Polymers are the main constituents of many materials and products in our modern world. However, their environmental safety is not assessed with the same level of detail as done for non-polymeric chemical substances. Moreover, the fundamentals of contemporary regulatory approaches for polymers were developed in the early 1990s, with little change occurring since then. Currently, the European Commission is working on a proposal to initiate registration of polymers under the European Union's (EU) chemicals legislation REACH. This provides a unique opportunity for regulation to catch up on recent scientific advances. To inform this process, we here critically appraise the suggested regulatory approaches to the environmental assessment and management of polymers against the latest scientific findings regarding their environmental fate, exposure, and effects, and identify the remaining critical knowledge gaps. While we use the EU draft proposal as an example, our findings are broadly applicable to other polymer legislations worldwide, due to the similarity of polymer assessment criteria being used. We emphasize four major aspects that require more attention in the regulation of polymers: (i) increased transparency about chemical identities, physical characteristics and grouping approaches for in-use polymers; (ii) improved understanding of the environmental fate of polymers and materials composed of polymers across size and density categories and exposure profiles; (iii) comprehensive assessment of the environmental hazards of polymers, considering the effects of degradation and weathering and taking into account the actual uptake, long-term toxicity, and geophysical impacts; and (iv) consideration of the production volume and use/release patterns in determining regulatory data and testing requirements. Transitioning toward a toxic-free and sustainable circular economy will likely require additional policy instruments that will reduce the overall complexity and diversity of in-use polymers and polymeric materials.

Polar regions should be given greater consideration with respect to the monitoring, risk assessment, and management of potentially harmful chemicals, consistent with requirements of the precautionary principle. Protecting the vulnerable polar environments requires (i) raising political and public awareness and (ii) restricting and preventing global emissions of harmful chemicals at their sources. The Berlin Statement is the outcome of an international workshop with representatives of the European Commission, the Arctic Council, the Antarctic Treaty Consultative Meeting, the Stockholm Convention on Persistent Organic Pollutants (POPs), environmental specimen banks, and data centers, as well as scientists from various international research institutions. The statement addresses urgent chemical pollution issues in the polar regions and provides recommendations for improving screening, monitoring, risk assessment, research cooperation, and open data sharing to provide environmental policy makers and chemicals management decision-makers with relevant and reliable contaminant data to better protect the polar environments. The consensus reached at the workshop can be summarized in just two words: “Act now!”

Specifically, “Act now!” to reduce the presence and impact of anthropogenic chemical pollution in polar regions by.

•Establishing participatory co-development frameworks in a permanent multi-disciplinary platform for Arctic-Antarctic collaborations and establishing exchanges between the Arctic Monitoring and Assessment Program (AMAP) of the Arctic Council and the Antarctic Monitoring and Assessment Program (AnMAP) of the Scientific Committee on Antarctic Research (SCAR) to increase the visibility and exchange of contaminant data and to support the development of harmonized monitoring programs.

•Integrating environmental specimen banking, innovative screening approaches and archiving systems, to provide opportunities for improved assessment of contaminants to protect polar regions.

Nontarget analysis by liquid chromatography-high-resolutionmass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation(MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI: FingerID software. The root mean square errors of the training and test sets were 0.55(3.5x) and 0.80 (6.3x) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2x to 6x. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4x, ageometric mean of 4.5x, and a median of 4.0x. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5x, a geometricmean of 5.6x, and a median of 5.2x on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. TheMS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.

The threat posed by plastic in the environment is poorly characterized due to uncertainties and unknowns about sources, transport, transformation and removal processes, and the properties of the plastic pollution itself. Plastic creates a footprint of particulate pollution with a diversity of composition, size and shape, and a halo of chemicals. In this Perspective, we argue that process-based mass-balance models could provide a platform to synthesize knowledge about plastic pollution as a function of its measurable intrinsic properties. 

Down-the-drain chemical spills that reach a sewage treatment plant (STP) can cause a biological toxic shock that may reduce or eliminate the capability of STP microorganisms to remove organic matter and nutrients for weeks to months. Thus, chemical spills are a threat to water quality. Here, we present a case study of toxic shock threat prioritization for chemicals used at industrial facilities connected to the Kappala STP in Stockholm, Sweden. We surveyed 60 facilities, collected information on the use and storage of bulk chemical products, and documented 8676 uses of constituent chemicals. In situ chemical tracer experiments were conducted in the primary sewer tunnel leading to Kappala to measure chemical spill dilution during transit to the plant. To assess chemical risks to the plant, we extracted data on toxicity to STP microorganisms for 6168 chemicals from European Chemicals Agency brief profiles and estimated exposure concentrations in the plant using conservative assumptions. Under a high-end spill scenario, the majority of chemicals in the survey posed a negligible risk for adverse effects on plant microorganisms, however 28 chemicals were identified as posing a potential risk and were prioritized for additional information gathering to refine our conservative assumptions. The analysis framework was built into an online tool (RAVEN STREAM) provided as free, open-source software for STP operators to screen for threats posed by possible chemical spills at connected facilities. The threat identification framework can facilitate communication between STPs and their upstream industrial clients to mitigate possible high-risk chemical spills before they happen.

