Abstract The availability of electron acceptors (EAs) in peatlands determines the potential of methane (CH 4 ) formation under waterlogged conditions. Previous studies suggested that EAs can suppress CH 4 production based on Gibbs free energy under the Redox Ladder Theory. However, growing evidence challenges this theory, raising the question of how the coupling of soil substrates with EAs influences CH 4 emissions. To answer this key question, peat soils were collected across different climatic zones with different degrees of soil degradation. Anoxic incubation experiments were set up, and continuous addition of SO 4 2− , Fe 3+ and humic acid (HA) at different concentrations was followed by characterization of dissolved organic matter using fluorescence spectroscopy. Results suggest that low concentrations of SO 4 2− (1000 μmol L −1 ), Fe 3+ (100 μmol L −1 ) and HA (30 mgC L −1 ) promoted CH 4 production in most of the peat soils. With the addition of SO 4 2− and HA, increased CH 4 emissions were attributed to the facilitation of dissolved organic carbon and increased quinone‐like component C1, which increased the substrate availability for methanogenesis. Furthermore, strengthened microbial activity as indicated by fluorescence component C2 led to higher CH 4 production under Fe 3+ treatments. On the other hand, at high concentrations of SO 4 2− (5000 μmol L −1 ), Fe 3+ (500 μmol L −1 ) and HA (50 mgC L −1 ), CH 4 emissions rapidly decreased by 70.65 ± 1.57% to 96.25 ± 0.45% compared to control group without EAs addition, accompanied by increased δ 13 C‐CH 4 signatures indicating the outweighed CH 4 production under anaerobic oxidation of methane (AOM) when coupling with reduced EAs. The effect of EAs on CH 4 emissions in peat soils could also be related to lability and characteristics of natural organic matter. Our results suggest that the CH 4 production in waterlogged peatlands could be facilitated by regulating organic substrates at low EAs concentrations, but excessive EAs will reduce net CH 4 emissions through AOM. The valuable discovery of CH 4 production and oxidation processes provides insights for mitigating methane emissions from peatlands and regulating global climate change.
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Brand names can be used to hold plastic companies accountable for their items found polluting the environment. We used data from a 5-year (2018-2022) worldwide (84 countries) program to identify brands found on plastic items in the environment through 1576 audit events. We found that 50% of items were unbranded, calling for mandated producer reporting. The top five brands globally were The Coca-Cola Company (11%), PepsiCo (5%), Nestlé (3%), Danone (3%), and Altria (2%), accounting for 24% of the total branded count, and 56 companies accounted for more than 50%. There was a clear and strong log-log linear relationship production (%) = pollution (%) between companies' annual production of plastic and their branded plastic pollution, with food and beverage companies being disproportionately large polluters. Phasing out single-use and short-lived plastic products by the largest polluters would greatly reduce global plastic pollution.
Earthquake disaster assessment is one of the most critical aspects in reducing earthquake disaster losses. However, traditional seismic intensity assessment methods are not effective in disaster-stricken areas with insufficient observation data. Social media data contain a large amount of disaster information with the advantages of timeliness and multiple temporal-spatial scales, opening up a new channel for seismic intensity assessment. Based on the earthquake disaster information on the microblog platform obtained by the network technique, a multi-model coupled seismic intensity assessment method is proposed, which is based on the BERT-TextCNN model, constrained by the seismaesthesia intensity attenuation model, and supplemented by the method of ellipse-fitting inverse distance interpolation. Taking four earthquakes in Sichuan Province as examples, the earthquake intensity was evaluated in the affected areas from the perspective of seismaesthesia. The results show that (1) the microblog data contain a large amount of earthquake information, which can help identify the approximate scope of the disaster area; (2) the influences of the subjectivity and uneven spatial distribution of microblog data on the seismic intensity assessment can be reduced by using the seismaesthesia intensity attenuation model and the method of ellipse-fitting inverse distance interpolation; and (3) the accuracy of seismic intensity assessment based on the coupled model is 70.81%. Thus, the model has higher accuracy and universality. It can be used to assess seismic intensity in multiple regions and assist in the formulation of earthquake relief plans.
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