Excess nitrogen (N) from agricultural sources is a major contributor to the water pollution of rivers in Europe. Floodplains are of tremendous importance as they can permanently remove nitrate (NO) from the environment by releasing reactive N to the atmosphere in its gaseous forms (NO, N) during denitrification. However, the quantitative assessment of this ecosystem function is still challenging, particularly on the national level. In this study, we modeled the potential of NO-N removal through microbial denitrification in soils of the active floodplains of the river Elbe and river Rhine in Germany. We combined laboratory measurements of soil denitrification potentials with straightforward modelling data, covering the average inundation duration from six study areas, to improve an existing Germany-wide proxy-based approach (PBAe) on NO-N retention potential. The PBAe estimates this potential to be 30-150 kg NO-N ha yr. However, with soil pH and Floodplain Status Category identified as essential parameters for the proxies, the improved PBA (PBAi) yields a removal potential of 5-480 kg N ha yr. To account for these parameters, we applied scaling factors using a bonus-malus system with a base value of 10-120 N ha yr. Upscaling the determined proxies of the PBAi to the entire active floodplains of the river Elbe and river Rhine results in similarly high NO-N retention sums of ~7000 t yr in spite of very different retention area sizes, strengthening the argument for area availability as the primary objective of restoration efforts. Although PBAs are always subject to uncertainty, the PBAi enables a more differentiated spatial quantification of denitrification because local key controlling parameters are included. Hence, the PBAi is an innovative and robust approach to quantify denitrification in floodplain soils, supporting a better assessment of ecosystem services for decision-making on floodplain restoration.
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http://dx.doi.org/10.1016/j.scitotenv.2023.164727 | DOI Listing |
Heliyon
November 2024
Département d'Analytique, Opérations et Technologies de l'Information, Université de Québec à Montreal, 315, Sainte-Catherine Street East, H2X 3X2, Montreal, Canada.
The budgeted influence maximization (BIM) problem aims to identify a set of seed nodes that adhere to predefined budget constraints within a specified network structure and cost model. However, it is difficult for the existing algorithms to achieve a balance between timeliness and effectiveness. To address this challenge, our study initially proposes a refined cost model through empirical scrutiny of Weibo's quote data.
View Article and Find Full Text PDFSci Total Environ
November 2024
Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China. Electronic address:
Mechanisms underlying the effects of ecological disturbance on aquatic ecosystems remain uncertain in subtropical regions. Here, we used a proxy-based approach to explore the community dynamics of testate amoebae (Arcellinida and Euglyphida) in two subtropical deep reservoirs (Tingxi and Shidou) in Xiamen, southeastern China, over a three-year period. Specifically, we employed drought and typhoon events recorded by weather station as proxies for ecological disturbance and chlorophyll-a estimated through fluorometry as a proxy for testate amoeba food.
View Article and Find Full Text PDFFront Artif Intell
July 2024
Department of Computer Science, The University of Manchester, Manchester, United Kingdom.
Deep learning models have achieved state-of-the-art performance for text classification in the last two decades. However, this has come at the expense of models becoming less understandable, limiting their application scope in high-stakes domains. The increased interest in explainability has resulted in many proposed forms of explanation.
View Article and Find Full Text PDFAm J Bot
July 2024
Earth Sciences, Negaunee Integrative Research Center, Field Museum, Chicago, 60605, IL, USA.
Premise: The Aptian-Albian (121.4-100.5 Ma) was a greenhouse period with global temperatures estimated as 10-15°C warmer than pre-industrial conditions, so it is surprising that the most reliable CO estimates from this time are <1400 ppm.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
October 2024
Transformers have shown remarkable performance, however, their architecture design is a time-consuming process that demands expertise and trial-and-error. Thus, it is worthwhile to investigate efficient methods for automatically searching high-performance Transformers via Transformer Architecture Search (TAS). In order to improve the search efficiency, training-free proxy based methods have been widely adopted in Neural Architecture Search (NAS).
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