The processing and impact of dissolved riverine nitrogen in the Arctic Ocean

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Estuaries and Coasts 35 (2012): 401-415, doi:10.1007/s12237-011-9417-3.Although the Arctic Ocean is the most riverine-influenced of all of the world’s oceans, the importance of terrigenous nutrients in this environment is poorly understood. This study couples estimates of circumpolar riverine nutrient fluxes from the PARTNERS (Pan-Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments) Project with a regionally configured version of the MIT general circulation model to develop estimates of the distribution and availability of dissolved riverine N in the Arctic Ocean, assess its importance for primary production, and compare these estimates to potential bacterial production fueled by riverine C. Because riverine dissolved organic nitrogen is remineralized slowly, riverine N is available for uptake well into the open ocean. Despite this, we estimate that even when recycling is considered, riverine N may support 0.5–1.5 Tmol C year−1 of primary production, a small proportion of total Arctic Ocean photosynthesis. Rapid uptake of dissolved inorganic nitrogen coupled with relatively high rates of dissolved organic nitrogen regeneration in N-limited nearshore regions, however, leads to potential localized rates of riverine-supported photosynthesis that represent a substantial proportion of nearshore production.Funding for this work was provided through NSFOPP- 0229302 and NSF-OPP-0732985.Support to SET was additionally provided by an NSERC Postdoctoral Fellowship

Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions

Nowadays, green energy is considered as a viable solution to hinder CO2 emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover 40% of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management. In this paper, we propose an innovative methodology for implementing two different non-linear autoregressive neural networks to forecast Global Horizontal Solar Irradiance (GHI) in short-term time periods (i.e. from future 15 to 120min). Both neural networks have been implemented, trained and validated exploiting a dataset consisting of four years of solar radiation values collected by a real weather station. We also present the experimental results discussing and comparing the accuracy of both neural networks. Then, the resulting GHI forecast is given as input to a Photovoltaic simulator to predict energy production in short-term time periods. Finally, we present the results of this Photovoltaic energy estimation discussing also their accuracy

Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations

Plant pathogens cause significant losses to agricultural yields, and increasingly threaten food security, ecosystem integrity, and societies in general. Xylella fastidiosa (Xf) is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates a dramatically broadened geographic range. The Xf pathogen has thus re-emerged as a global threat, with its poorly contained expansion in Europe creating a socio-economic, cultural, and political disaster. Xf represents a threat of global proportion because it can infect over 350 plant species worldwide, and the early detection of Xf has been identified as a critical need for its eradication. Here, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography reveal Xf infection in trees before symptoms are visible. We obtained accuracies of disease detection exceeding 80% when high-resolution solar-induced fluorescence quantified by 3D simulations and thermal-based stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected via spectral plant trait alterations (presumed false positives) developed Xf symptoms four months later at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant trait alterations caused by Xf infection are detectable at the landscape scale before symptoms are visible, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.JRC.D.1-Bio-econom

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers

Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis

10.1016/j.rser.2020.110087Renewable and Sustainable Energy Reviews135110087-11008