation in concentrations in the raw water (range 15-7995 ng L−1 for ∑177CECs). The variation was less pronounced in drinking water, with total concentration ranging from 35 to 919 ng L−1. Treatment efficiency was on average 65 ± 28%, with wide variation between different DWTPs. The DWTP with the highest ∑CEC concentrations in raw water had the most efficient treatment procedure (average treatment efficiency 89%), whereas the DWTP with the lowest ∑177CEC concentration in the raw water had the lowest average treatment efficiency (2.3%). Suspect screening was performed for 500 compounds ranked high as chemicals of concern for drinking water, using a prioritisation tool (SusTool). Overall, 208 features of interest were discovered and three were confirmed with reference standards. There was co-variation between removal efficiency in DWTPs for the target compounds and the suspected features detected using suspect screening, implying that removal of known contaminants can be used to predict overall removal of potential CECs for drinking water production. Our results can be of high value for DWTPs around the globe in their planning for future treatment strategies to meet the increasing concern about human exposure to unknown CECs present in their drinking water.

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.