Rivers of plastic: A socio-economic and topographic approach to modeling plastic transport from catchment to sea.

Marine litter caused by discharge of mismanaged plastic waste is considered to be one of the major environmental challenges by the international society. With the annual increase of plastic production, a growing number of plastic products are being used in people's daily lives. A large number of these plastics end up as waste emitted into rivers and subsequently into oceans through the effects of downpours or wind, posing a threat to the marine ecosystem. In this study, we developed a riverine plastic transport model based on catchment topography and social-economic factors. By applying reasonable compromise on the complexity of the model, this compromised simplified process-based model has the innovative capability to estimate plastic emissions effectively under the current conditions of limited data availability for model inputs. Compared to existing models, this novel model can also resolve challenges related to the contributions of various land use types and transport stages to plastic emissions into the oceans. To further explore the applicability of our results on a global scale, certain input parameter such as the proportion of mismanaged waste is crucial for users to acquire. Here, taking the S river catchment as our study area, the tourism-driven seasonal variation of land-based plastic emissions was quantified. According to our estimation, the emission flux in S river catchment in 2020 was 68 to 280 tons. 62.4% of riverine plastics reached the ocean. Although urban areas are the predominant contributors to the total emission flux, the contributions from other land use types such as forests and cultivated areas are also unignorable. For instance, forests and cultivated areas contribute 25.7% and 6.3%, respectively, even surpassing the contributions from high tourist activity (5.8%). Stricter waste collection legislations are imperatively needed particularly in these regions.

Measuring riverine macroplastic: Methods, harmonisation, and quality control.

River systems are a key environmental recipient of macroplastic pollution. Understanding the sources of macroplastic to rivers and the mechanisms controlling fate and transport is essential to identify and tailor measures that can effectively reduce global plastic pollution. Several guidelines exist for monitoring macroplastic in rivers; yet, no single method has emerged representing the standard approach. This reflects the substantial variability in river systems globally and the need to adapt methods to the local environmental context and monitoring goals. Here we present a critical review of methods used to measure macroplastic flows in rivers, with a specific focus on opportunities for methods testing, harmonisation, and quality assurance and quality control (QA/QC). Several studies have already revealed important findings; however, there is significant disparity in the reporting of methodologies and data. There is a need to converge methods, and their adaptations, towards greater comparability. This can be achieved through: i) methods testing to better understand what each method effectively measures and how it can be applied in different contexts; ii) incorporating QA/QC procedures during sampling and analysis; and iii) reporting methodological details and data in a more harmonised way to facilitate comparability and the utilisation of data by several end users, including policy makers. Setting this as a priority now will facilitate the collection of rigorous and comparable monitoring data to help frame solutions to limit plastic pollution, including the forthcoming global treaty on plastic pollution.

Levels of nitramines and nitrosamines in lake drinking water close to a CO2 capture plant: A modelling perspective.

While CO2 capture is considered a key climate change mitigation option, we must ensure that global implementation occurs without causing harm to the local environment and the human health. The most mature option for capture is using amines, which however, is associated with a risk of contaminating nearby drinking water sources with carcinogenic nitramines (NAs) and nitrosamines (NSAs). Here we present the first process-based simulation of NAs and NSAs in a catchment-lake system with the input of previously modelled atmospheric deposition rates. Considering full-scale CO2 capture at the Oslo waste incineration plant in Norway, future (∼10 y) levels in a nearby lake approach the national drinking water limit. We further quantified the effect of hydrological and biogeochemical processes and identified those with the highest sensitivity (NA biodegradation). The uncertainty of the results is presented by a probabilistic distribution (Monte Carlo analysis), incorporating variability in catchment, lake, and literature NA and NSA parameter values. This modelling tool allows for the site-specific assessment of the abovementioned risks related to amine-based CO2 capture and aspires to contribute to the sound evaluation of costly amine emission reduction measures.

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction.

Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions).