Environmental Science and Ecotechnology最新文献

Biophotovoltaics (BPV) represents an innovative biohybrid technology that couples electrochemistry with oxygenic photosynthetic microbes to harness solar energy and convert it into electricity. Central to BPV systems is the ability of microbes to perform extracellular electron transfer (EET), utilizing an anode as an external electron sink. This process simultaneously serves as an electron sink and enhances the efficiency of water photolysis compared to conventional electrochemical water splitting. However, optimizing BPV systems has been hindered by a limited understanding of EET pathways and their impacts on cellular physiology. Here we show photosynthetic electron flows in Synechocystis sp. PCC 6803 cultivated in a ferricyanide-mediated BPV system. By monitoring carbon fixation rates and photosynthetic oxygen exchange, we reveal that EET does not significantly affect cell growth, respiration, carbon fixation, or photosystem II efficiency. However, EET competes for electrons with the flavodiiron protein flv1/3, influencing Mehler-like reactions. Our findings suggest that the ferricyanide mediator facilitates photosynthetic electron extraction from ferredoxins downstream of photosystem I. Additionally, the mediator induces a more reduced plastoquinone pool, an effect independent of EET. At very high ferricyanide concentrations, the electron transport chain exhibits responses resembling the impact of trace cyanide. These insights provide a molecular-level understanding of EET pathways in Synechocystis within BPV systems, offering a foundation for the future refinement of BPV technologies.

The response to climate change and air pollution control demonstrates strong synergy across scientific mechanisms, targets, strategies, and governance systems. This report, based on a monitoring indicator system for coordinated governance of air pollution and climate change, employs an interdisciplinary approach combining natural and social sciences. It establishes 20 indicators across five key areas: air pollution and climate change, governance systems and practices, structural transformation and technologies, atmospheric components and emission reduction pathways, and health impacts and co-benefits. This report tries to provide actionable insights into the interconnectedness of air pollution and climate governance. It highlights key policy gaps, presents updated indicators, and offers a refined monitoring framework to track progress toward China's dual goals of reducing emissions and improving air quality. Compared to previous editions, this year's report has updated four key indicators: meteorological impacts on air quality, climate change and its effects, governance policies, and low-carbon building energy systems. The aim is to further refine the monitoring framework, track progress, and establish a comprehensive theory for collaborative governance while identifying challenges and proposing solutions for China's pathway to carbon neutrality and clean air. The report comprises six chapters. The executive summary chapter is followed by analyzing air pollution and climate change interactions. Governance systems and practices are discussed in the third chapter, focusing on policy implementation and local experiences. The fourth chapter addresses structural transformations and emission reduction technologies, including energy and industrial shifts, transportation, low-carbon buildings, carbon capture and storage, and power systems. The fifth chapter outlines atmospheric component dynamics and emission pathways, presenting insights into emission drivers and future strategies. The sixth chapter assesses health impacts and the benefits of coordinated actions. Since 2019, China Clean Air Policy Partnership has produced annual reports on China's progress in climate and air pollution governance, receiving positive feedback. In 2023, the report was co-developed with Tsinghua University's Carbon Neutrality Research Institute, involving over 100 experts and multiple academic forums. The collaboration aims to continuously improve the indicator system and establish the report as a key resource supporting China's efforts in pollution reduction, carbon mitigation, greening, and sustainable growth.

Mesozooplankton are critical components of marine ecosystems, acting as key intermediaries between primary producers and higher trophic levels by grazing on phytoplankton and influencing fish populations. They play pivotal roles in the pelagic food web and export production, affecting the biogeochemical cycling of carbon and nutrients. Therefore, accurately modeling and visualizing mesozooplankton community dynamics is essential for understanding marine ecosystem patterns and informing effective management strategies. However, modeling these dynamics remains challenging due to the complex interplay among physical, chemical, and biological factors, and the detailed parameterization and feedback mechanisms are not fully understood in theory-driven models. Graph neural network (GNN) models offer a promising approach to forecast multivariate features and define correlations among input variables. The high interpretive power of GNNs provides deep insights into the structural relationships among variables, serving as a connection matrix in deep learning algorithms. However, there is insufficient understanding of how interactions between input variables affect model outputs during training. Here we investigate how the graph structure of ecosystem dynamics used to train GNN models affects their forecasting accuracy for mesozooplankton species. We find that forecasting accuracy is closely related to interactions within ecosystem dynamics. Notably, increasing the number of nodes does not always enhance model performance; closely connected species tend to produce similar forecasting outputs in terms of trend and peak timing. Therefore, we demonstrate that incorporating the graph structure of ecosystem dynamics can improve the accuracy of mesozooplankton modeling by providing influential information about species of interest. These findings will provide insights into the influential factors affecting mesozooplankton species and emphasize the importance of constructing appropriate graphs for forecasting these species.

Antibiotics are extensively used in livestock production to prevent and treat diseases, but their environmental impact through contamination of rivers and groundwater is a growing concern. The specific antibiotics involved, their sources, and their geographic distribution remain inadequately documented, hindering effective mitigation strategies for river and groundwater pollution control caused by livestock production. Here we develope the spatially explicit MARINA-Antibiotics (China-1.0) model to estimate the flows of 24 antibiotics from seven livestock species into rivers and leaching into groundwater across 395 sub-basins in China, and examine changes between 2010 and 2020. We find that 8364 tonnes and 3436 tonnes of antibiotics entered rivers and groundwater nationwide in 2010 and 2020, respectively. Approximately 50-90% of these amounts originated from about 40% of the basin areas. Antibiotic inputs to rivers decreased by 59% from 2010 to 2020, largely due to reduced manure point sources. Conversely, antibiotic leaching into groundwater increased by 15%, primarily because of enhanced manure recycling practices. Pollution varied by antibiotic groups and livestock species: fluoroquinolones contributed approximately 55% to river pollution, mainly from pig, cattle, and chicken manure; sulfonamides accounted for over 90% of antibiotics in groundwater, predominantly from pig and sheep manure. While our findings support existing policies promoting manure recycling to mitigate river pollution in China, they highlight the need for greater attention to groundwater pollution. This aspect is essential to consider in developing and designing future reduction strategies for antibiotic pollution from livestock production.

Microbial electrochemical technologies (MET) can remove a variety of organic and inorganic pollutants from contaminated groundwater. However, despite significant laboratory-scale successes over the past decade, field-scale applications remain limited. We hypothesize that enhancing the electrochemical conductivity of the soil surrounding electrodes could be a groundbreaking and cost-effective alternative to deploying numerous high-surface-area electrodes in short distances. This could be achieved by injecting environmentally safe iron- or carbon-based conductive (nano)particles into the aquifer. Upon transport and deposition onto soil grains, these particles create an electrically conductive zone that can be exploited to control and fine-tune the delivery of electron donors or acceptors over large distances, thereby driving the process more efficiently. Beyond extending the radius of influence of electrodes, these diffuse electro-conductive zones (DECZ) could also promote the development of syntrophic anaerobic communities that degrade contaminants via direct interspecies electron transfer (DIET). In this review, we present the state-of-the-art in applying conductive materials for MET and DIET-based applications. We also provide a comprehensive overview of the physicochemical properties of candidate electrochemically conductive materials and related injection strategies suitable for field-scale implementation. Finally, we illustrate and critically discuss current and prospective electrochemical and geophysical methods for measuring soil electronic conductivity-both in the laboratory and in the field-before and after injection practices, which are crucial for determining the extent of DECZ. This review article provides critical information for a robust design and in situ implementation of groundwater electro-bioremediation processes.