Microplastics monitoring in freshwater systems: A review of global efforts, knowledge gaps, and research priorities.

The escalating production of synthetic plastics and inadequate waste management have led to pervasive microplastic (MP) contamination in aquatic ecosystems. MPs, typically defined as particles smaller than 5 mm, have become an emerging pollutant in freshwater environments. While significant concern about MPs has risen since 2014, research has predominantly concentrated on marine settings, there is an urgent need for a more in-depth critical review to systematically summarize the current global efforts, knowledge gaps, and research priorities for MP monitoring in freshwater systems. This review evaluates the current understanding of MP monitoring in freshwater environments by examining the distribution, characteristics, and sources of MPs, alongside the progression of analytical methods with quantitative evidence. Our findings suggest that MPs are widely distributed in global freshwater systems, with higher abundances found in areas with intense human economic activities, such as the United States, Europe, and China. MP abundance distributions vary across different water bodies (e.g., rivers, lakes, estuaries, and wetlands), with sampling methods and size range selections significantly influencing reported MP abundances. Despite great global efforts, there is still a lack of harmonized analyzing framework and understanding of MP pollution in specific regions and facilities. Future research should prioritize the development of standardized analysis protocols and open-source MP datasets to facilitate data comparison. Additionally, exploring the potential of state-of-the-art artificial intelligence for rapid, accurate, and large-scale modeling and characterization of MPs is crucial to inform effective strategies for managing MP pollution in freshwater ecosystems.

Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction.

Chemical information disseminated in scientific documents offers an untapped potential for deep learning-assisted insights and breakthroughs. Automated extraction efforts have shifted from resource-intensive manual extraction toward applying machine learning methods to streamline chemical data extraction. While current extraction models and pipelines have ushered in notable efficiency improvements, they often exhibit modest performance, compromising the accuracy of predictive models trained on extracted data. Further, current chemical pipelines lack both transferability─where a model trained on one task can be adapted to another relevant task with limited examples─and extensibility, which enables seamless adaptability for new extraction tasks. Addressing these gaps, we present ChemREL, a versatile chemical data extraction pipeline emphasizing performance, transferability, and extensibility. ChemREL utilizes a custom, diverse data set of chemical documents, labeled through an active learning strategy to extract two properties: normal melting point and lethal dose 50 (LD50). The normal melting point is selected for its prevalence in diverse contexts and wider literature, serving as the foundation for pipeline training. In contrast, LD50 evaluates the pipeline's transferability to an unrelated property, underscoring variance in its biological nature, toxicological context, and units, among other differences. With pretraining and fine-tuning, our pipeline outperforms existing methods and GPT-4, achieving F1-scores of 96.1% for entity identification and 97.0% for relation mapping, culminating in an overall F1-score of 95.4%. More importantly, ChemREL displays high transferability, effectively transitioning from melting point extraction to LD50 extraction with 10 randomly selected training documents. Released as an open-source package, ChemREL aims to broaden access to chemical data extraction, enabling the construction of expansive relational data sets that propel discovery.

Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales.

Soil organic carbon (SOC) plays a vital role in global carbon cycling and sequestration, underpinning the need for a comprehensive understanding of its distribution and controls. This study explores the importance of various covariates on SOC spatial distribution at both local (up to 1.25 km) and continental (USA) scales using a deep learning approach. Our findings highlight the significant role of terrain attributes in predicting SOC concentration distribution with terrain, contributing approximately one-third of the overall prediction at the local scale. At the continental scale, climate is only 1.2 times more important than terrain in predicting SOC distribution, whereas at the local scale, the structural pattern of terrain is 14 and 2 times more important than climate and vegetation, respectively. We underscore that terrain attributes, while being integral to the SOC distribution at all scales, are stronger predictors at the local scale with explicit spatial arrangement information. While this observational study does not assess causal mechanisms, our analysis nonetheless presents a nuanced perspective about SOC spatial distribution, which suggests disparate predictors of SOC at local and continental scales. The insights gained from this study have implications for improved SOC mapping, decision support tools, and land management strategies, aiding in the development of effective carbon sequestration initiatives and enhancing climate mitigation efforts.

Microplastic Human Dietary Uptake from 1990 to 2018 Grew across 109 Major Developing and Industrialized Countries but Can Be Halved by Plastic Debris Removal.

Microplastics (MPs), plastic particles smaller than 5 mm, are now a growing environmental and public health issue, as they are detected pervasively in freshwater and marine environments, ingested by organisms, and then enter the human body. Industrial development drives this environmental burden caused by MP formation and human uptake by elevating plastic pollution levels and shaping the domestic dietary structure. We map the MP human uptake across 109 global countries on five continents from 1990 to 2018, focusing on the world's major coastlines that are affected by plastic pollution that affects the United Nations' Sustainable Development Goals (SDGs): SDG 6 (Clean Water and Sanitation), SDG 14 (Life Below Water), and SDG 15 (Life on Land). Amid rapid industrial growth, Indonesia tops the global per capita MP dietary intake at 15 g monthly. In Asian, African, and American countries, including China and the United States, airborne and dietary MP uptake increased over 6-fold from 1990 to 2018. Eradicating 90% of global aquatic plastic debris can help decrease MP uptake by more than 48% in Southeast Asian countries that peak MP uptake. To reduce MP uptake and potential public health risks, governments in developing and industrialized countries in Asia, Europe, Africa, and North and South America should incentivize the removal of free plastic debris from freshwater and saltwater environments through advanced water treatment and effective solid waste management practices.

Climate sustainability through a dynamic duo: Green hydrogen and crypto driving energy transition and decarbonization.

Climate change persists as a pressing global issue due to high greenhouse gas emissions from fossil fuel-based energy sources. A transition to a greener energy matrix combined with carbon offsetting is imperative to mitigate the rate at which global temperature ascends. While countries have deployed faith in green hydrogen to accelerate worldwide decarbonization efforts, the concurrent rise of blockchain-operated crypto-applications, such as bitcoin, has exacerbated climate change concerns. In this study, we propose technological solutions that combine the green hydrogen infrastructure with bitcoin mining operations to catalyze environmental and socioeconomic sustainability in climate change mitigation strategies. Since the present state of crypto-operations undeniably contributes to worldwide carbon emissions, it becomes vital to explore opportunities for harnessing the widespread enthusiasm for bitcoin as an aid toward a sustainable and climate-friendly future. Our findings reveal that green hydrogen production, paired with crypto-operations, can accelerate the deployment of solar and wind power capacities to boost conventional mitigation frameworks. Specifically, leveraging the economic potential derived from green hydrogen and bitcoin for incremental investment in renewable energy penetration, this dynamic duo can enable capacity expansions of up to 25.5% and 73.2% for solar and wind power installations. Therefore, the proposed technological solutions that leverage green hydrogen and bitcoin mining, bolstered with appropriate policy interventions, can not only strengthen renewable power generation and carbon offsetting capacities but also contribute significantly to achieving climate sustainability.

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning.

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security. While the data-driven deep learning approach has shown great capacity in predicting yield patterns, its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown. In this study, we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018, with a special focus on the extreme yield loss in 2012. We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018 (R2 = 0.81) and well reproduced the extreme yield anomalies in 2012 (R2 = 0.79). Further attribution analysis indicated that extreme heat stress was the major cause for yield loss, contributing to 72.5% of the yield loss, followed by anomalies of vapor pressure deficit (17.6%) and precipitation (10.8%). Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012. Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.