The climate in Europe is warming twice as fast as it is across the rest of the globe, and in Sweden annual mean tempera-tures are forecast to increase by up to 3-6 & DEG;C by 2100, with increasing frequency and magnitude of floods, heatwaves, and other extreme weather. These climate change-related environmental factors and the response of humans at the individual and collective level will affect the mobilization and transport of and human exposure to chemical pollutants in the envi-ronment. We conducted a literature review of possible future impacts of global change in response to a changing climate on chemical pollutants in the environment and human exposure, with a focus on drivers of change in exposure of the Swedish population to chemicals in the indoor and outdoor environment. Based on the literature review, we formulated three alternative exposure scenarios that are inspired by three of the shared socioeconomic pathways (SSPs). We then con-ducted scenario-based exposure modelling of the >3000 organic chemicals in the USEtox (R) 2.0 chemical library, and fur-ther selected three chemicals (terbuthylazine, benzo[a]pyrene, PCB-155) from the USEtox library that are archetypical pollutants of drinking water and food as illustrative examples. We focus our modelling on changes in the population intake fraction of chemicals, which is calculated as the fraction of a chemical emitted to the environment that is ingested via food uptake or inhaled by the Swedish population. Our results demonstrate that changes of intake fractions of chemicals are possible by up to twofold increases or decreases under different development scenarios. Changes in intake fraction in the most optimistic SSP1 scenario are mostly attributable to a shift by the population towards a more plant-based diet, while changes in the pessimistic SSP5 scenario are driven by environmental changes such as rain fall and runoff rates.

European agricultural development in the 21st century will be affected by a host of global changes, including climate change, changes in agricultural technologies and practices, and a shift towards a circular economy. The type and quantity of chemicals used, emitted, and cycled through agricultural systems in Europe will change, driven by shifts in the use patterns of pesticides, veterinary pharmaceuticals, reclaimed wastewater used for irrigation, and biosolids. Climate change will also impact the chemical persistence, fate, and transport processes that dictate environmental exposure. Here, we review the literature to identify research that will enable scenario-based forecasting of environmental exposures to organic chemicals in European agriculture under global change. Enabling exposure forecasts requires understanding current and possible future 1.) emissions, 2.) persistence and transformation, and 3.) fate and transport of agricultural chemicals. We discuss current knowledge in these three areas, the impact global change drivers may have on them, and we identify knowledge and data gaps that must be overcome to enable predictive scenario-based forecasts of environmental exposure under global change. Key research gaps identified are: improved understanding of relationships between global change and chemical emissions in agricultural settings; better understanding of environment-microbe interactions in the context of chemical degradation under future conditions; and better methods for downscaling climate change-driven intense precipitation events for chemical fate and transport modelling. We introduce a set of narrative Agricultural Chemical Exposure (ACE) scenarios — augmenting the IPCC's Shared Socio-economic Pathways (SSPs) — as a framework for forecasting chemical exposure in European agriculture. The proposed ACE scenarios cover a plausible range of optimistic to pessimistic 21st century development pathways. Filling the knowledge and data gaps identified within this study and using the ACE scenario approach for chemical exposure forecasting will support stakeholder planning and regulatory intervention strategies to ensure European agricultural practices develop in a sustainable manner.

The environmental risk assessment of UVCBs (i.e., substances of unknown or variable composition, complex reaction products, or biological materials) is challenging due to their inherent complexity. A particular problem is that UVCBs can contain constituents with unidentified chemical structures and/or have variable composition of constituents from batch to batch. Moreover, the composition of a UVCB in the environment is not the same as that of the UVCB in a product, meaning that a risk assessment based on environmental exposure to the UVCB in a product does not represent the actual environmental risk. Here we propose an in silico fate-directed risk assessment framework for UVCBs using cedarwood oil as a case study. The framework uses Monte Carlo simulations and the mass-balance models SimpleTreat and RAIDAR to provide quantitative information on whether unidentified constituents within the physical–chemical property space of a UVCB can be the decisive factor for the environmental risk of the entire UVCB. Thereby the framework provides a robust decision tool to evaluate if a UVCB risk assessment requires additional tests or if the data on known constituents is representative for the risk of the entire UVCB. In the case of cedarwood oil, it could be shown that a risk assessment based on the known constituents (representing around 70% of the overall UVCB by weight) is representative for the environmental risk of the entire UVCB – reducing the need for additional testing and test animals.

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS2). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC50) values from the CompTox database and validated for 219 chemicals and 420 MS2 spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC50 values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC50 values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or logKOW.