Life cycle analysis of macauba palm cultivation: A promising crop for biofuel production

The 450 Scenario, which limits the increase in global average temperature to 2¿°C, makes it necessary to take steps towards a low-carbon economy. Since the energy sector is a major contribution to anthropogenic greenhouse gas (GHG) emissions, the production of biofuels can play a key role in strategies aimed at climate change mitigation. In this regard, the oil derived from macauba palm (Acrocomia aculeata), mainly constituted of saturated organic chains, has been claimed to hold promise for the production of liquid fuels. The high potential yield, diversity of co-products and various positive features of this emerging energy crop make it an interesting option both from a social and an environmental point of view. Nonetheless, a full environmental evaluation is still missing. In the study presented herein, the impacts produced in its plantation, cultivation and harvesting phases and the associated cumulative energy demand have been determined using a life cycle analysis methodology, in addition to shedding some light on its GHG intensity relative to the other energy crops it can displace. Excluding land use changes and biogenic CO2 fixed by the crop, it was concluded that to produce one ton of macauba fruit in Brazil, the system would absorb 1810.21¿MJ, with GHG emissions of 158.69¿kg CO2eq in the 20-year timeframe, and of 140.04¿kg CO2eq in the 100-year timeframe (comparable to those of African oil palm). Damage to human health, ecosystem quality, and resources would add up to 16¿Pt·t-1 according to Eco-indicator 99 methodology. In order to account for the uncertainty derived from improvement and domestication programs, which should affect current production levels, a sensitivity analysis for different productivities was performed. In all analyses, fertilization was found to be responsible for ca. 90% of the impacts, and hence special attention should be paid to the development of alternative fertilizer management schemes

Search for Gamma-Ray and Neutrino Coincidences Using HAWC and ANTARES Data

In the quest for high-energy neutrino sources, the Astrophysical Multimessenger Observatory Network (AMON) has implemented a new search by combining data from the High Altitude Water Cherenkov (HAWC) observatory and the Astronomy with a Neutrino Telescope and Abyss environmental RESearch (ANTARES) neutrino telescope. Using the same analysis strategy as in a previous detector combination of HAWC and IceCube data, we perform a search for coincidences in HAWC and ANTARES events that are below the threshold for sending public alerts in each individual detector. Data were collected between July 2015 and February 2020 with a livetime of 4.39 years. Over this time period, 3 coincident events with an estimated false-alarm rate of $< 1$ coincidence per year were found. This number is consistent with background expectations.Comment: 12 pages, 5 figures, 3 table

Multimessenger NuEM Alerts with AMON

The Astrophysical Multimessenger Observatory Network (AMON), has developed a real-time multi-messenger alert system. The system performs coincidence analyses of datasets from gamma-ray and neutrino detectors, making the Neutrino-Electromagnetic (NuEM) alert channel. For these analyses, AMON takes advantage of sub-threshold events, i.e., events that by themselves are not significant in the individual detectors. The main purpose of this channel is to search for gamma-ray counterparts of neutrino events. We will describe the different analyses that make-up this channel and present a selection of recent results

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Impact of Climatic Variables on Carbon Content in Sugar Beet Root

The impacts of climatic variables on the growth and carbon content of spring sown sugar beet (Beta vulgaris L.) in the Castilla y Leon region (Northwestern Spain) were assessed by analyzing 35 beet crop variables at four sites over two cultivation years. ANOVA analysis allowed to discern that the location was the factor that had the highest effect on those variables. Fertilization treatments only had a significant impact on the variables derived from the quantity of fresh material (leaves), while the beet variety choice influenced the amount of nitrogen in leaves and the carbon to nitrogen ratio. It could be inferred that the percentage of root carbon content depended mostly on the location and that a higher percentage of root carbon content led to a higher content of dry matter, with a positive relationship with the sucrose content for the two types of varieties that were tested. Principal Component Analysis distinguished the climatic factors that most influenced each cultivation area in each cultivation year and provided a clear separation of the data in clusters, evidencing the uniqueness of each site

Safety and Energy Implications of Setback Control in Operating Rooms during Unoccupied Periods

Health care facilities are high energy-demanding buildings. The energy-saving potential is limited due to safety regulations, especially in critical care areas like operating rooms (ORs). Reducing the supply airflows during unoccupied periods, also called ventilation turndown or setback, is accepted as an energy efficiency measure as long as it does not compromise the pressure relationship. In addition, temperature and relative humidity setbacks can introduce further energy savings. This work aims at studying the effect that a setback has on the OR-positive pressure and the savings achievable in both the energy supply and CO2 emissions. Towards this target, five tests are performed in two ORs of a public hospital during the summer, winter, and midseason. A setback is applied on the basis of an occupancy sensor, and the pressure difference from the OR adjacent spaces is monitored. The outdoor and supply air conditions and airflows, as well as fan energy consumption, are measured. Punctual pressure relationship losses are observed during the occupied periods due to doors opening but not during ventilation setback operations. The energy savings achieved accounted for 75% of the natural gas consumption and 69% of the electricity in the ORs. The yearly estimations imply economic savings of near 20,000 EUR and more than 100 tons of CO2 emissions

Artificial neural network based model to calculate the environmental variables of the tobacco drying process; Modelo basado en redes neuronales artificiales para el cálculo de parámetros ambientales en el proceso de curado del tabaco

This paper presents an Artificial Neural Network (ANN) based model for environmental variables related to the tobacco drying process. A fitting ANN was used to estimate and predict temperature and relative humidity inside the tobacco dryer: the estimation consists of calculating the value of these variables in different locations of the dryer and the prediction consists of forecasting the value of these variables with different time horizons. The proposed model has been validated with temperature and relative humidity data obtained from a real tobacco dryer using a Wireless Sensor Network (WSN). On the one hand, an error under 2% was achieved, obtaining temperature as a function of temperature and relative humidity in other locations in the estimation task. Besides, an error around 1.5 times lower than the one obtained with an interpolation method was achieved in the prediction task when the temperature inside the tobacco mass was predicted with time horizons over 2.5 hours as a function of its present and past values. These results show that ANN-based models can be used to improve the tobacco drying process because with these types of models the value of environmental variables can be predicted in the near future and can be estimated in other locations with low errors

Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and Le&oacute;n, Spain

In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and Le&oacute;n, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (&deg;C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons